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Clay-based composite materials offer a low-carbon pathway for improving the environmental performance of the construction sector while maintaining relevance for architectural and heritage applications. A structured qualitative literature review was conducted, supported by thematic classification and exploratory bibliometric mapping (VOSviewer), based on peer-reviewed studies published between 2015 and 2025 relevant to the topic of clay minerals, stabilization, fibers, polymers, alkali activation, properties, performance, and applicability in architecture. According to the results obtained from the synthesized literature, it is seen that clay-based composites achieve performance improvement through complementary mechanisms: fiber reinforcement improves ductility, crack behavior, and energy absorption, polymer modification helps improve cohesion and water resistance and alkali activation transforms calcined aluminosilicate precursors into high-strength binding systems. The synthesis identifies three dominant performance mechanisms governing clay-based composites. Selected alkali-activated clay composite materials are reported to exhibit compression strengths higher than 60 MPa, and certain optimized systems may be able to provide lower thermal conductivity and lower levels of carbon emission in comparison with ordinary cement-based materials. The contribution of this paper lies in the synthesis of these material modification techniques and resulting performance aspects for their applicability in architecture, clarifying the potential of clay-based composites for sustainable construction, heritage compatible interventions, and future material development. By integrating material science with architectural applications, this study identifies the potential of clay-based composites for sustainable and heritage-compatible approaches to contribute to sustainable and circular construction practices, while also outlining key challenges and future research directions focused on optimization, large-scale implementation, and heritage-compatible innovation. Full article

(This article belongs to the Section Building Materials, and Repair & Renovation)

30 pages, 7105 KB

Open AccessArticle

Vis-NIR Spectroscopy and Machine Learning for Prediction of Soil Fertility Indicators and Fertilizer Recommendation in Andean Highland and Rainforest Agroecosystems

by Samuel Pizarro, Dennis Ccopi, Kevin Ortega, Duglas Contreras, Javier Ñaupari, Deyvis Cano, Solanch Patricio, Hildo Loayza and Orly Enrique Apolo-Apolo

Remote Sens. 2026, 18(9), 1331; https://doi.org/10.3390/rs18091331 (registering DOI) - 26 Apr 2026

Abstract

This study evaluated the use of visible and near-infrared (Vis-NIR) spectroscopy combined with machine learning (ML) algorithms to predict soil fertility-related properties in two contrasting agroecological regions of Peru: the Highlands and the Rainforest. A total of 297 soil samples were analyzed using portable spectroradiometers covering a spectral range of 350–2500 nm, applying transformations such as Savitzky–Golay smoothing, first derivative, and band depth. Predictive models were developed using PLSR, Random Forest, Support Vector Machines, and neural networks. Results show variable predictive performance across soil properties and ecosystems. Organic matter in Highland soils and calcium in Rainforest soils achieved the strongest test-set accuracy (R² > 0.70), while pH and texture fractions showed moderate performance (R² = 0.42–0.67), and mobile nutrients including phosphorus, potassium, and sodium showed limited predictive accuracy due to their weak spectral expression. Spectral predictions were further integrated into a structured nutrient balance framework to assess agronomic reliability. Nitrogen fertilizer recommendations showed the strongest agreement between observed and predicted values across both ecosystems, whereas K₂O and CaO recommendations in Highland soils were substantially underestimated, demonstrating that property-level statistical performance does not guarantee agronomic reliability. These findings confirm that Vis-NIR spectroscopy combined with ML represents a fast, cost-effective, and sustainable alternative to conventional soil analysis, especially in rural areas with limited laboratory infrastructure. Expanding regional calibration datasets and exploring mid-infrared FTIR spectroscopy as a complementary technology are identified as priority directions for improving predictions of agronomically critical nutrients. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing for Soil Property Retrieval: Advances and Applications)

20 pages, 6122 KB

Open AccessArticle

Automated Detection and Classification of Lunar Linear Tectonic Features Using a Deep Learning Method

by Xiaoyang Liu, Yang Luo, Jianhui Wang, Denggao Qiu, Jianguo Yan, Wensong Zhang and Yaowen Luo

Remote Sens. 2026, 18(9), 1330; https://doi.org/10.3390/rs18091330 (registering DOI) - 26 Apr 2026

Abstract

On the lunar surface, wrinkle ridges, grabens, and lobate scarps represent key tectonic landforms that reflect the evolution of the Moon’s stress field and its tectonic processes. However, these linear structures often exhibit weak textures, low contrast, and large scale variations, making manual interpretation inefficient and subjective. To address this issue, this study introduces an improved YOLOv8 model, termed HL-YOLOv8, for the automated detection of lunar linear features. The model incorporates a multiscale lightweight channel attention (C2f_MLCA) module into the backbone network to enhance the extraction of fine-grained and weak-texture features and integrates a multihead self-attention (C2f_MHSA) module in the feature fusion stage to improve the modelling of long-range spatial dependencies. In addition, the combination of a dual focal loss and a diversified data augmentation strategy effectively mitigates the detection difficulties caused by class imbalance and weak-feature samples. The experimental results obtained using the global LROC-WAC image dataset demonstrate that HL-YOLOv8 significantly outperforms the baseline YOLOv8 and other comparative models in terms of precision, recall, and mAP@0.5. Specifically, the proposed model achieved an average precision of 73.5%, an average recall of 73.1%, and an average mAP@0.5 of 74.6% on the evaluation dataset, showing particularly strong performance in detecting elongated grabens and boundary-blurred lobate scarps. The global distribution maps derived from the model predictions indicate that HL-YOLOv8 can be applied to comprehensively reconstruct the spatial patterns of the three types of linear structures and identify potential new features in high-latitude and geologically complex regions, demonstrating excellent generalizability and robustness. This study provides an efficient and reliable framework for the automated identification and global mapping of lunar linear features and offers a transferable methodological reference for the tectonic interpretation of terrestrial planets. Full article

17 pages, 16006 KB

Open AccessArticle

Research on the Distribution and Prediction of Wind Pressure on Airport Terminal Glass Curtain Walls Based on Wind Tunnel Testing and Numerical Simulation

by Liang Li, Huizhen Li, Fang Pei, Cheng Pei and Xiaokang Cheng

Buildings 2026, 16(9), 1710; https://doi.org/10.3390/buildings16091710 (registering DOI) - 26 Apr 2026

Abstract

Glass curtain walls are widely used in the enclosure structure of airport terminals due to their advantages of lightweight and beautiful appearance, good lighting, and easy installation. However, coastal areas are constantly affected by typhoons, and under the influence of strong winds, complex pressure distributions are generated on the surface of curtain walls. Therefore, studying the wind pressure distribution characteristics of glass curtain walls is crucial for the structural safety and durability of coastal airport terminals. Based on this, accurately predicting wind pressure distribution not only helps to improve the design safety of airport terminals but also effectively prevents potential damage under strong wind conditions. To achieve effective prediction of wind pressure on glass curtain walls, this paper adopts a combination of wind tunnel tests and neural network prediction models. Real wind pressure coefficient data is obtained through wind tunnel tests, and a CNN–Transformer combination model is proposed to predict wind pressure coefficients. The results show that the prediction accuracy of the combined model is higher than that of the CNN and Transformer single models. MAE is optimized by 0.04~0.10 compared with the former and 0.16~0.34 compared with the latter; RMSE has been optimized by 0.02–0.10 and 0.30–0.34, respectively. This article can provide reference for the prediction research of wind pressure on the surface of glass curtain walls in airport terminals. Full article

(This article belongs to the Section Building Structures)

15 pages, 659 KB

Open AccessArticle

Parameter-Free Metaheuristic-Based Method for Reinforced Concrete Frame Cost Optimization

by Elmas Rakıcı Güldal, Sinan Melih Nigdeli, Gebrail Bekdaş and Zong Woo Geem

J. Compos. Sci. 2026, 10(5), 231; https://doi.org/10.3390/jcs10050231 (registering DOI) - 26 Apr 2026

Abstract

This study proposes a parameter-free metaheuristic optimization framework using the Jaya and Rao algorithms for the cost-based design of reinforced concrete (RC) frames in accordance with ACI 318-25. Beam and column dimensions are treated as design variables within predefined bounds, and the objective was to minimize the total construction cost including concrete and reinforcing steel. Structural analysis was performed using the matrix displacement method. The performance of the Jaya, Rao-1, Rao-2, and Rao-3 algorithms was evaluated through multiple independent runs. All methods achieved optimal or near-optimal solutions; however, Rao-2 demonstrated competitive performance with low mean values and favorable statistical performance. The results confirm the effectiveness of parameter-free metaheuristic methods for RC structural cost optimization. Unlike previous studies, this study explicitly focuses on parameter-free metaheuristic algorithms and evaluates their robustness through statistical analysis on reinforced concrete frame systems. The main contribution lies in demonstrating the comparative performance and practical applicability of parameter-free algorithms without the need for algorithm-specific parameter tuning. Full article

(This article belongs to the Special Issue Automated and Digital Construction of Low-Carbon and High-Performance Steel-Concrete Composite Systems)

23 pages, 415 KB

Open AccessArticle

Artificial Intelligence and Sustainable Aviation Manufacturing: A Perspective from Green Innovation in China

by Guangfan Sun, Yue Song, Jianqiang Xiao and Daosheng Xu

Sustainability 2026, 18(9), 4298; https://doi.org/10.3390/su18094298 (registering DOI) - 26 Apr 2026

Abstract

In the pursuit of global industrial sustainable development and carbon neutrality goals, the aviation manufacturing sector serves as a strategic pillar for advancing global economic growth, driving technological innovation and enhancing national competitiveness. Its green innovation has thus become a critical pathway to achieving carbon neutrality targets and spearheading the sustainable transformation of the industrial sector. This study investigates the enabling effect of artificial intelligence (AI) on green innovation within aviation manufacturing enterprises. The findings indicate that AI exerts a promotional impact on green innovation via three primary channels: technological empowerment, labor structure optimization and resource access improvement. Specifically, AI drives the digital transformation of operational processes in aviation manufacturing, rationalizes the human resource framework of the sector, and eases the financing pressures confronted by aviation manufacturing enterprises. A heterogeneity analysis reveals that regional resource endowments, enterprise production attribute characteristics and external market attention can form synergistic interactions with AI technology. What is more prominent is that the positive influence of AI on green innovation is especially distinct in three scenarios: in economically developed urban areas, among enterprises with traditional production attributes, and for enterprises that garner high levels of analyst attention. Full article

(This article belongs to the Special Issue Sustainable Technology Management: Green Innovation and Strategic Development)

11 pages, 2813 KB

Open AccessArticle

Realization of Laser Frequency Stabilization and Continuous Broadband Tuning via Sideband PDH Locking

by Zhuxiong Ye, Shu Liu, Mingkang Han, Jia Feng, Mustafa Shah, Yongze Zhao, Pengjun Wang, Liangchao Chen, Wei Han, Zengming Meng and Lianghui Huang

Photonics 2026, 13(5), 426; https://doi.org/10.3390/photonics13050426 (registering DOI) - 26 Apr 2026

Abstract

We demonstrate a sideband Pound–Drever–Hall (SPDH) locking scheme that enables the simultaneous narrow-linewidth stabilization and continuous broadband frequency tuning of a laser referenced to an ultra-stable cavity. The method employs dual-frequency modulation applied to a fiber electro-optic modulator, where high-frequency modulation generates tunable sidebands and low-frequency modulation provides the error signal. We experimentally stabilize a 922 nm seed laser to the cavity and achieve a laser linewidth of 85(1) kHz with frequency noise suppression of up to 25 dB. The residual amplitude modulation (RAM) remains below 0.08% across the full tuning range. In addition, we demonstrate a continuous frequency tuning range of 1.4 GHz for a frequency-doubled 461 nm laser, with scan rates up to 317 MHz/s, while preserving stable locking to the cavity. This approach avoids complex waveform generation and provides a simple and robust solution for broadband laser frequency control. Full article

(This article belongs to the Special Issue Advanced Research in Quantum Optics)

12 pages, 305 KB

Open AccessArticle

Assessment of the Functional Status of Patients After Stroke Depending on the Length of Stay in the Rehabilitation Ward—A Retrospective Single-Center Study

by Michał Małek, Anna Hozakowska, Zbigniew Guzek, Małgorzata Stefańska and Joanna Kowalska

J. Clin. Med. 2026, 15(9), 3306; https://doi.org/10.3390/jcm15093306 (registering DOI) - 26 Apr 2026

Abstract

Background/Objectives: Swift achievement of an optimal functional status after stroke may substantially reduce patients’ stay in a medical facility and enable their return home. The study aimed to assess the functional status in patients after their first stroke, depending on the time after stroke incident and the patient’s length of stay in the stroke rehabilitation ward. Methods: The data from 229 patients, aged 69.4 ± 11.3 years (120 men and 109 women), formed part of the analysis. Based on medical records, basic socio-demographic, clinical data, and the results of the tests—Mini Mental State Examination (MMSE), Barthel Index (BI), Time Walk Test 10 m (TWT), Trunk Control Test (TCT), Up and Go Test (TUG), and Berg Balance Scale (BBS)—were collected. Results: The risk groups for patients with longer post-stroke rehabilitation stays and poorer rehabilitation outcomes included women, older adults, those with poorer functional status, and patients admitted to the stroke rehabilitation ward after a longer period of time after stroke. Conclusions: The functional status and the length of stay in the post-stroke rehabilitation ward should be monitored and analyzed to find and support groups of patients who may rehabilitate more slowly and stay longer in the ward. A shorter patient stay can allow for more effective management of beds in the post-stroke rehabilitation wards. Full article

(This article belongs to the Special Issue Clinical Perspectives in Stroke Rehabilitation)

22 pages, 6358 KB

Open AccessArticle

IoT-Based Precision Irrigation System Featuring Multi-Sensor Monitoring and Scheduled Automated Water-Control Gates for Rice Production

by Mir Nurul Hasan Mahmud, Younsuk Dong, Md Mahbubul Alam and Jinat Sharmin

Sensors 2026, 26(9), 2692; https://doi.org/10.3390/s26092692 (registering DOI) - 26 Apr 2026

Abstract

Despite its significant water-saving potential, the adoption of alternate wetting and drying (AWD) irrigation remains limited due to infrastructure constraints and intensive manual monitoring requirements. An automated precision irrigation system was developed and tested at the Bangladesh Rice Research Institute research farm in Gazipur, Bangladesh. The system combined ultrasonic water-level sensors, capacitive soil moisture sensors, an Arduino-based microcontroller, a GSM communication module, and solar-powered automatic control gates. Field performance was evaluated following a Randomized Complete Block Design (RCBD) under four irrigation treatments: IRRISAT, IRRI35, IRRI25, and continuous flooding (CF). The first three irrigation treatments were operated using scheduled daily decision windows, in which irrigation actions were automatically triggered based on predefined schedules and sensor threshold values. In IRRISAT, irrigation started when soil moisture dropped slightly below saturation and stopped at a ponding depth of 5 cm, while IRRI35 and IRRI25 were triggered at volumetric soil water contents of 35% and 25%, respectively, with the same upper cutoff of 5 cm ponding depth; CF served as the control. The IRRI35 treatment achieved a high grain yield (7.76 t ha⁻¹) while reducing water use by 28% and energy consumption by 37% compared to CF. Water use efficiency was considerably higher under IRRI35 (9.4 kg ha⁻¹ mm⁻¹) than under CF (6.7 kg ha⁻¹ mm⁻¹). The automated system proved to be reliable and precise in scheduled irrigation control, significantly reducing water use and labor requirements. The findings suggest that large-scale adoption of the system under real-world cultivation conditions could reduce irrigation energy needs and contribute to sustainable water governance in rice production. Full article

(This article belongs to the Special Issue Feature Papers in Smart Agriculture 2026)

23 pages, 5919 KB

Open AccessArticle

Backbone and Feature Fusion Design for YOLOv8-Based Bacterial Microcolony Detection in Microscopy Images

by Malek Rababa, Anas AlSobeh, Namariq Dhahir and Amer AbuGhazaleh

Appl. Sci. 2026, 16(9), 4241; https://doi.org/10.3390/app16094241 (registering DOI) - 26 Apr 2026

Abstract

Foodborne bacterial contamination creates significant public health and economic challenges. In the United States, the CDC estimates that foodborne illness causes approximately 48 million illnesses and 3000 deaths annually. Rapid screening is important because conventional confirmation methods are time- and labor-intensive. Microscopy-based analysis of early bacterial microcolonies can enable detection within hours rather than days, yet manual inspection is slow, subjective, and impractical at scale. Although deep learning object detectors such as YOLO offer a promising solution, the impact of architectural design choices on microscopy-based bacterial detection has not been systematically characterized under controlled conditions. In this work, we conducted a controlled architectural evaluation of YOLOv8 for detecting bacterial microcolonies in high-resolution microscopy images. We replaced the CSP-Darknet backbone with EfficientNetV2 variants and evaluated three feature fusion designs: no neck, the original PAN-FPN neck, and a NAS-FPN-inspired neck. All experiments were performed under identical conditions on a two-class dataset of Salmonella and E. coli. Our results show that EfficientNetV2 architectures consistently outperform the YOLOv8x baseline, which achieved 0.891 precision, 0.867 recall, and 0.898 mAP@50. The best overall performance was obtained with EfficientNetV2-S and the original YOLOv8 neck, reaching 0.976 precision, 0.968 recall, and 0.987 mAP@50, with comparable performance of 0.986 mAP@50 achieved by EfficientNetV2-S + NAS-FPN. The highest precision was obtained with EfficientNetV2-L + NAS-FPN, reaching 0.978. These findings demonstrate that effective bacterial detection depends on the interaction between backbone capacity and feature fusion design rather than backbone scaling alone. Full article

(This article belongs to the Special Issue Innovative Computer Vision and Deep Learning Applications)

42 pages, 16998 KB

Open AccessArticle

FSD-Net: A Siamese Dual Detail Recovery Network for High Resolution Remote Sensing Change Detection Based on Frequency Domain Sensing

by Jiajian Li, Ran Peng, Yuhao Nie, Shengyuan Zhi, Zhuolun He and Xiaoyan Chen

Appl. Sci. 2026, 16(9), 4240; https://doi.org/10.3390/app16094240 (registering DOI) - 26 Apr 2026

Abstract

High-resolution remote sensing image change detection holds significant application value in the fields of urban planning, disaster assessment, and others. However, it faces the dual challenge of pseudo-change interference and loss of detailed information. To address these issues, a frequency-domain-aware Siamese detail recovery network (FSD-Net) is designed in this paper. Firstly, from the perspective of frequency domain analysis, a theory on the dual roles of frequency domain components is introduced to reveal the robustness of low-frequency components to pseudo-changes and the dual semantic noise attributes of high-frequency components. Based on this theory, a frequency-aware context-guided difference (FCGD) module is designed. By explicitly decoupling the difference features into low-frequency global components and high-frequency residual components, it utilizes the prior low-frequency scene as a semantic gate to adaptively modulate the high-frequency differences, which effectively suppress pseudo-change interference. Subsequently, a detail recovery block (DRB), based on sub-pixel convolution, is constructed. This achieves unbiased spatial rearrangement through the semantic redundancy of channel dimensions, which avoids the checkerboard artifacts of traditional upsampling, and by employing a progressive multi-stage upsampling strategy to integrate shallow detail features from the encoder. The experimental results on the three public datasets of LEVIR-CD, WHU-CD, and CDD-CD demonstrate that the FSD-Net outperforms current mainstream methods (e.g., ChangeFormer, BAN, and so on) in core metrics such as F1 score and IoU, with a particularly significant improvement in recall. The ablation experiments validate the effectiveness and complementarity of the FCGD and DRB. Parameter sensitivity analysis indicates that the auxiliary loss weight λ is dataset dependent, with λ = 0.1 serving as a robust default choice. This study provides an efficient and reliable solution for change detection in high-resolution remote sensing imagery. Full article

(This article belongs to the Special Issue Deep Learning and Machine Learning in Image Processing and Pattern Recognition—Second Edition)

29 pages, 15907 KB

Open AccessArticle

Recurrent Climate-Driven Dieback of Subalpine Grasslands in Central Europe Detected from Multi-Decadal Landsat and Sentinel-2 Time Series

by Olha Kachalova, Tomáš Řezník, Jakub Houška, Jan Řehoř, Miroslav Trnka, Jan Balek and Radim Hédl

Remote Sens. 2026, 18(9), 1328; https://doi.org/10.3390/rs18091328 (registering DOI) - 26 Apr 2026

Abstract

Subalpine grasslands represent highly sensitive ecosystems that are increasingly exposed to climate extremes, yet their long-term disturbance dynamics remain poorly documented. This study investigates climate-driven dieback of subalpine grasslands in Central Europe using a harmonized, multi-decadal satellite time series. We analyzed Landsat (TM, ETM+, OLI, OLI-2) and Sentinel-2 imagery spanning 1984–2024 to detect changes in grassland condition, supported by field-based validation, climatic indices, and geomorphological analysis. Several spectral indices related to non-photosynthetic vegetation were evaluated, with the Normalized Burn Ratio (NBR) providing the best discrimination of dead grassland. In spatially grouped cross-validation, NBR achieved very high accuracy for dead versus non-dead grassland, with AUC = 0.9996, precision = 1.00, recall = 0.82, and F1-score = 0.90 for Sentinel-2, and AUC = 0.9982, precision = 1.00, recall = 0.62, and F1-score = 0.76 for Landsat 9. Retrospective mapping revealed four dieback events since 2000: two short-term episodes with rapid within-season recovery (2000, 2003) and two long-term events characterized by persistent degradation and slow regeneration (2012, late 2018–2019). The largest short-term event, in 2003, affected 42.19 ha of total dieback and 96.95 ha including partially damaged or regenerating grassland. Dieback extent was negatively associated with water balance deficit, strongest for SPEI-12 (ρ = −0.548, p = 0.002), while winter frost under shallow-soil conditions likely contributed to long-term damage in 2012. Geomorphological analysis indicated that elevation, terrain curvature, and, to a lesser extent, wind exposure are the primary controls on dieback susceptibility, highlighting the importance of fine-scale environmental controls. Our results demonstrate the value of long-term, multi-sensor satellite observations for detecting and interpreting climate-driven disturbances in subalpine grasslands and provide a transferable framework to support monitoring and conservation of mountain ecosystems under ongoing climate change. Full article

(This article belongs to the Special Issue Assessment of Spatial and Temporal Patterns in Forest and Grassland Ecosystems Based on Spectral Metrics)

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16 pages, 607 KB

Open AccessArticle

Ionization of Helium by Proton Impact in a Quasi-Sturmian Approach Built upon the 3C Model

by Sergey A. Zaytsev, Darya S. Zaytseva, Alexander S. Zaytsev, Lorenzo U. Ancarani, Konstantin A. Kouzakov and Yury V. Popov

Atoms 2026, 14(5), 36; https://doi.org/10.3390/atoms14050036 (registering DOI) - 26 Apr 2026

Abstract

We investigate theoretically the 75 keV proton-impact ionization of atomic helium. The convoluted quasi-Sturmian approach is extended to treat, on an equal footing, both the direct mechanism and the electron capture to the continuum. This is achieved by proposing an ansatz of the [...] Read more.

(e^{-}, {H e}^{+}, p^{+})

that is compatible with the well-known 3C correlated continuum wave function. The model that stems from this approximation, named

3 \tilde{C}

, is tested numerically using parabolic Sturmian expansions. Calculations of fully differential cross sections are presented for different regimes of energy losses, namely for ejected electron energies below, nearly equal to, and above the cusp energy. Our results are compared with recent experimental measurements and other theoretical calculations. The proposed

3 \tilde{C}

model yields very encouraging results and paves the way towards a more advanced Lippmann–Schwinger approach based on the 3C model. Full article

(This article belongs to the Section Atomic, Molecular and Nuclear Spectroscopy and Collisions)

26 pages, 4555 KB

Open AccessReview

Progress and Trends in UAV-Based Soil Moisture Inversion: A Comparative Knowledge Mapping Analysis of CNKI and Web of Science

by Lu Wang, Taifeng Zhu, Weiwei Dai, Feng Liang, Chenglong Yu, Peng Xiong, Xiong Fang, Yanlan Huang and Wen Xie

Remote Sens. 2026, 18(9), 1327; https://doi.org/10.3390/rs18091327 (registering DOI) - 26 Apr 2026

Abstract

Soil moisture critically governs terrestrial energy and water cycles. Precise monitoring of soil water content is essential for precision agriculture, drought early warning, and water resource management. Ground-based observations offer limited spatial coverage, and satellite remote sensing generally lacks high spatial resolution. Unmanned aerial vehicle (UAV) remote sensing, which provides centimeter-level spatial detail, can effectively address this gap and has therefore attracted considerable attention in soil moisture inversion research. Using CiteSpace, we performed a bibliometric analysis of 97 Chinese papers from the China National Knowledge Infrastructure (CNKI) and 321 English papers from the Web of Science Core Collection (2014–2025). The field has expanded rapidly since 2018, with China occupying a leading role. Domestically, Northwest A&F University represents a major research cluster, while the Chinese Academy of Sciences leads internationally. Key research topics include UAVs, soil moisture, machine learning, hyperspectral sensing, canopy temperature, and precision agriculture. Research themes have progressed from reliance on vegetation indices and temperature data toward the integration of hyperspectral and thermal infrared measurements, and toward the use of machine learning approaches to improve inversion models and achieve more accurate estimations. This study delineates the classification and developmental context of a knowledge system for soil moisture inversion using UAV remote sensing. Current work concentrates on integrating multi-sensor data with machine learning, while future efforts will emphasize coupling physical mechanisms with deep learning. These findings offer researchers a clear view of the field’s frontiers and a basis for planning future studies. Full article

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16 pages, 1428 KB

Open AccessArticle

A Spore-Based Biosensor-on-Pillar Platform for Detecting ß-Lactam Antibiotics in Milk

by Sammer UƖ Hassan, Zhuoxin Liu, Prashant Goel, Naresh Kumar and Xunli Zhang

Molecules 2026, 31(9), 1436; https://doi.org/10.3390/molecules31091436 (registering DOI) - 26 Apr 2026

Abstract

Antimicrobial resistance (AMR) is increasingly becoming a major global public health concern, as antibiotics are losing their effectiveness at an alarming rate due to drug resistance. The ß-lactam group of antibiotics are widely used in dairy farms to treat animal infections, and their presence in the food chain is a significant concern. Addressing this issue requires the development of effective analytical tools for the rapid detection of antibiotics. In this work, a miniaturized Biosensor-on-Pillar platform was developed for detecting ß-lactam antibiotics in milk, which operates in a rapid, cost-effective, and user-friendly format, making it particularly suitable for resource-limited settings. The platform employs an enzyme induction-based approach, wherein Bacillus cereus spores germinate in the presence of β-lactam antibiotics, leading to the production of β-lactamase enzyme, which is then recognized using a chromogenic substrate functionalized on paper associated with the pillar platform. The developed biosensor can detect 12 β-lactam antibiotics with limits of detection (LODs) ranging from 1 to 1000 ppb, achieving sensitivity at or below the maximum residue limits (MRLs) set by regulatory bodies (FSSAI/CODEX) for the majority of the tested antibiotics. The performance of the platform, including the design, fabrication, and working principle, was further evaluated by analyzing six blind milk samples, yielding significant results compared to the commercially available AOAC-approved gold-standard method. Hence, the developed biosensor demonstrates promising potential for the rapid, cost-effective and high-throughput screening of milk samples for β-lactam antibiotics, benefiting the dairy industry and ensuring food safety. Full article

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