bluuebunny/crossref_metadata_2025 huggingface dataset:
Theoretical and Experimental Demonstration of Advantages of Microwave Peat Processing in Comparison with Thermal Exposure during Pyrolysis
Authors: Krapivnitckaia Tatiana , Ananicheva Svetlana , Alyeva Alisa , Denisenko Andrey , Glyavin Mikhail , Peskov Nikolay , Vikharev Alexander , Sachkova Anastasiia , Zelentsov Sergey , Shulaev Nikolay
Published: December 2023

A series of theoretical and experimental studies was carried out with the aim of a direct comparison of the thermal and microwave destruction of peat during pyrolysis. Different heating mechanisms in these processes were investigated in the framework of simulations conducted using a commercial 3D software package, CST Studio Suite, to define their specific features. Based on these simulations, identical reactors were constructed exploiting an electric element and a microwave complex as a heat source, and an experimental technique was developed that allows these processes to be correctly compared under similar conditions. Using these reactors based on different heat sources, comparative experiments on peat pyrolysis were performed, and the results of the theoretical analysis were confirmed. As a result, the advantages of microwave exposure were demonstrated to achieve a more uniform and deep fragmentation of peat, increase the reaction rate and reduce the processing time, with high energy efficiency accompanying this method. As part of the experiments, the yield of pyrolysis products was analyzed. During the microwave destruction of peat, a high gas output (up to 27%) was obtained, which allows for a further increase in the efficiency of this processing method when burning these gases. The conducted elemental analysis showed an increase in the percentage of carbon from about 50% to 78% after microwave processing, which can become a raw material for obtaining effective environmentally friendly sorbents. The prospects for creating industrial microwave complexes for processing organic materials are discussed.
A New Bottom-Hole Assembly Design Method to Maintain Verticality and Reduce Lateral Vibration
Authors: Cheng Zhong , Zhang Liang , Hao Zhouzheng , Ding Xiangxiang , Liu Zhikun , Li Tiantai
Published: December 2023

Well deviation is a prevalent problem in deep oil and gas exploration, leading to a significant increase in drilling costs. The conventional bottom-hole assembly (BHA) anti-deviation design method does not consider the impact of the BHA structure on lateral vibration. This paper proposes an integrated BHA design method that takes into account both anti-deviation and vibration reduction. This method evaluates the BHA’s anti-deviation ability using the drilling trend angle. A negative value of the drilling trend angle indicates that the BHA can correct well deviation. A finite element linearized dynamics method is used to evaluate the lateral vibration intensity of the BHA. This method involves calculating the bending displacement caused by mass imbalance and then determining the magnitude of the bending strain energy based on this displacement. The structural factors affecting the anti-deviation ability and potential lateral vibration intensity of pendulum BHAs and bent-housing mud motor (BHMM) BHAs were studied, and field tests were conducted for verification. The research shows that for pendulum BHAs, the factor that has the greatest impact on anti-deviation ability and vibration intensity is the distance from the stabilizer to the drill bit. For BHMM BHAs, the length of the short drill collar has a significant impact on the vibration intensity. Compared with current design methods, the mechanical specific energy (MSE) of the single stabilizer pendulum BHA decreased by 12%, while the MSE of the BHMM BHA decreased by 26.4%. Both decreases indicate a reduction in vibration intensity. This study will help to further increase drilling speed while preventing well deviation.
Wellbore Pressure Modeling for Pumping and Tripping Simultaneously to Avoid Severe Pressure Swab
Authors: Sheng Cancheng , Zhang Feifei , Tang Yaoyao , Li Yafeng , Liu Xuesong
Published: December 2023

A pumping-while-tripping method is proposed to mitigate pressure swabs during tripping out in wells with a narrow mud density window and extended reach. In the proposed tripping-out process, the fluid circulation is started by using a special pump from a customized circulation line before tripping is initiated. During the tripping out, drilling fluid is circulated in the wellbore simultaneously while the drilling string is moving. A model to simulate the dynamic pressure changes in this process is developed based on the Navier–Stokes (N-S) equations and a damped free vibration system. The model was initially developed for Herschel–Bulkley (H-B) fluid; however, it can be applied to other fluid models by eliminating the non-existing terms. An analysis was conducted to investigate the effect of tripping velocity and circulation pumping rate on the pressure changes. The results show that pumping-while-tripping is effective in mitigating the pressure swab during tripping out, which is especially useful for extended-reach wells. It can also help to increase tripping out velocity and save tripping time for drilling operations.
Proteomics of Toxigenic Corynebacteria
Authors: Burkovski Andreas
Published: December 2023

Within the genus Corynebacterium, six species are potential carriers of the tox gene, which encodes the highly potent diphtheria exotoxin: Corynebacterium diphtheriae, Corynebacterium belfantii, Corynebacterium rouxii, Corynebacterium ulcerans, Corynebacterium pseudotuberculosis and Corynebacterium silvaticum. Based on their potential to infect different host species and cause either human infections, zoonotic diseases or infections of economically important animals, these bacteria are of high scientific and economic interest and different research groups have carried out proteome analyses. These showed that especially the combination of MS-based proteomics with bioinformatic tools helped significantly to elucidate the functional aspects of corynebacterial genomes and to handle the genome and proteome complexity. The combination of proteomic and bioinformatic approaches was also used to discover new vaccine and drug targets. In addition, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry has been established as a fast and precise tool for the identification of these bacteria.
Thinking about the Law with Edmund Burke
Authors: Moreira Ivone
Published: December 2023

Burke intervened in a very significant way at various moments in the political history of the England of his time. Although being considered by some commentators to be a utilitarian, in fact, it is Burke’s fidelity to the principles of natural law that makes all his political interventions coherent. The aim of this paper is to analyze the subordination of positive law to natural law and original justice in Burke’s thought, and we will achieve that by perusing his works through different periods of his career. In order to better understand his position, we will call to the conversation John Locke, from whom he departs, and Francisco de Vitoria, with whom he shares the notion of a society-dependent human nature. From the prophetic views of Burke and Tocqueville about the future, and as a kind of conclusion, we will question if the reference to natural law makes sense in present times.
‘Falling Upward’ into Sports Retirement: A Rohrsian Exploration of the Sports Retirement Experience
Authors: Jones Luke , Watson Nick J.
Published: December 2023

Retirement from sport is widely reported as a challenging time of transition in the lives of elite athletes and is one that has been explored from a range of different perspectives both by sport psychologists and socio-cultural scholars of sport. However, of late, a small number of scholars have considered athlete career transition within the context of religion and spirituality, identifying the religious identity and belief of athletes as central to their transition experiences. That said, this work does not go as far as developing a theological understanding of sports retirement. Here, we explore and frame the phenomenon of sports retirement through the theological lens put forward by the neo-Franciscan priest, Richard Rohr in his book Falling Upward: A Spirituality for the Two Halves of Life. In this paper, we suggest how Rohr’s ideas might help develop an alternative and more nuanced understanding of sports retirement, building on those currently promoted in sport psychology and the sociology of sport the literature.
Decheng, Beitang and Tushanwan Cloisonné Workshops: A New Contribution on Chinese Christian Art
Authors: Parada López de Corselas Manuel
Published: December 2023

Recent research has played a pivotal role in advancing our understanding of cloisonné enamel production in China during the 19th and 20th centuries. However, Christian workshops, whether operating under missionary subcontracting or owned by the Catholic Church, have yet to be accurately identified and contextualized. This article delves into three significant contexts. Firstly, it identifies and contextualizes the Christian connections and interactions of the Decheng private cloisonné workshop, involving the French Lazarist Bishop Alphonse Favier, in Beijing. Secondly, it identifies the cloisonné workshop stablished by the Lazarists in the Beitang complex in Beijing and elucidates the role it played. Finally, this paper presents new evidence concerning cloisonné Christian objects crafted by the Tushanwan Jesuit workshop in Shanghai. Some of the primary works of these three workshops are identified for the first time. Additionally, this paper shows that certain cloisonné crosses, some of them thought to be originally Japanese, including those referred to as Namban, were, in fact, crafted in Beijing during the 1920s. These preliminary results will contribute to placing Chinese Christian cloisonné within the history of Chinese Art and its interactions at a global level in the context of evangelization in China, the production of the so-called export art, and the processes of indigenization carried out by the Lazarists and the Jesuits.
Optimal Static Hedging of Variable Annuities with Volatility-Dependent Fees
Authors: Tang Junsen
Published: December 2023

Variable annuities (VAs) and other long-term equity-linked insurance products are typically difficult to hedge in the incomplete markets. A state-dependent fee tied with market volatility for VAs is designed to contribute the risk-sharing mechanism between policyholders and insurers. Different from prior research, we discuss several aspects on a fair valuation, fee-rate determination and hedging with volatility-dependent fees from the perspective of a VA hedger. A method of efficient hedging strategy as a benchmark compared to other strategies is developed in the stochastic volatility setting. We illustrate this method in guaranteed minimum maturity benefits (GMMBs), but it is also applicable to other equity-linked insurance contracts.
Construction of Remote Sensing Indices Knowledge Graph (RSIKG) Based on Semantic Hierarchical Graph
Authors: Wang Chenliang , Shi Wenjiao , Lv Hongchen
Published: December 2023

Remote sensing indices are widely used in various fields of geoscience research. However, there are limits to how effectively the knowledge of indices can be managed or analyzed. One of the main problems is the lack of ontology models and research on indices, which makes it difficult to acquire and update knowledge in this area. Additionally, there is a lack of techniques to analyze the mathematical semantics of indices, making it difficult to directly manage and analyze their mathematical semantics. This study utilizes an ontology and mathematical semantics integration method to offer a novel knowledge graph for a remote sensing index knowledge graph (RSIKG) so as to address these issues. The proposed semantic hierarchical graph structure represents the indices of knowledge with an entity-relationship layer and a mathematical semantic layer. Specifically, ontologies in the entity-relationship layer are constructed to model concepts and relationships among indices. In the mathematical semantics layer, index formulas are represented using mathematical semantic graphs. A method for calculating similarity for index formulas is also proposed. The article describes the entire process of building RSIKG, including the extraction, storage, analysis, and inference of remote sensing index knowledge. Experiments provided in this article demonstrate the intuitive and practical nature of RSIKG for analyzing indices knowledge. Overall, the proposed methods can be useful for knowledge queries and the analysis of indices. And the present study lays the groundwork for future research on analysis techniques and knowledge processing related to remote sensing indices.
Mapping Changes in Fractional Vegetation Cover on the Namib Gravel Plains with Satellite-Retrieved Land Surface Emissivity Data
Authors: Obrecht Laura , Göttsche Frank-Michael , Senn Johannes Antenor , Cermak Jan
Published: December 2023

Monitoring changes in vegetation cover over time is crucial for understanding the spatial distribution of rainfall, as well as the dynamics of plants and animals in the Namib desert. Traditional vegetation indices have limitations in capturing changes in vegetation cover within water-limited ecosystems like the Namib gravel plains. Spectral emissivity derived from thermal infrared remote sensing has recently emerged as a promising tool for distinguishing between bare ground and non-green vegetation in arid environments. This study investigates the potential of satellite-derived emissivities for mapping changes in fractional vegetation cover across the Namib gravel plains. Analyzing Moderate Resolution Imaging Spectroradiometer (MODIS) band 29 (λ = 8.55 µm) emissivity time series from 2001 to 2021, our findings demonstrate the ability of both Normalized Difference Vegetation Index (NDVI) and emissivity to detect sudden vegetation growth on the gravel plains. Emissivity additionally allows monitoring the extent of desiccated grass over several years after a rainfall event. Our results support a relationship between the change in fractional vegetation cover, the amount of rainfall and emissivity change magnitude. Information from NDVI and emissivity therefore provide complementary information for assessing vegetation in arid environments.
Experimental Results of Underwater Sound Speed Profile Inversion by Few-Shot Multi-Task Learning
Authors: Huang Wei , Zhou Jixuan , Gao Fan , Wang Junting , Xu Tianhe
Published: December 2023

Underwater Sound Speed Profile (SSP) distribution is crucial for the propagation mode of acoustic signals, so fast and accurate estimation of SSP is of great importance in building underwater observation systems. The state-of-the-art SSP inversion methods include frameworks of matched field processing (MFP), compressive sensing (CS), and feed-forward neural networks (FNNs), among which the FNN shows better real-time performance while maintaining the same level of accuracy. However, the training of FNN needs quite a lot historical SSP samples, which is difficult to satisfy in many ocean areas. This situation is called few-shot learning. To tackle this issue, we propose a multi-task learning (MTL) model with partial parameter sharing among different training tasks. By MTL, common features could be extracted, which accelerates the learning process on given tasks, and reduces the demand for reference samples, enhancing the generalization ability in few-shot learning. To verify the feasibility and effectiveness of MTL, a deep-ocean experiment was held in April 2023 in the South China Sea. Results show that MTL outperforms the other mainstream methods in terms of accuracy for SSP inversion, while inheriting the real-time advantage of FNN during the inversion stage.
SDSNet: Building Extraction in High-Resolution Remote Sensing Images Using a Deep Convolutional Network with Cross-Layer Feature Information Interaction Filtering
Authors: Wang Xudong , Tian Mingliang , Zhang Zhijun , He Kang , Wang Sheng , Liu Yan , Dong Yusen
Published: December 2023

Building extraction refers to the automatic identification and separation of buildings from the background in remote sensing images. It plays a significant role in urban planning, land management, and disaster monitoring. Deep-learning methods have shown advantages in building extraction, but they still face challenges such as variations in building types, object occlusions, and complex backgrounds. To address these issues, SDSNet, a deep convolutional network that incorporates global multi-scale feature extraction and cross-level feature fusion, is proposed. SDSNet consists of three modules: semantic information extraction (SIE), multi-level merge (MLM), and semantic information fusion (SIF). The SIE module extracts contextual information and improves recognition of multi-scale buildings. The MLM module filters irrelevant details guided by high-level semantic information, aiding in the restoration of edge details for buildings. The SIF module combines filtered detail information with extracted semantic information for refined building extraction. A series of experiments conducted on two distinct public datasets for building extraction consistently demonstrate that SDSNet outperforms the state-of-the-art deep-learning models for building extraction tasks. On the WHU building dataset, the overall accuracy (OA) and intersection over union (IoU) achieved impressive scores of 98.86% and 90.17%, respectively. Meanwhile, on the Massachusetts dataset, SDSNet achieved OA and IoU scores of 94.05% and 71.6%, respectively. SDSNet exhibits a unique advantage in recovering fine details along building edges, enabling automated and intelligent building extraction. This capability effectively supports urban planning, resource management, and disaster monitoring.
Combined Methodology for Rockfall Susceptibility Mapping Using UAV Imagery Data
Authors: Gantimurova Svetlana , Parshin Alexander
Published: December 2023

Gravitational processes on cut slopes located close to infrastructure are a high concern in mountainous regions. There are many techniques for survey, assessment, and prognosis of hazardous exogenous geological processes. The given research describes using UAV data and GIS morphometric analysis for delineation of hazardous rockfall zones and 3D modelling to obtain an enhanced, detailed evaluation of slope characteristics. Besides the slope geomorphometric data, we integrated discontinuity layers, including rock plains orientation and fracture network density. Cloud Compare software 2.12 was utilised for facet extraction. Fracture discontinuity analysis was performed in QGIS using the Network GT plugin. The presented research uses an Analytical Hierarchy Process (AHP) to determine the weight of each contributing factor. GIS overlay of weighted factors is applied for rockfall susceptibility mapping. This integrated approach allows for a more comprehensive GIS-based rockfall susceptibility mapping by considering both the structural characteristics of the outcrop and the geomorphological features of the slope. By combining UAV data, GIS-based morphometric analysis, and discontinuity analysis, we are able to delineate hazardous rockfall zones effectively.
A Novel Polarization Scattering Decomposition Model and Its Application to Ship Detection
Authors: Fang Lu , Yang Ziyuan , Mu Wenxing , Liu Tao
Published: December 2023

In polarimetric synthetic aperture radar (POLSAR), it is of great significance for civil and military applications to find novel model-based decomposition methods suitable for ship detection in different detection backgrounds. Based on the physical interpretation of polarimetric decomposition theory and the Lasso rule for sparse features, we propose a four-component decomposition model, which is composed of surface scattering (Odd), double-bounce scattering (Dbl), volume scattering (Vol), and ±45° oriented dipole (Od). In principle, the Od component can describe the compounded scattering structure of a ship consisting of odd-bounce and even-bounce reflectors. Moreover, the pocket perceptron learning algorithm (PPLA) and support vector machine (SVM) are utilized to solve the linear inseparable problems in this study. Using large amounts of RADARSAT-2 (RS-2) fully polarized SAR data and AIRSAR data, our experimental results show that the Od component can make a great contribution to ship detection. Compared with other conventional decomposition methods used in the experiments, the proposed four-component decomposition method has better performance and is more effective and feasible to detect ships.
Ocean Colour Atmospheric Correction for Optically Complex Waters under High Solar Zenith Angles: Facilitating Frequent Diurnal Monitoring and Management
Authors: Wang Yongquan , Liu Huizeng , Zhang Zhengxin , Wang Yanru , Zhao Demei , Zhang Yu , Li Qingquan , Wu Guofeng
Published: December 2023

Accurate atmospheric correction (AC) is one fundamental and essential step for successful ocean colour remote-sensing applications. Currently, most ACs and the associated ocean colour remote-sensing applications are restricted to solar zenith angles (SZAs) lower than 70°. The ACs under high SZAs present degraded accuracy or even failure problems, rendering the satellite retrievals of water quality parameters more challenging. Additionally, the complexity of the bio-optical properties of the coastal waters and the presence of complex aerosols add to the difficulty of AC. To address this challenge, this study proposed an AC algorithm based on extreme gradient boosting (XGBoost) for optically complex waters under high SZAs. The algorithm presented in this research has been developed using pairs of Geostationary Ocean Colour Imager (GOCI) high-quality noontime remote-sensing reflectance (Rrs) and the Rayleigh-corrected reflectance (ρrc) derived from the Ocean Colour–Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART) in the morning (08:55 LT) and at dusk (15:55 LT). The algorithm was further examined using the daily GOCI images acquired in the morning and at dusk, and the hourly (total suspended sediment) TSS concentration was also obtained based on the atmospherically corrected GOCI data. The results showed that: (i) the model produced an accurate fitting performance (R2 ≥ 0.90, RMSD ≤ 0.0034 sr−1); (ii) the model had a high validation accuracy with an independent dataset (R2 = 0.92–0.97, MAPD = 8.2–26.81% and quality assurance (QA) score = 0.9–1); and (iii) the model successfully retrieved more valid Rrs for GOCI images under high SZAs and enhanced the accuracy and coverage of TSS mapping. This algorithm has great potential to be applied to AC for optically complex waters under high SZAs, thus increasing the frequency of available observations in a day.
Conditional Most-Correlated Distribution-Based Load-Balancing Scheme for Hybrid LiFi/WiGig Network
Authors: Farrag Mohammed , Al Ayidh Abdulrahman , Hussein Hany S.
Published: December 2023

A hybrid network has recently been proposed as a framework for a high-speed wireless communication network. Basically, it integrates light fidelity (LiFi) with radio frequency wireless gigabit alliance (WiGig) networks that operate, simultaneously, in a completely different frequency band. To assign the best access point (AP) and provide enough resources for each user, an effective load-balancing (LB) strategy is needed. However, the traditional LB strategies involve sophisticated iterative computing procedures whenever the user distribution changes. Hence, the first contribution of this work is to offer a more adaptable, two-step, conditional, and most-correlated distribution (CMCD) algorithm. Thus, the low-complexity most-correlated distribution (MCD) LB scheme is applied, and the average data rates for all users are then calculated. If the results achieve the predefined performance threshold (PDT), the decisions will be confirmed; otherwise, the proposed scheme automatically switches to the more accurate, but more complex, consecutive assign WiGig first separate optimization algorithms (CAWFS) algorithm. The suggested algorithm provides a clear performance-complexity trade-off, which could be simply controlled by choosing the suitable performance tolerance factor. The second contribution of this paper is the correlation-weighted majority voting (CWMV) method, which attempts to benefit from as many prior decision votes as possible, instead of relying just on one vote. In the CWMV technique, the weight of each vote is calculated based on the correlation between the history distribution vectors and the new user distribution vector. A significant increase in the system performance is evident from the simulation results.
Design and Characterization of Piezoresistive Sensors for Non-Planar Surfaces and Pressure Mapping: A Case Study on Kayak Paddle
Authors: Laaraibi Abdo-Rahmane Anas , Jodin Gurvan , Depontailler Corentin , Bideau Nicolas , Razan Florence
Published: December 2023

This article focuses on the design of a sensor system for a non-planar surface, in particular a cylindrical shape, such as a kayak paddle. The main objective is to develop a piezoresistive sensor system to measure the pressure exerted by the hand on the shaft. The study begins with static characterization of the sensors, including dispersion analysis to assess their sensitivity, linearity and measurement range. A calibration process is carried out using a dedicated test bench, and an inverse viscoelastic model is used to establish an accurate relationship between the measured resistance and the corresponding pressure. The sensor system is connected to a data acquisition board equipped with an analog-to-digital converter (ADC) that enables the direct conversion of analog data into digital resistance values. Furthermore, Bluetooth Low Energy (BLE) wireless communication is employed to facilitate data transfer to a computer, enabling a detailed pressure mapping of the kayak paddle and real-time data collection. The calibrated sensors are then tested and validated on the kayak paddle, facilitating the mapping of pressure zones on the paddle surface. This mapping provides information for locating areas of high pressure exertion during kayaker movements.
Adaptive Ultrasonic Full Matrix Capture Process for the Global Imaging of Complex Components with Curved Surfaces
Authors: Miao Wensong , Liu Ne , Huang Jingqiang , Lu Minghui
Published: December 2023

This work proposes a new global FD-RTM method to solve the problem of ultrasonic inspection of parts with complex geometric shapes. With this method, the frequency domain reverse time migration (FD-RTM) algorithm is used to adapt to the complex refraction of ultrasonic waves by the surface, while an interface solution algorithm based on tangent fitting is used to solve the interface position with high precision through the full matrix reception data. Based on high-precision interface information, a hybrid extrapolation algorithm and a situation-specific probe movement strategy are used to enable the probe to find the next sampling point according to the direction of the workpiece surface, allowing complex surface topography features to be identified without relying on the workpiece CAD drawing. This makes it possible to achieve the automated inspection of workpieces. To verify the proposed method’s effectiveness, an aluminum alloy model with side-drilled holes (SDH) is used. The geometry of the model consists of multiple convex and concave surfaces. By comparing the local FD-RTM imaging with images synthesized using the entire scan path, it is shown that gFD-RTM improved the imaging performance. Compared with FD-RTM, the average signal-to-noise ratio of gFD-RTM was increased by 20%, and the array performance index (API) was reduced by 70%, indicating effective detection coverage.
Seismic Beacon—A New Instrument for Detection of Changes in Rock Massif
Authors: Lukešová Renata , Málek Jiří
Published: December 2023

The seismic beacon is a new instrument that allows for the measurement of changes in a rock massif with high sensitivity. It is based on effects, which affect the propagation of harmonic seismic waves generated continuously with stable and precise frequency and amplitude. These seismic waves are registered by a system of seismic stations. The amplitude of the seismic signal is very small, and it is normally hidden in a seismic noise. Special techniques are applied to increase the signal-to-noise ratio. In 2020, the first prototype of the seismic beacon was constructed in a laboratory, and field tests were performed in 2022 and 2023. During the tests, the changes in spectral amplitude and phase of seismic waves were detected, which is interpreted as the changes in material properties. These measurements testified the basic functionality of the device. The seismic beacon has been developed primarily for the detection of critical stress before an earthquake, which is manifested by non-linear effects such as higher harmonics generation. In addition, it could be used, for example, in the detection of magma movements, groundwater level changes, changes in hydrocarbon saturation in rocks during the extraction of oil and natural gas, or the penetration of gases and liquids into the earth’s crust.
Non-Invasive Blood Pressure Tracking of Spontaneous Hypertension Rats Using an Electronic Nose
Authors: Zhang Fumei , Yang Lijing , Wei Jia , Tian Xiaojing
Published: December 2023

Traditional noninvasive blood pressure measurement methods in experimental animals are time consuming and difficult to operate, particularly for large numbers of animals. In this study, the possibility of sensing fecal odor to estimate the blood pressure status of spontaneous hypertension rats (SHRs) was explored with the aim of establishing a new method for non-invasive monitoring of blood pressure. The body weight and blood pressure of SHRs kept increasing with growth, and the odor information monitored using an E-nose varied with the blood pressure status, particularly for sensors S6 and S7. The fecal information was analyzed using principal component analysis, canonical discriminant analysis and multilayer perception neural networks (MLP) to discriminate SHRs from normal ones, with a 100% correct classification rate. For better prediction of blood pressure, the model built using multiple linear regression analysis, partial least squares regression analysis and multilayer perceptron neural network analysis were used, with coefficients of determination (R2) ranging from 0.8036 to 0.9926. Moreover, the best prediction model for blood pressure was established using MLP analysis with an R2¬ higher than 0.91. Thus, changes in blood pressure levels can be tracked non-invasively, and normotension can be distinguished from hypertension or even at different hypertension levels based on the odor information of rat feces, providing a foundation for non-invasive health monitoring. This work might provide potential instructions for functional food research aimed at lowering blood pressure.
Human-Robot Joint Misalignment, Physical Interaction, and Gait Kinematic Assessment in Ankle-Foot Orthoses
Authors: Andrade Ricardo Luís , Figueiredo Joana , Fonseca Pedro , Vilas-Boas João P. , Silva Miguel T. , Santos Cristina P.
Published: December 2023

Lower limb exoskeletons and orthoses have been increasingly used to assist the user during gait rehabilitation through torque transmission and motor stability. However, the physical human-robot interface (HRi) has not been properly addressed. Current orthoses lead to spurious forces at the HRi that cause adverse effects and high abandonment rates. This study aims to assess and compare, in a holistic approach, human-robot joint misalignment and gait kinematics in three fixation designs of ankle-foot orthoses (AFOs). These are AFOs with a frontal shin guard (F-AFO), lateral shin guard (L-AFO), and the ankle modulus of the H2 exoskeleton (H2-AFO). An experimental protocol was implemented to assess misalignment, fixation displacement, pressure interactions, user-perceived comfort, and gait kinematics during walking with the three AFOs. The F-AFO showed reduced vertical misalignment (peak of 1.37 ± 0.90 cm, p-value < 0.05), interactions (median pressures of 0.39–3.12 kPa), and higher user-perceived comfort (p-value < 0.05) when compared to H2-AFO (peak misalignment of 2.95 ± 0.64 and pressures ranging from 3.19 to 19.78 kPa). F-AFO also improves the L-AFO in pressure (median pressures ranging from 8.64 to 10.83 kPa) and comfort (p-value < 0.05). All AFOs significantly modified hip joint angle regarding control gait (p-value < 0.01), while the H2-AFO also affected knee joint angle (p-value < 0.01) and gait spatiotemporal parameters (p-value < 0.05). Overall, findings indicate that an AFO with a frontal shin guard and a sports shoe is effective at reducing misalignment and pressure at the HRI, increasing comfort with slight changes in gait kinematics.
Implementation of a Trust-Based Framework for Substation Defense in the Smart Grid
Authors: Boakye-Boateng Kwasi , Ghorbani Ali A. , Lashkari Arash Habibi
Published: December 2023

The Smart Grid is a cyber-integrated power grid that manages electricity generation, transmission, and distribution to consumers and central to its functioning is the substation. However, integrating cyber-infrastructure into the substation has increased its attack surface. Notably, sophisticated attacks such as the PipeDream APT exploit multiple device protocols, such as Modbus, DNP3, and IEC61850. The substation’s constraints pose challenges for implementing security measures such as encryption and intrusion detection systems. To address this, we propose a comprehensive trust-based framework aimed at enhancing substation security. The framework comprises a trust model, a risk posture model, and a trust transferability model. The trust model detects protocol-based attacks on Intelligent Electronic Devices and SCADA HMI systems, while the risk posture model dynamically assesses the substation’s risk posture. The trust transferability model evaluates the feasibility of transferring and integrating a device and its trust capabilities into a different substation. The practical substation emulation involves a Docker-based testbed, employing a multi-agent architecture with a real-time Security Operations Center-influenced dashboard. Assessment involves testing against attacks guided by the MITRE ICS ATT&CK framework. Our framework displays resilience against diverse attacks, identifies malicious behavior, and rewards trustworthy devices.
Exploring Generation Z’s Investment Patterns and Attitudes towards Greenness
Authors: Pašiušienė Inga , Podviezko Askoldas , Malakaitė Daiva , Žarskienė Laura , Liučvaitienė Aušra , Martišienė Rita
Published: December 2023

Financial technology is quickly developing, making the financial industry more accessible and encouraging individual investor engagement in the investing process. Generation Z, characterised by a high level of digital literacy, curiosity, and receptivity to innovation, tends to very quickly make decisions and rapidly consume. Since 2007, there has been an increase in the number of articles analysing investor behaviour, drawing on insights from financial and psychological theories. The purpose of this exploratory study is to categorise the behaviour of students surveyed by the type of their investments, while at the same time assessing their willingness to choose green investments. The survey used in the analysis not only aims at collecting data but also educates students on the importance of critical self-awareness and the identification of their emotions to make rational, responsible investment decisions and, at the same time, to form a responsible investor who understands that investing is not only a way to earn a return but also can make a positive impact on the world when green investments are chosen. This study shows that studying students tend to be very rational and interested in contributing to greening the world; however, they are still hesitant to put their theoretical skills into practise and are more likely to provide theoretical support for green investments rather than actually invest. Respondents are grouped according to their potential investment behaviour. The proportions of groups are assessed using statistical inference with a precision of 95% that allowed to propose the method of deriving confidence intervals for each group estimation and, thus, making estimates both reliable and available as statistical estimations.
How Digital Technologies Can Support Sustainability of the Waterborne Passenger Mobility Ecosystem: A Case Study Analysis of Smart Circular Practices in Northern Europe
Authors: Pirrone Laura , Bionda Arianna , Ratti Andrea
Published: December 2023

To tackle the significant increase in traffic congestion and pollution in urban areas, waterborne transport systems can offer a more efficient and environmentally friendly alternative, decreasing traffic congestion on roads, noise, and pollution emissions, with reduced infrastructure requirements. Developing a sustainable waterborne passenger system requires a multidisciplinary approach and a systemic view, which involves various stakeholders and knowledge. Digital transition can encourage a better management of resources and enables systems integration promoting circular economy and ecosystem models, which create interrelations among systems. The aim of this study is to identify circular practices adopted in the waterborne passenger mobility (WPM) ecosystem driven by digital technologies. A holistic perspective which considers all the system of actors and their mutual interactions has been adopted. Five case studies have been selected in Northern Europe and analyzed by applying the Smart Circular WPM Ecosystem framework, previously developed by the authors. The analysis identifies a list of Smart Circular practices resulting from the application of digital technologies during specific lifecycle stages, involving certain actors’ categories, and enabling circular principles. The case studies analysis also highlighted unexplored or under-considered fields of action which can be the base for further research.
Enhancing Precision of Crop Farming towards Smart Cities: An Application of Artificial Intelligence
Authors: Addas Abdullah , Tahir Muhammad , Ismat Najma
Published: December 2023

Water sustainability will be scarce in the coming decades because of global warming, an alarming situation for irrigation systems. The key requirement for crop production is water, and it also needs to fulfill the requirements of the ever-increasing population around the globe. The changing climate significantly impacts agriculture production due to the extreme weather conditions that prevail in various regions. Since urbanization is increasing worldwide, smart cities must find innovative ways to grow food sustainably within built environments. This paper explores how precision agriculture powered by artificial intelligence (AI) can transform crop farms (CF) to enhance food security, nutrition, and environmental sustainability. We developed a robotic CF prototype that uses deep reinforcement learning to optimize seeding, watering, and crop maintenance in response to real-time sensor data. The system was tested in a simulated CF setting and benchmarked. The results revealed a 26% increase in crop yield, a 41% reduction in water utilization, and a 33% decrease in chemical use. We employed AI-enabled precision farming to improve agriculture’s efficiency, sustainability, and productivity within smart cities. The widespread adoption of such technologies makes food supplies resilient, reduces land, and minimizes agriculture’s environmental footprint. This study also qualitatively assessed the broader implications of AI-enabled precision farming. Interviews with farmers and stakeholders were conducted, which revealed the benefits of the proposed approach. The multidimensional impacts of precision crop farming beyond measurable outcomes emphasize its potential to foster social cohesion and well-being in urban communities.
Strategies for OPC Paste Carbonation: Relationship between Microstructure, Performance and Net CO2 Balance
Authors: Silva André , Nogueira Rita , Bogas José Alexandre
Published: December 2023

Carbon capture storage and utilization is the main technology for reducing CO2 emissions, accounting for 56% of the overall reduction required to achieve the carbon neutrality of concrete by 2050. Different strategies have been explored in cement-based materials towards this end, namely, in concrete. However, the impact on carbonated concrete differs depending on the moment at which cementitious material comes into contact with CO2, either in terms of CO2 uptake or in terms of its lifetime performance. This paper presents three leading strategies that rely on the direct carbonation of a cementitious binder to reduce the carbon footprint. For each strategy, the effect of the carbonation process on the kinetics and microstructure of cementitious paste, the estimation of its carbon capture capability and the application feasibility are discussed. Accelerated carbonation curing is one approach widely studied by academics. However, despite some CO2 capture effectiveness, its industrial processing is still a long way off. A second strategy consists of incorporating CO2 during the mixing process, which has been shown to speed up the hardening reactions of cement. However, this effect is of short term and may negatively affect its long-term performance. Finally, the carbonation of hydrated cement waste is shown to be a very promising strategy that enables the recycling of hydrated cement waste as a supplementary cementitious material which also has a potentially high CO2 uptake. The integrated analysis of the three strategies highlights a wide variability in the reduction of CO2 emissions from 1% to 37% in relation to current emissions, where the best result was achieved using carbonated waste (third strategy) in the production of a concrete subjected to carbonation curing (first strategy).
An E-Commerce Personalized Recommendation Algorithm Based on Multiple Social Relationships
Authors: Bin Sheng
Published: December 2023

Environmental e-commerce is a sustainability-oriented e-commerce model. To address the problem of data sparsity and the lack of diversity in traditional e-commerce recommendation algorithms, a new collaborative filtering recommendation algorithm based on multiple social relationships is proposed in environmental e-commerce. In real social networks, there were many relationships between users. On the basis of the traditional matrix decomposition model, the proposed algorithm integrates multiple social relationships between users into the user feature matrix, and then the multiple social relationships between users and the user rating preference similarity were used to jointly predict the user’s rating value for commodity, thus the personalized recommendation for users was achieved. In order to verify the superiority of the proposed algorithm, in this paper, two open datasets were used to compare the performance of several recommendation algorithms. The experimental results show that compared with the traditional social recommendation algorithms, the proposed algorithm improves recommendation accuracy and diversity. In real environmental e-commerce recommendation systems, the proposed algorithm can provide users with more personalized recommendation results, and reduce the arbitrariness of customer purchases and frequent returns in reality.
Influence of Reservoir Heterogeneity on Simultaneous Geothermal Energy Extraction and CO2 Storage
Authors: Singh Mrityunjay , Mahmoodpour Saeed , Schmidt-Hattenberger Cornelia , Sass Ingo , Drews Michael
Published: December 2023

This numerical study delves into the dynamic interaction between reservoir heterogeneity and its impact on the dual objectives of geothermal energy extraction and CO2 sequestration. Employing finite element models, this research scrutinizes the effects of variable porosity, permeability, and capillary entry pressures on fluid dynamics and thermal processes within geothermal systems. Key findings reveal that these heterogeneities significantly dictate fluid behavior and heat distribution, influencing the operational efficiency and environmental sustainability of geothermal–CO2 storage operations. By integrating the nonlinear, temperature-dependent properties of fluids, simulations provide in-depth insights into the coupled fluid–thermal interactions that govern system performance. The outcomes offer a refined understanding of the complex interdependencies within heterogeneous reservoirs, underpinning the optimization of design and operational methodologies for co-optimized geothermal energy and CO2 storage solutions. Ultimately, this research contributes to the advancement of sustainable energy technologies, highlighting further investigative pathways to bolster the efficiency and longevity of two-phase water–CO2 geothermal systems.
European Union Tools for the Sustainable Development of Border Regions
Authors: Chirodea Florentina , Soproni Luminita , Marian Mihai
Published: December 2023

The European Union uses several instruments aimed at reducing disparities between regions and strengthening their competitiveness and sustainability. The border regions have a special relevance, given their position and characteristics, as well as the challenges faced by local actors. The European Commission has introduced the action “b-solutions” to tackle the specific border obstacles along European Union internal borders. The article aims to analyze the integration mechanisms at the micro-regional level, which is considered as a viable and sustainable solution for cross-border regions with resources which can be exploited to attract investment and generate wealth and well-being. The qualitative analysis method involved consulting thematic publications of the b-solution program and extracting data from the presentation sheets of accepted proposals published by the Association of European Border Regions. The collected data were processed according to criteria such as direction of action, types and causes of obstacles, solutions proposed by experts, etc. Addressing the legal and administrative obstacles that hamper cross-border flows proved to be a good initiative, with 120 cases selected. The solutions offered to the particular cases can also be replicated for other obstacles identified at the European Union’s internal borders so that cooperation between border regions is intensified to the benefit of increasing European territorial, economic, and social cohesion.
On the Quantum Deformations of Associative Sato Grassmannian Algebras and the Related Matrix Problems
Authors: Balinsky Alexander A. , Bovdi Victor A. , Prykarpatski Anatolij K.
Published: December 2023

We analyze the Lie algebraic structures related to the quantum deformation of the Sato Grassmannian, reducing the problem to studying co-adjoint orbits of the affine Lie subalgebra of the specially constructed loop diffeomorphism group of tori. The constructed countable hierarchy of linear matrix problems made it possible, in part, to describe some kinds of Frobenius manifolds within the Dubrovin-type reformulation of the well-known WDVV associativity equations, previously derived in topological field theory. In particular, we state that these equations are equivalent to some bi-Hamiltonian flows on a smooth functional submanifold with respect to two compatible Poisson structures, generating a countable hierarchy of commuting to each other’s hydrodynamic flows. We also studied the inverse problem aspects of the quantum Grassmannian deformation Lie algebraic structures, related with the well-known countable hierarchy of the higher nonlinear Schrödinger-type completely integrable evolution flows.