bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhancing Soybean and Maize Yields through Improved Nitrogen and Soil Water Use Efficiencies: A 40-Year Study on the Impact of Farmyard Manure Amendment in Northeast China
Authors: Liu Yuying , Bai Minsong , Shen Feng , Wu Zhengchao , Yang Jinfeng , Li Na , Liu Houjun , Dai Jian , Han Xiaori
Published: February 2024

The combined application of manure and chemical fertilizers has been recognized as a critical factor driving significant changes in crop yield and nutrient use efficiency, holding the potential to optimize agricultural management to achieve high yields. In this 40-year study, we investigated the effect of manure amendment on soybean and maize yields, water and nitrogen use efficiencies (WUE and NUE), and water and mineral N storage at 0–100 cm soil depths from 2017 to 2018 to explore the optimization of fertilization management strategies for soybean and maize production in Northeast China. To elucidate the impact of chemical fertilizers and manure, twelve treatments—control (CK); single N fertilizer at a low rate (N1) and that at a high rate (N2); N1, phosphorus (P), and potassium (K) fertilizer (N1PK); manure alone at 13.5 and 27 t ha−1 (M1 and M2); and those combined with N, P, or K fertilizer (M1N1, M1N2, and M1N1PK and M2N1, M2N2, and M2N1PK)—were selected and studied. The results showed that long-term amendment with manure significantly increased crop biomass and yield in the soybean–maize–maize rotation system. Combining with manure increased the WUE, the partial factor productivity of N fertilizer (PFPN), and N physiological efficiency (PEN) in both the soybean and maize seasons; conserved soil water (mainly at 40–60 cm); and increased soil N retention (in the upper 60 cm layer), which reduced the risk of N leaching, with a better effect being observed after the application of 13.5 t ha−1 manure. These results provide insight into the potential of using fertilization management strategies that include amendment with 13.5 t ha−1 manure in combination with N, P, and K fertilizer in the maize season and only chemical fertilizer in the soybean season, as these results indicate that such strategies can achieve high yields and be used to implement agricultural sustainable development in brown soil regions in Northeast China.
An Overview of Mycorrhiza in Pines: Research, Species, and Applications
Authors: Dyshko Valentyna , Hilszczańska Dorota , Davydenko Kateryna , Matić Slavica , Moser W. Keith , Borowik Piotr , Oszako Tomasz
Published: February 2024

In the latest literature, climate models show that the conditions for pines, spruces, larches, and birches will deteriorate significantly. In Poland, as well as in other European countries, there are already signs of the decline of these species. This review article deals with the symbiotic relationships between fungi and plants, which can hardly be overestimated, using the example of pine trees. These are the oldest known symbiotic relationships, which are of great benefit to both components and can help plants, in particular, survive periods of severe drought and the attack of pathogens on the roots. This article describes symbioses and their causal conditions, as well as the mycorrhizal components of pine trees and their properties; characterizes ectomycorrhizal fungi and their mushroom-forming properties; and provides examples of the cultivation of pure fungal cultures, with particular attention to the specificity of the mycorrhizal structure and its effects on the growth and development of Pinus species. Finally, the role of mycorrhiza in plant protection and pathogen control is described.
Multi-Omics Analysis Revealed the AGR-FC.C3 Locus of Brassica napus as a Novel Candidate for Controlling Petal Color
Authors: Ding Yiran , Li Huaixin , Liu Xinmin , Cheng Xin , Chen Wang , Wu Mingli , Chen Liurong , He Jianjie , Chao Hongbo , Jia Haibo , Fu Chunhua , Li Maoteng
Published: February 2024

Variations in the petal color of Brassica napus are crucial for ornamental value, but the controlled loci for breeding remain to be unraveled. Here, we report a candidate locus, AGR-FC.C3, having conducted a bulked segregant analysis on a segregating population with different petal colors. Our results showed that the locus covers 9.46 Mb of the genome, harboring 951 genes. BnaC03.MYB4, BnaC03.MYB85, BnaC03.MYB73, BnaC03.MYB98, and BnaC03.MYB102 belonging to MYB TFs families that might regulate the petal color were observed. Next, a bulk RNA sequencing of white and orange-yellow petals on three development stages was performed to further identify the possible governed genes. The results revealed a total of 51 genes by overlapping the transcriptome data and the bulked segregant analysis data, and it was found that the expression of BnaC03.CCD4 was significantly up-regulated in the white petals at three development stages. Then, several novel candidate genes such as BnaC03.ENDO3, BnaC03.T22F8.180, BnaC03.F15C21.8, BnaC03.Q8GSI6, BnaC03.LSD1, BnaC03.MAP1Da, BnaC03.MAP1Db, and BnaC03G0739700ZS putative to controlling the petal color were identified through deeper analysis. Furthermo re, we have developed two molecular markers for the reported functional gene BnaC03.CCD4 to discriminate the white and orange-yellow petal colors. Our results provided a novel locus for breeding rapeseed with multi-color petals.
Testing of Polymer Composites for Manufacturing of Sprayer Nozzles
Authors: Slavkina Victoria E. , Mirzaev Maksim A. , Kuzmin Anton M. , Kutyrev Alexey I. , Tuzhilin Sergey P. , Denisov Vyacheslav A. , Kataev Yuriy V.
Published: February 2024

Wear is the leading cause of nozzle failure. The durability of the nozzle is affected by the material it is made from. Traditional materials are ceramics, stainless steel, brass, and polymers. One of the possible ways to improve the wear resistance of polymer nozzles is through the incorporation of dispersed fillers into them. This paper presents the results of testing polymer composites for their chemical resistance to pesticides, examining the effects of different types and amounts of fillers on the chemical and abrasion resistance. When silicon carbide was used as a filler, the strength increased by 30.2%. The experiments on chemical resistance to pesticides revealed that the nature, shape, and volume content of filler particles do not significantly affect the resistance of the compounds obtained. Tests on hydro-abrasive wear have shown that graphite and silicon carbide are effective fillers capable of reducing wear by up to 7.5 times. Based on previous research, it is recommended to use a composite compound with 15% volume of silicon carbide for nozzle manufacturing.
Novel PVA–Hyaluronan–Siloxane Hybrid Nanofiber Mats for Bone Tissue Engineering
Authors: Sánchez-Téllez Daniela Anahí , Baltierra-Uribe Shantal Lizbeth , Vidales-Hurtado Mónica Araceli , Valdivia-Flores Alejandra , García-Pérez Blanca Estela , Téllez-Jurado Lucía
Published: February 2024

Hyaluronan (HA) is a natural biodegradable biopolymer; its biological functions include cell adhesion, cell proliferation, and differentiation as well as decreasing inflammation, angiogenesis, and regeneration of damaged tissue. This makes it a suitable candidate for fabricating nanomaterials with potential use in tissue engineering. However, HA nanofiber production is restricted due to the high viscosity, low evaporation rate, and high surface tension of HA solutions. Here, hybrids in the form of continuous and randomly aligned polyvinyl alcohol (PVA)–(HA)–siloxane nanofibers were obtained using an electrospinning process. PVA–HA fibers were crosslinked by a 3D siloxane organic–inorganic matrix via sol-gel that restricts natural hydrophilicity and stiffens the structure. The hybrid nanofiber mats were characterized by FT-IR, micro-Raman spectroscopy, SEM, and biological properties. The PVA/HA ratio influenced the morphology of the hybrid nanofibers. Nanofibers with high PVA content (10PVA-8 and 10PVA-10) form mats with few beaded nanofibers, while those with high HA content (5PVA-8 and 5PVA-10) exhibit mats with mound patterns formed by “ribbon-like” nanofibers. The hybrid nanofibers were used as mats to support osteoblast growth, and they showed outstanding biological properties supporting cell adhesion, cell proliferation, and cell differentiation. Importantly, the 5PVA-8 mats show 3D spherical osteoblast morphology; this suggests the formation of tissue growth. These novel HA-based nanomaterials represent a relevant advance in designing nanofibers with unique properties for potential tissue regeneration.
Mechanical Properties and Degradation Rate of Poly(Sorbitol Adipate-Co-Dioladipate) Copolymers Obtained with a Catalyst-Free Melt Polycondensation Method
Authors: Kavimani V. , Lakkaboyana Sivarama Krishna , Trilaksana Herri , Atanase Leonard I.
Published: February 2024

A new family of polyester-based copolymers—poly(sorbitol adipate-co-ethylene glycol adipate) (PSAEG), poly(sorbitol adipate-co-1,4 butane diol adipate) (PSABD), and poly (sorbitol adipate-co-1,6 hexane diol adipate) (PSAHD)—was obtained with a catalyst-free melt polycondensation procedure using the multifunctional non-toxic monomer sorbitol, adipic acid, and diol, which are acceptable to the human metabolism. Synthesized polyesters were characterized by FTIR and 1H NMR spectroscopy. The molecular weight and thermal properties of the polymers were determined by MALDI mass spectroscopy, differential scanning calorimetry (DSC), and thermogravimetric analysis. The degradation rate was investigated, at 37 °C, in 0.1M NaOH (pH 13) and in phosphate-buffered solution (PBS) at pH 7.4. It was found that the polymers degraded faster in NaOH (i.e., in a day) compared to their degradation in PBS, which was much slower (in a week). The highest degradation rate was noticed for the PSAEG sample in both media, whereas PSAHD was the most stable polymer at pH 7.4 and 13. A reduced hydrophilicity of the polymers with diol length was indicated by low swelling percentage and sol content in water and DMSO. Mechanical studies prove that all the polymers are elastomers whose flexibility increases with diol length, shown by the increase in percentage of elongation at break and the decrease in tensile stress and Young’s modulus. These biodegradable copolymers with adaptable physicochemical characteristics might be useful for a broad variety of biological applications by merely varying the length of the diol.
Enhancing the Thermal Performance of Shape Memory Polymers: Designing a Minichannel Structure
Authors: Beshkoofe Saed , Baniassadi Majid , Mahdavi Nejad Alireza , Sheidaei Azadeh , Baghani Mostafa
Published: February 2024

This research proposes a numerical approach to improve the thermal performance of shape memory polymers (SMPs) while their mechanical properties remain intact. Sixteen different 3D minichannel structures were numerically designed to investigate the impact of embedded water flow in microchannel networks on the thermal response and shape recovery of SMPs. This work employs two approaches, each with different physics: approach A focuses on solid mechanics analysis and, accordingly, thermal analysis in solids without considering the fluid. approach B tackles solid and fluid mechanics analysis and thermal analysis in both solid and fluid subdomains, which inherently calls for fluid–structure coupling in a uniform procedure. Finally, the results of these two approaches are compared to predict the SMP’s thermal and mechanical behavior. The structural designs are then analyzed in terms of their shape recovery speed, recovery ratio, and recovery parameters. The results indicate that isotropic structures thermally outperform their anisotropic counterparts, exhibiting improved thermal characteristics and faster shape recovery. Additionally, it was observed that polymeric structures with a low volume fraction of embedded branches thermally perform efficiently. The findings of this study predict that the geometrical angle between the main branch and sub-branches of SMP favorably impacts the enhancement of thermal characteristics of the structure, accelerating its shape recovery. Approach B accelerates the shape recovery rate in SMPs due to fluid flow and uniform heat transfer within the structures.
A Tale of Two Chains: Geometries of a Chain Model and Protein Native State Structures
Authors: Škrbić Tatjana , Giacometti Achille , Hoang Trinh X. , Maritan Amos , Banavar Jayanth R.
Published: February 2024

Linear chain molecules play a central role in polymer physics with innumerable industrial applications. They are also ubiquitous constituents of living cells. Here, we highlight the similarities and differences between two distinct ways of viewing a linear chain. We do this, on the one hand, through the lens of simulations for a standard polymer chain of tethered spheres at low and high temperatures and, on the other hand, through published experimental data on an important class of biopolymers, proteins. We present detailed analyses of their local and non-local structures as well as the maps of their closest contacts. We seek to reconcile the startlingly different behaviors of the two types of chains based on symmetry considerations.
A Facile Surface Modification Scheme for Medical-Grade Titanium and Polypropylene Using a Novel Mussel-Inspired Biomimetic Polymer with Cationic Quaternary Ammonium Functionalities for Antibacterial Application
Authors: Cheng Chi-Hui , Zeng Xiang-Zhen , Chiu Wen-Yuan , Lin Jui-Che
Published: February 2024

Medical device-associated infection remains a critical problem in the healthcare setting. Different clinical- or device-related methods have been attempted to reduce the infection rate. Among these approaches, creating a surface with bactericidal cationic functionality has been proposed. To do so, a sophisticated multi-step chemical procedure would be needed. Instead, a simple immersion approach was utilized in this investigation to render the titanium and polypropylene surface with the quaternary ammonium functionality by using a mussel-inspired novel lab-synthesized biomimetic catechol-terminated polymer, PQA-C8. The chemical oxidants, CuSO4/H2O2, as well as dopamine, were added into the novel PQA-C8 polymer immersion solution for one-step surface modification. Additionally, a two-step immersion scheme, in which the polypropylene substrate was first immersed in the dopamine solution and then in the PQA-C8 solution, was also attempted. Surface analysis results indicated the surface characteristics of the modified substrates were affected by the immersion solution formulation as well as the procedure utilized. The antibacterial assay has shown the titanium substrates modified by the one-step dopamine + PQA-C8 mixtures with the oxidants added and the polypropylene modified by the two-step scheme exhibited bacterial reduction percentages greater than 90% against both Gram-positive S. aureus and Gram-negative E. coli and these antibacterial substrates were non-cytotoxic.
Application of Electrospun Drug-Loaded Nanofibers in Cancer Therapy
Authors: Yang Yaoyao , Zhang Rui , Liang Zhiyuan , Guo Junli , Chen Bingying , Zhou Shengwei , Yu Dengguang
Published: February 2024

In the 21st century, chemotherapy stands as a primary treatment method for prevalent diseases, yet drug resistance remains a pressing challenge. Utilizing electrospinning to support chemotherapy drugs offers sustained and controlled release methods in contrast to oral and implantable drug delivery modes, which enable localized treatment of distinct tumor types. Moreover, the core–sheath structure in electrospinning bears advantages in dual-drug loading: the core and sheath layers can carry different drugs, facilitating collaborative treatment to counter chemotherapy drug resistance. This approach minimizes patient discomfort associated with multiple-drug administration. Electrospun fibers not only transport drugs but can also integrate metal particles and targeted compounds, enabling combinations of chemotherapy with magnetic and heat therapies for comprehensive cancer treatment. This review delves into electrospinning preparation techniques and drug delivery methods tailored to various cancers, foreseeing their promising roles in cancer treatment.
Thermal Stabilization of Lipases Bound to Solid-Phase Triazine-Scaffolded Biomimetic Ligands: A Preliminary Assessment
Authors: Ferreira-Faria Diogo , Taipa M. Ângela
Published: February 2024

Biomimetic ligands are synthetic compounds that mimic the structure and binding properties of natural biological ligands. The first uses of textile dyes as pseudo-affinity ligands paved the way for the rational design and de novo synthesis of low-cost, non-toxic and highly stable triazine-scaffolded affinity ligands. A novel method to assess and enhance protein stability, employing triazine-based biomimetic ligands and using cutinase from Fusarium solani pisi as a protein model, has been previously reported. This innovative approach combined the concepts of molecular modeling and solid-phase combinatorial chemistry to design, synthesize and screen biomimetic compounds able to bind cutinase through complementary affinity-like interactions while maintaining its biological functionality. The screening of a 36-member biased combinatorial library enabled the identification of promising lead ligands. The immobilization/adsorption of cutinase onto a particular lead (ligand 3′/11) led to a noteworthy enhancement in thermal stability within the temperature range of 60–80 °C. In the present study, similar triazine-based compounds, sourced from the same combinatorial library and mimicking dipeptides of diverse amino acids, were selected and studied to determine their effectiveness in binding and/or improving the thermal stability of several lipases, enzymes which are closely related in function to cutinases. Three ligands with different compositions were screened for their potential thermostabilizing effect on different lipolytic enzymes at 60 °C. An entirely distinct enzyme, invertase from Saccharomyces cerevisiae, was also assessed for binding to the same ligands and functioned as a ‘control’ for the experiments with lipases. The high binding yield of ligand 3′/11 [4-({4-chloro-6-[(2-methylbutyl)amino]-1,3,5-triazin-2-yl}amino)benzoic acid] to cutinase was confirmed, and the same ligand was tested for its ability to bind lipases from Aspergillus oryzae (AOL), Candida rugosa (CRL), Chromobacterium viscosum (CVL), Rhizomucor miehei (RML) and Rhizopus niveus (RNL). The enzymes CRL, CVL, RNL and invertase showed significant adsorption yields to ligand 3′/11—32, 29, 36 and 94%, respectively, and the thermal stability at 60 °C of free and adsorbed enzymes was studied. CVL and RNL were also stabilized by adsorption to ligand 3′/11. In the case of CRL and invertase, which bound but were not stabilized by ligand (3′/11), other ligands from the original combinatorial library were tested. Between the two alternative ligands, one was effective at stabilizing C. rugosa lipase, while none stabilized invertase.
A Deep Learning Approach to Estimate Soil Organic Carbon from Remote Sensing
Authors: Pavlovic Marko , Ilic Slobodan , Ralevic Neobojša , Antonic Nenad , Raffa Dylan Warren , Bandecchi Michele , Culibrk Dubravko
Published: February 2024

Monitoring soil organic carbon (SOC) typically assumes conducting a labor-intensive soil sampling campaign, followed by laboratory testing, which is both expensive and impractical for generating useful, spatially continuous data products. The present study leverages the power of machine learning (ML) and, in particular, deep neural networks (DNNs) for segmentation, as well as satellite imagery, to estimate the SOC remotely. We propose a new two-stage pipeline for remote SOC estimation, which relies on using a DNN trained to classify land cover to perform feature extraction, while the SOC estimation is performed by a different ML model. The first stage is an image segmentation DNN with the U-Net architecture, which is trained to estimate the land cover for an observed geographical region, based on multi-spectral images taken by the Sentinel-2 satellite constellation. This estimator is subsequently used to extract the latent feature vector for each of the output pixels, by rolling back from the output (dense) layer of the U-Net and accessing the last available convolutional layer of the same dimension as our desired output. The second stage is trained on a set of feature vectors extracted at the coordinates for which manual SOC measurements exist. We tested a variety of ML models and report on their performance. Using the best extremely randomized trees model, we generated a spatially continuous map of SOC estimations for the region of Tuscany, in Italy, with a resolution of 10 m, to share with the researchers as a means of validating the results and to demonstrate the efficiency of the proposed approach, which can can easily be scaled to create a global continuous SOC map.
The Analysis of Ionospheric TEC Anomalies Prior to the Jiuzhaigou Ms7.0 Earthquake Based on BeiDou GEO Satellite Data
Authors: Jia Xinyi , Liu Jing , Zhang Xuemin
Published: February 2024

The position between BeiDou geostationary Earth orbit (GEO) satellites and ground-based receiving stations can roughly be considered to be constant with negligible fluctuations; thus, the total electron content (TEC) data over a fixed ionospheric piercing point (IPP) can be continuously acquired, which is advantageous for monitoring ionospheric disturbances. Focused on the Jiuzhaigou Ms7.0 earthquake that occurred on 8 August 2017, the TEC data inverted by the BeiDou GEO satellite were analyzed to extract ionospheric disturbances potentially associated with the earthquake. It was found that significant anomalies in ionospheric TEC occurred 10–11 days, 6–7 days, and 1–9 h prior to the earthquake, which was mainly located in the southeast and southwest directions within about 2500 km distance from the epicenter. Comparing the spatial and temporal characteristics between the ionospheric disturbance and the radon gas near the surface, the atmospheric electric field, and the spectrum of TEC data, it was considered that the chemical and acoustic–gravity wave pathway may play an important role in the lithosphere–atmosphere–ionosphere coupling (LAIC) mechanism.
Investigation of Climate Effects on the Physiological Parameters of Dairy Livestock (Cow vs. Buffalo)
Authors: Piscopo Nadia , Matera Roberta , Cotticelli Alessio , Trapanese Lucia , Tamburis Oscar , Cimmino Roberta , Salzano Angela
Published: February 2024

Nowadays climate change is affecting the planet’s biodiversity, and livestock practices must adapt themselves to improve production without affecting animal welfare. This work investigates the influence that some climatic parameters such as Environment Temperature, Relative Humidity, Thermal excursion and Temperature–Humidity Index (THI), can have on milk quantity and quality in two different dairy species (buffaloes and cows) raised on the same farm. A further aim was to understand if THI threshold used for cows could also be used for buffaloes. The climatic parameters were recorded daily through a meteorological station located inside the farm. Milk quantity (converted into ECM) and quality (Fat Percentage—FP; Protein Percentage—PP; Somatic Cell Count—SCC) were measured. Data were analyzed with Spearman’s correlation index, separately for buffaloes and cows. The results indicate a greater sensitivity of cows to heat stress and a strong negative correlation of the ECM with meteorological data (p < 0.01). The results of this study may stimulate the use of integrated technologies (sensors, software) in the dairy sector, since the IoT (sensors, software) helps to enhance animal well-being and to optimize process costs, with a precision livestock farming approach.
Deeply Implanted Conformal Antenna for Real-Time Bio-Telemetry Applications
Authors: Matekovits Ladislau , Mir Farzad , Dassano Gianluca , Peter Ildiko
Published: February 2024

The design and experimental verification of a deeply implanted conformal printed antenna is presented. The hip implant acts as the ground plane for a coaxial-cable-fed trapezoidal radiator designed to transmit biological signals collected within the body by proper biosensors. The arrangement, consisting of a metallic (or equivalent) hip implant, bio-compatible gypsum-based dielectric, and conformal radiator, was tested when the hosting 3D-printed plastic bone was immersed in tissue-like liquid contained in a plastic bucket. The dimensions of the set-up are similar to a human leg. Matching and radiation characteristics are presented in the industrial, scientific, and medical (ISM) frequency band (2.4–2.5 GHz), showing the feasibility of the proposed arrangement.
EEG Dataset Collection for Mental Workload Predictions in Flight-Deck Environment
Authors: Hernández-Sabaté Aura , Yauri José , Folch Pau , Álvarez Daniel , Gil Debora
Published: February 2024

High mental workload reduces human performance and the ability to correctly carry out complex tasks. In particular, aircraft pilots enduring high mental workloads are at high risk of failure, even with catastrophic outcomes. Despite progress, there is still a lack of knowledge about the interrelationship between mental workload and brain functionality, and there is still limited data on flight-deck scenarios. Although recent emerging deep-learning (DL) methods using physiological data have presented new ways to find new physiological markers to detect and assess cognitive states, they demand large amounts of properly annotated datasets to achieve good performance. We present a new dataset of electroencephalogram (EEG) recordings specifically collected for the recognition of different levels of mental workload. The data were recorded from three experiments, where participants were induced to different levels of workload through tasks of increasing cognition demand. The first involved playing the N-back test, which combines memory recall with arithmetical skills. The second was playing Heat-the-Chair, a serious game specifically designed to emphasize and monitor subjects under controlled concurrent tasks. The third was flying in an Airbus320 simulator and solving several critical situations. The design of the dataset has been validated on three different levels: (1) correlation of the theoretical difficulty of each scenario to the self-perceived difficulty and performance of subjects; (2) significant difference in EEG temporal patterns across the theoretical difficulties and (3) usefulness for the training and evaluation of AI models.
Improved SSA-Based GRU Neural Network for BDS-3 Satellite Clock Bias Forecasting
Authors: Liu Hongjie , Liu Feng , Kong Yao , Yang Chaozhong
Published: February 2024

Satellite clock error is a key factor affecting the positioning accuracy of a global navigation satellite system (GNSS). In this paper, we use a gated recurrent unit (GRU) neural network to construct a satellite clock bias forecasting model for the BDS-3 navigation system. In order to further improve the prediction accuracy and stability of the GRU, this paper proposes a satellite clock bias forecasting model, termed ITSSA-GRU, which combines the improved sparrow search algorithm (SSA) and the GRU, avoiding the problems of GRU’s sensitivity to hyperparameters and its tendency to fall into local optimal solutions. The model improves the initialization population phase of the SSA by introducing iterative chaotic mapping and adopts an iterative update strategy based on t-step optimization to enhance the optimization ability of the SSA. Five models, namely, ITSSA-GRU, SSA-GRU, GRU, LSTM, and GM(1,1), are used to forecast the satellite clock bias data in three different types of orbits of the BDS-3 system: MEO, IGSO, and GEO. The experimental results show that, as compared with the other four models, the ITSSA-GRU model has a stronger generalization ability and forecasting effect in the clock bias forecasting of all three types of satellites. Therefore, the ITSSA-GRU model can provide a new means of improving the accuracy of navigation satellite clock bias forecasting to meet the needs of high-precision positioning.
Train Distance Estimation for Virtual Coupling Based on Monocular Vision
Authors: Hao Yang , Tang Tao , Gao Chunhai
Published: February 2024

By precisely controlling the distance between two train sets, virtual coupling (VC) enables flexible coupling and decoupling in urban rail transit. However, relying on train-to-train communication for obtaining the train distance can pose a safety risk in case of communication malfunctions. In this paper, a distance-estimation framework based on monocular vision is proposed. First, key structure features of the target train are extracted by an object-detection neural network, whose strategies include an additional detection head in the feature pyramid, labeling of object neighbor areas, and semantic filtering, which are utilized to improve the detection performance for small objects. Then, an optimization process based on multiple key structure features is implemented to estimate the distance between the two train sets in VC. For the validation and evaluation of the proposed framework, experiments were implemented on Beijing Subway Line 11. The results show that for train sets with distances between 20 m and 100 m, the proposed framework can achieve a distance estimation with an absolute error that is lower than 1 m and a relative error that is lower than 1.5%, which can be a reliable backup for communication-based VC operations.
Advancing Human Motion Recognition with SkeletonCLIP++: Weighted Video Feature Integration and Enhanced Contrastive Sample Discrimination
Authors: Yuan Lin , He Zhen , Wang Qiang , Xu Leiyang
Published: February 2024

This paper introduces ‘SkeletonCLIP++’, an extension of our prior work in human action recognition, emphasizing the use of semantic information beyond traditional label-based methods. The innovation, ‘Weighted Frame Integration’ (WFI), shifts video feature computation from averaging to a weighted frame approach, enabling a more nuanced representation of human movements in line with semantic relevance. Another key development, ‘Contrastive Sample Identification’ (CSI), introduces a novel discriminative task within the model. This task involves identifying the most similar negative sample among positive ones, enhancing the model’s ability to distinguish between closely related actions. Incorporating the ‘BERT Text Encoder Integration’ (BTEI) leverages the pre-trained BERT model as our text encoder to refine the model’s performance. Empirical evaluations on HMDB-51, UCF-101, and NTU RGB+D 60 datasets illustrate positive improvements, especially in smaller datasets. ‘SkeletonCLIP++’ thus offers a refined approach to human action recognition, ensuring semantic integrity and detailed differentiation in video data analysis.
Extraction of Phycocyanin and Chlorophyll from Spirulina by “Green Methods”
Authors: Nikolova Krastena , Petkova Nadezhda , Mihaylova Dasha , Gentscheva Galia , Gavrailov Georgi , Pehlivanov Ivaylo , Andonova Velichka
Published: February 2024

Phycocyanin is a pigment–protein complex from the group of phycobiliproteins obtained from Spirulina (Arthrospira platensis), with possibilities for various applications in food and pharmaceutical technologies. It is a natural colorant for food and cosmetic products. This study aimed to investigate the effect of ultrasonic and microwave extraction conditions on antioxidant activity (AOA), chlorophyll content, and the content and purity index of phycocyanin in aqueous and alcoholic extracts of Spirulina (Arthrospira platensis). For this purpose, ultrasonic extraction with water or ethanol was performed at 20 °C, 30 °C, and 40 °C for 1, 2, and 3 h at an ultrasonic frequency of 36 kHz, 40 kHz, and 45 kHz. Microwave water extraction was performed for 60 s, 120 s, and 180 s. For each of the obtained samples, three parallel measurements of antioxidant activity were made by DPPH and FRAP methods, and chlorophyll content and phycocyanin yield and purity index were determined spectrophotometrically. Ultrasonic extraction resulted in a higher yield and purity index of phycocyanin compared to microwave extraction. The highest yield of 14.88 mg g−1 with a purity index of 1.60 was achieved at a temperature of 40 °C for one hour and an ultrasonic wave frequency of 40 kHz. A relatively low yield of 4.21 mg g−1, but with a purity index of 2.67, was obtained at a temperature of 30 °C, a time of two hours, and an ultrasonic frequency of 40 kHz. Chlorophyll b content at 20 °C, for two hours and ultrasonic frequency 40 kHz was 1.400 mg g−1. The study proposes ultrasonic extraction as a green method to obtain phycocyanin of varying purity index that may be used for food, cosmetic, or biomedical purposes.
Digital Access to Judicial Services in the Brazilian Amazon: Barriers and Potential
Authors: de Moraes Beatriz Fruet , Lunardi Fabrício Castagna , Correia Pedro Miguel Alves Ribeiro
Published: February 2024

This study investigates the influence of geographical barriers and the challenges and advantages presented by information and communication technologies on digital governance within the judicial branch in the Brazilian Amazon region. The primary objective is to provide diagnoses and recommendations that can inform the construction of research for the development of policies aimed at enhancing access to judicial services by riverside populations. The methodology initially employed was a comprehensive literature review on digital governance within the judiciary and access to justice for vulnerable groups in a geographical context. Subsequently, a qualitative study was conducted, employing participant observation in the riverside communities of Itapéua and Boca do Una, situated along the Jaurucu River within the Porto de Moz District in the state of Pará. The insights garnered from respondent perceptions and participant observations were synthesized to formulate five key dimensions for digital governance and access to justice within Amazonian communities: (1) one’s experience with justice, (2) access to information, (3) geographical barriers, (4) user-friendliness of technology, and (5) resources and infrastructure supporting technology use. The study concludes that there are compelling indications that tailored digital governance and technology utilization by the judiciary, adapted to regional nuances, can significantly contribute to streamlining access to judicial services.
Research Trends in the Study of Acceptability of Digital Mental Health-Related Interventions: A Bibliometric and Network Visualisation Analysis
Authors: Armaou Maria
Published: February 2024

The acceptability of digital health interventions is a multifaceted concept that is central to user engagement. It is influenced by cultural and social norms and it is, also, a key consideration for intervention development and evaluation. For this reason, it is important to have a clear overview of how research in digital interventions’ acceptability has evolved, what type of measures or assessments have been most frequently utilised, and what may be the implications for the knowledge area and future research directions. The purpose of this bibliometric and network visualization analysis was to explore the main research patterns in the study of the acceptability of digital mental health interventions and highlight the key characteristics of knowledge production on this topic. The Web of Science was searched for relevant primary studies, with 990 documents selected for inclusion in this bibliometric analysis. Publications’ metrics, text and author keyword analysis, and bibliographical coupling of the documents provided insights into how technological developments, specific research interests, research priorities, and contexts have shaped research in the field. The main differentiation in acceptability approaches emanated from the studies’ research designs, the stage of intervention development and evaluation, and the extent to which there was a focus on user attitudes, experience, and engagement. These differentiations further indicate the importance of having clarity as to what concepts or elements of acceptability a study addresses as well as approaches that have the potential to address the complexities of acceptability.
Modular Autonomous Vehicles’ Application in Public Transport Networks: Conceptual Analysis on Airport Connection
Authors: Oargă Ioan-Tudor , Varga Bogdan Ovidiu , Moldovanu Dan , Cărăușan Horațiu , Prunean Gabriel
Published: February 2024

Increased efficiency and optimized operation of transport networks represent two of the main topics of interest when discussing modern road vehicle solutions. Taking steps towards more sustainable options, manufacturers of road vehicles are looking into advanced technologies that allow vehicles to run more efficiently and take advantage of all the available data on the road. When looking at public transportation applications, trends point in the direction of using varied types of vehicles that can carry people around. The intermodality of these types of vehicles represents the most optimized way of traveling, combining the fast and secure characteristics of airplanes and trains with the flexibility of last-mile options, such as taxis, buses, or trams. This paper discusses the aspects of implementing a modular autonomous vehicle (MAV) solution for the last-mile part of travel routes, connecting key points of a city, such as an airport or a train station, to other key locations in the city, such as the city center, important facilities, or marginal neighborhoods.
Optimization of Controlled Low-Strength Material from Multi-Component Coal-Based Solid Waste
Authors: Chen Tianxiang , Yuan Ning , Wang Shanhu , Zhang Xinling , Lin Chaoyang , Wu Xinyue , Wang Qibao , Wang Dongmin
Published: February 2024

Recently, controlled low-strength material (CLSM) has been considered an easy-to-mix material, and the raw material is usually derived from solid waste, suggesting lower production costs. Moreover, the resource utilization of waste fosters the sustainable advancement of both society and the environment. In the present work, a CLSM with excellent performance was developed by adopting fly ash, bottom ash, desulfuration gypsum, and cement as the main cementitious materials, as well as gasification coarse slag and coal gangue as aggregates. An orthogonal experiment with three factors and three levels was designed according to the ratio of cement to binder, the contents of water, and the water-reducing agent. Further, the macroscopic properties of flowability, dry density, bleeding, compressive strength, fresh density, porosity, and absorption rate of the CLSM mixtures were tested. To optimize the CLSM proportion, the ranges of three indicators of CLSM were calculated. Experimental results manifested that the fresh and dry densities of the mixtures were within the range recommended by ACI 229. The optimal levels of cement–binder ratio (i.e., the ratio of cement to binder), water content, and water-reducing agent content are 0.24, 248 kg·m−3, and 0.80 kg·m−3, respectively. Under this condition, the flowability was 251 mm, the bleeding was 3.96%, and the compressive strength for 3 d, 7 d, and 28 d was 1.50 MPa, 3.06 MPa, and 7.79 MPa, respectively. Furthermore, the leaching values of eight heavy metals in CLSM and raw materials were less than the standard requirements, indicative of no leaching risk.
Geospatial Analysis of Wind Energy Siting Suitability in the East African Community
Authors: Bimenyimana Samuel , Wang Chen , Asemota Godwin Norense Osarumwense , Ihirwe Jeanne Paula , Tuyizere Mucyo Ndera , Mwizerwa Fidele , Mo Yiyi , Abiyese Martine
Published: February 2024

Site investigation is essential for developing and constructing a dependable and effective wind engineering project. Also, the kinetic energy of moving air, used to drive a wind turbine, produces electricity. Having seen the shortage of previous studies on wind energy sites’ suitability across Africa and having read about the abundance of untapped wind energy resources in the East African region, this paper used Geographical Information System (GIS), multi-criteria, and Analytic Hierarchy techniques to provide a geospatial analysis of wind energy technology siting suitability in Eastern African Community Countries. Different data were acquired and processed from numerous open-access databases (Global Wind atlas, Regional Center for Mapping of Resources for Development (RCMRD), African Geoportal, East African community website, and Energy data Info.org). The results reveal Kenya has large parts of its land areas highly appropriate for wind energy siting (15.26%) and 1.55% of its land classified as unsuitable for wind energy generation. The rates of suitability and unsuitability were respectively 26.57% and 4.87% for Burundi, 20.6% and 10.21% for Rwanda, 20.39% and 10.44% for Tanzania, and 4.65% and 27.15% for South Sudan. The findings also show that East Africa exhibits moderate levels of wind energy siting suitability, with an estimated average of around 37.27% of its land area moderately suitable for wind energy technology installation, covering thousands of square kilometers. The study is advantageous to academia and industry-related personnel engaged in renewable energy-related activities in other African countries with similar topographies.
Advancement in Supercapacitors for IoT Applications by Using Machine Learning: Current Trends and Future Technology
Authors: Sial Qadeer Akbar , Safder Usman , Iqbal Shahid , Ali Rana Basit
Published: February 2024

Supercapacitors (SCs) are gaining attention for Internet of Things (IoT) devices because of their impressive characteristics, including their high power and energy density, extended lifespan, significant cycling stability, and quick charge–discharge cycles. Hence, it is essential to make precise predictions about the capacitance and lifespan of supercapacitors to choose the appropriate materials and develop plans for replacement. Carbon-based supercapacitor electrodes are crucial for the advancement of contemporary technology, serving as a key component among numerous types of electrode materials. Moreover, accurately forecasting the lifespan of energy storage devices may greatly improve the efficient handling of system malfunctions. Researchers worldwide have increasingly shown interest in using machine learning (ML) approaches for predicting the performance of energy storage materials. The interest in machine learning is driven by its noteworthy benefits, such as improved accuracy in predictions, time efficiency, and cost-effectiveness. This paper reviews different charge storage processes, categorizes SCs, and investigates frequently employed carbon electrode components. The performance of supercapacitors, which is crucial for Internet of Things (IoT) applications, is affected by a number of their characteristics, including their power density, charge storage capacity, and cycle longevity. Additionally, we provide an in-depth review of several recently developed ML-driven models used for predicting energy substance properties and optimizing supercapacitor effectiveness. The purpose of these proposed ML algorithms is to validate their anticipated accuracies, aid in the selection of models, and highlight future research topics in the field of scientific computing. Overall, this research highlights the possibility of using ML techniques to make significant advancements in the field of energy-storing device development.
Barriers to Peer-to-Peer Energy Trading Networks: A Multi-Dimensional PESTLE Analysis
Authors: Sun Zheyuan , Tavakoli Sara , Khalilpour Kaveh , Voinov Alexey , Marshall Jonathan Paul
Published: February 2024

The growing adoption of distributed energy production technologies and the potential for energy underutilisation when the energy is produced by non-connected groups has raised interest in developing ‘sharing economy’ concepts in the electricity sector. We suggest that mechanisms, such as peer-to-peer (P2P) energy trading, will allow users to exchange their surplus energy for mutual benefits, stimulate the adoption of renewable energy, encourage communities to ‘democratically’ control their own energy supplies for local development, improve energy efficiency, and create many other benefits This approach is receiving increasing attention across the world, particularly in Germany, the Netherlands and Australia. Nevertheless, the actual development and implementation of these platforms are slow and mostly limited to trial activities. This study investigates the challenges and barriers facing P2P energy trading developments based on previous academic and industry studies. We provide a comprehensive multidimensional barrier analysis through a PESTLE approach to assess the barriers from a variety of perspectives, including the political (P), economic (E), social (S), technological (T), legal (L), and environmental (E) aspects. This approach clarifies the many intersecting problem fields for P2P trading in renewable energy, and the paper identifies a list of such barriers and discusses the prospects for addressing these issues. We also elaborate on the importance of incentive-based P2P market design.
Electric Taxi Charging Load Prediction Based on Trajectory Data and Reinforcement Learning—A Case Study of Shenzhen Municipality
Authors: Liu Xiaojia , Liu Bowei , Chen Yunjie , Zhou Yuqin , Yu Dexin
Published: February 2024

In order to effectively solve the problem of electric taxi charging load prediction and reasonable charging behaviour discrimination, in this paper, we use taxi GPS trajectory data to mine the probability of operation behaviour in each area of the city, simulate the operation behaviour of a day by combining it with reinforcement learning ideas, obtain the optimal operation strategy through training, and count the spatial and temporal distributions and power values at the time of charging decision making, so as to predict the charging load of electric taxis. Experiments are carried out using taxi travel data in Shenzhen city centre. The results show that, in terms of taxi operation behaviour, the operation behaviour optimized by the DQN algorithm shows the optimal effect in terms of the passenger carrying time, mileage, and daily net income; in terms of the charging load distribution, the spatial charging demand of electric taxis in each area shows obvious differences, and the charging demand load located in the city centre area and close to the traffic hub is higher. In time, the peak charging demand is distributed around 3:00 to 4:00 and 14:00 to 15:00. Compared with the operating habits of drivers based on the Monte Carlo simulation, the DQN algorithm is able to optimise the efficiency and profitability of taxi drivers, which is more in line with the actual operating habits of drivers formed through accumulated experience, thus achieving a more accurate charging load distribution.