bluuebunny/crossref_metadata_2025 huggingface dataset:
Antimicrobial Activity In Vitro of Cream from Plant Extracts and Nanosilver, and Clinical Research In Vivo on Veterinary Clinical Cases
Authors: Popova Teodora P. , Ignatov Ignat , Petrova Toshka E. , Kaleva Mila D. , Huether Fabio , Karadzhov Stoil D.
Published: November 2022

The antimicrobial effect of a cream containing extracts of African geranium (Pelargonium sidoides DC.), black elderberry (Sambucus nigra L.), and St. John’s wort (Hypericum perforatum L.) in colloidal nanosilver (AgNPs) at a concentration of 30 ppm, denoted as SILVER STOP® cream (SS® cream), was examined in vitro. The research was performed with Escherichia coli (ATCC and two clinical isolates), Staphylococcus aureus (ATCC and two clinical strains), and Candida albicans (ATCC and two clinical isolates). The agar-gel diffusion method and suspension tests for determination of the time of antimicrobial action of SS® cream were used. SS® cream showed significant antimicrobial activity. The Gram-negative microorganisms tested died in a much shorter time than the Gram-positive ones. In suspension with a density of 104 cells·mL−1, E. coli died for 1 min, the oval fungus C. albicans—after 10 min and S. aureus—after 60 min of exposure to SS® cream. The highest sensitivity was found in E. coli. The curative effect of SILVER STOP® cream was also examined in vivo in dogs with different skin diseases. The results showed successful healing of the diseases and a very good curative effect of the cream.
Focused Delivery of Chemotherapy to Augment Surgical Management of Brain Tumors
Authors: Mehkri Yusuf , Woodford Samuel , Pierre Kevin , Dagra Abeer , Hernandez Jairo , Reza Hosseini Siyanaki Mohammad , Azab Mohammed , Lucke-Wold Brandon
Published: November 2022

Chemotherapy as an adjuvant therapy that has largely failed to significantly improve outcomes for aggressive brain tumors; some reasons include a weak blood brain barrier penetration and tumor heterogeneity. Recently, there has been interest in designing effective ways to deliver chemotherapy to the tumor. In this review, we discuss the mechanisms of focused chemotherapies that are currently under investigation. Nanoparticle delivery demonstrates both a superior permeability and retention. However, thus far, it has not demonstrated a therapeutic efficacy for brain tumors. Convection-enhanced delivery is an invasive, yet versatile method, which appears to have the greatest potential. Other vehicles, such as angiopep-2 decorated gold nanoparticles, polyamidoamine dendrimers, and lipid nanostructures have demonstrated efficacy through sustained release of focused chemotherapy and have either improved cell death or survival in humans or animal models. Finally, focused ultrasound is a safe and effective way to disrupt the blood brain barrier and augment other delivery methods. Clinical trials are currently underway to study the safety and efficacy of these methods in combination with standard of care.
Resemblance of the Recurrence Patterns in Primary Systemic, Primary Surgery and Secondary Oncoplastic Surgery
Authors: Dayan Davut , Ernst Kristina , Aktas Bahriye , Baierl Raffaela , Briest Susanne , Dengler Martin , Dieterle Daniela , Endres Amelie , Engelken Kathrin , Faridi Andree , Frenz Hannes , Hantschmann Peer , Janni Wolfgang , Kaiser Christina , Kokott Thorsten , Laufhütte Stefanie , Schober Florian , Ebner Florian
Published: November 2022

Purpose: Surgical interventions tend to have an effect on the generation of recurrences in tumor patients due to the anesthesia involved as well as tissue damage and subsequent inflammation. This can also be found in patients with breast cancer. Methods: In this multicenter study, we investigated data of 632 patients with breast cancer and the subsequent diagnosis of a recurrence. The patient data were acquired from 1 January 2006 to 31 December 2019 in eight different centers in Germany. The data sets were separated into those with primary surgery, primary systemic therapy with subsequent surgery, and reconstructive surgery. Three different starting points for observation were defined: the date of diagnosis, the date of first surgery, and the date of reconstructive surgery, if applicable. The observational period was divided into steps of six months and maxima of recurrences were compared. Furthermore, the variance was calculated using the difference of the distribution in percent. Results: The descriptive analysis showed no resemblance between the groups. The variance of the difference of the recurrence rates analysis using the surgical date as the starting point showed similarities in the age subgroup. Conclusion: Our clinical analysis shows different metastatic behavior in different analysis and treatment regimes. These findings justify further investigations on a larger database. These results may possibly identify an improved follow-up setting depending on tumor stage, biology, treatment, and patient factors (i.e., age, …).
An Analysis by State on The Effect of Movement Control Order (MCO) 3.0 Due to COVID-19 on Malaysians’ Mental Health: Evidence from Google Trends
Authors: Pang Nicholas Tze Ping , Kamu Assis , Ho Chong Mun , Wider Walton , Tseu Mathias Wen Leh
Published: November 2022

Due to significant social and economic upheavals brought on by the COVID-19 pandemic, there is a great deal of psychological pain. Google Trends data have been seen as a corollary measure to assess population-wide trends via observing trends in search results. Judicious analysis of Google Trends data can have both analytical and predictive capacities. This study aimed to compare nation-wide and inter-state trends in mental health before and after the Malaysian Movement Control Order 3.0 (MCO 3.0) commencing 12 May 2021. This was through assessment of two terms, “stress” and “sleep” in both the Malay and English language. Google Trends daily data between March 6 and 31 May in both 2019 and 2021 was obtained, and both series were re-scaled to be comparable. Searches before and after MCO 3.0 in 2021 were compared to searches before and after the same date in 2019. This was carried out using the differences in difference (DiD) method. This ensured that seasonal variations between states were not the source of our findings. We found that DiD estimates, β_3 for “sleep” and “stress” were not significantly different from zero, implying that MCO 3.0 had no effect on psychological distress in all states. Johor was the only state where the DiD estimates β_3 were significantly different from zero for the search topic ‘Tidur’. For the topic ‘Tekanan’, there were two states with significant DiD estimates, β_3, namely Penang and Sarawak. This study hence demonstrates that there are particular state-level differences in Google Trend search terms, which gives an indicator as to states to prioritise interventions and increase surveillance for mental health. In conclusion, Google Trends is a powerful tool to examine larger population-based trends especially in monitoring public health parameters such as population-level psychological distress, which can facilitate interventions.
A Primary Mediastinal Monophasic Spindle-Cell Synovial Sarcoma with Superior Venacaval Obstruction
Authors: Madi Deepak , Dsouza Nikhil Victor , Manoj Matthew Antony , Achappa Basavaprabhu , Boussios Stergios
Published: November 2022

Primary mediastinal sarcoma is a rare tumour that usually presents with nonspecific symptoms such as hoarseness, dyspnoea, and chest pain. Superior vena cava (SVC) syndrome is an extremely uncommon complication that is caused by the compression, invasion, and thrombosis of the SVC or brachiocephalic veins. SVC syndrome can present as asymptomatic cases or as rare life-threatening emergencies with upper airway obstruction and increased intracranial pressure. This report describes the case of a 58-year-old female who presented with swelling of the face, neck, and upper limbs associated with dyspnoea on exertion. The radiological investigations revealed a large well-defined central necrotic peripherally enhancing lesion in the superior mediastinum extending anteriorly with the compression of brachiocephalic veins. A histopathological examination detected spindle cells arranged in fascicles with nuclear atypia with immunohistochemistry positive for creatine kinase (CK), smooth muscle actin (SMA), desmin and CD99. These findings established the diagnosis of a mediastinal monophasic synovial sarcoma with SVC obstruction. The patient was initiated on palliative radiotherapy for the management of the SVC, followed by systemic biological treatment with the tyrosine kinase inhibitor pazopanib, and was clinically improved. It is essential to promptly diagnose and treat this condition, especially when SVC syndrome manifests.
Spatial Co-Clustering of Tuberculosis and HIV in Ethiopia
Authors: Gemechu Leta Lencha , Debusho Legesse Kassa
Published: November 2022

Background: Tuberculosis (TB) and HIV are epidemiologically associated, and their co-dynamics suggest that the two diseases are directly related at the population level and within the host. However, there is no or little information on the joint spatial patterns of the two diseases in Ethiopia. The main objective of the current study was to assess the spatial co-clustering of TB and HIV cases simultaneously in Ethiopia at the district level. Methods: District-level aggregated data collected from the national Health Management Information System (HMIS) for the years 2015 to 2018 on the number of TB cases enrolled in directly observed therapy, short course (DOTS) who were tested for HIV and the number of HIV patients enrolled in HIV care who were screened for TB during their last visit to health care facilities were used in this study. The univariate and bivariate global and local Moran’s I indices were applied to assess the spatial clustering of TB and HIV separately and jointly. Results: The results of this study show that the two diseases were significantly (p-value <0.001) spatially autocorrelated at the district level with minimum and maximum global Moran’s I values of 0.407 and 0.432 for TB, 0.102 and 0.247 for HIV, and 0.152 and 0.251 for joint TB/HIV. The district-level TB/HIV spatial co-clustering patterns in Ethiopia in most cases overlapped with the hot spots of TB and HIV. The TB/HIV hot-spot clusters may appear due to the observed high TB and HIV prevalence rates in the hot-spot districts. Our results also show that there were low-low TB/HIV co-clusters or cold spots in most of the Afar and Somali regions, which consistently appeared for the period 2015–2018. This may be due to very low notifications of both diseases in the regions. Conclusions: This study expanded knowledge about TB and HIV co-clustering in Ethiopia at the district level. The findings provide information to health policymakers in the country to plan geographically targeted and integrated interventions to jointly control TB and HIV.
Nutritional Support in Acute Liver Failure
Authors: Abenavoli Ludovico , Maurizi Valentina , Boccuto Luigi , Di Berardino Arianna , Giostra Nena , Santori Pierangelo , Scarcella Maria Laura , Procopio Anna Caterina , Rasetti Carlo , Scarpellini Emidio
Published: November 2022

Acute liver failure (ALF) presents with an acute abnormality of liver blood tests in an individual without underlying chronic liver disease. The clinical course leads to the development of coagulopathy and hepatic encephalopathy. The role of nutrition in its prevention and treatment remains uncertain. We aimed to review literature data on the concept of ALF and the role of nutrition in its treatment and prevention, considering the impact of gut microbiota dysbiosis and eubiosis. We conducted a review of the literature on the main medical databases using the following keywords and acronyms and their associations: liver failure, nutrition, branched-chain amino acids, gut microbiota, dysbiosis, and probiotics. Upon their arrival at the emergency department, an early, accurate nutritional assessment is crucial for individuals with ALF. Branched-chain amino acids (BCAAs), stable euglycemia maintenance, and moderate caloric support are crucial for this subset of patients. An excessive protein load must be avoided because it worsens hepatic encephalopathy. Preclinical evidence supports future probiotics use for ALF treatment/prevention. Nutritional support and treatment for ALF are crucial steps against patient morbidity and mortality. BCAAs and euglycemia remain the mainstay of nutritional treatment of ALF. Gut dysbiosis re-modulation has an emerging and natural-history changing impact on ALF.
Fast Obstacle Detection System for UAS Based on Complementary Use of Radar and Stereoscopic Camera
Authors: Bigazzi Luca , Miccinesi Lapo , Boni Enrico , Basso Michele , Consumi Tommaso , Pieraccini Massimiliano
Published: November 2022

Autonomous unmanned aerial systems (UAS) are having an increasing impact in the scientific community. One of the most challenging problems in this research area is the design of robust real-time obstacle detection and avoidance systems. In the automotive field, applications of obstacle detection systems combining radar and vision sensors are common and widely documented. However, these technologies are not currently employed in the UAS field due to the major complexity of the flight scenario, especially in urban environments. In this paper, a real-time obstacle-detection system based on the use of a 77 GHz radar and a stereoscopic camera is proposed for use in small UASs. The resulting system is capable of detecting obstacles in a broad spectrum of environmental conditions. In particular, the vision system guarantees a high resolution for short distances, while the radar has a lower resolution but can cover greater distances, being insensitive to poor lighting conditions. The developed hardware and software architecture and the related obstacle-detection algorithm are illustrated within the European project AURORA. Experimental results carried out employing a small UAS show the effectiveness of the obstacle detection system and of a simple avoidance strategy during several autonomous missions on a test site.
Genetic Algorithm Based on a New Similarity for Probabilistic Transformation of Belief Functions
Authors: Dong Yilin , Cao Lei , Zuo Kezhu
Published: November 2022

Recent studies of alternative probabilistic transformation (PT) in Dempster–Shafer (DS) theory have mainly focused on investigating various schemes for assigning the mass of compound focal elements to each singleton in order to obtain a Bayesian belief function for decision-making problems. In the process of such a transformation, how to precisely evaluate the closeness between the original basic belief assignments (BBAs) and transformed BBAs is important. In this paper, a new aggregation measure is proposed by comprehensively considering the interval distance between BBAs and also the sequence inside the BBAs. Relying on this new measure, we propose a novel multi-objective evolutionary-based probabilistic transformation (MOEPT) thanks to global optimizing capabilities inspired by a genetic algorithm (GA). From the perspective of mathematical theory, convergence analysis of EPT is employed to prove the rationality of the GA used here. Finally, various scenarios in evidence reasoning are presented to evaluate the robustness of EPT.
Fractional-Order Active Disturbance Rejection Control with Fuzzy Self-Tuning for Precision Stabilized Platform
Authors: Zhao Jianjian , Zhao Tao , Liu Nian
Published: November 2022

In this paper, a novel fractional-order active disturbance rejection control with fuzzy self-tuning method (FSFOADRC) is proposed for photoelectric tracking system (PTS). Firstly, aiming at the internal uncertainty of PTS and external disturbance, a fraction-order extended state observer (FOESO) is designed, and the FOESO can transform the plant into a simple form, which greatly simplifies the mathematical model. Secondly, a fuzzy regulator is applied to the proportion–differentiation controller (PD), increasing the flexibility and adaptivity of the controller. In addition, the stability of the whole control system can be guaranteed. Eventually, numerical comparative simulations are implemented to verify the feasibility and superiority of the proposed method. Compared with the integral-order active disturbance rejection control (IOADRC), fractional-order active disturbance rejection control (FOADRC) without the fuzzy regulator and proportion–integration–differentiation (PID) controller, the proposed method performs better with faster response, smaller overshoot, and stronger disturbance suppression capability.
Numerical Simulation of Heat and Mass Transfer in Sludge Low-Temperature Drying Process
Authors: Wang Zhenyu , Wang Qiang , Lai Ju , Liu Dong , Hu Anjie , Xu Lin , Chen Yongcan
Published: November 2022

Based on the sludge mass transfer flux model, this paper conducts a simulation study on the drying characteristics of sludge under low-temperature environment and compares it with the previous experimental results. It is found that when the sludge moisture content is low, the change of its drying curve is basically consistent with the experimental results, but there is a large error when the sludge moisture content is 0.4–0.6. In order to better simulate sludge drying characteristics, a model of cracking and shrinkage coefficients based on sludge moisture content is proposed, and the effective diffusion coefficient and mass transfer coefficient are modified. The maximum error between simulation and experiment is reduced to 23.78%. Based on this model, the sludge drying mechanism was studied. It was found that heat transfer and diffusion played a major role in the initial stage of sludge drying, and diffusion played a major role in sludge drying 30 min later.
Credit Constraints and Investment-Cash Flow Sensitivity in Declining Economic Conditions: The Role of Reliance on Bank Debt
Authors: Tayem Ghada
Published: November 2022

This paper examines the sensitivity of investment to cash flow in declining economic conditions, focusing on the impact of a firm’s reliance on bank debt. Using the context of Jordan, a developing Middle East and North Africa (MENA) country, the study utilizes the standard Q theory of investment augmented by cash flow, leverage, and liquidity. Then, it allows for differential loading on the cash flow coefficient pre- and post-2008, the year that marks the beginning of declining conditions, and by categorizing companies based on their reliance on bank debt, measured by having access to a bank line of credit. Using alternative estimation specifications, the findings indicate that firms’ investments decreased significantly in episodes of declining conditions. In addition, the findings indicate that firms’ investments exhibited more sensitivity to cash flow during declining conditions, especially for firms with access to lines of credit. The latter finding suggests that firms reliant on bank debt could not compensate for the credit shortages by switching to other sources of external funding and therefore they were compelled to use more of their internally generated funds to finance their investments.
Math Instrument Development for Examining the Relationship between Spatial and Mathematical Problem-Solving Skills
Authors: Sorby Sheryl Ann , Duffy Gavin , Yoon So Yoon
Published: November 2022

Spatial cognition involves the ability to acquire, organize, and use spatial information in the “mind’s eye.” Numerous correlational studies have demonstrated the strong relationship between spatial cognition and skill in problem-solving in mathematics. Educational researchers across the globe aim to improve students’ mathematics problem-solving ability since these skills are often viewed as the gateway to further study in the Science, Technology, Engineering, and Mathematics (STEM) careers of the future. As a result, causal studies showing that spatial cognition interventions can improve mathematics problem-solving skills have begun to appear with increasing frequency. In order to determine if a spatial intervention is effective in improving mathematics problem-solving skills, it is important to identify the type of problem that is likely to respond to improvements in spatial cognition. A large study was conducted in the United States, where a spatial skills intervention was implemented in 7th grade classrooms across the country to determine its impact on mathematics problem-solving skills through four waves of testing. Mathematics instruments for 7th to 9th grades were developed with items compared to the spatial scores of the individual students. In this paper, the instrument development process along with lessons learned are presented. Data are provided showing each item’s correlation with spatial skills test scores, difficulty level, and discrimination index, along with overall instrument reliability. Complete final instruments for each wave of testing are included in an Appendix. General guidelines are also presented discussing the type of math problems likely to be correlated with spatial ability.
Assessing Learning Preferences of Tertiary Education Students in Jordan Post COVID-19 Pandemic and the Associated Perceived Stress
Authors: Basheti Iman A. , Assaraira Thafer Yusif , Obeidat Nathir M. , Al-abed Al-haq Fawwaz , Refai Mashhoor
Published: November 2022

Background: The COVID-19 pandemic was associated with extensive lockdown strategies which included universities, forcing educational administrations to implement online learning and acknowledging the countless consequences it would have on the educational process. Those prompt changes highlighted the importance of online learning effects on educational outcomes. Aim: To assess students’ learning preferences and the stress associated with online and face-to-face learning. Methods: This is a multi-center cross-sectional study, employing a web-based Google Forms, which was conducted among four universities in Jordan. The survey assessed students’ demographic characteristics, educational methods received, assessment of factors that may have influenced students’ stress, and assessment of ‘stress’ using the Perceived Stress Scale (PSS). Results: Among 1241 participating students, most of the students preferred face-to-face learning (43.3%), although the majority believed that online learning is less stressful (42.2%). The majority believed that face-to-face learning is efficient (42.7%), and that online learning is moderately efficient (38.4%), while many (35.3%) reported that the future of learning will be blended 50/50 between online and face-to-face learning. The mean score of PSS was 20.88, with 62.9% reported to have experienced moderate perceived stress, and 22.4% experienced high perceived stress. Conclusions: Although Jordanian university students prefer face-to-face learning over online learning, they believe that online learning can be less stressful. In addition to that, Jordanian students experienced a high mean of the PSS score, with more than 20% of students reporting high perceived stress.
Efficient Vision-Based Face Image Manipulation Identification Framework Based on Deep Learning
Authors: Dang Minh
Published: November 2022

Image manipulation of the human face is a trending topic of image forgery, which is done by transforming or altering face regions using a set of techniques to accomplish desired outputs. Manipulated face images are spreading on the internet due to the rise of social media, causing various societal threats. It is challenging to detect the manipulated face images effectively because (i) there has been a limited number of manipulated face datasets because most datasets contained images generated by GAN models; (ii) previous studies have mainly extracted handcrafted features and fed them into machine learning algorithms to perform manipulated face detection, which was complicated, error-prone, and laborious; and (iii) previous models failed to prove why their model achieved good performances. In order to address these issues, this study introduces a large face manipulation dataset containing vast variations of manipulated images created and manually validated using various manipulation techniques. The dataset is then used to train a fine-tuned RegNet model to detect manipulated face images robustly and efficiently. Finally, a manipulated region analysis technique is implemented to provide some in-depth insights into the manipulated regions. The experimental results revealed that the RegNet model showed the highest classification accuracy of 89% on the proposed dataset compared to standard deep learning models.
FasterAI: A Lightweight Library for Neural Networks Compression
Authors: Hubens Nathan , Mancas Matei , Gosselin Bernard , Preda Marius , Zaharia Titus
Published: November 2022

FasterAI is a PyTorch-based library, aiming to facilitate the use of deep neural network compression techniques, such as sparsification, pruning, knowledge distillation, or regularization. The library is built with the purpose of enabling quick implementation and experimentation. More particularly, compression techniques are leveraging callback systems of libraries, such as fastai and Pytorch Lightning to propose a user-friendly and high-level API. The main asset of FasterAI is its lightweight, yet powerful, simplicity of use. Indeed, because it has been developed in a very granular way, users can create thousands of unique experiments by using different combinations of parameters, with only a single line of additional code. This allows FasterAI to be suited for practical usage, as it contains the most common compression techniques available out-of-the-box, but also for research, as implementing a new compression technique usually boils down to writing a single line of code. In this paper, we propose an in-depth presentation of the different compression techniques available in FasterAI. As a proof of concept and to better grasp how the library is used, we present results achieved by applying each technique on a ResNet-18 architecture, trained on CALTECH-101.
Energy–Water–Carbon Nexus Study for the Optimal Design of Integrated Energy–Water Systems Considering Process Losses
Authors: Naveed Urwah , Mohammad Rozali Nor Erniza , Mahadzir Shuhaimi
Published: November 2022

Integrated energy–water systems have been explored using different process integration techniques considering the energy–water–carbon nexus to minimize the carbon footprint, e.g., pinch analysis techniques (power cascade table, water cascade table, and energy planning pinch diagram). However, the power and water losses while considering the energy–water–carbon nexus have not been explored in detail in the previous works. This work focuses on the modifications of the existing pinch analysis methods for energy–water–carbon nexus study while considering power and water losses, for an optimized energy–water system. Power and water losses should not be neglected in the analysis as they have a significant impact on the carbon emissions and overall capacities of energy and water. The effect of losses on energy storage capacity, outsourced electricity, water supply volume and water storage capacity were evaluated on an industrial case study. Results from the case study demonstrate that, while considering power losses during power allocation can lower storage capacity, it tends to raise the needed outsourced electricity supply. As water supply volume tends to increase, the water storage capacity tends to decline when losses are considered. The results were compared to the data without losses, and it was observed that the storage capacity of energy decreases by 4% while outsourced energy increases by 6%. Water supply volume increases by 20% but water storage capacity decreases by 13.7%. The emissions from energy system remains same while from the water system the emissions rise significantly by 20%. It is expected that consumers that takes power and water losses into account will produce more realistic and reliable energy, water, and carbon reduction targets and prevent under-sizing issues in designing integrated energy–water systems.
Potential Application of Canola Hull Fuel Pellets for the Production of Synthesis Gas and Hydrogen
Authors: Azargohar Ramin , Nanda Sonil , Cheng He , Dalai Ajay K.
Published: November 2022

The production of biopellets from agricultural residues is an effective method to overcome the expected shortage in the supply of fuel pellets in the future. This work focused on the new potential applications of fuel pellets to produce liquid and gas fuels through thermochemical and hydrothermal biomass-to-gas technologies. The outcomes also provide a basis to compare the effects of steam and hydrothermal gasification techniques on the properties of product and byproduct, as well as their potentials. For steam gasification, the syngas yield increased from 10.7 to 27.8 mmol/g (on a dry and ash-free basis) by a rise in the gasification temperature at a constant steam-to-biomass ratio. In the case of hydrothermal gasification, there was no carbon monoxide, and hydrogen was the main gas product, and with an increase in the temperature, the hydrogen yield rose from 0.4 mmol/g to 3.17 mmol/g with temperatures from 350 to 650 °C. CO had the largest share in the gas product from steam gasification, which was between 23.3 and 31.3 mol%. The range of the molar ratio of H2/CO for the steam gasification (1.13–1.40) showed the necessity of the further purification of gas products to utilize them as feed for liquid fuel production using the Fischer–Tropsch process. Examination of the mineral content of biochar that remained after the gasification techniques showed large essential elements in them compared with heavy metals, which shows potential for soil amendment. The results highlight the possibility of converting an agricultural residue into a value-added product with potential applications in the energy sector and agriculture.
Occurrence, Fate, and Implications of Heavy Metals during Anaerobic Digestion: A Review
Authors: Kadam Rahul , Khanthong Kamonwan , Jang Heewon , Lee Jonghwa , Park Jungyu
Published: November 2022

Over the years, anaerobic digestion (AD) has evolved as a competent technology to retrieve energy potential from various organic substrates, including wastewater. However, the energy metabolisms of anaerobic microorganisms, biochemical reactions, and biogas production are affected by various parameters, including heavy metals. It is important to understand the interaction of heavy metals with anaerobes and their potential influence on the process to enhance energy potential. This review methodically outlines the occurrence and role of heavy metals in the AD process. Additionally, the repercussions of the most common heavy metals (i.e., Cu, Zn, Cd, Fe, and Ni) on each stage of AD (i.e., hydrolysis, acidogenesis, and methanogenesis) have been discussed. We found that traces of heavy metals can endorse anaerobic digestion, but inhibition increases with increasing concentration. Methanogenic archaea are more susceptible to heavy metal inhibition than hydrolytic and acidogenic archaea. An improved understanding and relevant intuition will help to promote biogas production along with heavy metal management.
Position Estimation of Multiple Receiving Coils and Power Transmission Control for WPT without Feedback
Authors: Heo Jun , Kim Sang-Won , Cho In-Kui , Park Yong Bae
Published: November 2022

It is important to determine the position of the receiver (Rx) coils in wireless power transfer (WPT) system, and to control the power transmitted to the Rx coil based on this result. In particular, in a situation where there is no feedback between the primary side and the secondary side, it is difficult to control the received power because the information is limited. In this paper, a method for determining the position of the Rx coils and controlling the received power using limited parameters in a feedback-free WPT system is proposed. The proposed method is verified by constructing a 4×2 WPT system, and it is validated that the simulation result and the experimental result are consistent well. Furthermore, arbitrary power can be transmitted to the Rx coil based on the result of the position of the Rx coil. The experiment is conducted by transmitting about 1W to Rx 1 and Rx 2, and the efficiency for Rx 1 is about 32.93%, Rx 2 is 25.03%, and the overall efficiency is confirmed to be 57.96%.
Thermodynamic and Economic Analyses of Zero-Emission Open Loop Offshore Regasification Systems Integrating ORC with Zeotropic Mixtures and LNG Open Power Cycle
Authors: Naveiro Manuel , Romero Gómez Manuel , Arias-Fernández Ignacio , Baaliña Insua Álvaro
Published: November 2022

The present study provides an energy, exergy and economic analysis of a seawater regasification system (open loop) combining stages of simple organic Rankine cycles (ORCs) arranged in series with an open organic Rankine cycle (OC) in order to exploit the cold energy of liquefied natural gas (LNG). The proposed system, termed ORC-OC, is implemented in a Floating Storage Regasification Unit (FSRU) to achieve the objective of zero greenhouse emissions during the regasification process. Configurations of up to three stages of ORCs and the use of zeotropic mixtures of ethane/propane and n-butane/propane as working fluids are considered in the study of the novel regasification system. Only the two-stage ORC-OC (2ORC-OC) and three-stage (3ORC-OC) configurations accomplish the objective of zero emissions, attaining exergy efficiencies of 61.80% and 62.04%, respectively. The overall cost rate of the latter, however, is 20.85% greater, so the 2ORC-OC results as being more cost-effective. A comparison with conventional regasification systems installed on board shows that the 2ORC-OC yields a lower total cost rate if the LNG price exceeds 8.903 USD/MMBtu. This value could be reduced, however, if the electrical power that exceeds the FSRU’s demand is exported and if compact heat exchangers are implemented.
Utilization of Electric Vehicle Grid Integration System for Power Grid Ancillary Services
Authors: Das Himadry , Nurunnabi Md , Salem Mohamed , Li Shuhui , Rahman Mohammad
Published: November 2022

Electric vehicle grid integration (EVGI) is one of the most important parts of transportation electrification. However, large-scale EV charging/discharging can have an adverse effect on the distribution grid, due to a large amount of load being drawn from or fed back to the power grid. Additionally, the power electronics used in the grid interaction may impose additional complications, such as voltage and frequency deviation, harmonic distortion, etc. With proper control scheme designs for the grid-connected inverters, such complications can be mitigated, and several grid ancillary services, such as voltage and frequency support, reactive power support, and harmonic mitigation, can be facilitated from large-scale EVGI. In this study, a large-scale EVGI system is developed where the vector control implementation of a grid-connected inverter in the d-q reference frame is presented for providing different grid ancillary services using the EVGI system. The EVGI system is operated in different control modes to ensure multiple ancillary services of the power grid. The study is supported by the electromagnetic transient simulation performed in Matlab/Simulink of a large-scale EVGI system. The simulation shows that with the proper control mechanism of grid-connected inverters, EVGI can be used to provide several useful grid ancillary services.
Assessment of Energy Recovery from Municipal Waste Management Systems Using Circular Economy Quality Indicators
Authors: Kowalski Zygmunt , Kulczycka Joanna , Makara Agnieszka , Verhé Roland , De Clercq Guy
Published: November 2022

A complex method developed to assess quality within a proposed framework and at a certain scope of measurement for circular economy (CE) quality indicators is presented. This was used to compare three different scenarios for municipal waste management systems: 1—incineration; 2—recycling and reuse of separated municipal waste and the transformation of the organic fraction into biodiesel and bio-coal; and 3—an upgraded Scenario 2 including decreased recycling of waste streams and the bioprocessing of paper/cardboard and processing the non-recycled fraction into bio-diesel, bio-coal, and second-generation biofuel. For the evaluation of the CE quality indicator, a set of technical, environmental, economic, and social elements was selected by a panel of experts, who also assigned them a qualitative assessment and weighting on the basis of the factors identified. The calculated Relative Increase in the CE indicator for the scenarios analyzed showed that Scenarios 3 and 2 are much more beneficial than Scenario 1 in technical, environmental, economic and social terms.
The Impact of Shale Energy on Population Dynamics, Labor Migration, and Employment
Authors: Sapci Onur
Published: November 2022

This paper is designed to determine whether producing oil and gas via shale has an economically significant effect on population migration dynamics and on the labor market in terms of the number of employed individuals, the number of establishments, total wages, and average annual pay per person in twenty-six counties in Ohio and Pennsylvania, USA. The analysis incorporates migration inflow and outflow between producing and nonproducing counties. The results of the analysis show that the counties that engage in shale gas extraction saw a negative impact on net migration but a much larger positive impact on labor market outcomes. Specifically, the number of jobs is higher by 2.4%, the number of establishments is higher by 1.1%, total wages are 3% more and the average annual pay is 1.5% more in producing counties after shale. The analysis reveals a small but statistically significant negative impact on migration, as shale regions experienced some migration outflows.
Development and Validation of a Nuclear Power Plant Fault Diagnosis System Based on Deep Learning
Authors: Liu Bing , Lei Jichong , Xie Jinsen , Zhou Jianliang
Published: November 2022

As artificial intelligence technology has progressed, numerous businesses have used intelligent diagnostic technology. This study developed a deep LSTM neural network for a nuclear power plant to defect diagnostics. PCTRAN is used to accomplish data extraction for distinct faults and varied fault degrees of the PCTRAN code, and some essential nuclear parameters are chosen as feature quantities. The training, validation, and test sets are collected using random sampling at a ratio of 7:1:2, and the proper hyperparameters are selected to construct the deep LSTM neural network. The test findings indicate that the fault identification rate of the nuclear power plant fault diagnostic model based on a deep LSTM neural network is more than 99 percent, first validating the applicability of a deep LSTM neural network for a nuclear power plant fault-diagnosis model.
Theoretical and Experimental Investigation of Explosion Characteristics of Hydrogen Explosion in a Closed Vessel
Authors: Xing Huadao , Yu Runze , Xu Guangan , Li Xiaodong , Qiu Yanyu , Wang Derong , Li Bin , Xie Lifeng
Published: November 2022

A simplified model that calculates the deflagration pressure–time curves of a hydrogen explosion was proposed. The deflagration parameters (pressure peak, duration, deflagration index, and impulse) of hydrogen–air mixtures with different hydrogen concentrations were experimentally investigated. The results show that the pressure curves calculated by the model are consistent with experimental data pertaining to a methane and hydrogen explosion. By comparison, the pressure peak and deflagration index are found to be influenced by the aspect ratio and surface area of vessels. The impulse and explosion times at fuel-lean hydrogen concentrations are greater than those at fuel-rich concentrations. When the hydrogen concentration is between 34 vol.% and 18 vol.%, the greatest explosion damage effect is formed by both the overpressure and the impulse, which should be considered for hydrogen explosion safety design in industrial production.
Nearshore Wave Energy Resource Assessment for Off-Grid Islands: A Case Study in Cuyo Island, Palawan, Philippines
Authors: Pacaldo Jonathan C. , Bilgera Princess Hope T. , Abundo Michael Lochinvar S.
Published: November 2022

Electrifying off-grid and isolated islands in the Philippines remains one of the challenges that hinders community development, and one of the solutions seen to ensure energy security, energy access and promote a low-carbon future is the use of renewable energy sources. This study determines the nearshore wave energy resource during monsoon seasons in Cuyo Island using a 40-year wave hindcast and 8-year on-site wind speed data as inputs to develop a high-resolution wave energy model using SWAN and assesses its annual energy production through matching with wave energy devices. The results show that the average significant wave height (Hs), peak period (Tp) and wave power density (Pd) during a northeast monsoon are Hs = 1.35 m, Tp = 4.79 s and Pd = 4.05 kW/m, respectively, while a southwest monsoon, which is sheltered by the mainland, results in Hs = 0.52 m, Tp = 3.37 s and Pd = 0.34 kW/m. While the simulated model was observed to overestimate the significant wave height (bias = 0.398, RMSE = 0.54 and SI = 1.34), it has a strong relationship with the “observed values” (average r = 0.9). The annual energy production for Wave Dragon, Archimedes Wave Swing and Seawave Slot-Cone Generator are highest at 1970.6 MWh, 2462.04 MWh, 62.424 MWh and 4099.23 MWh, respectively.
Prediction of Carbon Dioxide Emissions in China Using Shallow Learning with Cross Validation
Authors: Hou Yali , Wang Qunwei , Tan Tao
Published: November 2022

Accurately measuring carbon dioxide (CO2) emissions is critical for effectively implementing carbon reduction policies, and China’s increased investment in reducing CO2 emissions is expected to significantly impact the world. In this study, the potential of shallow learning for predicting CO2 emissions was explored. Data included CO2 emissions, renewable energy consumption, and the share of primary, secondary, and tertiary industries in China from 1965 to 2021. These time-series data were converted into labeled sample data using the sliding window method to facilitate a supervised learning model for CO2 emission prediction. Then, different shallow learning models with k-fold cross-validation were used to predict China’s short-term CO2 emissions. Finally, optimal models were presented, and the important features were identified. The key findings were as follows. (1) The combined model of RF and LASSO performed best at predicting China’s short-term CO2 emissions, followed by LASSO and SVR. The prediction performance of RF was very fragile to the window width. (2) The sliding window method is used to convert time series predictions into supervision learning problems, and historical data can be used to predict future carbon dioxide emissions. To ensure that the feature data are real, the model can predict CO2 emissions for up to six years ahead. (3) Cross-validation and grid search were critical for optimizing China’s CO2 emissions prediction with small datasets. (4) By 2027, carbon dioxide emissions will continue to grow and reach 10.3 billion tons. It can be seen that the task of China to achieve its carbon peak on schedule is very heavy. The results indicate that an increase in renewable energy consumption and adjustments in industrial structure will continue to play an important role in curbing China’s CO2 emissions.
A Novel Cost Allocation Mechanism for Local Flexibility in the Power System with Partial Disintermediation
Authors: Sleisz Ádám , Divényi Dániel , Polgári Beáta , Sőrés Péter , Raisz Dávid
Published: November 2022

Electricity markets are going through a comprehensive transformation that includes the large-scale appearance of intermittent renewable generators (RGs). To handle the local effects of new RGs on the distribution grid, the more efficient utilization of distributed local flexibility (LF) resources is necessary. However, the optimal market design is not yet known for LF products. This paper investigates a novel cost allocation mechanism in the context of this market challenge. The mechanism is designed to provide several important advantages of peer-to-peer trading without creating barriers to practical application. It provides partial disintermediation. The acquisition of LF remains the responsibility of the DSO, while the financial costs of the transaction are covered on power exchanges (PXs). To provide this functionality, the clearing algorithm of the PX in question has to incorporate a novel feature we call the Payment Redistribution Technique. This technique allows the buyers’ expenses to be larger than the sellers’ income, and the difference is used to finance flexibility costs. Its mathematical formulation is presented and analyzed in detail, considering computational efficiency and accuracy. Afterward, a realistic case study is constructed to demonstrate the operation of the algorithm and its energy market effects.
Battery Electric Tractors: Small-Scale Organic Growers’ Preferences, Perceptions, and Concerns
Authors: Bessette Douglas , Brainard Daniel , Srivastava Ajit , Lee Woongkul , Geurkink Sarah
Published: November 2022

Battery electric tractors (BETs) demonstrate considerable advantages over diesel-fueled tractors, including higher conversion efficiency, higher torque, less maintenance, and no tailpipe emissions. Converting to BETs also requires tradeoffs in the form of the batteries’ high cost, increased weight, limited energy capacity, finite charging cycles, and lengthy charging time. The extent to which small-scale organic vegetable, fruit and cut-flower growers are aware of these tradeoffs is unknown. Little research exists examining these growers’ perceptions, concerns, and willingness to pay for or adopt BETs. Here, we address that gap by conducting qualitative semi-structured interviews with 14 organic growers in the US Midwest, most operating in Michigan. We focus our questions on growers’ motivations, existing tractor-use patterns, and the evaluation of different configurations of a belly-mount open-station cultivating BET. Our results suggest interest in and potential for growers to transition to BETs, including an estimated willingness to pay 14 percent more for a BET compared to a diesel-fueled alternative. This premium is driven by most growers’ preferences for reduced noise, fumes, fuel, and greenhouse gases, as well as beliefs about BETs ultimately being a more sustainable long-term option than diesel-fueled tractors. Growers also identify significant concerns and uncertainty about the long-term performance, maintenance, storage, cost, safety, and weight of the tractors’ battery systems. While growers linked some environmental values and motivations to their interest in BETs, altruistic value signaling was absent, and growers focused considerably more on financial and instrumental concerns and motivations for BET adoption.