bluuebunny/crossref_metadata_2025 huggingface dataset:
A Hybrid Model for Electricity Demand Forecast Using Improved Ensemble Empirical Mode Decomposition and Recurrent Neural Networks with ERA5 Climate Variables
Authors: Chreng Karodine , Lee Han Soo , Tuy Soklin
Published: October 2022

By conserving natural resources and reducing the consumption of fossil fuels, sustainable energy development plays a crucial role in energy planning. Specifically, demand-side planning must be researched and anticipated based on electricity consumption at the grounded level. Due to the global warming crisis, atmospheric conditions are among the most influential components that have altered electricity consumption patterns. In this study, 66 climate variables from the ERA5 reanalysis and the observed power demand at four grid substations (GSs) in Cambodia were examined using recurrent neural networks (RNNs). Using the cross-correlation function between power demand and each climate variable, statistically significant climate variables were sorted out. In addition, a wide range of feedback delays (FDs) was generated from the data on power demand and defined using 95% confidence intervals. The combination of the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) technique with a nonlinear autoregressive neural network with exogenous inputs (NARX) and a nonlinear autoregressive neural network (NAR) produced a hybrid electricity forecasting model. The data were decomposed into the intrinsic mode functions (IMFs) and were then used as inputs in optimized NARX and NAR models. The performance of the various benchmarked models was analyzed and compared using mainly statistical indicators such as the normalized root mean square error (NMSE) and the coefficient of determination (R2). The hybrid models perform exceptionally well in predicting electricity demand, and the ICEEMDAN-NARX hybrid model with correlated climate variables performs the best among the tested experiments as a useful prediction tool.
Sustainable Transition from Fossil Fuel to Geothermal Energy: A Multi-Level Perspective Approach
Authors: Yudha Satya Widya , Tjahjono Benny , Longhurst Philip
Published: October 2022

Indonesia is currently undergoing the energy transition from heavily fossil fuel-dependent energy to cleaner sources of energy in order to achieve its net-zero emissions by 2060. In addition to reducing fossil fuel dependency, as one of the countries with the most geothermal reserves, the optimization of geothermal energy in Indonesia could be key to facilitating the energy transition. The objective of this paper is to elaborate on the transition process, which incorporates the destabilization of fossil fuel and the growth of geothermal energy, by analyzing the impact of both exogenous and endogenous factors on the supply chain structures of both sectors. This study employs workshop involving geothermal stakeholders in Indonesia, combined with the application of the Multi-Level Perspective (MLP) framework as the theoretical lens. The study found that energy demand, environmental awareness, energy regulations, energy supply chain, and geothermal potential breakthroughs are important aspects pertinent to the MLP components, namely the socio-technical landscape, socio-technical regime and niche innovations. The socio-technical landscapes are exogenous factors that pressurize the energy sector regime allowing the niche innovation, in the form of geothermal innovation, to penetrate the fossil fuel regime, allowing it to transition to a geothermal regime. The transition pathways include several measures that could break down the fossil fuel and build up geothermal energy, through a number of schemes and incentives.
ANFIS-Based Droop Control of an AC Microgrid System: Considering Intake of Water Treatment Plant
Authors: Rohmingtluanga C. , Datta Subir , Sinha Nidul , Ustun Taha Selim , Kalam Akhtar
Published: October 2022

Provision of an efficient water supply system (WSS) is one of the top priorities of all municipals to ascertain adequate water supply to the city. Intake is the lifeline of the water supply system and largely effects the overall plant efficiency. The required power supply is generally fed from the main grid, and a diesel generator is commonly used as a power backup source. This results in high pumping cost as well as high operational cost. Moreover, due to operation of motor pumps and other auxiliary loads, frequent maintenance is required. Therefore, to avoid various challenges and to efficiently operate the intake system, microgrid concept has been introduced in this paper. Various distributed generations (DGs) such as solar photovoltaic (PV), interior permanent magnet machine (IPM) wind turbine generator and Battery energy storage system (BESS) are incorporated in the microgrid system. Additionally, a new approach based on adaptive neuro-fuzzy inference system (ANFIS) is proposed, where P-f and Q-V droop is considered while training the ANFIS data; after successful training, the microgrid voltage and frequency are controlled as per system requirement. Simulation of the microgrid system shows good results and comparison with the generalized droop control (GDC) method is done using MATLAB/Simulink software.
Planning and Energy–Economy–Environment–Security Evaluation Methods for Municipal Energy Systems in China under Targets of Peak Carbon Emissions and Carbon Neutrality
Authors: Chen Weiwei , Wang Yibo , Zhang Jia , Dou Wei , Jiao Yaxuan
Published: October 2022

In order to mitigate the negative effects of global climate change, the Chinese government has committed to achieving peak carbon emissions by 2030 and carbon neutrality by 2060. Since municipal cities are the bottom administrative level for drawing up development plans, it is necessary and important to conduct decarbonization pathway research on municipal energy systems (MESs). However, there is little research on decarbonization at the municipal level, and the impact of development paths in each forecast scenario is mostly based on expert evaluation and qualitative assessment. Therefore, this study established a complete decarbonization framework for MESs, including general research procedures, models, and a sustainable evaluation method. The models of energy consumption and carbon emission were adapted and improved for MESs. In order to quantitatively evaluate the energy system development for each scenario, we proposed an energy–economy–environment–security (3E–S) evaluation method, in which principal component analysis (PCA) was adopted for multi-criterion decision making. According to the analysis results of the case city in Guangdong, this evaluation method was proved to be an effective way to identify the factors that may influence coordinated development. By adjusting the relevant parameters and factors in the model, the optimal decarbonization pathway can be found to promote sustainable and coordinated development, thus helping government decision makers to quantitatively evaluate planning paths.
High-Order Band-Pass Active Damping Control and Predictive Control for Three-Phase Small-Film DC-Link Capacitor IPMSM Drive Systems
Authors: Liu Tian-Hua , Cheng Sheng-Hsien , Fan Chong-Yi
Published: October 2022

Traditional three-phase rectifier DC-link inverters have been used in industry for more than 40 years. However, electrolytic capacitors, which are widely used in traditional inverters, have very large volumes and can only be used for five years. To solve this problem, a three-phase small-film DC-link capacitor interior permanent-magnet synchronous motor drive system is investigated in this paper. This small-film capacitor not only has a longer life and smaller size than an electrolytic capacitor, but it can also improve the input harmonic currents and power factor on the grid side. A high-order band-pass filter active damping control is proposed here. In addition, a constrained predictive speed controller is designed to enhance the transient, load disturbance, and tracking speed performance. Furthermore, a constrained predictive current controller is implemented to reduce the three-phase harmonic currents of the motor. A digital signal processor, type TMS-320F-28035, manufactured by Texas Instruments, is employed as a control center to conduct the whole control algorithms. Several simulated and measured results are compared to demonstrate the practicability and correctness of the proposed control algorithms.
Analysis of Deep Learning Neural Networks for Seismic Impedance Inversion: A Benchmark Study
Authors: Marques Caique Rodrigues , dos Santos Vinicius Guedes , Lunelli Rafael , Roisenberg Mauro , Rodrigues Bruno Barbosa
Published: October 2022

Neural networks have been applied to seismic inversion problems since the 1990s. More recently, many publications have reported the use of Deep Learning (DL) neural networks capable of performing seismic inversion with promising results. However, when solving a seismic inversion problem with DL, each author uses, in addition to different DL models, different datasets and different metrics for performance evaluation, which makes it difficult to compare performances. Depending on the data used for training and the metrics used for evaluation, one model may be better or worse than another. Thus, it is quite challenging to choose the appropriate model to meet the requirements of a new problem. This work aims to review some of the proposed DL methodologies, propose appropriate performance evaluation metrics, compare the performances, and observe the advantages and disadvantages of each model implementation when applied to the chosen datasets. The publication of this benchmark environment will allow fair and uniform evaluations of newly proposed models and comparisons with currently available implementations.
Optimization of Oil Pipeline Operations to Reduce Energy Consumption Using an Improved Squirrel Search Algorithm
Authors: Peng Shanbi , Zhang Zhe , Ji Yongqiang , Shi Laimin
Published: October 2022

To achieve the goal of achieving carbon-neutral by 2060, the government of China has put forward higher requirements for energy conservation and consumption reduction in the energy industry. Therefore, it is necessary to reduce energy consumption in the process of transporting oil. In this paper, an optimization model that minimizes the total energy consumption of the entire pipeline system is proposed and the squirrel search algorithm (SSA) is used to solve the optimization model. Meanwhile, to improve the performance of the SSA, two strategies are proposed. One is the adaptive inertia weight strategy, and the other is the multi-group co-evolution strategy. The adaptive inertia weight can adjust the step size of the flying squirrels according to the difference of the objective function value and multi-group co-evolution is introduced to improve population diversity. The improved SSA is named multigroup coevolution-adaptive inertia weight SSA (MASSA). A total of 20 benchmark functions are used to test the performance of MASSA, including unimodal functions and multimodal functions. Compared with the other four algorithms, MASSA has better performance and convergence capabilities. In the case study experiment, an optimization model of the oil pipeline is built, which takes the minimum energy consumption of the whole pipeline as the objective function. Compared with the actual operating conditions, the electricity consumption optimized by MASSA decreases by 399,018.94 kgce, and the thermal energy dissipation decreases by 113,759.25 kgce. The total energy consumption is reduced by 512,778.19 kgce, which is 9.62%. These results indicate that the two improvement strategies are significant, and optimizing the operating parameters can reduce energy consumption.
3D Geomechanical Model Construction for Wellbore Stability Analysis in Algerian Southeastern Petroleum Field
Authors: Eladj Said , Doghmane Mohamed Zinelabidine , Lounissi Tanina Kenza , Djeddi Mabrouk , Tee Kong Fah , Djezzar Sofiane
Published: October 2022

The main objective of this research work was the wellbore stability evaluation of oil and gas wells based on a 3D geomechanical model, which as constructed using seismic inversion in a southeastern Algerian petroleum field. The seismic inversion model was obtained by using an iterative method and Aki and Richards approximation. Since the correlation between the inversion model and the log data was high at the wells, the reservoir was efficiently characterized and its lithology carefully discriminated in order to build a reliable 3D geomechanical model. The latter was further used to suggest the drilling mud weight window for the ongoing wells (well 5) and to examine the stability of four previously drilled wells. The main contribution of this study is providing a 3D geomechanical model that allows the optimization of drilling mud weight parameters so that a wellbore’s stability is guaranteed, on the one hand, and, on the other hand, so that the reservoir damage brought about by excessive surfactant use can be prevented. Indeed, the mud parameters are not just important for the drilling process’s effectiveness but also for logging operations. Since the tools have limited investigation diameters, with excessive use of surfactant, the invaded zone can become larger than the tools’ investigation diameter, which makes their logs unreliable. Hence, the 3D geomechanical model presented here is highly recommendable for the proposition of new wells, entailing less exploration uncertainty and more controllable productivity.
The Effect of Carbon Price Volatility on Firm Green Transitions: Evidence from Chinese Manufacturing Listed Firms
Authors: Wu Xintong , Li Zhendong , Tang Fangcheng
Published: October 2022

Accelerating the promotion of the green transition can help to achieve high-quality development in manufacturing industries. In terms of policies that encourage the transition to green production, carbon trading is a direct and effective means of achieving this goal, and the carbon price is an important regulator in trading. Normally, firms respond to carbon prices by making three behavioral choices: production restrictions, pollution reduction, and the technological transition to green production. This study examines the effect of carbon price volatility on the decision to conduct green production, i.e., transforming to sustainable technologies and processes. In addition, this paper also investigates whether organizational resource slack and organizational technical standards moderate the relationship between the carbon price volatility and firms’ green transitions. The results suggest that a steadily increasing carbon price will motivate firms to make a green transition, but if the carbon price is volatile, firms will be reluctant to make a green transition. This tendency to make a green transition is stronger when firms have resource slack and have implemented green technical standards. The findings provide empirical evidence and policy implications regarding how manufacturing firms can accelerate their green transition.
A Review of Applications of Artificial Intelligence in Heavy Duty Trucks
Authors: Katreddi Sasanka , Kasani Sujan , Thiruvengadam Arvind
Published: October 2022

Due to the increasing use of automobiles, the transportation industry is facing challenges of increased emissions, driver safety concerns, travel demand, etc. Hence, automotive industries are manufacturing vehicles that produce fewer emissions, are fuel-efficient, and provide safety for drivers. Artificial intelligence has taken a major leap recently and provides unprecedented opportunities to enhance performance, including in the automotive and transportation sectors. Artificial intelligence shows promising results in the trucking industry for increasing productivity, sustainability, reliability, and safety. Compared to passenger vehicles, heavy-duty vehicles present challenges due to their larger dimensions/weight and require attention to dynamics during operation. Data collected from vehicles can be used for emission and fuel consumption testing, as the drive cycle data represent real-world operating characteristics based on heavy-duty vehicles and their vocational use. Understanding the activity profiles of heavy-duty vehicles is important for freight companies to meet fuel consumption and emission standards, prevent unwanted downtime, and ensure the safety of drivers. Utilizing the large amount of data being collected these days and advanced computational methods such as artificial intelligence can help obtain insights in less time without on-road testing. However, the availability of data and the ability to apply data analysis/machine learning methods on heavy-duty vehicles have room for improvement in areas such as autonomous trucks, connected vehicles, predictive maintenance, fault diagnosis, etc. This paper presents a review of work on artificial intelligence, recent advancements, and research challenges in the trucking industry. Different applications of artificial intelligence in heavy-duty trucks, such as fuel consumption prediction, emissions estimation, self-driving technology, and predictive maintenance using various machine learning and deep learning methods, are discussed.
Antifungals and Drug Resistance
Authors: Hossain Chowdhury Mobaswar , Ryan Lisa Kathleen , Gera Meeta , Choudhuri Sabyasachi , Lyle Nazmun , Ali Kazi Asraf , Diamond Gill
Published: October 2022

Antifungal drugs prevent topical or invasive fungal infections (mycoses) either by stopping growth of fungi (termed fungistatic) or by killing the fungal cells (termed fungicidal). Antibiotics also prevent bacterial infections through either bacteriostatic or bactericidal mechanisms. These microorganisms successfully develop resistance against conventional drugs that are designed to kill or stop them from multiplying. When a fungus no longer responds to antifungal drug treatments and continues to grow, this is known as antifungal drug resistance. Bacteria have an amazing capacity to become resistant to antibiotic action as well, and the effectiveness of the scarce antifungal arsenal is jeopardised by this antibiotic resistance, which poses a severe threat to public health.
Characteristics of Soil Organic Carbon Fractions and Stability along a Chronosequence of Cryptomeria japonica var. sinensis Plantation in the Rainy Area of Western China
Authors: Han Xin , Liu Xuan , Li Zhiwei , Li Jiao , Yuan Yaling , Li Han , Zhang Li , Liu Sining , Wang Lixia , You Chengming , Tan Bo , Xu Zhenfeng
Published: October 2022

Soil organic carbon (SOC) is critical for carbon cycling and sequestration in forest ecosystems. However, how stand age affects SOC components and stability still remains poorly understood. Here, soil samples (0–20 cm) were collected from Cryptomeria japonica var. sinensis (L. f.) D. Don plantations of seven stand ages (6, 12, 23, 27, 32, 46, 52 a) in the rainy area of western China. SOC fractions, including soil particulate organic carbon (POC), easily oxidizable carbon (EOC), labile organic carbon (LOC), recalcitrant organic carbon (ROC), and light fraction organic carbon (LFOC), were determined to explore the nature of carbon components and stability across a chronosequence of C. japonica plantation. Soil carbon fractions first increased and then trended to be stable with an increase in stand age. SOC concentrations were the largest in mature forests (27 or 32 a), but the concentrations of other carbon components often peaked in early over-mature forests (46 a). The concentrations of all carbon fractions were the lowest in the young forests (6 a). The ratios of ROC/SOC increased and LOC/SOC decreased with increasing stand age. Almost all carbon fractions were positively correlated with soil bulk density and negatively correlated with soil moisture. The allometric exponent of ROC or HFOC and soil physicochemical properties was higher as compared to LOC and LFOC. The results noted in this study indicate that SOC components often accumulate fast over the first 20 years of afforestation and SOC stability increases with increasing stand age for C. japonica plantation in this specific region.
Deep Learning Based Burnt Area Mapping Using Sentinel 1 for the Santa Cruz Mountains Lightning Complex (CZU) and Creek Fires 2020
Authors: Luft Harrison , Schillaci Calogero , Ceccherini Guido , Vieira Diana , Lipani Aldo
Published: October 2022

The study presented here builds on previous synthetic aperture radar (SAR) burnt area estimation models and presents the first U-Net (a convolutional network architecture for fast and precise segmentation of images) combined with ResNet50 (Residual Networks used as a backbone for many computer vision tasks) encoder architecture used with SAR, Digital Elevation Model, and land cover data for burnt area mapping in near-real time. The Santa Cruz Mountains Lightning Complex (CZU) was one of the most destructive fires in state history. The results showed a maximum burnt area segmentation F1-Score of 0.671 in the CZU, which outperforms current models estimating burnt area with SAR data for the specific event studied models in the literature, with an F1-Score of 0.667. The framework presented here has the potential to be applied on a near real-time basis, which could allow land monitoring as the frequency of data capture improves.
Plant-Based Innovations for the Transition to Sustainability: A Bibliometric and in-Depth Content Analysis
Authors: Krzywonos Małgorzata , Piwowar-Sulej Katarzyna
Published: October 2022

Plants maintain the ecological equilibrium of the earth and stabilize the ecosystem. Today, traditional commodities and new value-added markets can be served simultaneously. There is significant biosource and bioprocess innovation for biobased industrial products. Furthermore, plant-based innovation is associated with the transition to sustainability. This study performed a bibliometric and in-depth content analysis to review plant-based innovations in the research field between 1995 and 2022. A set of 313 articles was identified from the Scopus and Web of Science databases. Different analytical scientometric tools (topic mapping and overlay visualization networks) were used to analyze 124 articles; the most influential countries, institutions, authors, journals and articles were identified. Through in-depth studies, based on the grounded theory approach, five leading research areas related to plant-based innovation were determined: (1) agricultural/environmental innovation, (2) plant-based food or feed innovation, (3) innovation within the medical/pharmaceutical research area, (4) technology-related innovation and (5) economic/business aspects of plant-based innovations. Future research directions include exploring less examined and new topics, such as the sustainability implications of incorporating various plant-based foods and Industry 4.0 in plant-based innovation, and linking and developing findings from different research areas.
Investigation of Chemical Compounds and Evaluation of Toxicity, Antibacterial, and Anti-Inflammatory Activities of Three Selected Essential Oils and Their Mixtures with Moroccan Thyme Honey
Authors: Mekkaoui Mouna , Bouidida El Houcine , Naceiri Mrabti Hanae , Ouaamr Ahmed , Lee Learn-Han , Bouyahya Abdelhakim , Cherrah Yahya , Alaoui Katim
Published: October 2022

Throughout history, honey has been used to treat various diseases. The present work examined and assessed the in vivo anti-inflammatory potential of Moroccan thyme honey and its association with essential oils from three selected plants: Origanum vulgare L.; Mentha spicata L.; Eucalyptus globulus L. The chemical composition of the essential oils was studied, and preliminary toxicity, in vitro anti-inflammatory, and antibacterial tests were conducted. Then the anti-inflammatory effect was determined by applying carrageenan and an experimental trauma-induced paw edema test in rats. The essential oils were rich in phytochemicals and showed significant antibacterial activity against four selected ATCC bacterial strains. The results revealed the significant anti-inflammatory potential of honey and mixtures with essential oils and indicated higher efficiency of mixtures compared to honey alone. It can be concluded that the mixtures of honey and essential oils have advantageous anti-inflammatory effects and may be used for treating different types of inflammation in humans after certain clinical trials.
Analysis of Volatile Compounds, Composition, and Thermal Behavior of Coffee Beans According to Variety and Roasting Intensity
Authors: Dippong Thomas , Dan Monica , Kovacs Melinda Haydee , Kovacs Emoke Dalma , Levei Erika Andrea , Cadar Oana
Published: October 2022

This study aimed to investigate the ways in which the thermal behavior, composition, and volatile compound contents of roasted coffee beans depend on variety and roasting intensity. The thermal analysis revealed various transformations in coffee composition, namely, drying, water loss, and decomposition of polysaccharides, lipids, amino acids, and proteins. The results showed that volatile compounds are released differently in coffee depending on coffee type and degree of roasting. The most abundant volatile compounds present in the samples were 2-butanone, furan, 2-methylfuran, methyl formate, 2.3-pentanedione, methylpyrazine, acetic acid, furfural, 5-methyl furfural, and 2-furanmethanol. The total polyphenol contents ranged between 13.3 and 18.9 g gallic acid/kg, being slightly higher in Robusta than in Arabica varieties and in more intensely roasted beans compared to medium-roasted beans. The Robusta variety has higher mineral contents than Arabica, and the contents of most minerals (K, Ca, Mg, Fe, Cu, P, N, and S) increased with roasting intensity. Discrimination between coffee varieties and roasting intensities is possible based on mineral and polyphenol contents.
Oxidative Stability of Walnut Kernel and Oil: Chemical Compositions and Sensory Aroma Compounds
Authors: Ampofo Josephine , Grilo Filipa S. , Langstaff Sue , Wang Selina C.
Published: October 2022

The impact of storage temperature and time on quality of two walnut cultivars (Juglans regia Chandler and Howard) were evaluated. Free fatty acids, peroxides, and oxidative stabilities exhibited significant changes. After the storage period, γ-, δ-, and α-tocopherols in Howard oil significantly reduced by 42, 56, and 100% at 5 °C, while 23 °C showed 48, 42, and 100% losses, respectively. For Chandler oil, storage at 5 °C reduced γ-, δ-, and α-tocopherols by 19, 24, and 100%, while 23 °C caused 42, 45, and 100% losses, respectively. Storage of Howard kernels, up to month four, significantly reduced total phenolics by 9 and 18%, at 23 and 5 °C, respectively, whereas Chandler also reduced by 9 and 27%, at 23 and 5 °C, respectively. Additionally, 14 phenolic compounds were profiled in kernels, where flavonoids were dominant than phenolic acids. At the end of month four, the dominant phenolic compound was gallic acid at 23 °C (981.68 and 703 mg/kg for Chandler and Howard, respectively). Additionally, positive correlations were observed between rancid sensory perceptions and oxidative volatiles. Storage conditions are crucial for maintaining the sensory and nutritional attributes of walnuts during postharvest management.
Substituting Whole Wheat Flour with Pigeon Pea (Cajanus cajan) Flour in Chapati: Effect on Nutritional Characteristics, Color Profiles, and In Vitro Starch and Protein Digestion
Authors: Sachanarula Sirin , Chantarasinlapin Praew , Adisakwattana Sirichai
Published: October 2022

Pigeon pea (Cajanus cajan (L.) Millsp.), a potential legume as an economical source of protein, is commonly cultivated in tropical and subtropical regions of the world. Therefore, pigeon pea may be potentially used as a substitute to improve the nutritional profile of foods. In the present study, the effect of substitution of whole wheat flour (WWF) with 20% and 40% pigeon pea flour (PPF) on the nutritional properties, color profiles, and starch and protein digestibility of chapati was investigated. The results showed that PPF had higher protein content but less carbohydrate than WWF. The protein content of chapati substituted with 20% and 40% PPF increased by 1.18 and 1.34 times, respectively, compared to WWF chapati, along with a marked decrease in carbohydrate content. Analyses further revealed an increase in the lightness and yellowness and a decrease in the redness of the chapati. Furthermore, glucose release from chapati with 20% and 40% PPF under simulated digestion was attenuated, corresponding to decreased hydrolysis and a predicted glycemic index. In the 40% PPF chapati, a significant reduction in slowly digestible starch (SDS) with increased resistant starch (RS) proportions was achieved without altering the effect on rapidly digestible starch (RDS). In addition, the level of amino-group residues was markedly elevated in 20% and 40% PPF substituted chapati compared to WWF chapati. These findings suggest that PPF can serve as a promising plant-based alternative ingredient to improve the nutrient value of chapati by reducing starch and increasing protein digestibility.
An Adaptive Generalized Cauchy Model for Remaining Useful Life Prediction of Wind Turbine Gearboxes with Long-Range Dependence
Authors: Song Wanqing , Chen Dongdong , Zio Enrico , Yan Wenduan , Cai Fan
Published: October 2022

Remaining useful life (RUL) prediction is important for wind turbine operation and maintenance. The degradation process of gearboxes in wind turbines is a slowly and randomly changing process with long-range dependence (LRD). The degradation trend of the gearbox is constantly changing, and a single drift coefficient is not accurate enough to describe the degradation trend. This paper proposes an original adaptive generalized Cauchy (GC) model with LRD and randomness to predict the RUL of wind turbine gearboxes. The LRD is explained jointly by the fractal dimension and the Hurst exponent, and the randomness is explained by the diffusion term driven by the GC difference time sequence. The estimated value of the unknown parameter of adaptive GC model is deduced, and the specific expression of the RUL estimation is deduced. The adaptability is manifested in the time-varying drift coefficient of the GC model: by continuously updating the drift coefficient to adapt to the change in the degradation trend, the adaptive GC model offers high accuracy in the prediction of the degradation trend. The performance of the proposed model is analyzed using real wind turbine gearbox data.
Dendrimers-Based Drug Delivery System: A Novel Approach in Addressing Parkinson’s Disease
Authors: Ordonio Michaella B. , Zaki Randa Mohammed , Elkordy Amal Ali
Published: October 2022

Parkinson’s disease (PD) is a progressive neurodegenerative disease that is characterized by the loss of dopamine. Since dopamine has trouble entering the blood–brain barrier, the utilization of dendrimers and other nanomaterials is considered for conjugating the neurotransmitter and other PD drugs. Dendrimers are three-dimensional, hyper-branched structures that are categorized into several generations. Alpha-synuclein (ASN) is the protein involved in regulating dopaminergic functions and is the main aggregate found inside Lewy bodies. Different types of dendrimers have shown efficacy in disrupting the formation of unstable beta structures of ASN and fibrillation. The conjugation of PD drugs into nanomaterials has elicited a prolonged duration of action and sustained release of the drugs inside the BBB. The objectives of this study are to review the applications of a dendrimer-based drug delivery system in addressing the root cause of Parkinson’s disease and to emphasize the delivery of anti-Parkinson’s drugs such as rotigotine, pramipexole and dopamine using routes of administration other than oral and intravenous.
Transcriptomic Analysis Provides Insights into the Differential Effects of Aluminum on Peanut (Arachis hypogaea L.)
Authors: Bao Gegen , Li Shengyu , Zhou Qi , Ashraf Umair , Qiao Jingxuan , Li Xiaolin , Wan Xiaorong , Zheng Yixiong
Published: October 2022

In acidic soils, high concentrations of aluminum ions (Al3+) in dissolved form reduce root growth and development of most crops. In addition, Al3+ is also a beneficial element in some plant species in low concentrations. However, the regulatory mechanism of the growth and development of peanut (Arachis hypogaea L.) treated with different concentrations of Al3+ has been rarely studied. In this study, peanut seedlings were treated with AlCl3.18H2O in Hoagland nutrient solution at four different concentrations of Al3+, i.e., 0 (pH 6.85), 1.25 (pH 4.03), 2.5 (pH 3.85), and 5 (pH 3.69) mmol/L, which are regarded as Al0, Al1, Al2, and Al3. The results showed that low concentrations of Al treatment (Al1) promoted peanut growth, while high concentrations of Al treatments (Al2 and Al3) significantly inhibited peanut growth. Compared with the control (Al0), transcriptome analysis showed that the differentially expressed genes (DEGs) of starch and sucrose metabolic pathways were significantly enriched at low concentrations, i.e., Al1 treatment, whereas the expression of AhERD6 (sugar transporter) was significantly up-regulated, and the soluble sugar content was significantly increased. The DEGs of the plant hormone signaling transduction pathway were significantly enriched at high concentrations of Al2 and Al3 treatments, whereas the expression of AhNCED1 (9-cis-epoxycarotenoid dioxygenase) was significantly up-regulated, and the content of ABA was significantly increased. Moreover, the expression of transcription factors (TFs) in peanut was affected by different concentrations of Al. Overall, low concentrations of Al1 promoted peanut growth by increasing soluble sugar content, while high concentrations of Al2 and Al3 inhibited the growth of peanut, induced AhNCED1 gene expression, and increased endogenous ABA content. For peanut, the exposure of Al at low concentrations not only derived an adaptive mechanism to cope with Al stress, but also acted as a stimulator to promote its growth and development.
Advances of Brain Transcriptomics
Authors: Redina Olga E. , Babenko Vladimir N.
Published: October 2022

Advancements in RNA sequencing technology in past decade have underlined its power for elucidating the brain gene networks responsible for various stressful factors, as well as the pathologies associated with both genetically determined neurodegenerative diseases and those acquired during the lifespan [...]
A Reappraisal of the Destructive Earthquake (Mw5.9) of 15 July 1909 in Western Greece
Authors: Triantafyllou Ioanna , Koukouvelas Ioannis , Papadopoulos Gerassimos A. , Lekkas Efthymios
Published: October 2022

Studies on earthquakes that occurred in the early instrumental period of seismology are of importance for the seismic hazard assessment and are still under investigation since new data are being increasingly revealed. We study the case of a moderate-to-strong earthquake that occurred on 15 July 1909 in NW Peloponnese, Greece. Although the earthquake event was quite destructive, it remains little-known so far in the seismological tradition. We compiled a variety of documentary sources and showed that the earthquake caused extensive building destruction in Chavari and in many other villages with an estimated maximum intensity IX (in EMS-98 scale) and a death toll as high as 55. We also assigned macroseismic intensities in several observation points and drew isoseismal lines by applying the nearest-neighbor technique. From empirical relationships between magnitude and intensities, we estimated the macroseismic magnitude of proxy Ms5.9. Our examination also revealed a variety of earthquake associated phenomena including several types of precursors and abundant co-seismic hydrological changes and ground failures, such as soil liquefaction, surface ruptures, and rock falls. Since no surface fault-trace was reported, the determination of the causative blind fault remains an open issue for future investigation.
Genetic Diversity of Tea Plant (Camellia sinensis (L.) Kuntze) Germplasm Resources in Wuyi Mountain of China Based on Single Nucleotide Polymorphism (SNP) Markers
Authors: Liu Caiguo , Yu Wentao , Cai Chunping , Huang Shijian , Wu Huanghua , Wang Zehan , Wang Pan , Zheng Yucheng , Wang Pengjie , Ye Naixing
Published: October 2022

Wuyi Mountain in Southeast China is the origin of black tea and oolong tea. It is also considered the ‘treasure trove of tea cultivars’ because of its rich tea germplasm resources. In the present study, the population structure and genetic diversity of 137 tea germplasms from Wuyi Mountain and its adjacent areas were analyzed by SNPs. The information index (I), observed heterozygosity (Ho), expected heterozygosity (He) and fixation index (F) polymorphisms of the selected SNPs were high, stable and reliable. Ho had an average of 0.389, while He had an average of 0.324, indicating that Wuyi Mountain tea germplasms had rich genetic diversity. The AMOVA results showed that genetic variation came mainly from intrapopulation variation, accounting for 66% of the total variation. The differences in the Fst and Nei values of tea germplasm between Wuyi Mountain and its adjacent areas are similar to the geographical differences. Multiple analyses based on high-quality SNPs found that the landraces of tea plants on Wuyi Mountain had different genetic backgrounds from the wild-type landraces and the landraces of Wuyi Mountain tea plants underwent population differentiation. This study provides a basis for the effective protection and utilization of tea germplasms on Wuyi Mountain and lays a foundation for identifying potential parents to optimize tea cultivation.
New Evidence of Semi-Mangrove Plant Barringtonia racemosa in Soil Clean-Up: Tolerance and Absorption of Lead and Cadmium
Authors: Liang Fang , Hu Ju , Liu Bing , Li Lin , Yang Xiuling , Bai Caihong , Tan Xiaohui
Published: October 2022

Mangrove plants play an important role in the remediation of heavy-metal-contaminated estuarine and coastal areas; Barringtonia racemosa is a typical semi-mangrove plant. However, the effect of heavy metal stress on this plant has not been explored. In this study, tolerance characteristics and the accumulation profile of cadmium (Cd) and lead (Pb) in B. racemosa were evaluated. The results indicated that B. racemosa exhibited a high tolerance in single Cd/Pb and Cd + Pb stress, with a significant increase in biomass yield in all treatment groups, a significant increase in plant height, leaf area, chlorophyll and carotenoid content in most treatment groups and without significant reduction of SOD, POD, MDA, proline content, Chl a, Chl b, Chl a + b, Car, ratio of Chl a:b and ratio of Car:Chl (a + b). Cd and Pb mainly accumulated in the root (≥93.43%) and the content of Cd and Pb in B. racemosa was root > stem > leaf. Pb showed antagonistic effects on the Cd accumulation in the roots and Cd showed antagonistic or synergistic effects on the Pb accumulation in the roots, which depended on the concentration of Cd and Pb. There was a significant synergistic effect of Cd and Pb enrichment under a low Cd and Pb concentration treatment. Thus, phytoremediation could potentially use B. racemosa for Cd and Pb.
Sexual Health among Youth in Residential Care in Spain: Knowledge, Attitudes and Behaviors
Authors: Fernández-García Olga , Gil-Llario María Dolores , Ballester-Arnal Rafael
Published: October 2022

Adolescents in the child welfare system often face multiple maladaptive experiences that predispose them to worse sexual health outcomes. This study aims to (1) describe the sexual health of adolescents in Spanish residential care by exploring their sexual knowledge, attitudes toward sexuality, and sexual behaviors and (2) to find out whether there are certain characteristics that make a subgroup particularly vulnerable to engaging in risky sexual behaviors. A total of 346 adolescents recruited from 47 Spanish residential care facilities (34.1% girls, 65.9% boys) aged between 11 and 19 years old completed some self-report instruments. Descriptive analyses and tests to prove gender and age differences were conducted. Their knowledge of sexuality was lower than observed in the general adolescent population, their attitudes more negative, and their tendency to engage in risky sexual behaviors higher. Girls made very infrequent use of condoms, while boys had more sexist attitudes and made habitual use of withdrawal. Although more than 20% of them had experienced sexual exchange activities before the age of 13 until 17, they did not use condoms systematically. The low level of knowledge, the early initiation of sexual exchange activities, and the scarce use of protection methods, together with sexist attitudes, place this group in a situation of great vulnerability, increasing the risk of unwanted pregnancies, sexually transmitted infections, and even teen dating violence.
Long-Term Effects of a Kinematic Bikefitting Method on Pain, Comfort, and Fatigue: A Prospective Cohort Study
Authors: Scoz Robson Dias , de Oliveira Paulo Rui , Santos Cleyton Salvego , Pinto Júlia Ribeiro , Melo-Silva Cesar Augusto , de Júdice André Filipe Teixeira , Mendes José João Baltazar , Ferreira Luciano Maia Alves , Amorim César Ferreira
Published: October 2022

The purpose of this study is to analyze the long-term riders’ subjective responses to a standardized bikefitting method on their bicycles. Eighty-six amateur mountain bikers had their riding posture and bicycle components ergonomically adjusted through a 3D kinematic bikefitting method. Validated subjective scales (Feeling, OMNI, and Numerical Rating Pain Scale) were used to assess their overall riding comfort and fatigue along with localized pain for six body parts. Data were collected just before intervention (baseline or pre), immediately after (or post), and 30, 60, 90, and 120 days after the bikefit session. A Student’s t-test comparing before bikefit and after 120 days showed significant (p < 0.05) reduction in localized pain for all six body parts and riding comfort along with a large effect size effect (d = 1.18) for riding comfort. Although initially reduced, fatigue scores gradually increased over the months, showing a high correlation (r = 0.946) with increased monthly training volume. In conclusion, overall riding discomfort and pain were significantly decreased after a standardized kinematic bikefit session even after 120 days post intervention. However, fatigue scores began to rise after 30 days, showing a high correlation with increasing monthly training volume.
Are Healthcare Workers Infected with SARS-CoV-2 at Home or at Work? A Comparative Prevalence Study
Authors: Zahran Shadi , Nir-Paz Ran , Paltiel Ora , Stein-Zamir Chen , Oster Yonatan
Published: October 2022

Prior to the widespread use of vaccinations, healthcare workers (HCWs) faced the double burden of caring for unprecedented numbers of critically ill COVID-19 patients while also facing the risk of becoming infected themselves either in healthcare facilities or at home. In order to assess whether SARS-CoV-2-positivity rates in HCWs reflected or differed from those in their residential areas, we compared the SARS-CoV-2-positivity rates during 2020 among HCWs in Hadassah Hebrew University Medical Centers (HHUMC), a tertiary medical center in Jerusalem, Israel, to those of the general population in Jerusalem, stratified by neighborhood. Additionally, we compared the demographic and professional parameters in every group. Four percent of the adult population (>18 years) in Jerusalem tested positive for SARS-CoV-2 during 2020 (24,529/605,426) compared to 7.1% of HHUMC HCWs (317/4470), rate ratio 1.75 (95% CI 1.57–1.95), with wide variability (range 0.38–25.0) among different neighborhoods. Of the 30 neighborhoods with more than 50 infected HCWs, 25 showed a higher positivity rate for HCWs compared to the general population. The higher risk of HCWs compared to residents representing the general population in most neighborhoods in Jerusalem may be explained by their behavior in and out of the hospital.
Navigating Hidden Hunger: An Exploratory Analysis of the Lived Experience of Food Insecurity among College Students
Authors: Anderson Ashlyn , Lazarus Jacqueline , Anderson Steeves Elizabeth
Published: October 2022

College students are a vulnerable population to food insecurity (FI), which has significant implications for academic and health outcomes. The aims of this study were to explore the meaning of FI and its impact on students’ lived experiences and food decisions, facilitators and barriers to food access as a student, and students’ proposed solutions to address FI. Semi-structured, qualitative interviews were conducted with thirty students from a large, public land grant university in the Southeast United States. Grounded theory methodology was utilized with a constant comparative coding strategy to guide thematic analysis. Nine main themes emerged. Themes included the perceived meaning of FI, students’ lived experience with FI, and food related coping strategies and decisions. Facilitators to food access were found to be social-networks and on-campus resources, while barriers to food access included financial burden of higher education, and stigma and social comparison. Proposed solutions to FI aligned with two main themes: food access solutions and information access solutions. Both of these themes included multiple subthemes that provided specific suggestions to address food insecurity for students. The findings aid in understanding the complex lived experience of FI and can inform future efforts to center student experiences, perceptions, and feedback into institutional frameworks to best meet student needs.
Exploring Saudi Individuals’ Perspectives and Needs to Design a Hypertension Management Mobile Technology Solution: Qualitative Study
Authors: Alzahrani Adel , Gay Valerie , Alturki Ryan
Published: October 2022

Hypertension is a chronic condition caused by a poor lifestyle that affects patients’ lives. Adherence to self-management programs increases hypertension self-monitoring, and allows greater prevention and disease management. Patient compliance with hypertension self-management is low in general; therefore, mobile health applications (mHealth-Apps) are becoming a daily necessity and provide opportunities to improve the prevention and treatment of chronic diseases, including hypertension. This research aims to explore Saudi individuals’ perspectives and needs regarding designing a hypertension management mobile app to be used by hypertension patients to better manage their illnesses. Semi-structured interviews were conducted with 21 Saudi participants to explore their perspectives and views about the needs and requirements in designing a hypertension mobile technology solution, as well as usability and culture in the Saudi context. The study used NVivo to analyze data and divided the themes into four main themes: the app’s perceived health benefits, features and usability, suggestions for the app’s content, and security and privacy. The results showed that there are many suggestions for improvements in mobile health apps that developers should take into consideration when designing apps. The mobile health apps should include physical activity tracking, related diet information, and reminders, which are promising, and could increase adherence to healthy lifestyles and consequently improve the self-management of hypertension patients. Mobile health apps provide opportunities to improve hypertension patients’ self-management and self-monitoring. However, this study asserts that mobile health apps should not share users’ data, and that adequate privacy disclosures should be implemented.