bluuebunny/crossref_metadata_2025 huggingface dataset:
Elevating Teachers’ Professional Digital Competence: Synergies of Principals’ Instructional E-Supervision, Technology Leadership and Digital Culture for Educational Excellence in Digital-Savvy Era
Authors: Rasdiana  , Wiyono Bambang Budi , Imron Ali , Rahma Lailatul , Arifah Nur , Azhari Reza , Elfira  , Sibula Irvine , Maharmawan Muh. Asrandy
Published: March 2024

The educational landscape has been significantly influenced by the rapid development of technology, especially in the instructional process. Examining teachers’ professional digital competence (TPDC) in Indonesia, a developing country, is of utmost importance. It is vital to comprehend the extent of professional digital competence among teachers to identify potential gaps and areas for improvement through training programs. This investigation aims to shed light on disparities and formulate strategies to bridge the digital divide. In this context, the principal’s instructional e-supervision (PIS) and technology leadership (PTL) play a pivotal role in nurturing a school’s digital culture (SDC). This culture is crucial for ensuring the effectiveness of the collaborative learning process that meets the needs of students in this digital-savvy era. Therefore, this study delves into the structural impact of PIS and PTL on TPDC mediated by SDC. Quantitative methods were employed to address research hypotheses through structural equation modeling (SEM) analysis with AMOS, utilizing inner and outer model techniques. Carried out in seven senior high schools in Indonesia, the research involved 257 productive teachers randomly selected from a population of 450. The findings revealed that PIS directly influences TPDC, albeit with the most negligible coefficient (0.192). Simultaneously, PTL directly impacts SDC (0.663) and TPDC (0.229). Moreover, SDC significantly influences TPDC (0.816). However, the direct coefficient of PTL has a more substantial impact on SDC than on TPDC. Consequently, the structural model suggests that PTL will profoundly influence TPDC when mediated by SDC (0.541). In light of these results, this study recommends the application of principal technology leadership-based humbleness for future research.
Determining the Oxidation Stability of Electrolytes for Lithium-Ion Batteries Using Quantum Chemistry and Molecular Dynamics
Authors: Evshchik Elizaveta Y. , Borisevich Sophia S. , Ilyina Margarita G. , Khamitov Edward M. , Chernyak Alexander V. , Pugacheva Tatiana A. , Kolmakov Valery G. , Bushkova Olga V. , Dobrovolsky Yuri A.
Published: March 2024

Determining the oxidation potential (OP) of lithium-ion battery (LIB) electrolytes using theoretical methods will significantly speed up and simplify the process of creating a new generation high-voltage battery. The algorithm for calculating OP should be not only accurate but also fast. Our work proposes theoretical principles for evaluating the OP of LIB electrolytes by considering LiDFOB solutions with different salt concentrations in EC/DMC solvent mixtures. The advantage of the new algorithm compared to previous versions of the theoretical determination of the oxidation potential of electrolyte solutions used in lithium-ion batteries for calculations of statistically significant complexes, the structure of which was determined by the molecular dynamics method. This approach significantly reduces the number of atomic–molecular systems whose geometric parameters need to be optimized using quantum chemical methods. Due to this, it is possible to increase the speed of calculations and reduce the power requirements of the computer performing the calculations. The theoretical calculations included a set of approaches based on the methods of classical molecular mechanics and quantum chemistry. To select statistically significant complexes that can make a significant contribution to the stability of the electrochemical system, a thorough analysis of molecular dynamics simulation trajectories was performed. Their geometric parameters (including oxidized forms) were optimized by QM methods. As a result, oxidation potentials were assessed, and their dependence on salt concentration was described. Here, we once again emphasize that it is difficult to obtain, by calculation methods, the absolute OP values that would be equal (or close) to the OP values estimated by experimental methods. Nevertheless, a trend can be identified. The results of theoretical calculations are in full agreement with the experimental ones.
Design of a 1.2 kV SiC MOSFET with Buried Oxide for Improving Switching Characteristics
Authors: Yoon Hyowon , Seok Ogyun
Published: March 2024

The 1.2 kV SiC MOSFET with a buried oxide was verified to be effective in improving switching characteristics. It is crucial to reduce the gate–drain charge (QGD) of devices to minimize switching loss (Etotal). The SiC MOSFET with a split gate and device with a buffered oxide have been proposed by previous studies to reduce the QGD of the devices. However, both devices have a common issue of the concentration of the electric field at the gate oxide. In this paper, we propose the 1.2 kV SiC MOSFET with a buried oxide to reduce the QGD and suppress the electric field crowding effect at the gate oxide. We analyzed the specific on-resistance (Ron,sp), QGD and the maximum electric field at the gate oxide in the off state (Eox,max) according to the width (WBO) and thickness of the buried oxides (TBO). The device with the buried oxide, under optimal conditions, showed lower Eox,max and Etotal without significant increase in Ron,sp in comparison to the device with a conventional structure. These results indicate that the buried oxide can improve the switching characteristics of 1.2 kV SiC MOSFETs.
A Reconfigurable Local Oscillator Harmonic Mixer with Simultaneous Phase Shifting and Image Rejection
Authors: Wu Bin , Zheng Chaoyue , Zhang Hao , Zhao Qingchun
Published: March 2024

The multibeam high-throughput satellites (HTS) are regarded as a crucial component in the forthcoming space-based Internet of Things (S-IoT) network. The multi-band frequency conversion capability of HTS is essential for achieving high-capacity information interconnection in the S-IoT network. To enhance the frequency conversion capability of the on-board payload, a reconfigurable local oscillator (LO) harmonic mixer with simultaneous phase shifting and image-rejection is proposed and demonstrated based on a polarization division multiplexing dual-parallel Mach–Zehnder modulator (PDM-DPMZM). By adjusting the input radio frequency (RF) signal and direct current (DC) bias voltage of the modulator, four different LO frequency-multiplication mixing functions can be achieved. The phase of the generated signal can be flexibly tuned over a full 360° range by controlling the angle α between the polarization direction of the polarizer and one axis of the modulator, and it has a flat amplitude response. When combined with an optical frequency comb, the scheme can be extended to a multi-channel multi-band frequency conversion system with an independent phase tuning capability. Additionally, by adjusting the phase difference between dual channel output signals, it can be reconfigured to implement in-phase/quadrature (I/Q) mixing, double-balanced mixing and image-reject mixing.
Design of a Convolutional Neural Network Accelerator Based on On-Chip Data Reordering
Authors: Liu Yang , Zhang Yiheng , Hao Xiaoran , Chen Lan , Ni Mao , Chen Ming , Chen Rong
Published: March 2024

Convolutional neural networks have been widely applied in the field of computer vision. In convolutional neural networks, convolution operations account for more than 90% of the total computational workload. The current mainstream approach to achieving high energy-efficient convolution operations is through dedicated hardware accelerators. Convolution operations involve a significant amount of weights and input feature data. Due to limited on-chip cache space in accelerators, there is a significant amount of off-chip DRAM memory access involved in the computation process. The latency of DRAM access is 20 times higher than that of SRAM, and the energy consumption of DRAM access is 100 times higher than that of multiply–accumulate (MAC) units. It is evident that the “memory wall” and “power wall” issues in neural network computation remain challenging. This paper presents the design of a hardware accelerator for convolutional neural networks. It employs a dataflow optimization strategy based on on-chip data reordering. This strategy improves on-chip data utilization and reduces the frequency of data exchanges between on-chip cache and off-chip DRAM. The experimental results indicate that compared to the accelerator without this strategy, it can reduce data exchange frequency by up to 82.9%.
Survey on AI Applications for Product Quality Control and Predictive Maintenance in Industry 4.0
Authors: Andrianandrianina Johanesa Tojo Valisoa , Equeter Lucas , Mahmoudi Sidi Ahmed
Published: March 2024

Recent technological advancements such as IoT and Big Data have granted industries extensive access to data, opening up new opportunities for integrating artificial intelligence (AI) across various applications to enhance production processes. We cite two critical areas where AI can play a key role in industry: product quality control and predictive maintenance. This paper presents a survey of AI applications in the domain of Industry 4.0, with a specific focus on product quality control and predictive maintenance. Experiments were conducted using two datasets, incorporating different machine learning and deep learning models from the literature. Furthermore, this paper provides an overview of the AI solution development approach for product quality control and predictive maintenance. This approach includes several key steps, such as data collection, data analysis, model development, model explanation, and model deployment.
Multifunctional Superconducting Magnetic Energy Compensation for the Traction Power System of High-Speed Maglevs
Authors: Fu Lin , Chen Yu , Zhang Mingshun , Chen Xiaoyuan , Shen Boyang
Published: March 2024

With the global trend of carbon reduction, high-speed maglevs are going to use a large percentage of the electricity generated from renewable energy. However, the fluctuating characteristics of renewable energy can cause voltage disturbance in the traction power system, but high-speed maglevs have high requirements for power quality. This paper presents a novel scheme of a high-speed maglev power system using superconducting magnetic energy storage (SMES) and distributed renewable energy. It aims to solve the voltage sag caused by renewable energy and achieve smooth power interaction between the traction power system and maglevs. The working principle of the SMES power compensation system for topology and the control strategy were analyzed. A maglev train traction power supply model was established, and the results show that SMES effectively alleviated voltage sag, responded rapidly to the power demand during maglev acceleration and braking, and maintained voltage stability. In our case study of a 10 MW high-speed maglev traction power system, the SMES system could output/absorb power to compensate for sudden changes within 10 ms, stabilizing the DC bus voltage with fluctuations of less than 0.8%. Overall, the novel SMES power compensation system is expected to become a promising solution for high-speed maglevs to overcome the power quality issues from renewable energy.
Deep Multi-Instance Conv-Transformer Frameworks for Landmark-Based Brain MRI Classification
Authors: Li Guannan , Ji Zexuan , Sun Quansen
Published: March 2024

For brain diseases, e.g., autism spectrum disorder (ASD), with unclear biological characteristics, the detection of imaging-based biomarkers is a critical task for diagnosis. Several landmark-based categorization approaches have been developed for the computer-aided diagnosis of brain diseases, such as Alzheimer’s disease (AD), utilizing structural magnetic resonance imaging (sMRI). With the automatic detection of the landmarks of brain disease, more detailed brain features were identified for clinical diagnosis. Multi-instance learning is an effective technique for classifying brain diseases based on landmarks. The multiple-instance learning approach relies on the assumption of independent distribution hypotheses and is mostly focused on local information, thus the correlation among different brain regions may be ignored. However, according to previous research on ASD and AD, the abnormal development of different brain regions is highly correlated. Vision Transformers, with self-attention modules to capture the relationship between embedded patches from a whole image, have recently demonstrated superior performances in many computer vision tasks. Nevertheless, the utilization of 3D brain MRIs imposes a substantial computational load, especially while training with Vision Transformer. To address the challenges mentioned above, in this research, we proposed a landmark-based multi-instance Conv-Transformer (LD-MILCT) framework as a solution to the aforementioned issues in brain disease diagnosis. In this network, a two-stage multi-instance learning strategy was proposed to explore both spatial and morphological information between different brain regions; the Vision Transformer utilizes a multi-instance learning head (MIL head) to fully utilize the features that are not involved in the ultimate classification. We assessed our proposed framework using T1-weighted MRI images from both AD and ASD databases. Our method outperformed existing deep learning and landmark-based methods in terms of brain MRI classification tasks.
Geomechanical and Technical Aspects of Torpedo Blasting under Seismic and Rockburst Hazard Conditions in Legnica–Glogow Copper District Mines
Authors: Burtan Zbigniew , Cieślik Jerzy , Chlebowski Dariusz , Piasecki Paweł , Gzik Krzysztof
Published: March 2024

The dominant hazard in the Polish copper ore mining industry (LGCD mines—Legnica–Glogow Copper District) is the occurrence of mining tremors and rockbursts. One of the effective active methods of preventing this threat is torpedo blasting, which results in disturbing the structure of roof rocks. A change in the integrity of a roof, especially in the tremor-generating layer or in the contact between stiff rock layers, reduces the possibility of an elastic energy concentration and may also be a kind of stress concentrator, provoking the destruction of rocks. This article presents original solutions for determining the areas of rock mass within a mining area where it is advisable to conduct torpedo blasting, and the experience of blasting in the Rudna copper mine in the LGCD is described. The first part of this article presents the results and experiences of using torpedo blasting prevention in the LGCD mine conditions in the Rudna mine. Assuming that due to the stress of the rocks, torpedo blasting brings the greatest benefits in the areas of elastic energy concentration, the second part of this article presents the results of numerical modeling, on the basis of which the zones of elastic energy concentration in the form of total, shear and volume deformation were determined in the vicinity of mining activities for typical geological and mining conditions in the LGCD. The importance of methods for the verification of the proposed solutions based on the analysis of seismic activity and geophysical mechanisms of events’ foci and seismo-acoustic emissions was also emphasized. The numerical simulations performed and conclusions from in situ observations allowed the formulation of general principles for the selection of torpedo blasting parameters.
Dynamic Reactive Power Allocation Method of Power Grid with High-Permeability PV Considering Transient Voltage Stability
Authors: Li Yangyang , Yang Dongfeng , Liu Chuang , Guo Dongbo , Gu Yichen , Li Jiahe
Published: March 2024

In order to solve the risk of transient voltage instability caused by the increasing proportion of new energy represented by photovoltaic (PV) and dynamic load in the power grid, a dynamic reactive power compensation device configuration method with high-permeability PV is proposed considering transient voltage stability. Firstly, a typical reactive power compensation device configuration is constructed, and evaluation indexes based on transient voltage disturbance and transient voltage peak are proposed. The static index based on complex network characteristics and the dynamic index based on sensitivity theory are used to guide the candidate nodes of dynamic reactive power compensation. Secondly, when reactive power capacity is configured, a differentiated dynamic reactive power compensation optimization model is established, and the multi-objective marine predator algorithm is used to solve the configured capacity, aiming to improve the transient voltage stability at the lowest reactive power investment cost. The final configuration scheme is selected by using the improved entropy weight ideal solution sorting method. Finally, the simulation results of the improved IEEE39-node system verify the effectiveness of the proposed method.
Experimental Investigation of the Effects of Inorganic Components on the Supercritical Water Gasification of Semi-Coke
Authors: Sun Panpan , Lv Zhaobin , Sun Chuanjiang , Jin Hui , He Long , Ren Tong , Cheng Zening
Published: March 2024

Inorganic components in coal play a significant role during the supercritical water gasification (SCWG) process. This study comprehensively investigated the effect of major mineral components (SiO2, Al2O3, and CaO) on the SCWG of semi-coke with/without K2CO3. The inhibition/promotion mechanism and conversion of mineral chemical components were explored. The results showed that, without K2CO3, CaO promoted gasification because CaO’s adsorption of CO2 contributed to the fixed carbon steam reforming reaction and the catalysis of highly dispersed calcite. When K2CO3 was added, SiO2 and CaO were prone to sintering and agglomeration due to the formation of low-melting-point minerals, which hindered further gasification of fine carbon particles. Al2O3 prevented the aggregation of slags, increased the probability of fine carbon particles contacting SCW and K2CO3, and promoted complete gasification. This study’s results may provide theoretical guidance for the directional control of minerals in coal during SCWG, and complete gasification of solid-phase carbon can be achieved by properly adjusting the mineral components.
Experimental Study on Impinging Jet Atomization Using Doublet and Quadruplet Jets
Authors: Weng Jung-Yi , Liu Yao-Hsien
Published: March 2024

The process of impinging-jet atomization involves the collision of multiple liquid jets to create atomization. This study specifically focuses on a system that utilizes impinging atomization with multiple jets. The injectors used in this study are arranged in either a planar configuration for doublet injectors or a stereoscopic configuration for quadruplet injectors, both designed to facilitate impinging atomization. The angle at which the jets collide is set at 90°, with injector intersection angles of either 60° or 120°. The diameter of the jets ranges from 0.8 to 1.1 mm, while the length–diameter ratio of the pipe remains fixed at 10. To investigate the atomization process, experiments were conducted by varying flow rates (ranging from 30 to 130 mL/min) from each injector using pure water as the working fluid. This resulted in a range of Weber numbers spanning from 4 to 206 and Reynolds numbers ranging from 578 to 3443. Four atomization regimes were observed in the impinging atomization flow field: closed-rim mode, periodic drop mode, open rim mode, and fully developed mode. The experiment utilized a high-speed camera to observe the formation and breakup of the liquid sheet. However, increasing the number of jets and altering the impingement configuration had minimal impact on the liquid sheet patterns as the Weber number increased. Compared to traditional double jet atomization, quadruplet jet atomization resulted in the wider extension of liquid sheets and similar atomization patterns. This study is useful for designing jet impingement-atomization systems for confined spaces.
RF Energy Harvesting
Authors: Szut Jakub , Piątek Paweł , Pauluk Mariusz
Published: March 2024

This article presents research on the usefulness of three different electric circuit simulation environments for exploring energy harvesting from electromagnetic waves using energy harvesters. The software that is compared includes KiCad EDA, LT Spice and MATLAB Simscape Electrical.Too prepare a common background for the results comparison, crucial equations that combine RF transmission with energy are presented. Commercially available harvesters are also presented. An overview of the state-of-the-art research on this topic is summarised. In order to verify software using conditions that are similar to real ones, the power available at the 868 MHZ ISM band, which is close to the LTE bands used for telecommunications, is calculated. The results obtained using different software are close to being identical for all tested simulation environments.
A New Method for Determining Economic Well Pattern Density and Infilling Time of Tight Gas Reservoirs
Authors: Wang Daye , Fang Maojun , Li Hao , Cao Guangsheng , Fan Weipeng , Wang Bo
Published: March 2024

Well pattern infilling optimization is a crucial measure to enhance gas recovery, especially in tight gas reservoirs with low permeability and small-scale sand bodies. Traditional methods of determining well pattern density rely on qualitative analysis from the perspective of gas blocks. However, these methods are challenging to apply to sand bodies with different properties, and there have been no studies conducted on infilling time, which significantly impacts production increment. In response to this situation, this paper establishes a series of evaluation indexes and proposes a numerical simulation of economic well pattern density and infilling time based on real parameters obtained from the Linxing–Shenfu gas field. To quantitatively determine the economic well pattern density and infilling time based on fine characterization of sand bodies, a sensitivity analysis is conducted, considering various permeability levels and reserve abundance. Two intersection charts of economic well pattern density and infilling time, relating to reserve abundance and permeability, are then drawn. Furthermore, a real well site is selected as an example, and the infilling effect analysis confirms the reliability of the charts. The new method of determining economic well pattern density and infilling time presented in this article can provide theoretical support for the economic and efficient development of the Linxing–Shenfu gas field, serving as a useful reference for the beneficial development of similar gas reservoirs.
Correction: Chen et al. Constructing High-Performance Carbon Nanofiber Anodes by the Hierarchical Porous Structure Regulation and Silicon/Nitrogen Co-Doping. Energies 2022, 15, 4839
Authors: Chen Yujia , Wang Jiaqi , Wang Xiaohu , Li Xuelei , Liu Jun , Liu Jingshun , Nan Ding , Dong Junhui
Published: March 2024

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Large Differences in Bud Burst and Senescence between Low- and High-Altitude European Beech Populations along an Altitudinal Transect in the South-Eastern Carpathians
Authors: Ciocîrlan Mihnea Ioan Cezar , Ciocîrlan Elena , Chira Dănuț , Radu Gheorghe Raul , Păcurar Victor Dan , Beșliu Emanuel , Zormpa Ourania Grigoriadou , Gailing Oliver , Curtu Alexandru Lucian
Published: March 2024

Phenology is considered an indicator of environmental changes, with direct implications in the length of the growing season; therefore, it offers essential information for a better understanding of the tree–environment relationships that could lead to the right decisions for forests’ sustainable use and conservation. A better understanding of how European beech (Fagus sylvatica) phenology responds to predicted climate change effects is important for forest management. This study aimed to assess bud burst and senescence among and within beech populations located along a steep elevational gradient. Phenological observations were carried out on 150 beech individuals along an altitudinal transect in the south-eastern Carpathian Mountains, from 550 to 1450 m, in five study sites in two consecutive years. The start of the bud burst, of senescence, and the duration of the growing season varied inversely proportionally to the elevational gradient in both monitored years. Individuals located at the highest altitude need 28 more days to start the growing season than those at the lowest altitude. There is an average difference of 14 days at the start of the growing season in the same beech populations between the two consecutive years. The first stage of senescence (yellowing of leaves) lasted longer in 2021 (21–32 days) than in 2022 (18–25 days), with a difference of 16%–28%, proportional to the increase in altitude. The association of field phenological data with meteorological data indicates that the start of the growing season occurs when the thermal threshold of 10 °C is exceeded, with an accumulation of a least 60 GDD (growing degree days) with a threshold of 0 °C in the last 7 days as a complementary condition. The appearance of the first stage of senescence, the yellowing of the leaves, was also influenced by the temperature and the accumulation of at least 72 SDD (senescence degree days) with a threshold of 0 °C in the last 7 days. Our results confirm that the temperature is the triggering meteorological factor for the onset of bud burst and leaf senescence in European beech.
Genome-Wide Identification and Expression Profiling of the Response Regulator (RR) Gene Family in Pecan Reveals Its Possible Association with Callus Formation during Grafting
Authors: Zhang Yan , Jia Zhanhui , Wang Guoming , Hou Mengxin , Zhai Min , Hu Longjiao , Xuan Jiping , Mo Zhenghai
Published: March 2024

Response regulator (RR) is the core component of cytokinin (CK) signaling, and it regulates the expression of numerous downstream CK-responsive genes. However, the knowledge regarding the pecan RR (CiRR) gene family is still limited. In this study, we first monitored trans-zeatin riboside (tZR) content in the graft union 0, 7, 14, and 32 days after grafting and then conducted genome-wide analysis and expression profiling of the CiRR gene family using an available genome sequence and RNA-seq dataset, aiming to better understand the roles of CK during pecan grafting. The dynamic contents of tZR showed an increased trend during the specific period for both the scion and rootstock. There were 20 CiRRs in the pecan genome, including 12 type A CiRRs, 5 type B members, and 3 type C genes. All members contained a receiver domain and type B CiRRs possessed an additional Myb-like DNA-binding domain. Promoter analysis showed that the CiRR gene family contained cis-elements associated with growth and development, hormones, and stress. A total of 10 genes, including CiRR18/9/4a/14a/12c/5/12b/14b/2b/2a, were abundantly expressed in the samples of different tissues, drought stress, and kernel development. There were 12 genes (CiRR5/18/4a/12b/2b/12c/14b/2a/14a/4b/9/11a) showing active expressions during grafting, and weighted gene co-expression network analysis (WGCNA) grouped them into six modules. Among them, CiRR14a and CiRR12b were the hub genes for the turquoise and brown modules, respectively. Functional annotation indicated that the turquoise module was associated with gene transcription and translation, while the brown module was related to cell proliferation. Our results suggest that the CiRR gene family central to CK signaling is probably involved in callus formation during pecan grafting.
Mapping Forest Tree Species Using Sentinel-2 Time Series by Taking into Account Tree Age
Authors: Yang Ben , Wu Ling , Liu Meiling , Liu Xiangnan , Zhao Yuxin , Zhang Tingwei
Published: March 2024

Accurate classification of forest tree species holds great significance in the context of forest biodiversity assessment and the management of forest resources. In this study, we utilized Sentinel-2 time series data with high temporal and spatial resolution for tree species classification. To address potential classification errors stemming from spectral differences due to tree age variations, we implemented the Continuous Change Detection and Classification (CCDC) algorithm to estimate tree ages, which were integrated as additional features into our classification models. Four different combinations of classification features were created for both the random forest (RF) algorithm and extreme gradient boosting (XGB) algorithm: spectral band (Spec), spectral band combined with tree age feature (SpecAge), spectral band combined with spectral index (SpecVI), and spectral band combined with spectral index and tree age feature (SpecVIAge). The results demonstrated that the XGB-based models outperformed the RF-based ones, with the SpecVIAge model achieving the highest accuracy at 78.8%. The incorporation of tree age as a classification feature led to an improvement in accuracy by 2% to 3%. The improvement effect on classification accuracy varies across tree species, due to the varying uniformity of tree age among different tree species. These results also showed it is feasible to accurately map regional tree species based on a time-series multi-feature tree species classification model which takes into account tree age.
A Survey and Analysis of the History of Ernest Henry Wilson’s Specimen Collections in China
Authors: Zou Yongxi , Shi Ke , Liao Shuai , Xiang Zhoubing , Luo Jifan , Nan Xinge , Yan Hai , Bao Zhiyi , Nie Wenbin , Wu Renwu
Published: March 2024

Since the mid-17th century, Western plant collectors have explored Asia, particularly China, to collect native species for economic and botanical development. Ernest Henry Wilson (1876–1930) stands among the foremost figures in this pursuit. He conducted five plant collections in China between 1899 and 1918, gaining recognition for numerous valuable ornamental species. However, precise details such as the total number of specimens, a comprehensive species list, and the collection locations remained elusive, hindering a thorough assessment of his scientific contributions. To address this gap, we compiled data from various online databases and relevant publications, constructing a comprehensive dataset encompassing species names, collection dates, locations, and additional pertinent information. Employing Python, we organized and translated the species names and locations, facilitating analysis of families, genera, species, and the completeness and preferences of his collection. The conclusions drawn from the available data include 19,218 specimens, with 11,884 collection numbers, belonging to 200 families, 1046 genera, and 3794 species (including 342 infraspecific taxa). Wilson exhibited a preference for woody and flowering plants, resulting in a collection completeness of 25%. His collection spanned seven provinces, 28 prefecture-level cities, and 38 county-level areas, underscoring his significant contributions to the global dissemination of Chinese plant knowledge. This study conducts a comprehensive analysis of the plant specimens collected by Wilson in China, investigating their taxonomy, distribution, and historical context and evaluating Wilson’s contribution to plant dissemination. The findings serve as a valuable reference for subsequent biodiversity research endeavors.
Changes in Social Media Big Data on Healing Forests: A Time-Series Analysis on the Use Behavior of Healing Forests before and after the COVID-19 Pandemic in South Korea
Authors: Youn Ju-Yeong , Kim Sang-wook
Published: March 2024

This study aimed to identify changes in visitor behavior and visitor interest in healing forests before and after the COVID-19 pandemic. The study used text mining analysis techniques to identify changes in visitation behavior over time, divided into three periods, as follows: pre-COVID-19 (1 January to 31 December 2019), during the COVID-19 pandemic (1 November 2020 to 31 October 2022), and post-COVID-19 (1 November 2022 to 31 October 2023). After the COVID-19 outbreak, healing forest use behavior did not revert to pre-COVID-19 patterns. Moreover, the keywords “tourism” and “hiking” stood out as the main drivers of this change in behavior. Therefore, the Korea Forest Service and related authorities must examine the scalability of the functions, services, and programs of healing forests from a general healing space to a space for leisure and tourism. These findings will contribute to the development of future marketing strategies and programs for healing forests.
Changes in Physical Properties and Microstructure of Bamboo–Plastic Composites with Different Bamboo Powder/Polybutylene Succinate Ratios, Polypropylene, and Polyethylene
Authors: Niu Sijie , Han Ge , Chen Xinran , Liu Jiale , Wang Chuangui
Published: March 2024

As the most promising biomass material, bamboo has been widely used, but at the same time, it is subject to many problems, such as processing residues and the failure of high-value utilization of residues. In this paper, bamboo powder and polybutylene succinate (PBS) were used as the main raw materials, and by changing the ratio of bamboo powder to PBS and adding polypropylene (PP) or polyethylene (PE) in combination with PBS, the effects of the ratio, as well as the effects of the use of PP and PE, on the physical–mechanical, thermal, and degradation properties of bamboo–plastic composites were investigated, and the microscopic changes of the materials were studied by chemical component analysis. The optimal formulation of bamboo powder/PBS composite material has been identified through experimentation, yielding a flexural strength of 24.87 MPa and a compressive strength of 29.74 MPa. This material can be used for outdoor furniture, wall panels, flooring, road barriers, and other applications, providing a new environmentally friendly approach to the consumption of residual bamboo materials.
Enhancing Urban Above-Ground Vegetation Carbon Density Mapping: An Integrated Approach Incorporating De-Shadowing, Spectral Unmixing, and Machine Learning
Authors: Qie Guangping , Ye Jianneng , Wang Guangxing , Wang Minzi
Published: March 2024

Accurately mapping urban above-ground vegetation carbon density presents challenges due to fragmented landscapes, mixed pixels, and shadows induced by buildings and mountains. To address these issues, a novel methodological framework is introduced, utilizing a linear spectral unmixing analysis (LSUA) for shadow removal and vegetation information extraction from mixed pixels. Parametric and nonparametric models, incorporating LSUA-derived vegetation fraction, are compared, including linear stepwise regression, logistic model-based stepwise regression, k-Nearest Neighbors, Decision Trees, and Random Forests. Applied in Shenzhen, China, the framework integrates Landsat 8, Pleiades 1A & 1B, DEM, and field measurements. Among the key findings, the shadow removal algorithm is effective in mountainous areas, while LSUA-enhanced models improve urban vegetation carbon density mapping, albeit with marginal gains. Integrating kNN and RF with LSUA reduces errors, and Decision Trees, especially when integrated with LSUA, outperform other models. This study underscores the potential of the proposed framework, particularly the integration of Decision Trees with LSUA, for advancing the accuracy of urban vegetation carbon density mapping.
Association between Single Nucleotide Polymorphisms of PRKD1 and KCNQ3 Gene and Milk Quality Traits in Gannan Yak (Bos grunniens)
Authors: Ma Xiaoyong , Yang Guowu , Zhang Juanxiang , Ma Rong , Shen Jinwei , Feng Fen , Yu Daoning , Huang Chun , Ma Xiaoming , La Yongfu , Wu Xiaoyun , Guo Xian , Chu Min , Yan Ping , Liang Chunnian
Published: March 2024

Protein kinase D1 (PRKD1) functions primarily in normal mammary cells, and the potassium voltage-gated channel subfamily Q member 3 (KCNQ3) gene plays an important role in controlling membrane potential and neuronal excitability, it has been found that this particular gene is linked to the percentage of milk fat in dairy cows. The purpose of this study was to investigate the relationship between nucleotide polymorphisms (SNPs) of PRKD1 and KCNQ3 genes and the milk quality of Gannan yak and to find molecular marker sites that may be used for milk quality breeding of Gannan yak. Three new SNPs were detected in the PRKD1 (g.283,619T>C, g.283,659C>A) and KCNQ3 gene (g.133,741T>C) of 172 Gannan lactating female yaks by Illumina yak cGPS 7K liquid-phase microarray technology. Milk composition was analyzed using a MilkoScanTM milk composition analyzer. We found that the mutations of these three loci significantly improved the lactose, milk fat, casein, protein, non-fat milk solid (SNF) content and acidity of Gannan yaks. The lactose content of the TC heterozygous genotype population at g.283,619T>C locus was significantly higher than that of the TT wild-type population (p < 0.05); the milk fat content of the CA heterozygous genotype population at g.283,659C>A locus was significantly higher than that of the CC wild-type and AA mutant populations (p < 0.05); the casein, protein and acidity of the CC mutant and TC heterozygous groups at the g.133,741T>C locus were significantly higher than those of the wild type (p < 0.05), and the SNF of the TC heterozygous group was significantly higher than that of the mutant group (p < 0.05). The results showed that PRKD1 and KCNQ3 genes could be used as candidate genes affecting the milk traits of Gannan yak.
Enhancing Cookie Formulations with Combined Dehydrated Peach: A Machine Learning Approach for Technological Quality Assessment and Optimization
Authors: Lončar Biljana , Pezo Lato , Knežević Violeta , Nićetin Milica , Filipović Jelena , Petković Marko , Filipović Vladimir
Published: March 2024

This study focuses on predicting and optimizing the quality parameters of cookies enriched with dehydrated peach through the application of Support Vector Machine (SVM) and Artificial Neural Network (ANN) models. The purpose of the study is to employ advanced machine learning techniques to understand the intricate relationships between input parameters, such as the presence of dehydrated peach and treatment methods (lyophilization and lyophilization with osmotic pretreatment), and output variables representing various quality aspects of cookies. For each of the 32 outputs, including the parameters of the basic chemical compositions of the cookie samples, selected mineral contents, moisture contents, baking characteristics, color properties, sensorial attributes, and antioxidant properties, separate models were constructed using SVMs and ANNs. Results showcase the efficiency of ANN models in predicting a diverse set of quality parameters with r2 up to 1.000, with SVM models exhibiting slightly higher coefficients of determination for specific variables with r2 reaching 0.981. The sensitivity analysis underscores the pivotal role of dehydrated peach and the positive influence of osmotic pretreatment on specific compositional attributes. Utilizing established Artificial Neural Network models, multi-objective optimization was conducted, revealing optimal formulation and factor values in cookie quality optimization. The optimal quantity of lyophilized peach with osmotic pretreatment for the cookie formulation was identified as 15%.
Nondestructive Determination of Epicarp Hardness of Passion Fruit Using Near-Infrared Spectroscopy during Storage
Authors: Wang Junyi , Fu Dandan , Hu Zhigang , Chen Yan , Li Bin
Published: March 2024

The hardness of passion fruit is a critical feature to consider when determining maturity during post-harvest storage. The capacity of near-infrared diffuse reflectance spectroscopy (NIRS) for non-destructive detection of outer and inner hardness of passion fruit epicarp was investigated in this work. The passion fruits’ spectra were obtained using a near-infrared spectrometer with a wavelength range of 10,000–4000 cm−1. The hardness of passion fruit’s outer epicarp (F1) and inner epicarp (F2) was then measured using a texture analyzer. Moving average (MA) and mean-centering (MC) techniques were used to preprocess the collected spectral data. Competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA), and uninformative variable elimination (UVE) were used to pick feature wavelengths. Grid-search-optimized random forest (Grids-RF) models and genetic-algorithm-optimized support vector regression (GA-SVR) models were created as part of the modeling process. After MC preprocessing and CARS selection, MC-CARS-Grids-RF model with 7 feature wavelengths had the greatest prediction ability for F1. The mean square error of prediction set (RMSEP) was 0.166 gN. Similarly, following MA preprocessing, the MA-Grids-RF model displayed the greatest predictive performance for F2, with an RMSEP of 0.101 gN. When compared to models produced using the original spectra, the R2P for models formed after preprocessing and wavelength selection improved. The findings showed that near-infrared spectroscopy may predict the hardness of passion fruit epicarp, which can be used to identify quality during post-harvest storage.
Fatty Acid Release and Gastrointestinal Oxidation Status: Different Methods of Processing Flaxseed
Authors: Zhang Mingkai , Chen Yashu , Chen Hongjian , Deng Qianchun
Published: March 2024

Flaxseed has been recognized as a superfood worldwide due to its abundance of diverse functional phytochemicals and nutrients. Various studies have shown that flaxseed consumption is beneficial to human health, though methods of processing flaxseed may significantly affect the absorption and metabolism of its bioactive components. Hence, flaxseed was subjected to various processing methods including microwaving treatment, microwave-coupled dry milling, microwave-coupled wet milling, and high-pressure homogenization. In vitro digestion experiments were conducted to assess the impact of these processing techniques on the potential gastrointestinal fate of flaxseed oil. Even though more lipids were released by the flaxseed at the beginning of digestion after it was microwaved and dry-milled, the full digestion of flaxseed oil was still restricted in the intestine. In contrast, oil droplets were more evenly distributed in wet-milled flaxseed milk, and there was a greater release of fatty acids during simulated digestion (7.33 ± 0.21 μmol/mL). Interestingly, wet-milled flaxseed milk showed higher oxidative stability compared with flaxseed powder during digestion despite the larger specific surface area of its oil droplets. This study might provide insight into the choice of flaxseed processing technology for better nutrient delivery efficiency.
Nutritional Data on Selected Food Products Consumed in Oman: An Update of the Food Composition Table and Use for Future Food Consumption Surveys
Authors: Almaamari Salima , Al-Jawaldeh Ayoub , Al Ghammari Ibtisam , Al Shammakhi Saleh , Al Aamri Jokha , El Ati Jalila
Published: March 2024

Food composition data in the Eastern Mediterranean Region countries are often lacking, obsolete, or unreliable. The study aims to provide reliable nutrient data on food products consumed in Oman in order to evaluate their nutritional quality, the consistency of the nutrition labeling and claims, and, ultimately, the use for food consumption surveys and update the current food composition database. Contents of fat, fatty acids, carbohydrates, protein, sugars, and sodium were chemically analyzed in 221 foods and beverages. Products were classified according to their nutritional composition and the extent of processing and coded according to the FoodEx2 system. Labels and laboratory values were compared using the tolerance levels of the European Union. Results indicate that the nutrition labeling aligns with the values obtained in the laboratory, with the exception of 6.3% discrepancies in TFA content, where the reported values are higher than the appropriate reference values. The most frequent category (71.5%) was ultra-processed foods. In terms of inconsistencies in the nutritional claims, 5.1% of food products with claims did not comply with the statement “sugar-free” or “low salt”. Our study provides evidence to support the necessity of comprehensive recommendations for consumers and food industries, which are aimed at enhancing the nutritional quality of products and augmenting consumer awareness.
The Effects of Sunflower and Maize Crop Residue Extracts as a New Ingredient on the Quality Properties of Pork Liver Pâtés
Authors: Glišić Milica , Bošković Cabrol Marija , Čobanović Nikola , Starčević Marija , Samardžić Stevan , Veličković Ivona , Maksimović Zoran
Published: March 2024

The present study aimed to evaluate the antioxidant capacity of ethanolic extracts from post-harvest sunflower and maize stalk residues, and their impact on the chemical composition, physicochemical parameters, lipid oxidative stability, microbiological properties, and sensory characteristics of pork liver pâtés over a 90-day storage period. Four formulations were prepared: a control group (CON), a batch with butylated hydroxytoluene as a synthetic antioxidant (BHT), 1% ethanolic extract from sunflower residues (SSRE), and 1% ethanolic extract from maize residues (MSRE). The MSRE had a higher total phenol content and showed better antioxidant activity relative to the SSRE (p < 0.01). The addition of SSRE decreased the lightness and increased the redness in the pork liver pâtés, with these pâtés showing the highest total color difference compared to the control (p < 0.01). The crop extracts increased the n-6 and total PUFA contents in pâtés and improved the PUFA/SFA ratio (p < 0.01). Formulations containing crop residue extracts showed higher TBARs and POV values than the control and BHT group (p < 0.01), indicating a pro-oxidant effect and accelerated lipid oxidation in pâtés during storage. As far as microbiological quality, the presence of crop residue extracts decreased the total viable count, lactic acid bacteria, and psychotropic aerobic bacteria (p < 0.01). The incorporation of crop extracts in the pork pâtés impaired their sensory quality, particularly color, odor, aroma, and flavor, and decreased their overall acceptability. These results indicated that, while the crop residue extracts were not as effective as synthetic antioxidants in preserving the lipid stability of pâtés, they demonstrated potential for enhancing the microbial quality of this type of meat product.
Preharvest Methyl Jasmonate Treatment Affects the Mineral Profile, Metabolites, and Antioxidant Capacity of Radish Microgreens Produced without Substrate
Authors: Tilahun Shimeles , Baek Min Woo , An Ki-Seok , Choi Han Ryul , Lee Jong Hwan , Tae Su Ho , Park Do Su , Hong Jin Sung , Jeong Cheon Soon
Published: March 2024

This study investigated the impact of Methyl Jasmonate (MeJA) application on the nutritional content and yield of five different colored radish microgreens. Microgreens were produced without substrate and subjected to 0.5 mM and 1.0 mM MeJA treatments on the 7th day, three days before harvest. The parameters measured included yield, dry matter, minerals, amino acids, secondary metabolites such as chlorophylls (Chls), anthocyanins, flavonoids, phenolics, glucosinolates (GSLs), vitamin C, and antioxidant capacity. MeJA at 1.0 mM generally improved yield and dry weight across cultivars, and all microgreens exhibited rich mineral and amino acid composition, with the influence of cultivar being more significant than MeJA treatment. However, MeJA enhanced all cultivars’ anthocyanins, GSLs, phenolics, flavonoids, and antioxidant activities. Generally, as the antioxidant capacity is the primary factor influencing the nutritional quality of microgreens, MeJA-treated microgreens, especially with selected superior cultivars such as ‘Asia purple’ and ‘Koregon red’, could offer a potential for cultivation of value-added, eco-friendly microgreens with substrate-free cultivation.