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Because of the theoretical challenges posed by the Efficient Market Hypothesis with respect to technical analysis, the effectiveness of technical indicators in high-frequency trading remains inadequately explored, particularly at the minute-level frequency, where the effects of the microstructure of the market dominate. This study evaluates the integration of traditional technical indicators with Random Forest regression models using minute-level SPY data, analyzing 13 distinct model configurations. Our empirical results reveal a stark contrast between in-sample and out-of-sample performance, with

R^{2}

values deteriorating from 0.749–0.812 during training to negative values in testing. A feature importance analysis demonstrates that primary price-based features dominate the predictions made by the model, accounting for over 60% of the importance, while established technical indicators, such as RSI and Bollinger Bands, account for only 14–15%. Although the indicator-enhanced models achieved superior risk-adjusted metrics, with Rachev ratios between 0.919 and 0.961, they consistently underperformed a simple buy-and-hold strategy, generating returns ranging from −2.4% to −3.9%. These findings challenge conventional assumptions about the usefulness of technical indicators in algorithmic trading, suggesting that in high-frequency contexts, they may be more relevant to risk management rather than to predicting returns. For practitioners and researchers, our findings indicate that successful high-frequency trading strategies should focus on adaptive feature selection and regime-specific modeling rather than relying on traditional technical indicators, as well as indicating the critical importance of robust out-of-sample testing in the development of a model. Full article

(This article belongs to the Special Issue Machine Learning Applications in Finance, 2nd Edition)

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27 pages, 3681 KiB

Open AccessArticle

Combination of Wall Insulation and PCMs in External Walls of Typical Residential Buildings in the UK and Their Impact on Building Energy Consumption

by Yue Zhang, Siddig Omer and Ruichang Hu

Buildings 2025, 15(6), 854; https://doi.org/10.3390/buildings15060854 (registering DOI) - 9 Mar 2025

Abstract

With growing concerns over global warming and the significant contribution of buildings to energy consumption, reducing energy demand in buildings has become crucial. This study addresses this issue by investigating the integration of phase-change materials (PCMs) with wall insulation on the inside surface of building exterior walls as a strategy to reduce energy consumption. The methodology involved conducting simulations using OpenStudio and EnergyPlus software to assess the thermal performance and energy savings of this approach. The parameters evaluated include energy consumption reduction, material selection and thickness, cost savings, and payback period. The results show that combining a 100 mm Celotex TB4000 Insulation Board with a 1 cm PCM RT24HC layer can reduce energy consumption by 65.4%, save approximately GBP 1645.67 annually, and achieve a payback period of 13 years. Additionally, the selection of the PCM phase-change temperature, thickness, insulation layer thickness, and indoor temperature settings are crucial to optimizing the combined effect. Based on these results, it is recommended that designers and practitioners consider these factors when conducting pre-retrofit simulations to ensure that PCM-enhanced insulation operates within its optimal temperature range. Full article

(This article belongs to the Section Building Energy, Physics, Environment, and Systems)

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24 pages, 1814 KiB

Open AccessArticle

Nutritional and Bioactive Lipid Composition of Amaranthus Seeds Grown in Varied Agro-Climatic Conditions in France

by Ahlem Azri, Sameh Sassi Aydi, Samir Aydi, Mohamed Debouba, Jalloul Bouajila, Muriel Cerny, Romain Valentin, Lucas Tricoulet, Patrice Galaup and Othmane Merah

Agronomy 2025, 15(3), 672; https://doi.org/10.3390/agronomy15030672 (registering DOI) - 9 Mar 2025

Abstract

Increasing interest has been devoted to the seeds of the amaranth, a plant that has garnered attention for its multifaceted uses in daily life. In this research, we focused on four genotypes of two amaranth species cultivated in two different sites in the southwest of France. Oil content, fatty acid composition, and unsaponifiable levels were carried out. The lipid composition was analyzed using Gas Chromatography with Flame Ionization Detection (GC-FID) analysis. The total polyphenol contents (TPC) of different seed extracts were measured by a Folin–Ciocalteu assay. Antioxidants and cytotoxic activities were additionally assessed for the methanol (70%), ethyl acetate, and cyclohexane extracts. Results showed that oil content varied greatly and ranged from 4.3 to 6.4%. Lera cultivated at Riscle had the highest squalene yield, reaching 7.7%. Linoleic acid and oleic acid were the most abundant fatty acids for the four genotypes in two sites, followed by palmitic acid. Triglycerides (TAGs) were the main glycerides in all samples growing in both sites. A total of 44 volatile compounds were identified in Amaranthus seed extracts. The chemical compositions of the amaranth have been discussed as influenced by genetic and environmental factors. These data highlight the bioactive potential of the amaranth seed. Full article

(This article belongs to the Section Plant-Crop Biology and Biochemistry)

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16 pages, 4856 KiB

Open AccessArticle

The Composition of Volatiles in Quartz and Pyrite from the Konduyak Gold Deposit (Yenisei Ridge, Russia)

by Elena Shaparenko, Taras Bul’bak, Anatoly Tomilenko, Anatoly Sazonov, Marina Petrova, Sergey Silyanov, Nadezhda Gibsher and Margarita Khomenko

Minerals 2025, 15(3), 278; https://doi.org/10.3390/min15030278 (registering DOI) - 9 Mar 2025

Abstract

The Konduyak gold–quartz–sulfide deposit is one of the most promising gold mines in the Ayakhta gold ore cluster on the Yenisei ridge. This article is devoted to the study of the composition of the volatile compounds in the ore-forming fluid, since this is one of the key aspects in understanding the conditions of deposit formation. The compositions of the fluids that formed quartz and pyrite in the deposit ore zone were determined using Raman spectroscopy and pyrolysis-free gas chromatography–mass spectrometry. The study of the fluid inclusions in the minerals showed that complex C-H-O-S-N multi-component fluids formed the quartz–sulfide ore zones. A range of 232 to 302 various volatile compounds were found in the fluids. The mineralizing fluids mainly consist of H₂O (14.25–96.02 rel. %) and CO₂ (2.07–54.44 rel. %). A high SO₂ content (14.60–44.95 rel. %) is typical of fluids trapped by pyrites. Moreover, a wide range of hydrocarbons (oxygen-free aliphatic, cyclic, heterocyclic, and oxygenated) and nitrogenated and sulfur compounds were found among the volatiles in the fluid. The variable H/(H + O) ratios, from 0.51 to 0.81, and CO₂/(CO₂ + H₂O) ratios, from 0.02 to 0.56, indicate changes in the redox conditions during ore formation. Full article

(This article belongs to the Special Issue Native Gold as a Specific Indicator Mineral for Gold Deposits, 2nd Edition)

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13 pages, 5926 KiB

Open AccessArticle

Long-Term (1979–2024) Variation Trend in Wave Power in the South China Sea

by Yifeng Tong, Junmin Li, Wuyang Chen and Bo Li

J. Mar. Sci. Eng. 2025, 13(3), 524; https://doi.org/10.3390/jmse13030524 (registering DOI) - 9 Mar 2025

Abstract

Wave power (WP) is a strategic oceanic resource. Previous studies have extensively researched the long-term variations in WP in the South China Sea (SCS) for energy planning and utilization. This study extends the analysis of long-term trends to the last year based on ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis v5) reanalysis data from 1979 to 2024. Our results mainly indicate that the trends in WP after 2011 are significantly different from those before 2011. Before 2011, the WP in the SCS primarily showed an increasing trend, but, after 2011, it shifted to a decreasing trend. This trend has seasonal differences, manifested as being consistent with the annual trend in winter and spring while being inconsistent with the annual trend in summer and autumn. It indicates that the opposite trend in WP before and after 2011 was mainly the result of WP variations in winter and spring. To illustrate the driving factor for the WP’s variations, the contemporary long-term trend of the wind fields is systematically analyzed. Analysis results reveal that, regardless of seasonal differences or spatial distribution, the two trends are consistent in most situations, indicating that wind fields are the dominant factor for the long-term variations in WP. Meanwhile, the effects of the wind fields on the WP variations can also be modulated by environmental factors such as oceanic swell propagation and local topography. This study contributes to the knowledge of the latest trends and driving factors regarding the WP in the SCS. Full article

(This article belongs to the Special Issue Advances in Offshore Wind and Wave Energies—2nd Edition)

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16 pages, 2218 KiB

Open AccessArticle

Application of Simultaneous Active and Passive Fluorescence Observations: Extending a Fluorescence-Based q_L Estimation Model

by Chenhui Guo, Zhunqiao Liu and Xiaoliang Lu

Sensors 2025, 25(6), 1700; https://doi.org/10.3390/s25061700 (registering DOI) - 9 Mar 2025

Abstract

The fraction of open Photosystem II (PSII) reaction centers (q_L) is critical for connecting broadband PSII fluorescence (ChlF_PSII) with the actual electron transport from PSII to Photosystem I. Accurately estimating q_L is fundamental for determining ChlF_PSII, which, in turn, is vital for mechanistically estimating the actual electron transport rate and photosynthetic CO₂ assimilation. Chlorophyll fluorescence provides direct physiological insights, offering a robust foundation for q_L estimation. However, uncertainties in the ChlF_PSII–q_L relationship across different plant functional types (PFTs) limit its broader application at large spatial scales. To address this issue, we developed a leaf-level instrument capable of simultaneously measuring actively and passively induced chlorophyll fluorescence. Using this system, we measured light response, CO₂ response, and temperature response curves across 52 species representing seven PFTs. Our findings reveal the following: (1) a strong linear correlation between ChlF_PSII derived from passively induced fluorescence and that from actively induced fluorescence (R² = 0.85), and (2) while the parameters of the ChlF_PSII–q_L relationship varied among PFTs, ChlF_PSII reliably modeled q_L within each PFT, with the R² ranging from 0.85 to 0.96. This study establishes quantitative ChlF_PSII–q_L relationships for various PFTs by utilizing passively induced fluorescence to calculate ChlF_PSII. The results demonstrate the potential for remotely sensed chlorophyll fluorescence data to estimate q_L and strengthen the use of fluorescence-based approaches for mechanistic GPP estimation at large spatial scales. Full article

(This article belongs to the Section Smart Agriculture)

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12 pages, 1834 KiB

Open AccessArticle

Examination of Intermolecular Forces Influencing Headspace Analysis of Biological Samples

by Young Eun Lee and Bruce A. Kimball

Metabolites 2025, 15(3), 183; https://doi.org/10.3390/metabo15030183 (registering DOI) - 9 Mar 2025

Abstract

Headspace analysis is an effective method for assessing the concentrations of volatile and semi-volatile metabolites in biological samples. In particular, solid-phase microextraction (SPME) is an efficient tool for headspace analyses. Metabolites present in the sample are the typical targets of headspace analysis (rather than the vapor phase concentration) for making measurements on sample donors (e.g., biomarkers of health or disease). Accordingly, intermolecular forces between metabolites and matrix may prevent a complete profile of the metabolite composition in the biosamples from being revealed. To assess sources of such interactions, several volatile compounds in various sample mediums were examined. Small volatile metabolites typical of human biosamples were the volatile compounds selected for this study. Test media included lipid or serum solution to simulate biological samples commonly encouraged in biomarker discovery. Headspace concentrations of volatile analytes were compared using solid-phase microextraction gas chromatography-mass spectrometry (SPME-GC-MS). Observed levels of metabolites in headspace varied among the different media, despite being fortified at equal concentrations in the samples. Overall, lower headspace responses were observed in samples containing proteins or lipids. It was found that these strong intermolecular interactions arose from irreversible chemical bonds between the volatile molecules and component of the sample matrix. However, headspace responses could be maximized when the analysis was performed at temperatures ranging from 60 to 70 °C. Furthermore, normalization of peak responses to an internal standard did not always account for these interactions. Full article

(This article belongs to the Special Issue Advanced Metabolic Profiling via Gas Chromatography–Mass Spectrometry and Liquid Chromatography–Mass Spectrometry)

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19 pages, 2935 KiB

Open AccessArticle

Detection and Severity Classification of Sleep Apnea Using Continuous Wearable SpO₂ Signals: A Multi-Scale Feature Approach

by Nhung H. Hoang and Zilu Liang

Sensors 2025, 25(6), 1698; https://doi.org/10.3390/s25061698 (registering DOI) - 9 Mar 2025

Abstract

The use of wearable devices for sleep apnea detection is growing, but their limited signal resolution poses challenges for accurate diagnosis. This study explores the feasibility of using SpO

_{2}

signals from wearable sensors for detecting sleep apnea and classifying its severity. We [...] Read more.

The use of wearable devices for sleep apnea detection is growing, but their limited signal resolution poses challenges for accurate diagnosis. This study explores the feasibility of using SpO

_{2}

signals from wearable sensors for detecting sleep apnea and classifying its severity. We propose a novel multi-scale feature engineering approach, which extracts features from coarsely grained SpO

_{2}

signals across timescales ranging from 1 s to 600 s. Our results show that traditional SpO

_{2}

markers, such as the oxygen desaturation index (ODI) and Lempel–Zip complexity, lose their relevance with the Apnea–Hypopnea Index (AHI) at longer timescales. In contrast, non-linear features like complex entropy, sample entropy, and fuzzy entropy maintain strong correlations with AHI, even at the coarsest timescales (up to 600 s), making them well suited for low-resolution data. Multi-scale feature extraction improves model performance across various machine learning algorithms by alleviating model bias, particularly with the Bayes and CatBoost models. These findings highlight the potential of multi-scale feature engineering for wearable device applications where only low-resolution data are commonly available. This could improve accessibility to low-cost, at-home sleep apnea screening, reducing reliance on expensive and labor-intensive polysomnography. Moreover, it would allow even healthy individuals to proactively monitor their sleep health at home, facilitating the early identification of potential sleep problems. Full article

(This article belongs to the Special Issue Wearable Sensors for Continuous Health Monitoring and Analysis)

23 pages, 8860 KiB

Open AccessArticle

Oxygen and Sulfur Isotope Systematics of Dissolved Sulfate in a Nonvolcanic Geothermal System: Sulfate Source, Evolution and Impact on Geothermometers

by Yinlei Hao, Zhonghe Pang, Qinghua Gong, Nianqing Li, Dawei Liao and Zhengyu Luo

Water 2025, 17(6), 788; https://doi.org/10.3390/w17060788 (registering DOI) - 9 Mar 2025

Abstract

Dual isotopes of sulfate (δ³⁴S_SO₄ and δ¹⁸O_SO₄), along with isotopes in water and trace elements of geothermal waters, are systematically investigated to quantitatively elucidate sulfate sources and oxygen and sulfur isotopic behaviors during deep groundwater circulation and to constrain reservoir temperatures in the Jimo nonvolcanic geothermal system on the eastern coast of China. The results show that δ³⁴S_SO₄ and δ¹⁸O_SO₄ values in geothermal waters ranged from −21.0 to 5.7‰ and from 1.1 to 8.8‰, respectively. An increase in SO₄ concentrations (140–796 mg/L) with a systematic decrease in δ³⁴S_SO₄ and δ¹⁸O_SO₄ values was observed along the flow path from the central to eastern and western parts. The sulfate in the Middle Group was predominantly from atmospheric deposition, with sulfide oxidation contributions of <27%. In contrast, 80–85% of SO₄ in the Eastern Group is derived from pyrite oxidation. In the Western Group, the oxidation of multiple metal sulfides contributed 43–66% of SO₄. Sulfate oxidation and mixing of shallow groundwater caused reservoir temperatures to be underestimated by 9 ± 6–14 ± 16% using silica and K-Mg geothermometers but overestimated by up to 52–62% using sulfate–water oxygen isotope geothermometers. The estimated average target reservoir temperature was 144 ± 8 °C, with geothermal waters circulating to depths of 3.6–4.6 km. This study offers new insights into the significant impact of sulfate-related processes on geothermometric estimates, a factor often overlooked when using aqueous geothermometers. It also provides valuable guidance for accurately estimating target geothermal reservoir temperatures and advancing exploration in nonvolcanic geothermal systems. Full article

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25 pages, 9300 KiB

Open AccessArticle

Monitoring Canopy Height in the Hainan Tropical Rainforest Using Machine Learning and Multi-Modal Data Fusion

by Qingping Ling, Yingtan Chen, Zhongke Feng, Huiqing Pei, Cai Wang, Zhaode Yin and Zixuan Qiu

Remote Sens. 2025, 17(6), 966; https://doi.org/10.3390/rs17060966 (registering DOI) - 9 Mar 2025

Abstract

Biomass carbon sequestration and sink capacities of tropical rainforests are vital for addressing climate change. However, canopy height must be accurately estimated to determine carbon sink potential and implement effective forest management. Four advanced machine-learning algorithms—random forest (RF), gradient boosting decision tree, convolutional neural network, and backpropagation neural network—were compared in terms of forest canopy height in the Hainan Tropical Rainforest National Park. A total of 140 field survey plots and 315 unmanned aerial vehicle photogrammetry plots, along with multi-modal remote sensing datasets (including GEDI and ICESat-2 satellite-carried LiDAR data, Landsat images, and environmental information) were used to validate forest canopy height from 2003 to 2023. The results showed that RH80 was the optimal choice for the prediction model regarding percentile selection, and the RF algorithm exhibited the optimal performance in terms of accuracy and stability, with R² values of 0.71 and 0.60 for the training and testing sets, respectively, and a relative root mean square error of 21.36%. The RH80 percentile model using the RF algorithm was employed to estimate the forest canopy height distribution in the Hainan Tropical Rainforest National Park from 2003 to 2023, and the canopy heights of five forest types (tropical lowland rainforests, tropical montane cloud forests, tropical seasonal rainforests, tropical montane rainforests, and tropical coniferous forests) were calculated. The study found that from 2003 to 2023, the canopy height in the Hainan Tropical Rainforest National Park showed an overall increasing trend, ranging from 2.95 to 22.02 m. The tropical montane cloud forest had the highest average canopy height, while the tropical seasonal forest exhibited the fastest growth. The findings provide valuable insights for a deeper understanding of the growth dynamics of tropical rainforests. Full article

(This article belongs to the Special Issue New Methods and Applications in Remote Sensing of Tropical Forests)

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18 pages, 259 KiB

Open AccessArticle

Deep Learning for Predicting Rehabilitation Success: Advancing Clinical and Patient-Reported Outcome Modeling

by Yasser Mahmoud, Kaleb Horvath and Yi Zhou

Electronics 2025, 14(6), 1082; https://doi.org/10.3390/electronics14061082 (registering DOI) - 9 Mar 2025

Abstract

Predicting rehabilitation outcomes is essential for guiding clinical decisions and improving patient care. Traditional machine learning methods, while effective, are often limited in their ability to capture complex, nonlinear relationships in data. This study investigates the application of deep learning techniques, including hybrid Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), to predict rehabilitation success based on clinical and patient-reported outcome measures (CROMs and PROMs). Using a dataset of 1047 rehabilitation patients encompassing diverse musculoskeletal conditions and treatment protocols, we compare the performance of deep learning models with previously established machine learning approaches such as Random Forest and Extra Trees classifiers. Our findings reveal that deep learning significantly enhances predictive performance. The weighted F1-score for direct classification improved from 65% to 74% using a CNN-RNN architecture, and the mean absolute error (MAE) for regression-based success metrics decreased by 12%, translating to more precise estimations of functional recovery. These improvements hold clinical significance as they enhance the ability to tailor rehabilitation interventions to individual patient needs, potentially optimizing recovery timelines and resource allocation. Moreover, attention mechanisms integrated into the deep learning models provided improved interpretability, highlighting key predictors such as age, range of motion, and PROM indices. This study underscores the potential of deep learning to advance outcome prediction in rehabilitation, offering more precise and interpretable tools for clinical decision-making. Future work will explore real-time applications and the integration of multimodal data to further refine these models. Full article

(This article belongs to the Special Issue Artificial Intelligence and Deep Learning Techniques for Healthcare)

23 pages, 5268 KiB

Open AccessArticle

Computer Modelling of Heliostat Fields by Ray-Tracing Techniques: Simulating Shading and Blocking Effects

by José Carlos Garcia Pereira and Luís Guerra Rosa

Appl. Sci. 2025, 15(6), 2953; https://doi.org/10.3390/app15062953 (registering DOI) - 9 Mar 2025

Abstract

In this work, solar concentrating heliostat fields are modelled using computer ray-tracing techniques to investigate the parameters controlling the optical efficiency of those solar facilities. First, it is explained how the non-trivial problem of heliostat blocking and shading can be efficiently handled in ray-tracing simulations. These numerical techniques were implemented in our Light Analysis Modelling (LAM) software, which was then used to study realistic heliostat fields for a range of different geometries. Two locations were chosen, with the highest and the lowest latitudes, from the SFERA-III EU list of solar concentrating facilities with heliostat fields: Jülich (Germany) and Protaras (Cyprus). The results indicate that shading and blocking can substantially reduce the radiation collected during the year (up to 20%). Accurate figures of merit are proposed to quantify the thermal efficiency of a heliostat field, independently of its size. Increasing the tower height mostly reduces blocking (especially when the sun is high and most energy is collected), while increasing the distance between heliostats or increasing the ground slope mostly reduces shading (especially when the sun is low and little energy is collected). Full article

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24 pages, 4323 KiB

Open AccessArticle

NDMI-Derived Field-Scale Soil Moisture Prediction Using ERA5 and LSTM for Precision Agriculture

by Elham Koohikeradeh, Silvio Jose Gumiere and Hossein Bonakdari

Sustainability 2025, 17(6), 2399; https://doi.org/10.3390/su17062399 (registering DOI) - 9 Mar 2025

Abstract

Accurate soil moisture prediction is fundamental to precision agriculture, facilitating optimal irrigation scheduling, efficient water resource allocation, and enhanced crop productivity. This study employs a Long Short-Term Memory (LSTM) deep learning model, integrated with high-resolution ERA5 remote sensing data, to improve soil moisture estimation at the field scale. Soil moisture dynamics were analyzed across six commercial potato production sites in Quebec—Goulet, DBolduc, PBolduc, BNiquet, Lalancette, and Gou-new—over a five-year period. The model exhibited high predictive accuracy, with correlation coefficients (R) ranging from 0.991 to 0.998 and Nash–Sutcliffe efficiency (NSE) values reaching 0.996, indicating strong agreement between observed and predicted soil moisture variability. The Willmott index (WI) exceeded 0.995, reinforcing the model’s reliability. The integration of NDMI assessments further validated the predictions, demonstrating a strong correlation between NDMI values and LSTM-based soil moisture estimates. These findings confirm the effectiveness of deep learning in capturing spatiotemporal variations in soil moisture, underscoring the potential of AI-driven models for real-time soil moisture monitoring and irrigation optimization. This research study provides a scientifically robust framework for enhancing data-driven agricultural water management, promoting sustainable irrigation practices, and improving resilience to soil moisture variability in agricultural systems. Full article

(This article belongs to the Special Issue Advancing Sustainable Development Through Artificial Intelligence (AI))

19 pages, 584 KiB

Open AccessArticle

Riemannian Topological Analysis of Neuronal Activity

by Manuel Rivas and Manuel Reina

Symmetry 2025, 17(3), 412; https://doi.org/10.3390/sym17030412 (registering DOI) - 9 Mar 2025

Abstract

Cerebral dynamics emerge from the brain’s substrate due to the anatomical patterns of its physical connections, which we know are not a fixed structure but are subject to temporal and local modifications. This circumstance makes it possible for a more or less fixed number of neurons to generate a range of complex networks. By studying the topological space associated with these physical connections and their geometric dynamics, we can use Differential Geometry to study the foundations of the brain’s connectome. Full article

(This article belongs to the Special Issue Symmetry and the Nanoscale: Matter and Mind - Mathematical Modeling and Phenomenology)

19 pages, 1740 KiB

Open AccessArticle

Coupled Resonance Fiber-Optic SPR Sensor Based on TRIZ

by Cuilan Zhu, Haodi Zhai, Yonghao Wang, Xiangru Suo, Tianyu Zhu and Shuowei Jin

Photonics 2025, 12(3), 244; https://doi.org/10.3390/photonics12030244 (registering DOI) - 9 Mar 2025

Abstract

: This paper aims to enhance the sensitivity of fiber-optic surface plasmon resonance (SPR) sensors by innovatively applying TRIZ (Theory of Inventive Problem Solving). To identify the key challenges faced by current SPR sensors, methods such as functional analysis, causal analysis, and the Nine-Window method are employed. Utilizing TRIZ tools, including Technical Contradiction, Physical Contradiction, the Smart Little Man method, and object–field analysis, innovative solutions are proposed, involving transparent indium tin oxide (ITO) thin films, an asymmetric photonic crystal fiber structure with elliptical pores, and titanium dioxide (TiO₂) thin films. Experimental results reveal a significant improvement in sensitivity, with an average of 9961.90 nm/RIU and a peak of 12,503.56 nm/RIU within the refractive index range of 1.33061 to 1.40008, representing a 456% increase compared to the original gold-film fiber-optic SPR sensor. These findings have potential applications in biosensing, environmental monitoring, and food safety. Full article

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