bluuebunny/crossref_metadata_2025 huggingface dataset:
Development and Characterization of Silibinin-Loaded Nanoemulsions: A Promising Mucoadhesive Platform for Enhanced Mucosal Drug Delivery
Authors: Tartari Ana Paula Santos , Jacumazo Joslaine , Lorenzett Ariane Krause Padilha , Freitas Rilton Alves de , Mainardes Rubiana Mara
Published: February 2025

Background: Silibinin (SLB), a flavonoid derived from milk thistle, exhibits promising therapeutic properties but faces significant clinical limitations due to poor solubility and bioavailability. Objectives: This study focuses on the development and characterization of SLB-loaded nanoemulsions designed for mucosal delivery. Methods: Nanoemulsions were prepared using the spontaneous emulsification method, guided by pseudoternary phase diagrams to determine selected component ratios. Comprehensive characterization included particle size, polydispersity index (PDI), zeta potential, encapsulation efficiency, rheological properties, and surface tension. Mucoadhesive properties were evaluated using quartz crystal microbalance with dissipation (QCM-D) to quantify interactions with mucin layers. Results: The combination of Capryol 90, Tween 80, and Transcutol in selected proportions yielded nanoemulsions with excellent stability and solubilization capacity, enhancing the solubility of silibinin by 625 times compared to its intrinsic solubility in water. The ternary phase diagram indicated that achieving nanoemulsions with particle sizes between 100 and 300 nm required higher concentrations of surfactants (60%), relative to oil (20%) and water (20%), with formulations predominantly composed of Smix (surfactant and cosurfactant mixture in a 1:1 ratio). Rheological analysis revealed Newtonian behavior, characterized by constant viscosity across varying shear rates and a linear torque response, ensuring ease of application and mechanical stability. QCM-D analysis confirmed strong mucoadhesive interactions, with significant frequency and dissipation shifts, indicative of prolonged retention and enhanced mucosal drug delivery. Furthermore, contact angle measurements showed a marked reduction in surface tension upon interaction with mucin, with the SLB-loaded nanoemulsion demonstrating superior wettability and strong mucoadhesive potential. Conclusions: These findings underscore the suitability of SLB-loaded nanoemulsions as a robust platform for effective mucosal drug delivery, addressing solubility and bioavailability challenges while enabling prolonged retention and controlled therapeutic release.
Intranasal Mucoadhesive In Situ Gel of Glibenclamide-Loaded Bilosomes for Enhanced Therapeutic Drug Delivery to the Brain
Authors: Tripathi Meenakshi , Gharti Laxmi , Bansal Amit , Kaurav Hemlata , Sheth Sandeep
Published: February 2025

Background: The neuroprotective efficacy of glibenclamide (GLIB) has been demonstrated in multiple rodent models of ischemia, hemorrhagic stroke, traumatic brain damage, spinal cord injury, and metastatic brain tumors. Due to its poor solubility, GLIB has low oral bioavailability, limiting its transportation to the brain via the oral route. Objectives: Here, we attempted to develop and optimize an intranasal mucoadhesive in situ gel of GLIB-loaded bilosomes using a 32 Box–Behnken design for brain drug delivery. Methods: To facilitate a longer residence time of the administered dose within the nasal cavity, the prepared bilosomes were loaded into a mucoadhesive in situ gel providing resistance to rapid mucociliary clearance. The amounts of sodium deoxycholate, the cholesterol/Span 40 mixture, and the molar ratio between the mixture’s components were chosen as independent variables, while the entrapment efficiency and in vitro drug release were selected as dependent variables. Results and conclusions: The optimal formulation was analyzed for particle size and entrapment efficiency, which were found to be 270.6 nm and 68.39%, respectively. In vitro drug release from optimal formulation after 12 h was 87.29 ± 1.98% as compared to 52.01 ± 2.04% of plain in situ gel of drug. An in vivo brain drug delivery study performed on Swiss albino mice showed that the brain concentration of drug through intranasal administration from mucoadhesive in situ gel of GLIB-bilosomes after 12 h was 2.12 ± 0.16 µg/mL as compared to 0.68 ± 0.04 µg/mL from plain in situ gel of drug. Conclusively, the developed bilosomal formulation offers a favorable intranasal substitute with enhanced therapeutic drug delivery to the brain.
Complex Seed Dormancy in Parrotia subaequalis: Identification, Breaking Mechanisms, and Conservation Strategies for an Endangered Species in China
Authors: Yang Yanfang , Luo Laikai , Zhu Ling , Cheng Ying , Yuan Meng , Ruan Xiangdong , Zhao Kai
Published: February 2025

Parrotia subaequalis, an endangered plant unique to China, is highly valued for its significant ecological and ornamental value. The specific type of seed dormancy in this species has not been clearly reported, which limits its natural regeneration and artificial propagation, posing a challenge to its conservation and sustainable use. To address this, we conducted a comprehensive analysis of the seed dormancy mechanism of P. subaequalis and explored various methods to break dormancy, including cold and warm stratification, after-ripening, seed coat removal, and hormone soaking. Our analysis of the seeds’ physical properties, water absorption patterns, seed coat structure, embryo development, and endogenous inhibitors revealed that P. subaequalis seeds exhibit complex characteristics of physical and non-deep physiological dormancy. Experimental results showed that soaking the seeds in gibberellin (GA3) followed by seed coat removal effectively promoted germination. The optimal GA3 concentration for germination was 800 mg·L−1. Additionally, cold and warm stratification and after-ripening treatments significantly increased the germination percentage. These findings provide important technical support for dormancy release and seedling growth, which is crucial for the artificial propagation and population recovery of P. subaequalis.
Structural Particularities of Gall Neoformations Induced by Monarthropalpus flavus in the Leaves of Buxus sempervirens
Authors: Gostin Irina Neta , Popescu Irinel Eugen , Blidar Cristian Felix
Published: February 2025

The boxwood leafminer Monarthropalpus flavus (Diptera, Cecidomyiidae) has historically been considered a leafminer, but some researchers suggested it induced galls on Buxus species leaves. The larvae of M. flavus create small blister-like galls on Buxus sempervirens leaves, causing tissue hypertrophy and hyperplasia. Histological examination reveals that M. flavus larvae cause the formation of small blister galls, which involve tissue reorganization in the mesophyll. Unlike typical leafminers, which only disrupt existing tissues, M. flavus induces the appearance of a neo-formed tissue, near the larval chamber. This tissue, originating primarily from spongy parenchyma cells, significantly increases as the leaf thickens. Various histochemical analyses show that the new tissue contains starch, lipids, terpenes, and proteins, providing evidence of reprogramming in the plant’s metabolism. The study concludes that M. flavus induces rudimentary galls, not simply mines, due to the formation of new tissue, whose cells have cytological characteristics distinct from those found in non-galled leaves. However, despite some gall-like features, it does not create new vascular elements, distinguishing it from more complex galls formed by other gall-inducing species.
Polymers Derived from Agro-Industrial Waste in the Development of Bioactive Films in Food
Authors: Culqui-Arce Carlos , Mori-Mestanza Diner , Fernández-Jeri Armstrong B. , Cruzalegui Robert J. , Mori Zabarburú Roberto Carlos , Vergara Alex J. , Cayo-Colca Ilse S. , da Silva Juliana Guimarães , Araujo Nayara Macêdo Peixoto , Castro-Alayo Efraín M. , Balcázar-Zumaeta César R.
Published: February 2025

This review explores the potential of biopolymers as sustainable alternatives to conventional plastics in food packaging. Biopolymers derived from plant or animal sources are crucial in extending food shelf life, minimizing degradation, and protecting against oxidative and microbial agents. Their physical and chemical properties, influenced by the raw materials used, determine their suitability for specific applications. Biopolymers have been successfully used in fruits, vegetables, meats, and dairy products, offering antimicrobial and antioxidant benefits. Consequently, they represent a functional and eco-friendly solution for the packaging industry, contributing to sustainability while maintaining product quality.
Research on Laser Direct Transmission Welding of Transparent Polystyrene and Polycarbonate Based on Laser Surface Modification
Authors: Zhai Kehui , Yang Fuhao , Gu Qiyan , Lin Yu , Liu Minqiu , Ouyang Deqin , Chen Yewang , Zhang Ying , Lue Qitao , Ruan Shuangchen
Published: February 2025

The conventional near-infrared laser transmission welding (LTW) process for joining dissimilar transparent polymers is limited by the need to incorporate optical absorbents, which compromises joint performance and raises biocompatibility concerns. To address these issues, this study proposed a surface modification technique using femtosecond laser ablation prior to the welding process. Experiments involved 520 nm femtosecond laser ablation of transparent polymers, followed by LTW of dissimilar transparent polymers using an 808 nm laser, with subsequent characterization and mechanical property evaluations. A maximum joint strength of 13.65 MPa was achieved. A comprehensive investigation was conducted into the physical and chemical mechanisms through which laser ablation improved the welding performance of dissimilar transparent polymers. The results demonstrated that laser ablation generated microstructures that serve as substitutes for optical absorbents while also facilitating the formation of numerous oxygen-containing functional groups. These enhancements improve miscibility and bonding performance between dissimilar polymers, enabling absorbent-free welding between ablated polycarbonate (PC) and polystyrene (PS). This work confirms both the feasibility and potential application of this process for direct LTW of dissimilar transparent polymers.
Printability and Performance Metrics of New-Generation Multifunctional PMMA/Antibacterial Blend Nanocomposites in MEX Additive Manufacturing
Authors: Petousis Markos , Nasikas Nektarios K. , Papadakis Vassilis , Valsamos Ioannis , Gkagkanatsiou Katerina , Mountakis Nikolaos , Argyros Apostolos , Dimitriou Evgenia , Michailidis Nikolaos , Vidakis Nectarios
Published: February 2025

Poly(methyl methacrylate) (PMMA) is a thermoplastic widely utilized in civilian-, defense-, and medicine-related applications. Therefore, inducing antibacterial properties is an additional asset when infection control is prioritized. To counter this, PMMA was mixed, for the first time, with antibacterial agents (antibacterial blend nanopowder, AP) to curb bacterial proliferation and therefore reduce the chances of infection. The reinforcing efficacy of the blend in PMMA was also assessed. Nanocomposites were developed with various nanopowder concentrations for 3D printing material extrusion (MEX). PMMA/AP nanocomposites were evaluated for their mechanical and rheological properties, thermal stability, morphological, structural, and chemical characteristics, and bacterial resistance (against Staphylococcus aureus and Escherichia coli (E. Coli) using the well diffusion method). The effect on quality metrics, such as the geometrical accuracy and pores of the 3D-printed structure was examined with micro-computed tomography. The modified PMMA had improved properties, such as increased tensile (~20% increase at 2 wt.%) and flexural strength (~10.8% at 4 wt.%), while also having strong antibacterial properties against Staphylococcus aureus and mild antibacterial properties against E. Coli. Such improvements add to the expanding portfolio of biomaterials, such as their use in the demanding defense sector and the medical field.
Adaptation Skills and Temporomandibular Joint Neutrality: A Case Report of a Failed Orthognathic Surgery Intervention
Authors: Pollis Matteo , Colonna Anna , Manfredini Daniele , Ferrari Marco , Ferrari Cagidiaco Edoardo
Published: February 2025

The present study describes the case of a severe surgical complication in a 42-year-old female patient undergoing bimaxillary orthognathic surgery for the correction of skeletal class III, where a detachment of the surgical plates and fibrous healing of the palatal suture occurred. The aim of this study was to enlighten two important concepts: (I) how occlusal and mandible position changes can be well tolerated by the temporomandibular joint even in the case of a failed orthognathic surgery; (II) how a prosthodontic approach based on the search for occlusal stabilization made it possible to manage a complicated clinical case. Clinical Presentation: Clinically, the patient presented an occlusal instability and a split and mobile maxillary bone with respect to the cranial base. The case was resolved using full-mouth prosthodontic rehabilitation to fix the occlusal instability and guide maxillofacial surgeons, establishing the new occlusal position during an orthognathic reintervention. Clinical Relevance: The function was reestablished independently on any treatment planning centered on the temporomandibular joint repositioning concepts. However, although neuromuscular plasticity and the patient’s adaptation skills can explain the clinical success in such complex rehabilitations, these findings must be interpreted with caution due to the limited generalizability inherent to the study’s design.
The Role of Resilience in Maintaining Religious Identity—The Life Story of a Nun
Authors: Bögre Zsuzsanna
Published: February 2025

In Hungary, the monastic orders were dissolved in 1950 during the harshest communist dictatorship. A total of 11,538 people were affected by the ban. The consequences of the dissolution of the monastic orders not only affected the people who were directly involved but also practically the whole society. The orders had an important role on the education, society and culture of the country, and their banning for ideological reasons had enormous consequences. The question is how dissolved monks and nuns managed to integrate into a society where they were marginalized. Through the life story of a nun, named Speravia (1914–2009), I present the behavioral patterns which helped to survive the persecution of religion during communism and then in socialism. I found that she maintained her religious identity through change and resilient means. Identity maintenance in modern society is a dynamic process in which there is room for change through constant adaptation to the environment. Her obedience was transformed into accommodation to the political system by making deals with the system. This behavior can be called resilience. Resilient behavior is characterized by both resistance and accommodation. To do this, she had to be active and have the support of her environment. In this study, I used the principles of Grounded Theory, which is a specific content analysis method. I analyzed the interview using the Grounded Theory approach, at least some of its techniques. I coded some parts of the interview line by line and other parts only as units of thought. Then, I aggregated the codes and I formulated categories. After this process, I compared the meanings of the categories and it became clear to me that my interviewee’s behavior could be understood as resilient. This is why I emphasized the role of resilience in identity maintenance in the title of the study. My conclusion is that while Sperávia flexibly adapted to the new political system, she could also preserve her identity as a nun. I call this survival strategy religious resilience.
Inter-Annual Variability of Peatland Vegetation Captured Using Phenocam- and UAV Imagery
Authors: Simpson Gillian , Wade Tom , Helfter Carole , Jones Matthew R. , Yeung Karen , Nichol Caroline J.
Published: February 2025

Plant phenology is an important driver of inter-annual variability in peatland carbon uptake. However, the use of traditional phenology datasets (e.g., manual surveys, satellite remote sensing) to quantify this link is hampered by their limited spatial and temporal coverage. This study examined the use of phenology cameras (phenocams) and uncrewed aerial vehicles (UAVs) for monitoring phenology in a Scottish temperate peatland. Data were collected at the site over multiple growing seasons using a UAV platform fitted with a multispectral Parrot Sequoia camera. We found that greenness indices calculated using data from both platforms were in strong agreement with each other, and exhibited strong correlations with rates of gross primary production (GPP) at the site. Greenness maps generated with the UAV data were combined with fine-scale vegetation classifications, and highlighted the variable sensitivity of different plant species to dry spells over the study period. While a lack of suitable weather conditions for surveying limited the UAV data temporally, the phenocam provided a near-continuous record of phenology. The latter revealed substantial temporal variability in the relationship between canopy greenness and peatland GPP, which although strong over the growing season as a whole (rs = 0.88, p < 0.01), was statistically insignificant during the peak growing season.
Application of a Random Forest Method to Estimate the Water Use Efficiency on the Qinghai Tibetan Plateau During the 1982–2018 Growing Season
Authors: Wu Xuemei , Zhou Tao , Zeng Jingyu , Zhang Yajie , Zhang Jingzhou , Tan E , Yu Yin , Zhang Qi , Qu Yancheng
Published: February 2025

Water use efficiency (WUE) reflects the quantitative relationship between vegetation gross primary productivity (GPP) and surface evapotranspiration (ET), serving as a crucial indicator for assessing the coupling of carbon and water cycles in ecosystems. As a sensitive region to climate change, the Qinghai Tibetan Plateau’s WUE dynamics are of significant scientific interest for understanding carbon water interactions and forecasting future climate trends. However, due to the scarcity of observational data and the unique environmental conditions of the plateau, existing studies show substantial errors in GPP simulation accuracy and considerable discrepancies in ET outputs from different models, leading to uncertainties in current WUE estimates. This study addresses these gaps by first employing a machine learning approach (random forest) to integrate observed GPP flux data with multi-source environmental information, developing a predictive model capable of accurately simulating GPP in the Qinghai Tibetan Plateau (QTP). The accuracy of the random forest simulation results, RF_GPP (R2 = 0.611, RMSE = 69.162 gC·m−2·month−1), is higher than that of the multiple linear regression model, regGPP (R2 = 0.429, RMSE = 86.578 gC·m−2·month−1), and significantly better than the accuracy of the GLASS product, GLASS_GPP (R2 = 0.360, RMSE = 91.764 gC·m−2·month−1). Subsequently, based on observed ET flux data, we quantitatively evaluate ET products from various models and construct a multiple regression model that integrates these products. The accuracy of REG_ET, obtained by integrating five ET products using a multiple linear regression model (R2 = 0.601, RMSE = 21.04 mm·month−1), is higher than that of the product derived through mean processing, MEAN_ET (R2 = 0.591, RMSE = 25.641 mm·month−1). Finally, using the optimized GPP and ET data, we calculate the WUE during the growing season from 1982 to 2018 and analyze its spatiotemporal evolution. In this study, GPP and ET were optimized based on flux observation data, thereby enhancing the estimation accuracy of WUE. On this basis, the interannual variation of WUE was analyzed, providing a data foundation for studying carbon water coupling in QTP ecosystems and supporting the formulation of policies for ecological construction and water resource management in the future.
Experimental Study on Vibration Attenuation Characteristics of Ballastless Track Structures in Urban Rail Transit
Authors: Guo Wuji , Zeng Zhiping , Ye Mengxuan , Liu Fushan , Wang Weidong , Chang Cheng , Li Qiuyi , Li Ping
Published: February 2025

With the rapid development of urban rail transit, the intensity and impact range of train-induced vibrations are increasing. Investigating the transmission characteristics and attenuation patterns of these vibrations in track structures aids in understanding train-induced environmental vibrations. This study conducted rail impact experiments on a long sleeper integrated slab of a straight section of a subway tunnel. The hammer struck the rail at various positions, and acceleration sensors recorded the responses of the rail, slab, and tunnel. In order to determine the impact force, the vertical wheel–rail force and the vibration response of track structures were measured. Then, the Lance-LC1304B force hammer was selected for the experiment, and the hammer impact force reached 30 kN, the magnitude of which reached the measured wheel–rail force size for the line. Based on the results of the impact tests, the vibration attenuation characteristics of the track structure were analyzed. Accordingly, reference values for the truncation time and truncation distance in the vehicle–track coupled dynamics model’s moving window were provided. By comparing the results of the hammering experiment with the train-induced vibration results, the main excitation frequencies during train operation were determined. These findings provide valuable insights for the development of rail transit systems.
Research on Mobile Robot Path Planning Based on MSIAR-GWO Algorithm
Authors: Chen Danfeng , Liu Junlang , Li Tengyun , He Jun , Chen Yong , Zhu Wenbo
Published: February 2025

Path planning is of great research significance as it is key to affecting the efficiency and safety of mobile robot autonomous navigation task execution. The traditional gray wolf optimization algorithm is widely used in the field of path planning due to its simple structure, few parameters, and easy implementation, but the algorithm still suffers from the disadvantages of slow convergence, ease of falling into the local optimum, and difficulty in effectively balancing exploration and exploitation in practical applications. For this reason, this paper proposes a multi-strategy improved gray wolf optimization algorithm (MSIAR-GWO) based on reinforcement learning. First, a nonlinear convergence factor is introduced, and intelligent parameter configuration is performed based on reinforcement learning to solve the problem of high randomness and over-reliance on empirical values in the parameter selection process to more effectively coordinate the balance between local and global search capabilities. Secondly, an adaptive position-update strategy based on detour foraging and dynamic weights is introduced to adjust the weights according to changes in the adaptability of the leadership roles, increasing the guiding role of the dominant individual and accelerating the overall convergence speed of the algorithm. Furthermore, an artificial rabbit optimization algorithm bypass foraging strategy, by adding Brownian motion and Levy flight perturbation, improves the convergence accuracy and global optimization-seeking ability of the algorithm when dealing with complex problems. Finally, the elimination and relocation strategy based on stochastic center-of-gravity dynamic reverse learning is introduced for the inferior individuals in the population, which effectively maintains the diversity of the population and improves the convergence speed of the algorithm while avoiding falling into the local optimal solution effectively. In order to verify the effectiveness of the MSIAR-GWO algorithm, it is compared with a variety of commonly used swarm intelligence optimization algorithms in benchmark test functions and raster maps of different complexities in comparison experiments, and the results show that the MSIAR-GWO shows excellent stability, higher solution accuracy, and faster convergence speed in the majority of the benchmark-test-function solving. In the path planning experiments, the MSIAR-GWO algorithm is able to plan shorter and smoother paths, which further proves that the algorithm has excellent optimization-seeking ability and robustness.
Detailed Modeling of Surface-Plasmon Resonance Spectrometer Response for Accurate Correction
Authors: Araguillin-López Ricardo David , Méndez-Cevallos Angel Dickerson , Costa-Vera César
Published: February 2025

This work identifies and models the inline devices in an experimental surface-plasmon resonance spectroscopy setup to determine the system’s transfer function. This allows for the comparison of theoretical and experimental responses and the analysis of the dynamics of the components of an analyte placed on the sensor at the nanometer scale. The transfer functions of individual components, including the light source, polarizers, spectrometer, optical fibers, and the SPR sensor, were determined experimentally and theoretically. The theoretical model employed Planck’s law for the light source, manufacturer specifications for some components, and experimental characterization for others, such as the polarizers and optical fibers. The SPR sensor was modeled using characteristic matrix theory, incorporating the optical constants of the prism, gold film, chromium adhesive layer, and analyte. The combined transfer functions created a comprehensive model of the entire experimental system. This model successfully reproduced the experimental SPR spectrum with a similarity greater than 95%. The system’s operational range was also extended, constrained by the signal-to-noise ratio at the spectrum’s edges. The detailed model allows for the accurate correction of the measured spectra, which will be essential for the further analysis of nanosuspensions and molecules dissolved in liquids.
An Iterative Shifting Disaggregation Algorithm for Multi-Source, Irregularly Sampled, and Overlapped Time Series
Authors: Quinn Colin O. , Brown Ronald H. , Corliss George F. , Povinelli Richard J.
Published: February 2025

Accurate time series forecasting often requires higher temporal resolution than that provided by available data, such as when daily forecasts are needed from monthly data. Existing temporal disaggregation techniques, which typically handle only single, uniformly sampled time series, have limited applicability in real-world, multi-source scenarios. This paper introduces the Iterative Shifting Disaggregation (ISD) algorithm, designed to process and disaggregate time series derived from sensor-sourced low-frequency measurements, transforming multiple, nonuniformly sampled sensor data streams into a single, coherent high-frequency signal. ISD operates in an iterative, two-phase process: a prediction phase that uses multiple linear regression to generate high-frequency series from low-frequency data and correlated variables, followed by an update phase that redistributes low-frequency observations across high-frequency periods. This process repeats, refining estimates with each iteration cycle. The ISD algorithm’s key contribution is its ability to disaggregate multiple, nonuniformly spaced time series with overlapping intervals into a single daily representation. In two case studies using natural gas data, ISD successfully disaggregates billing cycle and grouped residential customer data into daily time series, achieving a 1.4–4.3% WMAPE improvement for billing cycle data and a 4.6–10.4% improvement for residential data over existing methods.
Efficient Elliptic-Curve-Cryptography-Based Anonymous Authentication for Internet of Things: Tailored Protocols for Periodic and Remote Control Traffic Patterns
Authors: Hu Shunfang , Zhang Yuanyuan , Guo Yanru , Chen Yanru , Chen Liangyin
Published: February 2025

IoT-based applications require effective anonymous authentication and key agreement (AKA) protocols to secure data and protect user privacy due to open communication channels and sensitive data. While AKA protocols for these applications have been extensively studied, achieving anonymity remains a challenge. AKA schemes using one-time pseudonyms face resynchronization issues after desynchronization attacks, and the high computational overhead of bilinear pairing and public key encryption limits its applicability. Existing schemes also lack essential security features, causing issues such as vulnerability to ephemeral secret leakage attacks and key compromise impersonation. To address these issues, we propose two novel AKA schemes, PUAKA and RCAKA, designed for different IoT traffic patterns. PUAKA improves end device anonymity in the periodic update pattern by updating one-time pseudonyms with authenticated session keys. RCAKA, for the remote control pattern, ensures anonymity while reducing communication and computation costs using shared signatures and temporary random numbers. A key contribution of RCAKA is its ability to resynchronize end devices with incomplete data in the periodic update pattern, supporting continued authentication. Both protocols’ security is proven under the Real-or-Random model. The performance comparison results show that the proposed protocols exceed existing solutions in security features and communication costs while reducing computational overhead by 32% to 50%.
Evaluation of Average Quantum Efficiency of Industrial Digital Camera
Authors: Deng Zhuochen , Chen Lingfeng , Wei Xuemeng , Zhang Xusheng
Published: February 2025

Quantum efficiency (QE) is a critical metric for assessing the performance of industrial digital cameras. The current EMVA1288 standard relies on monochromatic light for QE measurements. Comprehensive QE tests across the visible spectrum often involve elaborate setups and extensive data acquisition. Additionally, such tests may not fully capture camera performance under broadband illumination, which is frequently encountered in industrial applications. This study introduces the concept of average quantum efficiency (AQE) using white light sources and proposes a novel testing method. Systematic experiments and data analyses were performed on two industrial digital cameras under white light sources with different spectral distributions. The results suggest that AQE testing offers a practical and efficient means to evaluate camera performance under broadband illumination, complementing existing monochromatic QE measurement methods.
Yttrium Doping of Perovskite Oxide La2Ti2O7 Nanosheets for Enhanced Proton Conduction and Gas Sensing Under HighHumidity Levels
Authors: Wang Jian , Sun Caicai , Bao Jusheng , Yang Zhiwei , Zhang Jian , Huang Xiao
Published: February 2025

Water molecules from the environment or human breath are one of the main factors affecting the accuracy, efficiency, and long-term stability of electronic gas sensors. In this contribution, yttrium (Y)-doped La2Ti2O7 (LTO) nanosheets were synthesized by a hydrothermal reaction, demonstrating improved proton conductivity compared to their non-doped counterparts. The response of Y-doped LTO with the optimal doping concentration to 100 ppm NO2 at 43% relative humidity (RH) was −21%, which is four times higher than that of bare La2Ti2O7. As the humidity level increased to 75%, the response of Y-doped LTO further increased to −64%. Unlike the gas doping effect observed in previous studies of semiconducting metal oxides, the sensing mechanism of Y-doped LTO nanosheets is based on the enhanced dissociation of H2O in the presence of target NO2 molecules, leading to the generation of more protons for ion conduction. This also resulted in a greater resistance drop and thus a larger sensing response at elevated humidity levels. Our work demonstrates that proton-conductive oxide materials are promising gas-sensing materials under humid conditions.
Federated Learning for IoMT-Enhanced Human Activity Recognition with Hybrid LSTM-GRU Networks
Authors: Albogamy Fahad R.
Published: February 2025

The proliferation of wearable sensors and mobile devices has fueled advancements in human activity recognition (HAR), with growing importance placed on both accuracy and privacy preservation. In this paper, the author proposes a federated learning framework for HAR, leveraging a hybrid Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) model to enhance feature extraction and classification in decentralized environments. Utilizing three public datasets—UCI-HAR, HARTH, and HAR7+—which contain diverse sensor data collected from free-living activities, the proposed system is designed to address the inherent privacy risks associated with centralized data processing by deploying Federated Averaging for local model training. To optimize recognition accuracy, the author introduces a dual-feature extraction mechanism, combining convolutional blocks for capturing local patterns and a hybrid LSTM-GRU structure to detect complex temporal dependencies. Furthermore, the author integrates an attention mechanism to focus on significant global relationships within the data. The proposed system is evaluated on the three public datasets—UCI-HAR, HARTH, and HAR7+—achieving superior performance compared to recent works in terms of F1-score and recognition accuracy. The results demonstrate that the proposed approach not only provides high classification accuracy but also ensures privacy preservation, making it a scalable and reliable solution for real-world HAR applications in decentralized and privacy-conscious environments. This work showcases the potential of federated learning in transforming human activity recognition, combining advanced feature extraction methodologies and privacy-respecting frameworks to deliver robust, real-time activity classification.
Relationship Between Signals from Cerebral near Infrared Spectroscopy Sensor Technology and Objectively Measured Cerebral Blood Volume: A Systematic Scoping Review
Authors: Silvaggio Noah , Stein Kevin Y. , Sainbhi Amanjyot Singh , Vakitbilir Nuray , Bergmann Tobias , Islam Abrar , Hasan Rakibul , Hayat Mansoor , Zeiler Frederick A.
Published: February 2025

Cerebral blood volume (CBV) is an essential metric that indicates and evaluates various healthy and pathologic conditions. Most methods of CBV measurement are cumbersome and have a poor temporal resolution. Recently, it has been proposed that signals and derived metrics from cerebral near-infrared spectroscopy (NIRS), a non-invasive sensor, can be used to estimate CBV. However, this association remains vastly unexplored. As such, this scoping review aimed to examine the literature on the relationship between cerebral NIRS signals and CBV. A search of six databases was conducted conforming to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to assess the following search question: What are the associations between various NIRS cerebral signals and CBV? The database search yielded 3350 unique results. Seven of these articles were included in this review based on the inclusion and exclusion criteria. An additional study was identified and included while examining the articles’ reference sections. Overall, the literature for this systematic scoping review shows extreme variation in the association between cerebral NIRS signals and CBV, with few sources objectively documenting a true statistical association between the two. This review highlights the current critical knowledge gap and emphasizes the need for further research in the area.
Adaptive Grasp Pose Optimization for Robotic Arms Using Low-Cost Depth Sensors in Complex Environments
Authors: Chen Aiguo , Li Xuanfeng , Cen Kerui , Hon Chitin
Published: February 2025

This paper presents an efficient grasp pose estimation algorithm for robotic arm systems with a two-finger parallel gripper and a consumer-grade depth camera. Unlike traditional deep learning methods, which suffer from high data dependency and inefficiency with low-precision point clouds, the proposed approach uses ellipsoidal modeling to overcome these issues. The algorithm segments the target and then applies a three-stage optimization to refine the grasping path. Initial estimation fits an ellipsoid to determine principal axes, followed by nonlinear optimization for a six-degree-of-freedom grasp pose. Validation through simulations and experiments showed a target grasp success rate (TGSR) of over 83% under low noise, with only a 4.9% drop under high noise—representing a 68.0% and a 42.4% improvement over GPD and PointNetGPD, respectively. In real-world tests, success rates ranged from 95 to 100%, and the computational efficiency was improved by 56.3% compared to deep learning methods, proving its practicality for real-time applications. These results demonstrate stable and reliable grasping performance, even in noisy environments and with low-cost sensors.
RFCS-YOLO: Target Detection Algorithm in Adverse Weather Conditions via Receptive Field Enhancement and Cross-Scale Fusion
Authors: Liu Gang , Huang Yingzheng , Yan Shuguang , Hou Enxiang
Published: February 2025

The paper proposes a model based on receptive field enhancement and cross-scale fusion (RFCS-YOLO). It addresses challenges like complex backgrounds and problems of missing and mis-detecting traffic targets in bad weather. First, an efficient feature extraction module (EFEM) is created. It reconfigures the backbone network. This helps to make the receptive field better and improves its ability to extract features of targets at different scales. Next, a cross-scale fusion module (CSF) is introduced. It uses the receptive field coordinate attention mechanism (RFCA) to fuse information from different scales well. It also filters out noise and background information that might interfere. Also, a new Focaler-Minimum Point Distance Intersection over Union (F-MPDIoU) loss function is proposed. It makes the model converge faster and deals with issues of leakage and false detection. Experiments were conducted on the expanded Vehicle Detection in Adverse Weather Nature dataset (DWAN). The results show significant improvements compared to the conventional You Only Look Once v7 (YOLOv7) model. The mean Average Precision (mAP@0.5), precision, and recall are enhanced by 4.2%, 8.3%, and 1.4%, respectively. The mean Average Precision is 86.5%. The frame rate is 68 frames per second (FPS), which meets the requirements for real-time detection. A generalization experiment was conducted using the autonomous driving dataset SODA10M. The mAP@0.5 achieved 56.7%, which is a 3.6% improvement over the original model. This result demonstrates the good generalization ability of the proposed method.
Design of Bilayer Crescent Chiral Metasurfaces for Enhanced Chiroptical Response
Authors: Asefa Semere A. , Seong Myeongsu , Lee Dasol
Published: February 2025

Chiral metasurfaces exploit structural asymmetry to control circular polarized light, presenting new possibilities for the design of optical devices, specifically in the dynamic control of light and enhanced optical sensing fields. This study employed theoretical and computational methods to examine the chiroptical properties of a bilayer crescent chiral metasurface, demonstrating the effect of the angle of rotation on the chiroptical response. We particularly investigated the changes in transmittance, electric field distribution, and circular dichroism (CD) across various rotation angles. The crescent chiral metasurface demonstrated the maximum CD and showed significant control over the CD and electric field distribution across different rotation angles in the near-infrared region. The highest CD value was observed at a 23° rotation angle, where the chiroptical response reached its maximum. In addition, the left circular polarized light showed a stronger intensity of the electric field along the crescent metasurface edge relative to the right circular polarized light, underscoring the significant difference in the intensity and field localization. It was also shown that the configuration with a 2 by 2-unit cell, compared with a single-unit cell, exhibited significantly enhanced CD, thus underlining the importance of the unit cell arrangement in optimizing the chiroptical properties of metasurfaces for advanced photonic applications. These results prove that the 2 by 2 bilayer crescent chiral metasurface can be tailored to a fine degree for specific applications such as improved biosensing, enhanced optical communications, and precise polarization control by optimizing the configuration. The insight presented by this theoretical and computational study will contribute to the broad understanding of chiroptical phenomena as well as pave the way for potential applications in developing advanced optical devices with tuned chiroptical interactions.
CAREUP: An Integrated Care Platform with Intrinsic Capacity Monitoring and Prediction Capabilities
Authors: Kolakowski Marcin , Lupica Andrea , Ben Bader Seif , Djaja-Josko Vitomir , Kolakowski Jerzy , Cichocki Jacek , Ayadi Jaouhar , Gilardi Luca , Consoli Angelo , Mocanu Irina Georgiana , Cramariuc Oana , Ferrazzini Lionello , Reithner Eva , Velciu Magdalena , Borgogni Barbara , Rivaira Sofia , Leonzi Sara , Cucchieri Giacomo , Stara Vera
Published: February 2025

This paper describes CAREUP, a novel older adult healthy aging support platform based on Intrinsic Capacity (IC) monitoring. Besides standard functionalities like storing health measurement data or providing users with personalized recommendations, the platform includes novel intrinsic capacity assessment and prediction algorithms. Older adults’ performance is continuously monitored in all five IC domains—locomotion, psychology, cognition, vitality, and sensory capacity—based on measurement results and answers to questionnaires gathered using the platform’s mobile applications. The users are also presented with a machine learning-based prediction of how their intrinsic capacity might change over the following years. The platform’s operation was successfully tested with the participation of older adults and their caregivers in three countries: Austria, Italy, and Romania.
RGANet: A Human Activity Recognition Model for Extracting Temporal and Spatial Features from WiFi Channel State Information
Authors: Hu Jianyuan , Ge Fei , Cao Xinyu , Yang Zhimin
Published: February 2025

With the rapid advancement of communication technologies, wireless networks have not only transformed people’s lifestyles but also spurred the development of numerous emerging applications and services. Against this backdrop, research on Wi-Fi-based human activity recognition (HAR) has become a hot topic in both academia and industry. Channel State Information (CSI) contains rich spatiotemporal information. However, existing deep learning methods for human activity recognition (HAR) typically focus on either temporal or spatial features. While some approaches do combine both types of features, they often emphasize temporal sequences and underutilize spatial information. In contrast, this paper proposes an enhanced approach by modifying residual networks (ResNet) instead of using simple CNN. This modification allows for effective spatial feature extraction while preserving temporal information. The extracted spatial features are then fed into a modifying GRU model for temporal sequence learning. Our model achieves an accuracy of 99.4% on the UT_HAR dataset and 99.24% on the NTU-FI HAR dataset. Compared to other existing models, RGANet shows improvements of 1.21% on the UT_HAR dataset and 0.38% on the NTU-FI HAR dataset.
Fiber Fabry–Perot Sensor Based on Ion-Imprinted Sodium Alginate/Graphene Oxide Hydrogel for Copper Ion Detection Using Vernier Effect
Authors: Wang Ning , Liu Shiqi , Xu Liang , Wang Longjiao , He Ming , Lei Chuanjie , Xiao Linyufan
Published: February 2025

This work proposes an optical fiber copper ion sensor, which is fabricated by an ion-imprinted sodium alginate/graphene oxide (SA/GO) hydrogel and single-mode fiber (SMF). This sensing Fabry–Perot Interferometer (FPI) achieves −1.98 nm/(mg/L) sensitivity with 0.998 linearity. To achieve higher sensitivity, we add a reference FPI to create a Vernier effect. We achieve 19.58 nm/mg/L sensitivity and 0.989 linearity at a concentration range of 0 mg/L–1.4 mg/L. It was 9.9 times higher than that of a single-sensing FPI. The experimental results also demonstrate that when the FSR values of two FPIs are closer, the higher response sensitivity is achieved. The sensor also has good measurement repeatability and dynamic response. In addition, the experimental results of response selectivity show that its response sensitivity to copper ions is significantly higher than other six types of ions, including iron ions, lead ions, magnesium ions, manganese ion, zinc ions, chromium ions. The copper ion is also mixed with six types of ions to deeply investigate the response selectivity. Good response selectivity and cross-responding are demonstrated by experimental results.
Open-Source Algorithm for Automated Vigilance State Classification Using Single-Channel Electroencephalogram in Rodents
Authors: Saevskiy Anton , Suntsova Natalia , Kosenko Peter , Alam Md Noor , Kostin Andrey
Published: February 2025

Accurate identification of sleep stages is essential for understanding sleep physiology and its role in neurological and behavioral research. Manual scoring of polysomnographic data, while reliable, is time-intensive and prone to variability. This study presents a novel Python-based algorithm for automated vigilance state scoring using single-channel electroencephalogram (EEG) recordings from rats and mice. The algorithm employs artifact processing, multi-band frequency analysis, and Gaussian mixture model (GMM)-based clustering to classify wakefulness, non-rapid, and rapid eye movement sleep (NREM and REM sleep, respectively). Combining narrow and broad frequency bands across the delta, theta, and sigma ranges, it uses a majority voting system to enhance accuracy, with tailored preprocessing and voting criteria improving REM detection. Validation on datasets from 10 rats and 10 mice under standard conditions showed sleep–wake state detection accuracies of 92% and 93%, respectively, closely matching manual scoring and comparable to existing methods. REM sleep detection accuracies of 89% (mice) and 91% (rats) align with previously reported (85–90%). Processing a full day of EEG data within several minutes, the algorithm is advantageous for large-scale and longitudinal studies. Its open-source design, flexibility, and scalability make it a robust, efficient tool for automated rodent sleep scoring, advancing research in standard experimental conditions, including aging and sleep deprivation.
Comparative Study of Selected Order-Picking Methods: Efficiency, Ergonomics, and Adaptation Rate of New Employees
Authors: Łopuszyński Marcin , Janusz Kamil , Karwat Dawid
Published: February 2025

The human manual order-picking process in the warehouse is still the leading method despite increasing automation. This manual process is supported by indicating and receipt systems to reduce the order-picking time and the number of errors. Many studies in the literature compare the Pick-by-Light system with the Pick-by-Paper and other systems, and it is more challenging to study the Pick-by-Point system. This paper presents the results of laboratory comparative studies of the most straightforward Pick-by-Paper system with Pick-by-Light and Pick-by-Point systems supported by receipt systems. In the case of the Pick-by-Light system, the receipt system is a button on the module that the picker presses to confirm the pick-up of an item. In the case of Pick-by-Point, the receipt system is a wrist scanner that the picker uses to confirm the pick-up of an item. A total of 71 people participated in the study. Participants completed five orders with five items per system. Comparisons were made of the time it took to pick the orders with the support of these systems, the number of errors made, ergonomics, and the speed of adaptation for new employees without experience. A person with nine years of experience in the picking process took part in the study, whose order-picking times were compared with those of the others. In the study, the Pick-by-Light system proved to be the fastest regarding order picking and the adaptation of new employees. On the other hand, the Pick-by-Point system was the most error-proof.