bluuebunny/crossref_metadata_2025 huggingface dataset:
Isolation and Characterization of Extracellular Vesicles from Arabidopsis thaliana Cell Culture and Investigation of the Specificities of Their Biogenesis
Authors: Yugay Yulia , Tsydeneshieva Zhargalma , Rusapetova Tatiana , Grischenko Olga , Mironova Anastasia , Bulgakov Dmitry , Silant’ev Vladimir , Tchernoded Galina , Bulgakov Victor , Shkryl Yury
Published: October 2023

Over recent years, extracellular vesicles (EVs), commonly termed exosomes, have gained prominence for their potential as natural nanocarriers. It has now been recognized that plants also secrete EVs. Despite this discovery, knowledge about EV biogenesis in plant cell cultures remains limited. In our study, we have isolated and meticulously characterized EVs from the callus culture of the model plant, Arabidopsis thaliana. Our findings indicate that the abundance of EVs in calli was less than that in the plant’s apoplastic fluid. This difference was associated with the transcriptional downregulation of the endosomal sorting complex required for transport (ESCRT) genes in the calli cells. While salicylic acid increased the expression of ESCRT components, it did not enhance EV production. Notably, EVs from calli contained proteins essential for cell wall biogenesis and defense mechanisms, as well as microRNAs consistent with those found in intact plants. This suggests that plant cell cultures could serve as a feasible source of EVs that reflect the characteristics of the parent plant species. However, further research is essential to determine the optimal conditions for efficient EV production in these cultured cells.
New Insights into the Enhancement of Adventitious Root Formation Using N,N′-Bis(2,3-methylenedioxyphenyl)urea
Authors: Ricci Ada , Polverini Eugenia , Bruno Stefano , Dramis Lucia , Ceresini Daniela , Scarano Antonio , Diaz-Sala Carmen
Published: October 2023

Adventitious rooting is a process of postembryonic organogenesis strongly affected by endogenous and exogenous factors. Although adventitious rooting has been exploited in vegetative propagation programs for many plant species, it is a bottleneck for vegetative multiplication of difficult-to-root species, such as many woody species. The purpose of this research was to understand how N,N′-bis-(2,3-methylenedioxyphenyl)urea could exert its already reported adventitious rooting adjuvant activity, starting from the widely accepted knowledge that adventitious rooting is a hormonally tuned progressive process. Here, by using specific in vitro bioassays, histological analyses, molecular docking simulations and in vitro enzymatic bioassays, we have demonstrated that this urea derivative does not interfere with polar auxin transport; it inhibits cytokinin oxidase/dehydrogenase (CKX); and, possibly, it interacts with the apoplastic portion of the auxin receptor ABP1. As a consequence of this dual binding capacity, the lifespan of endogenous cytokinins could be locally increased and, at the same time, auxin signaling could be favored. This combination of effects could lead to a cell fate transition, which, in turn, could result in increased adventitious rooting.
Preparation, Characterization, Dielectric Properties, and AC Conductivity of Chitosan Stabilized Metallic Oxides CoO and SrO: Experiments and Tight Binding Calculations
Authors: Elfadl Azza Abou , Bashal Ali H. , Habeeb Talaat H. , Khalafalla Mohammed A. H. , Alkayal Nazeeha S. , Khalil Khaled D.
Published: October 2023

Polymeric films made from chitosan (CS) doped with metal oxide (MO = cobalt (II) oxide and strontium oxide) nanoparticles at different concentrations (5, 10, 15, and 20% wt. MO/CS) were fabricated with the solution cast method. FTIR, SEM, and XRD spectra were used to study the structural features of those nanocomposite films. The FTIR spectra of chitosan showed the main characteristic peaks that are usually present, but they were shifted considerably by the chemical interaction with metal oxides. FTIR analysis of the hybrid chitosan-CoO nanocomposite exhibited notable peaks at 558 and 681 cm−1. Conversely, the FTIR analysis of the chitosan-SrO composite displayed peaks at 733.23 cm−1, 810.10 cm−1, and 856.39 cm−1, which can be attributed to the bending vibrations of Co-O and Sr-O bonds, respectively. In addition, the SEM graphs showed a noticeable morphological change on the surface of chitosan, which may be due to surface adsorption with metal oxide nanoparticles. The XRD pattern also revealed a clear change in the crystallinity of chitosan when it is in contact with metal oxide nanoparticles. The presence of characteristic signals for cobalt (Co) and strontium (Sr) are clearly shown in the EDX examinations, providing convincing evidence for their incorporation into the chitosan matrix. Moreover, the stability of the nanoparticle-chitosan coordinated bonding was verified from the accurate and broadly parametrized semi-empirical tight-binding quantum chemistry calculation. This leads to the determination of the structures’ chemical hardness as estimated from the frontier’s orbital calculations. We characterized the dielectric properties in terms of the real and imaginary dielectric permittivity as a function of frequency. Dielectric findings reveal the existence of extensive interactions of CoO and SrO, more pronounced for SrO, with the functional groups of CS through coordination bonding. This induces the charge transfer of the complexes between CoO and SrO and the CS chains and a decrease in the amount of the crystalline phase, as verified from the XRD patterns.
Virtual Reality Mindfulness for Meta-Competence Training among People with Different Mental Disorders: A Systematic Review
Authors: Mitsea Eleni , Drigas Athanasios , Skianis Charalabos
Published: October 2023

International psychiatry is facing major challenges due to the rapid increase in mental health issues. The forthcoming mental health crisis has opened the debate about the need to broaden the therapeutic horizons with the implementation of digitally assisted mindfulness practices within psychotherapeutic interventions. Mindfulness training is developing into a promising intervention for a variety of health problems and a booster of well-being. At the same time, virtual reality (VR) and especially immersive technologies are increasingly being used as assistive tools in the training of people with special education needs and disabilities (SEND). Μeta-competences refer to a set of self-development skills that incorporate meta-cognitive and meta-emotional attributes, enabling individuals to be self-conscious, self-regulated, and flexible in every aspect of human life. The current review aims to investigate (i) the efficacy of mindfulness strategies in meta-competence training for SEND and (ii) the role of VR as an assistive technology in mindfulness training. The PRISMA 2020 methodology was utilized to respond to the objectives and research questions. The database search provided 1380 records, and 29 studies met the inclusion criteria. The results showed that mindfulness training has the potential to train meta-cognitive and meta-emotional competences among people with different mental disorders, including Attention Deficit/Hyperactivity Disorder (ADHD), Autism Spectrum Disorder (ASD), Intellectual Disability (ID), Depressive Disorder, Stress Disorder, and Specific Learning Disorder. VR was found to be an effective assistive technology, providing significant advantages compared to conventional mindfulness interventions. Mindfulness training assisted by immersive technologies was found to significantly improve a wide range of cognitive and socio-emotional meta-competences, including self-awareness, inhibition control, attention regulation, flexibility, positive thinking, and emotional regulation. The results of this systematic review may provide positive feedback for creating inclusive digital training environments.
Internet of Things and Big Data Analytics for Risk Management in Digital Tourism Ecosystems
Authors: Popova Petya , Marinova Kremena , Popov Veselin
Published: October 2023

Participation and inclusion in the business ecosystem have emerged as a growing trend for company collaboration in areas such as innovation, product development, and research. Collaborations can take many forms, ranging from the traditional value chain to strategic alliances, corporate networks, and digital ecosystems. The Internet of Things (IoT) and Big Data Analytics (BDA) play key roles in developing smart tourism destinations by delivering efficient management solutions, increased public safety, and improved operational efficiency while managing different risks and challenges, while also being a source of such risks and challenges. The objective of this article was to investigate the potential of IoT and BDA to properly control the risks associated with participants in a tourism destination’s digital ecosystem. The authors used the systematic literature review (SLR) method to examine scientific and applied articles on this subject. As a result, the main risks of the digital tourism ecosystem (DTE) as a whole and of the IoT and BDA technologies used in it were identified and classified; the features of DTE that affect risk management in it were distinguished; IoT technologies and their applications used in DTE were outlined; and the roles of DTE participants and the possible IoT technologies that can successfully address the risks associated with a given role were defined.
Assessing the Allometric Scaling of Vectorized Branch Lengths of Trees with Terrestrial Laser Scanning and Quantitative Structure Modeling: A Case Study in Guyana
Authors: Sun Jingjing , Lin Yi
Published: October 2023

Allometric scaling is closely related to the morphology, function and behavior of trees, which are of great significance to the study of ecology. However, most of the traditional allometric scaling studies used the scalar attributes of trees, without considering the 3D vector mode of tree growth. In order to investigate the allometric relationships between branch lengths in 3D vector mode, in this study, an accurate and detailed quantitative structure model was used to reconstruct tree architectures from 3D point cloud data collected by terrestrial laser scanning and extract the structural parameters of each branch (length, branching level and zenith angle). The standardized major axis was used to establish and analyze the scalar and vector allometric relationships between branch lengths. Our results show that at the same branching level and using the same allometric model, the scaling exponents between the lengths of branches and the lengths of cumulative child (descendant) branches (no matter whether the lengths are in scalar or vector form) are similar among trees, and there is no significant difference between the scaling exponents of most trees. And the scaling exponents between the lengths of the horizontal components of branches and the cumulative lengths of the horizontal components of the child (descendant) branches are much larger than those between the lengths of the vertical components of branches and the cumulative lengths of vertical components of the child (descendant) branches. At different branching levels, the scaling exponents between the lengths of branches and the cumulative lengths of descendant branches tend to decrease with the increase in the branching level. The allometric models in terms of the cumulative lengths of horizontal components of the child (descendant) branches and the allometric models in terms of the cumulative lengths of child (descendant) branches have similar model accuracy and scaling exponents. The study results of allometric relationships between tree branch lengths in 3D mode are of great importance for understanding the crown morphology and the branching rule, which is helpful to further understand the growth strategies and adaptation mechanisms of trees and explain the growth and development mechanisms of trees from a physiological perspective.
Geo-Environment Vulnerability Assessment of Multiple Geohazards Using VWT-AHP: A Case Study of the Pearl River Delta, China
Authors: Huang Peng , Wu Xiaoyu , Ma Chuanming , Zhou Aiguo
Published: October 2023

Geohazards pose significant risks to communities and infrastructure, emphasizing the need for accurate susceptibility assessments to guide land-use planning and hazard management. This study presents a comprehensive method that combines Variable Weight Theory (VWT) with Analytic Hierarchy Process (AHP) to assess geo-environment vulnerability based on susceptibility to various geohazards. The method was applied to the Pearl River Delta in China, resulting in the classification of areas into high vulnerability (5961.85 km2), medium vulnerability (19,227.93 km2), low vulnerability (14,892.02 km2), and stable areas (1616.19 km2). The findings demonstrate improved accuracy and reliability compared to using AHP alone. ROC curve analysis confirms the enhanced performance of the integrated method, highlighting its effectiveness in discerning susceptibility levels and making informed decisions in hazard preparedness and risk reduction. Additionally, this study assessed the risks posed by geohazards to critical infrastructures, roads, and artificial surfaces, while discussing prevention strategies. However, this study acknowledges certain limitations, including the subjective determination of its judgment matrix and data constraints. Future research could explore the integration of alternative methods to enhance the objectivity of factor weighting. In practical applications, this study contributes to the understanding of geo-environment vulnerability assessments, providing insight into the intricate interplay among geological processes, human activities, and disaster resilience.
Preliminary Assessment of BDS Radio Occultation Retrieval Quality and Coverage Using FY-3E GNOS II Measurements
Authors: Liu Congliang , Liao Mi , Sun Yueqiang , Wang Xi , Liang Jiahui , Hu Xiuqing , Zhang Peng , Yang Guanglin , Liu Yan , Wang Jinsong , Bai Weihua , Du Qifei , Meng Xiangguang , Hu Peng , Tan Guangyuan , Wang Xianyi , Xia Junming , Huang Feixiong , Yin Cong , Cai Yuerong , Li Wei , Li Peixian , Kirchengast Gottfried
Published: October 2023

The FengYun-3E Global Navigation Satellite System (GNSS) occultation sounder II (FY-3E GNOS II) was launched on 5 July 2021. For the first time, based on the new GNOS II sensor, this mission realizes radio occultation (RO) and reflectometry observations using the navigation signals from the third-generation BeiDou System (BDS-3), and it is hence important to assess and analyze the BDS-3 remote sensing performances relative to other systems. Here, we assessed FY-3E GNOS II RO atmospheric retrievals by inter-comparing with corresponding data from the NCEP FNL global atmospheric analysis and FY-3D GNOS mission. The GNOS RO data quality and consistency of the different FY-3 meteorological satellites, i.e., FY-3D and FY-3E, as well as different GNSS systems (GPS, BDS-2, BDS-3) were analyzed. We find that the FY-3E GNOS II RO data exhibit better quality than FY-3D GNOS, particularly in the number, penetration height toward surface, and global coverage by BDS RO profiles, due to the integration of BDS-2 and BDS-3. Additionally, comparing with co-located NCEP FNL analysis profiles, the mean difference (and standard deviation) of the FY-3E GNOS II RO atmospheric refractivity profile retrievals is found to be smaller than 0.2% (and 1%), in the upper troposphere and lower stratosphere, from 5 to 30 km, and remains consistent at this accuracy and precision level with the FY-3D GNOS RO data. These features provide clear evidence for a high utility of the new GNOS II RO data for weather and climate research and applications.
Adaptive Signal-to-Noise Ratio Indicator for Wearable Bioimpedance Monitoring
Authors: Lapsa Didzis , Janeliukstis Rims , Elsts Atis
Published: October 2023

Bioimpedance monitoring is an increasingly important non-invasive technique for assessing physiological parameters such as body composition, hydration levels, heart rate, and breathing. However, sensor signals obtained from real-world experimental conditions invariably contain noise, which can significantly degrade the reliability of the derived quantities. Therefore, it is crucial to evaluate the quality of measured signals to ensure accurate physiological parameter values. In this study, we present a novel wrist-worn wearable device for bioimpedance monitoring, and propose a method for estimating signal quality for sensor signals obtained on the device. The method is based on the continuous wavelet transform of the measured signal, identification of wavelet ridges, and assessment of their energy weighted by the ridge duration. We validate the algorithm using a small-scale experimental study with the wearable device, and explore the effects of variables such as window size and different skin/electrode coupling agents on signal quality and repeatability. In comparison with traditional wavelet-based signal denoising, the proposed method is more adaptive and achieves a comparable signal-to-noise ratio.
In-Bulk Temperature Profile Mapping Using Fiber Bragg Grating in Fluids
Authors: Su Sylvie , Niu Tianyi , Vogt Tobias , Eckert Sven
Published: October 2023

The capabilities of Fiber Bragg Grating (FBG) sensors to measure temperature variations in the bulk of liquid flows were considered. In the first step of our research project, reported in this paper, we investigated to what extent the use of thin glass fibers without encapsulation, which only minimally disturb a flow, can fulfill the requirements for robustness and measurement accuracy. Experimental tests were performed in a benchmark setup containing 24 FBG measuring positions, which were instrumented in parallel with thermocouples for validation. We suggest a special assembly procedure in which the fiber is placed under a defined tension to improve its stiffness and immobility for certain flow conditions. This approach uses a single FBG sensor as a reference that measures the strain effect in real time, allowing accurate relative temperature measurements to be made at the other FBG sensor points, taking into account an appropriate correction term. Absolute temperature readings can be obtained by installing another well-calibrated, strain-independent thermometer on the reference FBG. We demonstrated this method in two test cases: (i) a temperature gradient with stable density stratification in the liquid metal GaInSn and (ii) the heating of a water column using a local heat source. In these measurements, we succeeded in recording both spatial and temporal changes in the linear temperature distribution along the fiber. We present the corresponding results from the tests and, against this background, we discuss the capabilities and limitations of this measurement technique with respect to the detection of temperature fields in liquid flows.
High-Power-Efficiency Readout Circuit Employing Average Capacitance-to-Voltage Converter for Micro-Electro-Mechanical System Capacitive Accelerometers
Authors: Li Linxi , Lai Xinquan , Wang Yuheng , Niu Zhiwen
Published: October 2023

A capacitance-to-voltage converter (CVC) is proposed in this paper and applied to a readout circuit for a micro-electro-mechanical system (MEMS) accelerometer to improve the power efficiency. In a traditional readout circuit, the front-end CVC has to operate at a high sampling frequency to resist thermal noise deterioration due to the large parasitic capacitance introduced by the mechanical sensing element. Thus, the back-end analog-to-digital converter (ADC) also has to operate at a high sampling frequency to avoid noise aliasing when sampling the output signal of the CVC, which leads to high power consumption. The average CVC technique is proposed in this paper to reduce the sampling frequency requirement of the back-end ADC and thus reduce the power consumption. Both the traditional readout circuit and the proposed readout circuit are simulated with a commercial 0.18 μm BCD process. The simulation results show that noise aliasing occurs, and the noise power spectral density (PSD) of the traditional readout circuit increases by 12 dB when the sampling frequency of back-end ADC is reduced by 24 dB. However, in the proposed readout circuit, a noise aliasing effect does not occur. Moreover, the proposed readout circuit reduces the power consumption by 53% without thermal noise deterioration. In addition, the proposed CVC circuits are fabricated in an 0.18 μm BCD process, and the test results show that the presented readout circuit based on the average CVC technique can obtain better performance than the traditional CVC-based readout circuit.
Analysis of Train–Track–Bridge Coupling Vibration Characteristics for Heavy-Haul Railway Based on Virtual Work Principle
Authors: Wu Nanhao , Yang Hongyin , Afsar Haleem , Wang Bo , Fan Jianfeng
Published: October 2023

This paper introduces an innovative model for heavy-haul train–track–bridge interaction, utilizing a coupling matrix representation based on the virtual work principle. This model establishes the relationship between the wheel–rail contact surface and the bridge–rail interface concerning internal forces and geometric constraints. In this coupled system’s motion equation, the degrees of freedom (DOFs) of the wheelsets in a heavy-haul train lacking primary suspension are interdependent. Additionally, the vertical and nodding DOFs of the bogie frame are linked with the rail element. A practical application, a Yellow River Bridge with a heavy-haul railway line, is used to examine the accuracy of the proposed model with regard to discrepancy between the simulated and measured displacement ranging from 1% to 11%. A comprehensive parametric analysis is conducted, exploring the impacts of track irregularities of varying wavelengths, axle load lifting, and the degradation of bridge stiffness and damping on the dynamic responses of the coupled system. The results reveal that the bridge’s dynamic responses are particularly sensitive to track irregularities within the wavelength range of 1 to 20 m, especially those within 1 to 10 m. The vertical displacement of the bridge demonstrates a nearly linear increase with heavier axle loads of the heavy-haul trains and the reduction in bridge stiffness. However, there is no significant rise in vertical acceleration under these conditions.
A Low-Complexity Hand Gesture Recognition Framework via Dual mmWave FMCW Radar System
Authors: Mao Yinzhe , Zhao Lou , Liu Chunshan , Ling Minhao
Published: October 2023

In this paper, we propose a novel low-complexity hand gesture recognition framework via a multiple Frequency Modulation Continuous Wave (FMCW) radar-based sensing system. In this considered system, two radars are deployed distributively to acquire motion vectors from different observation perspectives. We first independently extract reflection points of the interested target from different radars by applying the proposed neighboring reflection points detection method after processing the traditional 2-dimensional Fast Fourier Transform (2D-FFT). The obtained sufficient corresponding information of detected reflection points, e.g., distances, velocities, and angle information, can be exploited to synthesize motion velocity vectors to achieve a high signal-to-noise ratio (SNR) performance, which does not require knowledge of the relative position of the two radars. Furthermore, we utilize a long short-term memory (LSTM) network as well as the synthesized motion velocity vectors to classify different gestures, which can achieve a significantly high accuracy of gesture recognition with a 1600-sample data set, e.g., 98.0%. The experimental results also illustrate the robustness of the proposed gesture recognition systems, e.g., changing the environment background and adding new gesture performers.
Adversarially Learning Occlusions by Backpropagation for Face Recognition
Authors: Zhao Caijie , Qin Ying , Zhang Bob
Published: October 2023

With the accomplishment of deep neural networks, face recognition methods have achieved great success in research and are now being applied at a human level. However, existing face recognition models fail to achieve state-of-the-art performance in recognizing occluded face images, which are common scenarios captured in the real world. One of the potential reasons for this is the lack of large-scale training datasets, requiring labour-intensive and costly labelling of the occlusions. To resolve these issues, we propose an Adversarially Learning Occlusions by Backpropagation (ALOB) model, a simple yet powerful double-network framework used to mitigate manual labelling by contrastively learning the corrupted features against personal identity labels, thereby maximizing the loss. To investigate the performance of the proposed method, we compared our model to the existing state-of-the-art methods, which function under the supervision of occlusion learning, in various experiments. Extensive experimentation on LFW, AR, MFR2, and other synthetic masked or occluded datasets confirmed the effectiveness of the proposed model in occluded face recognition by sustaining better results in terms of masked face recognition and general face recognition. For the AR datasets, the ALOB model outperformed other advanced methods by obtaining a 100% recognition rate for images with sunglasses (protocols 1 and 2). We also achieved the highest accuracies of 94.87%, 92.05%, 78.93%, and 71.57% TAR@FAR = 1 × 10−3 in LFW-OCC-2.0 and LFW-OCC-3.0, respectively. Furthermore, the proposed method generalizes well in terms of FR and MFR, yielding superior results in three datasets, LFW, LFW-Masked, and MFR2, and producing accuracies of 98.77%, 97.62%, and 93.76%, respectively.
Data Augmentation Techniques for Machine Learning Applied to Optical Spectroscopy Datasets in Agrifood Applications: A Comprehensive Review
Authors: Gracia Moisés Ander , Vitoria Pascual Ignacio , Imas González José Javier , Ruiz Zamarreño Carlos
Published: October 2023

Machine learning (ML) and deep learning (DL) have achieved great success in different tasks. These include computer vision, image segmentation, natural language processing, predicting classification, evaluating time series, and predicting values based on a series of variables. As artificial intelligence progresses, new techniques are being applied to areas like optical spectroscopy and its uses in specific fields, such as the agrifood industry. The performance of ML and DL techniques generally improves with the amount of data available. However, it is not always possible to obtain all the necessary data for creating a robust dataset. In the particular case of agrifood applications, dataset collection is generally constrained to specific periods. Weather conditions can also reduce the possibility to cover the entire range of classifications with the consequent generation of imbalanced datasets. To address this issue, data augmentation (DA) techniques are employed to expand the dataset by adding slightly modified copies of existing data. This leads to a dataset that includes values from laboratory tests, as well as a collection of synthetic data based on the real data. This review work will present the application of DA techniques to optical spectroscopy datasets obtained from real agrifood industry applications. The reviewed methods will describe the use of simple DA techniques, such as duplicating samples with slight changes, as well as the utilization of more complex algorithms based on deep learning generative adversarial networks (GANs), and semi-supervised generative adversarial networks (SGANs).
Fast Automatic Registration of UAV Images via Bidirectional Matching
Authors: Luo Xin , Wei Zuqi , Jin Yuwei , Wang Xiao , Lin Peng , Wei Xufeng , Zhou Wenjian
Published: October 2023

Image registration plays a vital role in the mosaic process of multiple UAV (Unmanned Aerial Vehicle) images acquired from different spatial positions of the same scene. Aimed at the problem that many fast registration methods cannot provide both high speed and accuracy simultaneously for UAV visible light images, this work proposes a novel registration framework based on a popular baseline registration algorithm, ORB—the Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elemental Features) algorithm. First, the ORB algorithm is utilized to extract image feature points fast. On this basis, two bidirectional matching strategies are presented to match obtained feature points. Then, the PROSRC (Progressive Sample Consensus) algorithm is applied to remove false matches. Finally, the experiments are carried out on UAV image pairs about different scenes including urban, road, building, farmland, and forest. Compared with the original version and other state-of-the-art registration methods, the bi-matching ORB algorithm exhibits higher accuracy and faster speed without any training or prior knowledge. Meanwhile, its complexity is quite low for on-board realization.
Improved GNSS Ambiguity Fast Estimation Reduction Algorithm
Authors: Li Xinzhong , Xiong Yongliang , Chen Weiwei , Xu Shaoguang , Zhang Rui
Published: October 2023

The fast and accurate solution of integer ambiguity is the key to achieve GNSS high-precision positioning. Based on the lattice theory of high-dimensional ambiguity solving, the reduction time consumption is much larger than the search time consumption, and it is especially important to improve the efficiency of the lattice basis reduction algorithm. The Householder QR decomposition with minimal column pivoting is utilized to pre-sort the basis vectors and reduce the number of basis vector exchanges during the reduction process by partial size reduction and relaxing the basis vector exchange condition to improve the reduction efficiency of the LLL algorithm. The improved algorithm is validated using simulated and measured data, respectively, and the performance advantages and disadvantages of the improved algorithm are evaluated from the perspectives of the extent of reduction basis orthogonality and the quality of reduction basis size reduction. The results show that the improved LLL algorithm can significantly reduce the number of basis vector exchanges and the reduction time consumption. The HSLLL and PSLLL algorithms with the Siegel condition as the basis vector exchange condition have a better reduction effect, but are slightly less stable. The PLLLR algorithm significantly improves the search ambiguity resolution efficiency, which is conducive to the rapid realization of ambiguity resolution.
Immuno-Modulatory Effects of Korean Mistletoe in MDA-MB-231 Breast Cancer Cells and THP-1 Macrophages
Authors: Lim Wan-Taek , Hong Chang-Eui , Lyu Su-Yun
Published: October 2023

Korean mistletoe (Viscum album var. coloratum) has been traditionally used as a remedy for cancer, diabetes, and hypertension. This study investigated the immuno-modulatory effects of Korean mistletoe water extract, specifically on MDA-MB-231 cells, a highly metastatic breast cancer cell line, when co-cultured with THP-1 human macrophage cells. When compared to MDA-MB-231 cells cultured alone, the co-culture of MDA-MB-231/THP-1 cells treated with mistletoe extract showed a significant reduction in IL-6 secretion. Additionally, these co-cultures exhibited elevated levels of IL-4, TGF-β, and IFN-y. These results suggest that water extracts from mistletoe have the potential to induce mitochondria-targeted apoptosis in MDA-MB 231 cells stimulated by THP macrophages. Regarding apoptosis, in MDA-MB-231 cells co-cultured with THP-1 macrophages, mistletoe water extract treatment triggered a significant increase in Bax/Bcl-2 ratio, caspase-3 activation, and PARP inactivation. In addition, there was a significant increase in E-cadherin and a decrease in N-cadherin. Treatment of Korean mistletoe also led to significant reductions in both MMP-2 and -9. Furthermore, inhibition of cell migration in MDA-MB-231/THP-1 co-cultured cells was observed. In summary, this study highlights the potential of Korean mistletoe as a prospective drug for the treatment of triple-negative breast cancer, particularly through its ability to regulate human immunity.
Is the Coming Out of an LGBTQIA+ Child a Death-like Event for Italian Parents?
Authors: Biancotto Nicola , Biancalani Gianmarco , Ronconi Lucia , Testoni Ines
Published: October 2023

Parents of LGBTQIA+ individuals often report experiencing an affective state similar to grief after their children’s coming out. The current study explores whether this experience resembles that of people who have recently lost someone close. Furthermore, we tested whether the parents’ alexythimic traits are associated with their grief-like experience. In a sample of 194 parents who experienced their children’s coming out, we administered the Integration of Stressful Life Events Scale (ISLES), the Social Meaning In Life Events Scale (SMILES), and the Toronto Alexithymia Scale (TAS-20). The results showed no significant differences in the mean scores of ISLES and SMILES between the present and bereaved samples by their creators. In addition, in the present sample, lower ISLES and SMILES scores were associated with higher alexithymic traits. Overall, these findings suggest a resemblance between the experience of parents following their children’s coming out and that of bereaved individuals. Therefore, they could inform on how to assist parents in coming to terms with the coming out of an LGBTQIA+ child.
Rotational Tillage Practices to Deal with Soil Compaction in Carbon Farming
Authors: Cavalaris Chris , Gemtos Theofanis , Karamoutis Christos
Published: October 2023

Conservation tillage practices, such as reduced tillage and no-tillage, have recently garnered significant attention as core elements of the regenerative agriculture and carbon farming concepts. By minimizing mechanical soil disturbance, these practices preserve soil carbon and facilitate CO2 fixation in the soil. Despite the widely acknowledged benefits, many farmers still approach no-tillage with skepticism. Their primary concerns are weed management and soil compaction. While weeds can be effectively controlled with the deployment of integrated weed management strategies, urgent soil compaction problems can be rapidly resolved only with mechanical interventions. That is why many no-till farmers resort to occasional heavy tillage, in a scheme characterized as rotational tillage, inadvertently sacrificing their regenerative assets in soil carbon. This is also a pivotal issue within carbon farming: the fate of soil carbon at the end of a compliant scheme focused on carbon fixation. The present study explores data of soil organic matter (SOM), soil penetration resistance (PR), and dry bulk density (DBD) from the initial, six-year period of a long-term tillage experiment in Greece. During that period, modifications to the experimental design allowed diverse combinations of five tillage methods (conventional tillage, 3 reduced tillage methods, and no-tillage). The findings indeed underscore the farmers’ concerns about soil compaction. High levels of PR and DBD were observed even at the topsoil layer of the no-tillage (NT). Conventional, moldboard plowing (MP) or reduced, chisel plowing (CP) applied after four years of uninterrupted no-tillage ameliorated most of the soil compaction; however, at the same time, this induced unfavorable consequences for SOM. In contrast, NT applied permanently for six years resulted in a substantial enhancement in SOM that reached 2.24%, for a sampling depth 0–0.30 m compared to 1.54% for permanent MP. When no-tillage was rotated with plowing in the fifth year, almost 50% of the sequestered carbon was lost and the SOM dropped to 1.87%. Nevertheless, the amount of SOM observed at the deeper 0.15–0.30 m layer was greater compared to permanent NT. This suggests that while plowing induced some loss of SOM, it also facilitated the uniform distribution into the soil profile, in contrast with the accumulation in the topsoil at prolonged NT. The permanent CP method and the NT/CP rotation provided comparative outcomes in terms of both soil compaction and soil carbon sequestration with the rotational NT/MP scheme, while all the other tillage combinations were inferior.
Relationship between Longitudinal Upper Body Rotation and Energy Cost of Running in Junior Elite Long-Distance Runners
Authors: Lang Charlotte , Schleichardt Axel , Warschun Frank , Walter Nico , Fleckenstein Daniel , Berkel Fides , Ueberschär Olaf
Published: October 2023

Running is a basic form of human locomotion and one of the most popular sports worldwide. While the leg biomechanics of running have been studied extensively, few studies have focused on upper-body movement. However, an effective arm swing and longitudinal rotation of the shoulders play an important role in running efficiency as they must compensate for the longitudinal torques generated by the legs. The aim of this study is to assess the upper-body rotation using wearable inertial sensors and to elucidate its relation to energy expenditure. Eighty-six junior elite middle- and long-distance runners (37 female, 49 male) performed an incremental treadmill test with sensors attached on both shoulders, tibiae and the sacrum. The mean and total horizontal shoulder and pelvis rotations per stride were derived while energy costs were determined using respiratory gas analysis and blood sampling. Results show that shoulder and pelvis rotations increase with running speed. While shoulder rotation is more pronounced in female than in male runners, there is no sex difference for pelvis rotation. The energy cost of running and upper trunk rotation prove to be slightly negatively correlated. In conclusion, upper body rotation appears to be an individual characteristic influenced by a sex-specific body mass distribution.
Utilization of Multilayered Polyethylene Terephthalate (PET)-Based Film Packaging Waste Using Reactive Compatibilizers and Impact Modifier
Authors: Szymczak Piotr , Dziadowiec Damian , Piasecki Adam , Szostak Marek , Andrzejewski Jacek
Published: October 2023

This research aimed to evaluate the material properties of reactive extrusion-modified blends containing PET multilayered foil waste. Three types of PET-based multilayer foil waste were used as the compound during the reprocessing of standard bottle-grade PET. Flakes used for this purpose were made from laminated foils: (A) PET/PE, (B) PET/EVOH/PE, and PET/PE/met. All types of the prepared materials were compounded with 30% of the waste foil flakes. Additionally, the blend was modified with an epoxy-based chain extender and polyolefin-based impact modifier. The prepared blends were processed using two methods; initially, the materials were prepared by injection molding, while cast-film samples were also prepared. All samples were subjected to full characterization using mechanical testing methods, thermal analysis, and structural observations. The study shows that the addition of multilayered foil waste is leading to significant deterioration of PET-based material properties. While, in most cases, the use of a chain extender led to some improvement in mechanical characteristics, the impact modifier addition strongly influenced most of the properties. It was also observed that the reactive extrusion procedure led to melt strength improvement, which greatly facilitates the film production process. Due to the limited possibility of separating the film components, the developed method of foil recycling might be useful for the utilization of multilayered packaging.
Greenhouse Gas (GHG) Emission Estimation for Cattle: Assessing the Potential Role of Real-Time Feed Intake Monitoring
Authors: Berdos Janine I. , Ncho Chris Major , Son A-Rang , Lee Sang-Suk , Kim Seon-Ho
Published: October 2023

This study investigated the impact of feeding systems on the determination of enteric methane (CH4) emissions factor in cattle. Real-time feed intake data, a crucial CH4 conversion rate (Ym value) parameter, were obtained using a roughage intake control (RIC) unit within a smart farm system. Greenhouse gas (GHG) emissions, including CH4 and carbon dioxide (CO2), from Holstein steers were monitored using a GreenFeed (GF) 344 unit. The results revealed satisfactory body weight (383 ± 57.19 kg) and daily weight gain (2.00 ± 0.83 kg), which are crucial factors. CO2 production exhibited positive correlations with the initial body weight (r = 0.72, p = 0.027), feed intake (r = 0.71, p = 0.029), and feed conversion ratio (r = 0.69, p = 0.036). Five different emission factors (EFs), EFA (New Equation 10.21A) and Equation 10.21 (EFB, EFC, EFD, and EFE), were used for GHG calculations following the Intergovernmental Panel on Climate Change (IPCC) Tier 2 approach. The estimated CH4 EFs using these equations were 69.91, 69.91, 91.79, 67.26, and 42.60 kg CH4/head/year. These findings highlight the potential for further exploration and adoption of smart farming technology, which has the potential to enhance prediction accuracy and reduce the uncertainty in Ym values tailored to specific countries or regions.
Advancing Sustainability and Efficiency in Supply Chains: Insights from the Special Issue on Sustainable Supply Chain and Lean Manufacturing
Authors: Jagtap Sandeep , Afy-Shararah Mohamed , Raut Rakesh , Gupta Sumit
Published: October 2023

Supply chains play a pivotal role in our global economy, connecting producers to consumers and influencing the environmental and social impacts of the products we consume [...]
Gender, Sustainability, and Urbanism: A Systematic Review of Literature and Cross-Cluster Analysis
Authors: Gudekli Aysad , Dogan Murat Ertan , Goru Dogan Tulay , Gudekli Duygu
Published: October 2023

Gender diversity and equality have a significant influence on policymaking regarding sustainable development, environmental issues, and urbanism. This study examines the general bibliometric outlook and research patterns of publications on gender equality, urbanism, and sustainability to provide a general perspective on the relevant literature and trends for institutions and scholars who wish to conduct research within the framework of gender, sustainability, and urbanism. The findings of this study show that there are a limited number of studies dealing with gender equality, sustainability, and urbanism. The study analyzed 308 papers in total, utilizing data mining and analytics techniques such as t-SNE and SNA for a systematic review process. The study utilized the PRISMA protocol as the research method. The results showed that research on the frame of gender, sustainability, and urbanism peaked in 2021, and the top countries for studying gender, sustainability, and urbanism are the USA, the UK, Spain, and China. The research fields that contributed the most were those dealing with environmental studies and green and sustainable technologies, followed by those dealing with business and women’s studies. The following three thematically inclined clusters were revealed by the t-SNE analysis: (1) Gender Diversity, Corporate Sustainability, and Board Governance; (2) Gender, Environmental Sustainability, Sustainable Development, and Policy Agenda; and (3) Gender, Sustainable Urbanism, and Community Development. The findings of the study revealed that fostering gender equality with policies such as gender mainstreaming, as in SDG 5 and SDG 11, and gender equality strategies of the EU or UN will help to overcome discrimination against women in the urban space and empower sustainable development.
Review on Causes of Power Outages and Their Occurrence: Mitigation Strategies
Authors: Salman Hasan M. , Pasupuleti Jagadeesh , Sabry Ahmad H.
Published: October 2023

For power plant networks in developing countries like Iraq, balancing electricity demand and generation continues to be a major challenge. Energy management (EM) in either demand-side (DS) or generation-side (GS) strategies, which is frequently utilized in Iraq due to a lack of adequate power generation, has a small impact on the power balancing mechanism. Most previous studies in similar countries discussed only the application of DS strategies. The purpose of this paper is to contrast and review various energy management methodologies being used in developing nations facing power outages, to be able to recommend suitable ones according to the country’s situation. To assess potential EM-based solutions to improve the total energy efficiency of the Iraqi electrical community, a thorough and methodical analysis was carried out. The main objective of this review paper is to discuss the causes of power outages and the energy management strategies addressed here as methods to mitigate or avoid power outages. Unlike existing reviews that concentrated on demand-side energy management, this study specifically focuses on power outage causes in developing countries like Iraq rather than all management strategies. It also introduces the consequences of power outages including analysis of distribution power losses, financial loss from power blackouts, and power blackouts in firms in a typical month. Therefore, it presents readers with state-of-the-art strategies and recommends a generation-based EM strategy to mitigate such issues.
Servitization Process Analysis: A Case Study of Automotive Headrest Manufacturing
Authors: Han Junghee
Published: October 2023

Few papers have dealt with the process of servitization at the firm level. There has been a particular lack of research in manufacturing. Business model innovation through convergence between products and services is an indispensable aspect of a firm’s sustainable strategy. The current paper analyzes servitization as a process innovation by performing a single case study of a manufacturing firm. Extending product value, or servitization, is an archetype of business model innovation. To investigate and identify the real problem through so-called service development in a product-service system, we utilized a single case of a headrest in a car seat. Based on study findings, this paper proposes that a product-service system could be classified into three steps: (1) understanding and discovering the service; (2) discovering service conceptualization and developing a scenario phase; and (3) service prototyping and marketization using an infusion of technological utilities. We argue that the process of servitization can be regarded as one of the processes of service design, which is a process of value creation that comes from the synchronization of customer empathy.
A Market-Based Payment Study for Forest Water Purification Service in Loess Plateau of Yellow River Basin, China
Authors: Li Huilin , Wen Zuomin
Published: October 2023

Freshwater scarcity is increasingly threatening social development and human survival, and more effective watershed environmental management measures are yet to be developed. Market-based payment is an innovative tool to coordinate the relationship between ecosystem services’ supply and demand sides in watersheds, emphasizing the market (rather than government intervention) as the main means to regulate and control the behavior of interest-related subjects. We proposed a marked-based plan for forestry water purification service and stimulate the potential benefits of this plan under the zero transaction cost assumption. We applied and demonstrated the approach in the Loess Plateau of the Yellow River Basin (LPB) in China. By constructing the relationship between the higher reaches of annual forestry investment and the corresponding lower reaches of sediment concentration, we established forestry water purification service supply function (R2 = 0.956). Then, connecting the agents’ cost function of water treatment plants in the lower reaches with the forest water purification service, we obtained the forestry water purification service demand function (R2 = 0.943). Combining both the service supply and demand functions, we stimulated the market equilibrium state. The results show that higher reaches will provide 13.164 kg/m3 of water purification service by afforestation, and lower reaches can save RMB 2.131 billion annually via this service. This study suggests that marked-based payment between areas is feasible for a watershed ecosystem service, and promoting the establishment of watershed compensation market is a rewarding development direction. All of these insights provide a valuable reference point for Chinese horizontal ecological compensation practice.