bluuebunny/crossref_metadata_2025 huggingface dataset:
Study on the Influence of Calcination Temperature of Iron Vitriol on the Coloration of Ancient Chinese Traditional Iron Red Overglaze Color
Authors: Li Qijiang , Wu Anjian , Zhang Maolin , Li Jinwei , Cao Jianwen , Li Haorui , Jiang Yimei
Published: June 2024

Iron red, a traditional Jingdezhen overglaze color, is primarily colored with iron oxide (Fe2O3). In traditional processes, the main ingredient for the iron red overglaze color, raw iron red, is produced by calcining iron vitriol (FeSO4·7H2O). Analysis of ancient iron red porcelain samples indicates that the coloration is unstable, ranging from bright red to dark red and occasionally to black. Addressing this, the present study, from a ceramic technology standpoint, conducts a series of calcination experiments on industrial iron vitriol at varying temperatures. Utilizing methodologies such as differential scanning calorimetry-thermogravimetry (DSC-TG), Raman spectroscopy, X-ray diffraction (XRD), scanning electron microscopy with X-ray energy dispersive spectrometry (SEM-EDS), and optical microscopy (OM), this research scientifically explores the impact of iron vitriol’s calcination temperature on the coloration of traditional Jingdezhen iron red overglaze color. The findings indicate that from room temperature to 550 °C, the dehydration of iron vitriol resulted in the formation of Fe2(SO4)3 and a minimal amount of α-Fe2O3, rendering the iron red overglaze color a yellowish-red shade. At 650 °C, the coexistence of Fe2(SO4)3 and α-Fe2O3 imparted a brick-red color to the iron red. As the temperature was elevated to 700 °C, the desulfurization of Fe2(SO4)3 produced α-Fe2O3, transitioning the iron red to an orange red. With further temperature increase to 750 °C, the particle size of α-Fe2O3 grew and the crystal reflectivity decreased, resulting in a purplish-red hue. Throughout this stage, the powder remained in a single α-Fe2O3 phase. Upon further heating to 800 °C, the crystallinity of α-Fe2O3 enhanced, giving the iron red overglaze color a dark red or even black appearance.
Influence of Runner Downstream Structure on the Flow Field in the Runner of Small-Sized Water Turbine
Authors: Tang Lingdi , Wang Wei , Zhang Chenjun , Wang Zanya , Yuan Shouqi
Published: June 2024

Unstable flows in the runner of water turbines, such as reverse flow, vorticity and flow direction transition, are the main factors causing increased losses and decreased efficiency, and changing the geometry structure in the downstream of the runner is an important means of mitigating these instabilities. The different flow fields downstream of runners induced by different locking nut structures are numerically calculated and verified by experimental results. The flow states are evaluated in terms of characteristic quantities such as pressure gradient, swirling flow, reverse flow, and vorticity. The results show a non-negligible effect of the locking nut, which leads to a more uniform pressure distribution, increases the descending speed of the reverse flow rate, and reduces the volume and strength of the vortex. The small locking nut significantly weakens the pressure gradient, reduces the top reverse flow zone, and decreases the vortex volume at the blade flow passage outlet and the size of the downstream disturbance vortex. The extended lock nut reduces the growth rate of the vortex generation rate and the size of the partial vortex, but increases the range of the high-pressure zone, causing the bottom reverse flow and increasing the vortex.
Advanced Copula-Based Models for Type II Censored Data: Applications in Industrial and Medical Settings
Authors: Almetwally Ehab M. , Fayomi Aisha , Qura Maha E.
Published: June 2024

Copula models are increasingly recognized for their ability to capture complex dependencies among random variables. In this study, we introduce three innovative bivariate models utilizing copula functions: the XLindley (XL) distribution with Frank, Gumbel, and Clayton copulas. The results highlight the fundamental characteristics and effectiveness of these newly introduced bivariate models. Statistical inference for the distribution parameters is conducted using a Type II censored sampling design. This employs maximum likelihood and Bayesian estimation techniques. Asymptotic and credible confidence intervals are calculated, and numerical analysis is performed using the Markov Chain Monte Carlo method. The proposed methodology’s applicability is illustrated by analyzing several real-world datasets. The initial dataset examines burr formation occurrences and consists of two observation sets. Additionally, the second and third datasets contain medical information. The second dataset focuses on diabetic nephropathy, while the third dataset explores infection and recurrence time among kidney patients.
DEEP-STA: Deep Learning-Based Detection and Localization of Various Types of Inter-Frame Video Tampering Using Spatiotemporal Analysis
Authors: Akhtar Naheed , Hussain Muhammad , Habib Zulfiqar
Published: June 2024

Inter-frame tampering in surveillance videos undermines the integrity of video evidence, potentially influencing law enforcement investigations and court decisions. This type of tampering is the most common tampering method, often imperceptible to the human eye. Until now, various algorithms have been proposed to identify such tampering, based on handcrafted features. Automatic detection, localization, and determine the tampering type, while maintaining accuracy and processing speed, is still a challenge. We propose a novel method for detecting inter-frame tampering by exploiting a 2D convolution neural network (2D-CNN) of spatiotemporal information and fusion for deep automatic feature extraction, employing an autoencoder to significantly reduce the computational overhead by reducing the dimensionality of the feature’s space; analyzing long-range dependencies within video frames using long short-term memory (LSTM) and gated recurrent units (GRU), which helps to detect tampering traces; and finally, adding a fully connected layer (FC), with softmax activation for classification. The structural similarity index measure (SSIM) is utilized to localize tampering. We perform extensive experiments on datasets, comprised of challenging videos with different complexity levels. The results demonstrate that the proposed method can identify and pinpoint tampering regions with more than 90% accuracy, irrespective of video frame rates, video formats, number of tampering frames, and the compression quality factor.
Fredholm Theory Relative to Any Algebra Homomorphisms
Authors: Kong Yingying , Wang Yabo , Yang Jingen
Published: June 2024

In this paper, we give another definition of Ruston elements and almost Ruston elements, which is equivalent to the definitions given by Mouton and Raubenheimer in the case that the homomorphism has a closed range and Riesz property. For two homomorphisms, we consider the preserver problems of Fredholm theory and Fredholm spectrum theory. In addition, we study the spectral mapping theorems of Fredholm (Weyl, Browder, Ruston, and almost Ruston) elements relative to a homomorphism. Last but not least, the dependence of Fredholm theory on three homomorphisms is considered, and meanwhile, the transitivity of Fredholm theory relative to three homomorphisms is illustrated. Furthermore, we consider the Fredholm theory relative to more homomorphisms.
Enhancing Mobile Robot Navigation: Optimization of Trajectories through Machine Learning Techniques for Improved Path Planning Efficiency
Authors: Al-Kamil Safa Jameel , Szabolcsi Róbert
Published: June 2024

Efficient navigation is crucial for intelligent mobile robots in complex environments. This paper introduces an innovative approach that seamlessly integrates advanced machine learning techniques to enhance mobile robot communication and path planning efficiency. Our method combines supervised and unsupervised learning, utilizing spline interpolation to generate smooth paths with minimal directional changes. Experimental validation with a differential drive mobile robot demonstrates exceptional trajectory control efficiency. We also explore Motion Planning Networks (MPNets), a neural planner that processes raw point-cloud data from depth sensors. Our tests demonstrate MPNet’s ability to create optimal paths using the Probabilistic Roadmap (PRM) method. We highlight the importance of correctly setting parameters for reliable path planning with MPNet and evaluate the algorithm on various path types. Our experiments confirm that the trajectory control algorithm works effectively, consistently providing precise and efficient trajectory control for the robot.
Elements in Serum, Muscle, Liver, and Kidney of Rabbits Fed Macroalgae-Supplemented Diets
Authors: Al-Soufi Sabela , Miranda Marta , García Javier , Muíños Antonio , Cegarra Eugenio , Nicodemus Nuria , Herrero-Latorre Carlos , López-Alonso Marta
Published: June 2024

The addition of marine macroalgae to animal feed has garnered interest due to the demonstrated benefits of gut health in many livestock species. Most macroalgae have a higher mineral content than terrestrial vegetables, making them an attractive, sustainable source of minerals. However, some macroalgae contain elevated concentrations of iodine and arsenic, which may be transferred to the meat of livestock fed with macroalgae. This study evaluated the mineral profile of rabbit serum, muscle, liver, and kidney of rabbits fed diets supplemented with different marine macroalgae, with the goal of improving post-weaning gut health and reducing reliance on antibiotics. We found increased deposition of iodine in muscle, liver, and kidney due to macroalgae supplementation, which is particularly promising for regions with low iodine endemicity. Higher, though relatively low arsenic concentrations, compared to those in other animal meats and food sources, were also detected in the muscle, liver, and kidney of macroalgae-fed rabbits. The absence of apparent interactions with other micronutrients, particularly selenium, suggests that the inclusion of macroalgae in rabbit diets will not affect the overall mineral content. Enhanced bioavailability of elements such as phosphorus and iron may provide additional benefits, potentially reducing the need for mineral supplementation.
Benefits and Challenges of Applying Bacteriophage Biocontrol in the Consumer Water Cycle
Authors: Reyneke Brandon , Havenga Benjamin , Waso-Reyneke Monique , Khan Sehaam , Khan Wesaal
Published: June 2024

Bacteria (including disinfection- and antibiotic-resistant bacteria) are abundant in the consumer water cycle, where they may cause disease, and lead to biofouling and infrastructure damage in distributions systems, subsequently resulting in significant economic losses. Bacteriophages and their associated enzymes may then offer a biological control solution for application within the water sector. Lytic bacteriophages are of particular interest as biocontrol agents as their narrow host range can be exploited for the targeted removal of specific bacteria in a designated environment. Bacteriophages can also be used to improve processes such as wastewater treatment, while bacteriophage-derived enzymes can be applied to combat biofouling based on their effectiveness against preformed biofilms. However, the host range, environmental stability, bacteriophage resistance and biosafety risks are some of the factors that need to be considered prior to the large-scale application of these bacterial viruses. Characteristics of bacteriophages that highlight their potential as biocontrol agents are thus outlined in this review, as well as the potential application of bacteriophage biocontrol throughout the consumer water cycle. Additionally, the limitations of bacteriophage biocontrol and corresponding mitigation strategies are outlined, including the use of engineered bacteriophages for improved host ranges, environmental stability and the antimicrobial re-sensitisation of bacteria. Finally, the potential public and environmental risks associated with large-scale bacteriophage biocontrol application are considered, and alternative applications of bacteriophages to enhance the functioning of the consumer water cycle, including their use as water quality or treatment indicators and microbial source tracking markers, are discussed.
The Millennia-Long Development of Drugs Associated with the 80-Year-Old Artificial Intelligence Story: The Therapeutic Big Bang?
Authors: Crouzet Aurore , Lopez Nicolas , Riss Yaw Benjamin , Lepelletier Yves , Demange Luc
Published: June 2024

The journey of drug discovery (DD) has evolved from ancient practices to modern technology-driven approaches, with Artificial Intelligence (AI) emerging as a pivotal force in streamlining and accelerating the process. Despite the vital importance of DD, it faces challenges such as high costs and lengthy timelines. This review examines the historical progression and current market of DD alongside the development and integration of AI technologies. We analyse the challenges encountered in applying AI to DD, focusing on drug design and protein–protein interactions. The discussion is enriched by presenting models that put forward the application of AI in DD. Three case studies are highlighted to demonstrate the successful application of AI in DD, including the discovery of a novel class of antibiotics and a small-molecule inhibitor that has progressed to phase II clinical trials. These cases underscore the potential of AI to identify new drug candidates and optimise the development process. The convergence of DD and AI embodies a transformative shift in the field, offering a path to overcome traditional obstacles. By leveraging AI, the future of DD promises enhanced efficiency and novel breakthroughs, heralding a new era of medical innovation even though there is still a long way to go.
Cornus officinalis Extract Enriched with Ursolic Acid Ameliorates UVB-Induced Photoaging in Caenorhabditis elegans
Authors: Yue Zengwang , Liu Han , Liu Manqiu , Wang Ning , Ye Lin , Guo Chaowan , Zheng Bisheng
Published: June 2024

Ultraviolet B (UVB) exposure can contribute to photoaging of skin. Cornus officinalis is rich in ursolic acid (UA), which is beneficial to the prevention of photoaging. Because UA is hardly soluble in water, the Cornus officinalis extract (COE) was obtained using water as the antisolvent to separate the components containing UA from the crude extract of Cornus officinalis. The effect of COE on UVB damage was assessed using Caenorhabditis elegans. The results showed that COE could increase the lifespan and enhance the antioxidant enzyme activity of C. elegans exposed to UVB while decreasing the reactive oxygen species (ROS) level. At the same time, COE upregulated the expression of antioxidant-related genes and promoted the migration of SKN-1 to the nucleus. Moreover, COE inhibited the expression of the skn-1 downstream gene and the extension of the lifespan in skn-1 mutants exposed to UVB, indicating that SKN-1 was required for COE to function. Our findings indicate that COE mainly ameliorates the oxidative stress caused by UVB in C. elegans via the SKN-1/Nrf2 pathway.
Latest Developments of the Julia–Kocienski Olefination Reaction: Mechanistic Considerations
Authors: Chrenko Daniel , Pospíšil Jiří
Published: June 2024

Since its discovery, the Julia–Kocienski olefination reaction has over past 30 years become one of the key C-C connective methods that is used in late-stage natural product synthesis. The reaction proceeds under mild reaction conditions, with a wide substrate scope and functional group tolerance range and with high (E) selectivity. In this focused review, we discuss the reaction from a mechanistic point of view and disclose key features that play an important role in reaction selectivity. Finally, the mechanistic aspects of the newly developed modification of the Julia–Kocienski reaction, which allows the formation of both (E) and (Z) olefins from the same reaction partners, are discussed.
Au-Based Bimetallic Catalysts for Aerobic Oxidation of 5-Hydroxymethylfurfural to 2,5-Furandicarboxylic Acid under Base-Free Reaction Conditions
Authors: Su Juan , Liu Zongyang , Tan Yuan , Xiao Yan , Zhan Nannan , Ding Yunjie
Published: June 2024

The aerobic oxidation of 5-hydroxymethylfurfural (HMF) to 2,5-furandicarboxylic acid (FDCA) plays a pivotal role in the synthesis of renewable, biodegradable plastics and sustainable chemicals. Although supported gold nanoclusters (NCs) exhibit significant potential in this process, they often suffer from low selectivity. To address this challenge, a series of gold-M (M means Ni, Fe, Cu, and Pd) bimetallic NCs catalysts were designed and synthesized to facilitate the selective oxidation of HMF to FDCA. Our findings indicate that the introduction of doped metals, particularly Ni and Pd, not only improves the reaction rates for HMF tandem oxidation but also promotes high yields of FDCA. Various characterizations techniques, including X-ray diffraction (XRD), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), in situ diffuse reflectance infrared Fourier transform spectroscopy of CO adsorption (CO-DRIFTS), and temperature-programmed desorption of oxygen (O2-TPD), were employed to scrutinize the structural and electronic properties of the prepared catalysts. Notably, an electronic effect was observed across the Au-based bimetallic catalysts, facilitating the activation of reactant molecules and enhancing the catalytic performance. This study provides valuable insights into the alloy effects, aiding in the development of highly efficient Au-based bimetallic catalysts for biomass conversions.
Reduction of Toxic Metal Ions and Production of Bioelectricity through Microbial Fuel Cells Using Bacillus marisflavi as a Biocatalyst
Authors: Segundo Rojas-Flores , De La Cruz-Noriega Magaly , Luis Cabanillas-Chirinos , Otiniano Nélida Milly , Soto-Deza Nancy , Rojas-Villacorta Walter , De La Cruz-Cerquin Mayra
Published: June 2024

Industrialization has brought many environmental problems since its expansion, including heavy metal contamination in water used for agricultural irrigation. This research uses microbial fuel cell technology to generate bioelectricity and remove arsenic, copper, and iron, using contaminated agricultural water as a substrate and Bacillus marisflavi as a biocatalyst. The results obtained for electrical potential and current were 0.798 V and 3.519 mA, respectively, on the sixth day of operation and the pH value was 6.54 with an EC equal to 198.72 mS/cm, with a removal of 99.08, 56.08, and 91.39% of the concentrations of As, Cu, and Fe, respectively, obtained in 72 h. Likewise, total nitrogen concentrations, organic carbon, loss on ignition, dissolved organic carbon, and chemical oxygen demand were reduced by 69.047, 86.922, 85.378, 88.458, and 90.771%, respectively. At the same time, the PDMAX shown was 376.20 ± 15.478 mW/cm2, with a calculated internal resistance of 42.550 ± 12.353 Ω. This technique presents an essential advance in overcoming existing technical barriers because the engineered microbial fuel cells are accessible and scalable. It will generate important value by naturally reducing toxic metals and electrical energy, producing electric currents in a sustainable and affordable way.
Asymmetrical Three-Dimensional Conformal Imaging Lens
Authors: Gong Desen , Ge Yixiao , Xiao Wen , Chen Huanyang
Published: June 2024

Absolute instrument refers to a media that can make light rays to propagate in a closed orbit and perform imaging and self-imaging. In the past few decades, traditional investigations into absolute instrument have been centered on the two-dimensional plane and rotational symmetry situations, and have paid less attention to three-dimensional counterparts. In this article, we design two types of three-dimensional non-spherically symmetric absolute instruments based on conformal inverse transformation, which originated from the three-dimensional Luneburg lens and Lissajous lens. We carry out ray tracing on the optical performance of these new lenses and analyze the imaging laws. Our work enlarges the family of absolute instruments from two dimensions to three dimensions and symmetry to asymmetry, which may allow for imaging applications in optical waves.
Miniaturized Multi-Platform Free-Space Laser-Communication Terminals for Beyond-5G Networks and Space Applications
Authors: Carrasco-Casado Alberto , Shiratama Koichi , Kolev Dimitar , Ono Fumie , Tsuji Hiroyuki , Toyoshima Morio
Published: June 2024

Beyond-5G (B5G) technology plays a pivotal role in the next generation of communication infrastructure to support the future Society 5.0, a concept introduced in the 5th Basic Plan for Science and Technology by the Japanese Cabinet to define the long-term growth strategy for reconciling economic development with the resolution of social issues through the promotion of science and technologies. Free-space laser communication is a key element in boosting the data transmission capabilities required for B5G applications. The NICT will complete in 2024 the first fully functional prototypes of a series of miniaturized laser-communication terminals for multiple platforms. These terminals are designed to adapt to a wide range of requirements to address scenarios where laser communications can offer a competitive, enhanced solution compared to existing technologies. This paper provides an overview of these terminals’ capabilities and the plans for their functional validation, as well as preliminary data from the first full-system tests. A number of innovations integrated into the terminals are introduced, such as the manufacture of the smallest miniaturized EDFA with integrated HPA and LNA and full space qualification to date, the first-ever integration of a beam-divergence control system in a practical communication terminal, the development of the most compact Tbit/s-class modem prototype documented in the literature, and the smallest gimbal design integrated in a lasercom terminal. Furthermore, this paper outlines the mid-term plans for demonstration in the most significant realistic scenarios, emphasizing the use of High-Altitude Platform Stations (HAPSs) and ultra-small satellites.
Leveraging Designed Simulations and Machine Learning to Develop a Surrogate Model for Optimizing the Gas–Downhole Water Sink–Assisted Gravity Drainage (GDWS-AGD) Process to Improve Clean Oil Production
Authors: Al-Mudhafar Watheq J. , Rao Dandina N. , Wojtanowicz Andrew K.
Published: June 2024

The Gas and Downhole Water Sink–Assisted Gravity Drainage (GDWS-AGD) process addresses gas flooding limitations in reservoirs surrounded by infinite-acting aquifers, particularly water coning. The GDWS-AGD technique reduces water cut in oil production wells, improves gas injectivity, and optimizes oil recovery, especially in reservoirs with high water coning. The GDWS-AGD process installs two 7-inch production casings bilaterally. Then, two 2-3/8-inch horizontal tubings are completed. One tubing produces oil above the oil–water contact (OWC) area, while the other drains water below it. A hydraulic packer in the casing separates the two completions. The water sink completion uses a submersible pump to prevent water from traversing the oil column and entering the horizontal oil-producing perforations. To improve oil recovery in the heterogeneous upper sandstone pay zone of the South Rumaila oil field, which has a strong aquifer and a large edge water drive, the GDWS-AGD process evaluation was performed using a compositional reservoir flow model in a 10-year prediction period in comparison to the GAGD process. The results show that the GDWS-AGD method surpasses the GAGD by 275 million STB in cumulative oil production and 4.7% in recovery factor. Based on a 10-year projection, the GDWS-AGD process could produce the same amount of oil in 1.5 years. In addition, the net present value (NPV) given various oil prices (USD 10–USD 100 per STB) was calculated through the GAGD and GDWS-AGD processes. The GDWS-AGD approach outperforms GAGD in terms of NPV across the entire range of oil prices. The GAGD technique became uneconomical when oil prices dropped below USD 10 per STB. Design of Experiments–Latin Hypercube Sampling (DoE-LHS) and radial basis function neural networks (RBF-NNs) were used to determine the optimum operational decision variables that influence the GDWS-AGD process’s performance and build the proxy metamodel. Decision variables include well constraints that control injection and production. The optimum approach increased the recovery factor by 1.7525% over the GDWS-AGD process Base Case. With GDWS-AGD, water cut and coning tendency were significantly reduced, along with reservoir pressure, which all led to increasing gas injectivity and oil recovery. The GDWS-AGD technique increases the production of oil and NPV more than the GAGD process. Finally, the GDWS-AGD technique offers significant improvements in oil recovery and income compared to GAGD, especially in reservoirs with strong water aquifers.
Bibliometric Analysis: Use of Agricultural Waste in the Generation of Electrical Energy
Authors: Segundo Rojas-Flores , Magaly De La Cruz-Noriega , Luis Cabanillas-Chirinos , Otiniano Nélida Milly , Soto-Deza Nancy , Terrones-Rodriguez Nicole , Mayra De La Cruz-Cerquin
Published: June 2024

Scientific reports have diversified enormously in the various thematic areas of the scientific world, generating errors and biases in the searches and directing the various investigations. For this reason, this article provides an approach, from the point of view of bibliometric analysis, to reveal the trends of agricultural waste for its potential use as fuel in generating electrical energy. This research has used the programs RStudio, VosViewer, and Excel for the compilation and analysis of data, whose data were extracted from Scopus during the period from 2013 to 15 March 2024, filtering all types of publications that were not original articles and English language. Scientific reports have found that biodigesters are constantly evolving, improving valves, studying and analyzing different types of agricultural waste, and using microorganisms to accelerate the fermentation process of agricultural waste. The terms biogas (330 occurrences), anaerobic digestion (214 occurrences), and agriculture (212 occurrences) were the words with the highest occurrences. At the same time, the author that stood out was Liu Y., who had an H index of 6 and 117 citations in his six articles published in the Scopus database. Moreover, China (1900 citations) was the country with the highest citation numbers, followed by the United States (1060 citations) and India (967 citations). The designs of biogas production increase efficiency and can increase biodigesters’ performance. The research also reveals the different types of development and trends that stood out and emerged in the last decade, such as the authors who have had the most impact on this topic that has recently emerged and the countries that have obtained the most significant number of publications on the topic.
IoT-Based Data Mining Framework for Stability Assessment of the Laser-Directed Energy Deposition Process
Authors: Hartmann Sebastian , Vykhtar Bohdan , Möbs Nele , Kelbassa Ingomar , Mayr Peter
Published: June 2024

Additive manufacturing processes are prone to production errors. Specifically, the unique physical conditions of Laser-Directed Energy Deposition (DED-L) lead to unexpected process anomalies resulting in subpar part quality. The resulting costs and lack of reproducibility are two major barriers hindering a broader adoption of this innovative technology. Combining sensor data with data from relevant steps before and after the production process can lead to an increased understanding of when and why these process anomalies occur. In the present study, an IoT-based data mining framework is presented to assess the stability of processing Ti6Al4V on an industrial-grade DED-L machine. The framework employs an edge-cloud computing methodology to collect data efficiently and securely from various steps in the part lifecycle. During manufacturing, multiple sensors are employed to monitor the essential process characteristics in situ. Mechanical properties of the 160 printed specimens were obtained using appropriate destructive testing. All data are stored on a central database and can be accessed via the web for data analytics. The results prove the successful implementation of the proposed IoT framework but also indicate a lack of process stability during manufacturing. The occurring part errors can only be partially correlated with anomalies in the in situ sensor data.
A Soft Actor-Critic Deep Reinforcement-Learning-Based Robot Navigation Method Using LiDAR
Authors: Liu Yanjie , Wang Chao , Zhao Changsen , Wu Heng , Wei Yanlong
Published: June 2024

When there are dynamic obstacles in the environment, it is difficult for traditional path-generation algorithms to achieve desired obstacle-avoidance results. To solve this problem, we propose a robot navigation control method based on SAC (Soft Actor-Critic) Deep Reinforcement Learning. Firstly, we use a fast path-generation algorithm to control the robot to generate expert trajectories when the robot encounters danger as well as when it approaches a target, and we combine SAC reinforcement learning with imitation learning based on expert trajectories to improve the safety of training. Then, for the hybrid data consisting of agent data and expert data, we use an improved prioritized experience replay method to improve the learning efficiency of the policies. Finally, we introduce RNN (Recurrent Neural Network) units into the network structure of the SAC Deep Reinforcement-Learning navigation policy to improve the agent’s transfer inference ability in a new environment and obstacle-avoidance ability in dynamic environments. Through simulation and practical experiments, it is fully verified that our method has a higher training efficiency and navigation success rate compared to state-of-the-art reinforcement-learning algorithms, which further enhances the obstacle-avoidance capability of the robot system.
Antagonist Activation Measurement in Triceps Surae Using High-Density and Bipolar Surface EMG in Chronic Hemiparesis
Authors: Ghédira Mouna , Vieira Taian Martins , Cerone Giacinto Luigi , Gazzoni Marco , Gracies Jean-Michel , Hutin Emilie
Published: June 2024

After a stroke, antagonist muscle activation during agonist command impedes movement. This study compared measurements of antagonist muscle activation using surface bipolar EMG in the gastrocnemius medialis (GM) and high-density (HD) EMG in the GM and soleus (SO) during isometric submaximal and maximal dorsiflexion efforts, with knee flexed and extended, in 12 subjects with chronic hemiparesis. The coefficients of antagonist activation (CAN) of GM and SO were calculated according to the ratio of the RMS amplitude during dorsiflexion effort to the maximal agonist effort for the same muscle. Bipolar CAN (BipCAN) was compared to CAN from channel-specific (CsCAN) and overall (OvCAN) normalizations of HD-EMG. The location of the CAN centroid was explored in GM, and CAN was compared between the medial and lateral portions of SO. Between-EMG system differences in GM were observed in maximal efforts only, between BipCAN and CsCAN with lower values in BipCAN (p < 0.001), and between BipCAN and OvCAN with lower values in OvCAN (p < 0.05). The CAN centroid is located mid-height and medially in GM, while the CAN was similar in medial and lateral SO. In chronic hemiparesis, the estimates of GM hyperactivity differ between bipolar and HD-EMGs, with channel-specific and overall normalizations yielding, respectively, higher and lower CAN values than bipolar EMG. HD-EMG would be the way to develop personalized rehabilitation programs based on individual antagonist activations.
Rapid Prototyping for Nanoparticle-Based Photonic Crystal Fiber Sensors
Authors: Sherburne Michael , Harjes Cameron , Klitsner Benjamin , Gigax Jonathan , Ivanov Sergei , Schamiloglu Edl , Lehr Jane
Published: June 2024

The advent of nanotechnology has motivated a revolution in the development of miniaturized sensors. Such sensors can be used for radiation detection, temperature sensing, radio-frequency sensing, strain sensing, and more. At the nanoscale, integrating the materials of interest into sensing platforms can be a common issue. One promising platform is photonic crystal fibers, which can draw in optically sensitive nanoparticles or have its optical properties changed by specialized nanomaterials. However, testing these sensors at scale is limited by the the need for specialized equipment to integrate these photonic crystal fibers into optical fiber systems. Having a method to enable rapid prototyping of new nanoparticle-based sensors in photonic crystal fibers would open up the field to a wider range of laboratories that could not have initially studied these materials in such a way before. This manuscript discusses the improved processes for cleaving, drawing, and rapidly integrating nanoparticle-based photonic crystal fibers into optical system setups. The method proposed in this manuscript achieved the following innovations: cleaving at a quality needed for nanoparticle integration could be done more reliably (≈100% acceptable cleaving yield versus ≈50% conventionally), nanoparticles could be drawn at scale through photonic crystal fibers in a safe manner (a method to draw multiple photonic crystal fibers at scale versus one fiber at a time), and the new photonic crystal fiber mount was able to be finely adjusted when increasing the optical coupling before inserting it into an optical system (before, expensive fusion splicing was the only other method).
Characterizing Smart Cities Based on Artificial Intelligence
Authors: Hammoumi Laaziza , Maanan Mehdi , Rhinane Hassan
Published: June 2024

Cities worldwide are attempting to be labelled as smart, but truly classifying as such remains a great challenge. This study aims to use artificial intelligence (AI) to classify the performance of smart cities and identify the factors linked to their smartness. Based on residents’ perceptions of urban structures and technological applications, this study included 200 cities globally. For 147 cities, we gathered the perceptions of 120 residents per city through a survey of 39 questions covering two main pillars: ‘Structures’, referring to the existing infrastructure of the city, and the ‘Technology’ pillar that describes the technological provisions and services available to the inhabitants. These pillars were evaluated across five key areas: health and safety, mobility, activities, opportunities, and governance. For the remaining 53 cities, scores were derived by analyzing pertinent data collected from various online resources. Multiple machine learning algorithms, including Random Forest, Artificial Neural Network, Support Vector Machine, and Gradient Boost, were tested and compared in order to select the best one. The results showed that Random Forest and the Artificial Neural Network are the best trained models that achieved the highest levels of accuracy. This study provides a robust framework for using machine learning to identify and assess smart cities, offering valuable insights for future research and urban planning.
Changes in Lower-Extremity Gait Biomechanics Following High-Cadence Cycling
Authors: Thorsen Tanner A. , Hester Rials J. , Keating Christopher J.
Published: June 2024

We sought to investigate the lower-extremity biomechanics underlying increased gait velocity following high-cadence cycling. Ground reaction forces (GRF) and lower-extremity kinematics and kinetics were recorded as 15 healthy adults walked at a self-selected pace prior to and immediately following a 15 min bout of cycling at a cadence of 75 rotations per minute. Propulsive GRF and stance-phase peak dorsiflexion and knee extension angles increased, while peak plantarflexion and hip extension angles decreased. Swing-phase peak dorsiflexion, plantarflexion, knee flexion, and hip flexion angles increased, while peak knee extension angle decreased. Peak dorsiflexion, knee extension, and hip extension angular velocity also increased during swing. No changes in peak joint moments were observed; however, peak positive ankle, knee, and hip joint power generation increased following cycling. Completing high-cadence cycling improves gait velocity by increasing propulsive GRF; increasing joint angular velocity during the swing phase of gait for the ankle, knee, and hip; and increasing positive power production by the ankle, knee, and hip during the stance phase. Increased gait velocity post cycling exercise did not increase lower-extremity joint moments. Cycling may be a viable exercise-based modality for increasing gait velocity, especially in populations where gait ability or joint loading is of particular concern.
Thermal and Oxidative Stability of Biocrude Oil Derived from the Continuous Hydrothermal Liquefaction of Spirulina
Authors: Wang Yingxian , Cao Maojiong , Lan Weijuan , Yin Dongxue
Published: June 2024

The stability of biocrude oil is a significant challenge for its storage, transportation, and refining. In this investigation, the thermal and oxidative stability of Spirulina-biocrude oil derived from a plug-flow continuous hydrothermal reactor was systematically studied. The biocrude oil was stored at three temperatures to simulate the winter (4 °C), spring and autumn (15 °C), and summer (35 °C) seasons, and in two atmospheres (air and N2) to simulate the conditions of a storage tank being sealed or kept open. Results demonstrated that the physicochemical properties of biocrude oil were highly influenced by the storage environment. The viscosity of biocrude oil increased with increasing storage temperature and time. The maximum viscosity (17,577 mPa·s) was observed when biocrude oil was stored at 35 °C and in an air condition over 84 days, 145% higher than fresh biocrude oil (7164.2 mPa·s). The viscosity increased by 10.9% when biocrude oil was sorted at 4 °C in an N2 atmosphere after being stored for 28 days. After long-term storage, biocrude oil still exhibited comparable characteristics to petroleum, with a slight decrease in HHV (31.36–33.97 MJ·kg−1) and the nitrogen-to-carbon ratio (0.087–0.092). This study indicated that the viscosity and HHV of the biocrude oil derived from a continuous reactor stored at 4 °C in an N2 atmosphere condition remained relatively unchanged, which enables the scheduling of oil refining production.
Integrated Sports Information Systems: Enhancing Data Processing and Information Provision for Sports in Slovakia
Authors: Varmus Michal , Kubina Milan , Mičiak Martin , Šarlák Michal
Published: June 2024

Integrated information systems in sports have the potential to improve the efficiency of data management and support the managers’ decision-making. However, this implementation faces challenges such as inefficiency, data duplicity, and time demands. This study represents a comparative analysis of sports information systems’ implementation in four countries—Slovakia, Czech Republic, England, and Denmark. The originality of this study stems from the fact that there is currently no research background examining this issue to the same extent. This study’s methodology focuses on the identification of the benefits and challenges occurring while implementing sports information systems and performing data management and analysis. This study also focuses on the potential of these systems to support managerial decision-making in this area. Data were collected from national sports databases and other relevant sources. Verification of the hypotheses showed that the implementation of sports information systems in Slovakia is inefficient in terms of costs and technology. Nevertheless, the systems that were implemented support managerial decision-making and their success is comparable to other EU countries within the aspects studied. Following the results, the main recommendation is to ensure transparency, automation, and strategic planning in the implementation of sports information systems. Future research directions include ethical and legal issues related to the utilization of technology in sports and the improvement of the user experience.
A Dynamic Analysis to Examine Regional Development in the Context of a Digitally Enabled Regional Innovation System: The Case of Western and Central Macedonia (Greece)
Authors: Samara Elpida , Kilintzis Pavlos , Katsoras Efthymios , Martinidis George , Kosti Paraskevi
Published: June 2024

The significance of Regional Innovation Systems (RIS) as a strategic tool for enhancing a region’s competitiveness has been increasingly recognized. This paper presents a model of RIS that was developed using the system dynamics (SD) methodology. The goal of this model is to amalgamate the systemic approach with computer modeling and simulation disciplines into a comprehensive dynamic framework for analyzing RIS. Within this framework, the paper explores the impact of smart technologies on regional development through the RIS. Specifically, the SD model serves as an ‘experimental tool’ for conducting extensive what-if scenario analyses concerning smart technologies. The efficacy of these technologies is examined in terms of their dynamic influence on regional development, with insights derived from simulation outcomes. Data from two Greek regions provides a strategic analysis over a designated time horizon.
Advancements in 3D Printing: Directed Energy Deposition Techniques, Defect Analysis, and Quality Monitoring
Authors: Imran Muhammad Mu’az , Che Idris Azam , De Silva Liyanage Chandratilak , Kim Yun-Bae , Abas Pg Emeroylariffion
Published: June 2024

This paper provides a comprehensive analysis of recent advancements in additive manufacturing, a transformative approach to industrial production that allows for the layer-by-layer construction of complex parts directly from digital models. Focusing specifically on Directed Energy Deposition, it begins by clarifying the fundamental principles of metal additive manufacturing as defined by International Organization of Standardization and American Society for Testing and Materials standards, with an emphasis on laser- and powder-based methods that are pivotal to Directed Energy Deposition. It explores the critical process mechanisms that can lead to defect formation in the manufactured parts, offering in-depth insights into the factors that influence these outcomes. Additionally, the unique mechanisms of defect formation inherent to Directed Energy Deposition are examined in detail. The review also covers the current landscape of process evaluation and non-destructive testing methods essential for quality assurance, including both traditional and contemporary in situ monitoring techniques, with a particular focus given to advanced machine-vision-based methods for geometric analysis. Furthermore, the integration of process monitoring, multiphysics simulation models, and data analytics is discussed, charting a forward-looking roadmap for the development of Digital Twins in Laser–Powder-based Directed Energy Deposition. Finally, this review highlights critical research gaps and proposes directions for future research to enhance the accuracy and efficiency of Directed Energy Deposition systems.
Effective Technique and Mechanism for Simultaneous Adsorption of As(III/V) from Wastewater by Fe-ZIF-8@MXene
Authors: Zang Shuyan , Zhang Qing , Hu Baoli , Zhang Yaqian , Pu Jaan H. , Lv Meiheng
Published: June 2024

Arsenic (As) contamination of surface water has become a global concern, especially for the third world countries, and it is imperative to develop advanced materials and an effective treatment method to address the issue. In this paper, iron doped ZIF-8@MXene (Fe-ZIF-8@MXene) was prepared as a potential adsorbent to effectively and simultaneously remove As(III/V) from wastewater. To investigate this, Fe-ZIF-8@MXene was characterized before and after the removal of mixed As(III/V). The results of Fourier transform infrared (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), specific surface area (BET) and point of zero charge (pHpzc) showed that Fe-ZIF-8@MXene was prepared successfully and kept a stable structure after As(III) and As(V) adsorption. The particle size of Fe-ZIF-8@MXene was in the range of 0.5 μm to 2.5 μm, where its BET was 531.7 m2/g. For both contaminants, adsorption was found to follow pseudo-second-order kinetics and was best-fitted by the Langmuir adsorption model with correlation coefficients (R2) of 0.998 and 0.997, for As(III) and As(V), respectively. The adsorbent was then applied to remove As from two actual water samples, giving maximum removal rates of 91.07% and 98.96% for As(III) and As(V), respectively. Finally, removal mechanisms for As(III/V) by Fe-ZIF-8@MXene were also explored. During the adsorption, multiple complexes were formed under the effect of its abundant surface functional groups involving multiple mechanisms, which included Van der Waals force, surface adsorption, chemical complexation and electrostatic interactions. In conclusion, this study demonstrated that Fe-ZIF-8@MXene was an advanced and reusable material for simultaneous removal of As(III/V) in wastewater.
Development of a modular structural geographic information system for spatial data analysis
Authors:   Kuban State Technological University, Krasnodar, Russia, Gura D. A. , Dyachenko R. A. ,   Kuban State Technological University, Krasnodar, Russia, Shalaya A. A. ,   Kuban State Technological University, Krasnodar, Russia
Published: June 2024

This work contains the research on the development and implementation of a modular structural geographic information system (GIS) for spatial data analysis, which is based on artificial intelligence technologies, mainly machine learning. The article discusses the basic principles and concepts of modularity, and also proposes a methodology for developing a modular GIS structure using modern technologies and tools. The study has practical significance for specialists in the field of geoinformatics and spatial analysis, and can also be useful for GIS developers and scientific researchers interested in creating modular GIS structures for analyzing spatial data.