bluuebunny/crossref_metadata_2025 huggingface dataset:
Liposome-Based Drug Delivery Systems in Cancer Research: An Analysis of Global Landscape Efforts and Achievements
Authors: Hamad Islam , Harb Amani A. , Bustanji Yasser
Published: March 2024

Lipid-bilayer-based liposomes are gaining attention in scientific research for their versatile role in drug delivery. With their amphiphilic design, liposomes efficiently encapsulate and deliver drugs to targeted sites, offering controlled release. These artificial structures hold great promise in advancing cancer therapy methodologies. Bibliometric research analyzes systematic literary data statistically. This study used bibliometric indicators to examine, map, and evaluate the applications of liposomes in cancer therapy. A Scopus search was conducted to identify all English-language peer-reviewed scientific publications on the applications of liposomes in cancer therapy within the past twenty years. Bibliometric indicators were calculated using VOSviewer and Biblioshiny. We produced thematic, conceptual, and visualization charts. A total of 14,873 published documents were obtained. The procedure of keyword mapping has effectively identified the main areas of research concentration and prevailing trends within this specific field of study. The significant clusters discovered through theme and hotspot analyses encompassed many topics such as the use of multiple strategies in chemotherapy and different forms of cancer, the study of pharmacokinetics and nanomedicine, as well as the investigation of targeted drug delivery, cytotoxicity, and gene delivery. Liposomes were employed as drug delivery systems so as to selectively target cancer cells and improve the bioavailability of anticancer drugs. The work showcased the capacity to tailor these liposomes for accurate drug delivery by including potent anticancer medications. Our findings not only bring attention to the latest progress in utilizing liposomes for cancer treatment but also underscore the vital need for ongoing research, collaborative efforts, and the effective translation of these breakthroughs into tangible clinical applications, emphasizing the dynamic and evolving nature of cancer therapeutics.
Femtosecond Laser Microfabrication of Artificial Compound Eyes
Authors: Zhang Fan , Xu Huacheng , Yang Qing , Lu Yu , Du Guangqing , Chen Feng
Published: March 2024

Over millions of years of evolution, arthropods have intricately developed and fine-tuned their highly sophisticated compound eye visual systems, serving as a valuable source of inspiration for human emulation and tracking. Femtosecond laser processing technology has attracted attention for its excellent precision, programmable design capabilities, and advanced three-dimensional processing characteristics, especially in the production of artificial bionic compound eye structures, showing unparalleled advantages. This comprehensive review initiates with a succinct introduction to the operational principles of biological compound eyes, providing essential context for the design of biomimetic counterparts. It subsequently offers a concise overview of crucial manufacturing methods for biomimetic compound eye structures. In addition, the application of femtosecond laser technology in the production of biomimetic compound eyes is also briefly introduced. The review concludes by highlighting the current challenges and presenting a forward-looking perspective on the future of this evolving field.
Nuclear Magnetic Resonance Fingerprints and Mini DNA Markers for the Authentication of Cinnamon Species Ingredients Used in Food and Natural Health Products
Authors: Ragupathy Subramanyam , Thirugnanasambandam Arunachalam , Vinayagam Varathan , Newmaster Steven G.
Published: March 2024

Cinnamomum verum (syn C. zeylanicum) is considered ‘true’ cinnamon. However, it is reported that less expensive sources of cinnamon from C. cassia (syn C. aromaticum), C. loureiroi, and C. burmannii (toxic coumarin) may be used in the place of C. verum. We lack the quality assurance tools that are required to differentiate C. verum from other cinnamon species when verifying that the correct species is sourced from ingredient suppliers. The current research on cinnamon species authentication using DNA tools is limited to a few species and the use of high-quality DNA extracted from raw leaf materials. The cinnamon bark traded in the supply chain contains much less DNA and poorer-quality DNA than leaves. Our research advances DNA methods to authenticate cinnamon, as we utilized full-length chloroplast genomes via a genome skimming approach for C. burmannii and C. cassia to facilitate the design of optimal mini DNA markers. Furthermore, we developed and validated the use of NMR fingerprints for several commercial cinnamon species, including the quantification of 16 molecules. NMR fingerprints provided additional data that were useful for quality assessment in cinnamon extract powders and product consistency. Both the new mini DNA markers and NMR fingerprints were tested on commercial cinnamon products.
Nitrogen Fertilization and Cultivar Interactions Determine Maize Yield and Grain Mineral Composition in Calcareous Soil under Semiarid Conditions
Authors: Djalovic Ivica , Prasad P. V. Vara , Akhtar Kashif , Paunović Aleksandar , Riaz Muhammad , Dugalic Marijana , Katanski Snežana , Zaheer Sajjad
Published: March 2024

Identifying the contributions of climate factors and fertilization to maize yield is significant for the assessment of climate change impacts on maize production under semiarid conditions. This experiment was conducted with an overall objective to find how N fertilization and cultivar interactions along with climatic conditions determine the mineral composition and maize yield responses of four divergent maize cultivars grown under eight different fertilization levels. The results showed that element contents were significantly affected by year (Y), cultivar (C), N fertilization, and N × C interaction. The element contents of grains were mainly influenced by N rate or N × C interactions. The results showed that maize yield was significantly affected by year (Y), genotype (G), N fertilization (N), and Y × G × N interaction. These results implied that the maize yield was significantly affected by changes in genotypes and environments. Overall, our findings are a result of the interactions of genetic, environmental, and agronomic management factors. Future studies could evaluate more extreme plant densities, N fertilizer levels, and environments to further enhance our understanding of management effects on the mineral composition and maize yield in calcareous soil.
A New Record of Pogonatum tahitense (Polytrichaceae) from Tibet, China: Taxonomic Description, Range Expansion, and Biogeographic History
Authors: Sun Yu , Song Xiaotong , Chen Chunfa , Li Shuang , Gu Jiqi , Shao Xiaoming
Published: March 2024

The genus Pogonatum stands out as the most diverse within the family Polytrichaceae, encompassing over 50 species. Pogonatum tahitense has been recorded across various Pacific regions, including Hawaii in the United States and Tahiti in French Polynesia, as well as in Asia, such as in Taiwan in China, Java in Indonesia, and Sabah in Malaysia. In the current study, a specimen collected in Tibet, China, is described, confirming its taxonomic classification as P. tahitense through a comprehensive analysis integrating morphological evidence and molecular study based on sequences from the plastid (rbcL, rps4, trnL-F), mitochondrial (nad5), and nuclear (ITS2) regions. This documentation represents the first record of the species within mainland China. A time-calibrated, molecular-based phylogenetic analysis was conducted, employing various approaches for ancestral range inference. The findings suggest that P. tahitense originated during the Pleistocene epoch, approximately 1.8 mya, in Tibet, China.
Revalorization of Yerba Mate Residues: Biopolymers-Based Films of Dual Wettability as Potential Mulching Materials
Authors: Sanchez Laura M. , de Haro Jorge , Domínguez Eva , Rodríguez Alejandro , Heredia Antonio , Benítez José J.
Published: March 2024

Biodegradable mulching films are a very attractive solution to agronomical practices intended to achieve more successful crop results. And, in this context, the employment of agricultural and industrial food residues as starting material for their production is an alternative with economic and environmental advantages. This work reports the preparation of bilayer films having two different wettability characteristics from three bio-derived biopolymers: TEMPO-oxidized cellulose nanofibers isolated from infused Yerba Mate residues, Chitosan and Polylactic acid. The infused Yerba Mate residues, the isolated and oxidized cellulose nanofibers, and the films were characterized. Nanofibrillation yield, optical transmittance, cationic demand, carboxyl content, intrinsic viscosity, degree of polymerization, specific surface area and length were studied for the (ligno)cellulose nanofibers. Textural and chemical analysis, thermal and mechanical properties studies, as well as water and light interactions were included in the characterization of the films. The bilayer films are promising materials to be used as mulching films.
Property Enhancement of Waste Printed Circuit Boards Powders Reinforced Polypropylene by In Situ Magnesium Hydroxide Impregnation from Waste Lye
Authors: Tian Shenghui , Liu Jingwei , Gu Jiabao , Xie Chaoting , Zhang Xiong , Liu Xinlu
Published: March 2024

Using alkali pretreatment can effectively remove residual variable-valence metals from non-metallic powder (WPCBP) in waste printed circuit boards. However, substantial amounts of waste lye are generated, which causes secondary pollution. On this basis, this study innovatively utilized waste alkali lye to prepare nano-magnesium hydroxide. When the dispersant polyethylene glycol 6000 was used at a dosage of 3 wt.% of the theoretical yield of magnesium hydroxide, the synthesized nano-magnesium hydroxide exhibited well-defined crystallinity, good thermal stability and uniform particle size distribution, with a median diameter of 197 nm. Furthermore, the in situ method was selected to prepare WPCBP/Mg(OH)2 hybrid filler (MW) and the combustion behavior, thermal and mechanical properties of PP blends filled with MW were evaluated. The combustion behavior of the PP/MW blends increased with the increasing hybrid ratio of Mg(OH)2, and the MW hybrid filler reinforced PP blends showed better thermal and mechanical properties compared to the PP/WPCBP blends. Furthermore, the dynamic mechanical properties of the PP/MW blends were also increased due to the improved interfacial adhesion between the MW fillers and PP matrix. This method demonstrated high economic and environmental value, providing a new direction for the high value-added utilization of WPCBP.
The Application of Pipette-Tip and Magnetic Dummy-Template Molecularly Imprinted Solid-Phase Extraction Coupled with High-Performance Liquid Chromatography with Diode Array and Spectrofluorimetric Detection for the Determination of Coumarins in Cosmetic Samples
Authors: Špačková Andrea , Hroboňová Katarína , Jablonský Michal
Published: March 2024

In this study, adsorbents based on molecularly imprinted polymers (MIPs) in two solid-phase extraction application forms, pipette tip and magnetic extraction, were used for the selective extraction of coumarins. The pipette-tip solid-phase extraction reduced solvent volumes; the magnetic MIP extraction was simple and effective for phase separation. Parameters affecting extraction, such as the amount of adsorbent, type of washing solvent, volume of the elution solvent, and extraction times for magnetic extraction, were optimized. The MIP-based adsorbents displayed high selectivity and extraction efficiency, resulting in recoveries ranging from 70.3 to 102.0% (RSD % less than 5.5%) for five coumarins under study, 6,7-dihydroxycoumarin-6-β-D-glucoside, coumarin, 7-methoxycoumarin, 6-methylcoumarin, and dicoumarol. The extracts were analyzed by high-performance liquid chromatography with diode array (DAD) and fluorescence (FLD) detectors, reaching limits of quantification of 0.5 and 0.9 µg·mL−1 for coumarin and dicoumarol detected by DAD and 0.001–0.012 µg·mL−1 for the other prohibited simple coumarins when used as a fragrance (detected by FLD). The proposed method was validated and its applicability was shown for the analysis of cosmetic samples like shower gel and perfume.
Study on Hydrogen Direct Injection in RNG Combustion under Various Ignition Timings for Power Generation in a Retrofitted Gas Engine
Authors: Yu Meiqi , Luo Hongliang , Zhou Beini , Liu Yang , Zhai Chang , Nishida Keiya , Ge Jun-Cong
Published: March 2024

Renewable natural gas (RNG) is attractive for energy policy goals in the world. Therefore, a regional system is designed to explore RNG combustion for power generation in localities. This study investigates a direct injection (DI) engine fueled with hydrogen (H2) blended into the simulated renewable natural gas, which consists of 50% methane (CH4) and 50% carbon dioxide (CO2) in volume. In order to obtain higher efficiency, comparisons between DI and port fuel injection (PFI) of H2 addition were made. Then, the volume percentage of H2 was changed from 20% to 100% by keeping the volume ratio of CH4 and CO2 at 1:1. Finally, results of power output, brake mean effective pressure (BMEP), brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC) were discussed. Results showed that in contrast to PFI, H2 DI injection could increase efficiency by 4%. Additionally, H2 DI could retard the MBT ignition timing at 5 °CA. Compared with CH4/CH4 + CO2 combustion, under stoichiometric combustion, BMEP increases with H2 addition but BTE decreases significantly. However, by enlarging the excess air ratio (λ) to 1.24, both BMEP and BTE increase obviously with H2 addition. Moreover, when λ < 1.3, the MBT ignition timing should be advanced from −10 to 15 °CA top dead center (TDC). But the MBT ignition timing is fixed at −25 °CA TDC when λ is larger than 1.3. Furthermore, if efficiency is the priority, 30% H2 addition with λ at 1.24 (−15 °CA TDC) should be selected. If higher BMEP is preferred, 20% H2 addition with λ at 0.99 (−10 °CA TDC) should be selected.
Design, Construction, and Characterization of a Solar Photovoltaic Hybrid Heat Exchanger Prototype
Authors: Perez Grajales Sandro Guadalupe , Hernández Angel Horacio , Juárez-Romero David , Lopez Lopez Guadalupe , Urquiza-Beltran Gustavo
Published: March 2024

In this experimental work, a prototype of a hybrid solar–thermal–photovoltaic (HE-PV/T) heat exchanger has been designed, built, and characterized, with rectangular geometry and 12 fins inside, to obtain better heat flow and higher performance in order to achieve a better heat transfer coefficient, reducing and optimizing the working area. The heat exchanger contains 12 photovoltaic cells connected in series, with an angle of inclination of approximately 18° towards the south and a surface area of 0.22 m2, smaller than those available on the market, which individually capture 147.05 W/m2 as a photovoltaic panel and 240 W/m2 as a solar collector. Mathematical models found in the literature from previous work were used for the electrical and thermal evaluations. The temperature of the PV cells was reduced to 13.2 °C and the thermal level of the water was raised to a temperature above 70 °C, with a photovoltaic–thermal coupling power of 307.11 W and a heat transfer coefficient of 5790 W/m2 °C. The efficiencies obtained were as follows: thermal up to 0.78 and electrical up to 0.095. The novelty of these results was achieved in a reduced space of 40% less than those reported and available on the market.
Study on the Simplified Chemical Kinetic Combustion Mechanism of Mixed Methanol/PODE Fuel for Marine Diesel Engines
Authors: Li Changxiong , Hu Yihuai , Guo Hao
Published: March 2024

As a clean alternative fuel oil for marine engines, methanol has received increasing attention, but its low cetane number requires diesel ignition, which increases the difficulty of retrofitting existing engine fuel injection systems. Polymethoxy dimethyl ether (PODEn) is an ether fuel mixture whose chemical structural formula can be expressed as CH3O(CH2O)nCH3 (n≥2). PODE3 is the predominant component in the blend, and its properties are representative of the blend. PODE is a low-carbon fuel with a high cetane number and is easy to compression ignite, and, as such, can be used to ignite methanol in a marine diesel engine. This article explores the combustion mechanism of mixed methanol–PODE fuel using the characteristics of PODE that can be easily mixed with methanol for combustion. Taking methanol and PODE3 as representative fuels, the detailed combustion mechanism of PODE3 and the detailed combustion mechanism of methanol are simplified using a DRGEPSA (direct relationship graph with error propagation (DRGEP) and sensitivity analysis (SA)) method. Based on the target engine cylinder combustion environment, a simplified mechanism for mixed methanol–PODE fuel is obtained, and the new mechanism is validated in terms of the ignition delay period and laminar flame speed. The results indicate that the newly constructed simplified mechanism is basically consistent with the ignition delay data and flame propagation speed data measured by a rapid compression machine (RCM), laying the foundation for the application of alternative methanol fuels in marine engines.
Post-Production Finishing Processes Utilized in 3D Printing Technologies
Authors: Kantaros Antreas , Ganetsos Theodore , Petrescu Florian , Ungureanu Liviu , Munteanu Iulian
Published: March 2024

Additive manufacturing (AM) has revolutionized production across industries, yet challenges persist in achieving optimal part quality. This paper studies the enhancement of post-processing techniques to elevate the overall quality of AM-produced components. This study focuses on optimizing various post-processing methodologies to address prevalent issues such as surface roughness, dimensional accuracy, and material properties. Through an extensive review, this article identifies and evaluates a spectrum of post-processing methods, encompassing thermal, chemical, and mechanical treatments. Special attention is given to their effects on different types of additive manufacturing technologies, including selective laser sintering (SLS), fused deposition modeling (FDM), and stereolithography (SLA) and their dedicated raw materials. The findings highlight the significance of tailored post-processing approaches in mitigating inherent defects, optimizing surface finish, and enhancing mechanical properties. Additionally, this study proposes novel post-processing procedures to achieve superior quality while minimizing fabrication time and infrastructure and material costs. The integration of post-processing techniques such as cleaning, surface finishing, heat treatment, support structure removal, surface coating, electropolishing, ultrasonic finishing, and hot isostatic pressing (HIP), as steps directly within the additive manufacturing workflow can immensely contribute toward this direction. The outcomes displayed in this article not only make a valuable contribution to the progression of knowledge regarding post-processing methods but also offer practical implications for manufacturers and researchers who are interested in improving the quality standards of additive manufacturing processes.
Fluvial Morphology in Different Permafrost Environments—A Review
Authors: Vandenberghe Jef
Published: March 2024

This review presents a synthesis of the interaction between the hydro-morphological processes on interfluves and channels within fluvial catchments in permafrost regions. Both in modern and ancient permafrost catchments, this integrated landscape is quite diverse because of a variegated extent of frozen ground, density of vegetation cover, snow thickness, and other local factors. Moreover, temporal changes in environmental conditions are expressed in the morphological evolution of catchments. Channel patterns vary between single- and different multi-channel forms while the interfluves show a high diversity ranging from complete stability to intense denudation by surface runoff. It appears that braided channels, despite their high energy, were only significant during short intervals of peak discharge and transported only limited amounts of eroded sediment, while other channel patterns required more subdued annual discharge variability. Further, denudational processes on interfluves were a specific characteristic of landscape evolution during subsequent ice ages, especially in conditions of snow-rich and poorly vegetated, seasonal frost, or discontinuous permafrost resulting in the formation of extended planforms (cryopediments). In contrast, interfluves appeared to be rather stable on continuously frozen soils.
Deep Learning-Based Spatiotemporal Fusion Architecture of Landsat 8 and Sentinel-2 Data for 10 m Series Imagery
Authors: Cheng Qing , Xie Ruixiang , Wu Jingan , Ye Fan
Published: March 2024

Medium- to high-resolution imagery is indispensable for various applications. Combining images from Landsat 8 and Sentinel-2 can improve the accuracy of observing dynamic changes on the Earth’s surface. Many researchers use Sentinel-2 10 m resolution data in conjunction with Landsat 8 30 m resolution data to generate 10 m resolution data series. However, current fusion techniques have some algorithmic weaknesses, such as simple processing of coarse or fine images, which fail to extract image features to the fullest extent, especially in rapidly changing land cover areas. Facing the aforementioned limitations, we proposed a multiscale and attention mechanism-based residual spatiotemporal fusion network (MARSTFN) that utilizes Sentinel-2 10 m resolution data and Landsat 8 15 m resolution data as auxiliary data to upgrade Landsat 8 30 m resolution data to 10 m resolution. In this network, we utilized multiscale and attention mechanisms to extract features from coarse and fine images separately. Subsequently, the features outputted from all input branches are combined and further feature information is extracted through residual networks and skip connections. Finally, the features obtained from the residual network are merged with the feature information of the coarsely processed images from the multiscale mechanism to generate accurate prediction images. To assess the efficacy of our model, we compared it with existing models on two datasets. Results demonstrated that our fusion model outperformed baseline methods across various evaluation indicators, highlighting its ability to integrate Sentinel-2 and Landsat 8 data to produce 10 m resolution data.
Fine-Scale (10 m) Dynamics of Smallholder Farming through COVID-19 in Eastern Thailand
Authors: Chen Gang , Hammelman Colleen , Anantsuksomsri Sutee , Tontisirin Nij , Todd Amelia R. , Hicks William W. , Robinson Harris M. , Calloway Miles G. , Bell Grace M. , Kinsey John E.
Published: March 2024

This study aims to understand the spatiotemporal changes in patterns of tropical crop cultivation in Eastern Thailand, encompassing the periods before, during, and after the COVID-19 pandemic. Our approach involved assessing the efficacy of high-resolution (10 m) Sentinel-2 dense image time series for mapping smallholder farmlands. We integrated harmonic regression and random forest to map a diverse array of tropical crop types between summer 2017 and summer 2023, including durian, rice, rubber, eucalyptus, oil palm, pineapple, sugarcane, cassava, mangosteen, coconut, and other crops. The results revealed an overall mapping accuracy of 85.6%, with several crop types exceeding 90%. High-resolution imagery demonstrated particular effectiveness in situations involving intercropping, a popular practice of simultaneously growing two or more plant species in the same patch of land. However, we observed overestimation in the majority of the studied cash crops, primarily those located in young plantations with open tree canopies and grass-covered ground surfaces. The adverse effects of the COVID-19 pandemic were observed in specific labor-intensive crops, including rubber and durian, but were limited to the short term. No discernible impact was noted across the entirety of the study timeframe. In comparison, financial gain and climate change appeared to be more pivotal in influencing farmers’ decisions regarding crop cultivation. Traditionally dominant crops such as rice and oil palm have witnessed a discernible decline in cultivation, reflecting a decade-long trend of price drops preceding the pandemic. Conversely, Thai durian has seen a significant upswing even over the pandemic, which ironically served as a catalyst prompting Thai farmers to adopt e-commerce to meet the surging demand, particularly from China.
High-Resolution Nitrogen Dioxide Measurements from an Airborne Fiber Imaging Spectrometer over Tangshan, China
Authors: Zhang Xiaoli , Xi Liang , Zhou Haijin , Wang Wei , Chang Zhen , Si Fuqi , Wang Yu
Published: March 2024

The pollution caused by nitrogen dioxide is a major environmental problem in China. This study introduces a new type of atmospheric trace gas remote-sensing instrument, an airborne fiber imaging spectrometer. This spectrometer has a spectral range of 300–410 nm and works in push-broom mode with a 30° field of view on a flight path. Flight experiments were conducted on 30 December 2022 and 5 January 2023, covering heavily polluted areas east of Beijing and Tangshan. This equipment obtained the density distribution of NO2 over the flight area. The results showed that pollution was mainly concentrated in the Caofeidian area and at the power station in the north, and the main source of pollution was anthropogenic. Satellite and airborne data near the pollution points were compared, and the two datasets showed a positive correlation with a correlation coefficient of 0.78 and 0.7, on the two days, respectively. This study demonstrates the capability of an airborne fiber imaging spectrometer for NO2 regional emission remote sensing and identifying the pollution points.
Identification of Ground Fissure Development in a Semi-Desert Aeolian Sand Area Induced from Coal Mining: Utilizing UAV Images and Deep Learning Techniques
Authors: Tao Tao , Han Keming , Yao Xin , Chen Ximing , Wu Zuoqi , Yao Chuangchuang , Tian Xuwen , Zhou Zhenkai , Ren Kaiyu
Published: March 2024

The occurrence of surface strata movement in underground coal mining leads to the generation of numerous ground fissures, which not only damage the ecological environment but also disrupt building facilities, lead to airflow and easily trigger coal spontaneous combustion, induce geological disasters, posing a serious threat to people’s lives, property, and mining production. Therefore, it is particularly important to quickly and accurately obtain the information of ground fissures and then study their distribution patterns and the law of spatial-temporal evolution. The traditional field investigation methods for identifying fissures have low efficiency. The rapid development of UAVs has brought an opportunity to address this issue. However, it also poses new questions, such as how to interpret numerous fissures and the distribution law of fissures with underground mining. Taking a mine in the Shenfu coalfield on the semi-desert aeolian sand surface as the research area, this paper studies the fissure recognition from UAV images by deep learning, fissure development law, as well as the mutual feed of surface condition corresponding to the under-ground mining progress. The results show that the DRs-UNet deep learning method can identify more than 85% of the fissures; however, due to the influence of seasonal vegetation changes and different fissure development stages, the continuity and integrity of fissure recognition methods need to be improved. Four fissure distribution patterns were found. In open-cut areas, arc-shaped fissures are frequently observed, displaying significant dimensions in terms of depth, length, and width. Within subsidence basins, central collapse areas exhibit fissures that form perpendicular to the direction of the working face. Along roadways, parallel or oblique fissures tend to develop at specific angles. In regions characterized by weak roof strata and depressed basins, abnormal reverse-“C”-shaped fissures emerge along the mining direction. The research results comprehensively demonstrate the process of automatically identifying ground fissures from UAV images as well as the spatial distribution patterns of fissures, which can provide technical support for the prediction of ground fissures, monitoring of geological hazards in mining areas, control of land environmental damage, and land ecological restoration. In the future, it is suggested that this method be applied to different mining areas and geotechnical contexts to enhance its applicability and effectiveness.
OPTILOD: Optimal Beacon Placement for High-Accuracy Indoor Localization of Drones
Authors: Famili Alireza , Stavrou Angelos , Wang Haining , Park Jung-Min (Jerry)
Published: March 2024

For many applications, drones are required to operate entirely or partially autonomously. In order to fly completely or partially on their own, drones need to access location services for navigation commands. While using the Global Positioning System (GPS) is an obvious choice, GPS is not always available, can be spoofed or jammed, and is highly error-prone for indoor and underground environments. The ranging method using beacons is one of the most popular methods for localization, especially for indoor environments. In general, the localization error in this class is due to two factors: the ranging error, and the error induced by the relative geometry between the beacons and the target object to be localized. This paper proposes OPTILOD (Optimal Beacon Placement for High-Accuracy Indoor Localization of Drones), an optimization algorithm for the optimal placement of beacons deployed in three-dimensional indoor environments. OPTILOD leverages advances in evolutionary algorithms to compute the minimum number of beacons and their optimal placement, thereby minimizing the localization error. These problems belong to the Mixed Integer Programming (MIP) class and are both considered NP-hard. Despite this, OPTILOD can provide multiple optimal beacon configurations that minimize the localization error and the number of deployed beacons concurrently and efficiently.
CM-YOLOv8: Lightweight YOLO for Coal Mine Fully Mechanized Mining Face
Authors: Fan Yingbo , Mao Shanjun , Li Mei , Wu Zheng , Kang Jitong
Published: March 2024

With the continuous development of deep learning, the application of object detection based on deep neural networks in the coal mine has been expanding. Simultaneously, as the production applications demand higher recognition accuracy, most research chooses to enlarge the depth and parameters of the network to improve accuracy. However, due to the limited computing resources in the coal mining face, it is challenging to meet the computation demands of a large number of hardware resources. Therefore, this paper proposes a lightweight object detection algorithm designed specifically for the coal mining face, referred to as CM-YOLOv8. The algorithm introduces adaptive predefined anchor boxes tailored to the coal mining face dataset to enhance the detection performance of various targets. Simultaneously, a pruning method based on the L1 norm is designed, significantly compressing the model’s computation and parameter volume without compromising accuracy. The proposed algorithm is validated on the coal mining dataset DsLMF+, achieving a compression rate of 40% on the model volume with less than a 1% drop in accuracy. Comparative analysis with other existing algorithms demonstrates its efficiency and practicality in coal mining scenarios. The experiments confirm that CM-YOLOv8 significantly reduces the model’s computational requirements and volume while maintaining high accuracy.
Optimization of 3D Passive Acoustic Mapping Image Metrics: Impact of Sensor Geometry and Beamforming Approach
Authors: Therre Sarah , Fournelle Marc , Tretbar Steffen
Published: March 2024

Three-dimensional passive acoustic mapping (PAM) with matrix arrays typically suffers from high demands on the receiving electronics and high computational load. In our study, we investigated, both numerically and experimentally, the influence of matrix array aperture size, element count, and beamforming approaches on defined image metrics. With a numerical Vokurka model, matrix array acquisitions of cavitation signals were simulated. In the experimental part, two 32 × 32 matrix arrays with different pitches and aperture sizes were used. After being reconstructed into 3D cavitation maps, defined metrics were calculated for a quantitative comparison of experimental and numerical data. The numerical results showed that the enlargement of the aperture from 5 to 40 mm resulted in an improvement of the full width at half maximum (FWHM) by factors of 6 and 13 (in lateral and axial dimension, respectively). A larger array sparsity influenced the point spread function (PSF) only slightly, while the grating lobe level (GLL) remained more than 12 dB below the main lobe. These results were successfully experimentally confirmed. To further reduce the GLL caused by array sparsity, we adapted a non-linear filter from optoacoustics for use in PAM. In combination with the delay, multiply, sum, and integrate (DMSAI) algorithm, the GLL was decreased by 20 dB for 64-element reconstructions, resulting in levels that were identical to the fully populated matrix reconstruction levels.
Logistics Center Selection and Logistics Network Construction from the Perspective of Urban Geographic Information Fusion
Authors: Ma Zhanxin , Zheng Xiyu , Liang Hejun , Luo Ping
Published: March 2024

The last-mile logistics in cities have become an indispensable part of the urban logistics system. This study aims to explore the effective selection of last-mile logistics nodes to enhance the efficiency of logistics distribution, strengthen the image of corporate distribution, further reduce corporate operating costs, and alleviate urban traffic congestion. This paper proposes a clustering-based approach to identify urban logistics nodes from the perspective of geographic information fusion. This method comprehensively considers several key indicators, including the coverage, balance, and urban traffic conditions of logistics distribution. Additionally, we employed a greedy algorithm to identify secondary nodes around primary nodes, thus constructing an effective nodal network. To verify the practicality of this model, we conducted an empirical simulation study using the logistics demand and traffic conditions in the Xianlin District of Nanjing. This research not only identifies the locations of primary and secondary logistics nodes but also provides a new perspective for constructing urban last-mile logistics systems, enriching the academic research related to the construction of logistics nodes. The results of this study are of significant theoretical and practical importance for optimizing urban logistics networks, enhancing logistics efficiency, and promoting the improvement of urban traffic conditions.
A Vascular Feature Detection and Matching Method Based on Dual-Branch Fusion and Structure Enhancement
Authors: Xu Kaiyang , Wu Haibin , Iwahori Yuji , Yu Xiaoyu , Hu Zeyu , Wang Aili
Published: March 2024

How to obtain internal cavity features and perform image matching is a great challenge for laparoscopic 3D reconstruction. This paper proposes a method for detecting and associating vascular features based on dual-branch weighted fusion vascular structure enhancement. Our proposed method is divided into three stages, including analyzing various types of minimally invasive surgery (MIS) images and designing a universal preprocessing framework to make our method generalized. We propose a Gaussian weighted fusion vascular structure enhancement algorithm using the dual-branch Frangi measure and MFAT (multiscale fractional anisotropic tensor) to address the structural measurement differences and uneven responses between venous vessels and microvessels, providing effective structural information for vascular feature extraction. We extract vascular features through dual-circle detection based on branch point characteristics, and introduce NMS (non-maximum suppression) to reduce feature point redundancy. We also calculate the ZSSD (zero sum of squared differences) and perform feature matching on the neighboring blocks of feature points extracted from the front and back frames. The experimental results show that the proposed method has an average accuracy and repeatability score of 0.7149 and 0.5612 in the Vivo data set, respectively. By evaluating the quantity, repeatability, and accuracy of feature detection, our method has more advantages and robustness than the existing methods.
Application of Convolutional Neural Network for Decoding of 12-Lead Electrocardiogram from a Frequency-Modulated Audio Stream (Sonified ECG)
Authors: Krasteva Vessela , Iliev Ivo , Tabakov Serafim
Published: March 2024

Research of novel biosignal modalities with application to remote patient monitoring is a subject of state-of-the-art developments. This study is focused on sonified ECG modality, which can be transmitted as an acoustic wave and received by GSM (Global System for Mobile Communications) microphones. Thus, the wireless connection between the patient module and the cloud server can be provided over an audio channel, such as a standard telephone call or audio message. Patients, especially the elderly or visually impaired, can benefit from ECG sonification because the wireless interface is readily available, facilitating the communication and transmission of secure ECG data from the patient monitoring device to the remote server. The aim of this study is to develop an AI-driven algorithm for 12-lead ECG sonification to support diagnostic reliability in the signal processing chain of the audio ECG stream. Our methods present the design of two algorithms: (1) a transformer (ECG-to-Audio) based on the frequency modulation (FM) of eight independent ECG leads in the very low frequency band (300–2700 Hz); and (2) a transformer (Audio-to-ECG) based on a four-layer 1D convolutional neural network (CNN) to decode the audio ECG stream (10 s @ 11 kHz) to the original eight-lead ECG (10 s @ 250 Hz). The CNN model is trained in unsupervised regression mode, searching for the minimum error between the transformed and original ECG signals. The results are reported using the PTB-XL 12-lead ECG database (21,837 recordings), split 50:50 for training and test. The quality of FM-modulated ECG audio is monitored by short-time Fourier transform, and examples are illustrated in this paper and supplementary audio files. The errors of the reconstructed ECG are estimated by a popular ECG diagnostic toolbox. They are substantially low in all ECG leads: amplitude error (quartile range RMSE = 3–7 μV, PRD = 2–5.2%), QRS detector (Se, PPV > 99.7%), P-QRS-T fiducial points’ time deviation (<2 ms). Low errors generalized across diverse patients and arrhythmias are a testament to the efficacy of the developments. They support 12-lead ECG sonification as a wireless interface to provide reliable data for diagnostic measurements by automated tools or medical experts.
6G Networks and the AI Revolution—Exploring Technologies, Applications, and Emerging Challenges
Authors: Chataut Robin , Nankya Mary , Akl Robert
Published: March 2024

In the rapidly evolving landscape of wireless communication, each successive generation of networks has achieved significant technological leaps, profoundly transforming the way we connect and interact. From the analog simplicity of 1G to the digital prowess of 5G, the journey of mobile networks has been marked by constant innovation and escalating demands for faster, more reliable, and more efficient communication systems. As 5G becomes a global reality, laying the foundation for an interconnected world, the quest for even more advanced networks leads us to the threshold of the sixth-generation (6G) era. This paper presents a hierarchical exploration of 6G networks, poised at the forefront of the next revolution in wireless technology. This study delves into the technological advancements that underpin the need for 6G, examining its key features, benefits, and key enabling technologies. We dissect the intricacies of cutting-edge innovations like terahertz communication, ultra-massive MIMO, artificial intelligence (AI), machine learning (ML), quantum communication, and reconfigurable intelligent surfaces. Through a meticulous analysis, we evaluate the strengths, weaknesses, and state-of-the-art research in these areas, offering a wider view of the current progress and potential applications of 6G networks. Central to our discussion is the transformative role of AI in shaping the future of 6G networks. By integrating AI and ML, 6G networks are expected to offer unprecedented capabilities, from enhanced mobile broadband to groundbreaking applications in areas like smart cities and autonomous systems. This integration heralds a new era of intelligent, self-optimizing networks that promise to redefine the parameters of connectivity and digital interaction. We also address critical challenges in the deployment of 6G, from technological hurdles to regulatory concerns, providing a holistic assessment of potential barriers. By highlighting the interplay between 6G and AI technologies, this study maps out the current landscape and lights the path forward in this rapidly evolving domain. This paper aims to be a cornerstone resource, providing essential insights, addressing unresolved research questions, and stimulating further investigation into the multifaceted realm of 6G networks. By highlighting the synergy between 6G and AI technologies, we aim to illuminate the path forward in this rapidly evolving field.
Design of Network-on-Chip-Based Restricted Coulomb Energy Neural Network Accelerator on FPGA Device
Authors: Kang Soongyu , Lee Seongjoo , Jung Yunho
Published: March 2024

Sensor applications in internet of things (IoT) systems, coupled with artificial intelligence (AI) technology, are becoming an increasingly significant part of modern life. For low-latency AI computation in IoT systems, there is a growing preference for edge-based computing over cloud-based alternatives. The restricted coulomb energy neural network (RCE-NN) is a machine learning algorithm well-suited for implementation on edge devices due to its simple learning and recognition scheme. In addition, because the RCE-NN generates neurons as needed, it is easy to adjust the network structure and learn additional data. Therefore, the RCE-NN can provide edge-based real-time processing for various sensor applications. However, previous RCE-NN accelerators have limited scalability when the number of neurons increases. In this paper, we propose a network-on-chip (NoC)-based RCE-NN accelerator and present the results of implementation on a field-programmable gate array (FPGA). NoC is an effective solution for managing massive interconnections. The proposed RCE-NN accelerator utilizes a hierarchical–star (H–star) topology, which efficiently handles a large number of neurons, along with routers specifically designed for the RCE-NN. These approaches result in only a slight decrease in the maximum operating frequency as the number of neurons increases. Consequently, the maximum operating frequency of the proposed RCE-NN accelerator with 512 neurons increased by 126.1% compared to a previous RCE-NN accelerator. This enhancement was verified with two datasets for gas and sign language recognition, achieving accelerations of up to 54.8% in learning time and up to 45.7% in recognition time. The NoC scheme of the proposed RCE-NN accelerator is an appropriate solution to ensure the scalability of the neural network while providing high-performance on-chip learning and recognition.
Dopamine Measurement Using Engineered CNT–CQD–Polymer Coatings on Pt Microelectrodes
Authors: Darroudi Mahdieh , White Kevin A. , Crocker Matthew A. , Kim Brian N.
Published: March 2024

This study aims to develop a microelectrode array-based neural probe that can record dopamine activity with high stability and sensitivity. To mimic the high stability of the gold standard method (carbon fiber electrodes), the microfabricated platinum microelectrode is coated with carbon-based nanomaterials. Carboxyl-functionalized multi-walled carbon nanotubes (COOH-MWCNTs) and carbon quantum dots (CQDs) were selected for this purpose, while a conductive polymer like poly (3-4-ethylene dioxythiophene) (PEDOT) or polypyrrole (PPy) serves as a stable interface between the platinum of the electrode and the carbon-based nanomaterials through a co-electrodeposition process. Based on our comparison between different conducting polymers and the addition of CQD, the CNT–CQD–PPy modified microelectrode outperforms its counterparts: CNT–CQD–PEDOT, CNT–PPy, CNT–PEDOT, and bare Pt microelectrode. The CNT–CQD–PPy modified microelectrode has a higher conductivity, stability, and sensitivity while achieving a remarkable limit of detection (LOD) of 35.20 ± 0.77 nM. Using fast-scan cyclic voltammetry (FSCV), these modified electrodes successfully measured dopamine’s redox peaks while exhibiting consistent and reliable responses over extensive use. This electrode modification not only paves the way for real-time, precise dopamine sensing using microfabricated electrodes but also offers a novel electrochemical sensor for in vivo studies of neural network dynamics and neurological disorders.
Exploring the Neural Correlates of Flow Experience with Multifaceted Tasks and a Single-Channel Prefrontal EEG Recording
Authors: Hang Yuqi , Unenbat Buyanzaya , Tang Shiyun , Wang Fei , Lin Bingxin , Zhang Dan
Published: March 2024

Flow experience, characterized by deep immersion and complete engagement in a task, is highly recognized for its positive psychological impacts. However, previous studies have been restricted to using a single type of task, and the exploration of its neural correlates has been limited. This study aimed to explore the neural correlates of flow experience with the employment of multifaceted flow-induction tasks. Six tasks spanning mindfulness, artistic tasks, free recall, and varying levels of Tetris complexity (easy, flow, and hard conditions) were employed to have relatively complete coverage of the known flow-induction tasks for a better induction of individualized flow experience. Twenty-eight participants were recruited to perform these six tasks with a single-channel prefrontal EEG recording. Significant positive correlations were observed between the subjective flow scores of the individual’s best-flow-experience task and the EEG activities at the delta, gamma, and theta bands, peaking at latencies around 2 min after task onset. The outcomes of regression analysis yield a maximum R2 of 0.163. Our findings report the EEG correlates of flow experience in naturalistic settings and highlight the potential of portable and unobtrusive EEG technology for an objective measurement of flow experience.