Search Results (9,449)

Over the past two decades, organic semiconductors have attracted significant research interest due to their advantageous features, including low-cost fabrication, lightweight properties, and portability, for photonic device applications. In this study, nickel sulfide doped with cobalt $(\mathrm{N}\mathrm{i}\mathrm{S}/\mathrm{C}\mathrm{o})$ nanocomposites were successfully synthesized via a wet-chemical processing technique and used as a dopant in the active layer of thin-film organic solar cells (TFOSCs). The poly(3-hexylthiophene) (P3HT) and [6,6]-phenyl-C61-butyric acid methyl ester (PC61BM) blend was used as the active layer in this investigation. The devices were fabricated with $\mathrm{N}\mathrm{i}\mathrm{S}/\mathrm{C}\mathrm{o}$ nanocomposites at 1 wt%, 2 wt%, and 3 wt% in the active layer to determine the optimal dopant concentration. However, the experimental evidence clearly showed that the solar cell’s performance depends on the concentration of the $\mathrm{N}\mathrm{i}\mathrm{S}/\mathrm{C}\mathrm{o}$ nanocomposites. As a result, the highest power conversion efficiency (PCE) recorded in this experimental work was 6.11% at a 1% doping concentration, compared with 2.48% for the pristine reference device under AM 1.5G illumination (100 mW/cm²) in ambient conditions. The optical and electrical properties of the active layers are found to be strongly influenced by the inclusion of $\mathrm{N}\mathrm{i}\mathrm{S}/\mathrm{C}\mathrm{o}$ nanocomposites in the medium. However, the device doped with 1 wt% $\mathrm{N}\mathrm{i}\mathrm{S}/\mathrm{C}\mathrm{o}$ nanocomposite exhibits the highest absorption intensity, consistent with the better performance observed in this study, which can be attributed to the localized surface plasmon resonance (LSPR) effect. The optical and morphological characteristics of the synthesized $\mathrm{N}\mathrm{i}\mathrm{S}/\mathrm{C}\mathrm{o}$ nanocomposites were comprehensively analyzed using high-resolution transmission electron microscopy (HRTEM), high-resolution scanning electron microscopy (HRSEM), and additional complementary techniques. Full article

This study leverages surface plasmon resonance (SPR) Biacore^TM technology to unveil the diagnostic potential of detecting CXCR4 on extracellular vesicles (EVs). Despite its recognized potential as a cancer biomarker, the presence of CXCR4 on EVs remains underexplored for diagnostic purposes. Using reference material (rEVs), a standardized label-free and real-time SPR biosensor is established to molecularly profile CXCR4-positive EVs. The binding interactions between immobilized antibodies and EVs isolated from different cancer cell lines revealed a unique SPR molecular fingerprint (SPR-MFP) consisting of varying expression levels of the CD9, CD63 and CD81 EV biomarkers, as well as CXCR4. There was a strong correlation between CXCR4 expression on the cellular membrane measured by flow cytometry (FCM) and the CXCR4 SPR signal of purified EVs, indicating that the chemokine receptor is actively transferred to the extracellular space. The Biacore^TM biosensor is able to directly detect and molecularly profile EVs in buffer and spiked in cell culture supernatant supplemented with 10% EV-depleted serum. Altogether, our findings illuminate the potential of SPR Biacore^TM technology in EV-related research as well as reveal the diagnostic potential of EV-associated CXCR4, offering valuable insights and paving the way for medical applications in diseases associated with aberrant CXCR4 expression. Full article

Quantifying river surface flow velocity is essential for hydrodynamic modelling, flood forecasting, and water resource management. Traditional in situ methods provide accurate point measurements but are costly and limited in spatial coverage. Unmanned aerial vehicles (UAVs) offer a flexible, non-contact alternative for high-resolution monitoring. Optical flow is a tracer-independent technique for deriving velocity fields from RGB video, making it well suited to UAV-based surveys. However, its operational use is hindered by the limited availability of annotated datasets and by instability under low-texture or noisy conditions. This study combines a Finite element method (FEM)-based physical flow model with UAV video to generate reference datasets and introduces a modified Recurrent All-Pairs Field Transforms (RAFT) architecture based on multiframe sequences. A Gated Recurrent Unit fusion module (Fuse-GRU) is incorporated prior to correlation computation, improving robustness to illumination changes and surface homogeneity while maintaining computational efficiency. The proposed model delivers stable, physically consistent velocity estimates across multiple rivers and flow conditions. Accuracy improves with higher spatial resolution and moderate temporal spacing. Compared to field measurements, the average angular difference ranged from 8 to 15°. The high error values were mainly caused by inaccuracies in the physical model and by complex river features. These findings confirm that multiframe optical flow can reproduce realistic river flow patterns with accuracy comparable to physically-based simulations, thereby supporting UAV-based hydrometric monitoring and model validation. Full article

The deinking of plastic packaging waste offers the potential of decreasing contamination and thus increasing the overall quality of recycled plastics, enabling their use in more demanding applications. However, for flexible polyethylene packaging waste, deinking is not yet implemented on an industrial scale and there is currently no objective methodology to evaluate the deinking effect on those inhomogeneous flakes. In this study, a novel approach for the objective assessment of ink removal on flexible post-consumer waste (PCW) is proposed. Via an image-based analysis, the transparency of the flakes is transformed into the 8-bit grey scale, and the deinking efficiency of several experiments is compared via the skewness and median of grey value distributions. The method is compared to the International Commission on Illumination (CIE) Lab-method and its robustness against wrinkles and overlaps is critically discussed. Using this analysis method enables the investigation of the general behaviour of contaminated PCW materials in deinking and identifies the most effective parameters for ink removal on inhomogeneous flakes. Full article

Adverse weather and low illumination remain major challenges for autonomous driving perception, where semantic segmentation must stay reliable despite severe appearance degradation. In unsupervised domain adaptation without target annotations, self-training is widely used, but it is often limited by the inconsistent quality of teacher-generated pseudo labels across samples, regions, and training stages. This paper presents RaDA, a reliability-aware self-training framework that regulates pseudo supervision at three levels. First, a progressive exposure strategy determines which target images are admitted for training. Second, spatial reliability weighting suppresses gradients from degraded regions while retaining informative supervision. Third, adaptive teacher update scheduling stabilizes pseudo label generation over time. Experiments on real-world adverse driving benchmarks show that RaDA improves robustness, training stability, and cross-dataset generalization compared with strong baselines. Compared with the previous state-of-the-art method MIC, RaDA achieves mIoU gains of 10.6 percentage points on Foggy Zurich and 8.8 percentage points on the Foggy Driving benchmark. These results indicate that explicit reliability regulation can strengthen self-training domain adaptation for semantic segmentation in autonomous driving under challenging environmental conditions. Full article

This study analyses the influence of yarn composition and knitted macrostructure on the structural and functional performance of passive smart knitted fabrics with optical functionalities. Twelve knitted macrostructures were produced using folded composite yarns combining cotton, reflective, and photoluminescent components and different stitch patterns. Thickness, air permeability, and reflectance under UV and visible illumination were experimentally evaluated. The results indicate that knitted macrostructure primarily controls thickness and air permeability, whereas optical response is governed by yarn composition. Variations in stitch pattern enable regulation of air permeability independent of optical behaviour, while UV-responsive yarn components dominate reflectance performance. The findings support independent optimisation of structural and optical properties through combined yarn and macrostructural design. Full article

Medieval literature often seems to be a remote, irrelevant, incomprehensible world of narrative texts lost in heroic, religious, or courtly themes, limited to stories about King Arthur, courtly lovers, military heroes, and religious martyrs, saints, and prophets. In reality, as any expert can easily confirm, when we turn our full attention to pre-modern literature from across Europe (and also other parts of the world), we can often recognize the true extent to which poets utilized their narratives for spiritual, philosophical, religious, scientific, and medical explorations that have much to tell us today and prove to be deeply meaningful in a timeless manner. One key aspect, which was shared among virtually all medieval artists, poets, and theologians, consisted of the unique experience by an individual who is entitled through a physical opening to see into the depth or the height of all existence and can thus discover a wholly different world. Through this motif of the gaze, an entire epiphanic realization can set in, which thus quickly transforms the purely entertaining narrative medium into a narrative catalyst of profound spiritual experiences, helping the individual to gain inspiration from the Godhead (e.g., mysticism). Indeed, numerous times, medieval poets employed the motif of the visionary gaze, developed in very concrete terms, to trace and explain the process of perspicuity and accompanying acuity which ultimately leads to new intellectual, emotional, and religious understandings and experiences. While many intellectuals already embraced this notion of a visionary concept of spiritual comprehension, it might come as a surprise that secular and religious poets also operated quite intentionally with the concept of a hole in the wall or some other opening as a springboard for intellectual and spiritual experiences, directly drawing from the concepts of the optical sciences as understood at that time. Oddly but highly significantly, Christian and pagan notions tend to intersect in those narrative moments, particularly in late medieval literature, merging the visionary experience with the monstrous within human society, associating the gaze with the erotic and religious dimension. Full article

How does the complex, adaptive, and autonomous organization of life emerge from the laws of physics and information? This review argues that the answer lies in a convergent set of universal organizational principles that constitute a physical and informational grammar of the living. Living systems are dissipative structures that achieve organizational closure—materially and energetically open, yet causally closed—thereby attaining genuine autonomy and agency. Their architecture exhibits fractal and modular scaling laws that maximize energy flow, robustness, and evolvability under universal physical constraints. Critically, organisms operate at critical transitions—zones of controlled instability where fluctuations amplify information processing, transforming noise into adaptive signal. This self-organized criticality enables functional degeneracy, relational redundancy, and evolutionary antifragility. Cognition emerges as a distributed process of active inference, operating through a predictive–corrective cycle that integrates perception, action, and learning under the Free Energy Principle. From molecular networks to ecosystems, the same physico-informational grammars unfold recursively, revealing a deep organizational holography: the principles of organization are replicated across scales. Evolution under the Law of Increasing Functional Information is not random drift, but a directional expansion of functional complexity—a thermodynamic gradient towards greater agency. This synthesis challenges biological exceptionalism: the trajectory from thermodynamics to cognition is continuous, physically constrained, and potentially inevitable. Life does not violate physical laws—it fulfills them in regimes of high informational complexity, instantiating fundamental principles in self-organized architectures capable of prediction, memory, and purpose. The objective of this work is to articulate how the synthesis of these principles not only unifies physics and biology, but also illuminates the profound continuity between thermodynamics, chemistry, informational constraints, organization, and the mind. Full article

Hyperspectral remote sensing (HRS) is gaining widespread adoption within the geoscience and Earth observation communities. It fosters diverse applications, including precision agriculture, soil science, mineral exploration, and carbon detection, to name a few. Recent technological advancements facilitated a growing number of satellite missions as well as an increase in the availability of commercial sensors and platforms, such as drones. A significant challenge in deploying the varied platforms and sensors is the design and optimization of the hyperspectral surveys. Forward modelling simulators are valuable for optimizing mission parameters and estimating imaging performance. Limited accessibility of open-source simulators presents an obstacle for users who seek to benefit from such tools. To bridge this gap, HySIMU (Hyperspectral SIMUlator) was developed and described herein. It is an open-source, forward modelling toolkit that combines and integrates a primary processing pipeline with various open-source packages into a transparent and modular workflow. It offers a cost-effective approach to evaluating the performance of hyperspectral surveys. HySIMU is designed to simulate hyperspectral imagery based on user-defined targets, platforms, and sensor parameters. Features include (i) a ground truth data cube builder for customizable input parameters, (ii) a terrain-based solar and view geometry calculator for illumination modelling, (iii) integrated open-source radiative transfer models for incorporating atmospheric effects, and (iv) spatial resampling filters. In this manuscript, the initial framework for HySIMU is presented with some example applications, including two validation studies with real hyperspectral images. As remote sensing technologies advance, forward modelling toolkits such as HySIMU play a crucial role in refining mission designs and assessing survey feasibility. The scalability for arbitrary hyperspectral sensors, platforms, and spectral libraries ensures broad applicability. Of particular importance is support for parameter optimization for both scientific and commercial HRS campaigns. Full article

Underwater micro-bubble detection entails multiple challenges, including diminutive target sizes, sparse pixel information, pronounced specular highlights and water scattering, indistinct bubble boundaries, and adhesion or overlap between instances. To address these issues, we propose HSMD-YOLO, an improved detector tailored for high-resolution micro-bubble detection and built upon YOLOv11. The model incorporates three novel components: the Scale Switch Block (SSB), a scale-transformation module that suppresses artifacts and background noise, thereby stabilizing edges in thin-walled bubble regions and enhancing sensitivity to geometric contours; the Global Local Refine Block (GLRB), which achieves efficient global relationship modeling with an asymptotic linear complexity ($O\left(N\right)$) in spatial dimensions while further refining local features, thereby strengthening boundary perception and improving bubble–background separability; and the Bidirectional Exponential Moving Attention Fusion (BEMAF), which accommodates the multi-scale nature of bubbles by employing a parallel multi-kernel architecture to extract spatial features across scales, coupled with a multi-stage EMA based attention mechanism to enhance detection robustness under weak boundaries and complex backgrounds. Experiments conducted on an Side-Illuminated Light Field Bubble Database (SILB-DB) and a public gas–liquid two-phase flow dataset (GTFD) demonstrate that HSMD-YOLO achieves mAP@50 scores of 0.911 and 0.854, respectively, surpassing mainstream detection methods. Ablation studies indicate that SSB, GLRB, and BEMAF contribute performance gains of 1.3%, 2.0%, and 0.4%, respectively, thereby corroborating the effectiveness of each module for micro-scale object detection. Full article

Building extraction is a core task in the semantic segmentation of satellite remote sensing imagery. Conventional pixel-level segmentation methods often prioritize texture over geometric structure, resulting in suboptimal performance in complex scenes affected by illumination variations, shadows, and scale changes. In this article, an innovative object-level building extraction approach is introduced to better capture the geometric structure of buildings, which incorporates superpixel segmentation to represent images as a set of adjacent regions. The proposed model consists of a cascade multi-scale fusion module (CMSFM) that progressively integrates contextual information across different receptive fields, along with a boundary-assisted loss function designed to enhance edge delineation and improve object-level accuracy. The experimental results on the WHU building dataset and the Massachusetts Buildings Dataset show that the proposed method notably outperforms other representative semantic segmentation approaches, such as FCN, UNet, DeepLab V3, and SETR. On the WHU dataset, MRLNet achieves the largest MIoU of 90.14% and the highest F1 score of 92.47%. On the Massachusetts Buildings Dataset, MRLNet attains the best MIoU of 83.14% and the highest F1 score of 90.46%. In addition, our building extraction model achieves a substantial performance improvement after the addition of the CMSFM module and the boundary-assisted loss function, demonstrating the effectiveness of these two enhancements used in our proposed model. It is expected that this research can provide a promising tool for the accurate extraction of buildings using satellite remote sensing images, which is indispensable in urban planning, disaster assessment, and other fields. Full article

Photosynthetic algae–microbial fuel cells (PAMFCs) are attractive for energy-positive wastewater treatment and carbon mitigation. However, PAMFC performance under continuous flow is often constrained by limited cathodic electron-acceptor supply and unstable photosynthetic biofilms, while the extent to which cathode interfacial engineering can stabilize diurnal power output and assimilative NH₄⁺–N removal remains unclear. In this study, the sponge-like and petal-like ZnO_0.2-NiO@rGO-modified carbon fibers (ZnO_0.2-NiO@rGO-pCFs and ZnO_0.2-NiO@rGO-pCFp) and pre-fabricated carbon felt (pCF) were used as cathode materials to construct three sets of PAMFC systems. Under light–dark cycling, the engineered cathodes reached steady operation within about 6.5 d and increased the steady-state voltage to approximately 0.35 V, compared with approximately 0.08 V for pCF. Under continuous-flow conditions, cathodic NH₄⁺–N removal exhibited a stable diurnal rhythm, with higher removal during illumination at about 43–51% than in the dark at about 29–30%, consistent with algal assimilation as the primary nitrogen sink, while cathode modification mainly improved the cathodic microenvironment and response stability. Compared with pCF, the ZnO_0.2–NiO@rGO cathode enriched a more even, Chlorophyta-dominated algal biofilm with an approximate relative abundance of 80%, indicating that its selective interfacial environment favors biofilm stabilization and sustains in situ oxygen production and cathodic electron-acceptor supply. Consequently, the composite cathode enhanced voltage output and stabilized light-enhanced, assimilative NH₄⁺–N removal under aeration-free operation, while establishing an interpretable link between electrochemical performance and 18S rDNA-derived community assembly features, thereby providing a low-cost cathode design basis for nitrogen removal in wastewater treatment. Full article

Adaptive façade systems are increasingly used to mitigate glare in daylit spaces and improve visual comfort while supporting sustainable daylight utilisation and reduced reliance on electric lighting in buildings. However, their performance is often evaluated using illuminance-based metrics or uncalibrated simulations, limiting the reliability of glare assessment. This study proposes a calibrated experimental–simulation framework for evaluating glare reduction achieved by a kinetic horizontal shading system (KSS) under real daylight conditions. The approach integrates reduced-scale physical measurements with Radiance-based simulations using a digitally reconstructed twin of the experimental setup. Two geometrically identical test chambers positioned side-by-side—a static reference chamber and a kinetic chamber equipped with six adaptive fins (0.63 m real-scale depth)—were investigated using a 1:20 scale mock-up. Internal illuminance measurements were normalised between chambers, and a sky-scaling procedure was applied to calibrate simulated sky luminance distributions against measured data on an hourly basis, enabling photometrically validated HDR renderings for glare evaluation. Glare performance was analysed for three representative clear-sky days during periods of maximum solar exposure (11:00–17:00) under late-summer conditions at approximately 51° N latitude in Wrocław, Poland. Visual comfort was assessed using Daylight Glare Probability (DGP), Daylight Glare Index (DGI), and veiling luminance (L_veil). The kinetic shading system reduced mean DGP from 0.57 to 0.35 (−38%) and peak glare values by nearly half compared with the static configuration, while veiling luminance decreased by 73%, indicating substantial improvement in physiological visual comfort. These results demonstrate that adaptive fin movement effectively suppresses both perceptual and physiological glare during critical daylight hours. The proposed calibrated experimental–simulation workflow provides a robust and transferable methodology for evaluating the glare performance of adaptive façade systems and supports sustainable daylight management by enabling high daylight availability while maintaining acceptable glare levels in buildings. Full article

In recent years, deep learning has driven remarkable progress in remote sensing change detection (CD); however, practical deployment is still hindered by two limitations. First, CD results are easily degraded by imaging-induced uncertainties—mixed pixels and blurred boundaries, radiometric inconsistencies (e.g., shadows and seasonal illumination changes), and slight residual misregistration—leading to pseudo-changes and fragmented boundaries. Second, prevailing methods follow a static one-pass inference paradigm and lack an explicit feedback mechanism for adaptive error correction, which weakens generalization in complex or unseen scenes. To address these issues, we propose a feedback-driven CD framework that integrates a dual-branch U-Net with deep reinforcement learning (RL) for pixel-level probabilistic iterative refinement of an initial change probability map. The backbone produces a preliminary posterior estimate of change likelihood from multi-scale bi-temporal features, while a PPO-based RL agent formulates refinement as a Markov decision process. The agent leverages a state representation that fuses multi-scale features, prediction confidence/uncertainty, and spatial consistency cues (e.g., neighborhood coherence and edge responses) to apply multi-step corrective actions. From an imaging and interpretation perspective, the RL module can be viewed as a learnable, self-adaptive imaging optimization mechanism: for high-risk regions affected by blurred boundaries, radiometric inconsistencies, and local misalignment, the agent performs feedback-driven multi-step corrections to improve boundary fidelity and spatial coherence while suppressing pseudo-changes caused by shadows and illumination variations. Experiments on four datasets (CDD, SYSU-CD, PVCD, and BRIGHT) verify consistent improvements. Using SiamU-Net as an example, the proposed RL refinement increases mIoU by 3.07, 2.54, 6.13, and 3.1 points on CDD, SYSU-CD, PVCD, and BRIGHT, respectively, with similarly consistent gains observed when the same RL module is integrated into other representative CD backbones. Full article

This study offers a descriptive analysis of complex thinking as a form of cognitive competency among a group of 52 public servants holding local leadership positions in the Philippines. By extending the empirical examination of complex thinking beyond educational contexts and into local public leadership, the study contributes to an emerging line of research on the cognitive competencies associated with decision making in decentralized governance environments. Drawing on complexity theory applied to public decision making, it assumes that local governance requires the capacity to integrate heterogeneous information, anticipate interdependencies, and act under conditions of uncertainty. The assessment employed the eComplexity instrument using an adapted 21-item version structured into four dimensions: systemic, scientific, critical, and innovative thinking. Scores were rescaled to a 0–100 metric and, after confirming non-normality (Shapiro–Wilk), non-parametric tests were applied (Mann–Whitney, Kruskal–Wallis, and Dunn’s post hoc test with Bonferroni correction), along with Spearman’s rho correlations to examine dimensional coherence. No significant differences were observed by gender or income. Age showed overall variation across several dimensions, but robust pairwise differences were concentrated between the 31–40 and 41–50 age groups in systemic thinking and in the global score. Employment status differentiated only scientific thinking, with higher medians among permanent staff than contractual/project personnel. Correlations among dimensions were positive and significant, with particularly strong associations between systemic, critical, and innovative thinking, supporting the interpretation of complex thinking as an integrated competency in local public leadership. The findings should be interpreted considering the study’s descriptive design, localized convenience sample, and reliance on self-reported measures, which limit statistical generalizability beyond the analyzed context. Beyond its descriptive findings, the study offers initial empirical evidence relevant to governance research on the cognitive competencies associated with decision making among grassroots public leaders operating in decentralized institutional contexts. Examining complex thinking at this level helps illuminate how public actors interpret interdependencies, evaluate information, and navigate uncertainty in everyday governance practice. Full article