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15 pages, 283 KB

Open AccessArticle

‘Look! […] Things People Can’t See!’ Wordbooks, Reader-Listenership, and Invisible Theatre in Handel’s Oratorios

by Cathal Twomey

Arts 2025, 14(6), 144; https://doi.org/10.3390/arts14060144 (registering DOI) - 17 Nov 2025

In eighteenth-century England, anyone attending an opera, an oratorio, or even a church service would typically have had a printed ‘wordbook’ made available to them to read during the performance. Such wordbooks, whether available for purchase or distributed free of charge, contained the [...] Read more.

In eighteenth-century England, anyone attending an opera, an oratorio, or even a church service would typically have had a printed ‘wordbook’ made available to them to read during the performance. Such wordbooks, whether available for purchase or distributed free of charge, contained the words to be sung (the libretto), usually with translations if necessary, and sometimes also explanatory footnotes, prefaces or plot summaries, or lists of dramatis personae. Examining several oratorios of George Frideric Handel, especially Saul and Theodora, this article asks how the wordbook influenced the drama of a performed work and to what extent this impact made it necessary to have an actively reading audience. The article also explores the use of stage directions in oratorio wordbooks, arguing that they provide rich opportunities for the audience’s imagination by suggesting images that the performance alone cannot provide (since English oratorio probably included no stage action). It notes the wordbook’s necessity in determining which singer is portraying which character, as well as the expressive and dramatic use to which these character identifications can be put. And it compares the practices of different oratorio librettists, suggesting great sensitivity to the unique imaginative power of the oratorio-with-wordbook medium. Full article

(This article belongs to the Special Issue Creating Musical Experiences)

26 pages, 1680 KB

Open AccessArticle

Digital Empowerment and Sustainable Tourism: Spatiotemporal Coupling Coordination Analysis of Digital Technology and High-Quality Development in China’s A-Level Scenic Spots

by Hongmei Dong and Jiali Zeng

Sustainability 2025, 17(22), 10293; https://doi.org/10.3390/su172210293 (registering DOI) - 17 Nov 2025

Abstract

The rapid advancement of digital technology has profoundly transformed the tourism industry, driving a shift from scale expansion toward high-quality and sustainable growth. However, spatiotemporal nature of digital empowerment’s support for sustainable tourism, particularly under heterogeneous regional conditions, remains insufficiently examined. To address [...] Read more.

The rapid advancement of digital technology has profoundly transformed the tourism industry, driving a shift from scale expansion toward high-quality and sustainable growth. However, spatiotemporal nature of digital empowerment’s support for sustainable tourism, particularly under heterogeneous regional conditions, remains insufficiently examined. To address this gap, this study constructs a dual-system evaluation framework and employs the entropy method to measure the spatiotemporal Coupling Coordination Degree (CCD) between digital technology and tourism development of A-Level Scenic Spots across 30 Chinese provinces (2013–2020). The entropy method is employed to build indicator systems for both subsystems, and CCD is calculated to assess the interaction strength and coordination level. The results reveal that: (1) A-level scenic spot development exhibits significant spatial heterogeneity, declining clearly from Eastern/Central to Western/Northeast regions; (2) CCD showed a general upward trend during 2013–2019 and it followed a nonlinear trajectory of decline first but then recovery, establishing a stable spatial pattern: East > Central > West/Northeast; (3) The COVID-19 pandemic in 2020 caused a temporary drop in CCD nationwide, but regional resilience varied considerably; (4) Provinces in the disordered stage are generally of the digital technology lagging type. Economic foundation, digital facilities, industrial structure and innovation capability are key drivers of coordination differences. We propose that leading regions should focus on cross-regional data sharing and green-smart upgrading, while lagging regions must prioritize digital infrastructure investment and talent introduction to effectively bridge the digital divide and foster equitable and high-quality sustainable tourism development. This study provides new insights for promoting regional sustainability through digital technology development and offers policy recommendations for advancing digital–tourism synergy in different regional contexts. Full article

(This article belongs to the Special Issue Sustainable Development of Regional Tourism)

19 pages, 3298 KB

Open AccessArticle

GPLVINS: Tightly Coupled GNSS-Visual-Inertial Fusion for Consistent State Estimation with Point and Line Features for Unmanned Aerial Vehicles

by Xinyu Chen, Shuaixin Li, Ruifeng Lu and Xiaozhou Zhu

Drones 2025, 9(11), 801; https://doi.org/10.3390/drones9110801 (registering DOI) - 17 Nov 2025

Abstract

The employment of linear features to enhance the positioning precision and robustness of point-based VIO (visual-inertial odometry) has attracted mounting attention, especially for UAV (unmanned aerial vehicle) applications where reliable 6-DoF pose estimation is critical for autonomous navigation, mission execution, and safety. This [...] Read more.

The employment of linear features to enhance the positioning precision and robustness of point-based VIO (visual-inertial odometry) has attracted mounting attention, especially for UAV (unmanned aerial vehicle) applications where reliable 6-DoF pose estimation is critical for autonomous navigation, mission execution, and safety. This paper presents GPLVINS—GNSS (global navigation satellite system)-point-line-visual-inertial navigation system—a UAV-tailored enhancement of the nonlinear optimization-based GVINS (GNSS-visual-inertial navigation system). Unlike GVINS, which struggles with feature extraction in weak-texture environments and depends entirely on point features, GPLVINS innovatively integrates line features into its state optimization framework to enhance robustness and accuracy. While existing studies adopt the LSD (line segment detector) algorithm for line feature extraction, this approach often generates numerous short line segments in real-world scenes. Such an outcome not only increases computational costs but also degrades pose estimation performance. In order to address this issue, the present study proposes an NMS (non-maximum suppression) strategy for the refinement of LSD. The line reprojection residual is then formulated as the distance between point and line, which is incorporated into the nonlinear optimization process. Experimental validations on open-source datasets and self-collected UAV datasets across indoor, outdoor, and indoor–outdoor transition scenarios demonstrate that GPLVINS exhibits superior positioning performance and enhanced robustness for UAVs in environments with feature degradation or drastic lighting intensity variations. Full article

(This article belongs to the Topic International Conference on Autonomous Unmanned Systems (5th ICAUS 2025))

11 pages, 2390 KB

Open AccessArticle

Integrated Quasi-Optical Terahertz Liquid Sensor Leveraging Mode-Parity-Dependent Interaction with a Capillary-Confined Analyte

by Andreas K. Klein, Julian Webber, Guillermo Carpintero, Masayuki Fujita and Daniel Headland

Sensors 2025, 25(22), 7026; https://doi.org/10.3390/s25227026 (registering DOI) - 17 Nov 2025

Abstract

The integration of terahertz (THz) sensing technology into compact, on-chip platforms is essential to the advancement of high-precision chemical and biomedical analysis, promising to bring analytics closer to the point of care and to enable in situ analysis of industrial processes. This study [...] Read more.

The integration of terahertz (THz) sensing technology into compact, on-chip platforms is essential to the advancement of high-precision chemical and biomedical analysis, promising to bring analytics closer to the point of care and to enable in situ analysis of industrial processes. This study presents an integrated quasi-optical THz liquid sensor that features a longitudinal cavity in a silicon slab waveguide, in which a capillary-confined analyte interacts with guided slab modes on resonance. The sensor design leverages mode-parity-dependent field distributions: even-parity resonances exhibit strong analyte-field interaction, whilst odd-parity modes remain largely unaffected by the presence of the analyte, enabling intrinsic self-calibration. The device is fabricated using deep reactive ion etching of high-resistivity silicon and monolithically integrates all required components. Experimental measurements with water and isopropanol demonstrate alternating resonance peaks with distinct sensitivity to refractive index and absorption, validated by linear shifts in frequency and transmission loss. The self-calibrating feature allows for real-time compensation of system fluctuations towards automated continuous monitoring applications. These findings establish the sensor’s capability for simultaneous, precise material characterization and calibration, highlighting its potential for in-line process monitoring and other high-bandwidth sensing applications. Full article

(This article belongs to the Special Issue Terahertz Sensors)

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17 pages, 3614 KB

Open AccessArticle

Excavation-Induced Disturbance in Natural Structured Clay: In-Situ Tests and Numerical Analyses

by Fangtong Wang, Taishan Lu, Zhigang Shan, Kanmin Shen, Yong Wang, Dingwen Zhang and Huan He

Appl. Sci. 2025, 15(22), 12201; https://doi.org/10.3390/app152212201 (registering DOI) - 17 Nov 2025

Abstract

Deep excavation in natural structured clay causes disturbance to the surrounding soil, which damages the soil structure and results in soil strength reduction. This study investigates excavation-induced disturbance in natural clay based on a case of subway station excavation. A series of piezocone [...] Read more.

Deep excavation in natural structured clay causes disturbance to the surrounding soil, which damages the soil structure and results in soil strength reduction. This study investigates excavation-induced disturbance in natural clay based on a case of subway station excavation. A series of piezocone tests was performed adjacent to the diaphragm wall before and after excavation to determine the disturbance degree based on cone tip resistance. The stress and deformation variations in soil were also obtained via numerical simulations, and the mechanisms of excavation-induced disturbance were proposed based on the numerical simulation results. The results showed that excavation caused a decrease in the cone tip resistance, and the disturbance degree of soil determined by cone tip resistance ranged from 0% to 50%. At identical locations, the disturbance degree of soil increased with excavation depth. The main reason for excavation disturbance is the increase in shear stress. Therefore, shear strain can serve as an indicator of the degree of disturbance, and the relationship between disturbance degree and shear strain can be expressed by a power function. The degree of soil disturbance is affected not only by the magnitude of the diaphragm wall horizontal displacement but also by its deformation distribution pattern. Full article

13 pages, 2928 KB

Open AccessArticle

Application Research on General Technology for Safety Appraisal of Existing Buildings Based on Unmanned Aerial Vehicles and Stair-Climbing Robots

by Zizhen Shen, Rui Wang, Lianbo Wang, Wenhao Lu and Wei Wang

Buildings 2025, 15(22), 4145; https://doi.org/10.3390/buildings15224145 (registering DOI) - 17 Nov 2025

Abstract

Structure detection (SD) has emerged as a critical technology for ensuring the safety and longevity of infrastructure, particularly in housing and civil engineering. Traditional SD methods often rely on manual inspections, which are time-consuming, labor-intensive, and prone to human error, especially in complex [...] Read more.

Structure detection (SD) has emerged as a critical technology for ensuring the safety and longevity of infrastructure, particularly in housing and civil engineering. Traditional SD methods often rely on manual inspections, which are time-consuming, labor-intensive, and prone to human error, especially in complex environments such as dense urban settings or aging buildings with deteriorated materials. Recent advances in autonomous systems—such as Unmanned Aerial Vehicles (UAVs) and climbing robots—have shown promise in addressing these limitations by enabling efficient, real-time data collection. However, challenges persist in accurately detecting and analyzing structural defects (e.g., masonry cracks, concrete spalling) amidst cluttered backgrounds, hardware constraints, and the need for multi-scale feature integration. The integration of machine learning (ML) and deep learning (DL) has revolutionized SD by enabling automated feature extraction and robust defect recognition. For instance, RepConv architectures have been widely adopted for multi-scale object detection, while attention mechanisms like TAM (Technology Acceptance Model) have improved spatial feature fusion in complex scenes. Nevertheless, existing works often focus on singular sensing modalities (e.g., UAVs alone) or neglect the fusion of complementary data streams (e.g., ground-based robot imagery) to enhance detection accuracy. Furthermore, computational redundancy in multi-scale processing and inconsistent bounding box regression in detection frameworks remain underexplored. This study addresses these gaps by proposing a generalized safety inspection system that synergizes UAV and stair-climbing robot data. We introduce a novel multi-scale targeted feature extraction path (Rep-FasterNet TAM block) to unify automated RepConv-based feature refinement with dynamic-scale fusion, reducing computational overhead while preserving critical structural details. For detection, we combine traditional methods with remote sensor fusion to mitigate feature loss during image upsampling/downsampling, supported by a structural model GIOU [Mathematical Definition: GIOU = IOU − (C − U)/C] that enhances bounding box regression through shape/scale-aware constraints and real-time analysis. By siting our work within the context of recent reviews on ML/DL for SD, we demonstrate how our hybrid approach bridges the gap between autonomous inspection hardware and AI-driven defect analysis, offering a scalable solution for large-scale housing safety assessments. In response to challenges in detecting objects accurately during housing safety assessments—including large/dense objects, complex backgrounds, and hardware limitations—we propose a generalized inspection system leveraging data from UAVs and stair-climbing robots. To address multi-scale feature extraction inefficiencies, we design a Rep-FasterNet TAM block that integrates RepConv for automated feature refinement and a multi-scale attention module to enhance spatial feature consistency. For detection, we combine dynamic-scale remote feature fusion with traditional methods, supported by a structural GIOU model that improves bounding box regression through shape/scale constraints and real-time analysis. Experiments demonstrate that our system increases masonry/concrete assessment accuracy by 11.6% and 20.9%, respectively, while reducing manual drawing restoration workload by 16.54%. This validates the effectiveness of our hybrid approach in unifying autonomous inspection hardware with AI-driven analysis, offering a scalable solution for SD in housing infrastructure. Full article

(This article belongs to the Special Issue AI-Powered Structural Health Monitoring: Innovations and Applications)

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18 pages, 3672 KB

Open AccessReview

Response of Plants to Touch Stress at Morphological, Physiological and Molecular Levels

by Agata Jędrzejuk and Natalia Kuźma

Int. J. Mol. Sci. 2025, 26(22), 11120; https://doi.org/10.3390/ijms262211120 (registering DOI) - 17 Nov 2025

Abstract

Thigmomorphogenesis denotes a suite of anatomical, physiological, biochemical, biophysical, and molecular responses of plants to mechanical stimulation. This phenomenon is evolutionarily conserved among diverse plant lineages; however, the magnitude and character of the response are strongly determined by both the frequency and intensity [...] Read more.

Thigmomorphogenesis denotes a suite of anatomical, physiological, biochemical, biophysical, and molecular responses of plants to mechanical stimulation. This phenomenon is evolutionarily conserved among diverse plant lineages; however, the magnitude and character of the response are strongly determined by both the frequency and intensity of the applied stimulus. In angiosperms, thigmomorphogenetic reactions typically occur gradually, reflecting a complex interplay of morphological alterations, biochemical adjustments, and genetic reprogramming. In dicotyledonous plants, thigmomorphogenesis is commonly expressed as a reduction in leaf blade surface area, shortening of petioles, decreased plant height, radial thickening of stems, and modifications in root system architecture. In monocotyledons, in turn, mechanical stress frequently results in stem rupture below the inflorescence, with concomitant shortening and increased flexibility of younger internodes. These specific traits can be explained by structural features of monocot secondary walls as well as by the absence of vascular cambium and lateral meristems. Mechanical stimulation has been shown to initiate a cascade of responses across multiple levels of plant organization. The earliest events involve activation of mechanoresponsive genes (e.g., TCH family), followed by enzymatic activation, biochemical shifts, and downstream physiological and molecular adjustments. Importantly, recent findings indicate that prolonged mechanical stress may significantly suppress auxin biosynthesis, while leaving auxin transport processes unaffected. Moreover, strong interdependencies have been identified between thigmostimulation, gibberellin biosynthesis, and flowering intensity, as well as between mechanical stress and signaling pathways of other phytohormones, including abscisic acid, jasmonic acid, and ethylene. At the molecular scale, studies have demonstrated a robust correlation between the expression of specific calmodulin isoforms and the GH3.1 gene, suggesting a mechanistic link between mechanosensing, hormone homeostasis, and regulatory feedback loops. The present study consolidates current knowledge and integrates novel findings, emphasizing both morphological and cellular dimensions of thigmomorphogenesis. In particular, it provides evidence that mechanical stress constitutes a critical modulator of hormonal balance, thereby shaping plant growth, development, and adaptive potential. Full article

(This article belongs to the Section Molecular Plant Sciences)

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17 pages, 726 KB

Open AccessArticle

Multilevel Intersectional Analysis to Identify Extreme Profiles in Italian Student Achievement Data

by Enrico Contin and Leonardo Grilli

Soc. Sci. 2025, 14(11), 672; https://doi.org/10.3390/socsci14110672 (registering DOI) - 17 Nov 2025

Abstract

Students have diverse identities and social characteristics. The different combinations of these factors create a stratification that affects the learning outcomes. This study aims to identify the student profiles associated with the highest and lowest academic performance. To this end, we analyse data [...] Read more.

Students have diverse identities and social characteristics. The different combinations of these factors create a stratification that affects the learning outcomes. This study aims to identify the student profiles associated with the highest and lowest academic performance. To this end, we analyse data from the 2022/23 INVALSI Mathematics test for fifth-grade students. The approach used is the Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA), which highlights the intersectional nature of social inequalities in shaping academic achievement. The strata are defined by the intersections of sex, origin, family environment, parental education, and parental occupation. Moreover, recognising the critical role of the school context, we fit a cross-classified multilevel model with random effects for both intersectional strata and schools. Indeed, model fitting reveals that the school-level variance is substantial, being about three-fourths of the variance due to the intersectional strata. The results show that the lowest-performing students are characterised by an unfavourable family environment, parents with compulsory or unknown education, and parents who are unemployed or in blue-collar jobs. Full article

(This article belongs to the Special Issue Tackling Educational Inequality: Issues and Solutions)

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28 pages, 4025 KB

Open AccessArticle

Quantitative Detection of Key Parameters and Authenticity Verification for Beer Using Near-Infrared Spectroscopy

by Yongshun Wei, Jinming Liu, Guiqing Xi and Yuhao Lu

Foods 2025, 14(22), 3936; https://doi.org/10.3390/foods14223936 (registering DOI) - 17 Nov 2025

Abstract

Alcohol content and original wort concentration are key indicators of beer quality. The detection of these metrics and the authentication of beer authenticity are crucial for protecting consumer rights. To this end, this study investigates quantitative detection methods for beer alcohol content and [...] Read more.

Alcohol content and original wort concentration are key indicators of beer quality. The detection of these metrics and the authentication of beer authenticity are crucial for protecting consumer rights. To this end, this study investigates quantitative detection methods for beer alcohol content and original wort concentration based on near-infrared spectroscopy (NIRS), as well as authenticity verification methods for craft, industrial, and non-fermented beers. Convolutional neural networks combined with a long short-term memory networks (CNN-LSTM) feature extraction method was proposed for establishing multiple regression models and partial least squares discriminant analysis (PLS-DA) model. The results indicate that the CNN-LSTM combined with the support vector machine regression demonstrates optimal performance, with coefficients of determination exceeding 0.99 for the alcohol content calibration, validation, and independent test sets, and all relative root mean square errors below 2.67%. For original wort concentration, the coefficients of determination exceeded 0.97 across the calibration, validation, and independent test sets, with relative root mean square errors below 4.05%. The CNN-LSTM combined with the PLS-DA approach exhibited the lowest variable dimension while achieving 100% classification accuracy. This method offers rapid, non-destructive, and efficient advantages, making it suitable for beer quality control and market regulation. Full article

(This article belongs to the Section Food Analytical Methods)

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18 pages, 2913 KB

Open AccessArticle

Enhanced Damping Method for Suppressing Sub-Synchronous Oscillations of Grid-Forming Permanent Magnet Synchronous Generator

by Hongke Li, Xiaohe Wang, Ming Yan, Jinhao Wang and Chao Wu

Electronics 2025, 14(22), 4489; https://doi.org/10.3390/electronics14224489 (registering DOI) - 17 Nov 2025

Abstract

With the increase in wind power penetration, the stable operation of wind turbines under the new power system is facing severe challenges. The grid-forming wind power technology operates in a self-synchronous mode, which can provide voltage and frequency support for the system without [...] Read more.

With the increase in wind power penetration, the stable operation of wind turbines under the new power system is facing severe challenges. The grid-forming wind power technology operates in a self-synchronous mode, which can provide voltage and frequency support for the system without being affected by the phase-locked loop, and is also suitable for operation under weak power grids. However, the current research for the grid-forming (GFM) permanent magnet synchronous generator (PMSG) ignores the DC-link dynamics generated by the wind turbine, which makes the sub-synchronous oscillation (SSO) phenomenon under different grid conditions and lacks a physical explanation. In this paper, the SSO problem in the grid-forming PMSG is studied, and the study reveals that the reduction in the DC-link voltage control bandwidth of the machine-side converter (MSC) is the main cause. To this end, an improved damping method is proposed, which introduces a low-pass filter branch in the reactive power control loop and takes the DC-link voltage tracking error as a compensation term. The small-signal analysis and simulation results show that the proposed method has significant effectiveness. Full article

20 pages, 4111 KB

Open AccessArticle

Exploring the Spatially Heterogeneous Relationships Between Biodiversity Maintenance Function and Socio-Ecological Drivers in Liaoning Province, China

by Yajun Qiao, Zhi Wang, Haonan Zhang, Kun Liu and Wanggu Xu

Land 2025, 14(11), 2276; https://doi.org/10.3390/land14112276 (registering DOI) - 17 Nov 2025

Abstract

Biodiversity maintenance function (BMF) denotes the capacity of ecosystems to sustain genetic, species, ecosystem, and landscape diversity. Assessing the spatial distribution and underlying drivers of BMF at the regional scale is essential for biodiversity management. However, research on the socio-ecological drivers of BMF [...] Read more.

Biodiversity maintenance function (BMF) denotes the capacity of ecosystems to sustain genetic, species, ecosystem, and landscape diversity. Assessing the spatial distribution and underlying drivers of BMF at the regional scale is essential for biodiversity management. However, research on the socio-ecological drivers of BMF from a geographical perspective remains scarce. Therefore, this study developed an integrated assessment framework encompassing climatic factors, species richness, vegetation status, ecosystem protection, and anthropogenic disturbance. We analyzed the BMF spatial patterns across Liaoning Province, China, and identified the dominant drivers and their spatial heterogeneity using multi-scale geographically weighted regression and geographical detector. The results show that (1) the eastern/western mountainous regions and Liaohe River estuary are critical BMF zones for prioritized conservation; (2) BMF spatial variation is mainly shaped by precipitation, temperature, slope, and forestland/farmland proportion, with factor interactions amplifying their impacts; (3) drivers show distinct spatial heterogeneity. Specifically, precipitation, slope, and NDVI exert homogeneous effects, whereas elevation, temperature, farmland/wetland proportion, and GDP exhibit pronounced heterogeneity. Natural factors generally exert positive effects, while the farmland/urban proportion tends to exert negative impacts—for example, farmland’s negative influence is stronger in the west, whereas the forestland and temperature exert more positive effects in the east. The results enhance the methodological framework for elucidating the spatial relationships between BMF and drivers, providing a scientific basis for biodiversity conservation and ecosystem management in Liaoning Province and similar regions. Full article

17 pages, 2643 KB

Open AccessArticle

MCPA Optical Fiber Sensors via Molecularly Imprinted Polymers Combined with Intensity-Based and Plasmonic Platforms

by Ines Tavoletta, Francesco Arcadio, Luigi Zeni, Ricardo Oliveira, Rogério Nunes Nogueira, Giancarla Alberti and Nunzio Cennamo

Polymers 2025, 17(22), 3048; https://doi.org/10.3390/polym17223048 (registering DOI) - 17 Nov 2025

Abstract

Two low-cost optical–chemical sensors based on plastic optical fibers (POFs) and molecularly imprinted polymers (MIPs) are developed and tested for the detection of 4-chloro-2-methylphenoxyacetic acid (MCPA), a herbicide of great interest in environmental monitoring. The first sensor is based on an optical splitter [...] Read more.

Two low-cost optical–chemical sensors based on plastic optical fibers (POFs) and molecularly imprinted polymers (MIPs) are developed and tested for the detection of 4-chloro-2-methylphenoxyacetic acid (MCPA), a herbicide of great interest in environmental monitoring. The first sensor is based on an optical splitter composed of two modified POFs coupled with an MIP for measuring MCPA. The second type of sensor is based on a surface plasmon resonance (SPR) D-shaped POF platform combined with the same MIP receptor for MCPA. The two proposed polymer-based sensors, exploiting different optical phenomena, were tested using similar equipment, consisting of white light sources and spectrometers. The experimental results show that both MCPA sensors present high selectivity for the target analyte and similar performances in terms of detection limits (LODs) of 3 nM and detection ranges (between 3 nM and 500 nM) by exploiting the MIP’s sites with a similar affinity constant. The polymer-based sensors exhibited better performances than those achieved by the electrochemical technique combined with the same MIP presented in the literature. Then, tests performed on real samples demonstrated good recovery values (between 82% and 116%), assessing the applicability of both sensors in real-world scenarios. Moreover, the POF-MIP splitter sensor configuration can be fabricated without expensive fabrication steps, such as spinning and sputtering processes. Full article

(This article belongs to the Section Polymer Applications)

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17 pages, 1785 KB

Open AccessArticle

Bactericidal Activity of Pradofloxacin and Other Antimicrobials Against Swine Respiratory Bacterial Pathogens

by Joseph M. Blondeau and Shantelle D. Fitch

Pathogens 2025, 14(11), 1171; https://doi.org/10.3390/pathogens14111171 (registering DOI) - 17 Nov 2025

Abstract

Swine respiratory disease (SRD) is a complex interaction whereby viral infection predisposes the host to secondary bacterial pulmonary invasion, which may be fatal. Antimicrobial agents remain an important therapy and serve to reduce morbidity and mortality in treated animals. Pradofloxacin is the newest [...] Read more.

Swine respiratory disease (SRD) is a complex interaction whereby viral infection predisposes the host to secondary bacterial pulmonary invasion, which may be fatal. Antimicrobial agents remain an important therapy and serve to reduce morbidity and mortality in treated animals. Pradofloxacin is the newest of the veterinary antibiotics to be approved to treat SRD. It is a dual-targeting fluoroquinolone with in vitro and clinical activity against Gram-negative and -positive bacteria, along with atypical agents including anaerobes. In this study, we compared the killing of Actinobacillus pleuropneumoniae, Pasteurella multocida, and Streptococcus suis by pradofloxacin and comparator antibiotics in a 3 h kill assay, using four clinically relevant drug concentrations. Pradofloxacin was bactericidal against the three pathogens, with kill rates ranging from 94.4 to 99.9% (A. pleuropneumoniae) following 15–20 min of exposure to the maximum serum and maximum tissue drug concentration. For P. multocida, the kill rates were 68.7–96.9% following 5–30 min of drug exposure at the maximum serum drug concentration, and 91.7% following 5 min of drug exposure at the maximum tissue drug concentration. For S. suis, pradofloxacin killed 92.4–99.4% and 71.6–97.1% of cells following 60–180 min of drug exposure at the maximum serum and maximum tissue drug concentration, respectively. Pradofloxacin appears to be an important addition to the drugs currently available for treating SRD. Full article

(This article belongs to the Special Issue Diagnostics, Antimicrobial Resistance, and Emerging Bacterial Pathogens)

21 pages, 5337 KB

Open AccessArticle

Sign Gradient Descent Algorithms for Accelerated Kinetostatic Protein Folding in Nanorobotics Design

by Alireza Mohammadi and Mohammad Al Janaideh

Robotics 2025, 14(11), 167; https://doi.org/10.3390/robotics14110167 (registering DOI) - 17 Nov 2025

Abstract

Numerical simulations of protein folding enable the design of protein-based nanomachines and nanorobots by predicting folded three-dimensional protein structures with high accuracy and revealing the protein conformation transitions during folding and unfolding. In the kinetostatic compliance method (KCM) for folding simulations, protein molecules [...] Read more.

Numerical simulations of protein folding enable the design of protein-based nanomachines and nanorobots by predicting folded three-dimensional protein structures with high accuracy and revealing the protein conformation transitions during folding and unfolding. In the kinetostatic compliance method (KCM) for folding simulations, protein molecules are represented as ensembles of rigid nano-linkages connected by chemical bonds, and the folding process is driven by the kinetostatic influence of nonlinear interatomic force fields until the system converges to a free-energy minimum of the protein. Despite its strengths, the conventional KCM framework demands an excessive number of iterations to reach folded protein conformations, with each iteration requiring costly computations of interatomic force fields. To address these limitations, this work introduces a family of sign gradient descent (SGD) algorithms for predicting folded protein structures. Unlike the heuristic-based iterations of the conventional KCM framework, the proposed SGD algorithms rely on the sign of the free-energy gradient to guide the kinetostatic folding process. Owing to their faster and more robust convergence, the proposed SGD-based algorithms reduce the computational burden of interatomic force field evaluations required to reach folded conformations. Their effectiveness is demonstrated through numerical simulations of KCM-based folding of protein backbone chains. Full article

(This article belongs to the Section Intelligent Robots and Mechatronics)

22 pages, 17758 KB

Open AccessReview

Emerging Implantable Sensor Technologies at the Intersection of Engineering and Brain Science

by Lihong Qi, Yuheng Wang and Xuemei Liang

Biosensors 2025, 15(11), 762; https://doi.org/10.3390/bios15110762 (registering DOI) - 17 Nov 2025

Abstract

Advances in implantable sensor technologies are revolutionizing the landscape of brain science by enabling chronic, precise, and multimodal interfacing with neural tissues. With the convergence of material science, electronics, and neurobiology, flexible, wireless, bioresorbable, and multimodal sensors are expanding the frontiers of diagnosis, [...] Read more.

Advances in implantable sensor technologies are revolutionizing the landscape of brain science by enabling chronic, precise, and multimodal interfacing with neural tissues. With the convergence of material science, electronics, and neurobiology, flexible, wireless, bioresorbable, and multimodal sensors are expanding the frontiers of diagnosis, therapy, and brain-machine interfacing. This review presents the latest breakthroughs in implantable neural sensor technologies, emphasizing bio-integration, signal fidelity, and functional adaptability. We highlight innovations such as CMOS-integrated flexible probes, internal ion-gated organic electrochemical transistors (IGTs), multimodal neurotransmitter-electrophysiology sensors, and wireless energy systems. Finally, we discuss the clinical potential, translational challenges, and future directions for brain science and neuroengineering. We further benchmark transduction and analytical performance in physiological media and outline in vivo calibration, antifouling/packaging, and on-node data-efficient processing for long-term stability. Full article

(This article belongs to the Section Wearable Biosensors)

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16 pages, 5525 KB

Open AccessArticle

Fast Thermal Resistance Distribution Analysis in High-Power VCSEL Array Module

by Dezhen Li, Tian Lan, Zhiyong Wang and Zhengyu Ye

Materials 2025, 18(22), 5210; https://doi.org/10.3390/ma18225210 (registering DOI) - 17 Nov 2025

Abstract

Vertical-cavity surface-emitting lasers (VCSELs) have generated extensive enthusiasm in scientific research on and applications of lasers. However, thermal resistance has seriously limited the performance of such devices for a long time, especially in high-power single-chip large-area VCSEL array modules. In this study, in [...] Read more.

Vertical-cavity surface-emitting lasers (VCSELs) have generated extensive enthusiasm in scientific research on and applications of lasers. However, thermal resistance has seriously limited the performance of such devices for a long time, especially in high-power single-chip large-area VCSEL array modules. In this study, in order to determine the packaging thermal resistance bottleneck of the high-power VCSEL array laser module and achieve better performance, the thermal characteristics of an 808 nm VCSEL module were analyzed quickly with electrical transient measurements without any damage, which consisted of a 6 mm × 6 mm, 85 W, AlGaAs/GaAs VCSEL array chip encapsulated on a submount and a water-cooled heat sink. The quantitative components of the device’s thermal resistance were clearly segmented and rapidly obtained within merely 25 s using the structure function algorithm. The packaging thermal resistances together accounted for an astonishing 70% of the total thermal resistance when the loading current was 8 A. Among them, R_submount and R_solder2 were the main focus areas, which accounted for 54% of the total thermal resistance. We also applied the spectroscopy method to calculate the total thermal resistance of the module on a large scale from another perspective for the comparative verification of the electrical transient method. The values obtained by the two methods were relatively close. More importantly, this research will have a positive impact and an indicative effect on reducing the main thermal resistances of the VCSEL array module. Full article

(This article belongs to the Topic Advances in Microelectronics and Semiconductor Engineering)

15 pages, 2715 KB

Open AccessArticle

A Green-Synthesized Fluorescent Carbon Dot Probe Derived from Banana Peel for Cellular Imaging and Sensing of Tetracycline

by Sihua Zeng, Chunrong Qin, Yuzhu Zhang, Haoyu Chen and Hua Lin

Materials 2025, 18(22), 5211; https://doi.org/10.3390/ma18225211 (registering DOI) - 17 Nov 2025

Abstract

The valorization of biomass waste represents an important direction in green chemistry. This study successfully prepared blue fluorescent carbon dots (BP-CDs) from waste banana peels via a one-step hydrothermal method, establishing a dual-functional platform for both pollutant detection and cellular imaging. The resulting [...] Read more.

The valorization of biomass waste represents an important direction in green chemistry. This study successfully prepared blue fluorescent carbon dots (BP-CDs) from waste banana peels via a one-step hydrothermal method, establishing a dual-functional platform for both pollutant detection and cellular imaging. The resulting material exhibited uniform particle size (~2.05 nm), good water dispersibility, and strong fluorescence emission at 445 nm under 360 nm excitation. It maintained over 93% of its initial fluorescence intensity after 20 days, demonstrating excellent stability. Based on the inner filter effect, the probe enabled a highly selective detection of tetracycline with a detection limit of 0.191 µM and two wide linear ranges (0–15 µM, R² = 0.996; 15–95 µM, R² = 0.991). Cellular experiments confirmed the good biocompatibility of BP-CDs (cell viability > 84%) and their successful application in cell imaging. More importantly, the probe achieved visual observation and semi-quantitative analysis of the distribution and content of tetracycline in living cells, providing a direct tool for studying the cellular behavior of antibiotics. This work not only offers a new strategy for banana peel valorization but also develops a green fluorescence imaging platform suitable for tracking intracellular pollutants. Full article

(This article belongs to the Topic Circular Materials Engineering: Waste Valorization and Sustainable Applications)

24 pages, 4000 KB

Open AccessArticle

Towards Robust Physical Adversarial Attacks on UAV Object Detection: A Multi-Dimensional Feature Optimization Approach

by Hailong Xi, Le Ru, Jiwei Tian, Wenfei Wang, Rui Zhu, Shiliang Li, Zhenghao Zhang, Longhao Liu and Xiaohui Luan

Machines 2025, 13(11), 1060; https://doi.org/10.3390/machines13111060 (registering DOI) - 17 Nov 2025

Abstract

Deep neural network (DNN)-based object detection has been extensively implemented in Unmanned Aerial Vehicles (UAVs). However, these architectures reveal significant vulnerabilities when faced with adversarial attacks, particularly the physically realizable adversarial patches, which are highly practicable. Existing methods for generating adversarial patches are [...] Read more.

Deep neural network (DNN)-based object detection has been extensively implemented in Unmanned Aerial Vehicles (UAVs). However, these architectures reveal significant vulnerabilities when faced with adversarial attacks, particularly the physically realizable adversarial patches, which are highly practicable. Existing methods for generating adversarial patches are easily affected by factors such as motion blur and color distortion, leading to a decline in the attack success rate (ASR). To address these limitations, a low-frequency robust adversarial patch (LFRAP) generation framework that integrates three dimensions of color, texture, and frequency domain is proposed. Firstly, a dynamic extraction mechanism for the environmental color pool based on clustering is proposed. This mechanism not only improves the degree of environmental integration but also reduces printing losses. Secondly, mathematical modeling of the effects of Unmanned Aerial Vehicle (UAV) high-speed motion is incorporated into the patch training process. The specialized texture derived from this modeling alleviates patch blurring and the subsequent decrease in attack efficiency caused by the high-speed movement of UAVs. Finally, a frequency domain separation strategy is introduced in the generation process to optimize the frequency space distribution, thereby reducing information loss during image recapture by UAV vision systems. The experimental results show that this framework increased the environment integration rate of the generated patches by 18.9%, and the attack success rate under the condition of motion blur increased by 19.2%, which significantly outperformed conventional methods. Full article

(This article belongs to the Special Issue Intelligent Control Techniques for Unmanned Aerial Vehicles)

15 pages, 1161 KB

Open AccessArticle

Effects of Leg-Length Discrepancy Compensation and Wedge Foot-Orthoses on Tensor Fasciae Latae EMG in Runners

by Ruben Sanchez-Gomez, Boon Peng Chang, Vitali Lipik, Paola Sanz-Wozniak, Dan Iulian Alexe, Jimena Garrido Cebrecos, Marta Martín Vega and Alvaro Gomez Carrion

Sports 2025, 13(11), 412; https://doi.org/10.3390/sports13110412 (registering DOI) - 17 Nov 2025

Abstract

Aims: Structural lower limb-length discrepancies (LLLD) have been classically associated with the etiology of low back pain. However, their biomechanical effects on lower-limb muscle activity during running remain unclear. This pilot crossover study aimed to evaluate the influence of orthotic interventions—designed to compensate [...] Read more.

Aims: Structural lower limb-length discrepancies (LLLD) have been classically associated with the etiology of low back pain. However, their biomechanical effects on lower-limb muscle activity during running remain unclear. This pilot crossover study aimed to evaluate the influence of orthotic interventions—designed to compensate for LLLD and modify foot biomechanics—on the electromyographic (EMG) activity of the contralateral tensor fasciae latae (TFL) in healthy runners. Methods: A total of 41 recreational male and female runners (mean age 32.27 ± 6.09) with structural LLLD were recruited and classified as neutral (Ng), supinated (SPg), or pronated (PRg) based on their foot posture. Surface EMG activity of the TFL in the longer leg was recorded with specific surface electrodes while participants ran on a treadmill at a constant speed of 9 km/h for 3 min. Each subject randomly wore standard orthoses with 5 mm pronating (PRO), supinating (SUP) wedges or orthoses with a heel lift (TAL) to compensate for the shorter leg, alongside the baseline condition (SIN). Results: Perfect reliability (close to 1) was obtained for all measurements. A statistically significant reduction in TFL EMG activity was recorded in the Ng group: SIN 105.64 ± 50.6%MVC vs. PRO 100.16 ± 48.61%MVC (p < 0.05), and SIN vs. TAL 93.49 ± 15.88%MVC (p < 0.001). A significant reduction was also observed in the PRg group: SIN 91.82 ± 40.75%MVC vs. TAL 80.08 ± 31.75%MVC (p < 0.05). Conclusion: Orthotic compensation for LLLD and foot pronation modifications produced measurable changes in TFL EMG activity during running. These findings provide mechanistic insight into the interaction between limb-length asymmetry, foot biomechanics, and proximal muscle activation in runners, and may inform future studies on overuse injuries such as iliotibial band syndrome. Full article

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18 pages, 7938 KB

Open AccessArticle

A Numerical Study on Heat Transfer Enhancement Mechanism of Composite Materials Based on Oriented Multi-Dimensional Fillers

by Hongjie Luo, Bin Liu, Wenbin Dou, Xinzhan Zhou, Xiao Jia and Lin Chen

Electron. Mater. 2025, 6(4), 19; https://doi.org/10.3390/electronicmat6040019 (registering DOI) - 17 Nov 2025

Abstract

The rapid development of electronic devices has led to increasing requirements for higher-performance thermal interface materials (TIMs). Based on the finite element method, this study investigates the heat transfer enhancement mechanism of polymer-based TIMs reinforced by carbon fiber and boron nitride fillers. An [...] Read more.

The rapid development of electronic devices has led to increasing requirements for higher-performance thermal interface materials (TIMs). Based on the finite element method, this study investigates the heat transfer enhancement mechanism of polymer-based TIMs reinforced by carbon fiber and boron nitride fillers. An ordered aggregation algorithm and a collision detection algorithm were developed to construct representative volume element models, enabling filler volume fractions exceeding 50 vol% in the simulation. A predictive thermal resistance model was developed and validated, demonstrating good agreement with experimental results. Then, the effects of filler ratio, orientation angle, and size on thermal conductivity were systematically analyzed. Results demonstrate that a high CF/BN ratio can construct more efficient thermal conduction pathways and the optimal ratio is 4 (13.72 W/m∙K). The thermal conductivity exhibits extreme sensitivity to filler orientation, showing an increase of 17.68 times when the angle decreases from 45° to 0°. Meanwhile, the BN particle diameters have less impact on heat transfer; thermal conductivity only increased by 19.9% when D_BN rose from 10 μm to 45 μm. The predictive model based on thermal resistance theory was developed, and the average prediction error was only 5.18%. These findings provide quantitative design principles for developing high-efficiency thermal interface materials through rational filler selection and structural optimization. Full article

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11 pages, 722 KB

Open AccessArticle

Context Matters: How Decontextualization Influences Public Perception and Conservation Attitudes Toward Barbary Macaques in Algeria

by Imane Razkallah, Sadek Atoussi, Thais Queiroz Morcatty, Rabah Zebsa, Cédric Sueur and Anne-Isola Nekaris

Animals 2025, 15(22), 3319; https://doi.org/10.3390/ani15223319 (registering DOI) - 17 Nov 2025

Abstract

The decontextualization (the portrayal of wildlife removed from their natural ecological context through social media), can distort the public perception of these animals and harm conservation efforts. This paper presents an exploratory case study based on two highly visible Facebook videos. To explore [...] Read more.

The decontextualization (the portrayal of wildlife removed from their natural ecological context through social media), can distort the public perception of these animals and harm conservation efforts. This paper presents an exploratory case study based on two highly visible Facebook videos. To explore this, we analyzed Facebook comments (n = 720) and emoji-based reactions (n = 23,024) regarding Barbary macaques (Macaca sylvanus) in two contexts: entertainment (macaque dressed in sports attire during political protests) and natural habitat (macaque being fed soda by tourists in its forest environment). This is the first study to examine how social media context influences public perception of Barbary macaque conservation status and welfare through analysis of viewer engagement on viral videos. The results indicated that videos depicting macaques in their natural habitat elicited significantly more positive conservation sentiments (68.4% of comments) compared to entertainment contexts (6.04% of comments). Conversely, the entertainment video generated predominantly negative conservation sentiments (54.95% of comments), with viewers expressing amusement rather than concern for species protection. Videos showing macaques in natural settings, particularly when depicting problematic feeding behaviors, prompted more critical engagement and awareness of conservation issues. This pattern suggests that anthropomorphized contexts may obscure recognition of species threats and normalize inappropriate human–wildlife interactions. Given the small dataset, these findings should be interpreted cautiously and as illustrative rather than generalizable. These findings lend preliminary support to the animal decontextualization hypothesis and underscore the importance of context in shaping public perceptions of wildlife and conservation priorities. Full article

(This article belongs to the Section Human-Animal Interactions, Animal Behaviour and Emotion)

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21 pages, 2245 KB

Open AccessArticle

Research on the Recycling Strategy of End-of-Life Power Battery for Electric Vehicles Based on Evolutionary Game

by Fangfang Zhao, Yiqi Geng, Wenhui Shi and Yingxue Ren

World Electr. Veh. J. 2025, 16(11), 625; https://doi.org/10.3390/wevj16110625 (registering DOI) - 17 Nov 2025

Abstract

The rapid growth of China’s electric vehicle (EV) market has led to a peak in end-of-life (EOL) power batteries, yet the recycling sector remains dominated by informal operations. This paper incorporates the formal and informal recycling participation behaviours of EV owners into the [...] Read more.

The rapid growth of China’s electric vehicle (EV) market has led to a peak in end-of-life (EOL) power batteries, yet the recycling sector remains dominated by informal operations. This paper incorporates the formal and informal recycling participation behaviours of EV owners into the framework of evolutionary games, systematically examines the mechanism by which governmental incentive and disincentive mechanisms influence the evolutionary stability of each party, and constructs a tripartite evolutionary game model involving the government, recycling enterprises, and EV owners. Numerical simulation experiments conducted using PyCharm 2.3 provide an in-depth exploration of the strategic evolutionary trajectories of each participating agent. The findings indicate that (1) the stable strategy for the game-theoretic system of EOL power battery recycling is government non-regulation, recycling enterprises adopting formal recycling practices, and EV owners participating in formal recycling; (2) strengthening penalties against recycling enterprises will accelerate their transition towards formal recycling strategies, while increasing incentive levels can significantly enhance the steady-state probability of firms opting for formal recycling; (3) government subsidies for EV owners encourage both EV owners and recycling enterprises to adopt formal recycling, with recycling enterprises shifting first. This study enriches the application of evolutionary game theory in the field of EOL power battery recycling and further provides guidance for the healthy development of the recycling industry. Full article

(This article belongs to the Section Energy Supply and Sustainability)

22 pages, 2033 KB

Open AccessArticle

Stress-Based Optimization of Components and Supports for Sinter-Based Additive Manufacturing

by David Stachg, Jaco Beckmann and Jens Telgkamp

Appl. Sci. 2025, 15(22), 12198; https://doi.org/10.3390/app152212198 (registering DOI) - 17 Nov 2025

Abstract

Sinter-based additive manufacturing (SBAM) processes, such as Cold Metal Fusion (CMF), combine the geometric freedom of additive manufacturing with the scalability of powder metallurgy, but part distortion and collapse during debinding and sintering remain critical design challenges. This study presents a revised stress-based [...] Read more.

Sinter-based additive manufacturing (SBAM) processes, such as Cold Metal Fusion (CMF), combine the geometric freedom of additive manufacturing with the scalability of powder metallurgy, but part distortion and collapse during debinding and sintering remain critical design challenges. This study presents a revised stress-based optimization framework to address these issues by integrating sintering-specific load cases into topology optimization. In contrast to earlier approaches, the revised workflow applies all load cases to the upscaled green-part geometry. This adjustment mitigates the non-linear scaling effects of dead load-induced stresses. A Case study, including a steering bracket for a Formula Student racing car, demonstrates that the revised method improves not only sinterability but also application-related performance compared to earlier approaches. In addition, a semi-automated procedure for generating sinter supports is introduced, allowing stable processing of geometries without planar bearing surfaces. Experimental validation confirms that optimized supports effectively prevent part failure during post-processing, though challenges remain in separating complex freeform geometries. Finally, the influence of stiffness on sintering-induced deformations is investigated, showing that higher stiffness configurations significantly reduce dimensional errors. Together, these results highlight stress- and stiffness-based optimization as tools to enhance the reliability, efficiency, and design freedom of SBAM. Full article

(This article belongs to the Section Additive Manufacturing Technologies)

42 pages, 44879 KB

Open AccessReview

Recent Developments in Novel TPMS Lattice Materials: Design Optimization, Performance Control, and Applications in Biomimetic Scaffolds

by Syed Zahid Ahmad, Muhammad Hassan Masood, Muhammad Umar Khattab, Syed Sulman Ahmad, Syed Asad Ali Zaidi and Shoaib Z. Khan

Materials 2025, 18(22), 5209; https://doi.org/10.3390/ma18225209 (registering DOI) - 17 Nov 2025

Abstract

Triply Periodic Minimal Surfaces (TPMSs) are mathematically defined surfaces that exhibit periodicity in three dimensions while maintaining a minimal surface property. TPMS-based lattices have gained significant attention in recent years, fueled by advancements in Additive Manufacturing (AM). These structures exhibit exceptional mechanical, thermal, [...] Read more.

Triply Periodic Minimal Surfaces (TPMSs) are mathematically defined surfaces that exhibit periodicity in three dimensions while maintaining a minimal surface property. TPMS-based lattices have gained significant attention in recent years, fueled by advancements in Additive Manufacturing (AM). These structures exhibit exceptional mechanical, thermal, and mass transfer properties, positioning them as a promising class of next-generation materials. However, fully leveraging their potential requires a comprehensive understanding of their design, properties, optimization, and applications. Given the hierarchical nature of TPMSs, achieving optimal performance requires multiscale optimization at the macro- and micro-levels. Addressing these complexities requires advanced computational methods to balance structural integrity and functional performance. In this narrative review, design strategies like functional grading and hybridization to create optimized TPMS-based lattices are summarized. Herein, the performance of such lattices in the mechanical, thermal, and mass transfer domains is focused upon. The role of topology optimization (TO) in the creation of architectured materials for specific application is discussed along with the emerging integration of machine learning. Furthermore, multidisciplinary applications of TPMS structures are examined, particularly in heat sinks, interpenetrating phase composites (IPCs), and biomimetic scaffolds, with their potential to enhance heat dissipation, structural resistance, and biomimicry of biological scaffolds. In addition, various additive manufacturing technologies for fabricating TPMS structures are reviewed, emphasizing how additive manufacturing allows high reproducibility construction of their complex geometry in a precise manner. Further unexplored areas of research are also discussed. Full article

(This article belongs to the Section Porous Materials)

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15 pages, 1802 KB

Open AccessArticle

Antioxidant Status and CO₂ Biofixation of Chlorella sp. Strain Under Sequential Photoautotrophic Cultivation with Aphotic Induction of Biotechnologically Valuable Compounds Accumulation

by Aleksandr Yakoviichuk, Irina Maltseva, Angelika Kochubey, Yevhen Maltsev, Ekaterina Lysova and Evilina Sheludko

Phycology 2025, 5(4), 75; https://doi.org/10.3390/phycology5040075 (registering DOI) - 17 Nov 2025

Abstract

Chlorella is a valuable object of biotechnology with high productivity of biomass and metabolites. The use of Chlorella for CO₂ binding in autotrophic metabolism is also discussed. Various types of stress are used to increase the yield of valuable metabolites. One of [...] Read more.

Chlorella is a valuable object of biotechnology with high productivity of biomass and metabolites. The use of Chlorella for CO₂ binding in autotrophic metabolism is also discussed. Various types of stress are used to increase the yield of valuable metabolites. One of the effective approaches may be dark stress. However, there is insufficient data to fully understand the effect of dark stress on productivity, biochemical parameters, the antioxidant system, and the rate of CO₂ fixation by Chlorella during the transfer from autotrophic culture to aphotic conditions. To study these processes, we used two-step cultivation. In the second step, the biomass was grown for 96 h on a BBM medium under standard lighting and in aphotic conditions. According to the results of the study, the metabolic systems of the studied strain of Chlorella sp. CAMU G–145 specifically react to cultivation under aphotic conditions. The greatest response was found in lipid–protein metabolism and the antioxidant defense system, which determines an increase in the overall antioxidant status of cells. At the same time, productivity, CO₂ absorption characteristics, and pigment composition of the photosynthetic system did not change after 96 h of darkening. In general, this approach is a promising strategy for increasing biotechnological productions efficiency. Full article

(This article belongs to the Special Issue Development of Algal Biotechnology)

12 pages, 831 KB

Open AccessArticle

Effects of Modifying Supportive Care Medications in Combination Therapy with Pertuzumab, Trastuzumab, and Taxane Anticancer Drugs

by Mina Takagi, Shinichiro Maeda, Makiko Maeda, Yasushi Fujio and Sachiko Hirobe

Pharmacy 2025, 13(6), 168; https://doi.org/10.3390/pharmacy13060168 (registering DOI) - 17 Nov 2025

Abstract

Chemotherapy for breast cancer includes pertuzumab and trastuzumab regimens with docetaxel (PHD) or paclitaxel (PHP). Current approaches for using supportive care drugs to manage the side effects of PHD and PHP are unclear. Here, we investigated the occurrence of side effects before and [...] Read more.

Chemotherapy for breast cancer includes pertuzumab and trastuzumab regimens with docetaxel (PHD) or paclitaxel (PHP). Current approaches for using supportive care drugs to manage the side effects of PHD and PHP are unclear. Here, we investigated the occurrence of side effects before and after supportive care medications were modified by discontinuing antipyretic analgesics. We retrospectively analyzed adverse events that occurred within 24 h of treating 76 patients with PHD or PHP. The frequencies of adverse effects in the groups before and after modification did not differ significantly (45.5% [15/33] and 44.2% [19/43], respectively). Severity also did not significantly differ between the groups. Therefore, discontinuing antipyretic analgesics as supportive care drugs had little effect on the frequency of side effects. Symptoms of feeling hot, pyrexic, and flushed were frequent, and their severity increased in the group after the support drugs were modified. Discontinuation of supportive care medications, including antipyretic analgesics, might affect the severity of certain symptoms and lead to the development of side effects that require medical intervention. Overall, our findings indicate the need to consider premedication with antipyretic analgesics, including further analysis of the risk factors that can predict symptoms. Full article

(This article belongs to the Section Pharmacy Practice and Practice-Based Research)

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17 pages, 1210 KB

Open AccessArticle

An Adaptive Protocol Selection Framework for Energy-Efficient IoT Communication: Dynamic Optimization Through Context-Aware Decision Making

by Dmitrij Żatuchin and Maksim Azarskov

Informatics 2025, 12(4), 125; https://doi.org/10.3390/informatics12040125 (registering DOI) - 17 Nov 2025

Abstract

The rapid growth of Internet of Things (IoT) deployments has created an urgent need for energy-efficient communication strategies that can adapt to dynamic operational conditions. This study presents a novel adaptive protocol selection framework that dynamically optimizes IoT communication energy consumption through context-aware [...] Read more.

The rapid growth of Internet of Things (IoT) deployments has created an urgent need for energy-efficient communication strategies that can adapt to dynamic operational conditions. This study presents a novel adaptive protocol selection framework that dynamically optimizes IoT communication energy consumption through context-aware decision making, achieving up to 34% energy reduction compared to static protocol selection. The framework is grounded in a comprehensive empirical evaluation of three widely used IoT communication protocols—MQTT, CoAP, and HTTP—using Intel’s Running Average Power Limit (RAPL) for precise energy measurement across varied network conditions including packet loss (0–20%) and latency variations (1–200 ms). Our key contribution is the design and validation of an adaptive selection mechanism that employs multi-criteria decision making with hysteresis control to prevent oscillation, dynamically switching between protocols based on six runtime metrics: message frequency, payload size, network conditions, packet loss rate, available energy budget, and QoS requirements. Results show MQTT consumes only 40% of HTTP’s energy per byte at high volumes (>10,000 messages), while HTTP remains practical for low-volume traffic (<10 msg/min). A novel finding reveals receiver nodes consistently consume 15–20% more energy than senders, requiring new design considerations for IoT gateways. The framework demonstrates robust performance across simulated real-world conditions, maintaining 92% of optimal performance while requiring 85% less computation than machine learning approaches. These findings offer actionable guidance for IoT architects and developers, positioning this work as a practical solution for energy-aware IoT communication in production environments. Full article

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