bluuebunny/crossref_metadata_2025 huggingface dataset:
Phytochemical Investigation of Polyphenols from the Aerial Parts of Tanacetum balsamita Used in Transylvanian Ethnobotany and Parallel Artificial Membrane Permeability Assay
Authors: Alberti Ágnes , Riethmüller Eszter , Felegyi-Tóth Csenge Anna , Czigle Szilvia , Czégényi Dóra , Filep Rita , Papp Nóra
Published: June 2024

In this study, based on ethnobotanical data recorded in Transylvania, the polyphenolic compounds and the permeability of the aerial part’s extract of Tanacetum balsamita were investigated. Ultrahigh-performance liquid chromatography-tandem mass spectrometry was applied for the analysis of the extracts. Parallel artificial membrane permeability assay (PAMPA) for the gastrointestinal tract and the blood–brain barrier was conducted. In the ethanolic and aqueous extracts of the species traditionally used for wound, furuncle, and liver disorders, 92 polyphenols were characterized (e.g., flavonoid, hydroxycinnamic acid, catechin, dihydroxybenzoyl, lignan derivatives, and a monoterpene) including 54 compounds identified for the first time in the plant. In the PAMPA tests, eight components were shown to be capable of passive diffusion across the studied membranes. These include apigenin and seven methoxylated flavonoid derivatives. Based on these results, methoxylated flavonoids might promote the pharmacological potential of T. balsamita to be applied in the enhancement of novel remedies.
Phylogeographic Structure and Population Dynamics of Baoxing Osmanthus (Osmanthus serrulatus), an Endemic Species from the Southwest Sichuan Basin, China
Authors: Wang Zhibei , Wu Xi , Liu Xin , Zhang Min , Xie Chunping , Chen Lin
Published: June 2024

The mountainous regions of southwest China are recognized as pivotal centers for the origin and evolution of Osmanthus species. Baoxing Osmanthus (Osmanthus serrulatus Rehder), a rare and endemic species known for its spring blooms, is sparsely distributed within the high altitude evergreen broad-leaved forests surrounding the southwestern Sichuan Basin. However, persistent anthropogenic disturbances and habitat fragmentation have precipitated a significant decline in its natural population size, leading to the erosion of genetic resources. To assess the genetic status of O. serrulatus and formulate effective conservation strategies, we conducted sampling across ten wild populations, totaling 148 individuals in their natural habitats. We employed two cpDNA fragments (matK and trnS-trnG) to elucidate the phylogeographic structure and historical population dynamics. The results revealed low species-level genetic diversity, alongside pronounced regional differentiation among populations (FST = 0.812, p < 0.05) and a notable phylogeographic structure (NST = 0.698 > GST = 0.396, p < 0.05). Notably, genetic variation was predominantly observed among populations (81.23%), with no evidence of recent demographic expansion across the O. serrulatus distribution range. Furthermore, divergence dating indicated a timeline of approximately 4.85 Mya, corresponding to the late Miocene to early Pleistocene. This temporal correlation coincided with localized uplift events in the southwestern mountains and heightened Asian monsoons, suggesting pivotal roles for these factors in shaping the current phylogeographic pattern of O. serrulatus. These findings support the effective conservation of O. serrulatus germplasm and offer insights into the impact of Quaternary climate oscillations on companion species within evergreen broad-leaved forests. They also enhance our understanding of the origin and evolution of these forests in the southwestern mountains, aiding biodiversity conservation efforts in the region.
Damage Resistance of Kevlar® Fabric, UHMWPE, PVB Multilayers Subjected to Concentrated Drop-Weight Impact
Authors: Nael Manal A. , Dikin Dmitriy A. , Admassu Natnael , Elfishi Omar Bahgat , Percec Simona
Published: June 2024

The impact resistance of layered polymer structures using polyvinyl butyral (PVB) in combination with Kevlar® fabric and ultra-high molecular weight polyethylene (UHMWPE) were fabricated and tested. Methods of wet impregnation and hot-press impregnation and consolidation of fabric with PVB and UHMWPE were used to manufacture multilayer constructs. All sandwich constructs were fixed to the surface of ballistic clay and subject to a free drop-weight test with a conical impactor having a small contact area. All tests were made at the same impact energy of 9.3 J and velocity of 2.85 m/s. The change in the resistance force was recorded using a piezoelectric force sensor at the time intervals of 40 μs. Using experimental force–time history, the change in the impactor’s velocity, the depth of impactor penetration, the energy transformation at various stages of impactor interaction with the sample, and other parameters were obtained. Three indicators were considered as the main criteria for the effectiveness of a sample’s resistance to impact: (1) minimum deformation, bulging, of the panel backside at the moment of impact, (2) minimum absorption of impact energy per areal density, and (3) minimal or, better yet, no destruction of structural integrity. Under the tested conditions, the rigid Kevlar–PVB–Kevlar sandwich at the frontside and relatively soft but flexible UHMWPE–Kevlar–UHMWPE layers in the middle helped to localize and absorb impact energy, while the backside Kevlar–PVB–Kevlar sandwich minimized local bulging providing the best overall performance. The front layer damage area was very shallow and less than two impactor tip diameters. The backside bulging was also less than in any other tested configurations.
Optimization Course of Titanium Nitride Nanofiller Loading in High-Density Polyethylene: Interpretation of Reinforcement Effects and Performance in Material Extrusion 3D Printing
Authors: Petousis Markos , Sagris Dimitris , Papadakis Vassilis , Moutsopoulou Amalia , Argyros Apostolos , David Constantine , Valsamos John , Spiridaki Mariza , Michailidis Nikolaos , Vidakis Nectarios
Published: June 2024

In this study, titanium nitride (TiN) was selected as an additive to a high-density polyethylene (HDPE) matrix material, and four different nanocomposites were created with TiN loadings of 2.0–8.0 wt. % and a 2 wt. % increase step between them. The mixtures were made, followed by the fabrication of the respective filaments (through a thermomechanical extrusion process) and 3D-printed specimens (using the material extrusion (MEX) technique). The manufactured specimens were subjected to mechanical, thermal, rheological, structural, and morphological testing. Their results were compared with those obtained after conducting the same assessments on unfilled HDPE samples, which were used as the control samples. The mechanical response of the samples improved when correlated with that of the unfilled HDPE. The tensile strength improved by 24.3%, and the flexural strength improved by 26.5% (composite with 6.0 wt. % TiN content). The dimensional deviation and porosity of the samples were assessed with micro-computed tomography and indicated great results for porosity improvement, achieved with 6.0 wt. % TiN content in the composite. TiN has proven to be an effective filler for HDPE polymers, enabling the manufacture of parts with improved mechanical properties and quality.
Ceria Quantum Dot Filler-Modified Polymer Electrolytes for Three-Dimensional-Printed Sodium Solid-State Batteries
Authors: Zhang Yi , Zheng Haoran , Ding Honggeng , Jabbar Khan Abdul , Gao Ling , Zhao Guowei
Published: June 2024

Solid polymer electrolytes have been considered as promising candidates for solid-state batteries (SSBs), owing to their excellent interfacial compatibility and high mechanical toughness; however, they suffer from intrinsic low ionic conductivity (lower than 10−6 S/cm) and large thickness (usually surpassed over 100 μm or even 500 μm), which has a negative influence on the interface resistance and ionic migration. In this work, ceria quantum dot (CQD)-modified composite polymer electrolyte (CPE) membranes with a thickness of 20 μm were successfully manufactured via 3D printing technology. The CQD fillers can reduce the crystallinity of the polymer, and the oxygen vacancies on CQDs can facilitate the dissociation of ion pairs in the NaTFSI salt to release more free Na+, improving the ionic conductivity. Meanwhile, tailoring the thickness of the CPE-CQDs membrane via 3D printing can further promote the migration and transport of Na+. Furthermore, the printed NNM//CPE-CQDs//Na SSB exhibited outstanding rate capability and cycling stability. The combination of CQD modification and thickness tailoring through 3D printing paves a new avenue for achieving high performance solid electrolyte membranes for practical application in Na SSBs.
Fault Detection and Isolation for Time-Varying Processes Using Neural-Based Principal Component Analysis
Authors: Kazemi Pezhman , Masoumian Armin , Martin Philip
Published: June 2024

This paper introduces a new adaptive framework for fault detection and diagnosis using neural-based PCA. This framework addresses the limitations of traditional PCA in handling time-varying processes. The adaptive framework updates the correlation matrix recursively, allowing it to adapt to the natural time-varying behavior of processes. It also recursively determines the number of principal components and the confidence limits for three process monitoring statistics (T2, Q, and the combined index φ). To diagnose faults, four different types of contribution plots are used as follows: complete decomposition contributions (CDC), partial decomposition contributions (PDC), diagonal-based contributions (DBCs), and reconstruction-based contributions (RBCs). The evaluation through three simulation studies—including a numerical example, the continuous stirred tank reactor (CSTR) process, and water resource recovery facilities (WRRFs)—demonstrates that the combined statistics provided superior fault detection and diagnosis performance compared with individual statistics. Additionally, the study of the isolation method shows that no single method can definitively be claimed as superior. Overall, our study highlights the strength and versatility of neural-based PCA for detecting and diagnosing faults in dynamic processes.
Machine Learning in Reservoir Engineering: A Review
Authors: Zhou Wensheng , Liu Chen , Liu Yuandong , Zhang Zenghua , Chen Peng , Jiang Lei
Published: June 2024

With the rapid progress of big data and artificial intelligence, machine learning technologies such as learning and adaptive control have emerged as a research focus in petroleum engineering. They have various applications in oilfield development, such as parameter prediction, optimization scheme deployment, and performance evaluation. This paper provides a comprehensive review of these applications in three key scenarios of petroleum engineering, namely hydraulic fracturing and acidizing, chemical flooding and gas flooding, and water injection. This article first introduces the steps and methods of machine learning processing in these scenarios, then discusses the advantages, disadvantages, existing challenges, and future prospects of these machine learning methods. Furthermore, this article compares and contrasts the strengths and weaknesses of these machine learning methods, aiming to help researchers select and improve their methods. Finally, this paper identifies some potential development trends and research directions of machine learning in petroleum engineering based on the current issues.
A Decision Tree-Based Method for Evaluating the Remanufacturability of Used Parts
Authors: Chen Shuhua , Hao Jian , Chen Yanxiang , Yang Zhongyuan
Published: June 2024

Assessing the remanufacturability of used parts is a crucial basis for determining their value and optimal utilization methods. Due to the uncertain quality of used parts and the varying processing capacity of enterprises, coupled with the continuous expansion of the scale of the remanufacturing industry, the traditional weighted-analysis model, which considers all indicators at the same level, is inefficient for decision-making. In order to evaluate the remanufacturability of used parts more efficiently, a decision tree-based method is proposed, which hierarchically processes the evaluation criteria to enhance decision-making efficiency and adaptability. First, using a data platform, the remaining value of used parts reflected in the failure degree is analyzed and predicted, with the aid of artificial neural networks and the Weibull model, providing an initial remanufacturability assessment. Then, remanufacturability is assessed sequentially from the technical, economic, and environmental feasibility aspects, based on the enterprise’s processing capabilities. Finally, the effectiveness of the proposed method is validated through a case study on the remanufacturing of used blades.
Investigating the Reliability of an Existing Top Angle and Seat Pad Semi-Rigid Connection System Using Advanced Modelling Techniques
Authors: Yonis Ferid A. , Li Chun-Qing , Saliva Saman De
Published: June 2024

The investigation introduces an advanced model procedure for evaluating the structural reliability of semi-rigid frame connections in existing aged buildings. Specifically, the focus is on the top angle and seat pad (TA–SP) semi-rigid connection, which was not initially considered in the current design standards. The approach employs a plastic hinge model to predict the ultimate strength of the connection and its beam-to-column behaviour. In order to increase computational efficiency, the investigation leverages the nonlinear behaviour of the finite element (FE) model to validate critical parameters. The statistical properties of the existing connection were obtained based on past experimental data, highlighting the weakest elements in the system. The first-order and second-order reliability methods and Monte Carlo simulations were employed to estimate the reliability index. Percentile errors were assessed to understand their impact on higher-order interactions. This new technique of identification quantifies the probability of the system failure interactions. Notably, a 45% lesser error aligns with the target reliability index, while a 114.5% larger error indicates a significant deviation from actual failure probability values. Each methodology introduced adheres to the current standard, and the system reliability analysis provides a vigorous conclusions scheme framework for assessing the existing TA–SP semi-rigid connection.
Biosurfactants: An Overview of Their Properties, Production, and Application in Mineral Flotation
Authors: Simões Carolina Rossini , da Silva Matheus Willian Pereira , de Souza Rodrigo Fernandes Magalhães , Hacha Ronald Roja , Merma Antônio Gutierrez , Torem Maurício Leonardo , Silvas Flávia Paulucci Cianga
Published: June 2024

The quest for sustainable mining processes has directed research towards environmentally friendly alternatives to conventional beneficiation practices, with biosurfactants emerging as a viable option due to their lower environmental impact. This study reviews the application of biosurfactants as bioreagents in mineral flotation, exploring their production, their mechanisms of action, and the sustainability benefits they offer. Methods include a decade-long text mining analysis of relevant literature, utilizing software tools like Zotero on platforms like Web of Science to extract and analyze data. The results underscore the growing interest in biosurfactants over the last ten years, highlighting their increasing relevance in industrial applications such as mineral flotation, where they replace synthetic surfactants. Particularly, biosurfactants have shown effectiveness in enhancing the froth flotation process, leading to more efficient mineral recovery while mitigating environmental harm. In conclusion, the adoption of biosurfactants in mineral processing not only aligns with sustainability goals but also demonstrates potential improvements in operational efficiency, offering a dual benefit of environmental protection and enhanced resource recovery.
Contemporary and Future Secondary Copper Reserves of Vietnam
Authors: Le Thi Van , Yamamoto Ryota , Dente Sebastien Michael Rene , Hashimoto Seiji
Published: June 2024

As ASEAN countries, including Vietnam, approach the living standards of developed countries, their copper demand is set to rise. This study investigates Vietnam’s copper stock and flow from 1995 to 2050, employing dynamic material flow analysis and five socioeconomic pathway scenarios (SSPs). Based on this, the secondary copper reserves of Vietnam were assessed. The results showed that the domestic copper demand is expected to grow to 526–1062 kt, resulting in a rapid increase in scrap generation. In 2022, Vietnam’s secondary copper reserves stood at 2.2 Mt and are projected to reach 6.8–8.6 Mt by 2050 under the SSP2 scenario. This corresponds to 3.6–4.6 times the 1.8 Mt primary copper reserve of Vietnam. However, these primary and secondary reserves cannot meet the cumulated demand by 2050. On the other hand, a large amount of copper, 8.9 Mt to 10 Mt, will become difficult-to-recover resources, such as waste in landfill sites, dissipated materials, or mixed metal loss. To promote the sustainable use of copper in Vietnam, we recommend increased geological expedition and mining investment, and improved waste management systems related to secondary resources.
Expected Utility Optimization with Convolutional Stochastically Ordered Returns
Authors: Gauchon Romain , Barigou Karim
Published: June 2024

Expected utility theory is critical for modeling rational decision making under uncertainty, guiding economic agents as they seek to optimize outcomes. Traditional methods often require restrictive assumptions about underlying stochastic processes, limiting their applicability. This paper expands the theoretical framework by considering investment returns modeled by a stochastically ordered family of random variables under the convolution order, including Poisson, Gamma, and exponential distributions. Utilizing fractional calculus, we derive explicit, closed-form expressions for the derivatives of expected utility for various utility functions, significantly broadening the potential for analytical and computational applications. We apply these theoretical advancements to a case study involving the optimal production strategies of competitive firms, demonstrating the practical implications of our findings in economic decision making.
Pose Estimation of a Container with Contact Sensing Based on Discrete State Discrimination
Authors: Kato Daisuke , Kobayashi Yuichi , Takamori Daiki , Miyazawa Noritsugu , Hara Kosuke , Usui Dotaro
Published: June 2024

In cases where vision is not sufficiently reliable for robots to recognize an object, tactile sensing can be a promising alternative for estimating the object’s pose. In this paper, we consider the task of a robot estimating the pose of a container aperture in order to select an object. In such a task, if the robot can determine whether its hand with equipped contact sensor is inside or outside the container, estimation of the object’s pose can be improved by reflecting the discrimination to the robotic hand’s exploration strategy. We propose an exploration strategy and an estimation method using discrete state recognition on the basis of a particle filter. The proposed method achieves superior estimation in terms of the number of contact actions, operation time, and stability of estimation efficiency. The pose is estimated with sufficient accuracy that the hand can be inserted into the container.
Effective Training and Inference Strategies for Point Classification in LiDAR Scenes
Authors: Carós Mariona , Just Ariadna , Seguí Santi , Vitrià Jordi
Published: June 2024

Light Detection and Ranging systems serve as robust tools for creating three-dimensional representations of the Earth’s surface. These representations are known as point clouds. Point cloud scene segmentation is essential in a range of applications aimed at understanding the environment, such as infrastructure planning and monitoring. However, automating this process can result in notable challenges due to variable point density across scenes, ambiguous object shapes, and substantial class imbalances. Consequently, manual intervention remains prevalent in point classification, allowing researchers to address these complexities. In this work, we study the elements contributing to the automatic semantic segmentation process with deep learning, conducting empirical evaluations on a self-captured dataset by a hybrid airborne laser scanning sensor combined with two nadir cameras in RGB and near-infrared over a 247 km2 terrain characterized by hilly topography, urban areas, and dense forest cover. Our findings emphasize the importance of employing appropriate training and inference strategies to achieve accurate classification of data points across all categories. The proposed methodology not only facilitates the segmentation of varying size point clouds but also yields a significant performance improvement compared to preceding methodologies, achieving a mIoU of 94.24% on our self-captured dataset.
Efficient and Rapid Modeling of Radar Echo Responses for Complex Targets under Arbitrary Wideband Excitation
Authors: Hua Mengbo , He Xiangxuan , He Siyuan , Chen Rumeng
Published: June 2024

In practical radar applications, the diversity of wideband transmission signals presents a significant challenge for radar target recognition systems. Traditional electromagnetic (EM) simulation methods often require the point-by-point sampling of target echo signals under specific radiation sources, and this process must be repeated for different transmission signals. This results in extensive EM computations and low computational efficiency. Additionally, the physical attribute information of the target is often obscured within the simulated echoes, complicating feature extraction. To address these issues, we propose a novel method for the efficient and rapid modeling and analysis of complex target time-domain echoes based on a forward scattering center model. This method constructs an impulse response model of the target under unit impulse signal excitation, representing the inherent scattering properties of the target and remaining unaffected by changes in the radiation source. Subsequently, this model is convolved with arbitrary wideband input signals to quickly reconstruct the wideband echo signals of the target. Numerical examples validate the accuracy and effectiveness of the proposed model. This method offers several advantages: firstly, it provides high computational efficiency by quickly convolving with the input signals, thereby avoiding the extensive EM calculations required by traditional methods due to changes in the radiation source. Secondly, it achieves significant data compression; the proposed echo model only requires the model parameters to be calculated at the central frequency point, enabling the rapid reconstruction of target echo data across a wideband range and effectively reducing storage requirements. Finally, it presents clear and complete physical attributes by employing a “cause-to-effect” forward-modeling approach, where the model parameters fully represent the inherent physical properties of the target. This effectively addresses the issue of physical interpretability in optical region radar target recognition.
An Efficient Pulse Circuit Design for Magnetic Stimulation with Diversified Waveforms and Adjustable Parameters
Authors: Fang Xiao , Zhang Tao , Luo Yaoyao , Wang Shaolong
Published: June 2024

As a noninvasive neuromodulation technique, transcranial magnetic stimulation (TMS) has important applications both in the exploration of mental disorder causes and the treatment of mental disorders. During the stimulation, the TMS system generates the intracranial time-varying induced E-field (E-field), which alters the membrane potential of neurons and subsequently exerts neural regulatory effects. The temporal waveform of the induced E-fields is directly related to the stimulation effect. To meet the needs of scientific research on diversified stimulation waveforms and flexible adjustable stimulation parameters, a novel efficient pulse magnetic stimulation circuit (the EPMS circuit) design based on asymmetric cascaded multilevel technology is proposed in this paper. Based on the transient analysis of the discharge circuit, this circuit makes it possible to convert the physical quantity (the intracranial induced E-field) that needs to be measured after magnetic stimulation into easily analyzable electrical signals (the discharge voltage at both ends of the stimulation coil in the TMS circuit). This EPMS circuit can not only realize monophasic and biphasic cosine-shaped intracranial induced E-fields, which are widely used in the market, but also realize three types of new intracranial induced E-field stimulation waveform with optional amplitude and adjustable pulse width, including monophasic near-rectangular, biphasic near-rectangular and monophasic/biphasic ladder-shaped stimulation waveform, which breaks through the limitation of the stimulation waveform of traditional TMS systems. Among the new waveforms produced by the EPMS circuit, further research was conducted on the dynamic response characteristics of neurons under the stimulation of the biphasic four-level waveform (the BFL waveform) with controllable parameters. The relationship between TMS circuit parameters (discharge voltage level and duration) and corresponding neural response characteristics (neuron membrane potential change and neuronal polarizability ratio) was explained from a microscopic perspective. Accordingly, the biological physical quantities (neuronal membrane potential) that are difficult to measure can be transformed into easily analyzable electrical signals (the discharge voltage level and duration). Results showed that compared with monophasic and biphasic cosine induced E-fields with the same energy loss, the neuron polarization ratio is decreased by 54.5% and 87.5%, respectively, under the stimulation of BFL waveform, which could effectively enhance the neuromodulation effect and improve the stimulation selectivity.
Wavelength Tuning in Resonant Cavity Interband Cascade Light Emitting Diodes (RCICLEDs) via Post Growth Cavity Length Adjustment
Authors: Schäfer Nicolas , Weih Robert , Scheuermann Julian , Rothmayr Florian , Koeth Johannes , Höfling Sven
Published: June 2024

We demonstrate substrate-emitting resonant cavity interband cascade light emitting diodes (RCICLEDs) based on a single distributed Bragg reflector (DBR). These devices operate in continuous wave mode at room temperature. Compared to standard ICLEDs without a cavity, we achieved an 89% reduction in the emission spectrum width, as indicated by the Full Width Half Maximum (FWHM) of 70 nm. Furthermore, we observed far-field narrowing and improved thermal stability. A single DBR configuration allows the cavity length to be adjusted by adding refractive index-matched material to the top of the epitaxial structure after epitaxial growth. This modification effectively shifts the cavity response towards longer wavelengths. We fabricated emitters comprising two cavities of different lengths, resulting in the emission of two distinct spectral lines that can be independently controlled. This dual-color capability enables one of the emission lines to serve as a built-in reference channel, making these LEDs highly suitable for cost-effective gas-sensing applications.
Nonlinear Regularization Decoding Method for Speech Recognition
Authors: Zhang Jiang , Wang Liejun , Yu Yinfeng , Xu Miaomiao
Published: June 2024

Existing end-to-end speech recognition methods typically employ hybrid decoders based on CTC and Transformer. However, the issue of error accumulation in these hybrid decoders hinders further improvements in accuracy. Additionally, most existing models are built upon Transformer architecture, which tends to be complex and unfriendly to small datasets. Hence, we propose a Nonlinear Regularization Decoding Method for Speech Recognition. Firstly, we introduce the nonlinear Transformer decoder, breaking away from traditional left-to-right or right-to-left decoding orders and enabling associations between any characters, mitigating the limitations of Transformer architectures on small datasets. Secondly, we propose a novel regularization attention module to optimize the attention score matrix, reducing the impact of early errors on later outputs. Finally, we introduce the tiny model to address the challenge of overly large model parameters. The experimental results indicate that our model demonstrates good performance. Compared to the baseline, our model achieves recognition improvements of 0.12%, 0.54%, 0.51%, and 1.2% on the Aishell1, Primewords, Free ST Chinese Corpus, and Common Voice 16.1 datasets of Uyghur, respectively.
Sensory Polymers: Trends, Challenges, and Prospects Ahead
Authors: Virumbrales Cintia , Hernández-Ruiz Raquel , Trigo-López Miriam , Vallejos Saúl , García José M.
Published: June 2024

In recent years, sensory polymers have evolved significantly, emerging as versatile and cost-effective materials valued for their flexibility and lightweight nature. These polymers have transformed into sophisticated, active systems capable of precise detection and interaction, driving innovation across various domains, including smart materials, biomedical diagnostics, environmental monitoring, and industrial safety. Their unique responsiveness to specific stimuli has sparked considerable interest and exploration in numerous applications. However, along with these advancements, notable challenges need to be addressed. Issues such as wearable technology integration, biocompatibility, selectivity and sensitivity enhancement, stability and reliability improvement, signal processing optimization, IoT integration, and data analysis pose significant hurdles. When considered collectively, these challenges present formidable barriers to the commercial viability of sensory polymer-based technologies. Addressing these challenges requires a multifaceted approach encompassing technological innovation, regulatory compliance, market analysis, and commercialization strategies. Successfully navigating these complexities is essential for unlocking the full potential of sensory polymers and ensuring their widespread adoption and impact across industries, while also providing guidance to the scientific community to focus their research on the challenges of polymeric sensors and to understand the future prospects where research efforts need to be directed.
DELTA: Integrating Multimodal Sensing with Micromobility for Enhanced Sidewalk and Pedestrian Route Understanding
Authors: Akhavi Zadegan Alireza , Vivet Damien , Hadachi Amnir
Published: June 2024

Urban environments are undergoing significant transformations, with pedestrian areas emerging as complex hubs of diverse mobility modes. This shift demands a more nuanced approach to urban planning and navigation technologies, highlighting the limitations of traditional, road-centric datasets in capturing the detailed dynamics of pedestrian spaces. In response, we introduce the DELTA dataset, designed to improve the analysis and mapping of pedestrian zones, thereby filling the critical need for sidewalk-centric multimodal datasets. The DELTA dataset was collected in a single urban setting using a custom-designed modular multi-sensing e-scooter platform encompassing high-resolution and synchronized audio, visual, LiDAR, and GNSS/IMU data. This assembly provides a detailed, contextually varied view of urban pedestrian environments. We developed three distinct pedestrian route segmentation models for various sensors—the 4K camera, stereocamera, and LiDAR—each optimized to capitalize on the unique strengths and characteristics of the respective sensor. These models have demonstrated strong performance, with Mean Intersection over Union (IoU) values of 0.84 for the reflectivity channel, 0.96 for the 4K camera, and 0.92 for the stereocamera, underscoring their effectiveness in ensuring precise pedestrian route identification across different resolutions and sensor types. Further, we explored audio event-based classification to connect unique soundscapes with specific geolocations, enriching the spatial understanding of urban environments by associating distinctive auditory signatures with their precise geographical origins. We also discuss potential use cases for the DELTA dataset and the limitations and future possibilities of our research, aiming to expand our understanding of pedestrian environments.
The Effect of Changing the Angle of the Passenger Car Seat Backrest on the Head Trajectories of the 50th Percentile Male Dummy
Authors: Frej Damian
Published: June 2024

The aim of the study is to compare the head displacement of the KPSIT C50 dummy during a frontal collision at a speed of 20 km/h, along with the change in the angle of the car seat backrest. Passenger car manufacturers recommend setting the backrest angle of the car seat between 100 and 125 degrees. It should be noted that the driver’s position is of great importance in the event of a collision injury. In the event of a rear-end collision, the position of the headrest of the car seat is an element that affects the degree of the driver’s injuries. In extreme cases, incorrect positioning of the headrest, even at low speed, can lead to serious injuries to the cervical spine and even death. The article is part of a large-scale study on low-speed crash testing. The research problem concerned the influence of the seat backrest angle on the head displacement during a low-speed collision. The article compares the displacement of the head of the KPSIT C50 dummy during a series of crash tests, where the angle of the car seat backrest was changed. On the basis of the research, it was found that the optimal angle of the car seat backrest is 110 degrees. In addition, a preliminary analysis of the displacements of the dummy’s head showed a high risk of whiplash injury in people sitting in a fully reclined seat.
Subsidence Characteristics in North Anhui Coal Mining Areas Using Space–Air–Ground Collaborative Observations
Authors: Quan Li’ao , Jin Shuanggen , Zhang Jianxin , Chen Junyun , He Junjun
Published: June 2024

To fully comprehend the patterns of land and ecological damage caused by coal mining subsidence, and to scientifically carry out ecological mine restoration and management, it is urgent to accurately grasp the information of coal mining, particularly in complex coaling areas, such as North Anhui, China. In this paper, a space–air–ground collaborative monitoring system was constructed for coal mining areas based on multi-source remote sensing data and subsidence characteristics of coaling areas were investigated in North Anhui. It was found that from 2019 to 2022, 16 new coal mining subsidence areas were found in northern Anhui, with the total area increasing by 8.1%. In terms of land use, water areas were increased by 101.9 km2 from 2012 to 2022, cultivated land was decreased by 99.3 km2, and residence land was decreased by 11.8 km2. The depth of land subsidence in the subsidence areas is divided into 307.9 km2 of light subsidence areas with a subsidence depth of less than 500 mm; 161.8 km2 of medium subsidence areas with a subsidence depth between 500 mm and 1500 mm; and 281.2 km2 of heavy subsidence areas with a subsidence depth greater than 1500 mm. The total subsidence governance area is 191.2 km2, accounting for 26.5% of the total subsidence area. From the perspective of prefecture-level cities, the governance rate reaches 51.3% in Huaibei, 10.1% in Huainan, and 13.6% in Fuyang. The total reclamation area is 68.8 km2, accounting for 34.5% of the subsidence governance area. At present, 276.1 km2 within the subsidence area has reached stable subsidence conditions, mainly distributed in the Huaibei mining area, which accounts for about 60% of the total stable subsidence area.
The Role of Artificial Intelligence in Patient Education: A Bladder Cancer Consultation with ChatGPT
Authors: Guo Allen Ao , Razi Basil , Kim Paul , Canagasingham Ashan , Vass Justin , Chalasani Venu , Rasiah Krishan , Chung Amanda
Published: June 2024

Objectives: ChatGPT is a large language model that is able to generate human-like text. The aim of this study was to evaluate ChatGPT as a potential supplement to urological clinical practice by exploring its capacity, efficacy and accuracy when delivering information on frequently asked questions from patients with bladder cancer. Methods: We proposed 10 hypothetical questions to ChatGPT to simulate a doctor–patient consultation for patients recently diagnosed with bladder cancer. The responses were then assessed using two predefined scales of accuracy and completeness by Specialist Urologists. Results: ChatGPT provided coherent answers that were concise and easily comprehensible. Overall, mean accuracy scores for the 10 questions ranged from 3.7 to 6.0, with a median of 5.0. Mean completeness scores ranged from 1.3 to 2.3, with a median of 1.8. ChatGPT was also cognizant of its own limitations and recommended all patients should adhere closely to medical advice dispensed by their healthcare provider. Conclusions: This study provides further insight into the role of ChatGPT as an adjunct consultation tool for answering frequently asked questions from patients with bladder cancer diagnosis. Whilst it was able to provide information in a concise and coherent manner, there were concerns regarding the completeness of information conveyed. Further development and research into this rapidly evolving tool are required to ascertain the potential impacts of AI models such as ChatGPT in urology and the broader healthcare landscape.
A Comparative Analysis of Acute Physiological and Perceptual Responses in Whole-Body and Ergometer-Based High-Intensity Interval Training Protocols
Authors: Schaun Gustavo Z. , Orcy Rafael B. , Del Vecchio Fabrício B.
Published: June 2024

The primary aim of the present investigation was to compare the acute physiological and perceptual responses between two modes of interval training using a randomized crossover design. More specifically, eleven young adult participants (23 ± 4 years, 77 ± 13 kg, 178 ± 7 cm) performed two protocols: one composed of whole-body calisthenics exercises and another on a cycle ergometer. Both protocols encompassed eight 20 s bouts at intensities equivalent to all-out (HIIT-WB) and 170% of the maximal power output (HIIT-C), respectively, interspersed with 10 s of passive rest. The peak and average heart rate, the rating of perceived effort, and blood lactate, creatine kinase, and lactate dehydrogenase concentrations were measured. Aside from blood lactate (HIIT-WB = 9.4 ± 1.8 mmo/L; HIIT-C = 12.5 ± 2.5 mmol/L, p < 0.05) and the rating of perceived exertion (HIIT-WB = 8.8 ± 0.9; HIIT-C = 9.6 ± 0.5, p < 0.05), physiological responses did not significantly differ between protocols (all p > 0.05), with high average heart rate values (HIIT-WB = 86 ± 6% HRmax; HIIT-C = 87 ± 4% HRmax) and a low magnitude of muscle damage, as inferred by CK and LDH concentrations (HIIT-WB = 205.9 ± 56.3 and 203.5 ± 72.4 U/L; HIIT-C = 234.5 ± 77.1 and 155.1 ± 65.3 U/L), respectively. It can be concluded that both protocols elicit vigorous heart rate responses and a low magnitude of muscle damage and, therefore, appear as viable alternatives to improve aerobic fitness. The inclusion of a whole-body HIIT protocol may be an interesting alternative for training prescription in relation to more common interval training protocols.
Single-Shot Visual Relationship Detection for the Accurate Identification of Contact-Driven Hazards in Sustainable Digitized Construction
Authors: Kim Daeho , Goyal Ankit , Lee SangHyun , Kamat Vineet R. , Liu Meiyin
Published: June 2024

Deploying construction robots alongside workers presents the risk of unwanted forcible contact—a critical safety concern. To address a semantic digital twin where such contact-driven hazards can be monitored accurately, the authors present a single-shot deep neural network (DNN) model that can perform proximity and relationship detections simultaneously. Given that workers and construction robots must sometimes collaborate in close proximity, their relationship must be considered, along with proximity, before concluding an event is a hazard. To address this issue, we leveraged a unique two-in-one DNN architecture called Pixel2Graph (i.e., object + relationship detections). The potential of this DNN architecture for relationship detection was confirmed by follow-up testing using real-site images, achieving 90.63% recall@5 when object bounding boxes and classes were given. When integrated with existing proximity monitoring methods, single-shot visual relationship detection will enable the accurate identification of contact-driven hazards in a digital twin platform, an essential step in realizing sustainable and safe collaboration between workers and robots.
The Nonlinear Effect of Economic Policy Uncertainty on Corporate Social Responsibility
Authors: Ou Cuiling , Yan Kegao
Published: June 2024

The debate on how corporate social responsibility (CSR) responds to and reacts to the increasing economic policy uncertainty (EPU) is still unsettled. The present study sheds light on the curvilinear relationship between EPU and CSR, employing the provincial EPU index in China for the period of 2010–2016. More precisely, when EPU is in an appropriate range, as EPU grows, its CSR engagement experiences an initial increase; however, once the threshold value is reached, the additional increments in EPU result in a reduction in CSR engagement. Additionally, the inflection point of the state-owned firms, large-sized firms, and firms in their mature stage are large, indicating that economic policy uncertainty influences corporate social responsibility positively in a rather wider range. We also show that the inverted U-shaped effect is negatively moderated by financial constraints and government interventions. The findings of this study hold significance for policy-makers, offering valuable insights related to the structuring of efficient policies.
Spatial and Temporal Dynamic Evolution and Correlation of Ecological Quality and Ecosystem Service Value in Fujian Province
Authors: Zheng Peng , Jin Lanting , Huang Yuxiao , Pan Wenbin
Published: June 2024

To promote sustainable development and improve human well-being, understanding how ecological quality influences ecosystem service value is essential. In this study, we quantified the ecological quality and ecosystem service value in Fujian Province from 2000 to 2021 using the Remote-Sensing Ecological Index (RSEI) and the Equivalent Factor Approach, respectively. This analysis aimed to examine their spatial and temporal dynamic evolution and to explore the correlation between the two. The results indicate that the temporal and spatial patterns of ecological quality and ecosystem service value in Fujian Province from 2000 to 2021 were similar, with both showing fluctuating upward trends over time. The spatial distributions were high in central and northern Fujian and low in the southeastern coastal areas. The Pearson correlation analysis revealed reveals a significant positive correlation between the RSEI and ecosystem service value (r = 0.673, p < 0.01), suggesting a synergistic relationship. The highest correlation was observed between the supporting service value and the RSEI indicators (|r| = 0.449–0.815, p < 0.01), and between the NDVI and the supporting service value (r = 0.815, p < 0.01). The spatial autocorrelation analysis also showed that ecological quality and ecosystem service value were positively correlated spatially, with high–high agglomerations concentrated in northwest and central Fujian, and low–low agglomerations in the coastal area of south Fujian.
Ecolodge Tourism Dynamics: A Village-Level Analysis of Marketing and Policy Indicators in Iran’s Hawraman Region
Authors: Pourtaheri Mehdi , Torabi Zabih-Allah , Khavarian-Garmsir Amir Reza , Sajadi Saeed , Hall Colin Micheal
Published: June 2024

Despite growing interest in community-based ecotourism as a pathway to sustainable development, there is a critical lack of research on how localized configurations of marketing and policy factors shape ecolodge performance at the village level. This study addresses this gap by pioneering a comprehensive, contextually grounded analysis of the availability and desirability of key marketing and policy indicators across 25 rural villages in Iran’s culturally and ecologically rich Hawraman region. By employing an innovative mixed-methods approach blending statistical analysis, GIS spatial modeling, surveys, and interviews, the research reveals striking heterogeneity in ecolodge constraints and opportunities, even within a localized setting. Spatial analysis uncovers distance decay effects, with peripheral villages facing exacerbated accessibility deficits compared to centrally located ones. Substantial variations in marketing effectiveness and governance capacities underscore the inadequacy of one-size-fits-all tourism planning. The findings make a novel contribution by demonstrating the imperative for transitioning from monolithic interventions to hyper-localized, place-based strategies tailored to each village’s unique barrier and asset profiles. Embracing this paradigm shift promises to enhance the equity and sustainability of ecolodge tourism in rural contexts globally. The study provides an original methodological blueprint for harnessing multi-modal diagnostics to unpack complex tourism dynamics and catalyze transformative, community-centric solutions.
Rural Area Resilience during the COVID-19 Pandemic as Exemplified by Urban–Rural Communes in Poland
Authors: Zwolińska-Ligaj Magdalena Anna , Guzal-Dec Danuta Jolanta
Published: June 2024

The purpose of the paper is to characterize the outcomes of the COVID-19 pandemic for farms and resilience activities performed by farmers in response to the economic crisis caused by the COVID-19 pandemic in the context of building rural area resilience. Research was carried out in all 87 urban–rural communes in Poland and focused on special determinants of rural resilience such as connections between small cities and rural areas, as well as the location of the territorial unit (peripheral versus non-peripheral). The purpose of the survey was to poll local government representatives on the outcomes of the COVID-19 pandemic for farms and identify resilience activities performed by farmers in response to the economic crisis caused by the COVID-19 pandemic. Empirical research was performed from September to October 2021. The results emphasize the significance of the diversification of farms and networks for strategies for coping with the COVID-19 crisis. The research revealed processes wherein farms adapted, even if to a small extent, to crisis conditions. The COVID-19 pandemic brought new challenges, at the same time stimulating innovative responses in communities and businesses in rural areas. This study also confirms the role of ITC solutions in the process of adaptation to the crisis and implies a need to strengthen local links between the rural area and the city, especially those relevant to peripheral areas. Local government authorities play a crucial role in this process.