bluuebunny/crossref_metadata_2025 huggingface dataset:
Crystalline Flat Surface Recovered by High-Temperature Annealing after Laser Ablation
Authors: Smith Daniel , Ng Soon Hock , Tang Amanda , Katkus Tomas , Moraru Daniel , Juodkazis Saulius
Published: May 2023

Ultra-short laser pulses (1030 nm/230 fs) were used to laser ablate the surface of crystalline sapphire (Al2O3) at high intensity per pulse 20–200 TW/cm2/pulse. Laser-ablated patterns were annealed at a high temperature of 1500 °C. Surface reconstruction took place, removing the ablation debris field at the edges of ablated pits in oxygen flow (O2 flow). Partial reconstruction of ripples was also observed when multi-pulse ablated surfaces were annealed at high temperature in O2 flow. Back-side ablation of a 0.5-mm-thick Al2O3 produced high surface roughness ∼1μm which was reduced to ∼0.2μm by high-temperature annealing at 1500 °C for 2 h in O2. Improvement of surface quality was due to restructuring of the crystalline surface and sublimation, while the defined 3D shape of a micro-lens was not altered after HTA (no thermal morphing).
Assessing Diatom Community Dynamics in a Recovering Agricultural Stream in Middle Georgia, USA
Authors: Brown Sydney , Manoylov Kalina M.
Published: May 2023

Streams play vital roles in surrounding communities and provide essential ecosystem services. The protection of streams is important, they are legally protected under the Clean Water Act, and they can be monitored through the continuous analyses of biological data, such as algal or other aquatic communities. The goals of this study were to analyze the long-term recovery of Tobler Creek, a recovering agricultural stream on the site of a National Historic Landmark, and yield comparisons to a local low-impact stream, Murder Creek, located within the Oconee National Forest. In 2011, Tobler Creek exceeded reference criteria values for total phosphorus (TP) but met the nitrate nitrogen criteria. With an 11-year recovery period, Tobler Creek met both nutrient reference criteria. In 2022, periphyton samples were collected according to standard protocols, confirming that diatoms are the dominant algal group in the community. With recovery, Tobler Creek showed an increase in diatom species richness (χ2 = 116.11, df = 5, p < 0.01) but this was significantly lower than the values documented in Murder Creek. The multi-metric index (MMI), calculated using diatom community analysis to estimate ecological health, indicated that Tobler Creek experienced degradation from 2011 to 2022 (χ2 = 55.97, df = 5, p < 0.05) and is below the regional 25th percentile. The percentage of sediment-tolerant taxa (surirelloid, naviculoid, and nitzschoid) was significantly higher in Tobler Creek in 2022 (χ2 = 500.96, df = 5, p < 0.01) compared to Murder Creek in 2022 (t = −4.67, df = 10, p < 0.01). Despite a reduction in nutrients given the 42-year recovery period, the diatom community in Tobler Creek was significantly different than other regional protected streams. Ecological degradation of the habitat was likely driven by sedimentation due to run-off in the recovering agriculture stream. These findings highlight the importance of protecting water quality, as the recovery of nutrients can be a decades-long process overlayed with many potentially new stressors influencing aquatic organisms.
Experimental Study of In-Process Heat Treatment on the Mechanical Properties of 3D Printed Thermoplastic Polymer PLA
Authors: Tamașag Ioan , Beșliu-Băncescu Irina , Severin Traian-Lucian , Dulucheanu Constantin , Cerlincă Delia-Aurora
Published: May 2023

The scientific literature regarding additive manufacturing, mainly the material extrusion method, suggests that the mechanical characteristics of the parts obtained by this technology depend on a number of the input factors specific to the printing process, such as printing temperature, printing trajectory, layer height, etc., and also on the post-process operations for parts, which, unfortunately, requires supplementary setups, equipment, and multiple steps that raise the overall costs. Therefore, this paper aims to investigate the influence of the printing direction, the thickness of the deposited material layer, and the temperature of the previously deposited material layer on the part tensile strength, hardness by means of Shore D and Martens hardness, and surface finish by using an in-process annealing method. A Taguchi L9 DOE plan was developed for this purpose, where the test specimens, with dimensions according to ISO 527-2 type B, were analysed. The results showed that the presented in-process treatment method is possible and could lead to sustainable and cost-effective manufacturing processes. The varied input factors influenced all the studied parameters. Tensile strength tended to increase, up to 12.5%, when the in-process heat treatment was applied, showed a positive linear variation with nozzle diameter, and presented considerable variations with the printing direction. Shore D and Martens hardness had similar variations, and it could be observed that by applying the mentioned in-process heat treatment, the overall values tended to decrease. Printing direction had a negligible impact on the additively manufactured parts’ hardness. At the same time, the nozzle diameter presented considerable variations, up to 36% for Martens hardness and 4% for Shore D, when higher diameter nozzles were used. The ANOVA analysis highlighted that the statistically significant factors were the nozzle diameter for the part’s hardness and the printing direction for the tensile strength.
Composite Hydrogels Based on Cross-Linked Chitosan and Low Molecular Weight Hyaluronic Acid for Tissue Engineering
Authors: Drozdova Maria , Vodyakova Marina , Tolstova Tatiana , Chernogortseva Marina , Sazhnev Nikita , Demina Tatiana , Aksenova Nadezhda , Timashev Peter , Kildeeva Nataliya , Markvicheva Elena
Published: May 2023

The objectives of the study were as follows: (1) to develop two methods for the preparation of macroporous composite chitosan/hyaluronic acid (Ch/HA) hydrogels based on covalently cross-linked Ch and low molecular weight (Mw) HA (5 and 30 kDa); (2) to investigate some properties (swelling and in vitro degradation) and structures of the hydrogels; (3) to evaluate the hydrogels in vitro as potential biodegradable matrices for tissue engineering. Chitosan was cross-linked with either genipin (Gen) or glutaraldehyde (GA). Method 1 allowed the distribution of HA macromolecules within the hydrogel (bulk modification). In Method 2, hyaluronic acid formed a polyelectrolyte complex with Ch over the hydrogel surface (surface modification). By varying compositions of the Ch/HA hydrogels, highly porous interconnected structures (with mean pore sizes of 50–450 μm) were fabricated and studied using confocal laser scanning microscopy (CLSM). Mouse fibroblasts (L929) were cultured in the hydrogels for 7 days. Cell growth and proliferation within the hydrogel samples were studied via MTT-assay. The entrapment of low molecular weight HA was found to result in an enhancement of cell growth in the Ch/HA hydrogels compared to that in the Ch matrices. The Ch/HA hydrogels after bulk modification promoted better cell adhesion, growth and proliferation than the samples prepared by using Method 2 (surface modification).
Polysaccharides—Naturally Occurring Immune Modulators
Authors: Murphy Emma J. , Fehrenbach Gustavo Waltzer , Abidin Ismin Zainol , Buckley Ciara , Montgomery Therese , Pogue Robert , Murray Patrick , Major Ian , Rezoagli Emanuele
Published: May 2023

The prevention of disease and infection requires immune systems that operate effectively. This is accomplished by the elimination of infections and abnormal cells. Immune or biological therapy treats disease by either stimulating or inhibiting the immune system, dependent upon the circumstances. In plants, animals, and microbes, polysaccharides are abundant biomacromolecules. Due to the intricacy of their structure, polysaccharides may interact with and impact the immune response; hence, they play a crucial role in the treatment of several human illnesses. There is an urgent need for the identification of natural biomolecules that may prevent infection and treat chronic disease. This article addresses some of the naturally occurring polysaccharides of known therapeutic potential that have already been identified. This article also discusses extraction methods and immunological modulatory capabilities.
Two-Way Reversible Shape Memory Behavior of Chitosan/Glycerol Film Triggered by Water
Authors: Li Shuozi , Lyu Hu , Wang Yujia , Kong Xianzhi , Wu Xiangxian , Zhang Lina , Guo Xiaojuan , Zhang Dawei
Published: May 2023

Reversible shape memory polymers (SRMPs) have been identified as having great potential for biomedical applications due to their ability to switch between different shapes responding to stimuli. In this paper, a chitosan/glycerol (CS/GL) film with a reversible shape memory behavior was prepared, and the reversible shape memory effect (SME) and its mechanism were systematically investigated. The film with 40% glycerin/chitosan mass ratio demonstrated the best performance, with 95.7% shape recovery ratio to temporary shape one and 89.4% shape recovery ratio to temporary shape two. Moreover, it shows the capability to undergo four consecutive shape memory cycles. In addition, a new curvature measurement method was used to accurately calculate the shape recovery ratio. The suction and discharge of free water change the binding form of the hydrogen bonds inside the material, which makes a great reversible shape memory impact on the composite film. The incorporation of glycerol can enhance the precision and repeatability of the reversible shape memory effect and shortens the time used during this process. This paper gives a hypothetical premise to the preparation of two-way reversible shape memory polymers.
Melanin Nanoparticles Obtained from Preformed Recombinant Melanin by Bottom-Up and Top-Down Approaches
Authors: Alcalá-Alcalá Sergio , Casarrubias-Anacleto José Eduardo , Mondragón-Guillén Maximiliano , Tavira-Montalvan Carlos Alberto , Bonilla-Hernández Marcos , Gómez-Galicia Diana Lizbeth , Gosset Guillermo , Meneses-Acosta Angélica
Published: May 2023

Melanin is an insoluble, amorphous polymer that forms planar sheets that aggregate naturally to create colloidal particles with several biological functions. Based on this, here, a preformed recombinant melanin (PRM) was utilized as the polymeric raw material to generate recombinant melanin nanoparticles (RMNPs). These nanoparticles were prepared using bottom-up (nanocrystallization—NC, and double emulsion–solvent evaporation—DE) and top-down (high-pressure homogenization—HP) manufacturing approaches. The particle size, Z-potential, identity, stability, morphology, and solid-state properties were evaluated. RMNP biocompatibility was determined in human embryogenic kidney (HEK293) and human epidermal keratinocyte (HEKn) cell lines. RMNPs prepared by NC reached a particle size of 245.9 ± 31.5 nm and a Z-potential of −20.2 ± 1.56 mV; 253.1 ± 30.6 nm and −39.2 ± 0.56 mV compared to that obtained by DE, as well as RMNPs of 302.2 ± 69.9 nm and −38.6 ± 2.25 mV using HP. Spherical and solid nanostructures in the bottom-up approaches were observed; however, they were an irregular shape with a wide size distribution when the HP method was applied. Infrared (IR) spectra showed no changes in the chemical structure of the melanin after the manufacturing process but did exhibit an amorphous crystal rearrangement according to calorimetric and PXRD analysis. All RMNPs presented long stability in an aqueous suspension and resistance to being sterilized by wet steam and ultraviolet (UV) radiation. Finally, cytotoxicity assays showed that RMNPs are safe up to 100 μg/mL. These findings open new possibilities for obtaining melanin nanoparticles with potential applications in drug delivery, tissue engineering, diagnosis, and sun protection, among others.
Urushiol-Based Benzoxazine Containing Sulfobetaine Groups for Sustainable Marine Antifouling Applications
Authors: Zhao Jing , Chen Jipeng , Zheng Xiaoxiao , Lin Qi , Zheng Guocai , Xu Yanlian , Lin Fengcai
Published: May 2023

Benzoxazine resins are new thermosetting resins with excellent thermal stability, mechanical properties, and a flexible molecular design, demonstrating promise for applications in marine antifouling coatings. However, designing a multifunctional green benzoxazine resin-derived antifouling coating that combines resistance to biological protein adhesion, a high antibacterial rate, and low algal adhesion is still challenging. In this study, a high-performance coating with a low environmental impact was synthesized using urushiol-based benzoxazine containing tertiary amines as the precursor, and a sulfobetaine moiety into the benzoxazine group was introduced. This sulfobetaine-functionalized urushiol-based polybenzoxazine coating (poly(U−ea/sb)) was capable of clearly killing marine biofouling bacteria adhered to the coating surface and significantly resisting protein attachment. poly(U−ea/sb) exhibited an antibacterial rate of 99.99% against common Gram negative bacteria (e.g., Escherichia coli and Vibrio alginolyticus) and Gram positive bacteria (e.g., Staphylococcus aureus and Bacillus sp.), with >99% its algal inhibition activity, and it effectively prevented microbial adherence. Here, a dual-function crosslinkable zwitterionic polymer, which used an “offensive-defensive” tactic to improve the antifouling characteristics of the coating was presented. This simple, economic, and feasible strategy provides new ideas for the development of green marine antifouling coating materials with excellent performance.
The Effects of Cross-Linking Agents on the Mechanical Properties of Poly (Methyl Methacrylate) Resin
Authors: Ceylan Gulsum , Emik Serkan , Yalcinyuva Tuncer , Sunbuloğlu Emin , Bozdag Ergun , Unalan Fatma
Published: May 2023

Cross-linking agents are incorporated into denture base materials to improve their mechanical properties. This study investigated the effects of various cross-linking agents, with different cross-linking chain lengths and flexibilities, on the flexural strength, impact strength, and surface hardness of polymethyl methacrylate (PMMA). The cross-linking agents used were ethylene glycol dimethacrylate (EGDMA), tetraethylene glycol dimethacrylate (TEGDMA), tetraethylene glycol diacrylate (TEGDA), and polyethylene glycol dimethacrylate (PEGDMA). These agents were added to the methyl methacrylate (MMA) monomer component in concentrations of 5%, 10%, 15%, and 20% by volume and 10% by molecular weight. A total of 630 specimens, comprising 21 groups, were fabricated. Flexural strength and elastic modulus were assessed using a 3-point bending test, impact strength was measured via the Charpy type test, and surface Vickers hardness was determined. Statistical analyses were performed using the Kolmogorov–Smirnov Test, Kruskal–Wallis Test, Mann–Whitney U Test, and ANOVA with post hoc Tamhane test (p ≤ 0.05). No significant increase in flexural strength, elastic modulus, or impact strength was observed in the cross-linking groups compared to conventional PMMA. However, surface hardness values notably decreased with the addition of 5% to 20% PEGDMA. The incorporation of cross-linking agents in concentrations ranging from 5% to 15% led to an improvement in the mechanical properties of PMMA.
Exploring Relationships among Crude Oil, Bitcoin, and Carbon Dioxide Emissions: Quantile Mediation Analysis
Authors: Hsu Tzu-Kuang , Lien Wan-Chu , Lee Yao-Hsien
Published: May 2023

Crude oil, Bitcoin, and carbon dioxide emissions are major issues that are significantly impacting the global economy and environment. These three issues are complexly interlinked, with profound economic and environmental implications. In this study, we explore the correlation among these three issues and attempt to understand the influence of crude oil and Bitcoin on carbon dioxide emissions. We created a novel approach, named quantile mediation analysis, which blends mediation regression with quantile regression, enabling us to explore the influence of Brent crude oil on carbon dioxide emissions by considering the mediating impact of Bitcoin. According to the findings from using our new approach, the impact of Brent crude oil on carbon dioxide emissions is partly mediated by Bitcoin, and the association between Brent crude oil and carbon dioxide emissions involves both direct and indirect effects. Since the carbon dioxide generated by the extraction of crude oil and Bitcoin has a great impact on the environment, accelerating the use of clean energy technologies to reduce our reliance on crude oil should be the direction that the cryptocurrency industry ought to pursue in the future.
Research on Landslide Displacement Prediction Based on DES-CGSSA-BP Model
Authors: Fang Lu , Yue Jianping , Xing Yin
Published: May 2023

A landslide is a type of natural disaster that has the highest frequency, the widest distribution and the heaviest losses worldwide; landslides seriously threaten human life and property and major engineering facilities. Therefore, it is important to improve landslide displacement prediction technology to avoid and mitigate landslide disasters. A landslide displacement prediction method based on a chaotic Gaussian mutation sparrow search algorithm-optimised BP neural network (CG-SSA-BP) is proposed to address the problems of the traditional sparrow search algorithm (SSA)-optimised BP (SSA-BP) neural network; it tends to fall into local optima, and it has slow convergence and a low prediction accuracy for landslide displacement prediction. This paper takes the Baishui River landslide in the Three Gorges reservoir area as the research object, and the double exponential smoothing (DES) method is used to decompose the landslide displacement into a trend term and a periodic term to solve the nonlinear landslide system problem. The results show that the prediction model based on CG-SSA-BP has a better prediction accuracy and better stability compared with the model based on SSA-BP.
The Adriatic Catholic Marian Pilgrimage in Nin near Zadar as a Maritime Pilgrimage
Authors: Hrovatin Mirela
Published: May 2023

Following the general approach to pilgrimage as established by anthropologists and other scientists, the paper analyses the pilgrimage in Nin to Our Lady of Zečevo. More specifically, this pilgrimage will be observed as a maritime pilgrimage, following relevant recent research. Based on the oral story about the apparition of Virgin Mary to a widow, the statue of Mary is transported from Nin in a boat procession via sea to a mediaeval church on the nearby uninhabited island of Zečevo. Pilgrimage practices include many sensorial and symbolic practices, so it will be analysed from several points of view and more than one theoretical approach, including the relational approach and mobility turn, applied also to maritime pilgrimage with a reflection on influence of tourism on pilgrimage activities, especially in the Mediterranean. The paper relies on the field research from 2020–2023 in Nin near Zadar in Croatia which has been supported in part by the Croatian Science Foundation under the project ‘PILGRIMAR’ (UIP-2019-04-8226).
Coping and Religiosity of Polish Breast Cancer Patients
Authors: Żołnierz Joanna , Sak Jarosław
Published: May 2023

Religiosity can support a patient in coping with a stressful situation such as breast cancer. In this study, the authors aimed to explain the relationships between the religiosity of the respondents and the religious crises they experienced and coping strategies, as well as between coping strategies and the disease duration. The research method used is the method of diagnostic survey, and the tools: a questionnaire of our own, making it possible to determine sociodemographic variables and standardized scales: the Inventory for Measuring Coping with Stress—Mini-COPE (the brief COPE), the Polish Centrality of Religiosity Scale (CRS) and the Religious Crisis Scale by W. Prężyna (RCS). With approval from the Bioethics Committee at the Medical University of Lublin (KE-0254/133/2015), 69 female subjects with breast cancer were studied. The results showed statistically significant positive correlations between the centrality of religiosity and selected components of religiosity and action-oriented coping strategies. RCS scores correlate negatively with more adaptive coping strategies and positively with ineffective ones. Additionally, patients suffering from breast cancer for more than five years, are statistically significantly different from those with shorter disease duration only in their scores for the CRS “public practice” subscale. Mature religiosity promotes the adoption of constructive coping strategies, while religious crisis hinders the process of coping with stressful situations. It appears necessary to integrate spiritual care into the treatment process of cancer patients.
Towards an Accurate and Reliable Downscaling Scheme for High-Spatial-Resolution Precipitation Data
Authors: Zhu Honglin , Liu Huizeng , Zhou Qiming , Cui Aihong
Published: May 2023

Accurate high-spatial-resolution precipitation is significantly important in hydrological and meteorological modelling, especially in rain-gauge-sparse areas. Some methods and strategies have been applied for satellite-based precipitation downscaling, residual correction and precipitation calibration. However, which downscaling scheme can provide reliable high-resolution precipitation efficiently remains unanswered. To address this issue, this study aimed to present a framework combining the machine learning downscaling algorithm and post-process procedures. Firstly, four ML-based models, namely support vector regression, random forest, spatial random forest (SRF) and eXtreme gradient boosting (XGBoost), were tested for downscaling and compared with conventional downscaling methods. Then, the effectiveness of the residual correction process using ordinary Kriging and the calibration process using the geographical difference analysis (GDA) method was investigated. The results showed that the ML-based methods had better performance than the conventional regression and interpolation approaches. The SRF and XGBoost outperformed others in generating accurate precipitation estimation with a high resolution. The GDA calibration process significantly improved the downscaled results. However, the residual correction process decreased the downscaling performance of the ML-based models. Combining the SRF or XGBoost downscaling algorithm with the GDA calibration method could be a promising downscaling scheme for precipitation data. The scheme could be used to generate high-resolution precipitation, especially in areas urgently requiring data, which would benefit regional water resource management and hydrological disaster prevention.
Multi-Scale Depthwise Separable Convolution for Semantic Segmentation in Street–Road Scenes
Authors: Dai Yingpeng , Li Chenglin , Su Xiaohang , Liu Hongxian , Li Jiehao
Published: May 2023

Vision is an important way for unmanned mobile platforms to understand surrounding environmental information. For an unmanned mobile platform, quickly and accurately obtaining environmental information is a basic requirement for its subsequent visual tasks. Based on this, a unique convolution module called Multi-Scale Depthwise Separable Convolution module is proposed for real-time semantic segmentation. This module mainly consists of concatenation pointwise convolution and multi-scale depthwise convolution. Not only does the concatenation pointwise convolution change the number of channels, but it also combines the spatial features from the multi-scale depthwise convolution operations to produce additional features. The Multi-Scale Depthwise Separable Convolution module can strengthen the non-linear relationship between input and output. Specifically, the multi-scale depthwise convolution module extracts multi-scale spatial features while remaining lightweight. This fully uses multi-scale information to describe objects despite their different sizes. Here, Mean Intersection over Union (MIoU), parameters, and inference speed were used to describe the performance of the proposed network. On the Camvid, KITTI, and Cityscapes datasets, the proposed algorithm compromised between accuracy and memory in comparison to widely used and cutting-edge algorithms. In particular, the proposed algorithm acquired 61.02 MIoU with 2.68 M parameters on the Camvid test dataset.
Attention-Aware Spectral Difference Representation for Hyperspectral Anomaly Detection
Authors: Zhang Wuxia , Guo Huibo , Liu Shuo , Wu Siyuan
Published: May 2023

Hyperspectral Anomaly Detection (HAD) aims to detect the pixel or target whose spectral characteristics are significantly different from the surrounding pixels or targets. The effectiveness of reconstructing the background model is an essential element affecting the improvement of the HAD performance. This paper proposes a Hyperspectral Anomaly Detection method based on Attention-aware Spectral Difference Representation (HAD-ASDR) to reconstruct more accurate background models by using the generated noise distribution matchable to the background as input. The proposed HAD-ASDR mainly includes three modules: Attention-aware Spectral Difference Representation Module (ASDRM), Convolutional Auto-Encoder based Background Reconstruction Module (CAE-BRM) and Joint Spectrum Intensity and Angle based Anomaly Detection Module (JSIA-ADM). First, inspired by Generative Adversarial Network (GAN), ASDRM is proposed to generate a noise distribution that better matches the background by the attention mechanism and the different operation. Then, CAE-BRM is employed to reconstruct the accurate background using the generated noise distribution as input and the convolutional auto-encoder with skip connections. Finally, JSIA-ADM is presented to detect anomalies more accurately by calculating the reconstructed errors from both spectral intensity and angle perspectives. The proposed HAD-ASDR has been verified on five data sets and achieves better or comparable HAD results compared to six other comparison methods. The average AUC of HAD-ASDR on these five data sets is 0.9817 higher than that of the comparison methods, resulting in an improvement of 0.0253. The experimental results demonstrate its superior performance and stability.
Algorithm for the Reconstruction of the Ground Surface Reflectance in the Visible and Near IR Ranges from MODIS Satellite Data with Allowance for the Influence of Ground Surface Inhomogeneity on the Adjacency Effect and of Multiple Radiation Reflection
Authors: Tarasenkov Mikhail V. , Belov Vladimir V. , Engel Marina V. , Zimovaya Anna V. , Zonov Matvei N. , Bogdanova Alexandra S.
Published: May 2023

An atmospheric correction algorithm is proposed for the reconstruction of the ground surface reflectance from the data of satellite measurements. A distinctive feature of the algorithm is that it takes into account the influence of the ground surface inhomogeneity on the adjacency effect and additional illumination of the ground surface by reflected radiation. These factors are important for the reconstruction of the reflectance of ground surface fragments with sharp reflectance changes and high atmospheric turbidity. The algorithm is based on Monte Carlo programs developed by the authors. To reduce the computing time, we have proposed some original criteria and approaches. To estimate the capabilities of the developed algorithm, its results have been validated by comparing with the results of the MOD09 algorithm for four MODIS bands and measurements for the Portugal surface fragment with coordinates 38.829 N, 8.791 W. Good agreement of the results obtained by the proposed algorithm with the surface measurements and the data obtained by the MOD09 algorithm demonstrates the efficiency of the proposed algorithm in the reconstruction of the ground surface reflectance.
Can ChatGPT Help in Electronics Research and Development? A Case Study with Applied Sensors
Authors: Tafferner Zoltán , Balázs Balázs , Krammer Olivér , Géczy Attila
Published: May 2023

In this paper, we investigated the applicability of ChatGPT AI in electronics research and development via a case study of applied sensors in embedded electronic systems, a topic that is rarely mentioned in the recent literature, thus providing new insight for professionals and academics. The initial electronics-development tasks of a smart home project were prompted to the ChatGPT system to find out its capabilities and limitations. We wanted to obtain detailed information on the central processing controller units and the actual sensors usable for the specific project, their specifications and recommendations on the hardware and software design flow additionally. Furthermore, an extensive literature survey was requested to see if the bot could offer scientific papers covering the given topic. It was found that the ChatGPT responded with proper recommendations on controllers. However, the suggested sensor units, the hardware and software design were only partially acceptable, with occasional errors in specifications and generated code. The results of the literature survey showed that non-acceptable, fabricated citations (fake authors list, title, journal details and DOI—Digital Object identifier) were presented by the bot. The paper provides a detailed qualitative analysis, a performance analysis and critical discussion of the aforementioned aspects while providing the query set, the generated answers and codes as supplied data with the goal to give added value to electronics researchers and developers if trying to reach out for the tools in their profession.
Functional Objects in Urban Walking Environments and Pedestrian Trajectory Modelling
Authors: Lui Andrew Kwok Fai , Chan Yin Hei , Hung Kevin
Published: May 2023

Functional objects are large and small physical entities installed in urban environments to offer specific functionalities to visitors, such as shops, escalators, and information kiosks. Instances of the novel notion are focal points of human activities and are significant in pedestrian movement. Pedestrian trajectory modelling in an urban scene is a challenging problem because of the complex patterns resulting from social interactions of the crowds and the diverse relation between pedestrians and functional objects. Many data-driven methods have been proposed to explain the complex movements in urban scenes. However, the methods considering functional objects in their formulation are rare. This study aims to reduce the knowledge gap by demonstrating the importance of pedestrian–object relations in the modelling task. The proposed modelling method, called pedestrian–object relation guided trajectory prediction (PORTP), uses a dual-layer architecture that includes a predictor of pedestrian–object relation and a series of relation-specific specialized pedestrian trajectory prediction models. The experiment findings indicate that the inclusion of pedestrian–object relation results in more accurate predictions. This study provides an empirical foundation for the novel notion and a strong baseline for future work on this topic.
A Compact Modified Two-Arm Rectangular Spiral Implantable Antenna Design for ISM Band Biosensing Applications
Authors: Ucar Mustafa Hikmet Bilgehan , Uras Erdem
Published: May 2023

This paper presents a new microstrip implantable antenna (MIA) design based on the two-arm rectangular spiral (TARS) element for ISM band (Industrial, Scientific, and Medical 2.4–2.48 GHz) biotelemetric sensing applications. In the antenna design, the radiating element consists of a two-arm rectangular spiral placed on a ground-supported dielectric layer with a permittivity of ϵr = 10.2 and a metallic line surrounding this spiral. Considering the practical implementation, in the proposed TARS-MIA, a superstrate of the same material is used to prevent contact between the tissue and the metallic radiator element. The TARS-MIA has a compact size of 10 × 10 × 2.56 mm3 and is excited by a 50 Ω coaxial feed line. The impedance bandwidth of the TARS-MIA is from 2.39 to 2.51 GHz considering a 50 Ω system, and has a directional radiation pattern with directivity of 3.18 dBi. Numerical analysis of the proposed microstrip antenna design is carried out in an environment with dielectric properties of rat skin (Cole–Cole model ϵf (ω), ρ = 1050 kg/m3) via CST Microwave Studio. The proposed TARS-MIA is fabricated using Rogers 3210 laminate with dielectric permittivity of ϵr = 10.2. The in vitro input reflection coefficient measurements are realized in a rat skin-mimicking liquid reported in the literature. It is observed that the in vitro measurement and simulation results are compatible, except for some inconsistencies due to manufacturing and material tolerances. The novelty of this paper is that the proposed antenna has a unique two-armed square spiral geometry along with a compact size. Moreover, an important contribution of the paper is the consideration of the radiation performance of the proposed antenna design in a realistic homogeneous 3D rat model. Ultimately, the proposed TARS-MIA may be a good alternative for ISM-band biosensing operations with its miniature size and acceptable radiation performance compared to its counterparts.
A New Contactless Cross-Correlation Velocity Measurement System for Gas–Liquid Two-Phase Flow
Authors: Sheng Bixia , Huang Junchao , Ji Haifeng , Huang Zhiyao
Published: May 2023

Based on the principle of Contactless Conductivity Detection (CCD), a new contactless cross-correlation velocity measurement system with a three-electrode construction is developed in this work and applied to the contactless velocity measurement of gas–liquid two-phase flow in small channels. To achieve a compact design and to reduce the influence of the slug/bubble deformation and the relative position change on the velocity measurement, an electrode of the upstream sensor is reused as an electrode of the downstream sensor. Meanwhile, a switching unit is introduced to ensure the independence and consistency of the upstream sensor and the downstream sensor. To further improve the synchronization of the upstream sensor and the downstream sensor, fast switching and time compensation are also introduced. Finally, with the obtained upstream and downstream conductance signals, the velocity measurement is achieved by the principle of cross-correlation velocity measurement. To test the measurement performance of the developed system, experiments are carried out on a prototype with a small channel of 2.5 mm. The experimental results show that the compact design (three-electrode construction) is successful, and its measurement performance is satisfactory. The velocity range for the bubble flow is 0.312–0.816 m/s, and the maximum relative error of the flow rate measurement is 4.54%. The velocity range for the slug flow is 0.161 m/s–1.250 m/s, and the maximum relative error of the flow rate measurement is 3.70%.
Resource- and Neighbor-Aware Observation Transmission Scheme in Satellite Networks
Authors: Ko Haneul , Kyung Yeunwoong
Published: May 2023

The observation satellite can exploit its own storage and computational resources to reduce the transmission delay. However, too excessive usage of these resources can have negative effects on the queuing delay at the relay satellite and/or on conducting other tasks at each observation satellite. In this paper, we proposed a new resource- and neighbor-aware observation transmission scheme (RNA-OTS). In RNA-OTS, each observation satellite decides whether to use its resources and the resources of the relay satellite at each time epoch by considering its resource utilization and transmission policies of neighbor observation satellites. For the optimal decision of each observation satellite in a distributed manner, the operation of observation satellites is modeled by means of a constrained stochastic game, and a best-response-dynamics-based algorithm is devised to find the Nash equilibrium. The evaluation results demonstrate that RNA-OTS can decrease the delay to deliver the observation to the destination by up to 87% compared to a relay-satellite-based scheme while guaranteeing a sufficiently low average utilization of the resources of the observation satellite.
Hardware–Software Partitioning for Real-Time Object Detection Using Dynamic Parameter Optimization
Authors: Zaharia Corneliu , Popescu Vlad , Sandu Florin
Published: May 2023

Computer vision algorithms implementations, especially for real-time applications, are present in a variety of devices that we are currently using (from smartphones or automotive applications to monitoring/security applications) and pose specific challenges, memory bandwidth or energy consumption (e.g., for mobility) being the most notable ones. This paper aims at providing a solution to improve the overall quality of real-time object detection computer vision algorithms using a hybrid hardware–software implementation. To this end, we explore the methods for a proper allocation of algorithm components towards hardware (as IP Cores) and the interfacing between hardware and software. Addressing specific design constraints, the relationship between the above components allows embedded artificial intelligence to select the operating hardware blocks (IP cores)—in the configuration phase—and to dynamically change the parameters of the aggregated hardware resources—in the instantiation phase, similar to the concretization of a class into a software object. The conclusions show the benefits of using hybrid hardware–software implementations, as well as major gains from using IP Cores, managed by artificial intelligence, for an object detection use-case, implemented on a FPGA demonstrator built around a Xilinx Zynq-7000 SoC Mini-ITX sub-system.
SPT: Single Pedestrian Tracking Framework with Re-Identification-Based Learning Using the Siamese Model
Authors: Manzoor Sumaira , An Ye-Chan , In Gun-Gyo , Zhang Yueyuan , Kim Sangmin , Kuc Tae-Yong
Published: May 2023

Pedestrian tracking is a challenging task in the area of visual object tracking research and it is a vital component of various vision-based applications such as surveillance systems, human-following robots, and autonomous vehicles. In this paper, we proposed a single pedestrian tracking (SPT) framework for identifying each instance of a person across all video frames through a tracking-by-detection paradigm that combines deep learning and metric learning-based approaches. The SPT framework comprises three main modules: detection, re-identification, and tracking. Our contribution is a significant improvement in the results by designing two compact metric learning-based models using Siamese architecture in the pedestrian re-identification module and combining one of the most robust re-identification models for data associated with the pedestrian detector in the tracking module. We carried out several analyses to evaluate the performance of our SPT framework for single pedestrian tracking in the videos. The results of the re-identification module validate that our two proposed re-identification models surpass existing state-of-the-art models with increased accuracies of 79.2% and 83.9% on the large dataset and 92% and 96% on the small dataset. Moreover, the proposed SPT tracker, along with six state-of-the-art (SOTA) tracking models, has been tested on various indoor and outdoor video sequences. A qualitative analysis considering six major environmental factors verifies the effectiveness of our SPT tracker under illumination changes, appearance variations due to pose changes, changes in target position, and partial occlusions. In addition, quantitative analysis based on experimental results also demonstrates that our proposed SPT tracker outperforms the GOTURN, CSRT, KCF, and SiamFC trackers with a success rate of 79.7% while beating the DiamSiamRPN, SiamFC, CSRT, GOTURN, and SiamMask trackers with an average of 18 tracking frames per second.
LWIR Lateral Effect Position Sensitive HgCdTe Photodetector at 205 K
Authors: Pawluczyk Jarosław , Żbik Mateusz , Piotrowski Józef
Published: May 2023

We describe in detail the construction and characterization of a Peltier-cooled long-wavelength infrared (LWIR) position-sensitive detector (PSD) based on the lateral effect. The device was recently reported for the first time to the authors’ knowledge. It is a modified PIN HgCdTe photodiode, forming the tetra-lateral PSD, with a photosensitive area of 1 × 1 mm2, operating at 205 K in the 3–11 µm spectral range, capable of achieving a position resolution of 0.3–0.6 µm using 10.5 µm 2.6 mW radiation focused on a spot of the 1/e2 diameter 240 µm, with a box-car integration time of 1 µs and correlated double sampling.
Combining Low-Light Scene Enhancement for Fast and Accurate Lane Detection
Authors: Ke Changshuo , Xu Zhijie , Zhang Jianqin , Zhang Dongmei
Published: May 2023

Lane detection is a crucial task in the field of autonomous driving, as it enables vehicles to safely navigate on the road by interpreting the high-level semantics of traffic signs. Unfortunately, lane detection is a challenging problem due to factors such as low-light conditions, occlusions, and lane line blurring. These factors increase the perplexity and indeterminacy of the lane features, making them hard to distinguish and segment. To tackle these challenges, we propose a method called low-light enhancement fast lane detection (LLFLD) that integrates the automatic low-light scene enhancement network (ALLE) with the lane detection network to improve lane detection performance under low-light conditions. Specifically, we first utilize the ALLE network to enhance the input image’s brightness and contrast while reducing excessive noise and color distortion. Then, we introduce symmetric feature flipping module (SFFM) and channel fusion self-attention mechanism (CFSAT) to the model, which refine the low-level features and utilize more abundant global contextual information, respectively. Moreover, we devise a novel structural loss function that leverages the inherent prior geometric constraints of lanes to optimize the detection results. We evaluate our method on the CULane dataset, a public benchmark for lane detection in various lighting conditions. Our experiments show that our approach surpasses other state of the arts in both daytime and nighttime settings, especially in low-light scenarios.
From Traffic Congestion to Sustainable Mobility: A Case Study of Public Transport in Odesa, Ukraine
Authors: Myronenko Sergii , Oborskyi Hennadii , Dmytryshyn Dmytro , Shobik Vyacheslav , Lauwers Dirk , Witlox Frank
Published: May 2023

Consistent and reliable information on passenger traffic is considered crucial for the efficient operation of the public transport (PT) network. The PT network is used to improve public services and thus attract more passengers. This study evaluated the passenger traffic in Odesa, Ukraine, due to the inefficient urban transport system. The main aim of this study was to make PT better by examining passenger distribution on traffic routes and specifying characteristics of PT travel influencing individual satisfaction. The metric-tabular method was used to collect data and examine the number of incoming and outgoing passengers at each bus stop. The results of the passenger and PT analysis provide valuable recommendations for optimizing future routes. It is beneficial for transport companies to implement such recommendations so that inefficient transport on the route can be reduced by either reforming the route network or choosing the optimal number of buses. According to the findings of this study, understanding PT services is the most important determinant of PT adoption. The main implications of the findings are of particular interest to policymakers who develop policies in the field of passenger transport and also to transport scientists and students.
A Cloud-Based Data Storage and Visualization Tool for Smart City IoT: Flood Warning as an Example Application
Authors: Leal Sobral Victor Ariel , Nelson Jacob , Asmare Loza , Mahmood Abdullah , Mitchell Glen , Tenkorang Kwadwo , Todd Conor , Campbell Bradford , Goodall Jonathan L.
Published: May 2023

Collecting, storing, and providing access to Internet of Things (IoT) data are fundamental tasks to many smart city projects. However, developing and integrating IoT systems is still a significant barrier to entry. In this work, we share insights on the development of cloud data storage and visualization tools for IoT smart city applications using flood warning as an example application. The developed system incorporates scalable, autonomous, and inexpensive features that allow users to monitor real-time environmental conditions, and to create threshold-based alert notifications. Built in Amazon Web Services (AWS), the system leverages serverless technology for sensor data backup, a relational database for data management, and a graphical user interface (GUI) for data visualizations and alerts. A RESTful API allows for easy integration with web-based development environments, such as Jupyter notebooks, for advanced data analysis. The system can ingest data from LoRaWAN sensors deployed using The Things Network (TTN). A cost analysis can support users’ planning and decision-making when deploying the system for different use cases. A proof-of-concept demonstration of the system was built with river and weather sensors deployed in a flood prone suburban watershed in the city of Charlottesville, Virginia.
Internationalization Pace, Social Network Effect, and Performance among China’s Platform-Based Companies
Authors: Lyu Jiayi , Jiang Wanxing
Published: May 2023

Platform-based companies are rapidly emerging and occupy major positions in the global market. In addition, the business model that relies on digital technology makes platform-based companies sustainable. This paper empirically examines the impact of internationalization breadth on corporate performance and explores the moderating role of the platform enterprise network effect and innovation investment. Annual panel data from listed Chinese platform-based companies between 2012 to 2020 were used to also verify the scale of overseas companies’ association and social networks and the moderating effect of centrality and innovation research and development (R&D) investment on these two factors. The results indicate that the irregular internationalization rhythm of platform-based companies was negatively correlated with corporate performance, and the association network and the social network centrality of overseas companies had a positive moderating effect. Innovative R&D investment could help platform-based companies expand overseas to improve their corporate performance, and its moderating effect was more significant for platform companies in the early stages of internationalization. In terms of the internationalization pace, overseas network construction, and innovative R&D investment strategies, this paper provides valuable suggestions for platform-based companies to expand internationally and improve their corporate performance.