bluuebunny/crossref_metadata_2025 huggingface dataset:
Distance Matters: A Distance-Aware Medical Image Segmentation Algorithm
Authors: Feng Yuncong , Cong Yeming , Xing Shuaijie , Wang Hairui , Zhao Cuixing , Zhang Xiaoli , Yao Qingan
Published: August 2023

The transformer-based U-Net network structure has gained popularity in the field of medical image segmentation. However, most networks overlook the impact of the distance between each patch on the encoding process. This paper proposes a novel GC-TransUnet for medical image segmentation. The key innovation is that it takes into account the relationships between patch blocks based on their distances, optimizing the encoding process in traditional transformer networks. This optimization results in improved encoding efficiency and reduced computational costs. Moreover, the proposed GC-TransUnet is combined with U-Net to accomplish the segmentation task. In the encoder part, the traditional vision transformer is replaced by the global context vision transformer (GC-VIT), eliminating the need for the CNN network while retaining skip connections for subsequent decoders. Experimental results demonstrate that the proposed algorithm achieves superior segmentation results compared to other algorithms when applied to medical images.
Identification of Solid and Liquid Materials Using Acoustic Signals and Frequency-Graph Features
Authors: Zhang Jie , Zhou Kexin
Published: August 2023

Material identification is playing an increasingly important role in various sectors such as industry, petrochemical, mining, and in our daily lives. In recent years, material identification has been utilized for security checks, waste sorting, etc. However, current methods for identifying materials require direct contact with the target and specialized equipment that can be costly, bulky, and not easily portable. Past proposals for addressing this limitation relied on non-contact material identification methods, such as Wi-Fi-based and radar-based material identification methods, which can identify materials with high accuracy without physical contact; however, they are not easily integrated into portable devices. This paper introduces a novel non-contact material identification based on acoustic signals. Different from previous work, our design leverages the built-in microphone and speaker of smartphones as the transceiver to identify target materials. The fundamental idea of our design is that acoustic signals, when propagated through different materials, reach the receiver via multiple paths, producing distinct multipath profiles. These profiles can serve as fingerprints for material identification. We captured and extracted them using acoustic signals, calculated channel impulse response (CIR) measurements, and then extracted image features from the time–frequency domain feature graphs, including histogram of oriented gradient (HOG) and gray-level co-occurrence matrix (GLCM) image features. Furthermore, we adopted the error-correcting output code (ECOC) learning method combined with the majority voting method to identify target materials. We built a prototype for this paper using three mobile phones based on the Android platform. The results from three different solid and liquid materials in varied multipath environments reveal that our design can achieve average identification accuracies of 90% and 97%.
Students’ Voices: A Qualitative Study on Contextual, Motivational, and Self-Regulatory Factors Underpinning Language Achievement
Authors: Katsantonis Ioannis , McLellan Ros
Published: August 2023

A lack of qualitative studies examining adolescent students’ voices regarding the contextual, relational, and self-regulatory factors that drive their language achievement has been observed. Therefore, the present study aimed to address this issue. Sixteen face-to-face semi-structured interviews were conducted with secondary school students in Greece to document and analyse their perceptions of the factors that influence language achievement. The study was guided by social-cognitive theory and models of self-regulated learning. Computer-assisted thematic analysis was performed using abductive open coding followed by refinement of codes. Three overarching global themes were identified in line with theoretical expectations, namely personal factors (motivation and self-regulatory strategies), relational factors (teachers’ practices, parental achievement expectations, peer influences), and structural factors (the educational system). Afterwards, an abductive thematic network analysis was conducted to explore and theorise about potential relationships between the emergent themes in the data. It was found that parental expectations for higher performance and the teachers’ behaviours were associated with students’ language achievement. Task-related characteristics and peers influenced students’ regulation of effort in learning. The students believed that they were not objectively graded and frequent summative assessments created test anxiety. The findings are discussed in light of existing empirical evidence and in terms of educational implications.
How Can Interprofessional Skills Be Taught during University Studies? Student Teachers, and Social Work and Law Students Solving Complex Student Welfare Cases
Authors: Lakkala Suvi , Nikupeteri Anna , Lantela Lauri
Published: August 2023

This study focused on the learning of interprofessional skills through an interdisciplinary university course provided to student teachers, and social work and law students. This study explored the development of the course, the aim of which was to raise the student’s understanding of interprofessional work to a more conscious level. The key research question was as follows: what kind of assignments and arrangements can be used to promote the interprofessional skills of students and their ability to solve complex student welfare cases? In this practice-oriented case study, we analyzed our own development process for the course, as well as the student’s reflective essays. Through our analysis, we identified four central themes of an interdisciplinary course that can enhance interprofessional skills in students: (1) identifying discipline-specific expertise as a part of a multi-professional network; (2) gaining an understanding of interprofessional working processes; (3) realizing the complexity of student welfare cases; and (4) orienting towards working life. Our research demonstrates that building an interdisciplinary university course based on flipped learning principles, which incorporates collaborative and reflective assignments supported by various course materials and literature, can promote the learning of interprofessional skills and processes by students.
185–215 GHz CMOS Frequency Doubler with a Single Row Staggered Distribution Layout Design
Authors: Dong Ruibing , You Chengwu
Published: August 2023

This paper presents a 220 GHz × 2 amplifier–doubler chain composed of a rat-race balun, a 6-stage driver amplifier, and a frequency doubler. The presented amplifier–doubler chain was fabricated in commercial 40 nm bulk CMOS technology. The maximum cutoff frequency fmax for the NMOS transistor produced by this manufacturing process was 290 GHz. The saturation output power of the six-stage driver amplifier at 110 GHz was 11.5 dBm. The transistor of the frequency doubler consisted of a single-row interleaved Poly-Diffusion Contact balancing structure. Theoretically, the single-row interleaved Poly-Diffusion Contact balancing structure was able to effectively avoid parasitic components. The simulated results demonstrate that the presented structure achieves a higher output than the conventional designs. Based on these measured results, the presented amplifier–doubler chain provides a peak output power of 7.9 dBm at 200 GHz and a 3-dB bandwidth of 30 GHz. Based on the comparison with other reported results, the presented amplifier–doubler chain provides the highest output power among reported frequency doublers fabricated in CMOS technology.
Cascading and Ensemble Techniques in Deep Learning
Authors: de Zarzà I. , de Curtò J. , Hernández-Orallo Enrique , Calafate Carlos T.
Published: August 2023

In this study, we explore the integration of cascading and ensemble techniques in Deep Learning (DL) to improve prediction accuracy on diabetes data. The primary approach involves creating multiple Neural Networks (NNs), each predicting the outcome independently, and then feeding these initial predictions into another set of NN. Our exploration starts from an initial preliminary study and extends to various ensemble techniques including bagging, stacking, and finally cascading. The cascading ensemble involves training a second layer of models on the predictions of the first. This cascading structure, combined with ensemble voting for the final prediction, aims to exploit the strengths of multiple models while mitigating their individual weaknesses. Our results demonstrate significant improvement in prediction accuracy, providing a compelling case for the potential utility of these techniques in healthcare applications, specifically for prediction of diabetes where we achieve compelling model accuracy of 91.5% on the test set on a particular challenging dataset, where we compare thoroughly against many other methodologies.
Research on Stability Control of Shields at Working Face with Large Dip Angle
Authors: Shi Bao , Wang Pengfei
Published: August 2023

Coal is the main energy source in China. As flat and shallow coal seams are being depleted, adverse coal seams such as inclined and steeply inclined coal seams account for larger proportion of seams that are mined. For these coal seams, instability such as slip and tipping of mining equipment due to the large inclination is a significant challenge for the productive operation of intelligent or smart mines. Therefore, this paper serves to provide some insights into improving their stability. In this paper, research on the anti-tipping and anti-slip technology of shields is carried out on an intelligent working face with a large dip angle. A mechanical model of “support-surrounding rock” was established. Through the analysis of the influence of the self-weight of the support on its stability and through theoretical analysis and field practice, it was found that the critical tipping angle of the support in the free state is 27.8, the critical slip angle is 16, and the support is more prone to slip in the free state; the shields in the middle of the working face are the key area for stability control. Suitable technical measures are taken to ensure the stability of the supports, which provides the management and practical basis for safe and efficient mining in the intelligent working face with a large dip angle.
Carbon-Neutral City Transformation with Digitization: Guidelines from International Standardization
Authors: Ziozias Christos , Kontogianni Elli , Anthopoulos Leonidas
Published: August 2023

Establishing carbon neutrality (or Net Zero) in cities appears to be an ambitious challenge, which drives urban transformation efforts around the world to enhance people-centric living. Cities launch corresponding strategies focusing mainly on the United Nations 2030 goals to form their climate-neutral future. This paper provides practical guidance on city digital transformation to achieve carbon Net Zero. These guidelines illustrate actionable recommendations to reduce their carbon footprint using digital technologies. Following a multi-method research approach, findings from a bibliometric literature and a standards’ gap analysis were combined with practical evidence and circulated with focus group experts to conclude with these guidelines, which are the foundation for further standardization. The outcomes provide concrete guidelines to policymakers, planners, and other stakeholders for city digital transformation to establish carbon neutrality, via emphasizing the need for digital technologies to enhance efficiency in several areas and via leveraging green Information and Communication Technology (ICT) for city economic growth, while also transforming citizen behavior.
Pack-Level Modeling and Thermal Analysis of a Battery Thermal Management System with Phase Change Materials and Liquid Cooling
Authors: Sun Jixian , Dan Dan , Wei Mingshan , Cai Senlin , Zhao Yihang , Wright Edward
Published: August 2023

Electric vehicles are seen as the prevailing choice for eco-friendly transportation. In electric vehicles, the thermal management system of battery cells is of great significance, especially under high operating temperatures and continuous discharge conditions. To address this issue, a pack-level battery thermal management system with phase change materials and liquid cooling was discussed in this paper. A dynamic electro-thermal coupled model for cells, the enthalpy–porosity model for phase change materials, and the k-ε model for the coolant flow were used. Various parameters, such as ambient temperatures, discharge rates, components of phase change materials, inlet mass flow rates, and temperatures of the coolant were considered. The results indicated that a battery thermal management system with both phase change materials and liquid cooling is more effective than the one with only liquid cooling. The phase change material with a mass fraction of 10% expanded graphite in paraffin wax had a favorable performance for the battery thermal management system. Additionally, increasing the mass flow rate or decreasing the flow temperature of the coolant can reduce the maximum temperature of the battery pack. However, the former can limit the maximum temperature difference, while the latter will deteriorate the temperature uniformity. The present work may shed light on the design of battery thermal management systems in the electric vehicle industry.
Helical Static Mixer Simulations for Its Integration in the Pour Plate Method: Mixing Agar and a Nutrient Solution
Authors: Díaz Ana M. , Terrones-Fernandez Inés , Gamez-Montero Pedro Javier , Castilla Robert
Published: August 2023

In microbiology laboratories, it is essential to obtain high-quality samples where the culture media are completely homogeneous. The pour plate method includes having to melt the culture media—a mixture of agar and a nutrient solution—before seeding. A static mixer is designed for this purpose, employing CFD (Computational Fluid Dynamics) with the software OpenFOAM to simulate the fluid’s behaviour in a helical static mixer with both internal and external configurations. The objective is to validate the CFD model by comparing it with the literature and provide a first approach to the mixer design. After satisfactory validation of the model, the results of the initial designs for Reynolds number 14 reveal the notably different behaviour of the fluids during mixing due to their differing properties, since agar and the nutrient solution present a high viscosity ratio. While the mixing efficiency is similar for the internal and external mixers, improved performance is demonstrated in the internal mixer, even for a shorter version. The external version of the mixer reaches a value for a mixing efficiency of 0.89 whereas the internal version performs more homogeneous mixing for the same number of mixing elements. This evaluation is based on a simplified internal design for computational simulations, whereas the external mixer is easier to manufacture but more complex to implement computationally. Finally, homogeneous mixing is achieved for the internal mixer configuration when adjusting its dimensions to those available on the market.
Characterizing the Wake Effects on Wind Power Generator Operation by Data-Driven Techniques
Authors: Astolfi Davide , De Caro Fabrizio , Vaccaro Alfredo
Published: August 2023

Wakes between neighboring wind turbines are a significant source of energy loss in wind farm operations. Extensive research has been conducted to analyze and understand wind turbine wakes, ranging from aerodynamic descriptions to advanced control strategies. However, there is a relatively overlooked research area focused on characterizing real-world wind farm operations under wake conditions using Supervisory Control And Data Acquisition (SCADA) parameters. This study aims to address this gap by presenting a detailed discussion based on SCADA data analysis from a real-world test case. The analysis focuses on two selected wind turbines within an onshore wind farm operating under wake conditions. Operation curves and data-driven methods are utilized to describe the turbines’ performance. Particularly, the analysis of the operation curves reveals that a wind turbine operating within a wake experiences reduced power production not only due to the velocity deficit but also due to increased turbulence intensity caused by the wake. This effect is particularly prominent during partial load operation when the rotational speed saturates. The turbulence intensity, manifested in the variability of rotational speed and blade pitch, emerges as the crucial factor determining the extent of wake-induced power loss. The findings indicate that turbulence intensity is strongly correlated with the proximity of the wind direction to the center of the wake sector. However, it is important to consider that these two factors may convey slightly different information, possibly influenced by terrain effects. Therefore, both turbulence intensity and wind direction should be taken into account to accurately describe the behavior of wind turbines operating within wakes.
The Effects of Electrospinning Structure on the Ion Conductivity of PEO-Based Polymer Solid-State Electrolytes
Authors: Sun Qihang , Liu Zhanna , Zhu Ping , Liu Jie , Shang Shenglong
Published: August 2023

To overcome the safety hazard of the liquid electrolytes used in traditional lithium batteries, solid electrolytes have drawn more attention because of their advantages such as non-volatility, easy processing, good mechanical properties, and stability. In this paper, sodium alginate (SA) nanofiber membranes were used as the backbone of PEO-based solid electrolytes. SA nanofiber membranes were prepared by electrospinning with assistance from PEO and cross-linked with calcium ions to construct a nanofiber network skeleton, which provided a guarantee for the stability of the subsequent electrolyte preparation process. The effects of spinning conditions and crosslinking time on the structure and performances of the nanofiber membranes were investigated. Meanwhile, the relationship between the skeleton of nanofiber membranes cross-linked with calcium ions and ion conductivity was investigated. The optimal parameters of the electrospinning process including concentration, voltage, distance, and SA content were discussed, and the fiber diameter and its distribution were analyzed. Furthermore, Fourier transform infrared (FTIR) spectrometer, thermal gravimetric analyzer analysis (TGA), X-ray diffraction (XRD), and energy dispersive spectrometer (EDS) maps were used to characterize the nanofiber membranes and electrolytes. The results showed that the thermal performance of cross-linked nanofiber membranes improved and the crystallinity of the PEO matrix decreased. The ion conductivity of the electrolytes was characterized by electrochemical impedance spectroscopy (EIS) testing, and the results showed that the assembled lithium symmetric battery had a good ion conductivity of 6.82 × 10−5 S/cm at 30 °C.
Energy Efficiency Indicator of Pumping Equipment Usage
Authors: Ivchenko Oleksandr , Andrusiak Vladyslav , Kondus Vladyslav , Pavlenko Ivan , Petrenko Serhii , Krupińska Andżelika , Włodarczak Sylwia , Matuszak Magdalena , Ochowiak Marek
Published: August 2023

The rational choice of pumping units and the effective usage of the corresponding pumping equipment are urgent problems in terms of ensuring the energy efficiency of up-to-date enterprises. The research aims to develop an energy efficiency indicator for torque flow pump usage to improve the decision-making procedure for choosing the corresponding pumping equipment for specific production conditions. Based on a study of the requirements of international standards for energy efficiency, a methodology for evaluating torque flow pumps according to the energy efficiency indicators is proposed, as the ratio between the values of the average compensated network power and the reference pump inlet power can determine the degree of reduced energy equipment operation efficiency in terms of the energy efficiency of pumping liquids. It was proposed to apply the base point method to determine the so-called compromise point of energy consumption for liquid pumping. This is a point for which another point, by the Pareto compromise, is likely to have a more significant (worse) value for energy consumption than the rest of the set of possible values.
Seismic Characterization of the Blue Mountain Geothermal Field
Authors: Gao Kai , Huang Lianjie , Cladouhos Trenton
Published: August 2023

Subsurface characterization is crucial for geothermal energy exploration and production. Yet hydrothermal reservoirs usually reside in highly fractured and faulted zones where accurate characterization is very challenging because of low signal-to-noise ratios of land seismic data and lack of coherent reflection signals. We perform an active-source seismic characterization for the Blue Mountain geothermal field in Nevada using active seismic data to reveal the elastic medium property complexity and fault distribution at this field. We first employ an unsupervised machine learning method to attenuate groundroll and near-surface guided-wave noise and enhance coherent reflection and scattering signals from noisy seismic data. We then build a smooth initial P-wave velocity model based on an existing magnetotellurics survey result, and use 3D first-arrival traveltime tomography to refine the initial velocity model. We then derive a set of elastic wave velocities and anisotropic parameters using elastic full-waveform inversion, and obtain PP and PS images using elastic reverse-time migration. We identify major faults by analyzing the variations of seismic velocities and anisotropy parameters, and reveal mid- to small-scale faults by applying a supervised machine learning method to the seismic migration images. Our characterization reveals complex velocity heterogeneities and anisotropies, as well as faults, with a high spatial resolution. These results can provide valuable information for optimal placement of future injection and production wells to increase geothermal energy production at the Blue Mountain geothermal power plant.
A Multiphysics-Multiscale Model for Particle–Binder Interactions in Electrode of Lithium-Ion Batteries
Authors: Ali Yasir , Shah Imran , Khan Tariq Amin , Iqbal Noman
Published: August 2023

Understanding the electrochemical and mechanical degradations inside the electrodes of lithium-ion battery is crucial for the design of robust electrodes. A typical lithium-ion battery electrode consists of active particles enclosed with conductive binder and an electrolyte. During the charging and discharging process, these adjacent materials create a mechanical confinement which suppresses the expansion and contraction of the particles and affects overall performance. The electrochemical and mechanical response mutually affect each other. The particle level expansion/contraction alters the electrochemical response at the electrode level. In return, the electrode level kinetics affect the stress at the particle level. In this paper, we developed a multiphysics–multiscale model to analyze the electrochemical and mechanical responses at both the particle and cell level. The 1D Li-ion battery model is fully coupled with 2D representative volume element (RVE) model, where the particles are covered in binder layers and bridged through the binder. The simulation results show that when the binder constraint is incorporated, the particles achieve a lower surface state of charge during charging. Further, the cell charging time increases by 7.4% and the discharge capacity reduces by 1.4% for 1 C-rate charge/discharge. In addition, mechanical interaction creates inhomogeneous stress inside the particle, which results in particle fracture and particle–binder debonding. The developed model will provide insights into the mechanisms of battery degradation for improving the performance of Li-ion batteries.
Weightless Neural Network-Based Detection and Diagnosis of Visual Faults in Photovoltaic Modules
Authors: Sridharan Naveen Venkatesh , Joseph Jerome Vasanth , Vaithiyanathan Sugumaran , Aghaei Mohammadreza
Published: August 2023

The present study introduces a novel approach employing weightless neural networks (WNN) for the detection and diagnosis of visual faults in photovoltaic (PV) modules. WNN leverages random access memory (RAM) devices to simulate the functionality of neurons. The network is trained using a flexible and efficient algorithm designed to produce consistent and precise outputs. The primary advantage of adopting WNN lies in its capacity to obviate the need for network retraining and residual generation, making it highly promising in classification and pattern recognition domains. In this study, visible faults in PV modules were captured using an unmanned aerial vehicle (UAV) equipped with a digital camera capable of capturing RGB images. The collected images underwent preprocessing and resizing before being fed as input into a pre-trained deep learning network, specifically, DenseNet-201, which performed feature extraction. Subsequently, a decision tree algorithm (J48) was employed to select the most significant features for classification. The selected features were divided into training and testing datasets that were further utilized to determine the training, test and validation accuracies of the WNN (WiSARD classifier). Hyperparameter tuning enhances WNN’s performance by achieving optimal values, maximizing classification accuracy while minimizing computational time. The obtained results indicate that the WiSARD classifier achieved a classification accuracy of 100.00% within a testing time of 1.44 s, utilizing the optimal hyperparameter settings. This study underscores the potential of WNN in efficiently and accurately diagnosing visual faults in PV modules, with implications for enhancing the reliability and performance of photovoltaic systems.
TS2ARCformer: A Multi-Dimensional Time Series Forecasting Framework for Short-Term Load Prediction
Authors: Li Songjiang , Zhang Wenxin , Wang Peng
Published: August 2023

Accurately predicting power load is a pressing concern that requires immediate attention. Short-term load prediction plays a crucial role in ensuring the secure operation and analysis of power systems. However, existing research studies have limited capability in extracting the mutual relationships of multivariate features in multivariate time series data. To address these limitations, we propose a multi-dimensional time series forecasting framework called TS2ARCformer. The TS2ARCformer framework incorporates the TS2Vec layer for contextual encoding and utilizes the Transformer model for prediction. This combination effectively captures the multi-dimensional features of the data. Additionally, TS2ARCformer introduces a Cross-Dimensional-Self-Attention module, which leverages interactions across channels and temporal dimensions to enhance the extraction of multivariate features. Furthermore, TS2ARCformer leverage a traditional autoregressive component to overcome the issue of deep learning models being insensitive to input scale. This also enhances the model’s ability to extract linear features. Experimental results on two publicly available power load datasets demonstrate significant improvements in prediction accuracy compared to baseline models, with reductions of 43.2% and 37.8% in the aspect of mean absolute percentage error (MAPE) for dataset area1 and area2, respectively. These findings have important implications for the accurate prediction of power load and the optimization of power system operation and analysis.
The Effects of Dynamic and Static Forest Bathing (Shinrin-yoku) on Physiological and Psychological Health in Males and Females
Authors: Wen Ye , Gu Xinren , Deng Wenping , Zou Qin , Hu Yuan , Yan Qi , Pan Yangliu , Wen Zhaojie , Wan Renhui , Sheng Gonghan , Liu Yuanqiu , He Mei
Published: August 2023

This study aimed to investigate the effects of dynamic and static forest bathing (Shinrin-yoku) on the physiological and psychological health of males and females. Dynamic pre-test and post-test forest bathing was performed on 11 participants (5 males and 6 females) as a single group in a forest environment. In addition, a randomized controlled trial involving 20 participants (10 males and 10 females) was conducted to evaluate static forest bathing in both forest and urban environments. Various physiological indicators, including systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse, heart rate variability (HRV), and self-assessed psychological indicators such as profile of mood states, were measured. Dynamic forest bathing resulted in a significant increase in the natural logarithmic value of the high frequency (lnHF) of HRV and significantly decreased ratio of the natural logarithmic value of the low frequency (lnLF) to lnHF (lnLF/lnHF) of HRV. Static forest bathing not only had the effects of dynamic forest bathing but also significantly decreased the participants’ SBP, DBP, and pulse. Both dynamic and static forest bathing enhanced human parasympathetic nervous system activity and reduced sympathetic nervous system activity, particularly affecting females. Negative mood state scores (tension, anger, fatigue, depression, and confusion) and total mood disturbance scores significantly decreased after forest bathing. In contrast, positive mood state (vigor) scores significantly increased, indicating an enhancement in positive mood. These improvements in mood were particularly pronounced in male individuals. Short-term exposure to a forest environment has positive effects on both physical and mental health of individuals. The extent of these improvements varied according to factors such as engagement in physical activity and gender.
Assessment of the Impact of Forest Reclamation Measures for the Adaptation of Agriculture to Climate Change in the South of the Russian Plain
Authors: Korneeva Evgenia A. , Belyaev Alexander I.
Published: August 2023

The aim of this study was to study the effectiveness and economic efficiency of the impact of agroforestry complexes on the adaptation of agriculture to climate change in the south of the Russian Plain. It has been established that this manifests quantitatively in a significant decrease (by almost a third) in the area of wind-destroyed lands and drought-dead crops in farms protected by forest strips compared with open agricultural territories. The calculation of direct damage prevented by protective forest plantations from degradation and loss of soil fertility as a result of dust storms and indirect damage prevented by protective forest plantations from crop loss as a result of extreme droughts shows that the total amount of remuneration received by farmers from agroforestry in connection with the placement of a forest-forming element in their fields is EUR 317–1239 ha−1 year−1. This value is the contribution of agroforestry to adaptation to climate change and is subject to zonal dynamics—it depends on natural and climatic conditions. The application developed as part of this research has value for decision makers, since it allows for preliminary assessment of the effectiveness and efficiency of agroforestry for various areas of farms and various natural zones.
Refining Fire–Climate Relationship Methodologies: Southern California
Authors: Bleiman Benjamin , Rolinski Tom , Hoffman Eric , Kelsey Eric , Bangor David
Published: August 2023

Efforts to delineate the influence of atmospheric variability on regional wildfire activity have previously been complicated by the stochastic occurrence of ignition and large fire events, particularly for Southern California, where anthropogenic modulation of the fire regime is extensive. Traditional metrics of wildfire activity inherently contain this stochasticity, likely weakening regional fire–climate relationships. To resolve this complication, we first develop a new method of quantifying regional wildfire activity that aims to more clearly capture the atmospheric fire regime component by aggregating four metrics of fire activity into an annual index value, the Annual Fire Severity Index (AFSI), for the 27-year period of 1992–2018. We then decompose the AFSI into trend and oscillatory components using singular spectrum analysis (SSA) and relate each component to a set of five climate predictors known to modulate macroscale fire activity in Southern California. These include the Atlantic Multidecadal Oscillation (AMO), Pacific Decadal Oscillation (PDO), El Niño–Southern Oscillation (ENSO), and Santa Ana wind (SAW) events, and marine layer frequency. The results indicate that SSA effectively isolates the individual influence of each predictor on AFSI quantified by generally moderate fire–climate correlations, |r|>0.4, over the full study period, and |r|>0.5 over select 13–15-year periods. A transition between weaker and stronger fire–climate relationships for each of the oscillatory PC–predictor pairs is centered around the mid-2000s, suggesting a significant shift in fire–climate variability at this time. Our approach of aggregating and decomposing a fire activity index yields a straightforward methodology to identify the individual influence of climatic predictors on macroscale fire activity even in fire regimes heavily modified by anthropogenic influence.
Development and Application of an Intelligent Approach to Reconstruct the Location of Fire Sources from Soot Patterns Deposited on Walls
Authors: Shi Meng , Li Hanbo , Zhang Zhichao , Lee Eric Wai Ming
Published: August 2023

This study developed an objective approach for determining fire source location based on an artificial neural network (ANN) model. The samples for the ANN model were obtained from computational fluid dynamics simulations. A data preprocessor was devised to transform numerical simulation results into a format that could be used by the ANN model prior to network training, and bootstrap aggregation was used to improve the model’s predictive performance, which was evaluated by the leave-one-out approach. The results show that the 95% left-tailed confidence limit was 0.7921 m for planar dimensions of 5 m × 5 m, which is sufficiently accurate for practical application. Additionally, comprehensive experiments were conducted in the confined space of a fire compartment that was geometrically similar to various fire source locations to explore soot patterns and verify the ANN model. The experimental results reveal that the differences between the locations determined in scaling experiments and the locations predicted by the ANN were invariably less than 1 m. In particular, the difference was only 0.17 m when the fire source was located in the centre of the fire compartment. These results demonstrate the feasibility of the devised ANN model for reconstructing fire source location in engineering applications.
Handling Effects on Histological Identification of Female Reproductive Status: Examples from Tropical Deepwater Snappers
Authors: Schemmel Eva , Brown-Peterson Nancy J.
Published: August 2023

Post-mortem ovarian tissue degradation from handling effects, such as delays in preservation, can lead to inaccurate reproductive assessments. Therefore, it is important to understand the differences between handling effects and natural ovarian atresia. Seven treatments of various holding temperatures and times to preservation were applied to whole fish and extracted ovaries of three tropical deepwater snapper species. Ovarian post-mortem degradation was observed at 12 h at ambient temperature and after 48 h iced or refrigerated for both whole fish and extracted ovaries; however, degradation did not adversely affect correct reproductive phase classification in fishes stored in ice from 12 to 48 h post-capture. A clear histological indicator of post-mortem degradation was the breakdown of ooplasm organelles at the oocyte periphery observed in vitellogenic and cortical alveolar oocytes. However, the chorion typically remains intact during post-mortem degradation, in contrast to natural atresia. Effects on post-ovulatory follicles were most discernible after 96 h on ice or refrigeration. Freezing gonadal tissue resulted in drastic changes to oocyte shape and morphology, making reproductive phase classification challenging. We advise that (1) freezing ovaries should be avoided where possible, and (2) gonad tissue should ideally be preserved immediately but no later than 48 h post-capture.
Protective Effect of Long-Term Fermented Soybeans with Abundant Bacillus subtilis on Glucose and Bone Metabolism and Memory Function in Ovariectomized Rats: Modulation of the Gut Microbiota
Authors: Yang Hee-Jong , Zhang Ting , Yue Yu , Jeong Su-Ji , Ryu Myeong-Seon , Wu Xuangao , Li Chen , Jeong Do-Yeon , Park Sunmin
Published: August 2023

We investigated the effects of different types of long-term fermented soybeans (traditionally made doenjang; TMD) on glucose and bone metabolism and memory function in ovariectomized (OVX) rats. The rats were categorized into six groups: Control, cooked unfermented soybeans (CSB), and four TMDs based on Bacillus subtilis (B. subtilis) and biogenic amine contents analyzed previously: high B. subtilis (HS) and high biogenic amines (HA; HSHA), low B. subtilis (LS) and HA (LSHA), HS and low biogenic amines (LA; HSLA), and LS and LA (LSLA). The rats in the CSB and TMD groups fed orally had a 4% high-fat diet for 12 weeks. Rats in the Control (OVX rats) and Normal-control (Sham-operated rats) groups did not consume CSB or TMD, although macronutrient contents were the same in all groups. Uterine weight and serum 17β-estradiol concentrations were much lower in the Control than the Normal-control group, but CSB and TMD intake did not alter them regardless of B. subtilis and biogenic amine contents. HOMA-IR, a measure of insulin resistance, decreased with TMD with high B. subtilis (HSLA and HSHA) compared to the Control group. In OGTT and IPGTT, serum glucose concentrations at each time point were higher in the Control than in the Normal-control, and HSLA and HSHA lowered them. Memory function was preserved with HSHA and HSLA administration. Bone mineral density decline measured by DEXA analysis was prevented in the HSHA and HSLA groups. Bone metabolism changes were associated with decreased osteoclastic activity, parathyroid hormone levels, and osteoclastic activity-related parameters. Micro-CT results demonstrated that TMD, especially HSLA and HSHA, preserved bone structure in OVX rats. TMD also modulated the fecal bacterial community, increasing Lactobacillus, Ligalactobacillus, and Bacillus. In conclusion, through gut microbiota modulation, TMD, particularly with high B. subtilis content, acts as a synbiotic to benefit glucose, bone, and memory function in OVX rats. Further research is needed to make specific recommendations for B. subtilis-rich TMD for menopausal women.
Herbal Extracts of Ginseng and Maqui Berry Show Only Minimal Effects on an In Vitro Model of Early Fracture Repair of Smokers
Authors: Rinderknecht Helen , Mayer Alana , Histing Tina , Ehnert Sabrina , Nüssler Andreas
Published: August 2023

Smoking is a major risk factor for delayed fracture healing, affecting several aspects of early fracture repair, including inflammation, osteogenesis, and angiogenesis. Panax ginseng (GE) and maqui berry extract (MBE) were shown in our previous studies to reduce smoke-induced cellular damage in late bone-healing in vitro models. We aimed here to analyze their effects on the early fracture repair of smokers in a 3D co-culture model of fracture hematomas and endothelial cells. Both extracts did not alter the cellular viability at concentrations of up to 100 µg/mL. In early fracture repair in vitro, they were unable to reduce smoking-induced inflammation and induce osteo- or chondrogenicity. Regarding angiogenesis, smoking-induced stress in HUVECs could not be counteracted by both extracts. Furthermore, smoking-impaired tube formation was not restored by GE but was harmed by MBE. However, GE promoted angiogenesis initiation under smoking conditions via the Angpt/Tie2 axis. To summarize, cigarette smoking strikingly affected early fracture healing processes in vitro, but herbal extracts at the applied doses had only a limited effect. Since both extracts were shown before to be very effective in later stages of fracture healing, our data suggest that their early use immediately after fracture does not appear to negatively impact later beneficial effects.
Evaluation of Pallet Covers Performance for Produce Protection in Cold Chain Logistics for Chard, Cucumbers and Carrots
Authors: Badia-Melis Ricardo , Ruiz-Garcia Luis , Robla-Villalba Jose Ignacio , Hoyos-Echevarria Pedro
Published: August 2023

Cold chain disruption and refrigeration failures are common issues in the logistics of perishable food products. In these cases, the use of pallet covers should be very useful, delaying the increase of product temperatures inside the pallets until cooling conditions are restored. However, there are no studies about the performance of pallet covers in these types of situations, which could persist for hours. This paper evaluates the performance of three different types of cold chain covers versus having no cover for three different types of vegetables (chard, cucumbers, and carrots). A refrigeration failure during the cold chain was simulated. The three covers presented an improvement in temperature loss compared to the no-cover situation, with the average time for the temperature to increase from 4 to 10 °C with a cover being a range of 214 to 506 min, while for no cover, from 162 to 211 min. Relative humidity (RH) always presented improved preservation with a cover than with no cover, except for one case. The correlation between the thermal images and sensor temperatures was also studied.
The Impacts of Acidophilic Lactic Acid Bacteria on Food and Human Health: A Review of the Current Knowledge
Authors: Icer Mehmet Arif , Özbay Sena , Ağagündüz Duygu , Kelle Bayram , Bartkiene Elena , Rocha João Miguel F. , Ozogul Fatih
Published: August 2023

The need to improve the safety/quality of food and the health of the hosts has resulted in increasing worldwide interest in acidophilic lactic acid bacteria (LAB) for the food, livestock as well as health industries. In addition to the use of acidophilic LAB with probiotic potential for food fermentation and preservation, their application in the natural disposal of acidic wastes polluting the environment is also being investigated. Considering this new benefit that has been assigned to probiotic microorganisms in recent years, the acceleration in efforts to identify new, efficient, promising probiotic acidophilic LAB is not surprising. One of these effots is to determine both the beneficial and harmful compounds synthesized by acidophilic LAB. Moreover, microorganisms are of concern due to their possible hemolytic, DNase, gelatinase and mucinolytic activities, and the presence of virulence/antibiotic genes. Hence, it is argued that acidophilic LAB should be evaluated for these parameters before their use in the health/food/livestock industry. However, this issue has not yet been fully discussed in the literature. Thus, this review pays attention to the less-known aspects of acidophilic LAB and the compounds they release, clarifying critical unanswered questions, and discussing their health benefits and safety.
A Numerical Technique Based on Bernoulli Wavelet Operational Matrices for Solving a Class of Fractional Order Differential Equations
Authors: Arafa Heba M. , Ramadan Mohamed A. , Althobaiti Nesreen
Published: August 2023

In this paper, we present an efficient, new, and simple programmable method for finding approximate solutions to fractional differential equations based on Bernoulli wavelet approximations. Bernoulli Wavelet functions involve advantages such as orthogonality, simplicity, and ease of usage, in addition to the fact that fractional Bernoulli wavelets have exact operational matrices that improve the accuracy of the applied approach. A fractional differential equation was simplified to a system of algebraic equations using the fractional order integration operational matrices of Bernoulli wavelets. Examples are used to demonstrate the technique’s precision.
Hydration and Fractal Analysis on Low-Heat Portland Cement Pastes Using Thermodynamics-Based Methods
Authors: Zhou Yifan , Li Wenwei , Peng Yuxiang , Tang Shengwen , Wang Lei , Shi Yan , Li Yang , Wang Yang , Geng Zhicheng , Wu Kai
Published: August 2023

Low-heat Portland (LHP) cement is a kind of high-belite cement, which has the characteristic of low hydration heat. Currently, it is extensively used in the temperature control of mass concrete. Based on the thermodynamic database of OPC-based materials, the thermodynamic software GEM-Selektor (noted as GEMS) is used for simulating the hydration products of the LHP cement paste. Then, according to the GEMS thermodynamic simulation results, MATLAB is used to visualize the initial and ultimate stages of LHP cement pastes; the effects of curing temperature and water to cement (w/c) ratio on hydration products are addressed; and the porosity, fractal dimension, and tortuosity of different pastes are calculated. It is found that an appropriately high curing temperature is important for reducing porosity, especially in the early hydration stage. Hydration time also has a significant impact on the hydration of LHP cement paste; long hydration time may reduce the impact of temperature on hydration products. The w/c ratio is another important consideration regarding the hydration degree and porosity of LHP paste, and under different curing temperatures, hydration times, and w/c ratios, the porosity varies from 5.91–32.91%. The fractal dimension of this work agrees with the previous findings. From tortuosity analysis, it can be concluded that the high curing temperature may cause significant tortuosity, further affecting the effective diffusivity of LHP cement paste. For cement pastes with low w/c ratio, this high curing temperature effect is mainly reflected in the early hydration stage, for ones with high w/c ratio, it is in turn evident under long-term curing.