bluuebunny/crossref_metadata_2025 huggingface dataset:
Afrocentric Education for Liberation in the Classroom: It Takes a Village to Raise a Child
Authors: Ringstaff Tytianna Nikia Maria
Published: May 2023

Racist and inequitable schools in the United States espouse an anti-Black and color blind curriculum that negatively impacts Black students’ lives. Black schools, including homeschools, are a strategic response to racist public and private schools and a viable option to address the academic and cultural needs of Black students. This paper explores Afrocentric practices, including familial relationships through culturally responsive instructional practices: an African time orientation, a personalized learning plan, authoritative teaching, OurStory, and Rising Meeting. This paper provides evidence that these practices benefit Black students. I draw upon an Afrocentric theoretical framework in this qualitative study to analyze and interpret data collected at the Black Scholars Academy (BSA), a pre-kindergarten (Pre-K) through 12th-grade Black homeschool collective in the U.S. The data consist of classroom observations, individual interviews with current and former teachers and students, and textual artifacts collected between July and November 2019. Familial relationships helped students develop cultural pride, agency, self-determination, independence, and liberation through education. The employment of Afrocentricity as a best practice in a homeschool collective is considered advisable across every educational context. There is a need for more research on Afrocentric practices as one of many culturally responsive techniques to best teach culturally diverse students, especially Black students, in educational settings.
Feature Contrastive Learning for No-Reference Segmentation Quality Evaluation
Authors: Li Xiaofan , Peng Bo , Xie Zhuyang
Published: May 2023

No-reference segmentation quality evaluation aims to evaluate the quality of image segmentation without any reference image during the application process. It usually depends on certain quality criteria to describe a good segmentation with some prior knowledge. Therefore, there is a need for a precise description of the objects in the segmentation and an integration of the representation in the evaluation process. In this paper, from the perspective of understanding the semantic relationship between the original image and the segmentation results, we propose a feature contrastive learning method. This method can enhance the performance of no-reference segmentation quality evaluations and be applied in semantic segmentation scenarios. By learning the pixel-level similarity between the original image and the segmentation result, a contrastive learning step is performed in the feature space. In addition, a class activation map (CAM) is used to guide the evaluation, making the score more consistent with the human visual judgement. Experiments were conducted on the PASCAL VOC2012 dataset, with segmentation results obtained by state-of-the-art (SoA) segmentation methods. We adopted two meta-measure criteria to validate the efficiency of the proposed method. Compared with other no-reference evaluation methods, our method achieves a higher accuracy which is comparable to the supervised evaluation methods and partly even exceeds them.
Parallelized A Posteriori Multiobjective Optimization in RF Design
Authors: Míchal Jan , Dobeš Josef
Published: May 2023

A posteriori multiobjective optimization relies on a series of mutually independent single-objective optimization subproblems, which can be run in parallel, thus making full use of a multiprocessor (or multicore) computer. This paper presents a parallel process launching scheme, such that practically no computing capacity gets wasted. This is achieved using standard Windows API kernel objects for process synchronization of the semaphore and mutex types. The algorithm used was further modified to inherently generate the desired Pareto front in the convenient form of a contour plot.
Python-Based Circuit Design for Fundamental Building Blocks of Spiking Neural Network
Authors: Qin Xing , Li Chaojie , He Haitao , Pan Zejun , Lai Chenxiao
Published: May 2023

Spiking neural networks (SNNs) are considered a crucial research direction to address the “storage wall” and “power wall” challenges faced by traditional artificial intelligence computing. However, developing SNN chips based on CMOS (complementary metal oxide semiconductor) circuits remains a challenge. Although memristor process technology is the best alternative to synapses, it is still undergoing refinement. In this study, a novel approach is proposed that employs tools to automatically generate HDL (hardware description language) code for constructing neuron and memristor circuits after using Python to describe the neuron and memristor models. Based on this approach, HR (Hindmash–Rose), LIF (leaky integrate-and-fire), and IZ (Izhikevich) neuron circuits, as well as HP, EG (enhanced generalized), and TB (the behavioral threshold bipolar) memristor circuits are designed to construct the most basic connection of a SNN: the neuron–memristor–neuron circuit that satisfies the STDP (spike-timing-dependent-plasticity) learning rule. Through simulation experiments and FPGA (field programmable gate array) prototype verification, it is confirmed that the IZ and LIF circuits are suitable as neurons in SNNs, while the X variables of the EG memristor model serve as characteristic synaptic weights. The EG memristor circuits best satisfy the STDP learning rule and are suitable as synapses in SNNs. In comparison to previous works on hardware spiking neurons, the proposed method needed fewer area resources for creating spiking neurons models on FPGA. The proposed SNN basic components design method, and the resulting circuits, are beneficial for architectural exploration and hardware–software co-design of SNN chips.
Study on Driver Cross-Subject Emotion Recognition Based on Raw Multi-Channels EEG Data
Authors: Wang Zhirong , Chen Ming , Feng Guofu
Published: May 2023

In our life, emotions often have a profound impact on human behavior, especially for drivers, as negative emotions can increase the risk of traffic accidents. As such, it is imperative to accurately discern the emotional states of drivers in order to preemptively address and mitigate any negative emotions that may otherwise manifest and compromise driving behavior. In contrast to many current studies that rely on complex and deep neural network models to achieve high accuracy, this research aims to explore the potential of achieving high recognition accuracy using shallow neural networks through restructuring the structure and dimensions of the data. In this study, we propose an end-to-end convolutional neural network (CNN) model called simply ameliorated CNN (SACNN) to address the issue of low accuracy in cross-subject emotion recognition. We extracted features and converted dimensions of EEG signals from the SEED dataset from the BCMI Laboratory to construct 62-dimensional data, and obtained the optimal model configuration through ablation experiments. To further improve recognition accuracy, we selected the top 10 channels with the highest accuracy by separately training the EEG data of each of the 62 channels. The results showed that the SACNN model achieved an accuracy of 88.16% based on raw cross-subject data, and an accuracy of 91.85% based on EEG channel data from the top 10 channels. In addition, we explored the impact of the position of the BN and dropout layers on the model through experiments, and found that a targeted shallow CNN model performed better than deeper and larger perceptual field CNN models. Furthermore, we discuss herein the future issues and challenges of driver emotion recognition in promising smart city applications.
Short-Term Wind Power Forecasting Based on Feature Analysis and Error Correction
Authors: Liu Zifa , Li Xinyi , Zhao Haiyan
Published: May 2023

Accurate wind power forecasting is an important factor in ensuring the stable operation of a power system. In this paper, we propose a wind power forecasting method based on feature analysis and error correction in order to further improve its accuracy. Firstly, the correlation analysis is carried out on the features using the maximal information coefficient (MIC), and the main features are selected as the model input items. Then, the two primary factors affecting wind power forecasting—the wind speed and wind direction provided by numerical weather prediction (NWP)—are analyzed, and the data are divided and clustered from the above two perspectives. Next, the bidirectional long short-term memory network (BiLSTM) is used to predict the power of each group of sub data. Finally, the error is forecasted by a light gradient boosting machine (LightGBM) in order to correct the prediction results. The calculation example shows that the proposed method achieves the expected purpose and improves the accuracy of forecasting effectively.
Research on Downhole Gas Separation Method Based on a PDMS Separation Membrane
Authors: Pei Xueliang , Zhang Lei , Ma Yongqian , Zhang Hengtong , Zhao Xinxin , Gao Yonghai
Published: May 2023

Safe and efficient deep drilling is a fundamental requirement for the development of oil and gas resources. In this regard, the application of membrane separation technology for drilling fluid gas separation and monitoring is highly significant. In this study, several commonly used permeable membrane materials were analyzed, and a PDMS separation membrane was preliminarily selected as a suitable material for downhole gas separation. We designed an experimental setup to investigate the separation performance of PDMS membranes. The effects of the separation pressure difference, operating temperature, and membrane thickness on the performance of PDMS membranes were analyzed, and the microstructure changes in the PDMS membrane under high temperature and pressure were observed using a scanning electron microscopy. The experimental results showed that PDMS membranes with a thickness of 150–200 μm can work stably and maintain good strength and permeability at a separation pressure difference of 1.1 MPa and a temperature of 150 °C. The SEM observations revealed that the PDMS separation membrane had a smooth surface and uniform microstructure after continuous operations for 15 h under the temperature and pressure conditions, without any cracks, demonstrating high temperature and pressure resistance. These research results provide an important reference for the application of PDMS separation membranes in downhole gas separation technology.
Experimental Study on Temperature Sensitivity of the State of Charge of Aluminum Battery Storage System
Authors: Chen Bin-Hao , Hsieh Chen-Hsiang , Teng Li-Tao , Huang Chien-Chung
Published: May 2023

The operating temperature of a battery energy storage system (BESS) has a significant impact on battery performance, such as safety, state of charge (SOC), and cycle life. For weather-resistant aluminum batteries (AlBs), the precision of the SOC is sensitive to temperature variation, and errors in the SOC of AlBs may occur. In this study, a combination of the experimental charge/discharge data and a 3D anisotropic homogeneous (Ani-hom) transient heat transfer simulation is performed to understand the thermal effect of a novel battery system, say an aluminum-ion battery. The study conducts a turbulence fluid dynamics method to solve the temperature distribution of the battery rack, and the entropy generation method analyzes the heat generation of AlB during the charging/discharging process. The AlB is modeled by a second-order Thevenin equivalent circuit to estimate the status of the battery. An extended Kalman filter is applied to obtain the accurate SOC for monitoring the battery cell. The current study conducts the Galvanostatic Intermittent Titration Technique (GITT) on aluminum-ion batteries under different operation temperatures: 25 °C, 40 °C, 60 °C, and 80 °C. According to the sensitivity analysis of the SOC, the temperature sensitivity tends to or greater than one, ST≥1, while the operation temperature is above 40 °C, and the SOC modification of EKFtmep estimator improves the battery state of charge in the error range below 1%.
A Prediction Model for Spot LNG Prices Based on Machine Learning Algorithms to Reduce Fluctuation Risks in Purchasing Prices
Authors: Yang Sun-Feel , Choi So-Won , Lee Eul-Bum
Published: May 2023

The ongoing Russia–Ukraine conflict has exacerbated the global crisis of natural gas supply, particularly in Europe. During the winter season, major importers of liquefied natural gas (LNG), such as South Korea and Japan, were directly affected by fluctuating spot LNG prices. This study aimed to use machine learning (ML) to predict the Japan Korea Marker (JKM), a spot LNG price index, to reduce price fluctuation risks for LNG importers such as the Korean Gas Corporation (KOGAS). Hence, price prediction models were developed based on long short-term memory (LSTM), artificial neural network (ANN), and support vector machine (SVM) algorithms, which were used for time series data prediction. Eighty-seven variables were collected for JKM prediction, of which eight were selected for modeling. Four scenarios (scenarios A, B, C, and D) were devised and tested to analyze the effect of each variable on the performance of the models. Among the eight variables, JKM, national balancing point (NBP), and Brent price indexes demonstrated the largest effects on the performance of the ML models. In contrast, the variable of LNG import volume in China had the least effect. The LSTM model showed a mean absolute error (MAE) of 0.195, making it the best-performing algorithm. However, the LSTM model demonstrated a decreased in performance of at least 57% during the COVID-19 period, which raises concerns regarding the reliability of the test results obtained during that time. The study compared the ML models’ prediction performances with those of the traditional statistical model, autoregressive integrated moving averages (ARIMA), to verify their effectiveness. The comparison results showed that the LSTM model’s performance deviated by an MAE of 15–22%, which can be attributed to the constraints of the small dataset size and conceptual structural differences between the ML and ARIMA models. However, if a sufficiently large dataset can be secured for training, the ML model is expected to perform better than the ARIMA. Additionally, separate tests were conducted to predict the trends of JKM fluctuations and comprehensively validate the practicality of the ML models. Based on the test results, LSTM model, identified as the optimal ML algorithm, achieved a performance of 53% during the regular period and 57% d during the abnormal period (i.e., COVID-19). Subject matter experts agreed that the performance of the ML models could be improved through additional studies, ultimately reducing the risk of price fluctuations when purchasing spot LNG.
Substantiation of Drilling Parameters for Undermined Drainage Boreholes for Increasing Methane Production from Unconventional Coal-Gas Collectors
Authors: Malozyomov Boris V. , Golik Vladimir Ivanovich , Brigida Vladimir , Kukartsev Vladislav V. , Tynchenko Yadviga A. , Boyko Andrey A. , Tynchenko Sergey V.
Published: May 2023

Decarbonization of the mining industry on the basis of closing the energy generation, on the basis of cogeneration of coal mine methane, and on the internal consumption of the mine is a promising direction in ensuring sustainable development. Known problems of deep underground mining do not allow for realizing the potential of man-made gas reservoirs due to the deterioration of the conditions of development of reserves of georesources. The aim of the work was to improve recommendations for the substantiation of drilling parameters for undermined drainage boreholes for increasing methane production from unconventional coal-gas collectors. The authors’ approach innovation lies in the possibility of using the established patterns of better natural stability of undermined boreholes to optimize them as spatial orientation parameters in an existing drilling passport for the improvement of methane extraction productivity. For this purpose, smoothing (LOESS) of the experimental data of two similar types of wells was used; then deterministic interpolation methods in combination with a three-dimensional representation of the response function in “gnuplot” were used. As a result, it was found that the increase in the inclination angle from 40° to 60° leads to a significant transformation of the model of the studied process, accompanied by a decline in the dynamics of methane emission and a decrease in the distance of the productive work zone of this type of well from 13 to 5 m before the roof landing, which then is replaced by a sharp increase in the productive work zone up to 35 m ahead of the longwall face. This allows under specific conditions for recommending increasing the productivity of methane capex from technogenic disturbed coal-gas reservoir replacement of wells with a smaller angle of rise to the transition to a more frequent grid of clusters from wells #4.
Laplace and State-Space Methods for Calculating the Heat Losses in Case of Heavyweight Building Elements and Short Sampling Times
Authors: Gaši Mergim , Milovanović Bojan , Grozdek Marino , Bagarić Marina
Published: May 2023

Reducing heat losses through the building envelope is one of the most important aspects to be met if the targets set by the European Union are to be achieved. In order to obtain a more realistic energy demand, dynamic heat transfer simulations are used to calculate the energy consumption of buildings, since steady-state calculations do not take into account the thermal mass in buildings. These dynamic simulations employ methods based on analytical models since numerical models are unsuitable for longer time periods. The analytical models used herein fall into the category of conduction transfer functions (CTFs). Two methods for computing CTFs that are addressed in this research are the Laplace method and the State-Space method. The objective of this paper is to verify the efficiency of the Laplace and State-Space methods for calculating the energy demand of a building in the case of heavyweight building elements and shorter sampling times, and to provide a means for improving the algorithms used by these methods. The Laplace and State-Space method algorithms were implemented in Mathematica, and the results were compared to EnergyPlus and TRNSYS, which use similar algorithms to calculate energy demand. It was shown in this paper that for the heavyweight wall element and a time step of 0.25 h, the difference between the total energy transferred through the inner surface was about 31% for EnergyPlus and 78% for TRNSYS compared to the reference solution. For the lightweight wall element, the results were stable for the time step of 0.25 h, but for the time step of 0.1 h, the differences were 45.64% and 303% between EnergyPlus, TRNSYS and the reference solution, respectively, compared to the State-Space and Laplace methods for which the maximum difference was 12.03% with a time step of 0.1 h. While dynamic heat transfer simulations are better than calculations based on steady-state boundary conditions, they also have their limitations and could lead to unsatisfactory results for short sampling times and if not applied properly.
Comparison of Tender Criteria for Electric and Diesel Buses in Poland—Has the Ongoing Revolution in Urban Transport Been Overlooked?
Authors: Jagiełło Aleksander , Wołek Marcin , Bizon Wojciech
Published: May 2023

The electrification of public transport is an overwhelming trend, representing the first step in the energy transition of the transport sector. The transport sector is characterized by the prevalence of public ownership and the significant influence of the public sector. Accordingly, tendering procedures are widely utilized to identify the most efficient bus delivery options. This paper compares, evaluates, and identifies the differences in criteria used in tenders for battery electric buses and diesel buses in Poland based on a deep bus market analysis supported by in-depth individual interviews. The article also attempts to determine whether the weight of the “vehicle price” criterion corresponds to the share of the vehicle price in its life cycle cost or total cost of ownership. The results indicate no significant difference in the tender criteria between battery electric buses and diesel buses. In the vast majority of cases, institutions that had previously developed diesel bus acquisition patterns transferred these patterns to tenders for battery electric bus purchases. Therefore, the criteria and their weights used in tenders do not consider the advantages and disadvantages of both technologies. Tendering procedures are adapted to local conditions and operational requirements. Electric buses often replace conventionally powered vehicles on existing routes and schedules. Thus, operational requirements are known. As a result, the necessary number of vehicles and the basic technical and operational parameters (e.g., selection of the optimal charging method and battery capacity) can be determined. In turn, the charging method will influence the total cost of ownership, with overnight charging favored for shorter assignments and opportunity charging favored for longer mileages.
Assessment of Nutrient Removal in Surface Flow Constructed Wetland Treating Secondary Effluent with Low Organic, Nitrogen and Phosphorus Loads
Authors: El Hanandeh Ali , Akrami Kambez
Published: May 2023

Nutrient loads must be reduced to safe levels to protect sensitive receiving environments. This work presents the results of a 15-month monitoring program of a surface flow-constructed wetland (SFCW) in Queensland, Australia. The SFCW reduced the influent TN concentration by 54% and was able to retain 80% of the TN load, mainly due to the efficient removal of NOx and ammonium (92–100%). TP removal was negative due to the unaccounted loads from wildlife activity. During occasions of high loads, the wetland reduced TP concentrations by 77%. The hydraulic loading rate (HLR) correlated poorly to the TSS and TVS loads (r < 0.55); however, when adjusted to account for precipitation and evapotranspiration, stronger correlations (r > 0.78) were revealed. Strong correlations were revealed between adjusted HLR and TP (r = 0.87) and TN (r = 0.93). TN removal was highly governed by the inflow of TN concentration. TN removal could be predicted from the inflow concentration using the first-order plug-flow model (R2 = 0.72). The model suggests that the system has an irreducible threshold TN load of 0.115 kg-N per m2 per month. This work shows that SFCW can be effective in managing the nutrient loads even in systems that receive low organic and nutrient loads.
Insights into Seawater Biodegradation of Sustainable Mater-Bi/Poly(ε-caprolactone)-Based Biocomposites Filled with Diisocyanate-Modified Cellulose Particles
Authors: Hejna Aleksander , Kosmela Paulina , Mysiukiewicz Olga , Barczewski Mateusz
Published: May 2023

Due to rapid economic growth, the use of plastics in almost all areas of human life has significantly increased over recent decades, leading to massive pollution. Therefore, works dealing with sustainable and biodegradable polymer materials are vital now. Herein, sustainable Mater-Bi/poly(ε-caprolactone) (PCL)-based biocomposites, filled with diisocyanate-modified cellulose particles, were prepared and subjected to 12-week seawater degradation. Changes in the chemical structure and surface wettability pointed to the increasing hydrophilicity of materials over time, which was limited by diisocyanate modifications. Only minor changes in the thermal performance of analyzed materials have been observed, pointing to the limited biodegradation of the PCL phase. The most significant effects have been related to the composite yellowing due to the filler diisocyanate modifications and surface erosion increasing its roughness. Obtained results pointing to the low degradation rate bring into question the commonly-regarded biodegradable nature of PCL material.
Contaminant Risk and Social Vulnerability Associated with Crustacean Shellfish Harvest in the Highly Urbanized San Diego Bay, USA
Authors: Talley Theresa Sinicrope , Loflen Chad , Venuti Nina , Pedersen David , Gossett Richard , Baker Mark D.
Published: May 2023

People in coastal cities around the world harvest seafood from local bays despite well-documented health risks. In cities such as San Diego, California, USA, much information about contaminants and human consumption patterns exists for finfish but is largely lacking for shellfish. This study sought to better understand shellfish contamination risks and human vulnerability to inform management and advisories. In summer 2018 and winter 2019, we sampled crustaceans for chemical contaminants and anthropogenic debris and throughout the year surveyed people harvesting from three public fishing piers around San Diego Bay. Of the emerging contaminants found, pyrethroids, benzylbutyl phthalate, PFOS and anthropogenic debris were in differing concentrations in the muscle and viscera of the California spiny lobster and two species of crabs. Combined with previous metal and organic contaminant data from the lobster, 22 contaminants were detected with 5 exceeding consumption thresholds and 8 lacking defined thresholds. California spiny lobster was the main crustacean harvested from piers, attracting shellfishers from a range of ages, incomes, home locations and self-identified racial/ethnic groups. Consumption preferences (e.g., muscle or viscera) were non-discriminant, making lobster contamination a community-wide risk. More monitoring of emerging contaminants and different shellfish species (and tissues) of interest is recommended to capture the spatial and temporal dynamics of health risks, especially because the use of bivalves as sentinels may not reveal the same risks (e.g., PFAS, phosphate flame retardants).
Comparative Transcriptomic Analysis Reveals Adaptive Traits in Antarctic Scallop Adamussium colbecki
Authors: Greco Samuele , Voltarel Giacomo , Gaetano Anastasia Serena , Manfrin Chiara , Pallavicini Alberto , Giulianini Piero Giulio , Gerdol Marco
Published: May 2023

Antarctica is the most extreme continent of Earth, with strong winds, freezing temperatures on land, and ocean temperatures constantly below 0 °C. Nonetheless, the Antarctic Ocean is home to an astounding diversity of living organisms that adapted to the multiple challenges posed by this environment via a diverse set of evolutionary traits. Although the recent advancements in sequencing technologies clarified the molecular bases of such adaptations in Antarctic fishes, little information is available for Antarctic invertebrates. In this preliminary study, we address this knowledge gap with a comparative transcriptomic approach to obtain insights into some of the adaptations that allow the Antarctic scallop Adamussium colbecki to survive and thrive in the freezing waters of the Antarctic Ocean. Despite some limitations, our analyses highlighted significant over-expression of genes involved in regulation of mRNA transcription, maturation, and degradation, which might compensate for a reduced efficiency of these processes at low temperatures. Other alterations detected in the Antarctic scallop transcriptome include enhanced expression of genes that regulate degradation of misfolded protein products and allow maintenance of cytoskeletal structure and function at subzero temperatures. Altogether, these observations support the presence of multiple previously unreported molecular adaptive traits in A. colbecki, which have important implications for our understanding of adaptation of this important component of the Antarctic trophic chain to such an extreme, but stable environment.
Dissipation, Metabolism, Accumulation, Processing and Risk Assessment of Fluopyram and Trifloxystrobin in Cucumbers and Cowpeas from Cultivation to Consumption
Authors: Cui Kai , Guan Shuai , Liang Jingyun , Fang Liping , Ding Ruiyan , Wang Jian , Li Teng , Dong Zhan , Wu Xiaohu , Zheng Yongquan
Published: May 2023

Fluopyram and trifloxystrobin are widely used for controlling various plant diseases in cucumbers and cowpeas. However, data on residue behaviors in plant cultivation and food processing are currently lacking. Our results showed that cowpeas had higher fluopyram and trifloxystrobin residues (16.48–247.65 μg/kg) than cucumbers (877.37–3576.15 μg/kg). Moreover, fluopyram and trifloxystrobin dissipated faster in cucumbers (half-life range, 2.60–10.66 d) than in cowpeas (10.83–22.36 d). Fluopyram and trifloxystrobin were the main compounds found in field samples, and their metabolites, fluopyram benzamide and trifloxystrobin acid, fluctuated at low residue levels (≤76.17 μg/kg). Repeated spraying resulted in the accumulation of fluopyram, trifloxystrobin, fluopyram benzamide and trifloxystrobin acid in cucumbers and cowpeas. Peeling, washing, stir-frying, boiling and pickling were able to partially or substantially remove fluopyram and trifloxystrobin residues from raw cucumbers and cowpeas (processing factor range, 0.12–0.97); on the contrary, trifloxystrobin acid residues appeared to be concentrated in pickled cucumbers and cowpeas (processing factor range, 1.35–5.41). Chronic and acute risk assessments suggest that the levels of fluopyram and trifloxystrobin in cucumbers and cowpeas were within a safe range based on the field residue data of the present study. The potential hazards of fluopyram and trifloxystrobin should be continuously assessed for their high residue concentrations and potential accumulation effects.
Korean Proficiency Tests for Pesticide Residues in Rice: Comparison of Various Proficiency Testing Evaluation Methods and Identification of Critical Factors for Multiresidue Analysis
Authors: Lee Hyosub , Jung Gunhee , Min Juhyeon , Kim Hyanghee , Jeong Wontae , Kim Taekkyum
Published: May 2023

Establishing pesticide safety management for agricultural products necessitates accurate pesticide analysis at analytical laboratories. Proficiency testing is regarded an effective method for quality control. Herein, proficiency tests were carried out for residual pesticide analysis in laboratories. All samples satisfied the homogeneity and stability criteria of the ISO 13528 standard. The obtained results were analyzed using the ISO 17043 z-score evaluation. Both individual pesticide and multiresidue proficiency evaluations were performed, and the proportion of z-scores within the ±2 range (“Satisfactory” rating) obtained for seven pesticides ranged 79–97%. Of the laboratories, 83% were classified as Category A using the category A/B method, and these also received AAA ratings in the triple-A evaluations. Furthermore, 66–74% of the laboratories were rated “Good” via five evaluation methods based on their z-scores. The sum of weighted z-scores and scaled sum of squared z-scores were considered as the most suitable evaluation techniques, as they compensated for the drawbacks of good results and corrected the poor results. To identify the main factors affecting laboratory analysis, the experience of the analyst, sample weight, calibration curve preparation method, and cleanup status were considered. A dispersive solid phase extraction cleanup significantly improved the results (p < 0.01).
Effects of Extraction Methods on the Physicochemical Properties and Biological Activities of Polysaccharides from Polygonatum sibiricum
Authors: Jing Yongshuai , Yan Meng , Zhang Hao , Liu Dongbo , Qiu Xiaoyue , Hu Beibei , Zhang Danshen , Zheng Yuguang , Wu Lanfang
Published: May 2023

Polygonatum sibiricum polysaccharides (PSPs) have important biological functions, such as antioxidation, immunomodulatory, and hypolipidemic functions. Different extraction methods have effects on their structures and activities. In this study, six extraction methods, including hot water extraction (HWE), alkali extraction (AAE), ultrasound-assisted extraction (UAE), enzyme-assisted extraction (EAE), microwave-assisted extraction (MAE), and freeze–thaw-assisted extraction (FAE) were used to extract PSPs, and their structure–activity relationships were analyzed. The results showed that all six PSPs had similar functional group compositions, thermal stability, and glycosidic bond compositions. PSP-As (PSPs extracted by AAE) exhibited better rheological properties due to their higher molecular weight (Mw). PSP-Es (PSPs extracted by EAE) and PSP-Fs (PSPs extracted by FAE) had better lipid-lowering activity due to their lower Mw. PSP-Es and PSP-Ms (PSPs extracted by MAE), which do not contain uronic acid and have a moderate Mw, had better 1,1-diphenyl-2-picrylhydrazyl (DPPH)-radical-scavenging activity. On the contrary, PSP-Hs (PSPs extracted by HWE) and PSP-Fs, with the Mw of uronic acid, had the best OH-radical-scavenging activity. The high-Mw PSP-As had the best Fe2+-chelating ability. In addition, mannose (Man) may play an important role in the immunomodulatory activity. These results indicate that different extraction methods affect the structure and biological activity of polysaccharides to varying degrees, and these results are helpful for understanding the structure–activity relationship of PSPs.
Feasibility Study of Combining Hyperspectral Imaging with Deep Learning for Chestnut-Quality Detection
Authors: Zhong Qiongda , Zhang Hu , Tang Shuqi , Li Peng , Lin Caixia , Zhang Ling , Zhong Nan
Published: May 2023

The rapid detection of chestnut quality is a critical aspect of chestnut processing. However, traditional imaging methods pose a challenge for chestnut-quality detection due to the absence of visible epidermis symptoms. This study aims to develop a quick and efficient detection method using hyperspectral imaging (HSI, 935–1720 nm) and deep learning modeling for qualitative and quantitative identification of chestnut quality. Firstly, we used principal component analysis (PCA) to visualize the qualitative analysis of chestnut quality, followed by the application of three pre-processing methods to the spectra. To compare the accuracy of different models for chestnut-quality detection, traditional machine learning models and deep learning models were constructed. Results showed that deep learning models were more accurate, with FD-LSTM achieving the highest accuracy of 99.72%. Moreover, the study identified important wavelengths for chestnut-quality detection at around 1000, 1400 and 1600 nm, to improve the efficiency of the model. The FD-UVE-CNN model achieved the highest accuracy of 97.33% after incorporating the important wavelength identification process. By using the important wavelengths as input for the deep learning network model, recognition time decreased on average by 39 s. After a comprehensive analysis, FD-UVE-CNN was deter-mined to be the most effective model for chestnut-quality detection. This study suggests that deep learning combined with HSI has potential for chestnut-quality detection, and the results are encouraging.
Yak Milk: Nutritional Value, Functional Activity, and Current Applications
Authors: Wang Diandian , Zhou Yaxi , Zheng Xianping , Guo Jinhong , Duan Hao , Zhou Shiqi , Yan Wenjie
Published: May 2023

The yak is a special species that inhabits the Qinghai-Tibet Plateau and its surrounding areas. Its unique habitat gives yak milk certain distinct characteristics compared to regular cow milk. Yak milk not only has a high nutritional value but also holds potential benefits for human health. In recent years, there has been increasing research attention on yak milk. Studies have found that the bioactive components in yak milk have various functional properties, including antioxidant, anticancer, antibacterial, blood pressure-lowering, anti-fatigue, and constipation-relieving effects. However, more evidence is needed to confirm these functions in the human body. Therefore, by reviewing the current research status on the nutrition and functionality of yak milk, we aim to reveal its enormous potential as a source of nutritional and functional substances. This article primarily analyzed the nutritional composition of yak milk and the functional effects of its bioactive components, categorically elucidated the mechanisms behind its functional activities, and provided a brief introduction to related yak milk products. Our objective is to deepen people’s understanding of yak milk and provide some references for its further development and utilization.
Changes in the Physicochemical Properties of Chia (Salvia hispanica L.) Seeds during Solid-State and Submerged Fermentation and Their Influence on Wheat Bread Quality and Sensory Profile
Authors: Bartkiene Elena , Rimsa Arnoldas , Zokaityte Egle , Starkute Vytaute , Mockus Ernestas , Cernauskas Darius , Rocha João Miguel , Klupsaite Dovile
Published: May 2023

This study aimed at investigating the impacts of 24 h of either solid-state fermentation (SSF) or submerged fermentation (SMF) with Lactiplantibacillus plantarum strain No. 122 on the physico-chemical attributes of chia seeds (CS). Furthermore, this study examined how adding fermented chia seeds (10, 20, and 30% concentrations) affected the properties and sensory profile of wheat bread. Acidity, lactic acid bacteria (LAB) viable counts, biogenic amine (BA), and fatty acid (FA) profiles of fermented chia seeds were analysed. The main quality parameters, acrylamide concentration, FA and volatile compound (VC) profiles, sensory characteristics, and overall acceptability of the obtained breads, were analysed. A decline in the concentration of certain BA and saturated FA and an increase in polyunsaturated FA and omega-3 (ω-3) were found in fermented CS (FCS). The same tendency in the FA profile was observed in both breads, i.e., breads with non-fermented CS (NFCS) or FCS. The quality parameters, VC profile, and sensory attributes of wheat bread were significantly affected by the addition of NFCS or FCS to the main bread formula. All supplemented breads had reduced specific volume and porosity, but SSF chia seeds increased moisture and decreased mass loss after baking. The lowest acrylamide content was found in bread with a 30% concentration of SSF chia seeds (11.5 µg/kg). The overall acceptance of supplemented breads was lower than the control bread, but breads with 10 and 20% SMF chia seed concentrations were still well accepted (on average, 7.4 score). Obtained results highlight that fermentation with Lp. plantarum positively contributes to chia seed nutritional value, while incorporation of NFCS and FCS at certain levels results in an improved FA profile, certain sensory attributes, and reduced acrylamide content in wheat bread.
Food-Evoked Emotion, Product Acceptance, Food Preference, Food Choice and Consumption: Some New Perspectives
Authors: Prinyawiwatkul Witoon
Published: May 2023

Food is more than just a source of nutrients—it is a source of basic pleasure and aesthetic experiences [...]
Annona crassiflora Mart. Fruit Peel Polyphenols Preserve Cardiac Antioxidant Defense and Reduce Oxidative Damage in Hyperlipidemic Mice
Authors: Komino Eliana Akemi , Ramos Letícia Pereira Afonso , de Souza Adriele Vieira , Caixeta Douglas Carvalho , Bittar Vinicius Prado , Borges Ana Luiza , Botelho Françoise Vasconcelos , Espindola Foued Salmen , Justino Allisson Benatti
Published: May 2023

Dyslipidemia and oxidative stress are directly related to the pathogenesis of cardiovascular diseases. Annona crassiflora Mart. (ACM) has been traditionally used in folk medicine to alleviate inflammation and pain. This plant is rich in polyphenols, which exhibit high antioxidant capacity. The present study aimed to elucidate the antioxidant properties of ACM in the heart of hyperlipidemic mice. The animals were orally administered either a crude ethanol extract (CEAc) or a polyphenols-rich fraction (PFAc) obtained from ACM fruit peel. There were correlations between blood and fecal biochemical data with cardiac oxidative stress biomarkers. Here, the pre-treatment with CEAc for 12 d led to an increase in glutathione content (GSH) and a reduction in the activities of superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase. Moreover, PFAc was found to enhance the total antioxidant capacity as well as GSH, SOD and CAT activities, which were reduced by Triton WR-1339-induced hyperlipidemia. Moreover, the administration of PFAc before the treatment resulted in a decrease in protein carbonylation and lipid peroxidation levels, as well as a reduction in the activities of glutathione reductase and glucose-6-phosphate dehydrogenase. ACM fruit peel showed improvement in the glutathione system, mainly its polyphenols-rich fraction, indicating a potential cardioprotective antioxidant usage of this plant extract.
Progress of Mass Spectrometry-Based Lipidomics in the Dairy Field
Authors: Ren Wei , Sun Mengqi , Shi Xiaoyuan , Wang Tianqi , Wang Yonghui , Wang Changfa , Li Mengmeng
Published: May 2023

Lipids play important biological roles, such as providing essential fatty acids and signaling. The wide variety and structural diversity of lipids, and the limited technical means to study them, have seriously hampered the resolution of the mechanisms of action of lipids. With advances in mass spectrometry (MS) and bioinformatic technologies, large amounts of lipids have been detected and analyzed quickly using MS-based lipidomic techniques. Milk lipids, as complex structural metabolites, play a crucial role in human health. In this review, the lipidomic techniques and their applications to dairy products, including compositional analysis, quality identification, authenticity identification, and origin identification, are discussed, with the aim of providing technical support for the development of dairy products.
Techno-Functional Properties of Burgers Fortified by Wild Garlic Extract: A Reconsideration
Authors: Kurćubić Vladimir S. , Stajić Slaviša B. , Miletić Nemanja M. , Petković Marko M. , Dmitrić Marko P. , Đurović Vesna M. , Heinz Volker , Tomasevic Igor B.
Published: May 2023

The aim of this research was to examine the chemical properties of freshly squeezed wild garlic extract (FSWGE) and its use as an additive in burgers (BU). Technological and sensory properties of such fortified burgers (BU) were determined. LC-MS/MS analyses identified thirty-eight volatile BAC. Allicin prevalence (11.375 mg/mL) is the key parameter determining the amount of FSWGE added in raw BU (PS-I 1.32 mL/kg, PS-II 4.40 mL/kg, and PS-III 8.79 mL/kg). Minimum inhibitory concentrations (MIC) and minimum bactericidal concentration (MBC) values of the FSWGE and evaporated FSWGE (EWGE) were determined against the six microorganisms using a microdilution method. The data indicated that using FSWGE can result in a reduced risk of Serratia marcescens (MIC = 50 mg/mL; MBC = 60 mg/mL), Listeria monocytogenes (MIC = MBC = 90 mg/mL), Escherichia coli and Staphylococcus aureus (MIC = 90 mg/mL; MBC ≥ 100 mg/mL), and Salmonella enteritidis and Enterococcus faecium (MIC = 100 mg/mL; MBC > 100 mg/mL) in BU. Changes in antioxidant (AOX) capacity were followed during cold storage (up to 10 days) and freezing (90 days). It was shown that PS-III had the highest level of AOX capacity during the entire period of cold storage, revealing 8.79 mL FSWGE/kg BU as the most suitable effective concentration. Adding FSWGE did not negatively affect the technological and physico-chemical properties during both cold and freeze storage. Regarding sensory evaluation, modified BU received mostly higher scores compared to control. The results of this study have demonstrated the great potential of wild garlic extract usage in the creation of safe products with prolonged shelf life.
Stability, Data Dependence, and Convergence Results with Computational Engendering of Fractals via Jungck–DK Iterative Scheme
Authors: Guran Liliana , Shabbir Khurram , Ahmad Khushdil , Bota Monica-Felicia
Published: May 2023

We have developed a Jungck version of the DK iterative scheme called the Jungck–DK iterative scheme. Our analysis focuses on the convergence and stability of the Jungck–DK scheme for a pair of non-self-mappings using the more general contractive condition. We demonstrate that this iterative scheme converges faster than all other leading Jungck-type iterative schemes. To further illustrate its effectiveness, we provide an example to verify the rate of convergence and prove the data dependence result for the Jungck–DK iterative scheme. Finally, we calculate the escape criteria for generating Mandelbrot and Julia sets for polynomial functions and present visually appealing images of these sets by our modified iteration.
Burn Severity and Its Impact on Soil Properties: A Study of the 2016 Erskine Fire in the Southern Sierra Nevada, California
Authors: Haake Sade , Krugh William , Montoya Eduardo , Guo Junhua
Published: May 2023

Wildfires can cause debris flow events in affected areas due to changes in the physical properties of burned soils, which are linked to burn severity. A study in California’s Sierra Nevada explored the impact of burn severity on soil physical properties using various tests. Results showed that higher burn-severity soils had higher total organic carbon content and liquid limit, and the plastic limit was also higher. The plasticity index was highest among low burn-severity soils, and high burn-severity soils had lower smectite and kaolinite/chlorite abundances compared to lower burn-severity soils. Grain size distribution and shear strength were not significantly impacted by burn severity. The study suggests that total organic carbon content is the most significant factor affecting the physical and mechanical properties of soil. These findings may help assess debris flow hazards in burned areas and highlight the need for further research on the effects of wildfires on soil properties and their contribution to debris flow events.
Interprofessional Needs Analysis and User-Centred Prototype Evaluation as a Foundation for Building Individualized Digital Education in Dementia Healthcare Supported by Artificial Intelligence: A Study Protocol
Authors: Malek Manuela , Nitsche Julia , Dinand Claudia , Ehlers Jan , Lissek Vanessa , Böhm Patricia , Derksen Eva-Maria , Halek Margareta
Published: May 2023

Continuous profession-specific training is necessary to provide high-quality care for people with dementia. Research shows the need for more educational programmes that are personalized and responsive to the learning needs and preferences of staff. Digital solutions supported by artificial intelligence (AI) may be a means of making these improvements. There is a lack of formats that support learners in selecting the right content according to their learning needs and preferences. The project “My INdividual Digital EDucation.RUHR” (MINDED.RUHR) addresses this problem and seeks to develop an automatized delivery system for individual learning content using AI. The sub-project presented here aims to achieve the following: (a) explore learning needs and preferences regarding behavioural changes in people with dementia, (b) develop learning nuggets, (c) evaluate the feasibility of the digital learning platform, and (d) identify optimization criteria. Following the first phase of the framework for the design and evaluation of digital health interventions (DEDHI), we use a qualitative approach with focus group interviews for exploration and development, and co-design workshops and expert audits to evaluate the developed learning nuggets. The developed e-learning tool is the first step in supporting the digital training of healthcare professionals in the context of caring for people with dementia, individualized through AI.