bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring the Differential Impact of Salt Stress on Root Colonization Adaptation Mechanisms in Plant Growth-Promoting Rhizobacteria
Authors: Cappellari Lorena del Rosario , Bogino Pablo Cesar , Nievas Fiorela , Giordano Walter , Banchio Erika
Published: December 2023

Salinity inhibits plant growth by affecting physiological processes, but soil microorganisms like plant growth-promoting rhizobacteria (PGPR) can alleviate abiotic stress and enhance crop productivity. However, it should be noted that rhizobacteria employ different approaches to deal with salt stress conditions and successfully colonize roots. The objective of this study was to investigate the effect of salt stress on bacterial survival mechanisms such as mobility, biofilm formation, and the autoaggregation capacity of three plant growth-promoting strains: Pseudomonas putida SJ04, Pseudomonas simiae WCS417r, and Bacillus amyloliquefaciens GB03. These strains were grown in diluted LB medium supplemented with 0, 100, 200, or 300 mM NaCl. Swimming and swarming mobility were evaluated in media supplemented with 0.3 and 0.5% agar, respectively. Biofilm formation capacity was quantified using the crystal violet method, and the autoaggregation capacity was measured spectrophotometrically. In addition, we evaluated in vitro the capacity of the strains to ameliorate the effects of saline stress in Mentha piperita. The study found that the GB03 strain exhibited enhanced swarming mobility when the salt concentration in the medium increased, resulting in a two-fold increase in the halo diameter at 300 mM. However, high concentrations of NaCl did not affect the swimming mobility. In contrast, swimming motility was reduced in WCS417r and SJ04 under salt stress. On the other hand, exposure to 300 mM NaCl resulted in a 180% increase in biofilm formation and a 30% rise in the percentage of autoaggregation in WCS417r. Conversely, the autoaggregation percentage of the strains SJ04 and GB03 remained unaffected by saline stress. However, for GB03, biofilm formation decreased by 80% at 300 mM. Simultaneously, inoculation with the three evaluated strains alleviated the detrimental effects of salinity on plant growth. Under 150 mM salt stress, all strains showed increased fresh weight, with GB03 and WCS417r improving by 40% and SJ04 exhibiting the most remarkable effect with a 70% rise compared to non-inoculated plants. Despite their different strategies for mitigating salt stress, the application of these strains presents a promising strategy for effectively mitigating the negative consequences of salt stress on plant cultivation.
Physiological and Physical Strategies to Minimize Damage at the Branch–Stem Junction of Trees: Using the Finite Element Method to Analyze Stress in Four Branch–Stem Features
Authors: Liu Tung-Chi , Peng Yi-Sen , Cheng Bai-You
Published: December 2023

This study analyzed the mechanical and physiological strategies associated with four features in the branch–stem junction of a tree, namely the U-shaped branch attachment, the branch collar, the branch bark ridge, and the roughened lower stem. Models were established for each stage of tree growth by adding these four features sequentially to a base model, and the finite element method (FEM) was employed to create three-dimensional models of an Acer tree’s branch–stem structure for static force analysis. According to the results, the development of the branch collar shifted the point of breakage to the outer part of the collar and, thus, constituted a physiological strategy that prevented decay in the stem. Additionally, the concentration of stress in the branch bark ridge limited the area of tear in the bark following breakage. Finally, the U-shaped branch attachment reduced stress and shifted the point of peak stress toward the branch, while the thickening of the lower stem reduced the overall stress. The development of these features, including the spatial positioning of the branch bark ridge and branch collar, resulted in two breakage points constituting a physical and a physiological strategy that limited damage to the tree and protected the xylem structure. This is the part that has been challenging to decipher in previous discussions of tree-related self-protection mechanisms.
Programming and Reprogramming the Viscoelasticity and Magnetic Response of Magnetoactive Thermoplastic Elastomers
Authors: Kostrov Sergei A. , Marshall Josiah H. , Maw Mitchell , Sheiko Sergei S. , Kramarenko Elena Yu.
Published: December 2023

We present a novel type of magnetorheological material that allows one to restructure the magnetic particles inside the finished composite, tuning in situ the viscoelasticity and magnetic response of the material in a wide range using temperature and an applied magnetic field. The polymer medium is an A-g-B bottlebrush graft copolymer with side chains of two types: polydimethylsiloxane and polystyrene. At room temperature, the brush-like architecture provides the tissue mimetic softness and strain stiffening of the elastomeric matrix, which is formed through the aggregation of polystyrene side chains into aggregates that play the role of physical cross-links. The aggregates partially dissociate and the matrix softens at elevated temperatures, allowing for the effective rearrangement of magnetic particles by applying a magnetic field in the desired direction. Magnetoactive thermoplastic elastomers (MATEs) based on A-g-B bottlebrush graft copolymers with different amounts of aggregating side chains filled with different amounts of carbonyl iron microparticles were prepared. The in situ restructuring of magnetic particles in MATEs was shown to significantly alter their viscoelasticity and magnetic response. In particular, the induced anisotropy led to an order-of-magnitude enhancement of the magnetorheological properties of the composites.
Does a Compatibilizer Enhance the Properties of Carbon Fiber-Reinforced Composites?
Authors: Gangwani Prashant , Kalin Mitjan , Emami Nazanin
Published: December 2023

We have evaluated the effectiveness of compatibilizers in blends and composites produced using a solvent manufacturing process. The compatibilizers were two different types of polyethylene (linear low-density and high-density) grafted with maleic anhydride (MAH) and a highly functionalized, epoxy-based compatibilizer with the tradename Joncryl. The selected material combinations were an ultra-high-molecular-weight polyethylene (UHMWPE) with MAH-based materials as compatibilizers and a polyphenylene sulfide plus polytetrafluoroethylene (PPS-PTFE) polymer blend with an epoxy-based compatibilizer. The findings revealed that while the compatibilizers consistently enhanced the properties, such as the impact strength and hardness of PPS-based compositions, their utility is constrained to less complex compositions, such as fibrous-reinforced PPS or PPS-PTFE polymer blends. For fibrous-reinforced PPS-PTFE composites, the improvement in performance does not justify the presence of compatibilizers. In contrast, for UHMWPE compositions, compatibilizers demonstrated negligible or even detrimental effects, particularly in reinforced UHMWPE. Overall, the epoxy-based compatibilizer Joncryl stands out as the only effective option for enhancing mechanical performance. Thermal and chemical characterization indicated that the compatibilizers function as chain extenders and enhance the fiber–matrix interface in PPS-based compositions, while they remain inactive in UHMWPE-based compositions. Ultimately, the incompatibility of the compatibilizers with certain aspects of the manufacturing method and the inconsistent integration with the polymer are the main reasons for their ineffectiveness in UHMWPE compositions.
Decolorization of Lactose-6-Phosphate Solutions Using Activated Carbon
Authors: Alsaleem Khalid A. , Hammam Ahmed R. A. , Metzger Lloyd E.
Published: December 2023

Sugar phosphorylation has many applications that can be used to develop dairy and food products. During the phosphorylation process, the color of the solution turns into a dark color. The dark color causes many challenges and limitations in using phosphorylation products. The dark color could cause unpleasant color changes in the products, so it is important to remove that color. Activated carbon has been utilized for decades to remove the dark color and improve the appearance of solutions such as sugar syrup and wastewater. This methodology is cheap and environmentally friendly. The objectives of this study were to develop a method to phosphorylate α-lactose monohydrate and milk permeate and to remove the dark color of solutions. The compositional characteristics of the solution, such as pH, total solids, and color parameters (L*—lightness; a*—redness; and b*—yellowness), were examined at different stages (seven stages) of washing the solutions. α-lactose monohydrate and MPP solutions were diluted with distilled water with a ratio of 1:2.2. Activated carbon was mixed with the solutions and left for 5 min at room temperature. Subsequently, the solutions were filtered. These steps were repeated seven times until there was a transparent (colorless) solutions. The experiment was repeated using three different batches of lactose and milk permeate solutions. Both solutions’ pH and total solids decreased with an increase in the number of washings with activated carbon. The International Commission on Illumination (CIE) L*a*b* scale was studied. The L* of the initial solutions was lower than that of the final solutions. However, the a* and b* of the initial solutions were higher than the final solutions. The total color difference (∆E) was calculated for both solutions. ∆E decreased with an increase in the number of washings with activated carbon in both solutions. We conclude that activated carbon can be used to remove the dark color that results from the phosphorylation process.
An Intelligent Design Method for Remanufacturing Considering Remanufacturability and Carbon Emissions
Authors: Peng Peng , Ke Chao , Han Jun
Published: December 2023

Design for remanufacturing (DfRem) is to consider the remanufacturability of the product at the design stage, which can improve the remanufacturability of the product. Moreover, the DfRem solution has a significant impact on the carbon emissions of manufacturing processes. Unreasonable design solutions can significantly increase carbon emissions from manufacturing processes. However, there is no direct link between DfRem solutions and remanufacturability as well as manufacturing carbon emissions, which makes it difficult to quickly generate a rational DfRem solution that can enhance product remanufacturability and reduce carbon emissions simultaneously. To this end, this paper proposes an intelligent design method for remanufacturing that considers remanufacturability and manufacturing carbon emissions. First, an intelligent DfRem framework is constructed, which includes information acquisition, virtual model construction of the DfRem solution, and multi-objective optimization of the design solution. Then, the design matrix and sensitivity analysis are used to construct the mapping models between remanufacturability, carbon emissions, and DfRem parameters. Meanwhile, a multi-objective optimization model of DfRem with remanufacturability requirements and carbon emissions as design objectives is constructed, and an adaptive teaching and learning optimization algorithm is applied to solve the optimization model to obtain a DfRem solution that satisfies the objective information. Finally, the feasibility of the method is verified by DfRem of the injection mold as an example.
Intelligent Fault Diagnosis of Marine Diesel Engines Based on Efficient Channel Attention-Improved Convolutional Neural Networks
Authors: Wang Jihui , Cao Hui , Cui Zhichao , Ai Zeren , Jiang Kuo
Published: December 2023

With the rapid development of smart ships, the ship maintenance model is also changing. In order to extract the fault characteristics of diesel engine thermal parameters more easily, reduce the model’s complexity and improve the model’s accuracy, a new approach is proposed: first, the traditional convolutional neural networks (improved convolutional neural networks (ICNN)) are improved by using Meta-ACON as the activation function, improved AdamP as the optimizer, and label smoothing regularization (LSR) as the loss function, which enhances the stability of the model. Secondly, efficient channel attention (ECA) is added to achieve the mastery of global feature information, reduce the complexity of the traditional self-attention module, and enhance the model’s feature extraction ability. Lastly, the accuracy and reliability of the model are verified through ablation and comparison experiments. The accuracy rate reaches 97.6%, which is significantly improved by 32.1% compared with the original model, and the robustness of the model is verified through the introduction of noise. The experimental results demonstrate the applicability of the model in the field of diesel engine fault diagnosis.
On the Knowledge of My Existence: Towards My Existence as the Adverbial Transcendent/Immanent
Authors: Shimizu Shogo
Published: December 2023

I exist in the universe in a unique manner. I seem to know this statement to be true. However, even if I did not exist, the human who happens to be me could be living and writing the same statement. Then, do I really know that the statement is true? Do I really have epistemic contact with my existence? The aim of paper is to clearly raise this question and to offer a positive answer. By drawing on the disjunctive theory of perception, I propose the account that my existence can be involved in experience. To consider how my existence can be involved in experience and can be known from within experience, I refer to Wittgenstein, Kuki Shūzō, and Nishida Kitarō, and present the panentheistic view that my actual existence can be a limit of experience, both transcendent of and immanent in experience. This view is made persuasive by understanding the transcendence and immanence of my existence as adverbial. My conclusion is that I do know with certainty that I exist in the universe in a unique manner, and that this knowledge lies beyond Cartesian certainty.
SSCNet: A Spectrum-Space Collaborative Network for Semantic Segmentation of Remote Sensing Images
Authors: Li Xin , Xu Feng , Yong Xi , Chen Deqing , Xia Runliang , Ye Baoliu , Gao Hongmin , Chen Ziqi , Lyu Xin
Published: December 2023

Semantic segmentation plays a pivotal role in the intelligent interpretation of remote sensing images (RSIs). However, conventional methods predominantly focus on learning representations within the spatial domain, often resulting in suboptimal discriminative capabilities. Given the intrinsic spectral characteristics of RSIs, it becomes imperative to enhance the discriminative potential of these representations by integrating spectral context alongside spatial information. In this paper, we introduce the spectrum-space collaborative network (SSCNet), which is designed to capture both spectral and spatial dependencies, thereby elevating the quality of semantic segmentation in RSIs. Our innovative approach features a joint spectral–spatial attention module (JSSA) that concurrently employs spectral attention (SpeA) and spatial attention (SpaA). Instead of feature-level aggregation, we propose the fusion of attention maps to gather spectral and spatial contexts from their respective branches. Within SpeA, we calculate the position-wise spectral similarity using the complex spectral Euclidean distance (CSED) of the real and imaginary components of projected feature maps in the frequency domain. To comprehensively calculate both spectral and spatial losses, we introduce edge loss, Dice loss, and cross-entropy loss, subsequently merging them with appropriate weighting. Extensive experiments on the ISPRS Potsdam and LoveDA datasets underscore SSCNet’s superior performance compared with several state-of-the-art methods. Furthermore, an ablation study confirms the efficacy of SpeA.
Interactive Change-Aware Transformer Network for Remote Sensing Image Change Captioning
Authors: Cai Chen , Wang Yi , Yap Kim-Hui
Published: December 2023

Remote sensing image change captioning (RSICC) aims to automatically generate sentences describing the difference in content in remote sensing bitemporal images. Recent works extract the changes between bitemporal features and employ a hierarchical approach to fuse multiple changes of interest, yielding change captions. However, these methods directly aggregate all features, potentially incorporating non-change-focused information from each encoder layer into the change caption decoder, adversely affecting the performance of change captioning. To address this problem, we proposed an Interactive Change-Aware Transformer Network (ICT-Net). ICT-Net is able to extract and incorporate the most critical changes of interest in each encoder layer to improve change description generation. It initially extracts bitemporal visual features from the CNN backbone and employs an Interactive Change-Aware Encoder (ICE) to capture the crucial difference between these features. Specifically, the ICE captures the most change-aware discriminative information between the paired bitemporal features interactively through difference and content attention encoding. A Multi-Layer Adaptive Fusion (MAF) module is proposed to adaptively aggregate the relevant change-aware features in the ICE layers while minimizing the impact of irrelevant visual features. Moreover, we extend the ICE to extract multi-scale changes and introduce a novel Cross Gated-Attention (CGA) module into the change caption decoder to select essential discriminative multi-scale features to improve the change captioning performance. We evaluate our method on two RSICC datasets (e.g., LEVIR-CC and LEVIRCCD), and the experimental results demonstrate that our method achieves a state-of-the-art performance.
Research on Rotating Machinery Fault Diagnosis Based on an Improved Eulerian Video Motion Magnification
Authors: Zhao Haifeng , Zhang Xiaorui , Jiang Dengpan , Gu Jin
Published: December 2023

Rotating machinery condition monitoring and fault diagnosis are important bases for maintenance decisions, as the vibrations generated during operation are usually imperceptible to the naked eye. Eulerian video motion magnification (EVMM) can reveal subtle changes and has been widely used in various fields such as medicine, structural analysis, and fault diagnosis, etc. However, the method has a bound relationship among three parameters: spatial wavelength, amplification factor, and displacement function, so it is necessary to adjust the parameters manually in practical applications. In this paper, on the basis of the original method, an automatic solution of spatial cutoff wavelength based on brightness is proposed. First, an input video is decomposed into image sequences, their RGB color spaces are transformed into HSV color spaces, and the Value channel image representing brightness is selected to automatically calculate the spatial cutoff frequency, and then the spatial cutoff wavelength is determined, and the motion magnification video in the specified frequency band is obtained by substituting it into the original method. Then, a publicly available video is taken as an example for simulation analysis. By comparing the time-brightness curves of the three videos (original video, motion magnification video obtained by the original method and the improved method), it is apparent that the proposed method exhibits the most significant brightness variation. Finally, taking an overhung rotor-bearing test device as the object, five conditions are set, respectively: normal, rotor unbalance, loosened anchor bolt of the bearing seat, compound fault, rotor misalignment. The proposed method is adopted to magnify the motion of the characteristic frequency bands including 1X frequency and 2X frequency. The results show that no obvious displacement is found in normal working conditions, and that the rotor unbalance fault has an overall axial shaking, the bearing seat at the loose place has an obvious vertical displacement, while the compound fault combines the both fault characteristics, and the rotor misalignment fault has an obvious axial displacement of the free-end bearing seat. The method proposed in this paper can automatically obtain the space cutoff wavelength, which solves the problem of defects arising from manually adjusting the parameters in the original method, and provides a new method for rotating machinery fault diagnosis and other fields of application.
Logistic Regression Ensemble Classifier for Intrusion Detection System in Internet of Things
Authors: Chalichalamala Silpa , Govindan Niranjana , Kasarapu Ramani
Published: December 2023

The Internet of Things (IoT) is a powerful technology that connect its users worldwide with everyday objects without any human interference. On the contrary, the utilization of IoT infrastructure in different fields such as smart homes, healthcare and transportation also raises potential risks of attacks and anomalies caused through node security breaches. Therefore, an Intrusion Detection System (IDS) must be developed to largely scale up the security of IoT technologies. This paper proposes a Logistic Regression based Ensemble Classifier (LREC) for effective IDS implementation. The LREC combines AdaBoost and Random Forest (RF) to develop an effective classifier using the iterative ensemble approach. The issue of data imbalance is avoided by using the adaptive synthetic sampling (ADASYN) approach. Further, inappropriate features are eliminated using recursive feature elimination (RFE). There are two different datasets, namely BoT-IoT and TON-IoT, for analyzing the proposed RFE-LREC method. The RFE-LREC is analyzed on the basis of accuracy, recall, precision, F1-score, false alarm rate (FAR), receiver operating characteristic (ROC) curve, true negative rate (TNR) and Matthews correlation coefficient (MCC). The existing researches, namely NetFlow-based feature set, TL-IDS and LSTM, are used to compare with the RFE-LREC. The classification accuracy of RFE-LREC for the BoT-IoT dataset is 99.99%, which is higher when compared to those of TL-IDS and LSTM.
N-Channel MOSFET Reliability Issue Induced by Visible/Near-Infrared Photons in Image Sensors
Authors: Liu Chun-Hsien , Lin Sheng-Di
Published: December 2023

Image sensors such as single-photon avalanched diode (SPAD) arrays typically adopt in-pixel quenching and readout circuits, and the under-illumination first-stage readout circuits often employs high-threshold input/output (I/O) or thick-oxide metal-oxide-semiconductor field-effect transistors (MOSFETs). We have observed reliability issues with high-threshold n-channel MOSFETs when they are exposed to strong visible light. The specific stress conditions have been applied to observe the drain current (Id) variations as a function of gate voltage. The experimental results indicate that photo-induced hot electrons generate interface trap states, leading to Id degradation including increased off-state current (Ioff) and decreased on-state current (Ion). The increased Ioff further activates parasitic bipolar junction transistors (BJT). This reliability issue can be avoided by forming an inversion layer in the channel under appropriate bias conditions or by reducing the incident photon energy.
A Novel Analytical Model for the IEEE 802.11p/bd Medium Access Control, with Consideration of the Capture Effect in the Internet of Vehicles
Authors: Wang Yang , Shi Jianghong , Fang Zhiyuan , Chen Lingyu
Published: December 2023

The traditional vehicular ad hoc network (VANET), which is evolving into the internet of vehicles (IoV), has drawn great attention for its enormous potential in road safety improvement, traffic management, infotainment service support, and even autonomous driving. IEEE 802.11p, as the vital standard for wireless access in vehicular environments, has been released for more than one decade and its evolution, IEEE 802.11bd, has also been released for a few months. Since the analytical models for the IEEE 802.11p/bd medium access control (MAC) play important roles in terms of performance evaluation and MAC protocol optimization, a lot of analytical models have been proposed. However, the existing analytical models are still not accurate as a result of ignoring some important factors of the MAC itself and real communication scenarios. Motivated by this, a novel analytical model is proposed, based on a novel two-dimensional (2-D) Markov chain model. In contrast to the existing studies, all the important factors are considered in this proposed model, such as the backoff freezing mechanism, retry limit, post-backoff states, differentiated packet arrival probabilities for empty buffer queue, and queue model of packets in the buffer. In addition, the influence of the capture effect under a Nakagami-m fading channel has also been considered. Then, the expressions of successful transmission, collided transmission, normalized unsaturated throughput, and average packet delay are all meticulously derived, respectively. At last, the accuracy of the proposed analytical model is verified via the simulation results, which show that it is more accurate than the existing analytical models.
Effective Zero-Shot Multi-Speaker Text-to-Speech Technique Using Information Perturbation and a Speaker Encoder
Authors: Bang Chae-Woon , Chun Chanjun
Published: December 2023

Speech synthesis is a technology that converts text into speech waveforms. With the development of deep learning, neural network-based speech synthesis technology is being researched in various fields, and the quality of synthesized speech has significantly improved. In particular, Grad-TTS, a speech synthesis model based on the denoising diffusion probabilistic model (DDPM), exhibits high performance in various domains, generates high-quality speech, and supports multi-speaker speech synthesis. However, speech synthesis for an unseen speaker is not possible. Therefore, this study proposes an effective zero-shot multi-speaker speech synthesis model that improves the Grad-TTS structure. The proposed method enables the reception of speaker information from speech references using a pre-trained speaker recognition model. In addition, by converting speaker information via information perturbation, the model can learn various types of speaker information, excluding those in the dataset. To evaluate the performance of the proposed method, we measured objective performance indicators, namely speaker encoder cosine similarity (SECS) and mean opinion score (MOS). To evaluate the synthesis performance for both the seen speaker and unseen speaker scenarios, Grad-TTS, SC-GlowTTS, and YourTTS were compared. The results demonstrated excellent speech synthesis performance for seen speakers and a performance similar to that of the zero-shot multi-speaker speech synthesis model.
YDD-SLAM: Indoor Dynamic Visual SLAM Fusing YOLOv5 with Depth Information
Authors: Cong Peichao , Liu Junjie , Li Jiaxing , Xiao Yixuan , Chen Xilai , Feng Xinjie , Zhang Xin
Published: December 2023

Simultaneous location and mapping (SLAM) technology is key in robot autonomous navigation. Most visual SLAM (VSLAM) algorithms for dynamic environments cannot achieve sufficient positioning accuracy and real-time performance simultaneously. When the dynamic object proportion is too high, the VSLAM algorithm will collapse. To solve the above problems, this paper proposes an indoor dynamic VSLAM algorithm called YDD-SLAM based on ORB-SLAM3, which introduces the YOLOv5 object detection algorithm and integrates deep information. Firstly, the objects detected by YOLOv5 are divided into eight subcategories according to their motion characteristics and depth values. Secondly, the depth ranges of the dynamic object and potentially dynamic object in the moving state in the scene are calculated. Simultaneously, the depth value of the feature point in the detection box is compared with that of the feature point in the detection box to determine whether the point is a dynamic feature point; if it is, the dynamic feature point is eliminated. Further, multiple feature point optimization strategies were developed for VSLAM in dynamic environments. A public data set and an actual dynamic scenario were used for testing. The accuracy of the proposed algorithm was significantly improved compared to that of ORB-SLAM3. This work provides a theoretical foundation for the practical application of a dynamic VSLAM algorithm.
BézierCE: Low-Light Image Enhancement via Zero-Reference Bézier Curve Estimation
Authors: Gao Xianjie , Zhao Kai , Han Lei , Luo Jinming
Published: December 2023

Due to problems such as the shooting light, viewing angle, and camera equipment, low-light images with low contrast, color distortion, high noise, and unclear details can be seen regularly in real scenes. These low-light images will not only affect our observation but will also greatly affect the performance of computer vision processing algorithms. Low-light image enhancement technology can help to improve the quality of images and make them more applicable to fields such as computer vision, machine learning, and artificial intelligence. In this paper, we propose a novel method to enhance images through Bézier curve estimation. We estimate the pixel-level Bézier curve by training a deep neural network (BCE-Net) to adjust the dynamic range of a given image. Based on the good properties of the Bézier curve, in that it is smooth, continuous, and differentiable everywhere, low-light image enhancement through Bézier curve mapping is effective. The advantages of BCE-Net’s brevity and zero-reference make it generalizable to other low-light conditions. Extensive experiments show that our method outperforms existing methods both qualitatively and quantitatively.
Orthogonal Space-Time Block Coding for Double Scattering V2V Links with LOS and Ground Reflections
Authors: Gaitán Miguel Gutiérrez , Javanmardi Gowhar , Sámano-Robles Ramiro
Published: December 2023

This work presents the performance analysis of space-time block codes (STBCs) for vehicle-to-vehicle (V2V) fast-fading channels in scenarios with modified line-of-sight (LOS). The objective is to investigate how the V2V MIMO (multiple-input multiple-output) system performance is influenced by two important impairments: deterministic ground reflections and an increased Doppler frequency (time-variant channels). STBCs of various coding rates (using an approximation model) are evaluated by assuming antenna elements distributed over the surface of two contiguous vehicles. A multi-ray model is used to study the multiple constructive/destructive interference patterns of the transmitted/received signals by all pairs of Tx–Rx antenna links considering ground reflections. A double scattering model is used to include the effects of stochastic channel components that depend on the Doppler frequency. The results show that STBCs are capable of counteracting fades produced by destructive self-interference components across a range of inter-vehicle distances and for a range of Doppler frequency values. Notably, the effectiveness of STBCs in deep fades is shown to outperform schemes with exclusive receive diversity, despite the interference created by the loss of orthogonality in time-varying channels with a moderate increase of Doppler frequency (mainly due to higher vehicle speeds, higher frequency or shorter time slots). Higher-order STBCs with rate losses are also evaluated using an approximation model, showing interesting gains even for low coding rate performance, particularly when accompanied by a multiple antenna receiver. Overall, these results can shed light on how to exploit transmit diversity in time-varying vehicular channels with modified LOS.
Study on the Dynamics Characteristics of HTS Maglev Train Considering the Aerodynamic Loads under Crosswinds
Authors: Li Zongpeng , Wang Xiaofei , Ding Yin , Wang Jukun , Liu Pengfei , Deng Zigang
Published: December 2023

High-temperature Superconducting (HTS) maglev trains are vulnerable to the effects of crosswinds when operating at high speeds in open-air conditions, potentially compromising riding comfort and safety. This study established a vehicle dynamic model based on the nonlinear maglev-track relationship and added aerodynamic loads under crosswinds to the train’s simplified load center to address this issue. Using the maximum vibration acceleration limit and the Sperling index, we evaluated the riding comfort of the HTS maglev train under different conditions. Further, the vibration acceleration power spectral density was analyzed to reveal the impact of increasing the train’s operating speed and crosswind speed. The results indicated that the lateral and vertical Sperling index achieved an “excellent” rating, even at crosswind speeds of up to 20.7 m/s when the train was traveling at speeds of up to 600 km/h. However, it was noted that particular attention should be given to the riding comfort in the head car when the speed reaches 600 km/h. Moreover, the influence of the increase in train speed on the vibration frequency domain distribution of the three car bodies and the train’s riding comfort is greater than that of the increase in the crosswind speed. These findings may provide a valuable reference for the future engineering application of the HTS maglev train.
Enhancing Coffee Quality in Rwanda: A Cost Benefit Analysis of Government Policies
Authors: Jenkins Glenn P. , Mbakop Ludovic , Miklyaev Mikhail
Published: December 2023

Over the past two decades, Rwanda has positioned itself as a leading producer of specialty coffee. The strategic move from ordinary to specialty coffee has overall been economically beneficial to the country. However, the multitude of incentives provided by both the Government and international donors spawned a rush to build a large number of coffee washing stations (CWS) throughout Rwanda. This trend gave rise to an oversupply of these plants, with most operating below their processing capacity. Our study uses cost benefit analysis to estimate the economic welfare loss that Rwanda has suffered owing to the combined effect of the oversupply of CWS, the coffee zoning policy, and the government regulated cherry coffee prices. Our results reveal that, if the coffee industry were rendered more competitive by dint of a reduction in the number of CWS, then the annual savings to Rwanda would be substantial. Furthermore, farmers could potentially receive prices that are 150% higher than the mandated fixed prices they are currently been paid. Our analysis could potentially be beneficial to Rwandese policy makers in devising fairer incentives to keep farmers interested in coffee farming, thus ensuring the sustainability of the coffee value chain in the long term.
Spatial and Paleoclimatic Reconstruction of the Peña Negra Paleoglacier (Sierra de Béjar-Candelario, Spain) during the Last Glacial Cycle (Late Pleistocene)
Authors: Nieto Carlos E. , Calvo Ana , Cruz Raquel , Martínez-Graña Antonio Miguel , Goy José Luis , González-Delgado José Ángel
Published: December 2023

The study of the Peña Negra paleoglacier during the Last Glacial Maximum reveals its sensitivity to paleoclimatic variations. The evolutionary phases of the paleoglacier are correlated with the evolutionary models proposed for the Sierra de Béjar-Candelario and the Central Iberian System. To recognize the mechanisms of ice advance/retreat and the response of the glacier to paleoclimatic variations, modeling is carried out based on a geographic information system tool. This model is key to establishing the spatial extent of the ice and the estimation of the Equilibrium line altitudeequilibrium line altitudes at each moment, which makes it easier to infer the approximate climatic conditions of each phase (temperature and precipitation) and allows us to improve the understanding of the glacial dynamics versus variations in paleoenvironmental conditions and paleoglacial morphometry. The spatial reconstruction data show that the paleoglacier had 0.526 km3 of ice during the phase of maximum extension, while the paleoclimatic data reflect an increase in precipitation and a slight decrease in average summer temperatures compared to today. The stability phases are associated with the periods of greatest precipitation when the mass balance was positive.
Deconstructing and Reconstructing the Theoretical Basis of the Ecological Scarcity Method
Authors: Heijungs Reinout
Published: December 2023

The ecological scarcity method (ESM) is a widely used system for assessing the environmental impact of pollutant emissions and resource extractions in the context of life cycle assessment (LCA). Its mathematical principles have been described in various reports, but not in scientific journals, which typically only quote the ESM or challenge the numerical values of the targets. It is, therefore, appropriate to carefully dissect the method and critically reassemble the resulting fragments. Our analysis introduces a substantial number of modifications, in terms of overall formulation, detail and interpretation, while it respects most of the existing numbers and is still applicable to the full range of pollutants and resources. It also yields the conclusion that, although the developers of ESM have tried to align the approach with the ISO 14040/14044 standards for LCA, this attempt has been less successful than foreseen. We finally conclude that the reference to ESM as a “distance-to-target” method further obscures the interpretation of the method.
Waste-Based Adsorbents for the Removal of Phenoxyacetic Herbicides from Water: A Comprehensive Review
Authors: Blachnio Magdalena , Kusmierek Krzysztof , Swiatkowski Andrzej , Derylo-Marczewska Anna
Published: December 2023

An overview of different adsorbents, based on agricultural and household waste, for chlorophenoxy herbicides removal from water is presented. Several groups of adsorbents are discussed, taking into account the modification method used on the initial material and the type of final product obtained. The adsorbent characteristics and the conditions of the adsorption measurements are given, and a discussion on the obtained results is presented, along with a theoretical description, following the application of various equations and models. A group of the most effective adsorbents is indicated, based on the analysis of the adsorption capacity, towards 2,4-D and/or MCPA, and the adsorption rate. Some important problems connected with adsorbent utility are discussed, taking into account economic and ecological aspects. Moreover, the effectiveness of the analyzed materials is observed through the analysis of its interactions with other components present in real systems.
Evaluating Construction Projects’ Alternatives Using Lean Construction and Sustainability Principles in an Information Model Framework
Authors: Faria Pedro Saieg , Sotelino Elisa Dominguez , Carmo Cristiano Saad Travassos do , Nascimento Daniel Luiz Mattos
Published: December 2023

This study evaluates and validates how BIM functionalities can foster the consideration of lean construction and sustainability principles in building design. To this end, a plugin was developed and implemented in a BIM-enabled visualization environment. This system aims to help a user decide the best design between alternatives, while considering the user’s perspective on what generates value in the project. To illustrate and validate the usability and importance of the system, the design of a warehouse with specific requirements was considered. Four design alternatives were modeled considering different types of superstructures, building envelopes, and materials. The system imported an external database and calculated the general costs, CO2 emissions, and thermal comfort indicators for each model. It also incorporated user-defined weights for each indicator and graphically displayed the results indicating the best options through a multiple attribute decision method. From the obtained results, it was concluded that it is possible to integrate BIM and lean principles to support sustainable development in the construction industry. Through an experiment with warehouse alternatives, the proposed approach revealed that the best project could save nearly 50% of CO2 emissions when compared to the worst project and that the second-best project had the best performance in terms of thermal heat flow (1.80 Megawatts). The results also indicate that the system can be a great asset in supporting the decision-making process of selecting the best design alternative and understanding the impact of design changes on cost and on the environment.
The Influence of Changing Socioeconomic Conditions in Europe on the Prioritisation of Risks in Travel Behaviour: A Case Study
Authors: Dudzińska Małgorzata , Gross Marta , Dawidowicz Agnieszka , Wolny-Kucińska Ada
Published: December 2023

Pandemics, political instability, social conflict and unrest, the economic crisis resulting from changes in energy supply contracts, terrorism, and civil and international wars exert a negative impact on the lives of millions of people. These events undermine economic and social stability, and they affect travel behaviour (TB). Understanding preferences in travel behaviour is essential for designing effective and sustainable transport systems. Agenda 2030 (the global sustainable development strategy) assigns a central role to mobility and transport in sustainable development and its components: economic, social, and environmental. Relevant research is scarce, which is why potential threats, including changes in socioeconomic factors that affect TB in functional urban areas (FUAs), should be identified and prioritised. The main aim of this study was to determine changes in the prioritisation of threats to TB resulting from the socioeconomic consequences of the COVID-19 pandemic and the energy crisis in Europe. The study involved an analysis of the literature, a comprehensive classification of potential threats that could impact TB, as well as an expert survey. The results of these analyses were used to develop a list of 46 factors that were grouped into six categories of key threats: social, economic, legal, infrastructural, technological/SMART, and environmental. The level of significance, similarities, and differences in the prioritisation of threats during the COVID-19 pandemic and the energy crisis after the outbreak of the military conflict in Ukraine (duration and range) were compared across 22 European countries.
Security Risk Assessment Framework for the Healthcare Industry 5.0
Authors: Baz Abdullah , Ahmed Riaz , Khan Suhel Ahmad , Kumar Sudesh
Published: December 2023

The relevance of Industry 5.0 confirms the collaborative relationship between humans and machines through an inclusive automation process. The healthcare industry at present is facilitated by the use of these emerging technologies, which promise a more personalized, patient-centric approach, enabling more prompt, cost-effective, and efficacious medical care to the affected. However, managing enormous data volumes, lack of standards, risks to data security, and regulatory obstacles, such as regulatory compliance, are critical issues that must be addressed to ensure that Industry 5.0 can be effectively integrated into the healthcare industry. This research assumes significance in the stated context as it seeks to reveal the gaps between security risks and threats assessments for personalized healthcare services based on Industry 5.0. The study’s investigations cite that the identification of security risks and various threats is an imperative need and must be prioritized so as to ensure optimal security for the healthcare system. Furthermore, the study peruses various security threats and security risk assessments for enhancing and safeguarding the healthcare industry. Moreover, the study also proposes a framework for security risk assessment based on Industry 5.0 (SRVFHI5.0) for the healthcare security system. A step-wise procedure is applied to validate the proposed framework and provide support for designing feasible security evaluation criteria and tools for future research. Statistical analysis was performed to evaluate the measure of the applicability of multiple criteria, the tool’s reliability, and factor analysis. This offers an adequate basis for accepting the suggested risk assessment methodology based on Healthcare Industry 5.0 for implementation as well as further research and analysis.
Dynamic Complexity Analysis of R&D Levels in the Automotive Industry under the Dual-Credit Policy
Authors: He Qing , Feng Yu , Li Zheyu
Published: December 2023

The dual-credit policy, as an important emerging policy in the Chinese automotive industry intended to achieve energy savings, emissions reductions, and promote the development of new energy vehicles (NEVs), has attracted considerable attention from scholars. This study investigates how this policy affects the research and development (R&D) levels of both component suppliers and vehicle manufacturers in the automotive supply chain. Assuming the bounded rationality of the participants, we construct a complex dynamic evolutionary model under Stackelberg games to explore the impact of the policy on the dynamic game behavior and equilibrium stability of R&D levels. Furthermore, we examine the influences of various parameters on the R&D level complex system. The findings reveal that the disparity in the proportion of NEVs in the policy should not be too large; otherwise, bifurcation and chaos may occur in the R&D level game system. Moreover, higher supplier research efficiency contributes to the stability of R&D levels, while the higher credit trading price is not suitable for stable R&D levels. This paper theoretically reveals the dynamic impact of the dual-credit policy on the R&D levels in the automotive supply chain, bridging the gap between previous studies assuming decision-makers as fully rational and the reality of bounded rationality. It also provides managerial recommendations for the implementation details of this policy.
Exploring Hemispheric Lateralization and Second Language Class Performance in 10 y.o. Students
Authors: Demareva Valeriia
Published: December 2023

This study investigated the relationship between functional hemispheric asymmetry at various brain levels and the performance of fourth-grade students in English as a second language classroom activities. Specifically, the study explored the impact of leftward and rightward asymmetry patterns on total classroom scores, considering lessons with a two-week interval and pre-lesson and post-lesson measurements. The sample comprised 27 right-handed students from an English-intensive school program. To assess functional hemispheric asymmetry before and after two English classes, computer laterometry based on a ‘two-source’ lead–lag dichotic paradigm was employed. Results revealed that leftward asymmetry in lability (brainstem-related) and excitability (primary auditory cortex-related) predicted higher total scores in classroom activities. The interaction between leftward lability and excitability asymmetries was a significant predictor of improved performance. These findings suggest that multiple regions of the left hemisphere are involved in supporting various linguistic tasks and emphasize the dynamic nature of functional hemispheric asymmetry. No significant relationship was observed between rightward asymmetry and classroom scores. However, future research may explore specific language tasks and sex-related differences in lateralization. The study underscores the importance of considering individual cognitive profiles in language learning and teaching, potentially improving language acquisition outcomes.