bluuebunny/crossref_metadata_2025 huggingface dataset:
A Microsimulation Model for Sustainability and Detailed Adequacy Analysis of the Retirement Pension System
Authors: Villanueva-García Jaime , Moral-Arce Ignacio , García Villalba Luis Javier
Published: January 2025

The sustainability and adequacy of pension systems are central to public policy debates in aging societies. This paper introduces a novel microsimulation model with probabilistic behavior to assess these dual challenges in the Spanish pension system. The model employs a mixed-projection method, integrating a macro approach—using economic and demographic aggregates from official sources such as the Spanish Statistics Office (INE) and Eurostat—with a micro approach based on the Continuous Sample of Working Lives (MCVL) dataset from Spanish Social Security. This framework enables individual-level projections of key labor market variables, including work time, salary, and initial pensions, under diverse reform scenarios. The results demonstrate the model’s ability to predict initial pensions with high accuracy, providing detailed insights into adequacy by age, gender, and income levels, as well as distributional measures such as density functions and quantiles. Sustainability findings indicate that pension expenditures are projected to stabilize at 13.9% of Gross Domestic Product (GDP) by 2050. The proposed model provides a robust and versatile tool for policymakers, offering a comprehensive evaluation of the long-term impacts of pension reforms on both system sustainability and individual adequacy.
Triple Mann Iteration Method for Variational Inclusions, Equilibria, and Common Fixed Points of Finitely Many Quasi-Nonexpansive Mappings on Hadamard Manifolds
Authors: Ceng Lu-Chuan , Huang Yun-Yi , Li Si-Ying , Yao Jen-Chih
Published: January 2025

In this paper, we introduce a triple Mann iteration method for approximating an element in the set of common solutions of a system of quasivariational inclusion issues, which is an equilibrium problem and a common fixed point problem (CFPP) of finitely many quasi-nonexpansive operators on a Hadamard manifold. Through some suitable assumptions, we prove that the sequence constructed in the suggested algorithm is convergent to an element in the set of common solutions. Finally, making use of the main result, we deal with the minimizing problem with a CFPP constraint and saddle point problem with a CFPP constraint on a Hadamard manifold, respectively.
From Anxiety to Hopelessness: Examining Influential Psychological Processes Affecting Mental Health Status of Spanish Nurses During the COVID-19 Pandemic
Authors: Peñacoba-Puente Cecilia , Gil-Almagro Fernanda , García-Hedrera Fernando José , Carmona-Monge Francisco Javier
Published: January 2025

Background and Objective: to test a model aimed at delving into the psychological processes that lead to hopelessness in Spanish nurses two years after a stressful work situation. The model proposed starts with the anxiety experienced at the beginning of the COVID-19 pandemic and includes cognitive fusion and emotional exhaustion, evaluated six months after the end of the confinement, as possible contributing factors to development of hopelessness. Design: prospective study with three data collection periods, May to June 2020 (period 1), January to April 2021 (period 2), April to July 2022 (period 3). Materials and Methods: The sample (n = 131 Spanish nurses) was selected by non-probabilistic convenience sampling. The inclusion criteria were as follows: being female, over 18 years of age, and working as a nurse in direct contact with COVID-19 patients. Results: The model proposed was statistically significant (B = 0.34, SE = 0.07, t = 5.15, p < 0.001, 95% CI = [0.21, 0.47]), contributing to the explanation of 28% of the variance of hopelessness, finding that the direct anxiety–hopelessness effect was equally significant (B = 0.19, SE = 0.08, t = 2.46, p = 0.014, 95% CI = [0.04, 0.34]). An effect of anxiety on cognitive fusion and on emotional exhaustion was observed. In turn, cognitive fusion had a significant effect on emotional exhaustion but not on hopelessness. Finally, emotional exhaustion had a significant effect on hopelessness. In this final model, years of experience had a significant effect (p = 0.004) on hopelessness. Conclusions: Cognitive fusion and emotional exhaustion are shown to be relevant psychological processes upon which to intervene to contribute to the improvement of the mental health of nurses regarding the COVID-19 pandemic.
Hypoxia Dependent Inhibition of Glioblastoma Cell Proliferation, Invasion, and Metabolism by the Choline-Kinase Inhibitor JAS239
Authors: Kelly Claire Louise , Wydrzynska Martyna , Phelan Marie M. , Osharovich Sofya , Delikatny Edward J. , Sée Violaine , Poptani Harish
Published: January 2025

Background: Elevated choline kinase alpha (ChoK) levels are observed in most solid tumors, including glioblastomas (GBM), and ChoK inhibitors have demonstrated limited efficacy in GBM models. Given that hypoxia is associated with resistance to GBM therapy, we hypothesized that tumor hypoxia could be responsible for the limited response. Therefore, we evaluated the effects of hypoxia on the function of JAS239, a potent ChoK inhibitor in four GBM cell lines. Methods: Rodent (F98 and 9L) and human (U-87 MG and U-251 MG) GBM cell lines were subjected to 72 h of hypoxic conditioning and treated with JAS239 for 24 h. NMR metabolomic measurements and analyses were performed to evaluate the signaling pathways involved. In addition, cell proliferation, cell cycle progression, and cell invasion parameters were measured in 2D cell monolayers as well as in 3D cell spheroids, with or without JAS239 treatment, in normoxic or hypoxic cells to assess the effect of hypoxia on JAS239 function. Results: Hypoxia and JAS239 treatment led to significant changes in the cellular metabolic pathways, specifically the phospholipid and glycolytic pathways, associated with a reduction in cell proliferation via induced cell cycle arrest. Interestingly, JAS239 also impaired GBM invasion. However, effects from JAS239 were variable depending on the cell line, reflecting the inherent heterogeneity of GBMs. Conclusions: Our findings indicate that JAS239 and hypoxia can deregulate cellular metabolism, inhibit cell proliferation, and alter cell invasion. These results may be useful for designing new therapeutic strategies based on ChoK inhibition, which can act on multiple pro-tumorigenic features.
Compact SPICE Model for TeraFET Resonant Detectors
Authors: Liu Xueqing , Zhang Yuhui , Ytterdal Trond , Shur Michael
Published: January 2025

This paper presents an improved compact model for TeraFETs employing a nonlinear transmission line approach to describe the non-uniform carrier density oscillations and electron inertia effects in the TeraFET channels. By calculating the equivalent components for each segment of the channel—conductance, capacitance, and inductance—based on the voltages at the segment’s nodes, our model accommodates non-uniform variations along the channel. We validate the efficacy of this approach by comparing terahertz (THz) response simulations with experimental data and MOSA1 and EKV TeraFET SPICE models, analytical theories, and Multiphysics simulations.
Two-Dimensional Nanostructured Ti3C2Tx MXene for Ceramic Materials: Preparation and Applications
Authors: Jia Xiao-Tong , Xing Hong-Wei , Cheng Xing-Wang , Zhang Zhao-Hui , Wang Qiang , Zhou Jin-Zhao , He Yang-Yu , Li Wen-Jun
Published: January 2025

Ti3C2Tx MXene, a novel two-dimensional transition metal carbide with nanoscale dimensions, has attracted significant attention due to its exceptional structural and performance characteristics. This review comprehensively examines various preparation methods for Ti3C2Tx MXene, including acid etching, acid–salt composite etching, alkali etching, and molten salt etching. It further discusses several strategies for interlayer exfoliation, highlighting the advantages and limitations of each method. The effects of these techniques on the nanostructure, surface functional groups, interlayer spacing, and overall performance of Ti3C2Tx MXene are evaluated. Additionally, this paper explores the diverse applications of Ti3C2Tx MXene in ceramic materials, particularly its role in enhancing mechanical properties, electrical and thermal conductivity, as well as oxidation and corrosion resistance. The primary objective of the review is to provide scientific insights and theoretical guidance for the preparation of Ti3C2Tx MXene and its further research and innovative applications in ceramic materials, advancing the development of high-performance, multifunctional ceramics.
Broadband Graphene-PbS Heterostructure Photodetector with High Responsivity
Authors: Mu Xinbo , Su Jinbao , Zhou Wenjuan , Chang Pengying , Deng Jun , Liu Ying , Ma Zhengtai , Xie Yiyang
Published: January 2025

Graphene-based photodetectors exhibit relatively low spectral absorption and rapid recombination of photogenerated carriers, which can limit their response performance. On the other hand, nanostructured lead sulfide (PbS) demonstrates a wide spectral absorption range from visible to near-infrared light. High-quality and evenly distributed PbS nanofilms were synthesized by chemical bath deposition and were applied to a graphene-PbS heterostructure photodetector. The heterostructure creates an inherent electric field that extends the lifetime of photogenerated carriers, leading to enhanced device response. We achieved a high-responsivity graphene-PbS photodetector by combining the high carrier mobility of graphene and the strong infrared absorption of PbS. The photodetector exhibits a responsivity of 72 A/W at 792 nm and 5.8 A/W at 1550 nm, with a response time of less than 20 ms. The optimized device features a broad spectral response ranging from 265 nm to 2200 nm.
Microporous Adsorbents for CH4 Capture and Separation from Coalbed Methane with Low CH4 Concentration: Review
Authors: Wei Xiao , Xia Yingkai , Wei Shuang , Chen Yuehui , Yang Shaobin
Published: January 2025

A rapid increase in natural gas consumption has resulted in a shortage of conventional natural gas resources, while an increasing concentration of CH4 in the atmosphere has intensified the greenhouse effect. The exploration and utilization of coalbed methane (CBM) resources not only has the potential to fill the gap in natural gas supply and promote the development of green energy, but could also reduce CH4 emissions into the atmosphere and alleviate global warming. However, the efficient separation of CH4 and N2 has become a significant challenge in the utilization of CBM, which has attracted significant attention from researchers in recent years. The development of efficient CH4/N2 separation technologies is crucial for enhancing the exploitation and utilization of low-concentration CBM and is of great significance for sustainable development. In this paper, we provide an overview of the current methods for CH4/N2 separation, summarizing their respective advantages and limitations. Subsequently, we focus on reviewing research advancements in adsorbents for CH4/N2 separation, including zeolites, metal–organic frameworks (MOFs), and porous carbon materials. We also analyze the relationship between the pore structure and surface properties of these adsorbents and their adsorption separation performances, and summarize the challenges and difficulties that different types of adsorbents face in their future development. In addition, we also highlight that matching the properties of adsorbents and adsorbates, controlling pore structures, and tuning surface properties on an atomic scale will significantly increase the potential of adsorbents for CH4 capture and separation from CBM.
Anti-Inflammatory and Pain-Relieving Effects of Arnica Extract Hydrogel Patch in Carrageenan-Induced Inflammation and Hot Plate Pain Models
Authors: Lee Sang Gil , Lee Eun Byul , Nam Tack Soo , You Sunho , Im Dahye , Kim Kyusun , Gu Bonseung , Nam Ga-young , Lee Hyerim , Kwon Soon Jae , Kim Yun Seok , Kim Sang Geon
Published: January 2025

Arnica montana (AM), which belongs to the daisy family Asteraceae, has a longstanding traditional use in Europe and North America for pain and inflammation treatment. This study investigates the inhibitory effects of ‘Arnica montana extract hydrogel patch (AHP)’ on Carrageenan-induced paw edema and hot plate-induced pain models. AHP exhibited transdermal permeability without the occurrence of issues like crystal precipitation. This study employed two animal model assessments using AHP, in comparison with Arnicare Gel (AG), to evaluate anti-inflammatory and pain relief effects. AHP treatment for 2 days showed a decrease in paw edema thickness in mice as compared to vehicle or AG groups; Carrageenan-induced swelling increased maximally at 1 h with the AHP group demonstrating a higher reduction. Thus, the AHP group exhibited a lower ratio of right/left paw thickness and a superior reduction in swelling, supportive of its ability to diminish edema. A histological analysis showed that AHP treatment reduced inflammatory cell infiltration. Consistently, the mRNA levels of inflammatory markers (tnfa, il1b, and il6) were decreased to a greater extent than the AG group. Particularly, tnfa inhibition was better in the AHP group, and the levels of il1b and il6 transcripts showed ~80% and 40% lower. Likewise, AHP reduced pain scores in a hot plate-induced rat model, although AG failed to do so. Together, these results demonstrate that AHP has long-lasting inhibitory effects on fluid effusion and edema formation, the production of inflammatory mediators, and pain-sensation, supporting its anti-inflammatory and pain-relieving pharmacological effects.
Inhibition of Kinase Activity and In Vitro Downregulation of the Protein Kinases in Lung Cancer and Cervical Cancer Cell Lines and the Identified Known Anticancer Compounds of Ziziphus mucronata
Authors: Sambo Themba , Mathe Emelinah , Shai Leswheni , Mapfumari Sipho , Gololo Stanley
Published: January 2025

Plants have long been used as sources of natural compounds with therapeutic benefits, providing molecules capable of inhibiting multiple kinases. Many medicinal plants are recognized for their anticancer properties and may offer ways to mitigate the adverse effects of conventional cancer treatments. In this study, the potential of Ziziphus mucronata methanol extract as a kinase inhibitor was assessed using the MTT assay, a universal kinase assay, and a human phosphokinase antibody array, along with a GC-MS analysis of volatile anticancer compounds. The MTT assay revealed strong cytotoxicity in A549 cells, with an IC50 of 31.25 µg/mL, while HeLa cells showed weaker cytotoxicity with an IC50 of 125 µg/mL. In comparison, paclitaxel exhibited potent inhibitory effects on A549 cells (IC50 of 31.25 µg/mL) and moderate inhibition on HeLa cells (IC50 of 65 µg/mL). Enzyme activity, measured by ADP production in the ADP-Glo assay, indicated that the extract inhibited protein kinase activity in both A549 and HeLa cells after 24 h of treatment. Additionally, the human phosphokinase antibody array, which includes 44 pre-spotted kinases, showed that the extract downregulated multiple phosphorylated kinases in both cell lines. Some of the affected kinases, such as TOR, Fyn, HcK, Fgr, STAT5b, PLC-γ1, p38α, ERK1/2, AMPKA, Akt1/2, GSK-3α/β, MSK1/2, CREB, RSK1/2/3, PLC-γ1, and STAT5a are critical regulators of various cellular processes, including apoptosis, differentiation, and proliferation. The findings of this study suggest that extract from Z. mucronata may have the capacity to regulate protein kinase activity, highlighting their significant potential as growth inhibitors for cancer cells.
Thomas/Twin in the Fourth Gospel and the Gospel of Thomas: The Mesopotamian Background of an Early Christian Motif
Authors: Baker Robin
Published: January 2025

This study examines the Thomas/twin motif in the Gospels of John and Thomas, which plays a significant role in each. By analyzing the motif’s meaning, deployment, and development in the two Gospels against Mesopotamian models, this study brings a fresh perspective to the much-debated topic of the Gospel of Thomas’s place of origin. This study demonstrates that Thomas betrays a knowledge of cuneiform polyvalence and argues that this corroborates the historical tradition that it originated in Mesopotamia. The findings also support the claim that the work is theologically sophisticated despite its disarming surface appearance. Similarly, Mesopotamian hermeneutics shed light on some enigmas of the Fourth Gospel’s symbolism, esotericism, and theology conveyed in the rhetorical and symbolic role of Thomas called Didymos. This study concludes that, in both Gospels, the figure of Thomas/twin is paradigmatic for every adherent of Jesus, but the Gospels have drawn on Mesopotamian sources in different ways.
A Self-Supervised Feature Point Detection Method for ISAR Images of Space Targets
Authors: Jiang Shengteng , Ren Xiaoyuan , Wang Canyu , Jiang Libing , Wang Zhuang
Published: January 2025

Feature point detection in inverse synthetic aperture radar (ISAR) images of space targets is the foundation for tasks such as analyzing space target motion intent and predicting on-orbit status. Traditional feature point detection methods perform poorly when confronted with the low texture and uneven brightness characteristics of ISAR images. Due to the nonlinear mapping capabilities, neural networks can effectively learn features from ISAR images of space targets, providing new ideas for feature point detection. However, the scarcity of labeled ISAR image data for space targets presents a challenge for research. To address the issue, this paper introduces a self-supervised feature point detection method (SFPD), which can accurately detect the positions of feature points in ISAR images of space targets without true feature point positions during the training process. Firstly, this paper simulates an ISAR primitive dataset and uses it to train the proposed basic feature point detection model. Subsequently, the basic feature point detection model and affine transformation are utilized to label pseudo-ground truth for ISAR images of space targets. Eventually, the labeled ISAR image dataset is used to train SFPD. Therefore, SFPD can be trained without requiring ground truth for the ISAR image dataset. The experiments demonstrate that SFPD has better performance in feature point detection and feature point matching than usual algorithms.
Angle and Range Unambiguous Estimation with Nested Frequency Diverse Array MIMO Radars
Authors: Wang Zhengxi , Li Ximin , Zhu Shengqi , Wei Fa , Liu Congfeng
Published: January 2025

This paper proposes an unambiguous method for joint angle and range estimation in colocated multiple-input multiple-output (MIMO) radar using the nested frequency diverse array (NFDA). Unlike a conventional phased array (PA), the transmission beampattern of FDA-MIMO radar depends not only on angle but also on range, which enables the precise identification of ambiguous regions in the two-dimensional frequency space. As a result, we can simultaneously estimate the angle and range of targets using FDA-MIMO radar, even when range ambiguity exists. By employing a nested array configuration, the degrees of freedom (DOFs) of the FDA are expanded. This expansion leads to improved accuracy in parameter estimation and enables a greater number of identifiable targets. In addition, the Cramér–Rao lower bound (CRLB) and the algorithm complexity are obtained to facilitate performance analysis. The simulation outcomes are presented to showcase the superior performance of the suggested approach.
Collaborative Sensing-Aware Task Offloading and Resource Allocation for Integrated Sensing-Communication- and Computation-Enabled Internet of Vehicles (IoV)
Authors: Huang Bangzhen , Fan Xuwei , Zheng Shaolong , Chen Ning , Zhao Yifeng , Huang Lianfen , Gao Zhibin , Chao Han-Chieh
Published: January 2025

Integrated Sensing, Communication, and Computation (ISCC) has become a key technology driving the development of the Internet of Vehicles (IoV) by enabling real-time environmental sensing, low-latency communication, and collaborative computing. However, the increasing sensing data within the IoV leads to demands of fast data transmission in the context of limited communication resources. To address this issue, we propose a Collaborative Sensing-Aware Task Offloading (CSTO) mechanism for ISCC to reduce the sensing tasks transmission delay. We formulate a joint task offloading and communication resource allocation optimization problem to minimize the total processing delay of all vehicular sensing tasks. To solve this mixed-integer nonlinear programming (MINLP) problem, we design a two-stage iterative optimization algorithm that decomposes the original optimization problem into a task offloading subproblem and a resource allocation subproblem, which are solved iteratively. In the first stage, a Deep Reinforcement Learning algorithm is used to determine task offloading decisions based on the initial setting. In the second stage, a convex optimization algorithm is employed to allocate communication bandwidth according to the current task offloading decisions. We conduct simulation experiments by varying different crucial parameters, and the results demonstrate the superiority of our scheme over other benchmark schemes.
Optimized Adaboost Support Vector Machine-Based Encryption for Securing IoT-Cloud Healthcare Data
Authors: Abushark Yoosef B. , Hassan Shabbir , Khan Asif Irshad
Published: January 2025

The Internet of Things (IoT) connects various medical devices that enable remote monitoring, which can improve patient outcomes and help healthcare providers deliver precise diagnoses and better service to patients. However, IoT-based healthcare management systems face significant challenges in data security, such as maintaining a triad of confidentiality, integrity, and availability (CIA) and securing data transmission. This paper proposes a novel AdaBoost support vector machine (ASVM) based on the grey wolf optimization and international data encryption algorithm (ASVM-based GWO-IDEA) to secure medical data in an IoT-enabled healthcare system. The primary objective of this work was to prevent possible cyberattacks, unauthorized access, and tampering with the security of such healthcare systems. The proposed scheme encodes the healthcare data before transmitting them, protecting them from unauthorized access and other network vulnerabilities. The scheme was implemented in Python, and its efficiency was evaluated using a Kaggle-based public healthcare dataset. The performance of the model/scheme was evaluated with existing strategies in the context of effective security parameters, such as the confidentiality rate and throughput. When using the suggested methodology, the data transmission process was improved and achieved a high throughput of 97.86%, an improved resource utilization degree of 98.45%, and a high efficiency of 93.45% during data transmission.
Aerial Imaging-Based Soiling Detection System for Solar Photovoltaic Panel Cleanliness Inspection
Authors: Naeem Umair , Chadda Ken , Vahaji Sara , Ahmad Jawad , Li Xiaodong , Asadi Ehsan
Published: January 2025

Unmanned Aerial Vehicles (UAVs) integrated with lightweight visual cameras hold significant promise in renewable energy asset inspection and monitoring. This study presents an AI-assisted soiling detection methodology for inspecting solar photovoltaic (PV) panels, using UAV-captured RGB images. The proposed scheme introduces an autonomous end-to-end soiling detection model for common types of soiling in solar panel installations, including bird droppings and dust. Detecting soiling, particularly bird droppings, is critical due to their pronounced negative impact on power generation, primarily through hotspot formation and their resistance to natural cleaning processes such as rain. A dataset containing aerial RGB images of PV panels with dust and bird droppings is collected as a prerequisite. This study addresses the unique challenges posed by the small size and indistinct features of bird droppings in aerial imagery in contrast to relatively large-sized dust regions. To overcome these challenges, we developed a custom model, named SDS-YOLO (Soiling Detection System YOLO), which features a Convolutional Block Attention Module (CBAM) and two dedicated detection heads optimized for dust and bird droppings. The SDS-YOLO model significantly improves detection accuracy for bird droppings while maintaining robust performance for the dust class, compared with YOLOv5, YOLOv8, and YOLOv11. With the integration of CBAM, we achieved a substantial 40.2% increase in mean Average Precision (mAP50) and a 26.6% improvement in F1 score for bird droppings. Dust detection metrics also benefited from this attention-based refinement. These results underscore the CBAM’s role in improving feature extraction and reducing false positives, particularly for challenging soiling types. Additionally, the SDS-YOLO parameter count is reduced by 24%, thus enhancing its suitability for edge computing applications.
Estimation of Power Output and Efficiency of Induction Motors: A New Non-Intrusive Approach
Authors: Paramo-Balsa Paula , Roldan-Fernandez Juan Manuel , Semião Jorge , Burgos-Payan Manuel
Published: January 2025

Industry 4.0 (I4.0) represents a transformative approach, integrating technology, production methods, and information and communication technology to enhance industrial value creation. A central I4.0 goal in the energy domain is improving energy efficiency to boost industrial competitiveness and profitability. Given that induction motors account for nearly two-thirds of industrial electrical energy consumption, optimizing their efficiency is crucial. Energy management systems (EMSs) need real-time data to assess motor efficiency, enabling prompt identification and replacement of inefficient motors with alternatives that have optimal efficiency class and rated power for specific applications. This paper introduces a novel non-intrusive method for estimating the load and efficiency of induction motors without disrupting their operation. To reach that goal, the proposed method optimizes the parameters of a set of relationships between output power, input power, and losses with the motor speed, minimizing the error in the estimates. It requires only input electrical power and motor speed measurements to set the model parameters and estimates the load and efficiency using either speed or input power measurements. The experimental results demonstrate that the proposed method, with a mean overall error of less than 3.5% in estimating output power and efficiency, outperforms conventional methods.
Ultraviolet Photodetector Using Nanostructured Hexagonal Boron Nitride with Gold Nanoparticles
Authors: Kim Dong Chan , Park Hamin
Published: January 2025

Ultraviolet (UV) photodetectors play a crucial role in various applications, ranging from environmental monitoring to biomedical diagnostics. This paper presents the fabrication and characterization of a high-performance UV photodetector using hexagonal boron nitride (hBN) decorated with gold nanoparticles (AuNPs). The hBN flakes were mechanically exfoliated onto SiO2 substrates, and AuNPs were formed via thermal evaporation, resulting in the creation of a plasmonically active surface that enhanced light absorption and carrier dynamics. Raman spectroscopy, transmission electron microscopy, and electrical measurements were performed to comprehensively analyze the device structure and performance. The photodetector exhibited significantly improved photocurrent and responsivity under UV-B (306 nm) and UV-C (254 nm) illumination, with the responsivity reaching an increase of nearly two orders of magnitude compared to that of the pristine hBN device. These improvements are attributed to the synergistic effects of the wide bandgap of hBN and the localized surface plasmon resonance of the AuNPs. These findings demonstrate the potential of AuNP-decorated hBN for advanced UV photodetection applications and provide a pathway toward more efficient and miniaturized optoelectronic devices.
SCR-Net: A Dual-Channel Water Body Extraction Model Based on Multi-Spectral Remote Sensing Imagery—A Case Study of Daihai Lake, China
Authors: Weng Zhi , Li Qiyan , Zheng Zhiqiang , Wang Lixin
Published: January 2025

Monitoring changes in lake area using remote sensing imagery and artificial intelligence algorithms is essential for assessing regional ecological balance. However, most current semantic segmentation models primarily rely on the visible light spectrum for feature extraction, which fails to fully utilize the multi-spectral characteristics of remote sensing images. Therefore, this leads to issues such as blurred segmentation of lake boundaries in the imagery, the loss of small water body targets, and incorrect classification of water bodies. Additionally, the practical applicability of existing algorithms is limited, and their performance under real-world conditions requires further investigation. To address these challenges, this paper introduces SCR-Net, a water body identification model designed for multi-spectral remote sensing images. SCR-Net employs a dual-channel encoding–decoding mechanism and alters the number of channels used for reading image data, enhancing feature learning for lakes while focusing on extracting information about the water body target locations, thereby ensuring accurate segmentation. Trained on multi-spectral remote sensing images, the model leverages the unique spectral properties of these images to improve segmentation accuracy. Extensive validation on two datasets demonstrates that SCR-Net outperforms state-of-the-art models in terms of segmentation accuracy. Based on the validation using this dataset, Daihai Lake in Inner Mongolia was additionally selected as a case study to calculate the lake area, providing valuable insights for interdisciplinary research in ecological environment monitoring and remote sensing image processing.
Noise Analysis for Correlation-Assisted Direct Time-of-Flight
Authors: Morsy Ayman , Vrijsen Jonathan , Coosemans Jan , Bruneel Tuur , Kuijk Maarten
Published: January 2025

The development of a correlation-assisted direct time-of-flight (CA-dToF) pixel provides a novel solution for time-of-flight applications that combines low power consumption, robust ambient shot noise suppression, and a compact design. However, the pixel’s implementation introduces systematic errors, affecting its performance. We investigate the pixel’s robustness against various noise sources, including timing jitter, kTC noise, switching noise, and photon shot noise. Additionally, we address limitations such as the SPAD deadtime, and source follower gain mismatch and offset, identifying their impact on performance. The paper also proposes solutions to enhance the pixel’s overall reliability and to improve the pixel’s implementation.
Novel Surveillance View: A Novel Benchmark and View-Optimized Framework for Pedestrian Detection from UAV Perspectives
Authors: Chen Chenglizhao , Gao Shengran , Pei Hongjuan , Chen Ning , Shi Lei , Zhang Peiying
Published: January 2025

To address the issues of insufficient samples, limited scene diversity, missing perspectives, and low resolution in existing UAV-based pedestrian detection datasets, this paper proposes a novel UAV-based pedestrian detection benchmark dataset named the Novel Surveillance View (NSV). This dataset encompasses diverse scenes and pedestrian information captured from multiple perspectives, and introduces an innovative data mining approach that leverages tracking and optical flow information. This approach significantly improves data acquisition efficiency while ensuring annotation quality. Furthermore, an improved pedestrian detection method is proposed to overcome the performance degradation caused by significant perspective changes in top-down UAV views. Firstly, the View-Agnostic Decomposition (VAD) module decouples features into perspective-dependent and perspective-independent branches to enhance the model’s generalization ability to perspective variations. Secondly, the Deformable Conv-BN-SiLU (DCBS) module dynamically adjusts the receptive field shape to better adapt to the geometric deformations of pedestrians. Finally, the Context-Aware Pyramid Spatial Attention (CPSA) module integrates multi-scale features with attention mechanisms to address the challenge of drastic target scale variations. The experimental results demonstrate that the proposed method improves the mean Average Precision (mAP) by 9% on the NSV dataset, thereby validating that the approach effectively enhances pedestrian detection accuracy from UAV perspectives by optimizing perspective features.
Detection of Sub-pT Field of Magnetic Responses in Metals and Magnetic Materials by Highly Sensitive Magnetoresistive Sensors
Authors: Ahn Hyuna , Tanaka Ayana , Kono Yuta , Trisnanto Suko Bagus , Kasajima Tamon , Shibuya Tomohiko , Takemura Yasushi
Published: January 2025

We developed a measurement system capable of detecting magnetic responses in various material samples. The system utilizes an excitation coil to apply an alternating magnetic field within the frequency range of 1–10 kHz. The magnetic field generated in the samples was detected using a highly sensitive magnetoresistive sensor. The system demonstrated a detection lower limit in the sub-pT range for magnetic fields arising from magnetic responses such as eddy currents and magnetization changes. The frequency dependence of the detected signal intensities correlated well with the physical mechanisms underlying the magnetic responses. Notably, the distance between the excitation coil and the magnetic sensor was maintained at 300 mm. These results, which demonstrate the detection of a sub-pT magnetic field using a highly sensitive magnetic sensor, have not been previously reported and provide valuable insights for advancing practical applications in non-destructive testing and clinical diagnostic imaging.
Commissioning an All-Sky Infrared Camera Array for Detection of Airborne Objects
Authors: Domine Laura , Biswas Ankit , Cloete Richard , Delacroix Alex , Fedorenko Andriy , Jacaruso Lucas , Kelderman Ezra , Keto Eric , Little Sarah , Loeb Abraham , Masson Eric , Prior Mike , Schultz Forrest , Szenher Matthew , Watters Wesley Andrés , White Abigail
Published: January 2025

To date, there is little publicly available scientific data on unidentified aerial phenomena (UAP) whose properties and kinematics purportedly reside outside the performance envelope of known phenomena. To address this deficiency, the Galileo Project is designing, building, and commissioning a multi-modal, multi-spectral ground-based observatory to continuously monitor the sky and collect data for UAP studies via a rigorous long-term aerial census of all aerial phenomena, including natural and human-made. One of the key instruments is an all-sky infrared camera array using eight uncooled long-wave-infrared FLIR Boson 640 cameras. In addition to performing intrinsic and thermal calibrations, we implement a novel extrinsic calibration method using airplane positions from Automatic Dependent Surveillance–Broadcast (ADS-B) data that we collect synchronously on site. Using a You Only Look Once (YOLO) machine learning model for object detection and the Simple Online and Realtime Tracking (SORT) algorithm for trajectory reconstruction, we establish a first baseline for the performance of the system over five months of field operation. Using an automatically generated real-world dataset derived from ADS-B data, a dataset of synthetic 3D trajectories, and a hand-labeled real-world dataset, we find an acceptance rate (fraction of in-range airplanes passing through the effective field of view of at least one camera that are recorded) of 41% for ADS-B-equipped aircraft, and a mean frame-by-frame aircraft detection efficiency (fraction of recorded airplanes in individual frames which are successfully detected) of 36%. The detection efficiency is heavily dependent on weather conditions, range, and aircraft size. Approximately 500,000 trajectories of various aerial objects are reconstructed from this five-month commissioning period. These trajectories are analyzed with a toy outlier search focused on the large sinuosity of apparent 2D reconstructed object trajectories. About 16% of the trajectories are flagged as outliers and manually examined in the IR images. From these ∼80,000 outliers and 144 trajectories remain ambiguous, which are likely mundane objects but cannot be further elucidated at this stage of development without information about distance and kinematics or other sensor modalities. We demonstrate the application of a likelihood-based statistical test to evaluate the significance of this toy outlier analysis. Our observed count of ambiguous outliers combined with systematic uncertainties yields an upper limit of 18,271 outliers for the five-month interval at a 95% confidence level. This test is applicable to all of our future outlier searches.
FL-TENB4: A Federated-Learning-Enhanced Tiny EfficientNetB4-Lite Approach for Deepfake Detection in CCTV Environments
Authors: Ha Jimin , El Azzaoui Abir , Park Jong Hyuk
Published: January 2025

The widespread deployment of CCTV systems has significantly enhanced surveillance and public safety across various environments. However, the emergence of deepfake technology poses serious challenges by enabling malicious manipulation of video footage, compromising the reliability of CCTV systems for evidence collection and privacy protection. Existing deepfake detection solutions often suffer from high computational overhead and are unsuitable for real-time deployment on resource-constrained CCTV cameras. This paper proposes FL-TENB4, a Federated-Learning-enhanced Tiny EfficientNetB4-Lite framework for deepfake detection in CCTV environments. The proposed architecture integrates Tiny Machine Learning (TinyML) techniques with EfficientNetB4-Lite, a lightweight convolutional neural network optimized for edge devices, and employs a Federated Learning (FL) approach for collaborative model updates. The TinyML-based local model ensures real-time deepfake detection with minimal latency, while FL enables privacy-preserving training by aggregating model updates without transferring sensitive video data to centralized servers. The effectiveness of the proposed system is validated using the FaceForensics++ dataset under resource-constrained conditions. Experimental results demonstrate that FL-TENB4 achieves high detection accuracy, reduced model size, and low inference latency, making it highly suitable for real-world CCTV environments.
Ableism and Employment: A Scoping Review of the Literature
Authors: Sharma Ramona H. , Asselin Renée , Stainton Timothy , Hole Rachelle
Published: January 2025

Background: Ableism obstructs employment equity for disabled individuals. However, research lacks a comprehensive understanding of how ableism multidimensionally manifests across job types, disability types, stages of employment, and intersecting identities. Objectives: This scoping review examines how ableism affects disabled workers and jobseekers, as well as its impacts on employment outcomes, variations across disabilities and identities, and the best practices for addressing these. Eligibility Criteria: The included articles were 109 peer-reviewed empirical studies conducted in the US, Canada, Australia, New Zealand, the UK, Ireland, Denmark, Sweden, Iceland, Norway, and Finland between 2018 and 2023. Sources of Evidence: Using terms related to disability, ableism, and employment, the databases searched included Sociology Collection, CINAHL, PsycInfo, Web of Science, SCOPUS, Education Source, Academic Search Complete, and ERIC. Charting Methods: Data were extracted in tabular form and analyzed through thematic narrative synthesis to identify study characteristics, ableist barriers within employment, intersectional factors, and best practices. Results: Ableism negatively impacts employment outcomes through barriers within the work environment, challenges in disclosing disabilities, insufficient accommodations, and workplace discrimination. Intersectional factors intensify inequities, particularly for BIPOC, women, and those with invisible disabilities. Conclusions: Systemic, intersectional strategies are needed to address ableism, improve policies, and foster inclusive workplace practices.
Application of Novel Biochar Derived from Experimental Sewage Sludge Gasification as an Adsorbent for Heavy Metals Removal
Authors: Nakić Domagoj , Posavčić Hana , Licht Katarina , Vouk Dražen
Published: January 2025

The growing amounts of sewage sludge (SS) and water pollution caused by heavy metals are major environmental concerns. This study addresses both issues by investigating the potential of biochar derived from SS gasification at an experimental plant as an effective adsorbent for the removal of selected heavy metals, cadmium, chromium, copper, and lead, from synthetic wastewater. A Box–Behnken design was used to determine the influence of the biochar mass, initial heavy metal concentration, pH, and time on the heavy metal removal. For the statistical analysis, 104 experiments were performed. The pristine SS biochar demonstrated an adsorption capacity reaching up to 46.64 mg/g for Cd, 43.89 mg/g for Cr, 42.42 mg/g for Cu, and 72.66 mg/g for Pb from single-component synthetic solutions in acidic-to-neutral conditions, with an over 99% removal efficiency for all four heavy metals under optimal conditions. The removal of all the tested metals followed pseudo-second-order kinetics, with Cd fitting the Langmuir model and Pb, Cr, and Cu fitting the Freundlich model. This paper also provides suggestions for further research focused on the multiple uses of biochar as an adsorbent and later as a substitute material in the construction industry, aiming to achieve an integrated approach and maximizing the overall sustainability of wastewater treatment and waste management by utilizing waste as a resource.
Integration of Machine Learning and Remote Sensing for Water Quality Monitoring and Prediction: A Review
Authors: Mohan Shashank , Kumar Brajesh , Nejadhashemi A. Pouyan
Published: January 2025

Aquatic ecosystems play a crucial role in sustaining life and supporting key green and blue economic sectors globally. However, the growing population and increasing anthropogenic pressures are significantly degrading terrestrial water resources, threatening their ability to provide essential socioeconomic services. To safeguard these ecosystems and their benefits, it is critical to continuously monitor changes in water quality. Remote sensing technologies, which offer high-resolution spatial and temporal data over large geographic areas, including surface water bodies, have become indispensable for these monitoring efforts. They enable the observation of various physical, chemical, and biological water quality indicators, which are essential for assessing ecosystem health. Machine learning algorithms are well suited to handle the complex and often non-linear relationships between remote sensing data and water quality parameters. By integrating remote sensing with machine learning techniques, it is possible to develop predictive models that enhance the accuracy and efficiency of water quality assessments. These models can identify and predict trends in water quality, supporting timely interventions to protect aquatic ecosystems. This paper provides a thorough review of the major remote sensing techniques for estimating water quality indicators (e.g., chlorophyll-a, turbidity, temperature, total nitrogen and total phosphorous, dissolved organic, total suspended solids, dissolved oxygen, and hydrogen power). It examines how machine learning can improve water quality assessments. Additionally, it identifies key research gaps in current methodologies and suggests future directions to address challenges in water quality monitoring, aiming to improve the precision and scope of these critical efforts.
Sustainable Approach to Utilization of Waste Glass Fibers and Basalt Powder as Potential Additives in Epoxy-Based Composites for Reparation of Concrete Structures
Authors: Ostrowski Krzysztof Adam , Piechaczek Marcin , Lach Michał , Sikora Oliwia , Furtak Kazimierz , Sajdak Katarzyna , Radecka-Trzop Martyna
Published: January 2025

The growing production of glass fibers is a major challenge due to the later problem of their sustainable recycling. This article reports the potential use of post-production waste glass fibers and basalt powder as additives in resin compounds designed for repairing concrete elements. The aim of this research was to develop a repair compound based on epoxy resin with the addition of waste materials, offering a competitive alternative to currently available repair compounds on the market by utilizing lower-cost materials and addressing pro-environmental aspects through waste reuse. The prepared research samples were characterized by varying proportions of basalt powder (0–20%), ground HP 12 fibers (40/60%), and HP 6 fibers (10/20%). A series of tests, including tensile strength tests, were conducted as part of this study to determine the effect of the applied additives on the ultimate tensile force and maximum deformations. Abrasion resistance tests were also carried out to evaluate the impact of basalt powder as a filler on enhancing the abrasion resistance of the designed repair compound. Additionally, SEM and EDS analyses were used to evaluate the uniformity and distribution of the additives within the sample. This study examined samples containing varying percentages of basalt powder and fibers, both virgin and milled. The most significant reinforcement effect was observed for sample E/20HP/20bp, where its tensile strength decreased by 8%, while its abrasion resistance increased by 44% compared to the reference sample. The obtained results confirm that incorporating waste materials as additives into epoxy resin can significantly enhance the mechanical properties of repair compounds while reducing cost and promoting environmental protection. In addition, the repair compound developed complies with the selected principles within the 6Rs environmental regulations: RECYCLING (reuse of waste glass fibers), RETHINK (reduce environmental impact by avoiding landfill), REDUCE (minimize the use of virgin glass fibers in the production of repair compounds) and REPAIR (increase the efficiency of repairing damaged concrete structures). Furthermore, as the percentage of basalt powder increases, the abrasion resistance of the repair compound improves. The obtained repair compounds may serve as an alternative to currently used compounds for the repair of bridges and factory floors.
Research on Home Environment Design to Improve the Autonomy of Elderly People with Dementia
Authors: Zhang Jijuan , Yu Yandan , Hu Lincai , Song Feifei , Wu Weiming , Zhang Zhongfeng
Published: January 2025

Under the trend of increasing global aging, this study is based on the theory of mental model to conduct a study on the design of living environments for demented elderly, which is dedicated to meeting the special needs of this special group of demented elderly, improving their quality of life and self-care ability, and reducing the dependence on social medical and nursing resources to achieve the goal of social sustainability. The field research method was used to observe and record the behavior of elderly people with dementia in their homes, which included 20 elderly people aged 60–94 years with mild dementia; the ratio of male to female is 1:1. The Kawakita Jiro (KJ) method is used to extract a mental model of elderly people with dementia, obtain their subjective needs for the home environment, and propose related design principles. The research indicates that the mental space of elderly people with dementia regarding the home environment is mainly composed of three parts: information perception, environmental cognition, and environmental shaping. Based on this mental model, design principles of the home environment of elderly people with dementia are designed, aiming to improve their living experience via environmental information perception design, the enhancement of environmental cognition, and the reduction in the difficulty of shaping the environment.