bluuebunny/crossref_metadata_2025 huggingface dataset:
A Multiple-Model Second-Level Adaptive Compensation Scheme for High-Speed Trains with Unknown Actuator Failures
Authors: Tan Chang , Zeng Jing , Jiang Kun , Li Zhongqi , Wu Yuntao
Published: October 2024

In this paper, a novel multiple-model second-level adaptive failure compensation scheme is proposed for high-speed trains with unknown failures to achieve the desired system performance. High-speed train dynamics with unknown system parameters actuator failures are first derived in detail. Multiple identification models are used to handle the parametric uncertainties and realize the desired speed and position tracking of a high-speed train with actuator failures in a first-level adaptation; identification errors of multiple identification models are used to obtain a virtual model for improving the convergence rate of parameters in a second-level adaptation. A second-level adaptive controller set is designed by using the parameters of the corresponding virtual model and the convex combination of the parameter estimates from the identification models. Finally, a switching mechanism associated with individual controllers is introduced to select the correct controller from the controller set. The stability of the closed-loop system and the performance of asymptotic state tracking are proved via the Lyapunov stability theory. Further, a simulation study was carried out by using the real line data of the CRH380A-type high-speed train from the Jinan section to the Xuzhou section. The simulation results show that in the presence of unknown actuator failures, the control scheme can control the speed tracking error within 0.4 km/h, and the switching strategy can quickly switch to the correct controller within 1 s, which verifies the effectiveness and feasibility of the proposed failure compensation scheme.
Substance-Induced Psychosis: Diagnostic Challenges and Phenomenological Insights
Authors: Ricci Valerio , Martinotti Giovanni , Maina Giuseppe
Published: October 2024

Substance-induced psychosis (SIP) is an increasingly prevalent and complex condition that presents significant diagnostic and therapeutic challenges. With the rising use of novel psychoactive substances (NPSs) and potent traditional compounds like cannabis and cocaine, particularly among adolescents and young adults, understanding the mechanisms and clinical manifestations of SIP has become more critical. This review examines the psychopathological characteristics of SIP, focusing on the differentiation between substance-induced and primary psychotic disorders. Two key concepts are explored: Gaëtan de Clérambault’s “mental automatism”, which describes involuntary thoughts and sensations experienced as alien to the self, and the “twilight state” of consciousness, characterized by an altered perception of space, time, and social interaction. These phenomena are essential in understanding how substance use triggers psychotic processes and how SIP differs from endogenous psychosis. This review also highlights the clinical and phenomenological distinctions between acute and chronic psychotic episodes, emphasizing the potential transition from SIP to persistent psychosis, especially in cases of frequent and potent substance use. It discusses the diagnostic and therapeutic importance of recognizing specific psychopathological markers such as alterations in temporality, spatiality, and intersubjectivity. Understanding these features enhances the clinician’s ability to accurately differentiate SIP from primary psychotic disorders, which is often complicated by the widespread use of substances that may not be easily detectable in standard tests. This review concludes by advocating for a clinical–phenomenological approach to empathically engage with patients and provide a solid diagnostic framework for improved treatment strategies, particularly for those at risk of transitioning to chronic psychosis.
The Level of Serotonin and the Parameters of Lipid Metabolism Are Dependent on the Mental Status of Patients with Suicide Attempts
Authors: Tatayeva Roza , Tussupova Aruzhan , Koygeldinova Sholpan , Serkali Sandugash , Suleimenova Ainash , Askar Bauyrzhan
Published: October 2024

Suicidal behavior is an important public health problem often associated with psychological and biological factors. Understanding the role of biochemical markers, such as cholesterol and serotonin levels, may provide insight into the mechanisms underlying suicidal tendencies. This study aimed to investigate the relationship between lipid profile parameters, serotonin levels, and psychological status in individuals who had attempted suicide. Patients with suicidal attempts underwent a psychiatric evaluation and blood samples were taken to measure serotonin levels, total cholesterol, and lipid metabolism parameters. Psychological assessments included measures of depression, stress, and hopelessness; assessments of motivation; suicidal tendencies; and assessments of psychological and physical aspects of fear of death. The study found a significant positive correlation between serotonin and cholesterol levels (r = 0.6883, p < 0.0001). Lower levels of serotonin and cholesterol were associated with higher rates of depression and stress. These findings suggest that dyslipidemia may be associated with reduced serotonin function, which may contribute to the development of suicidal behavior. The study highlights the potential role of cholesterol and serotonin in the development of suicidal behavior, along with the mental status of patients. Evaluating the interaction of neurobiological and social–psychological factors provide an opportunity to search for peripheral biomarkers of suicide risk.
Emotional Intelligence and University Students’ Happiness: The Mediating Role of Basic Psychological Needs’ Satisfaction
Authors: Vasiou Aikaterini , Vasilaki Eleni , Mastrothanasis Konstantinos , Galanaki Evangelia
Published: October 2024

Given the increasing importance of adjusting to university life and achieving happiness, identifying the effective role of emotional intelligence and psychological needs’ satisfaction in enhancing students’ well-being is crucial. This study investigated the relation between emotional intelligence, psychological need satisfaction, and university students’ happiness. Data were collected from 205 university students (mean age: 23.35; predominantly female) at the University of Crete. Participants completed the Emotional Intelligence Scale, the Oxford Happiness Questionnaire, and the Basic Psychological Needs Satisfaction Scale. The analysis began with a correlation matrix to explore preliminary relations among the key variables. A multiple-linear regression analysis was then conducted to predict happiness levels based on observed correlations. Following this, a multiple-mediation analysis examined how emotional intelligence affects happiness through psychological needs satisfaction. The results indicated a positive association between emotional intelligence and happiness, with psychological needs’ satisfaction also positively correlating with happiness. Specifically, the use and regulation of emotion, competence, and autonomy significantly predicted happiness. Path analysis revealed that emotional intelligence indirectly influences happiness, particularly through competence. The results indicate that high emotional intelligence, through the satisfaction of basic psychological needs, is significantly associated with university students’ happiness. The study suggests that institutions should guide students in emotional intelligence and competence to increase happiness during their studies.
Development of a Macro X-ray Fluorescence (MA-XRF) Scanner System for In Situ Analysis of Paintings That Operates in a Static or Dynamic Method
Authors: Freitas Renato P. de , Oliveira Miguel A. de , Oliveira Matheus B. de , Pimenta André R. , Felix Valter de S. , Pereira Marcelo O. , Gonçalves Elicardo A. S. , Grechi João V. L. , Silva Fabricio L. e. , Carvalho Cristiano de S. , Ataliba Jonas G. R. S. , Pereira Leandro O. , Muniz Lucas C. , Santos Robson B. dos , Vital Vitor da S.
Published: October 2024

This work presents the development of a macro X-ray fluorescence (MA-XRF) scanner system for in situ analysis of paintings. The instrument was developed to operate using continuous acquisitions, where the module with the X-ray tube and detector moves at a constant speed, dynamically collecting spectra for each pixel of the artwork. Another possible configuration for the instrument is static acquisitions, where the module with the X-ray tube and detector remains stationary to acquire spectra for each pixel. The work also includes the analytical characterization of the system, which incorporates a 1.00 mm collimator that allows for a resolution of 1.76 mm. Additionally, the study presents the results of the analysis of two Brazilian paintings using this instrument. The elemental maps obtained enabled the characterization of the pigments used in the creation of the artworks and materials used in restoration processes.
Production of Sugars and Ethanol from Acid–Alkaline-Pretreated Agave sisalana Residue
Authors: Thomas Habila Yusuf , Cavalcante José Demétrio Nery , dos Santos Willame Marinho , Souza Domingos Fabiano de Santana , Padilha Carlos Eduardo de Araújo , Menezes Rômulo Simões Cezar , de Souza Rafael Barros , Dutra Emmanuel Damilano
Published: October 2024

Drylands in Brazil have been exploring sisal (Agave sisalana) as an essential source of income. However, the solid residues generated because of this activity still need suitable destinations; therefore, research has been carried out to transform them into added-value products. Therefore, the present study evaluated the potential of sisal or agave solid residue as a precursor feedstock for second-generation ethanol production. Acid and acid–alkaline pretreatments were carried out on sisal residues to enrich the biomass with cellulose and maximize enzymatic digestibility. Second-generation ethanol production was carried out using Semi-simultaneous saccharification and fermentation (SSSF). Regardless of catalyst dosage and incubation time, oxalic acid pretreatments generated samples with a similar chemical composition to those pretreated with sulfuric acid. However, samples pretreated with oxalic acid showed lower enzymatic digestibility. Samples pretreated with oxalic acid and sodium hydroxide obtained 14.28 g/L of glucose and cellulose conversion of 79.1% (at 5% solids), while 21.49 g/L glucose and 91.2% of cellulose conversion were obtained in the hydrolysis of pretreated samples with sulfuric acid and sodium hydroxide combined pretreatments. The pretreatment sequence efficiently reduced cellulase dosage from 20 to 10 FPU/g without compromising sugar release. SSSF achieved maximum production of 40 g/L ethanol and 43% ethanol conversion using 30% solids and gradually adding biomass and cellulases.
The Second Protonation in the Bio-Catalytic Cycles of the Enzymes Cytochrome P450 and Superoxide Reductase
Authors: Spataru Tudor , Dascalu Lisa Maria , Moraru Andreea , Moraru Mariana
Published: October 2024

The enzymes Cytochrome P450 and Superoxide Reductase, which have a similar coordination center [FeN4S], begin their biochemical cycles similarly. They absorb an oxygen molecule, add two electrons, and link a hydrogen atom to the distal oxygen atom of the product obtained, creating the so-called Compound 0 in the case of the first enzyme. However, the bio-catalytic processes of these two enzymes continue in different ways. In the bio-catalytic cycle of Cytochrome P450, the enzyme binds another proton to the distal oxygen atom, producing a water molecule and Compound 1. In contrast, in the bio-catalytic cycle of the Superoxide Reductase, the enzyme binds a proton to the proximal oxygen atom, producing a hydrogen peroxide molecule, which later decomposes into oxygen and water. The MCSCF method in the CASSCF form was used to study the difference in Cytochrome P450 and Superoxide Reductase’s bio-catalytic cycles. The results of these enzymes’ hydroperoxo adduct models’ geometric optimization showed that, in fact, all their properties, including their spin states, the wave functions in their active zones, and the Fe-N, Fe-S, and Fe-O bond lengths, are different. The Fe-N, Fe-S, and Fe-O chemical bond lengths are much longer in the case of the second enzyme compared to the chemical bond lengths in the case of the first enzyme, reflecting a spin value equal to 5/2 in the second case and a spin value equal to 1/2 in the first. A decisive role in the difference in their bio-catalytic cycles is played by the fact that the first bonded hydrogen atom is linked to the distal oxygen atom in the side position in the case of Compound 0 and the up position in the case of the hydroperoxo adduct of the enzyme Superoxide Reductase, protecting the distal oxygen atom from possible interaction with the substrate. The second protonation to Compound 0 at the distal oxygen atom in the case of Cytochrome P450’s bio-catalytic cycle and the second protonation at the proximal oxygen atom in the case of the hydroperoxo adduct of Superoxide Reductase’s bio-catalytic cycle depend on the proton transfer through the Asp251 channel in the first case and on the transferal of H+ from the substrate to the water molecule and the proximal oxygen in the second case.
Revisiting Atmospheric Oxidation Kinetics of Nitrogen Oxides: The Use of Low-Cost Electrochemical Sensors to Measure Reaction Kinetics
Authors: Owen Steven M. , Yee Lachlan H. , Maher Damien T.
Published: October 2024

The high cost of equipment is a significant entry barrier to research for smaller organisations in developing solutions to air pollution problems. Low-cost electrochemical sensors have shown sensitivity at parts-per-billion by volume (ppbV) mixing ratios but are subject to variations due to changing environmental conditions, particularly temperature. We have previously demonstrated that under isothermal/isohume conditions such as those found in kinetic studies, very stable electrochemical responses occur. In this paper, we demonstrate the utility of a low-cost IoT-based sensor system that employs four-electrode electrochemical sensors under isothermal/isohume conditions for studying the kinetics of the atmospheric oxidation of nitrogen oxides. The results suggest that reproducible results for NO and NO2 kinetics can be achieved. The method produced oxidation rates of 7.95 × 103 L2 mol−2 s−1 (±1.3%), for NO and 7.99 × 10−4 s−1 (±2.1%) for NO2. This study suggests that the oxidation kinetics of nitrogen oxides can be assessed with low-cost sensors, which can support a wide range of industrial applications, such as designing biocatalytic coatings for air pollution remediation.
One-Pot Syntheses of [c2]Daisy-Chain Rotaxane Networks via Thiol-Ene Reaction and Its Application to Gel Electrolyte for Secondary Battery
Authors: Kamoto Risako , Onimura Kenjiro , Yamabuki Kazuhiro
Published: October 2024

A new topological material, the [c2]daisy-chain rotaxane network, was successfully synthesized via a thiol-ene reaction between a [c2]daisy-chain rotaxane, which consists of a host–guest compound (H–G compound) where a crown ether and a secondary ammonium salt are linked, and a multi-branched thiol compound. The resulting network polymer exhibited higher compressive strength compared to one without the [c2]daisy-chain rotaxane. Additionally, the neutralized [c2]daisy-chain rotaxane network, in which the ammonium salt was neutralized and there was no interaction with the crown ether, showed increased rigidity compared to its state before neutralization. Furthermore, a gel electrolyte was prepared by impregnating the [c2]daisy-chain rotaxane network with an organic electrolyte containing dissolved lithium salts, and its ionic conductivity was investigated. As a result, high ionic conductivity was achieved despite the high polymer content.
Enhancing Sewage Sludge Stabilization, Pathogen Removal, and Biomass Production through Indigenous Microalgae Promoting Growth: A Sustainable Approach for Sewage Sludge Treatment
Authors: Ben Hamed Hajer , Debuigne Antoine , Kleinjan Hetty , Toye Dominique , Léonard Angélique
Published: October 2024

Sewage sludge (SS), a byproduct of wastewater treatment plants, poses significant environmental and health risks if not properly handled. Conventional approaches for SS stabilization often involve costly and energy-consuming processes. This study investigated the effect of promoting native microalgae growth in SS on its stabilization, pathogen bacteria removal, and valuable biomass production. The effect on settleability, filterability, and extracellular polymeric substances (EPSs) was examined as well. Experiments were conducted in photobioreactors (PBRs) without O2 supply and CO2 release under controlled parameters. The results show a significant improvement in SS stabilization, with a reduction of volatile solids (VSs) by 47.55%. Additionally, fecal coliforms and E. coli were efficiently removed by 2.25 log and 6.72 log, respectively. Moreover, Salmonella spp. was not detected after 15 days of treatment. The settleability was improved by 71.42%. However, a worsening of the sludge filterability properties was observed, likely due to a decrease in floc size following the reduction of protein content in the tightly bound EPS fraction. Microalgae biomass production was 16.56 mg/L/day, with a mean biomass of 0.35 g/L at the end of the batch treatment, representing 10.35% of the total final biomass. These findings suggest that promoting native microalgal growth in SS could be sustainable and cost-effective for SS stabilization, microalgal biomass production, and the enhancement of sludge-settling characteristics, notwithstanding potential filtration-related considerations.
Utilizing Ceramic Factory Waste to Produce Low-Cost Refractory Ceramics
Authors: Khater Gamal A. , Romero Maximina , López-Delgado Aurora , Padilla Isabel , El-Kheshen Amany A. , Farag Mohammad M. , Elmaghraby Mohammad S. , Shendy Hussain , Nasralla Naglaa H. S.
Published: October 2024

The use of solid waste such as ceramic sludge, ceramic rollers, and magnesite was studied to obtain cheap refractory ceramics at temperatures of 1300 °C based on XRF, XRD SEM, EDX, bending strength, and dielectric properties. The prepared samples were examined. The results showed that the significant crystalline phases formed were mullite, spinel, and corundum. They also showed that mullite hindered the formation of cordierite and enhanced spinel formation. With increased cordierite content, the microstructure varied from fine grained to coarse grained. Bending strength increased with increasing mullite content and bulk density, ranging from 10.80 to 13.50 MPa. Bulk density increased with the increase in mullite content and sintering temperature and ranged from 1.99 to 1.94 g/cm3, while the percentage of porosity and water absorption decreased and ranged from 29.40 to 38.83, respectively. To examine the effect of the produced phases on the dielectric characteristics, the permittivity (ε′), dielectric loss (ε″), and AC conductivity (σac) were measured in the frequency range of 10−1 Hz to 106 Hz. As the concentration of cordierite increased, there was a noticeable drop in ε′ from 35.6 to 8.2 and σac from 10−8 s/cm to around 10−11 s/cm and high values of resistivity from 108 cm/s to about 1010 cm/s, suggesting that this material might be an excellent insulator.
From Unorthodox Sufism to Muslim Anarchism: The Disobedient Case of Islam-Based Political Thought in Turkey
Authors: Çelik Kadir Can
Published: October 2024

This paper examines Muslim anarchists in Turkey who developed an Islam-based anarchist theory opposing private property, the state, capitalism, and all forms of authority. By analyzing their online periodical itaatsiz (disobedient), published since 2013, and earlier works by Muslim anarchist writers, this study explores their perspectives on the West, Islam, the Qur’an, and Sufism. Muslim anarchists stand out for their opposition to the hegemony of Enlightenment-based, anti-theist, and positivist thought in anarchist movements in Turkey and for their encouragement to re-examine concepts such as authority, private property, capitalism, and the state within the framework of Islam-based political thought. Studying how Muslim anarchists construct a social movement in today’s Turkey is essential to understanding Islam-based conceptualizations of politics in Turkey and unpacking the relationship between Islam and anarchism.
Nutritive Valorisation of Banana Tree (Musa acuminata) By-Products with Different Levels of Sodium Hydroxide
Authors: Teixeira Sofia Margarida Pontes , Nunes Helder Patrício Barcelos , Vouzela Carlos Fernando Mimoso , Madruga João da Silva , Borba Alfredo Emílio Silveira
Published: October 2024

The aim of this study is to evaluate the effects of treatment with sodium hydroxide on the nutritional value of banana (Musa acuminata) trees to see if they can be used as a source of fibre for ruminants since there is large banana production and consequently some waste. The samples were collected and dried in an oven with a controlled air flow. They were then sprinkled with a sodium hydroxide solution (2, 4, 6 and 8% of dry matter) and placed in a suitable container. The chemical properties were analysed. The results show that sodium hydroxide treatment leads to a significant decrease in neutral fibre and acid lignin contents in both leaves and pseudostems. A significant (p < 0.05) increase was observed in ash, and digestibility values were lower in leaves, confirming the values for neutral detergent fibres. The total amount of gas produced was measured after 4, 8, 12, 24, 48, 72 and 96 h of incubation. The results showed that the gas production in the leaves was lower in all treatments compared to the pseudostem, which was consistent with the digestibility results. Analysis of the results showed that the best NaOH concentration to improve the nutritional value of M. acuminata was 6 and 8%.
Instance-Level Scaling and Dynamic Margin-Alignment Knowledge Distillation for Remote Sensing Image Scene Classification
Authors: Li Chuan , Teng Xiao , Ding Yan , Lan Long
Published: October 2024

Remote sensing image (RSI) scene classification aims to identify semantic categories in RSI using neural networks. However, high-performance deep neural networks typically demand substantial storage and computational resources, making practical deployment challenging. Knowledge distillation has emerged as an effective technique for developing compact models that maintain high classification accuracy in RSI tasks. Existing knowledge distillation methods often overlook the high inter-class similarity in RSI scenes, leading to low-confidence soft labels from the teacher model, which can mislead the student model. Conversely, overly confident soft labels may discard valuable non-target information. Additionally, the significant intra-class variability in RSI contributes to instability in the model’s decision boundaries. To address these challenges, we propose an efficient method called instance-level scaling and dynamic margin-alignment knowledge distillation (ISDM) for RSI scene classification. To balance the target and non-target class influence, we apply an entropy regularization loss to scale the teacher model’s target class at the instance level. Moreover, we introduce dynamic margin alignment between the student and teacher models to improve the student’s discriminative capability. By optimizing soft labels and enhancing the student’s ability to distinguish between classes, our method reduces the effects of inter-class similarity and intra-class variability. Experimental results on three public RSI scene classification datasets (AID, UCMerced, and NWPU-RESISC) demonstrate that our method achieves state-of-the-art performance across all teacher–student pairs with lower computational costs. Additionally, we validate the generalization of our approach on general datasets, including CIFAR-100 and ImageNet-1k.
DOA Estimation of GNSS Signals Based on Deconvolved Conventional Beamforming
Authors: Wu Jian , Li Chenglong , Lin Honglei , Tang Xiaomei , Wang Feixue
Published: October 2024

The Direction of Arrival (DOA) parameter is a key parameter in directional channel modeling for GNSS systems and multipath suppression. However, achieving high-precision, low-complexity DOA estimation of multiple signal sources without requiring a known source number is still a challenge. This paper introduces a satellite navigation DOA parameter estimation method based on deconvolution beamforming. By exploiting the translational invariance property of the uniform linear array pattern, the deconvolution process is applied to the de-spread array pattern of satellite navigation signals, achieving high-precision estimation of DOA parameters. This method can achieve high-precision blind DOA estimation of multiple signal sources while significantly reducing the estimation complexity. Compared with traditional methods, precise DOA estimation can be achieved even in low-signal-to-noise-ratio conditions and with a small number of elements in the array. The theoretical analysis and simulation results verify the effectiveness of the proposed algorithm.
Investigation of Global International GNSS Service Control Information Extraction for Geometric Calibration of Remote Sensing Images
Authors: Jiao Niangang , Xiang Yuming , Wang Feng , Zhou Guangyao , You Hongjian
Published: October 2024

Despite the swift advancement of geometric calibration techniques, the geometric performance of remote sensing imagery remains heavily contingent upon the quality and distribution of ground control data. Securing precise ground control data is often laborious, and the accuracy of open-source control data is subject to variability. This paper explores the potential of the globally dispersed International GNSS Service (IGS) network to enhance the geometric performance of remote sensing images. The IGS network, with its extensive reach, offers superior positioning and navigation products that surpass the previously mentioned sources. To establish a connection between the IGS network and remote sensing images, high-resolution GEM chips (GEMs) are firstly utilized for precise positioning. Geolocation biases of these GEMs are refined based on the identified IGS information. After that, the calibrated GEM chips are applied as control information for the geometric calibration of raw satellite images. A test dataset from the Chinese Gaofen-2 (GF-2) with various forms of coverage is experimented, with LiDAR-derived Digital Surface Models (DSMs) serving as reference for the validation of the proposed method. Compared with traditional methods using the GEMs as a direct reference, the experimental results demonstrate that the introduced IGS information enhances the geometric performance of remote sensing images, exhibiting robust generalization performance across remote sensing data from various source domains.
Multi-Scale Expression of Coastal Landform in Remote Sensing Images Considering Texture Features
Authors: Zhang Ruojie , Shen Yilang
Published: October 2024

The multi-scale representation of remote sensing images is crucial for information extraction, data analysis, and image processing. However, traditional methods such as image pyramid and image filtering often result in the loss of image details, particularly edge information, during the simplification and merging processes at different scales and resolutions. Furthermore, when applied to coastal landforms with rich texture features, such as biologically diverse areas covered with vegetation, these methods struggle to preserve the original texture characteristics. In this study, we propose a new method, multi-scale expression of coastal landforms considering texture features (METF-C), based on computer vision techniques. This method combines superpixel segmentation and texture transfer technology to improve the multi-scale representation of coastal landforms in remote sensing images. First, coastal landform elements are segmented using superpixel technology. Then, global merging is performed by selecting different classes of superpixels, with boundaries smoothed using median filtering and morphological operators. Finally, texture transfer is applied to create a fusion image that maintains both scale and level consistency. Experimental results demonstrate that METF-C outperforms traditional methods by effectively simplifying images while preserving important geomorphic features and maintaining global texture information across multiple scales. This approach offers significant improvements in edge preservation and texture retention, making it a valuable tool for analyzing coastal landforms in remote sensing imagery.
Category-Based Interactive Attention and Perception Fusion Network for Semantic Segmentation of Remote Sensing Images
Authors: Liu Tao , Cheng Shuli , Yuan Jian
Published: October 2024

With the development of CNNs and the application of transformers, the segmentation performance of high-resolution remote sensing image semantic segmentation models has been significantly improved. However, the issue of category imbalance in remote sensing images often leads to the model’s segmentation ability being biased towards categories with more samples, resulting in suboptimal performance for categories with fewer samples. To make the network’s learning and representation capabilities more balanced across different classes, in this paper we propose a category-based interactive attention and perception fusion network (CIAPNet), where the network divides the feature space by category to ensure the fairness of learning and representation for each category. Specifically, the category grouping attention (CGA) module utilizes self-attention to reconstruct the features of each category in a grouped manner, and optimize the foreground–background relationship and its feature representation for each category through the interactive foreground–background relationship optimization (IFBRO) module therein. Additionally, we introduce a detail-aware fusion (DAF) module, which uses shallow detail features to complete the semantic information of deep features. Finally, a multi-scale representation (MSR) module is deployed for each class in the CGA and DAF modules to enhance the description capability of different scale information for each category. Our proposed CIAPNet achieves mIoUs of 54.44%, 85.71%, and 87.88% on the LoveDA urban–rural dataset, and the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen and Potsdam urban datasets, respectively. Compared with current popular methods, our network not only achieves excellent performance but also demonstrates outstanding class balance.
Scalability Analysis of LoRa and Sigfox in Congested Environment and Calculation of Optimum Number of Nodes
Authors: Malik Mandeep , Kothari Ashwin , Pandhare Rashmi
Published: October 2024

Low-power wide area network (LPWAN) technologies as part of IoT are gaining a lot of attention as they provide affordable communication over large areas. LoRa and Sigfox as part of LPWAN have emerged as highly effective and promising non-3GPP unlicensed band IoT technologies while challenging the supremacy of cellular technologies for machine-to-machine-(M2M)-based use cases. This paper presents the design goals of LoRa and Sigfox while throwing light on their suitability in congested environments. A practical traffic generator of both LoRa and Sigfox is introduced and further interpolated for understanding simultaneous operation of 100 to 10,000 such nodes in close vicinity while establishing deep understanding on effects of collision, re-transmissions, and link behaviour. Previous work in this field have overlooked simultaneous deployment, collision issues, effects of re-transmission, and propagation profile while arriving at a number of successful receptions. This work uses packet error rate (PER) and delivery ratio, which are correct metrics to calculate successful transmissions. The obtained results show that a maximum of 100 LoRa and 200 Sigfox nodes can be deployed in a fixed transmission use case over an area of up to 1 km. As part of the future scope, solutions have been suggested to increase the effectiveness of LoRa and Sigfox networks.
Experimental Study of Hot-Sphere Anemometer Response in Stratospheric Environment
Authors: Li Xiyuan , Yang Xiaoning , Shen Xiaobin , Lin Guiping , Tao Dongxing , Wang Jing
Published: October 2024

Accurate wind speed measurement in low-pressure conditions is crucial for the thermal performance validation and attitude control of stratospheric aircraft. As air density decreases, traditional wind speed measurement systems based on principles such as dynamic pressure, heat transfer, ultrasound, and particle velocimetry face significant challenges when applied in low-pressure environments, often failing to achieve the required measurement accuracy. This paper presents the development of a wind speed simulation system based on a rotation method designed to operate in low-pressure conditions, utilizing a space environment simulation chamber in conjunction with a high-precision turntable. The system was employed to conduct response tests on a constant heat flow thermal sphere anemometer within a stratospheric pressure range of 1 kPa to 30 kPa. The experimental results revealed that at extremely low Reynolds numbers, the probe signal exhibited increasing nonlinearity, significantly affecting the response curve at pressures below 15 kPa. While the sensitivity of the hot-sphere probe remained relatively stable at wind speeds above 5 m/s, it decreased nonlinearly as the pressure dropped when wind speeds fell below 5 m/s. Furthermore, this paper analyzes the impact of various interpolation methods on wind speed conversion errors, providing valuable data to support the future development and validation of stratospheric aircraft.
Comparative Analysis of Machine Learning Techniques for Identifying Multiple Force Systems from Accelerometer Measurements
Authors: Pinheiro Giovanni de Souza , Setúbal Fábio Antônio do Nascimento , Filho Sérgio de Souza Custódio , Mesquita Alexandre Luiz Amarante , Nunes Marcus Vinicius Alves
Published: October 2024

The knowledge of the forces acting on a structure enables, among many other factors, assessments of whether the component’s useful life is compromised by the current machine condition. In many cases, a direct measurement of those forces becomes unfeasible, and an inverse problem must be solved. Among the solutions developed, machine learning techniques have stood out as powerful predictive tools increasingly applied to engineering problem-solving. This study evaluates the ability of different machine learning models to identify parameters of multi-force systems from accelerometer measurements. The models were assessed according to their prediction potential based on correlation coefficient (R2), mean relative error (MRE), and processing time. A computational numerical model using the finite element method was generated and validated by vibration measurements performed using accelerometers in the laboratory. A robust database created by the response surface methodology in conjunction with Design of Experiment (DOE) was used for the evaluation of the ability of machine learning models to predict the position, frequency, magnitude, and number of forces acting on a structure. Among the six machine learning models evaluated, k-NN was able to predict with a 0.013% error, and Random Forests showed a maximum error of 0.2%. The innovation of this study lies in the application of the proposed method for identifying parameters of multi-force systems.
A Depth Awareness and Learnable Feature Fusion Network for Enhanced Geometric Perception in Semantic Correspondence
Authors: Li Fazeng , Zou Chunlong , Yun Juntong , Huang Li , Liu Ying , Tao Bo , Xie Yuanmin
Published: October 2024

Deep learning is becoming the most widely used technology for multi-sensor data fusion. Semantic correspondence has recently emerged as a foundational task, enabling a range of downstream applications, such as style or appearance transfer, robot manipulation, and pose estimation, through its ability to provide robust correspondence in RGB images with semantic information. However, current representations generated by self-supervised learning and generative models are often limited in their ability to capture and understand the geometric structure of objects, which is significant for matching the correct details in applications of semantic correspondence. Furthermore, efficiently fusing these two types of features presents an interesting challenge. Achieving harmonious integration of these features is crucial for improving the expressive power of models in various tasks. To tackle these issues, our key idea is to integrate depth information from depth estimation or depth sensors into feature maps and leverage learnable weights for feature fusion. First, depth information is used to model pixel-wise depth distributions, assigning relative depth weights to feature maps for perceiving an object’s structural information. Then, based on a contrastive learning optimization objective, a series of weights are optimized to leverage feature maps from self-supervised learning and generative models. Depth features are naturally embedded into feature maps, guiding the network to learn geometric structure information about objects and alleviating depth ambiguity issues. Experiments on the SPair-71K and AP-10K datasets show that the proposed method achieves scores of 81.8 and 83.3 on the percentage of correct keypoints (PCK) at the 0.1 level, respectively. Our approach not only demonstrates significant advantages in experimental results but also introduces the depth awareness module and a learnable feature fusion module, which enhances the understanding of object structures through depth information and fully utilizes features from various pre-trained models, offering new possibilities for the application of deep learning in RGB and depth data fusion technologies. We will also continue to focus on accelerating model inference and optimizing model lightweighting, enabling our model to operate at a faster speed.
Polarization-Enhanced Underwater Laser Range-Gated Imaging for Subaquatic Applications
Authors: Chen Shuaibao , Liu Peng , He Wei , Luo Dong , Tan Yuguang , Chen Liangpei , Wang Jue , Zhao Qi , Jiao Guohua , Chen Wei
Published: October 2024

Laser range-gated underwater imaging technology, by removing most of the backscattering noise, can effectively increase image contrast and extend the detection range. The optical signal captured by a range-gated imaging system primarily comprises reflected light from the object and backscattered light from the surrounding water. Consequently, surfaces with low reflectivity or highly turbid water environments substantially constrain the applicability of the range-gated imaging system. To enhance the detection capability of underwater laser range-gated imaging, this paper proposes the incorporation of underwater polarized light imaging technology as an enhancement method. Based on polarization differences, backscattered light and reflected light from an object can be distinguished. Experimental results indicate that, compared to images obtained using a conventional range-gated laser imaging system, those captured with a polarization-enhanced system exhibit an increase of up to 47% for the corresponding Enhancement Measure Evaluation (EME) index. The proposed approach, which integrates polarization imaging with range-gated laser imaging, has the potential to broaden the applicability of underwater laser imaging scenarios, such as deep-sea exploration and military applications.
A Distributed VMD-BiLSTM Model for Taxi Demand Forecasting with GPS Sensor Data
Authors: Naji Hasan A. H. , Xue Qingji , Li Tianfeng
Published: October 2024

With the ubiquitous deployment of mobile and sensor technologies in modes of transportation, taxis have become a significant component of public transportation. However, vacant taxis represent an important waste of transportation resources. Forecasting taxi demand within a short time achieves a supply–demand balance and reduces oil emissions. Although earlier studies have forwarded highly developed machine learning- and deep learning-based models to forecast taxicab demands, these models often face significant computational expenses and cannot effectively utilize large-scale trajectory sensor data. To address these challenges, in this paper, we propose a hybrid deep learning-based model for taxi demand prediction. In particular, the Variational Mode Decomposition (VMD) algorithm is integrated along with a Bidirectional Long Short-Term Memory (BiLSTM) model to perform the prediction process. The VMD algorithm is applied to decompose time series-aware traffic features into multiple sub-modes of different frequencies. After that, the BiLSTM method is utilized to predict time series data fed with the relevant demand features. To overcome the limitation of high computational expenses, the designed model is performed on the Spark distributed platform. The performance of the proposed model is tested using a real-world dataset, and it surpasses existing state-of-the-art predictive models in terms of accuracy, efficiency, and distributed performance. These findings provide insights for enhancing the efficiency of passenger search and increasing the profit of taxicabs.
An 8 × 8 CMOS Optoelectronic Readout Array of Short-Range LiDAR Sensors
Authors: Chon Yeojin , Choi Shinhae , Joo Jieun , Park Sung-Min
Published: October 2024

This paper presents an 8 × 8 channel optoelectronic readout array (ORA) realized in the TSMC 180 nm 1P6M RF CMOS process for the applications of short-range light detection and ranging (LiDAR) sensors. We propose several circuit techniques in this work, including an amplitude-to-voltage (A2V) converter that reduces the notorious walk errors by intensity compensation and a time-to-voltage (T2V) converter that acquires the linear slope of the output signals by exploiting a charging circuit, thus extending the input dynamic range significantly from 5 μApp to 1.1 mApp, i.e., 46.8 dB. These results correspond to the maximum detection range of 8.2 m via the action of the A2V converter and the minimum detection range of 56 cm with the aid of the proposed T2V converter. Optical measurements utilizing an 850 nm laser diode confirm that the proposed 8 × 8 ORA with 64 on-chip avalanche photodiodes (APDs) can successfully recover the narrow 5 ns light pulses even at the shortest distance of 56 cm. Hence, this work provides a potential CMOS solution for low-cost, low-power, short-range LiDAR sensors.
Towards Autonomous Retail Stocking and Picking: Methods Enabling Robust Vacuum-Based Robotic Manipulation in Densely Packed Environments
Authors: Kmecl Peter , Munih Marko , Podobnik Janez
Published: October 2024

With the advent of robotics and artificial intelligence, the potential for automating tasks within human-centric environments has increased significantly. This is particularly relevant in the retail sector where the demand for efficient operations and the shortage of labor drive the need for rapid advancements in robot-based technologies. Densely packed retail shelves pose unique challenges for robotic manipulation and detection due to limited space and diverse object shapes. Vacuum-based grasping technologies offer a promising solution but face challenges with object shape adaptability. The study proposes a framework for robotic grasping in retail environments, an adaptive vacuum-based grasping solution, and a new evaluation metric—termed grasp shear force resilience—for measuring the effectiveness and stability of the grasp during manipulation. The metric provides insights into how retail objects behave under different manipulation scenarios, allowing for better assessment and optimization of robotic grasping performance. The study’s findings demonstrate the adaptive suction cups’ ability to successfully handle a wide range of object shapes and sizes, which, in some cases, overcome commercially available solutions, particularly in adaptability. Additionally, the grasp shear force resilience metric highlights the effects of the manipulation process, such as in shear force and shake, on the manipulated object. This offers insights into its interaction with different vacuum cup grasping solutions in retail picking and restocking scenarios.
The Role of Seed Characteristics on Water Uptake Preceding Germination
Authors: Upretee Prerana , Bandara Manjula S. , Tanino Karen K.
Published: October 2024

Seed germination is a complex process involving imbibition, activation and subsequent growth. In addition to re-establishing metabolic activity, water uptake helps stabilize macromolecules and biochemical reactions, resulting in radicle protrusion. Factors affecting water uptake include seed composition, water availability and seed coat permeability. Water entry sites vary with species and occur primarily through the hilum, micropyle or lens. In addition, seed size influences water uptake, where larger seeds are usually less permeable. The seed coat plays a significant role in regulating the water absorption process. Several seed coat characteristics, including color, thickness and differences in the anatomical structure, such as the presence of pores, cuticles and radicle pockets, alter water permeability. Similarly, the presence of either physical or physiological seed dormancy negatively affects water uptake. This review emphasizes that understanding seed characteristics, such as size, shape and seed coat permeability, and their relationships is essential for breeding and selecting seeds with desirable traits, as they directly influence water uptake, leading to improved germination and growth.
Research Progress on Physical and Chemical Remediation Methods for the Removal of Cadmium from Soil
Authors: Mu Yonglin , Zhang Chunhui , Li Yiyun , Zhou Weilong , Li Yanxin , Zhao Guifeng , Su Peidong
Published: October 2024

Soil cadmium contamination is a global environmental issue, threatening ecosystem health and human safety. Common remediation strategies, including phytoremediation and soil replacement, are typically hampered by their lengthy processes or high costs. The aim of this review is to explore and evaluate innovative physical and chemical remediation techniques to address cadmium pollution effectively. This review focuses on three promising approaches: the co-application of phosphate fertilizers and sepiolite, CaAl-layered double hydroxide (LDH) immobilization, and hydrochar treatments. The primary methodologies involved evaluating the adsorption capacity, ion exchange mechanisms, and remediation efficiency under varying environmental conditions. Results indicate that these techniques significantly enhance cadmium immobilization, with the co-application of phosphate fertilizers and sepiolite demonstrating up to 72.6% removal of HCl-extractable cadmium. The review concludes that these techniques offer superior cost-effectiveness and scalability for large-scale applications and recommends future research to optimize amendment formulations and develop renewable adsorbents to further improve sustainability.
Youth Voices Participating in the Improvement of Sexual Consent Awareness Campaigns
Authors: Cañaveras Paula , De Botton Lena , Carbonell Sara , Elboj Carmen , Aubert Adriana , Lopez de Aguileta Garazi
Published: October 2024

Communication concerning sexual consent among young people is a significant concern for prevention in our society today. While sexual consent awareness campaigns (SCACs) hold importance in various communication modes, they predominantly rely on speech acts despite scientific literature providing other elements beyond these. This research aims to fill this gap through dialogues between young people and the scientific literature. A content analysis of 23 international and national campaigns was conducted alongside fieldwork in Spain with 77 young participants (18–25 years old) and 24 professionals from the field of education and society engaging in dialogue with scientific evidence on sexual consent. The results provide three aspects for future campaigns: (a) to be based on scientific evidence, (b) to introduce clear examples of coercive discourse and interactive power, and (c) to aim at new alternative masculinities rather than targeting potential victims.