bluuebunny/crossref_metadata_2025 huggingface dataset:
Combined Effect of High-Resolution Land Cover and Grid Resolution on Surface NO2 Concentrations
Authors: Silveira Carlos , Ferreira Joana , Tuccella Paolo , Curci Gabriele , Miranda Ana I.
Published: February 2022

High-resolution air quality simulations are often performed using different nested domains and resolutions. In this study, the variability of nitrogen dioxide (NO2) concentrations estimated from two nested domains focused on Portugal (D2 and D3), with 5 and 1 km horizontal grid resolutions, respectively, was investigated by applying the WRF-Chem model for the year 2015. The main goal and innovative aspect of this study is the simulation of a whole year with high resolutions to analyse the spatial variability under the simulation grids in conjunction with detailed land cover (LC) data specifically processed for these high-resolution domains. The model evaluation was focused on Portuguese air quality monitoring stations taking into consideration the station typology. As main results, it should be noted that (i) D3 urban LC categories enhanced pollution hotspots; (ii) generally, modelled NO2 was underestimated, except for rural stations; (iii) differences between D2 and D3 estimates were small; (iv) higher resolution did not impact model performance; and (v) hourly D2 estimates presented an acceptable quality level for policy support. These modelled values are based on a detailed LC classification (100 m horizontal resolution) and coarse spatial resolution (approximately 10 km) emission inventory, the latter suitable for portraying background air pollution problems. Thus, if the goal is to characterise urban/local-scale pollution patterns, the use of high grid resolution could be advantageous, as long as the input data are properly represented.
Unravelling the Role of Nitrogen in Surface Chemistry and Oxidation Evolution of Deep Cryogenic Treated High-Alloyed Ferrous Alloy
Authors: Jovičević-Klug Patricia , Jovičević-Klug Matic , Podgornik Bojan
Published: February 2022

The role of nitrogen, introduced by deep cryogenic treatment (DCT), has been investigated and unraveled in relation to induced surface chemistry changes and improved corrosion resistance of high-alloyed ferrous alloy AISI M35. The assumptions and observations of the role of nitrogen were investigated and confirmed by using a multitude of complementary investigation techniques with a strong emphasis on ToF-SIMS. DCT samples display modified thickness, composition and layering structure of the corrosion products and passive film compared to a conventionally heat-treated sample under the same environmental conditions. The changes in the passive film composition of a DCT sample is correlated to the presence of the so-called ghost layer, which has higher concentration of nitrogen. This layer acts as a precursor for the formation of green rust on which magnetite is formed. This specific layer combination acts as an effective protective barrier against material degradation. The dynamics of oxide layer build-up is also changed by DCT, which is elucidated by the detection of different metallic ions and their modified distribution over surface thickness compared to its CHT counterpart. Newly observed passive film induced by DCT successfully overcomes the testing conditions in more extreme environments such as high temperature and vibrations, which additionally confirms the improved corrosion resistance of DCT treated high-alloyed ferrous alloys.
Synthesis and Characterization of Chanar Gum Films
Authors: Lazo Lismet , Melo Gisela M. , Auad María Luján , Filippa Mauricio , Masuelli Martin A.
Published: February 2022

New polysaccharides are being intensely studied as sources of edible materials, with potential application in food packaging systems, eco-materials and the pharmaceutical industry. This investigation aimed to develop biopolymer films based on the polysaccharides obtained from chañar (CH) fruit (Geoffrea decorticans). The resulting polysaccharides, from hydrothermal extraction (CHT) and acid extraction (CHA) were hydrodynamically characterized, with density, viscosity, and diffusion coefficient measurements to obtain their properties in an aqueous solution (intrinsic viscosity, shape factor, partial specific volume, hydration value, molecular weight, and hydrodynamic radius). The polysaccharides films (CHTF and CHAF) were characterized with SEM/EDX, DSC, TGA-DTG, FTIR, DRX, mechanical tests, water vapor permeation, colorimetry, antioxidant capacity, and biodegradability, to determine potential applications based on these properties. The results indicated that the extraction method affects the hydrodynamic properties of the obtained polysaccharide. They differ in molecular weight, and RH of CHT was greater than CHA. Both gums were quasi-spherical, and the νa/b value of CHT was more than CHA. The films properties did not present significant differences in most cases. SEM micrographs illustrate that CHAF presents a much rougher surface. The results of the mechanical analysis show that CHTF has better mechanical properties, it has higher elongation at break and tensile strength, with a Young Modulus of 2.8 MPa. Thermal analysis indicates good thermal stability of the films until about 150 °C. The degradation study shows that CH films are biodegradable in a 35 day range. The study of this properties is critical to demonstrate the functionality of biopolymers and their application. The obtained results represent an advantage and evidence that chañar is an interesting source for extract polysaccharides with film forming properties.
A Project-Scheduling and Resource Management Heuristic Algorithm in the Construction of Combined Cycle Power Plant Projects
Authors: Zohrehvandi Shakib
Published: February 2022

Given the growing number of development projects, proper project planning and management are crucial. The purpose of this paper is to introduce a heuristic algorithm for scheduling a power plant project construction and project resource management to determine the size of project buffers and feeding buffers. This algorithm consists of three steps: 1. estimating the duration of project activities; 2. determining the size of the project buffer and feeding buffers; and 3. simulating the mentioned algorithm, which will be explained below. Innovations of this research are as follows: estimating the exact duration of project activities by using a heuristic algorithm, in addition to determining the buffer size; calculating both project buffer and feeding buffers; and applying the algorithm to implement an ACC used in combined cycle power plant projects as a numerical example. In order to evaluate the proposed algorithm, inputs from this project were run through several algorithms recently presented. The results showed that a suitable amount of buffers can be allocated for projects using this algorithm.
Efficient Consecutive Synthesis of Ethyl-2-(4-Aminophenoxy) Acetate, a Precursor for Dual GK and PPARγ Activators, X-ray Structure, Hirshfeld Analysis, and DFT Studies
Authors: Altowyan Mezna , Soliman Saied , Ismail Magda , Haukka Matti , Barakat Assem , Ayoup Mohammed
Published: February 2022

Herein, we report a facile synthesis of ethyl-2-(4-aminophenoxy)acetate 4 as a building synthon for novel dual hypoglycemic agents. This building template was synthesized by alkylation of 4-nitrophenol with ethyl bromo-acetate followed by selective reduction of the nitro group. This reduction methoddoes not require nascent hydrogen or any reaction complexity; it goes easily via consecutive reaction in NH4Cl/Fe to yield our target synthon as very pure crystals. This product was characterized by 1HNMR, 13CNMR, COSY, NOESY NMR spectroscopy, and elemental analysis. Additionally, its structure was studied and approved by X-ray single crystal structure determination. The unit cell parameters are a = 8.2104(6)Å, b = 10.3625(9)Å, c = 11.9562(9)Å, α = 101.787(7), β = 91.849(6), and γ = 102.755(7)°, indicating that 4 was crystallized in the triclinic crystal system. The cooperative non-covalent interactions are also discussed with the aid of Hirshfeld surface analysis. The H…H, H…C, and O…H interactions have a major contribution in the molecular packing of 4. Moreover, different quantum chemical parameters were computed and discussed based on DFT calculations. The experimental UV/Vis spectra showed two bands at 299 and 234 nm, which were calculated using the TD-DFT method at 286 (f = 0.068) and 226 nm (f = 0.294), respectively. These bands were assigned to HOMO→LUMO (95%) and HOMO→LUMO+2 (86%) transitions, respectively.
Effects of Gradient Hot Rolled Deformation on Texture Evolution and Properties of 1561 Aluminum Alloy
Authors: Yang Lingfei , Wang Xiangjie , Yu Fang , Sun Wei , Li Lei , Cui Jianzhong
Published: February 2022

Through gradient hot rolling, a transition zone from the initial undeformed to 30% deformed microstructure was obtained in the 6 mm thick 1561 aluminum alloy hot rolled plate. The effect of gradient deformation on the evolution process of structure and texture characteristics to 1561 aluminum alloy were systematically investigated by X-ray diffractometer (XRD), optical microscope (OM), and electron back-scattered diffraction (EBSD) in this paper. The results showed that after gradient hot rolling, the grains were elongated along the rolled direction, and the average grain size decreased from 18.95 μm to 1.19 μm. After annealing, the average grain size decreased from 28.34 μm to 10.69 μm. The fraction of dynamic recrystallization is low in all cases. With the increase in gradient deformation, the fraction of the deformed texture (110) <100> Goss, (110) <112 > Brass and fiber texture increased under the action of shear strain, the hardness value of annealed 1561 aluminum alloy ranged from 83.8 HV up to as high as 104 HV, and the electrical conductivity (EC) value increased from 23.5% IACS to 24.3% IACS. Significantly, with the increment of the deformation, the dislocation density increases 2.4 × 1013 m−2 of the annealed hot rolled plates, which should be responsible for the hardness increase. While the structure of the alloy becomes more orderly, the EC increases. Work hardening, fine-grain strengthening and texture all influence the mechanical properties of the gradient hot rolled 1561 aluminum alloy plate.
A Guide to Implementing Immune Checkpoint Inhibitors within a Cancer Program: Experience from a Large Canadian Community Centre
Authors: Cheema Parneet K. , Iafolla Marco A. J. , Nematollahi Massey , Berco FeRevelyn , Kaushik Deepanjali , Matthews Priscilla , Raskin William R. , Perdrizet Kirstin A. , Dudani Shaan , Husain Juhi , Balcewicz Margaret , Kuruvilla Philip G. , Reingold Stephen M. , Conter Henry J.
Published: February 2022

The increased use of immune checkpoint inhibitors across cancer programs has created the need for standardized patient assessment, education, monitoring, and management of immune-related adverse events (irAEs). At William Osler Health System in Brampton, Ontario, a practical step-wise approach detailing the implementation of cancer immunotherapy in routine practice was developed. The approach focuses on four key steps: (1) identification of patient educators; (2) development of patient education materials; (3) development of patient monitoring tools; (4) involvement and education of multidisciplinary teams. Here, we provide an in-depth description of what was included in each step and how we integrated the different elements of the program. For each step, resources, tools, and materials that may be useful for patients, healthcare providers, and multidisciplinary teams were developed or modified based on existing materials. At our centre, the program led to improved patient comprehension of irAEs, the ability to act on symptoms (patient self-efficacy), and low rates of emergency room visits at first presentation for irAEs. We recognize that centres may need to tailor the approaches to their institutional policies and encourage centres to adapt and modify the forms and tools according to their needs and requirements.
Diet and Management of Type II Diabetes Mellitus in the United Kingdom: A Narrative Review
Authors: Rajput Sheeraz A. , Ashraff Suhel , Siddiqui Muhammad
Published: February 2022

Diabetes is a major public health problem and is emerging as a pandemic. The fundamental cause of obesity and overweight is an energy imbalance between calories consumed and calories burned up. Physical activity is one of the mainstay clinical interventions for preventing metabolic diseases, and dietary habits are the primary factor for the rapidly rising incidence of DM. Reducing weight and maintaining a healthy weight, reducing energy intake, and food intake high in vegetables, fruit, whole grains, legumes, nuts, and dairy products are core parts of management. We performed a narrative literature review, manual-search of reference lists of included articles, and relevant reviews. The main purpose of this review was to discuss the role of psychosocial factors and diet in the control of type II Diabetes.
Cardiodiagnostics Based on Photoplethysmographic Signals
Authors: Georgieva-Tsaneva Galya , Gospodinova Evgeniya , Cheshmedzhiev Krasimir
Published: February 2022

The article presents a methodology to support the process of correct cardiodiagnostics based on cardio signals recorded with modern optical photoplethysmographic (PPG) sensor devices. An algorithm for preprocessing registered PPG signals and the formation of a time series for the analysis of heart rate variability is presented, which is an important information indicator in the diagnosis of cardiovascular diseases. In order to validate the proposed algorithm, an experimental scheme for synchronous recordings of PPG and electrocardiographic (ECG) signals and the study of the accuracy of the registered signals was created. The obtained results show high accuracy of the studied signals in terms of the following parameters: number of QRS complexes/pulse waves and mean RR intervals/PP intervals and the finding that the proposed algorithm is suitable for preprocessing PPG signals, as well as the possibility of interchangeable use of PPG and ECG. The results of the mathematical analysis of heart rate variability by applying linear methods (Time-Domain and Frequency-Domain) to two groups of people are presented: healthy controls and patients with cardiovascular disease (syncope). After determining the values of the parameters of the methods used, in order to distinguish healthy subjects from sick ones, statistical analysis was applied using t-test and Receiver Operating Characteristics (ROC) analysis. The obtained results show that the linear methods used are suitable for analysing the dynamics of PP interval series and for distinguishing healthy subjects from those with pathological diseases. The presented research and analyses can find applications in guaranteeing correctness and accuracy of conducting cardiodiagnostics in clinical practice.
Deep Learning Approach to Automatize TMTV Calculations Regardless of Segmentation Methodology for Major FDG-Avid Lymphomas
Authors: Revailler Wendy , Cottereau Anne Ségolène , Rossi Cedric , Noyelle Rudy , Trouillard Thomas , Morschhauser Franck , Casasnovas Olivier , Thieblemont Catherine , Gouill Steven Le , André Marc , Ghesquieres Herve , Ricci Romain , Meignan Michel , Kanoun Salim
Published: February 2022

The total metabolic tumor volume (TMTV) is a new prognostic factor in lymphomas that could benefit from automation with deep learning convolutional neural networks (CNN). Manual TMTV segmentations of 1218 baseline 18FDG-PET/CT have been used for training. A 3D V-NET model has been trained to generate segmentations with soft dice loss. Ground truth segmentation has been generated using a combination of different thresholds (TMTVprob), applied to the manual region of interest (Otsu, relative 41% and SUV 2.5 and 4 cutoffs). In total, 407 and 405 PET/CT were used for test and validation datasets, respectively. The training was completed in 93 h. In comparison with the TMTVprob, mean dice reached 0.84 in the training set, 0.84 in the validation set and 0.76 in the test set. The median dice scores for each TMTV methodology were 0.77, 0.70 and 0.90 for 41%, 2.5 and 4 cutoff, respectively. Differences in the median TMTV between manual and predicted TMTV were 32, 147 and 5 mL. Spearman’s correlations between manual and predicted TMTV were 0.92, 0.95 and 0.98. This generic deep learning model to compute TMTV in lymphomas can drastically reduce computation time of TMTV.
Inferior Vena Cava Edge Tracking Echocardiography: A Promising Tool with Applications in Multiple Clinical Settings
Authors: Albani Stefano , Mesin Luca , Roatta Silvestro , De Luca Antonio , Giannoni Alberto , Stolfo Davide , Biava Lorenza , Bonino Caterina , Contu Laura , Pelloni Elisa , Attena Emilio , Russo Vincenzo , Antonini-Canterin Francesco , Pugliese Nicola Riccardo , Gallone Guglielmo , De Ferrari Gaetano Maria , Sinagra Gianfranco , Scacciatella Paolo
Published: February 2022

Ultrasound (US)-based measurements of the inferior vena cava (IVC) diameter are widely used to estimate right atrial pressure (RAP) in a variety of clinical settings. However, the correlation with invasively measured RAP along with the reproducibility of US-based IVC measurements is modest at best. In the present manuscript, we discuss the limitations of the current technique to estimate RAP through IVC US assessment and present a new promising tool developed by our research group, the automated IVC edge-to-edge tracking system, which has the potential to improve RAP assessment by transforming the current categorical classification (low, normal, high RAP) in a continuous and precise RAP estimation technique. Finally, we critically evaluate all the clinical settings in which this new tool could improve current practice.
Personalized Nutritional Intervention to Improve Mediterranean Diet Adherence in Female Patients with Multiple Sclerosis: A Randomized Controlled Study
Authors: Papandreou Panos , Gioxari Aristea , Daskalou Efstratia , Vasilopoulou Archontia , Skouroliakou Maria
Published: February 2022

Background: Multiple sclerosis (MS) is a chronic immune-mediated central nervous system disorder that affects females twice as often as males. MS patients show increased susceptibility to obesity and related cardiometabolic disorders, while diet may influence disease course. In the present randomized controlled study, we aimed to increase Mediterranean Diet (MedDiet) adherence in MS women and improve their nutritional status. Methods: Adult women with relapsing-remitting MS (n = 40) were randomly allocated to intervention (n = 20) or control group (n = 20). Individual dietary plans based on MedDiet together with nutritional consultation were provided to the intervention group. Controls received general lifestyle advice according to “National Dietary Guidelines”. Medical history, anthropometry, dietary records, and blood withdrawal were performed at baseline and at 3 months. Results: Compared to controls, the intervention group demonstrated greater MedDiet adherence (p < 0.001), which was negatively associated with cholesterol intake levels (p < 0.05). At 3 months, women following MedDiet had ameliorated body weight and body composition compared to baseline (p < 0.001). Serum 1,25(OH)2D was significantly higher in both study groups at 3 months (p < 0.001), but in the intervention group, the mean increment was twofold compared to controls (p < 0.001). Conclusion: Personalized nutritional intervention in MS patients may improve MedDiet adherence and nutritional status towards cardioprotective health outcomes.
A Characterization of Maximally Entangled Two-Qubit States
Authors: Duan Junjun , Zhang Lin , Qian Quan , Fei Shao-Ming
Published: February 2022

As already known by Rana’s result, all eigenvalues of any partial-transposed bipartite state fall within the closed interval [−12,1]. In this note, we study a family of bipartite quantum states where the minimal eigenvalues of partial-transposed states are −12. For a two-qubit system, we find that the minimal eigenvalue of its partial-transposed state is −12 if and only if such a two-qubit state is maximally entangled. However this result does not hold in general for a two-qudit system when the dimensions of the underlying space are larger than two.
Air Quality in Lombardy, Italy: An Overview of the Environmental Monitoring System of ARPA Lombardia
Authors: Maranzano Paolo
Published: February 2022

In this paper, we describe the structure and the features of the air quality and meteorological monitoring system adopted in the Lombardy region in Northern Italy. We are interested in describing which data the Regional Agency for Environmental Protection (ARPA Lombardia) must collect, how this process takes place and how they are disseminated to the public for institutional communication and research purposes. ARPA monitors the atmospheric conditions through a dense ground monitoring network composed mainly by permanent stations, but also by mobile samplers. We describe in a detailed fashion the structure of the network: how many stations the network consists of, their locations, which pollutants and weather events are monitored and with what frequency. Our main objective is to present to an international audience the case study of ARPA Lombardia and the main available public data, explicitly stating the sources of information and how to find them, and encouraging international researchers to deal with the subject. In spite of the significant and extensive efforts made to counteract the phenomenon of air pollution, the air quality recorded in the region is very poor, and the local authorities are struggling to comply with international regulations on the concentration of pollutants in the air, making Lombardy a relevant international case. In addition, we present in a synthetic and descriptive way, without any modeling ambition, some data observed in the last years in Lombardy regarding meteorology and the main pollutants (oxides and particulate matters). The empirical descriptive results have been obtained by analyzing sample data provided by ARPA Lombardia through the same sources described in the sections dedicated to the Agency. From the graphical analysis, it is noticeable that at aggregate (regional) level, the concentrations are affected by significant decreasing trends, but at a rather contained speed. This is particularly true for the concentrations of oxides (NO2 and NOX) in urban and industrial areas. However, particulate matters and ozone show a high persistence in the average concentrations, interrupted only by the alternation of climatic seasons. The data also show that the meteorology of the region does not seem favorable for the improvement of air quality, as the region is characterized by low precipitation, and wind almost everywhere is not very intense. This situation could be induced by the unfavorable geography of the area, which prevents adequate air recycling and facilitates the stagnation of pollutants. We suggest that any public policy intervention aimed at improving the air quality situation in the region should take into account this empirical evidence in the impact assessment phase.
Stock Price Forecasting for Jordan Insurance Companies Amid the COVID-19 Pandemic Utilizing Off-the-Shelf Technical Analysis Methods
Authors: Altarawneh Ghada A. , Hassanat Ahmad B. , Tarawneh Ahmad S. , Abadleh Ahmad , Alrashidi Malek , Alghamdi Mansoor
Published: February 2022

One of the most difficult problems analysts and decision-makers may face is how to improve the forecasting and predicting of financial time series. However, several efforts were made to develop more accurate and reliable forecasting methods. The main purpose of this study is to use technical analysis methods to forecast Jordanian insurance companies and accordingly examine their performance during the COVID-19 pandemic. Several experiments were conducted on the daily stock prices of ten insurance companies, collected by the Amman Stock Exchange, to evaluate the selected technical analysis methods. The experimental results show that the non-parametric Exponential Decay Weighted Average (EDWA) has higher forecasting capabilities than some of the more popular forecasting strategies, such as Simple Moving Average, Weighted Moving Average, and Exponential Smoothing. As a result, we show that using EDWA to forecast the share price of insurance companies in Jordan is good practice. From a technical analysis perspective, our research also shows that the pandemic had different effects on different Jordanian insurance companies.
Management of the Russian Interregional Investment Distribution Using the Autonomous Expenditure Multiplier Model
Authors: Silvestrov Sergey Nikolaevich , Pobyvaev Sergey Alekseevich , Reshetnikov Stanislav Borisovich , Firsov Dmitrii Vladimirovich
Published: February 2022

An effective and competitive investment policy requires improvements to the existing tools. The ongoing COVID-19 crisis requires understanding as to how the recovery processes should be implemented. This study aims to develop a model for determining the autonomous expenditure multiplier (AEM) values, considering the investment accelerator action. The scientific novelty consists of proving that the AEM is not only an effects enhancer of the government and private investment, but also a tool to specify on the regional industrial map of Russia where investment projects will allow significant economic growth. The work’s practical significance consists of determining the possibility of applying the AEM as a tool to improve investment efficiency. The key research method was paired linear regression analysis. Based on the developed model, the AEM values for the economies of the five Central Federal District regions are calculated. Additionally, authors provide an explanation on how AEM values correlate to regional economic specialization. For example, atypically low AEM values for Moscow can be explained by high daily workforce movement among Moscow and the Moscow region. The information support difficulties of the proposed model are defined, and the directions to overcome them are proposed. Empirical results show significant differences in the AEM values of the researched regions, and that the AEM as a management tool for interregional investment distribution will help to invest the limited resources of both the state and private businesses more effectively. Additionally, authors establish that the achieved results fall in line with real macroeconomic situations within the regions, which proves that the proposed model reflects real world processes. Primary beneficiaries and end users of the study are government agencies, state-owned corporations, and members of broader scientific community.
Cookie-Jar Alarms: An Analysis of First-Grade Students’ Gendered Conceptions of Engineers following a Programming Design Task
Authors: Mitchell April , Lott Kimberly H. , Tofel-Grehl Colby
Published: February 2022

Women remain underrepresented in engineering and broadening participation has recently become the focus of education reform efforts. Increased emphasis on K-12 engineering education calls for the design of learning environments and curricula that increase interest and conceptual understanding of engineering work, beginning in the early years of childhood. We seek to understand what works, for whom, in what contexts, how it works, and how engineering curricula can be improved to promote social justice. Here, we evaluate the impact of a curricular intervention designed to promote equity in elementary engineering education. The integrated STEM curriculum unit engages first-grade students in programming a cookie-jar alarm. Using a KIBO robot, students program a distance sensor to trigger the alarm and customize the sounds and flashing lights. Students’ pre-/post-drawings of ‘engineers at work’ were used to elicit reflection in semi-structured interviews, and to assess changes in students’ conceptions of engineering work following the curricular intervention. A mixed analysis of the data revealed students’ conceptual understanding of engineering work improved following the two-week curricular intervention, regardless of gender. However, the girls in the study were more likely than boys to identify as an engineer following the programming design task. We discuss the students’ self-reported explanations and elements of the intervention that may have facilitated conceptual change. We recommend that, to promote equity in engineering education, “gender-responsive” curricula are essential.
Bin-Picking Solution for Randomly Placed Automotive Connectors Based on Machine Learning Techniques
Authors: Torres Pedro , Arents Janis , Marques Hugo , Marques Paulo
Published: February 2022

This paper presents the development of a bin-picking solution based on low-cost vision systems for the manipulation of automotive electrical connectors using machine learning techniques. The automotive sector has always been in a state of constant growth and change, which also implies constant challenges in the wire harnesses sector, and the emerging growth of electric cars is proof of this and represents a challenge for the industry. Traditionally, this sector is based on strong human work manufacturing and the need arises to make the digital transition, supported in the context of Industry 4.0, allowing the automation of processes and freeing operators for other activities with more added value. Depending on the car model and its feature packs, a connector can interface with a different number of wires, but the connector holes are the same. Holes not connected with wires need to be sealed, mainly to guarantee the tightness of the cable. Seals are inserted manually or, more recently, through robotic stations. Due to the huge variety of references and connector configurations, layout errors sometimes occur during seal insertion due to changed references or problems with the seal insertion machine. Consequently, faulty connectors are dumped into boxes, piling up different types of references. These connectors are not trash and need to be reused. This article proposes a bin-picking solution for classification, selection and separation, using a two-finger gripper, of these connectors for reuse in a new operation of removal and insertion of seals. Connectors are identified through a 3D vision system, consisting of an Intel RealSense camera for object depth information and the YOLOv5 algorithm for object classification. The advantage of this approach over other solutions is the ability to accurately detect and grasp small objects through a low-cost 3D camera even when the image resolution is low, benefiting from the power of machine learning algorithms.
Data Preprocessing Combination to Improve the Performance of Quality Classification in the Manufacturing Process
Authors: Cho Eunnuri , Chang Tai-Woo , Hwang Gyusun
Published: February 2022

The recent introduction of smart manufacturing, also called the ‘smart factory’, has made it possible to collect a significant number of multi-variate data from Internet of Things devices or sensors. Quality control using these data in the manufacturing process can play a major role in preventing unexpected time and economic losses. However, the extraction of information about the manufacturing process is limited when there are missing values in the data and a data imbalance set. In this study, we improve the quality classification performance by solving the problem of missing values and data imbalances that can occur in the manufacturing process. This study proceeds with data cleansing, data substitution, data scaling, a data balancing model methodology, and evaluation. Five data balancing methods and a generative adversarial network (GAN) were used to proceed with data imbalance processing. The proposed schemes achieved an F1 score that was 0.5 higher than the F1 score of previous studies that used the same data. The data preprocessing combination proposed in this study is intended to be used to solve the problem of missing values and imbalances that occur in the manufacturing process.
Digital Pole Control for Speed and Torque Variation in an Axial Flux Motor with Permanent Magnets
Authors: González-Parada Adrián , Lozano-García José Merced , Ibarra-Manzano Mario Alberto
Published: February 2022

The use of renewable energies in the transportation industry has prompted the development of higher power electric motors and intelligent electronic traction systems. However, the typical coupling between the two continues to be mechanical, which reduces its efficiency and useful life. On the other hand, permanent magnet axial flux motor configurations make it possible to dispense with mechanical couplings, due to their high torque at low speeds due to their direct application on the wheels of vehicles. In this work, the design of a digital pole commutation system is presented, applied to an axial flux motor with permanent magnets for speed and torque control at a constant speed. The performance of the system is evaluated with experimental measurements; proving the effectiveness of the design, obtaining torques of up to 1784 Nm without extra mechanical couplings and maximum speed regulation errors of 8.43%.
Acknowledgment to Reviewers of Energies in 2021
Authors:   Energies Editorial Office
Published: February 2022

Rigorous peer-reviews are the basis of high-quality academic publishing [...]
Numerical Aspects of a Two-Way Coupling for Electro-Mechanical Interactions—A Wind Energy Perspective
Authors: Lüdecke Fiona Dominique , Schmid Martin , Rehe Eva , Panneer Selvam Sangamithra , Parspour Nejila , Cheng Po Wen
Published: February 2022

Generators in wind turbines are the key components to convert mechanical into electrical power. They are subject to electrical and mechanical excitation at the same time, which can cause electro-mechanical interactions. To avoid unwanted interactions, standard design approaches use conservative, stiff designs that lead to heavy generators of several hundred tons. New wind turbine designs, beyond 10 MW, need to revisit the conservative design approach as the tower top mass needs to be limited. To reduce the generator’s mass without large deformation that can damage the wind turbine, a better understanding of electro-mechanical interactions is key. This requires a detailed model including both the mechanical and the magnetic forces. This work presents a numerical setup of a coupled electromagnetic-structural multi-body model. While existing couplings are application-specific; the presented coupling is independent of the actual use case and allows for transient dynamic two-way coupled analyses. For validation, an experimental setup with basic components is introduced. The results show the applicability of the developed coupling for detailed analysis of general electro-mechanical interactions.
Effect of Double-Sided 3D Patterned Cathode Catalyst Layers on Polymer Electrolyte Fuel Cell Performance
Authors: Noh Taehyoung , Park Kayoung , Gao Ruijing , Kimura Naoki , Inoue Gen , Tsuge Yoshifumi
Published: February 2022

Optimization of the structure of cathode catalyst layers (CCLs) for promoting the transfer of reactants and products in polymer electrolyte fuel cells (PEFCs) is important for improving the cell performance. In this study, using theoretical equations, we confirmed that the shortened proton conduction path in the ionomer layer (IL) with a 3D-patterned structure, compared to that in the IL with a flat-patterned structure, can improve the cell performance. We experimentally investigated the effect of the IL with a 3D-patterned structure included in the CCLs on the cell performance. Based on the combination of the flat- or 3D-pattern of the IL and the catalyst layer (CL), the samples were categorized as Str. 1 (3D-patterned CL without IL), Str. 2 (flat-patterned IL and CL), Str. 3 (3D-patterned IL and flat-patterned CL), and Str. 4 (3D-patterned IL and CL). All of the samples had different morphologies. According to the I–V curves and impedance spectra data acquired at 80 °C and 40% relative humidity, Str. 4 showed superior cell performance relative to those of the other CCLs. These results indicate that the structure of Str. 4 enhanced the proton conductivity at a low humidity at which proton conduction is usually poor, thereby resulting in improved cell performance.
Investigating the Impact of Electric Vehicles Demand on the Distribution Network
Authors: Alquthami Thamer , Alsubaie Abdullah , Alkhraijah Mohannad , Alqahtani Khalid , Alshahrani Saad , Anwar Murad
Published: February 2022

Deployment of Electric Vehicles (EV) is increasing in recent years due to economic and environmental advantages compared with fossil fuel-based vehicles. As the market of EVs grows, new challenges to the electric grid are emerging to accommodate the EVs demand, especially in the distribution networks. In this paper, we investigate the impact of EVs deployment on the electricity demand and distributed network. We propose a model to generate EV demand profiles that consider the EV users’ driving pattern such as daily energy consumption and charging schedule, in addition to the EV’s charging characteristics. The EV demand model uses data we obtained from a survey to evaluate the model’s parameters. We use the EV demand model to simulate and evaluate the impact of EVs demand on the distribution network. We present a case study with an actual model for a distribution network to evaluate the impact of EVs on the distribution network in Saudi Arabia. We analyze the simulation results and show how EVs impact the demand and the distribution network performance.
Development of a Genetic Algorithm Code for the Design of Cylindrical Buoyancy Bodies for Floating Offshore Wind Turbine Substructures
Authors: Benifla Victor , Adam Frank
Published: February 2022

The Levelized Cost of Energy for floating offshore wind must decrease significantly to be competitive with fixed offshore wind projects or even with onshore wind projects. This study focuses on the design optimization of cylindrical buoyancy bodies for floating substructures of offshore wind turbines. The presented work is based on a previously studied buoyancy body design that allows an efficient manufacturing process and integration into different substructures. In this study, an optimization framework based on genetic algorithm is developed to parameterize the buoyancy body’s geometry and optimize its design in terms of cost, considering loads acting on the structure as well as manufacturing and floater specific dimension restrictions. The implementation of the optimization process is detailed, and tested for a given study case. Two structurally different genetic algorithms are considered in order to compare the results obtained and asses the performance of the presented optimization framework.
Repair Priority in Distribution Systems Considering Resilience Enhancement
Authors: Bae In-Su , Kim Sung-Yul , Kim Dong-Min
Published: February 2022

When a meteorological disaster occurs and equipment becomes damaged, a significant amount of time is required to repair the damaged components as it is impossible to repair several components simultaneously. Therefore, the determination of repair priority is a significant aspect of a distribution system’s resilience. This study proposes a technique to identify the unserved areas of a radial distribution system based on the bus injection to the branch current (BIBC) matrix, as opposed to a complex optimization technique, for evaluating the repair priority determination strategy for all the possible disaster scenarios. Generally, most resilience metrics include the concept of duration; therefore, the strategy for resilience enhancement must optimize the recovery priority using an objective function that consists of the recovered capacity increment, rather than the recovered capacity. To verify the proposed method, in this paper, the resilience is evaluated under all the disaster scenarios that can occur in contingencies from N-2 to N-5. Since complex restoration or repair strategies could be simplified using the proposed method, it is expected that this study will make a significant contribution to the resilience enhancement in distribution systems.
Logarithmic Mean Divisia Index Decomposition Based on Kaya Identity of GHG Emissions from Agricultural Sector in Baltic States
Authors: Makutėnienė Daiva , Perkumienė Dalia , Makutėnas Valdemaras
Published: February 2022

Greenhouse gas (GHG) emissions from agriculture contribute to climate change. The consequences of unsustainable agricultural activity are polluted water, soil, air, and food. The agricultural sector has become one of the major contributors to global GHG emissions and is the world’s second largest emitter after the energy sector, which includes emissions from power generation and transport. Latvian and Lithuanian agriculture generates about one fifth of GHG emissions, while Estonia generates only about one tenth of the country’s GHG emissions. This paper investigates the GHG trends in agriculture from 1995 to 2019 and the driving forces of changes in GHG emissions from the agricultural sectors in the Baltic States (Lithuania, Latvia, and Estonia), which are helpful for formulating effective carbon reduction policies and strategies. The impact factors have on GHG emissions was analysed by using the Logarithmic Mean Divisia Index (LMDI) method based on Kaya identity. The aim of this study is to assess the dynamics of GHG emissions in agriculture and to identify the factors that have had the greatest impact on emissions. The analysis of the research data showed that in all three Baltic States GHG emissions from agriculture from 1995 to 2001–2002 decreased but later exceeded the level of 1995 (except for Lithuania). The analysis of the research data also revealed that the pollution caused by animal husbandry activities decreased. GHG intensity declined by 2–3% annually, but the structure of agriculture remained relatively stable. The decomposition of GHG emissions in agriculture showed very large temporary changes in the analysed factors and the agriculture of the Baltic States. GHG emissions are mainly increased by pollution due to the growing economy of the sector, and their decrease is mainly influenced by two factors—the decrease in the number of people employed in the agriculture sector and the decreasing intensity of GHGs in agriculture. The dependence of the result on the factors used for the decomposition analysis was investigated by the method of multivariate regression analysis. Regression analysis showed that the highest coefficient of determination (R2 = 0.93) was obtained for Estonian data and the lowest (R2 = 0.54) for Lithuanian data. In the case of Estonia, all factors were statistically significant; in the case of Latvia and Lithuania, one of the factors was statistically insignificant. The identified GHG emission factors allowed us to submit our insights for the reduction of emissions in the agriculture of the Baltic States.
Estimating of Non-Darcy Flow Coefficient in Artificial Porous Media
Authors: Elsanoose Abadelhalim , Abobaker Ekhwaiter , Khan Faisal , Rahman Mohammad Azizur , Aborig Amer , Butt Stephen D.
Published: February 2022

This study conducted a radial flow experiment to investigate the existence of non-Darcy flow and calculate the non-Darcy “inertia” coefficient; the experiment was performed on seven cylindrical perforated artificial porous media samples. Two hundred thirty-one runs were performed, and the pressure drop was reported. The non-Darcy coefficient β was calculated and compared with available in the literature. The results showed that the non-Darcy coefficient decreased nonlinearly and converged on a value within a specific range as the permeability increased. Nonetheless, it was found that the non-Darcy flow exists even in the very low flow rate deployed in this study. In addition, it has been found that the non-Darcy effect is not due to turbulence but also the inertial effect. The existence of a non-Darcy flow was confirmed for all the investigated samples. The Forchheimer numbers for airflow at varied flow rates are determined using experimentally measured superficial velocity, permeability, and non-Darcy coefficient.
Evaluation of the Pavement Geothermal Energy Harvesting Technologies towards Sustainability and Renewable Energy
Authors: Al-Qadami Ebrahim Hamid Hussein , Mustaffa Zahiraniza , Al-Atroush Mohamed E.
Published: February 2022

Continually using fossil fuels as the main source for producing electricity is one of the main factors causing global warming. Through the past years, several efforts have been made, looking for sustainable, environmentally friendly, and clean energy alternatives. Harvesting geothermal energy from roadway pavement is one of the alternatives that have been developed and investigated recently. Herein, a systematic review and bibliometric analysis were conducted to provide a comprehensive overview of the potentials of harvesting thermal energy from asphalt pavement and to assess the level of achievement being attained towards developed technologies. A total of 713 articles were initially collected, considering the period between 2006 and 2021; later, a series of filtration processes were performed to reach 47 publications. The thermal energy harvesting technologies were categorized into three main sectors, at which their basics and principles were discussed. In addition, a detailed description of the systems’ configurations, materials, and efficiency was presented and described. Finally, gaps and future directions were summarized at the end of this paper. The fundamental knowledge introduced herein can inspire researchers to detect research gaps and serve as a wake-up call to motivate them to explore the high potentials of utilizing pavements as a clean and sustainable energy source.
Sustainable Rural Electrification Project Management: An Analysis of Three Case Studies
Authors: Del-Río-Carazo Laura , Acquila-Natale Emiliano , Iglesias-Pradas Santiago , Hernández-García Ángel
Published: February 2022

Universal access to energy is a global challenge for sustainable development that requires granting last-mile access to energy services to rural and isolated communities. However, achieving access is not sufficient: it must be done affordably, reliably and with an adequate quality. Universal access to energy goes beyond the mere selection of a technical solution or infrastructure; it demands being able to design management models for projects aiming to guarantee that households may access energy services in a sustainable way. This study analyzes the main elements (i.e., governance, technological and business models) of management models in universal access to energy projects and their impact on the different dimensions of sustainability (i.e., social, environmental, and economic). The study then presents three case studies of rural electrification projects having different configurations of the management model, with special focus on the differences in the business model, and it analyzes their outcomes from a sustainability perspective. The analysis of the three case studies suggests that the choice of the business model is key to ensuring sustainability, with fee-for-service models giving the best results. The analysis also highlights the importance of collaboration and involvement of the communities in projects engaging multiple agents with different roles.