Search Results (3,457)

Boron-dipyrromethene (BODIPY) dyes belong to a class of organoboron compounds that have become ubiquitous for researchers in areas of fluorescence imaging, photodynamic therapy, and optoelectronics. The intrinsic qualities of BODIPY dyes and their meso-modified structural analogs, Aza-BODIPY dyes, have propelled their recent increase in use in biomedical applications. The two scaffolds have high quantum yields, narrow absorption, and emission bandwidths with large Stokes’ shifts, and high photostability and thermal stability. Because their properties are independent of solvent polarity and dye functionality, they can be tuned to promote novel analytical methods, resulting in the adaptation of the physicochemical and spectral properties of the dyes. In this review of BODIPY and Aza-BODIPY scaffolds, we will summarize their spectral properties, synthetic methods of preparation, and applications reported between 2014 and 2025. This review aims to summarize the advances in chemosensing, especially pH sensor development, and the advances in NIR-II window bioimaging probes. We hope that this succinct overview of Aza-BODIPY scaffolds will highlight their untapped potential, elucidating insights that may catalyze novel ideas in the physical organic realm of BODIPY. Full article

The integration of solar energy into smart grids requires high-efficiency power conversion to support grid stability. However, Partial Shading Conditions (PSCs) remain a primary obstacle by inducing multiple local maxima on P–V characteristic curves. This paper presents a hardware-aware and memory-enhanced Maximum Power Point Tracking (MPPT) approach based on a modified Dragonfly Algorithm (DA) for grid-connected microinverter-based photovoltaic (PV) systems. The proposed method utilizes a quasi-switched Boost-Switched Capacitor (qSB-SC) topology, where the DA is specifically tailored by combining Lévy-flight exploration with a dynamic damping factor to suppress steady-state oscillations within the qSB-SC ripple constraints. Coupling the MPPT stage to a seven-level Packed-U-Cell (PUC) microinverter ensures that each PV module operates at its independent Global Maximum Power Point (GMPP). A ZigBee-based Wireless Sensor Network (WSN) facilitates rapid data exchange and supports ‘swarm-memory’ initialization, matching current shading patterns with historical data to seed the population near the most probable GMPP region. This integration reduces the overall response time to 0.026 s. Hardware-in-the-loop experiments validated the approach, attaining a tracking accuracy of 99.32%. Compared to current state-of-the-art benchmarks, the proposed model demonstrated a significant improvement in tracking speed, outperforming the most recent 2025 GWO implementation (0.0603 s) by approximately 56% and conventional metaheuristic variants such as GWO-Beta (0.46 s) by over 94%.These results confirmed that the modified DA-based MPPT substantially enhanced the microinverter efficiency under PSC through cross-layer parameter adaptation. Full article

Peatlands play a critical role in global and regional climate regulation by functioning as long-term carbon sinks, regulating hydrology, and modulating land–atmosphere energy exchange. Intact peat ecosystems store large amounts of organic carbon and stabilize local climate through high water retention and evapotranspiration, whereas peatland degradation disrupts these functions and can transform peatlands into significant sources of greenhouse gas emissions and climate extremes such as drought and fire. Indonesia contains approximately 13.6–40.5 Gt of carbon, around 40% of which is stored on the island of Sumatra. However, tropical peatlands in this region are highly vulnerable to climate anomalies and land-use change. This study investigates the impacts of major climate anomalies—specifically El Niño and positive Indian Ocean Dipole (pIOD) events in 1997/1998, 2015/2016, and 2019—on peatland cover change across South Sumatra, Jambi, Riau, and the Riau Islands. Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager/Thermal Infrared Sensor imagery were analyzed using a Random Forest machine learning classification approach. Climate anomaly periods were identified using El Niño-Southern Oscillation (ENSO) and IOD indices from the National Oceanic and Atmospheric Administration. To enhance classification accuracy and detect vegetation and hydrological stress, spectral indices including the Normalized Difference Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index (MSAVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI) were integrated. The results show classification accuracies of 89–92%, with kappa values of 0.85–0.90. The 2015/2016 El Niño caused the most severe peatland degradation (>51%), followed by the 1997/1998 El Niño (23–38%), while impacts from the 2019 pIOD were comparatively limited. These findings emphasize the importance of peatlands in climate regulation and highlight the need for climate-informed monitoring and management strategies to mitigate peatland degradation and associated climate risks. Full article

This study includes a comparative analysis of four graphene-based electrochemical sensors used for the detection of melatonin, an endogenous hormone involved in circadian rhythm regulation and associated with various neurological pathologies. The sensors were based on screen-printed electrodes (SPE) modified with graphene (G), graphene modified with gold nanoparticles (AuNPs/G), graphene oxide (GO), and reduced graphene oxide (rGO). Melatonin was extracted from commercially available pharmaceutical products, purified, and characterized using UV-Vis spectroscopy, FTIR spectrometry, and HPLC. The performance of the electrodes was evaluated via cyclic voltammetry, using potassium ferrocyanide and standard melatonin solutions to determine the kinetic characteristics, while square-wave voltammetry was employed to determine the detection and quantification limits. G/SPE showed the best performance, with a detection limit of 0.3424 μM, followed by AuNPs/G/SPE with an LOD of 1.2768 μM. GO/SPE had the poorest performance (LOD 23.1056 μM), and rGO/SPE had an LOD of 5.8503 μM. Testing of sensors on pharmaceuticals showed accurate quantification of melatonin in a complex environment. The results highlight the potential of G/SPE and AuNPs/G/SPE sensors for use in the rapid and accurate detection of melatonin in pharmaceutical and biomedical applications. Full article

Neuronal synapses are the functional units of communication in the central nervous system. This review describes the molecular mechanisms regulating synaptic transmission, plasticity, and circuit refinement. At the presynaptic active zone, scaffolding proteins including bassoon, piccolo, RIMs, and munc13 organize vesicle priming and the localization of voltage-gated calcium channels. Neurotransmitter release is mediated by the SNARE complex, comprising syntaxin-1, SNAP25, and synaptobrevin, and triggered by the calcium sensor synaptotagmin-1. Following exocytosis, synaptic vesicles are recovered through clathrin-mediated, ultrafast, bulk, or kiss-and-run endocytic pathways. Postsynaptically, the postsynaptic density (PSD) serves as a protein hub where scaffolds such as PSD-95, shank, homer, and gephyrin anchor excitatory (AMPA, NMDA) and inhibitory (GABA-A, Glycine) receptors are observed. Synaptic strength is modified during long-term potentiation (LTP) and depression (LTD) through signaling cascades involving kinases like CaMKII, PKA, and PKC, or phosphatases such as PP1 and calcineurin. These pathways regulate receptor trafficking, Arc-mediated endocytosis, and actin-dependent remodeling of dendritic spines. Additionally, synapse formation and elimination are guided by cell adhesion molecules, including neurexins and neuroligins, and by microglial pruning via the complement cascade (C1q, C3) and “don’t eat me” signals like CD47. Molecular diversity is further expanded by alternative splicing and post-translational modifications. A unified model of synaptic homeostasis is required to understand the basis of neuropsychiatric and neurological disorders. Full article

Pasture-based ruminant systems link nitrogen (N) nutrition with ecosystem N cycling. Grazing ruminants convert fibrous forages into milk and meat but excrete 65 to 80% of ingested N, creating excreta hotspots that drive ammonia volatilization, nitrate leaching, and nitrous oxide (N₂O) emissions. This review synthesizes ecological and ruminant nutrition evidence on N flows, emphasizing microbial processes, biological N₂ fixation, plant diversity, and urine patch biogeochemistry, and evaluates strategies to improve N use efficiency (NUE). We examine rumen N metabolism in relation to microbial protein synthesis, urea recycling, and dietary factors including crude protein concentration, energy supply, forage composition, and plant secondary compounds that modulate protein degradability and microbial N capture, thereby influencing N partitioning among animal products, urine, and feces, as reflected in milk and blood urea N. We also examine how grazing patterns and excreta distribution, assessed with sensor technologies, modify N flows. Evidence indicates that integrated management combining dietary manipulation, forage diversity, targeted grazing, and decision tools can increase farm-gate NUE from 20–25% to over 30% while sustaining performance. Framing these processes within the global N cycle positions pasture-based ruminant systems as critical leverage points for aligning ruminant production with environmental and climate sustainability goals. Full article

Crucial regulators of gamete metabolism and signaling, mitochondria synchronize energy generation with redox equilibrium and developmental proficiency. Once thought of as hazardous byproducts, reactive oxygen species (ROS) are now understood to be vital signaling molecules that provide a “redox window of competence” that is required for oocyte maturation, sperm capacitation, and early embryo development. This review presents the idea of mitochondrial metabolic checkpoints, which are phases that govern gamete quality and fertilization potential by interacting with cellular signaling, redox balance, and mitochondrial activity. Recent research shows that oocytes may sustain a nearly ROS-free metabolic state by blocking specific respiratory-chain components, highlighting the importance of mitochondrial remodeling in gamete competence. Evidence from in vitro and in vivo studies shows that ROS act as dynamic gatekeepers at critical points in oogenesis, spermatogenesis, fertilization, and early embryogenesis. However, assisted reproductive technologies (ARTs) may inadvertently disrupt this redox–metabolic equilibrium. Potential translational benefits can be obtained via targeted techniques that optimize mitochondrial function, such as modifying oxygen tension, employing mitochondria-directed antioxidants like MitoQ and SS-31, and supplementing with nutraceuticals like melatonin, CoQ10, and resveratrol. Understanding ROS-mediated checkpoints forms the basis for developing biomarkers of gamete competence and precision therapies to improve ART outcomes. By highlighting mitochondria as both metabolic sensors and redox regulators, this review links fundamental mitochondrial biology to clinical reproductive medicine. Full article

Pancreatic function tests are used to determine the presence of chronic pancreatitis, particularly in the early stage of the disease. Chymotrypsin is an indicator of pancreatic function and is thus related to pancreatic diseases. However, these methods often require specific equipment and cannot always meet on-site analysis requirements. Consequently, a highly sensitive detection method needs to be developed. This research employed graphene oxide modified with NHS sensors and peptides (RRHFFGC: Arginine-Arginine-Histidine-Phenylalanine-Phenylalanine-Glycine-Cysteine) tagged with gold nanoclusters (Au NCs) for the detection of chymotrypsin. The N-Hydroxysuccinimide-(Polyethylene Glycol)₄-Dibenzocyclooctyne (NHS-PEG₄-DBCO) and graphene oxide (GO)-N₃ click reaction yielded GO-NHS material, appropriate for fluorescence quenching. The peptide chain was accurately broken with the introduction of chymotrypsin, and the Au NCs were situated far from the GO-NHS surface. The detection limit was 2.014 pg/mL. The results showed that the detection method had high sensitivity in comparison with the previous studies. This method is relevant to real samples due to its potential efficacy. Therefore, it is a promising method in the biomedical field. Full article

The deployment of wireless seismic nodal systems necessitates the efficient identification of optimal locations for sensor installation, considering factors such as ground stability and the absence of interference. Semantic segmentation of satellite imagery has advanced significantly, and its application to this specific task remains unexplored. This work presents a baseline empirical evaluation of the U-Net architecture for the semantic segmentation of surfaces applicable for seismic sensor installation. We utilize a novel dataset of Sentinel-2 multispectral images, specifically labeled for this purpose. The study investigates the impact of pretrained encoders (EfficientNetB2, Cross-Stage Partial Darknet53—CSPDarknet53, and Multi-Axis Vision Transformer—MAxViT), different combinations of Sentinel-2 spectral bands (Red, Green, Blue (RGB), RGB+Near Infrared (NIR), 10-bands with 10 and 20 m/pix spatial resolution, full 13-band), and a technique for improving small object segmentation by modifying the input convolutional layer stride. Experimental results demonstrate that the CSPDarknet53 encoder generally outperforms the others (IoU = 0.534, Precision = 0.716, Recall = 0.635). The combination of RGB and Near-Infrared bands (10 m/pixel resolution) yielded the most robust performance across most configurations. Reducing the input stride from 2 to 1 proved beneficial for segmenting small linear objects like roads. The findings establish a baseline for this novel task and provide practical insights for optimizing deep learning models in the context of automated seismic nodal network installation planning. Full article

The Automatic Identification System (AIS) frequently suffers from data loss and irregular report intervals in real maritime environments, compromising the reliability of downstream navigation, monitoring, and trajectory reconstruction tasks. To address these challenges, we propose BAARTR (Boundary-Aware Adaptive Regression for Kinematically Consistent Vessel Trajectory Reconstruction), a novel kinematically consistent interpolation framework. Operating solely on time, latitude, and longitude inputs, BAARTR explicitly enforces boundary velocities derived from raw AIS data. The framework adaptively selects a velocity-estimation strategy based on the AIS reporting gap: central differencing is applied for short intervals, while a hierarchical cubic velocity regression with a quadratic acceleration constraint is employed for long or irregular gaps to iteratively refine endpoint slopes. These boundary slopes are subsequently incorporated into a clamped quartic interpolation at a 1 s resolution, effectively suppressing overshoots and ensuring velocity continuity across segments. We evaluated BAARTR against Linear, Spline, Hermite, Bezier, Piecewise cubic hermite interpolating polynomial (PCHIP) and Modified akima (Makima) methods using real-world AIS data collected from the Mokpo Port channel, Republic of Korea (2023–2024), across three representative vessels. The experimental results demonstrate that BAARTR achieves superior reconstruction accuracy while maintaining strictly linear time complexity ($\mathit{O}\left(\mathit{N}\right)$). BAARTR consistently achieved the lowest median Root Mean Square Error (RMSE) and the narrowest Interquartile Ranges (IQR), producing visibly smoother and more kinematically plausible paths-especially in high-curvature turns where standard geometric interpolations tend to oscillate. Furthermore, sensitivity analysis shows stable performance with a modest training window (n ≈ 16) and minimal regression iterations (m = 2–3). By reducing reliance on large training datasets, BAARTR offers a lightweight, extensible foundation for post-processing in Maritime Autonomous Surface Ship (MASS) and Vessel Traffic Service (VTS), as well as for accident reconstruction and multi-sensor fusion. Full article

Food loss and waste (FLW) is a chronic problem across food systems worldwide, with meat being one of the most resource-intensive and perishable categories. The perishable character of meat, combined with complex cold chain requirements and consumer behavior, makes the sector particularly sensitive to inefficiencies and loss across all stages from production to consumption. This review synthesizes the latest advancements in new preservation technologies and supply chain efficiency strategies to minimize meat wastage and also outlines current challenges and future directions. New preservation technologies, such as high-pressure processing, cold plasma, pulsed electric fields, and modified atmosphere packaging, have substantial potential to extend shelf life while preserving nutritional and sensory quality. Active and intelligent packaging, bio-preservatives, and nanomaterials act as complementary solutions to enhance safety and quality control. At the same time, blockchain, IoT sensors, AI, and predictive analytics-driven digitalization of the supply chain are opening new opportunities in traceability, demand forecasting, and cold chain management. Nevertheless, regulatory uncertainty, high capital investment requirements, heterogeneity among meat types, and consumer hesitancy towards novel technologies remain significant barriers. Furthermore, the scalability of advanced solutions is limited in emerging nations due to digital inequalities. Convergent approaches that combine technical innovation with policy harmonization, stakeholder capacity building, and consumer education are essential to address these challenges. System-level strategies based on circular economy principles can further reduce meat loss and waste, while enabling by-product valorization and improving climate resilience. By integrating preservation innovations and digital tools within the framework of UN Sustainable Development Goal 12.3, the meat sector can make meaningful progress towards sustainable food systems, improved food safety, and enhanced environmental outcomes. Full article

Cross-domain building segmentation in high-resolution remote sensing imagery underpins urban change monitoring, disaster assessment, and exposure mapping. However, differences in sensors, regions, and imaging conditions create structural and radiometric domain gaps that degrade model generalization. Most existing methods adopt model-centric domain adaptation with additional networks or losses, complicating training and deployment. We propose a data-centric framework, Hybrid Object-Based Augmentation and Histogram Matching (Hybrid OBA–HM), which improves cross-domain building segmentation without modifying the backbone architecture or using target-domain labels. The proposed framework comprises two stages: (i) object-based augmentation to increase structural diversity and building coverage, and (ii) histogram-based normalization to mitigate radiometric discrepancies across domains. Experiments on OpenEarthMap and cross-city transfer among three KOMPSAT-3A scenes show that Hybrid OBA–HM improves F1-scores from 0.808 to 0.840 and from 0.455 to 0.652, respectively, while maintaining an object-level intersection over union of 0.89 for replaced buildings. Domain-indicator analysis further reveals larger gains under stronger radiometric and geometric mismatches, indicating that the proposed framework strengthens cross-domain generalization and provides practical guidance by relating simple domain diagnostics (e.g., brightness/color and orientation mismatch indicators) to the expected benefits of augmentation and normalization when adapting to new domains. Full article

Stimuli-responsive materials based on renewable biopolymers are highly attractive for developing sustainable chemical sensors. Here, two spiropyran derivatives (SP1 and SP2) were synthesized and covalently grafted onto cellulose, yielding the functional materials Cel-SP1 and Cel-SP2. Cellulose was selected as a biocompatible, biodegradable, and renewable support able to provide a stable, hydrogen-bond-rich microenvironment for chromic responses. Raman spectroscopy confirmed successful esterification, while SEM-EDS analyses revealed preserved cellulose morphology and the incorporation of nitrogen-rich spiropyran moieties. Both materials exhibited pronounced solvatochromic and pH-dependent behaviors in the solid state. Diffuse reflectance measurements revealed distinct bathochromic or hypsochromic shifts depending on solvent polarity and specific solute–matrix interactions, with DMF and DMSO producing the strongest responses. Under acidic vapors, both materials generated new absorption bands consistent with the formation of protonated merocyanine species, whereas basic vapors promoted partial or full reversion to the spiropyran form. Cel-SP1 and Cel-SP2 also displayed solvent- and pH-dependent luminescence, with Cel-SP2 showing a markedly higher sensitivity to protonation. Prototype solvent strips and acid/base vapor indicators demonstrated fast, naked-eye, reversible chromic transitions. These results highlight spiropyran-modified cellulose as an effective, renewable platform for dual solvent and acid/base vapor sensing. Full article

It is understood that 4-nitrophenol (4-NP) and its derivatives/isomers, such as m-NP and o-NP, are considered toxic nitroaromatic pollutants that pose health risks for human beings and have negative impacts on the environment. Therefore, monitoring of 4-NP is of particular importance to avoid the negative impacts of these environmental pollutants on aquatic life and human health. Electrochemical sensors have emerged as the most promising next-generation technology for the detection of environmental pollutants. The electrochemical method has been extensively used for the detection of 4-NP, p-NP, etc., which has delivered an interesting electrochemical performance. This review provides an overview of the advances in electrode modifiers designed for the electrochemical detection of 4-NP and its isomers. This review includes the use of carbon-based materials, metal oxides, metal sulfides, metal-organic-frameworks (MOFs), conducting polymers, MXenes, covalent organic frameworks (COF), and composites for the development of 4-NP electrochemical sensors. Various electrochemical techniques, such as differential pulse voltammetry, square wave voltammetry, linear sweep voltammetry, cyclic voltammetry (CV), electrochemical impedance spectroscopy, and amperometry, are discussed for the detection of 4-NP and other isomers. Full article

Reliable models of the lung environment are important for research on inhalation products, drug delivery, and how aerosols interact with tissue. pH fluctuations frequently accompany real physiological processes in pulmonary environments, so monitoring pH changes in lung-on-a-chip devices is of considerable relevance. Presented here are flexible, miniaturized, inkjet-printed pH sensors that have been developed with the aim of integration into lung-on-a-chip systems. Different types of functional pH-sensitive materials were tested: hydrogen-selective plasticized PVC membranes and polyaniline (both electrodeposited and dropcast). Their deposition and performance were evaluated on different flexible conducting substrates, including screen-printed carbon electrodes (SPE) and inkjet-printed graphene electrodes (IJP-Gr). Finally, a biocompatible dropcast polyaniline-modified IJP was selected and paired with an inkjet-printed Ag/AgCl quasireference electrode. The printed potentiometric device showed Nernstian sensitivity (58.8 mV/pH) with good reproducibility, reversibility, and potential stability. The optimized system was integrated with a developed lung-on-a-chip model with an electrospun polycaprolactone membrane and alginate, simulating the alveolar barrier and the natural mucosal environment, respectively. The permeability of the system was studied by monitoring the pH changes upon the introduction of a 10 wt.% acetic acid aerosol. Overall, the presented approach shows that electrospun-hydrogel materials together with integrated microsensors can help create improved models for studying aerosol transport, diffusion, and chemically changing environments that are relevant for inhalation therapy and respiratory research. These results show that our system can combine mechanical behavior with chemical sensing in one platform, which may be useful for future development of lung-on-a-chip technologies. Full article