bluuebunny/crossref_metadata_2025 huggingface dataset:
Evolution Scenarios and Mitigation Strategies for COVID-19 in Peru, from the Complexity Approach and Agent-Based Modeling
Authors: Salinas Teresa , Tejada Magaly , Encinas Juan José , Talebzadehhosseini Seyyedmilad , Garibay Iván
Published: November 2021

The COVID-19 pandemic in Peru began during March 2020, generating a multidimensional crisis that has claimed 198,621 lives as of 8 September 2021. This study presents the simulation of multiple agents showing the emerging dynamics of the interaction and influence of a subset of biological and social factors in the development of the COVID-19 pandemic in Peru. The model is implemented in NetLogo to simulate different hypothetical scenarios that approximate the real behavior of the interaction between the virus, humans and their environment, adjusting demographic, medical, social, and institutional parameters associated with the evolution and spread of the virus. Four scenarios for the evolution of COVID-19 in Peru are investigated, with different levels of restriction on population mobility. This makes it possible to understand the country’s situation, the complex dynamics of the pandemic and simulate in a multidimensional context the non-linear effect of explanatory variables on the evolution of COVID-19. Social isolation is the measure that has the greatest impact on the behavior of the spread of the virus and, therefore, the one that most helps to prevent and slow down the spread of the infection.
Analysis and Optimization of the Machining Characteristics of High-Volume Content SiCp/Al Composite in Wire Electrical Discharge Machining
Authors: Yan Hongzhi , Kabongo Bakadiasa Djo , Zhou Hongbing , Wu Cheng , Chen Zhi
Published: November 2021

With the properties of high specific strength, small thermal expansion and good abrasive resistance, the particle-reinforced aluminum matrix composite is widely used in the fields of aerospace, automobile and electronic communications, etc. However, the cutting performance of the particle-reinforced aluminum matrix composite is very poor due to severe tool wear and low machining efficiency. Wire electrical discharge machining has been proven to be a good machining method for conductive material with any hardness. Even so, the high-volume SiCp/Al content composite is still a difficult-to-machine material in wire electrical discharge machining due to the influence of insulative the SiC particle. The goal of this paper is to analyze the machining characteristics and find the optimal process parameters for the high-volume content (65 vol.%) SiCp/Al composite in wire electrical discharge machining. Experimental results show that the material removal method of the SiCp/Al composite includes sublimating, decomposing and particle shedding. The material removal rate is found to increase with the increasing pulse-on time, first increasing and then decreasing with the increasing pulse-off time, servo voltage, wire feed and wire tension. Pulse-on time and servo voltage are the dominant factors for surface roughness. In addition, the multi-objective optimization method of the nondominated neighbor immune algorithm is presented to optimize the process parameters for a fast material removal rate and low surface roughness. The optimized process parameters can increase the material removal rate by 34% and reduce the surface roughness by 6%. Furthermore, the effectiveness of the Pareto optimal solution is proven by the verified experiment.
Advances in Diffraction Studies of Light-Induced Transient Species in Molecular Crystals and Selected Complementary Techniques
Authors: Deresz Krystyna A. , Łaski Piotr , Kamiński Radosław , Jarzembska Katarzyna N.
Published: November 2021

The review provides a summary of the current methods of tracing photoexcitation processes and structural dynamics in the solid state, putting major emphasis on the X-ray diffraction techniques (time-resolved Laue diffraction on synchrotron sources and time-resolved serial femtosecond crystallography on X-ray free-electron lasers). The recent developments and nowadays experimental possibilities in the field are discussed along with the data processing and analysis approaches, and illustrated with some striking literature examples of the respective successful studies. Selected complementary methods, such as ultrafast electron diffraction or time-resolved X-ray absorption spectroscopy, are briefly presented.
Contribution of the IdyllaTM System to Improving the Therapeutic Care of Patients with NSCLC through Early Screening of EGFR Mutations
Authors: Petiteau Constance , Robinet-Zimmermann Gwladys , Riot Adèle , Dorbeau Marine , Richard Nicolas , Blanc-Fournier Cécile , Bibeau Frédéric , Deshayes Simon , Bergot Emmanuel , Gervais Radj , Levallet Guénaëlle
Published: November 2021

Epidermal growth factor receptor (EGFR) genotyping, a critical examen for the treatment decisions of patients with non-small cell lung cancer (NSCLC), is commonly assayed by next-generation sequencing (NGS), but this global approach takes time. To determine whether rapid EGFR genotyping tests by the IdyllaTM system guides earlier therapy decisions, EGFR mutations were assayed by both the IdyllaTM system and NGS in 223 patients with NSCLC in a bicentric prospective study. IdyllaTM demonstrated agreement with the NGS method in 187/194 cases (96.4%) and recovered 20 of the 26 (77%) EGFR mutations detected using NGS. Regarding the seven missed EGFR mutations, five were not detected by the IdyllaTM system, one was assayed in a sample with insufficient tumoral cells, and the last was in a sample not validated by the IdyllaTM system (a bone metastasis). IdyllaTM did not detect any false positives. The average time between EGFR genotyping results from IdyllaTM and the NGS method was 9.2 ± 2.2 working days (wd) (12.6 ± 4.0 calendar days (cd)). Subsequently, based on the IdyllaTM method, the timeframe from tumor sampling to the initiation of EGFR-TKI was 7.7 ± 1.2 wd (11.4 ± 3.1 cd), while it was 20.3 ± 6.7 wd (27.2 ± 8.3 cd) with the NGS method (p < 0.001). We thus demonstrated here that the IdyllaTM system contributes to improving the therapeutic care of patients with NSCLC by the early screening of EGFR mutations.
Issi saaneq gen. et sp. nov.—A New Sauropodomorph Dinosaur from the Late Triassic (Norian) of Jameson Land, Central East Greenland
Authors: Beccari Victor , Mateus Octávio , Wings Oliver , Milàn Jesper , Clemmensen Lars B.
Published: November 2021

The Late Triassic (Norian) outcrops of the Malmros Klint Formation, Jameson Land (Greenland) have yielded numerous specimens of non-sauropod sauropodomorphs. Relevant fossils were briefly reported in 1994 and were assigned to Plateosaurus trossingensis. However, continuous new findings of early non-sauropod sauropodomorphs around the globe facilitate comparisons and allow us to now revise this material. Here, the non-sauropod sauropodomorph Issi saaneq gen. et sp. nov. is described based on two almost complete and articulated skulls. The two skulls represent a middle-stage juvenile and a late-stage juvenile or subadult. Issi saaneq differs from all other sauropodomorphs by several unique traits: (1) a small foramen at the medial surface of the premaxilla; (2) an anteroposteriorly elongated dorsoposterior process of the squamosal; (3) a relatively high quadrate relative to rostrum height; (4) a well-developed posterodorsal process of the articular. These features cannot be explained by taphonomy, ontogeny, or intraspecific variation. Issi saaneq shows affinities to Brazilian plateosaurids and the European Plateosaurus, being recovered as the sister clade of the latter in our phylogenetic analysis. It is the northernmost record of a Late Triassic sauropodomorph, and a new dinosaur species erected for Greenland. Issi saaneq broadens our knowledge about the evolution of plateosaurid sauropodomorphs.
Ultrasound Assessment of the Median Nerve Does Not Adequately Discriminate the Carpal Tunnel Syndrome among Patients Diagnosed with Diabetes
Authors: Guillen-Astete Carlos Antonio , Luque-Alarcon Monica , Garcia-Montes Nuria
Published: November 2021

Background: Carpal tunnel syndrome is the most prevalent peripheral nerve entrapment condition of the upper limb. Among metabolic risk factors, diabetes is considered the most relevant. Although wrist ultrasound assessment of the median nerve has demonstrated a good correlation with the gold standard for the diagnosis of this syndrome, neurophysiological study, its usefulness in patients with diabetes is questionable because the compressive phenomenon is not the predominant one. Method: We conducted a retrospective study to compare the clinical and median nerve ultrasound features of patients with carpal tunnel syndrome previously diagnosed or not diagnosed with diabetes. Additionally, a linear multivariate regression analysis was performed to determine to what extent the cross-sectional area of the median nerve was dependent on the condition of diabetes by fixing other variables such as sex, age, or time of evolution. Results: We included 303 records of patients (mean age 44.3 ± 11.7 years old, 57.89% female, mean of time of evolution 13.6 ± 8.3 months) from 2012 to 2020. The cross-sectional area of the median nerve was 10.46 ± 1.44 mm2 in non-diabetic patients and 8.92 ± 0.9 mm2 in diabetic patients (p < 0.001). Additionally, diabetic patients had a shorter time of evolution (7.91 ± 8.28 months vs. 14.36 ± 0.526 months, p < 0.001). In the multivariate analysis, the resultant model (fixed R-square = 0.659, p = 0.003) included a constant of the following four variables: the evolution time (Beta coeff. = 0.108, p < 0.001 95% CI 0.091 to 0.126, standardized coeff. = 0.611), the condition of diabetes (Beta coeff. = −0.623, p < 0.001 95% CI −0.907 to −0.339, standardized coeff. = −0.152), the severity (Beta coeff. = 0.359, p = 0.001 95% CI 0.147 to 0.571, standardized coeff. = 0.169), and the masculine sex (Beta coeff. = 0.309, p = 0.003, 95% CI 0.109 to 0.509, standardized coeff. = 0.103). Conclusions: Ultrasound assessment of the median nerve in patients with diabetes is not a useful tool to confirm whether carpal tunnel syndrome should be diagnosed or not diagnosed.
Are Fatigue and Pain Overlooked in Subjects with Stable Chronic Obstructive Pulmonary Disease?
Authors: Nishimura Koichi , Nakayasu Kazuhito , Mori Mio , Sanda Ryo , Shibayama Ayumi , Kusunose Masaaki
Published: November 2021

Although there have been many published reports on fatigue and pain in patients with chronic obstructive pulmonary disease (COPD), it is considered that these symptoms are seldom, if ever, asked about during consultations in Japanese clinical practice. To bridge this gap between the literature and daily clinical experience, the authors attempted to gain a better understanding of fatigue and pain in Japanese subjects with COPD. The Brief Fatigue Inventory (BFI) to analyse and quantify the degree of fatigue, the revised Short–Form McGill Pain Questionnaire 2 (SF-MPQ-2) for measuring pain and the Kihon Checklist to judge whether a participant is frail and elderly were administered to 89 subjects with stable COPD. The median BFI and SF-MPQ-2 Total scores were 1.00 [IQR: 0.11–2.78] and 0.00 [IQR: 0.00–0.27], respectively. They were all skewed toward the milder end of the respective scales. A floor effect was noted in around a quarter on the BFI and over half on the SF-MPQ-2. The BFI scores were significantly different between groups regarding frailty determined by the Kihon Checklist but not between groups classified by the severity of airflow limitation. Compared to the literature, neither fatigue nor pain are considered to be frequent, important problems in a real-world Japanese clinical setting, especially among subjects with mild to moderate COPD. In addition, our results might suggest that fatigue is more closely related to frailty than COPD.
Special Issue “Molecular Detection and Typing of Viruses”
Authors: Engelmann Ilka
Published: November 2021

I thank all authors, reviewers and the editorial staff who contributed to this special issue [...]
DIAROP: Automated Deep Learning-Based Diagnostic Tool for Retinopathy of Prematurity
Authors: Attallah Omneya
Published: November 2021

Retinopathy of Prematurity (ROP) affects preterm neonates and could cause blindness. Deep Learning (DL) can assist ophthalmologists in the diagnosis of ROP. This paper proposes an automated and reliable diagnostic tool based on DL techniques called DIAROP to support the ophthalmologic diagnosis of ROP. It extracts significant features by first obtaining spatial features from the four Convolution Neural Networks (CNNs) DL techniques using transfer learning and then applying Fast Walsh Hadamard Transform (FWHT) to integrate these features. Moreover, DIAROP explores the best-integrated features extracted from the CNNs that influence its diagnostic capability. The results of DIAROP indicate that DIAROP achieved an accuracy of 93.2% and an area under receiving operating characteristic curve (AUC) of 0.98. Furthermore, DIAROP performance is compared with recent ROP diagnostic tools. Its promising performance shows that DIAROP may assist the ophthalmologic diagnosis of ROP.
Deep Learning-Based Image Reconstruction for CT Angiography of the Aorta
Authors: Heinrich Andra , Streckenbach Felix , Beller Ebba , Groß Justus , Weber Marc-André , Meinel Felix G.
Published: November 2021

To evaluate the impact of a novel, deep-learning-based image reconstruction (DLIR) algorithm on image quality in CT angiography of the aorta, we retrospectively analyzed 51 consecutive patients who underwent ECG-gated chest CT angiography and non-gated acquisition for the abdomen on a 256-dectector-row CT. Images were reconstructed with adaptive statistical iterative reconstruction (ASIR-V) and DLIR. Intravascular image noise, the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR) were quantified for the ascending aorta, the descending thoracic aorta, the abdominal aorta and the iliac arteries. Two readers scored subjective image quality on a five-point scale. Compared to ASIR-V, DLIR reduced the median image noise by 51–54% for the ascending aorta and the descending thoracic aorta. Correspondingly, median CNR roughly doubled for the ascending aorta and descending thoracic aorta. There was a 38% reduction in image noise for the abdominal aorta and the iliac arteries, with a corresponding improvement in CNR. Median subjective image quality improved from good to excellent at all anatomical levels. In CT angiography of the aorta, DLIR substantially improved objective and subjective image quality beyond what can be achieved by state-of-the-art iterative reconstruction. This can pave the way for further radiation or contrast dose reductions.
Understanding Parents’ Perspectives of Support Services for People Living with Spina Bifida and/or Hydrocephalus in Ireland: A Qualitative Exploration
Authors: Buckley Maeve , Boland Pauline , Gowran Rosemary Joan
Published: November 2021

Purpose: In Ireland, the complex needs of people with Spina Bifida and/or Hydrocephalus (SB and/or H) are treated across primary care and tertiary specialist services. Traditionally, there has been much variation in how primary care services are delivered. To increase equity, ‘Progressing Disability Services for Children and Young People’ is a policy which is being implemented to reconfigure children’s services into multidisciplinary teams, for all disabilities. These changes, and an apparent discontinuity of support in the transition to adult services, requires further research exploring service delivery processes. Method: This study explored parents’ perspectives of support services for people with SB and/or H. Eight parents of people with SB and/or H participated in semi-structured interviews which were transcribed and analysed using thematic analysis. Results: Six themes were generated: (1) Difficulty accessing services; (2) Impact of waiting lists; (3) Onus on parents; (4) Importance of communication; (5) Reduced service provision following the implementation of ‘Progressing Disability Services’; and (6) Lack of adult services. Conclusions: While the service redesign for people with SB and/or H and their families is still in the implementation stage, this research contributes to the evolution of these changes by identifying the enhancing aspects such as effective communication and the inhibiting aspects including a parent’s perception of increased responsibility for supporting their family member and barriers in access to services.
Antibiotic Susceptibility, Biofilm Production, and Detection of mecA Gene among Staphylococcus aureus Isolates from Different Clinical Specimens
Authors: Gaire Upama , Thapa Shrestha Upendra , Adhikari Sanjib , Adhikari Nabaraj , Bastola Anup , Rijal Komal Raj , Ghimire Prakash , Banjara Megha Raj
Published: November 2021

The increasing incidence of methicillin-resistant and biofilm-forming S. aureus isolates in hospital settings is a gruesome concern today. The main objectives of this study were to determine the burden of S. aureus in clinical samples, assess their antibiotic susceptibility pattern and detect biofilm formation and mecA gene in them. A total of 1968 different clinical specimens were processed to isolate S. aureus following standard microbiological procedures. Antibiotic susceptibility test of the isolates was performed by Kirby–Bauer disc-diffusion method following CLSI guidelines. Biofilm was detected through tissue culture plate method. Methicillin-resistant S. aureus (MRSA) isolates were screened using cefoxitin (30 µg) discs and mecA gene was amplified by conventional polymerase chain reaction (PCR). Of 177 bacterial growth, the prevalence of S. aureus was 15.3% (n = 27). MRSA were 55.6% (15/27) and 44% (12/27) exhibited multidrug resistance (MDR). There was no significant association between methicillin resistance and MDR (p > 0.05). Both MRSA and MSSA were least sensitive to penicillin (100%, 75%) followed by erythromycin (86.6%, 66.6%). Most of the MRSA (93.4%) were susceptible to tetracycline. All S. aureus isolates were biofilm producers—19 (70%) were weak and only one (4%) was a strong biofilm producer. The strong biofilm-producing MSSA was resistant to most of the antibiotics except cefoxitin and clindamycin. None of the MSSA possessed mecA gene while 8 (53.3%) MRSA had it. More than half of S. aureus isolated were MRSA. High incidence of multidrug resistance along with capacity to form biofilm among clinical isolates of S.aureus is a matter of apprehension and prompt adoption of biosafety measures is suggested to curb their dissemination in the hospital environments.
Current Practices and Beliefs Regarding Screening Patients with Burns for Acute Stress Disorder and Posttraumatic Stress Disorder: A Survey of the American Burn Association Membership
Authors: Smith Mallory B. , Wiechman Shelley A. , Mandell Samuel P. , Gibran Nicole S. , Vavilala Monica S. , Rivara Frederick P.
Published: November 2021

Stress disorders are common after burn injuries and universal screening is recommended. This study describes current screening practices and provider beliefs regarding screening for stress disorders in patients with burns in the US. This was a 31-question survey distributed to the American Burn Association. Sixty-two percent of 121 respondents indicated their institution formally screened for ASD and/or PTSD. The most common reason for not screening was a lack of mental healthcare providers (46%), lack of funding (26%) and lack of time (20%). The timing of screening, the person administering the screening, and the method of screening varied for both pediatric and adult patients. Most respondents (87%) believed screening should be a standard of care, but only 32% were comfortable screening pediatric patients and 62% were comfortable screening adults. While screening for ASD and PTSD is recommended for patients with burns, our study indicates that screening is not a current standard of care. Lack of mental healthcare providers, funding, and time are contributing factors. Among those institutions that screen, a uniform screening practice does not exist.
A Pilot Trial Assessing the Feasibility and Efficacy of a Novel Powder for Rapid Wound Healing
Authors: Verly Myriam , Mason Emily , Sheikh-Oleslami Sara , Jalili Reza , Russ Breshell , Kilani Ruhangiz Taghi , Ghahary Aziz
Published: November 2021

It is well-understood that wound care poses a significant burden on the healthcare system and patient well-being. As such, it is imperative to develop efficient methods that facilitate tissue repair. Our group previously developed a nutritional gel scaffold, proven to accelerate wound repair. Due to its gel-like properties, this scaffold requires a time-consuming reconstitution, and is optimized for cavernous wounds. This pilot study examined the feasibility of a powdered form of this scaffold to accelerate healing of full-thickness wounds, thus broadening the range of applications, while providing a practical product. Splinted full-thickness wounds were generated on the backs of 6 mice, and treated with either powder, the original gel scaffold, or no treatment. Feasibility and efficacy of the powder was assessed through comparison of clinical wound measurements and histological assessments. There was a significant effect of treatment on rate of epithelialization [H(3) = 8.346, p = 0.0024] and on days to epithelial closure [H(3) = 8.482, p = 0.0061]. Post hoc analysis revealed that while requiring no reconstitution and simple to apply, the powder was sufficient to accelerate epithelialization compared to untreated wounds (p < 0.05). Furthermore, our results suggest that application of this powder did not alter certain processes associated with healing progress, such as epidermal thickness and collagen deposition. As such, this powder may provide a novel alternative to our previously developed gel scaffold by accelerating epithelialization, while providing a practical product. Future studies necessitate further evaluation of healing measures with a larger sample size.
Fault Structural Analysis Applied to Proton Exchange Membrane Fuel Cell Water Management Issues
Authors: Dijoux Etienne , Steiner Nadia Yousfi , Benne Michel , Péra Marie-Cécile , Grondin-Perez Brigitte
Published: November 2021

Proton exchange membrane fuel cells are relevant systems for power generation. However, they suffer from a lack of reliability, mainly due to their structural complexity. Indeed, their operation involves electrochemical, thermal, and electrical phenomena that imply a strong coupling, making it harder to maintain nominal operation. This complexity causes several issues for the design of appropriate control, diagnosis, or fault-tolerant control strategies. It is therefore mandatory to understand the fuel cell structure for a relevant design of these kinds of strategies. This paper proposes a fuel cell fault structural analysis approach that leads to the proposition of a structural graph. This graph will then be used to highlight the interactions between the control variables and the functionalities of a fuel cell, and therefore to emphasize how changing a parameter to mitigate a fault can influence the fuel cell state and eventually cause another fault. The final aim of this work is to allow an easier implementation of an efficient and fault-tolerant control strategy on the basis of the proposed graphical representation.
A Comprehensive Modeling of the Discrete and Dynamic Problem of Berth Allocation in Maritime Terminals
Authors: Mnasri Sami , Alrashidi Malek
Published: November 2021

In this study, the discrete and dynamic problem of berth allocation in maritime terminals, is investigated. The suggested resolution method relies on a paradigm of optimization with two techniques: heuristic and multi-agent. Indeed, a set of techniques such as the protocol of negotiation named contract net, the multi-agent interactions, and Worst-Fit arrangement technique, are involved. The main objective of the study is to propose a solution for attributing m parallel machines to a set of activities. The contribution of the study is to provide a detailed modeling of the discrete and dynamic berth allocation problem by establishing the corresponding models using a multi-agent methodology. A set of numerical experiments are detailed to prove the performance of the introduced multi-agent strategy compared with genetic algorithm and tabu search.
Strategies for Controlling Microgrid Networks with Energy Storage Systems: A Review
Authors: Al-Saadi Mudhafar , Al-Greer Maher , Short Michael
Published: November 2021

Distributed Energy Storage Systems are considered key enablers in the transition from the traditional centralized power system to a smarter, autonomous, and decentralized system operating mostly on renewable energy. The control of distributed energy storage involves the coordinated management of many smaller energy storages, typically embedded within microgrids. As such, there has been much recent interest related to controlling aspects of supporting power-sharing balance and sustainability, increasing system resilience and reliability, and balancing distributed state of charge. This paper presents a comprehensive review of decentralized, centralized, multiagent, and intelligent control strategies that have been proposed to control and manage distributed energy storage. It also highlights the potential range of services that can be provided by these storages, their control complications, and proposed solutions. Specific focus on control strategies based upon multiagent communication and reinforcement learning is a main objective of this paper, reflecting recent advancements in digitalization and AI. The paper concludes with a summary of emerging areas and presents a summary of promising future directions.
A Critical Review of Data-Driven Transient Stability Assessment of Power Systems: Principles, Prospects and Challenges
Authors: Zhang Shitu , Zhu Zhixun , Li Yang
Published: November 2021

Transient stability assessment (TSA) has always been a fundamental means for ensuring the secure and stable operation of power systems. Due to the integration of new elements such as power electronics, electric vehicles and renewable power generations, dynamic characteristics of power systems are becoming more and more complex, which makes TSA an increasingly urgent task. Since traditional time-domain simulations and direct method cannot meet the actual operation requirements of power systems, data-driven TSA has attracted growing attention from both academia and industry. This paper makes a comprehensive review from the following four aspects: feature extraction and selection, model construction, online learning and rule extraction; and then, summarizes the challenges and prospects for future research; finally, draws the conclusions of this review. This review will be beneficial for relevant researchers to better understand the research status, key technologies, and existing challenges in the field.
Electrical and Energy Systems Integration for Maritime Environment-Friendly Transportation
Authors: Vicenzutti Andrea , Sulligoi Giorgio
Published: November 2021

The policies against climate change require the reduction of greenhouse gas emissions of marine transportation. To reach the planned goals, the most promising approach is working both on ships improvement and ports redesign. The latter must enable the new green ships supply with sustainable electrical energy, by integrating shore connection systems, local renewables, and energy storage systems. In this paper, a methodology to obtain such an objective is proposed, capable of taking into account both ships’ and ports’ characteristics. The methodology workflow is explained through a case study, where two shore connection power sizes and two different operative approaches for recharging the ship onboard energy storage are considered. A discussion about the most suitable energy storage technologies is also provided. The case study shows how the methodology can be applied, as well as demonstrating that the port infrastructure has a direct effect on the ship environmental performance.
Cultural and Economic Barriers in Switching to Clean Cooking Energy: Does Women’s Agency Make a Difference?
Authors: Kelkar Govind , Nathan Dev
Published: November 2021

The major objective of this study is to identify and analyze cultural and economic barriers to sustained adoption of LPG (liquefied petroleum gas) as the primary clean cooking energy in India, and examining underpinning values and norms in socio-technical energy system of the country. In 2016, the Government of India introduced a mega scheme called Ujjwala for clean cooking energy with LPG connects in women’s name. This policy, however, experienced limited implementation, but did lead to enhancing women’s agency in many areas. Women’s agency is defined briefly as their ability to set goals, develop capacities, and act on their defined goals to realize desired outcomes in wellbeing and capabilities. In the case of switching to clean cooking energy, the question can be posed as: as women are the ones who carry out most of the onerous work of collecting and cooking with wood, are they able to make decisions on the adoption of clean cooking fuel, that enhance their agency and the wellbeing of their families? Male-centred cultural and economic norms can be changed by the exercise of women’s agency, when (1) women have unmediated asset ownership rights to land, houses, and energy technology; (2) they are organized in groups for earning cash incomes and energy access; (3) they have acquired new knowledge, skills, and finances to acquire and operate new technologies; and (4) women have experienced the effects of policy change addressing gendered norms.
Health Monitoring and Diagnosis System for a Small H-Type Darrieus Vertical-Axis Wind Turbine
Authors: Hwang Sungmok , Yoo Cheol
Published: November 2021

As the wind power market grows rapidly, the importance of technology for real-time monitoring and diagnosis of wind turbines is increasing. However, most of the developed technologies and research mainly focus on large horizontal-axis wind turbines, and research conducted on small- and medium-sized wind turbines is rare. In this study, a novel low-cost and real-time health monitoring and diagnosis system for the small H-type Darrieus vertical axis wind turbine is proposed. Turbine operating conditions were classified into parked/idle and power production. In the case of the power production condition, abnormality diagnosis was performed using key monitoring parameters, including vibration, fundamental frequency, the bending stress of the tower and generator vibration. The turbine abnormalities were diagnosed in two stages by applying the alert and alarm limits, determined by referring to international standards and material properties and the long-term measurement data together.
Does Air Pollution Impact Fiscal Sustainability? Evidence from Chinese Cities
Authors: Gao Ge , Li Xiuting , Liu Xiaoting , Dong Jichang
Published: November 2021

Fiscal sustainability is an issue of great concern for governments globally and air pollution control has become an important factor affecting fiscal sustainability. This study aims to examine the impact of air pollution on fiscal sustainability in the short and long run. We conducted an empirical analysis based on air pollution and local government debt data on China’s prefecture-level cities in 2014–2019, using regression discontinuity design (RDD) and a panel data model. The results show that air pollution reduces the debt burden of governments in the short run. However, in the long run, addressing the negative impacts of air pollution adds to the debt burden of local governments, hindering fiscal sustainability. Fiscal freedom and the level of public services significantly moderate the negative impact of air pollution on fiscal sustainability. A higher level of fiscal freedom generally indicates a greater incentive for local governments to raise pollutant emission standards, strengthen the construction of green infrastructure, and subsidize green enterprises. Furthermore, a higher level of public services reflects better infrastructure and higher levels of investment in environmental protection, which help to reduce the negative impact of air pollution. The governments are suggested to take measures to effectively control air pollution, so as to enhance fiscal stability in the long run.
A Multilevel Inverter Topology Using Diode Half-Bridge Circuit with Reduced Power Component
Authors: Sathik Jagabar , Aleem Shady H. E. Abdel , Shalchi Alishah Rasoul , Almakhles Dhafer , Bertilsson Kent , Bhaskar Mahajan Sagar , Fernandez Savier George , Dhandapani Karthikeyan
Published: November 2021

This paper presents a new multilevel converter with a reduced number of power components for medium voltage applications. Both symmetric and asymmetric structures of the presented multilevel converter are proposed. The symmetric topology requires equal dc source values, whereas the asymmetric topology uses minimum switch count. However, both structures suffer from high blocking voltage across the switches. To reduce the blocking voltage on switches, an optimal topology is presented and analyzed for the selection of the minimum number of switches and dc sources, while maintaining a low blocking voltage across the switches. A comparative analysis with recently published topologies was performed. The simulation results, as well as the comparative analysis, validated the robustness and effectiveness of the proposed topology in terms of the reduced power loss, lowered number of components, and cost. Furthermore, in addition to the simulation results, the performance of the proposed topology was verified using experimental results of 9, 17, and 25 levels.
Hybrid Forecast and Control Chain for Operation of Flexibility Assets in Micro-Grids
Authors: Mirtaheri Hamidreza , Macaluso Piero , Fantino Maurizio , Efstratiadi Marily , Tsakanikas Sotiris , Papadopoulos Panagiotis , Mazza Andrea
Published: November 2021

Studies on forecasting and optimal exploitation of renewable resources (especially within microgrids) were already introduced in the past. However, in several research papers, the constraints regarding integration within real applications were relaxed, i.e., this kind of research provides impractical solutions, although they are very complex. In this paper, the computational components (such as photovoltaic and load forecasting, and resource scheduling and optimization) are brought together into a practical implementation, introducing an automated system through a chain of independent services aiming to allow forecasting, optimization, and control. Encountered challenges may provide a valuable indication to make ground with this design, especially in cases for which the trade-off between sophistication and available resources should be rather considered. The research work was conducted to identify the requirements for controlling a set of flexibility assets—namely, electrochemical battery storage system and electric car charging station—for a semicommercial use-case by minimizing the operational energy costs for the microgrid considering static and dynamic parameters of the assets.
Power Plant Energy Predictions Based on Thermal Factors Using Ridge and Support Vector Regressor Algorithms
Authors: Afzal Asif , Alshahrani Saad , Alrobaian Abdulrahman , Buradi Abdulrajak , Khan Sher Afghan
Published: November 2021

This work aims to model the combined cycle power plant (CCPP) using different algorithms. The algorithms used are Ridge, Linear regressor (LR), and upport vector regressor (SVR). The CCPP energy output data collected as a factor of thermal input variables, mainly exhaust vacuum, ambient temperature, relative humidity, and ambient pressure. Initially, the Ridge algorithm-based modeling is performed in detail, and then SVR-based LR, named as SVR (LR), SVR-based radial basis function—SVR (RBF), and SVR-based polynomial regression—SVR (Poly.) algorithms, are applied. Mean absolute error (MAE), R-squared (R2), median absolute error (MeAE), mean absolute percentage error (MAPE), and mean Poisson deviance (MPD) are assessed after their training and testing of each algorithm. From the modeling of energy output data, it is seen that SVR (RBF) is the most suitable in providing very close predictions compared to other algorithms. SVR (RBF) training R2 obtained is 0.98 while all others were 0.9–0.92. The testing predictions made by SVR (RBF), Ridge, and RidgeCV are nearly the same, i.e., R2 is 0.92. It is concluded that these algorithms are suitable for predicting sensitive output energy data of a CCPP depending on thermal input variables.
Automatic Clutch Engagement Control for Parallel Hybrid Electric Vehicle
Authors: Vu Trieu Minh , Moezzi Reza , Cyrus Jindrich , Hlava Jaroslav , Petru Michal
Published: November 2021

Automatic clutch engagement control is essential for all kinds of vehicle power transmissions. The controllers for vehicle power transmissions may include model-based or model-free approaches and must provide high transmission efficiency, fast engagement and low jerk. Most vehicle automatic transmissions are using torque converters with transmission efficiencies up to 96%. This paper presents the use of fuzzy logic control for a dry clutch in parallel hybrid electric vehicles. This controller can minimize the loss of power transmission since it can offer a higher transmission efficiency, up to 99%, with faster engagement, lower jerk and, thus, higher driving comfortability with lower cost. Fuzzy logic control is one of the model-free schemes. It can be combined with AI algorithms, neuro networks and virtual reality technologies in future development. Fuzzy logic control can avoid the complex modelling while maintaining the system’s high stability amid uncertainties and imprecise information. Experiments show that fuzzy logic can reduce the clutch slip and vibration. The new system provides 2% faster engagement speed than the torque converter and eliminates 70% of noise and vibration less than the manual transmission clutch.
A Method for Determining the Impact of Ambient Temperature on an Electrical Cable during a Fire
Authors: Perka Bogdan , Piwowarski Karol
Published: November 2021

Evaluating environmental conditions that trigger fire-fighting equipment is one of the primary design tasks that have to be taken into account when engineering electrical systems supplying such devices. All of the solutions are aimed at, among others, preserving environmental parameters in a building being on fire for an assumed time and at a level enabling safe evacuation. These parameters include temperature, thermal radiation, visibility range, oxygen concentration, and environmental toxicity. This article presents a new mathematical model for heat exchange between the environment and an electric cable under thermal conditions exceeding permissible values for commonly used non-flammable installation cables. The method of analogy between thermal and electrical systems was adopted for modelling heat flow. Determining how the thermal conductivity of the cable and the thermal capacity of a conductor-insulation system can be applied to calculate the wire temperature depending on the heating time t and distance x from the heat source is discussed. Thermal conductivity and capacity were determined based on experimental tests for halogen-free flame-retardant (HFFR) cables with wire cross-sections of 2.5, 4.0, and 6.0 mm2. The conducted experimental tests enable verifying the results calculated by the mathematical model.
Modeling of Memristors under Periodic Signals of Different Parameters
Authors: Garda Bartłomiej
Published: November 2021

In this paper, the problem of modeling memristors is studied. Two types of memristors with carbon and tungsten doping fabricated by the Knowm Inc. are tested. The memristors have been examined with either sinusoidal or triangle voltage wave periodic excitation. Some different frequencies, amplitudes and signal shapes have been applied. The collected data have been averaged and subjected to high frequency filtering. The quality of measurement data has also been discussed. The averaged measurement has been modeled using three popular memristor models: Strukov, Biolek and VTEAM. Some additional feathers to the considered models have been proposed and tested. Memristor is usually modeled by a set of algebraic-differential equations which link both electrical values (i.e., voltage and current) and the internal variable(s) responsible for the element dynamics. The interior-point with box constrains optimization method has been used to obtain the optimal parameters of the memristor model that fit best to the collected data. The results of the optimization process have been discussed and compared. The sensitivity to the different frequency range has been also examined and reviewed. Some conclusions and future work ideas have been postulated.
Eco-Efficient Ventilated Facades Based on Circular Economy for Residential Buildings as an Improvement of Energy Conditions
Authors: Mercader-Moyano Pilar , Anaya-Durán Paula , Romero-Cortés Ana
Published: November 2021

In a planet with limited resources, climate change is a severe problem, intensified by industrial development. This is particularly important in the construction sector. According to the International Energy Agency (2021), the construction sector is responsible for 40% of the global energy consumption and 36% of CO2 emissions. In this way, sustainable architectural solutions should be a priority in our fight against climate change, for it is necessary to propose solutions that help to reuse existing resources, thus reducing consumption. In this sense, the rehabilitation of buildings with solutions that favor the circular economy will become a key element in the construction sector. This work provides the design of a facade for building rehabilitation based on the circular economy paradigm, which is implanted in a model building as a case study. The HULC tool quantifies the improvement in energy efficiency that this rehabilitation entails when compared to a conventional facade. With the designed facade, a reduction in energy losses through the envelope is achieved, as well as an improvement in living conditions and environmental impact. Next, an analysis of the building’s energy consumption and CO2 emissions is carried out with the Open BIM Quantities tool. This tool is used by construction researchers and professionals. Finally, the results show the improvements in the rehabilitation of the facade.
The Electrification of Conventional Industrial Processes: The Use of Mechanical Vapor Compression in an EtOH–Water Distillation Tower
Authors: Rispoli Andrea Liberale , Rispoli Giacomo , Verdone Nicola , Salladini Annarita , Agostini Emanuela , Boccacci Mirko , Parisi Maria Paola , Mazzarotta Barbara , Vilardi Giorgio
Published: November 2021

The aim of this study is to analyze the exergetic, environmental, and economic impact of the electrification of a bio-refinery plant, considering the application of Mechanical Vapor Compression (MVC) to a conventional water–ethanol distillation column in the context of bioethanol production. The process was implemented in AspenPlus® and Aspen Exchange Design and Rating (EDR) simulation environments, where a sensitivity analysis was also carried out, considering four scenarios characterized by different compressions’ operative conditions, and including a Coefficient of Performance (CoP) analysis of the proposed solution with MVC. Exergetic and economic analyses were performed, and the relevant impacts on Operative Expenditure (OpEx) and Capital Expenditure (CapEx) were analyzed. Comparing the base case scenario with the proposed solution, a reduction of operative costs of around 63% was achieved. Finally, an environmental analysis was carried out, showing a remarkable reduction in the carbon footprint of the unit, with a carbon dioxide emission reduction of almost 80% for the MVC solution, in line with RED target requirements.
A Survey Data Approach for Determining the Probability Values of Vehicle-to-Grid Service Provision
Authors: Zagrajek Krzysztof
Published: November 2021

One of the key aspects of vehicle-to-grid technology (V2G) is the analysis of uncertainty in electric vehicle user behavior. Correct estimation of the amount of available energy from electric vehicles that are expected to provide ancillary services to the electricity system operator or to secure the end user’s demand is essential to design these services in an appropriate way. Therefore, it is necessary to analyze the probabilities of V2G service performance for different scenarios. This paper presents the author’s approach to determining the values of V2G service provision probabilities using survey data. It was found that estimating these values using simulation and forecasting tools makes sense when the coefficients resulting from survey responses are used as initial data. Thus, the paper also presents the results of the surveys that were conducted. As the results from the simulations show, the values of the probabilities of V2G services are not high, which should induce future operators of V2G services to offer a beneficial product for the customer.
Sensor-Based Machine Learning Approach for Indoor Air Quality Monitoring in an Automobile Manufacturing
Authors: Wandy Yose , Vogt Marcus , Kansara Rushit , Felsmann Clemens , Herrmann Christoph
Published: November 2021

The alternative control concept using emission from the machine has the potential to reduce energy consumption in HVAC systems. This paper reports on a study of alternative inputs for a control system of HVAC using machine learning algorithms, based on data that are gathered in a welding area of an automotive factory. A data set of CO2, fine dust, temperatures and air velocity was logged using continuous and gravimetric measurements during two typical production weeks. The HVAC system was reduced gradually each day to trigger fluctuations of emission. The data were used to train and test various machine learning models using different statistical indices, consequently to choose a best fit model. Different models were tested and the Long Short-Term Memory model showed the best result, with 0.821 discrepancy on R2. The gravimetric samples proved that the reduction of air exchange rate does not correlate to escalation of fine dust linearly, which means one cannot rely on just gravimetric samples for HVAC system optimization. Furthermore, by using machine learning algorithms, this study shows that by using commonly available low cost sensors in a production hall, it is possible to correlate fine dust data cost effectively and reduce electricity consumption of the HVAC.
A Survey on Energy Efficiency in Smart Homes and Smart Grids
Authors: Prieto González Lisardo , Fensel Anna , Gómez Berbís Juan Miguel , Popa Angela , de Amescua Seco Antonio
Published: November 2021

Empowered by the emergence of novel information and communication technologies (ICTs) such as sensors and high-performance digital communication systems, Europe has adapted its electricity distribution network into a modern infrastructure known as a smart grid (SG). The benefits of this new infrastructure include precise and real-time capacity for measuring and monitoring the different energy-relevant parameters on the various points of the grid and for the remote operation and optimization of distribution. Furthermore, a new user profile is derived from this novel infrastructure, known as a prosumer (a user that can produce and consume energy to/from the grid), who can benefit from the features derived from applying advanced analytics and semantic technologies in the rich amount of big data generated by the different subsystems. However, this novel, highly interconnected infrastructure also presents some significant drawbacks, like those related to information security (IS). We provide a systematic literature survey of the ICT-empowered environments that comprise SGs and homes, and the application of modern artificial intelligence (AI) related technologies with sensor fusion systems and actuators, ensuring energy efficiency in such systems. Furthermore, we outline the current challenges and outlook for this field. These address new developments on microgrids, and data-driven energy efficiency that leads to better knowledge representation and decision-making for smart homes and SGs.