Abstract: Accurate detection of special targets in unmanned aerial vehicle (UAV) remote sensing imagery under complex degradation conditions remains a critical challenge for intelligent surveillance systems. Existing detectors exhibit significant performance degradation when confronted with composite degradation factors such as blur, rain, snow, fog, low illumination, strong light, and electromagnetic interference. To address this limitation, we propose RHG-DETR (Riemannian Hyper-Graph Detection Transformer), a novel detection framework for robust special target detection under multi-type degradation in UAV remote sensing imagery. Using RT-DETR as the baseline, three synergistic innovations are introduced at the backbone, neck, and encoder levels. The Dynamic Receptive-field Hyper-graph Attention Network (DRHANet) replaces the conventional ResNet backbone, employing anisotropic dynamic depthwise separable convolution and a Riemannian Hyper-graph Fusion (RHGF) mechanism to model high-order semantic topology dependencies among target components. The Bi-directional Weighted Adaptive Fusion Network (BWAFN) constructs a two-stage bidirectional feature pyramid with learnable scale contribution weights and a lightweight spatial compensation upsampler to maintain cross-scale semantic consistency under atmospheric degradation. The Adaptive Sparse Multi-scale Encoder with Dynamic normalization (ASMED) reconstructs the AIFI encoder module by introducing sparse window self-attention to suppress background interference, a spatial-gated feedforward fusion to preserve geometric topology constraints of target sub-components, and coordinated dynamic normalization modules to stabilize encoding under extreme illumination and electromagnetic interference. On a self-constructed special target dataset comprising tanks, multiple launch rocket systems, and soldiers under seven degradation types, RHG-DETR achieves an mAP50 of 78.5%, surpassing the RT-DETR baseline by 3.7%, while reducing GFLOPs and parameter count by 34.4% and 28.8%, respectively, at an inference speed of 84.2 FPS. Consistent improvements on VisDrone2019 and BDD100K further validate the cross-domain generalization capability of the proposed framework.

Abstract: Real-time quantitative precipitation estimation (QPE) from weather radar is essential for hydrological forecasting, flash flood warning systems, and water resource management. Despite significant advances in radar technology and signal processing, operational QPE systems face persistent challenges including non-meteorological clutter contamination, signal attenuation, vertical profile biases, and systematic errors that require integration with ground-based rain gauge networks. This review synthesizes recent developments in open-source frameworks for radar QPE, spanning the complete processing chain from raw signal correction to operative hydrological validation. We examine state-of-the-art methods for clutter removal (polarimetric fuzzy logic, CLEAN-AP, neural network quality control), C-band attenuation correction (self-consistent and KDP-based approaches), and vertical profile of reflectivity (VPR) correction for warm-rain events. We compare gauge-radar merging techniques including mean field bias adjustment, spatially variable corrections, Kriging with External Drift (KED), and Conditional Merging, with emphasis on real-time applicability and look-back window strategies. The review identifies key open-source Python libraries (wradlib, Py-ART, pySTEPS, radproc, weatherDataHarmonizer) and documents operational latency constraints for flash flood warning systems. A critical research gap is identified: current open-source solutions lack documented workflows for integrating delayed 24-hour manual gauge readings into real-time QPE streams while maintaining low latency. This review provides researchers and practitioners with a comprehensive roadmap for developing robust, open-source, real-time radar QPE systems suitable for operational hydrological applications.

Abstract: This study introduces the Children's Empathy for Older Adults (CEOA) eight-item scale, a novel image-based instrument designed to measure young children's views, empathy, and behavioural intentions toward older adults. CEOA was administered as a pre-test and post-test metric, following storytelling sessions, on 232 children aged 5-6 years in the multi-racial and multi-cultural context, Singapore. Findings revealed that children with regular exposure to grandparents demonstrated clearer, more distinct responses across all three domains, indicating a more developed understanding of older adults’ needs. In contrast, children without such exposure showed less differentiation between cognitive, affective, and behavioral components. These results underscore the importance of intergenerational contact in shaping children’s perceptions and empathy for older adults. The CEOA scale is a valuable tool for future research and interventions aimed at fostering positive intergenerational relationships.

Abstract: This paper examines whether the rise of remote work following the COVID-19 pandemic has generated a structural transformation in urban spatial organization across major metropolitan areas in advanced economies. While much of the existing literature treats COVID-19 as a temporary shock, this study argues that it has induced a persistent reconfiguration of cities toward more polycentric and decentralized spatial structures.Using a multi-source dataset combining Google mobility reports, NASA/VIIRS night-time light satellite data, OECD and national labor force surveys, and urban economic indicators, the study constructs a novel Urban Polycentricity Index (UPI) to measure spatial dispersion of economic activity. The empirical analysis covers New York, London, Paris, Berlin, and Munich over the period 2019–2025.The methodology integrates structural break tests, difference-in-differences estimation, and spatial equilibrium modeling to identify both the timing and magnitude of post-COVID spatial shifts. Results indicate a significant structural break around 2020–2021, followed by a sustained increase in remote work adoption and urban polycentricity. Satellite and mobility data confirm a systematic redistribution of economic activity from central business districts toward suburban and peripheral zones.Findings show that remote work is a statistically significant driver of urban decentralization, associated with flatter density gradients, reduced commuting intensity, and higher polycentricity. Counterfactual simulations further confirm that, without remote work expansion, cities would have remained substantially more monocentric. Overall, the study demonstrates that COVID-19 has permanently altered urban spatial equilibrium, positioning remote work as a key structural force reshaping metropolitan form.

Abstract: Integrated care is increasingly shaped by digital infrastructures, data governance, and AI-enabled analytics, yet the relevant literature remains fragmented across health-services research, digital health, and machine learning. This article presents a conceptual review informed by structured scoping searches across PubMed, Scopus, Semantic Scholar, Crossref, and selected policy sources covering January 2001–March 2026. The search component was used to map the field and identify representative frameworks, implementations, and technical advances rather than to estimate pooled effects. We synthesise the literature across four domains: conceptual foundations of integrated care, AI and multimodal analytics, implementation barriers, and digital-governance requirements. On that basis, we propose a five-level taxonomy ranging from disease-specific programmes to learning integrated care models and argue that most current deployments remain concentrated at digitally integrated but only weakly adaptive Type IV configurations. Across the literature, three recurrent constraints limit progression towards Type V learning systems: temporal blind spots, maintenance debt, and governance misalignment. Overall, the review positions AI-enabled integrated care less as a finished model than as an emerging design space requiring longitudinal data assets, stewarded model lifecycles, and accountable governance to support clinically useful, equitable, and trustworthy learning systems.

Abstract: The global drive towards sustainability and energy conservation has accelerated the development of intelligent buildings utilizing building management system (BMS). Occupants have profound impacts on building environment. Incorporating occupant-related factors into the environmental control process is essential for optimizing the efficiency of BMS, which thus give rise to the concept of occupant-centric control (OCC). Conventional methods rely on simplified models and fixed schedules that fail to satisfy environment control and occupant requirements, while constructing credible models places strict requirements on the dataset. In this paper, we propose a Model-Aware Predictive Control framework named MAPC, which can construct credible models with limited data and provide room-level control strategies allowing for occupant comfort and energy efficiency. Its technological innovations are twofold. On the one hand, we design a model construction and fine-tuning method combining data-driven subspace projection approach with physical priors, which can construct credible thermal dynamic models with limited data. On the other hand, to balance the potential conflicts between enhancing occupant comfort and saving energy, we present a hierarchical decision-making mechanism, which enables room-level global optimal control considering dynamic occupant comfort requirements and energy usage. Experimental results obtained on a typical duplex apartment dataset demonstrate that MAPC is able to provide room-level control strategies based on dynamic occupant requirements and user preferences, achieving improved occupant comfort and energy efficiency. The ablation experiments also demonstrated the superiority of MAPC in constructing reliable models on limited datasets.

Abstract: As the global average temperature increases, heat stress (HS) caused by high temperatures has become a key constraint to the development of the poultry industry. As the primary metabolic organ, HS can induce liver injury in chickens, thereby compromising food safety. However, the precise mechanisms underlying HS -induced liver injury remain to be elucidated. The objective of this study is to explore the impact of HS on liver damage, oxidative stress, the Keap1-Nrf2 pathway, ferroptosis and cuproptosis in chickens. A total of 70 chickens were selected for this experiment and divided into a CON group and HS group: the CON group was reared in a normal-temperature environment (24 ± 1 ℃), whilst the HS group was reared in a high-temperature environment (33 ± 1 ℃). The findings of the study suggested that HS has the potential to induce liver dysfunction, oxidative stress, and disruption of the Keap1-Nrf2 pathway. HS has been demonstrated to induce Fe2+ accumulation in chicken livers, inhibit the expression of FTH1, FSP1, SLC7A11 and Gpx4, and simultaneously upregulate the expression of CD71, PTGS2 and ACSL4, thereby promoting ferroptosis. Furthermore, Cu2+ accumulation in the liver upregulates HSP70, DLAT and Lip-DLAT levels and downregulates the expression of ATP7B, PDH1A, PDHB, PDK4, DLST and FDX1, thereby inducing cuproptosis. Subsequent correlation analysis revealed that HS can induce ferroptosis and cuproptosis via the HO-1/FDX1/Gpx4 pathway. This finding provides new insights into the mechanisms underlying HS-induced liver injury.

Abstract: Circadian rhythms are fundamental regulators of bone remodeling, orchestrating the co-ordinated actions of osteoblasts, osteocytes, and osteoclasts. Recent studies have high-lighted how core clock genes, such as Bmal1, Clock, Per1/2, and Cry1/2, exhibit rhythmic expression in bone tissue and modulate key markers of bone formation and resorption. Disruptions in circadian regulations, whether caused by environmental factors or genetic alterations, have been linked to osteoporosis, impaired fracture healing, and increased risk of bone fragility. This review provides a comprehensive evaluation of current experi-mental models used to study circadian regulation in skeletal biology, including in vivo, ex vivo, and in vitro approaches. We summarize their respective advantages and limitations and outline the molecular and cellular markers employed to assess circadian function in bone cells. We also discuss the emerging co-culture models and human-relevant plat-forms, for their potential to bridge the gaps between mechanistic research and transla-tional applications. By comparing model characteristics and highlighting integrated re-search strategies, this review aims to advance circadian bone research and inform future investigations into potential temporal aspects of skeletal health.

Abstract: This paper consolidates, corrects, and extends a research programme on the shifted-prime problem $p = q + r - 1$ with $p, q, r$ prime and its connections to the binary Goldbach conjecture and the non-trivial zeros of the Riemann zeta function $\zeta(s)$. New material over Version 6. The principal addition is a rigorous three-level treatment of the restricted Goldbach sum \[ P_{R_{3,4}}(N) = \sum_{\substack{p+q=N,\\ p\equiv 3\ (\mathrm{mod}\,4)}} (\log p)(\log q). \] At Level 1 [PROVED] (unconditional), the ``almost-all'' theorem of Montgomery--Vaughan type shows that the exceptional set of even integers $N\leq X$ for which $|R_{3,4}(N) - \tfrac{1}{2}C_2 S(N)N|$ exceeds $CN/(\log N)^3$ has measure $O_A\bigl(X/(\log X)^A\bigr)$ for every $A>0$. At Level 2 [PROVED] (unconditional), a transfer inequality bounds $|R_{a,q}(N)-\phi(q)^{-1}R(N)|$ in terms of twisted sums $S_\chi(N)$ with mean-square control. At Level 3 [COND. PROVED, GRH], for all sufficiently large even $N$ one has $R_{3,4}(N)=\tfrac{1}{2}C_2 S(N)N + O(N^{1/2+eps})$. Anderson's original claim of an explicit unconditional constant $K\leq 28.65$ for all $N$ is identified as relying on the Hardy--Littlewood binary asymptotic for each individual $N$, which is itself a conjecture; the claim is accordingly downgraded and the gap stated precisely. Retained from Version 6. Five analytical gaps (A--E) in the Goldbach--Riemann bridge for $\Psi^*(x)$ are fully closed unconditionally (Gaps D1, D2, D3, E) or under GRH (Gap C). The corrected spectral-detection results stand: $\lambda_1/\lambda_2 = 182.63$ ($n=892\,206$); 129/200 Riemann zeros detected at $p<0.01$ ($n=1\,310\,763$); Mellin--Lomb--Scargle concordance 29/30 versus 0/30; 9/10 direct Pearson correlations significant; heteroscedasticity of $eps(p)$ formally confirmed ($p=4.7\times 10^{-14}$). Principal corrections retained from Version 6. The $k=3$ existence problem is equivalent to binary Goldbach (open). The permutation-test bug in scripts~6.py--8.py is corrected ($199/200\to 129/200$). The formula for $S_\infty^{(k)}$ is corrected for $k\geq 3$. None of these results constitutes a proof of the Riemann Hypothesis. All claims carry explicit epistemic labels.

Abstract: Recently, a new generalization of hyperbolic functions called para-hyperbolic functions has been introduced. However, properties of the para-hyperbolic functions have not been investigated yet. In this paper, we derive the correct explicit formulas of the para-hyperbolic sine and cosine, study elementary properties of these functions, and explore which of the inequalities that hold for trigonometric and hyperbolic functions find their counterparts for para-hyperbolic functions. Namely, we prove a Wilker type inequality, Cusa-Huygens and Lazarević type inequality, Wu-Debnah modification of Wilker type inequality and Shafer type inequality for para-hyperbolic functions.

Abstract: The Collatz (3x + 1) conjecture remains one of the most challenging open problems in number theory, largely due to the unpredictable, pseudo-random fluctuations of its discrete integer o rbits. This paper introduces an interdisciplinary approach by translating discrete arithmetic rules into a continuous dynamical sandbox. Specifically, we construct a symbolic analogy between the 3x + 1 map and the Logistic map f (x) = 1 − µx² locked at the superstable period-3 window (µ ≈ 1.7549). By building a customized threshold partition anchored at the unstable fixed point, the continuous system naturally enforces a “forbidden word 11” grammar, mirroring the arithmetic constraint that an odd operation (T(n) = 3n + 1) must produce an even number. Through the eigenspectrum of the Perron-Frobenius transfer operator, we demonstrate a 2:1 ergodic measure ratio for contraction (even) and expansion (odd) states—a direct geometric consequence of the period-3 attractor structure. We validate the ro-bustness of the spectral quantities through convergence studies across multiple discretization schemes. Null-model controls show that the sandbox captures aspects of the global stopping-time distribution that a generic forbidden-11 Markov chain does not, while run-length analysis reveals that local arith-metic statistics (ν2-valuations) are better reproduced by the simpler null model. This mixed result delineates the sandbox as a partial surrogate: useful for global transient statistics, but not a replacement for the actual arithmetic dynamics. This study offers a heuristic framework positioning coarse-grained transient dynamics as a null-model approach for Collatz statistics, with explicitly characterized failure modes.

Abstract: The bioactive plant peptides represent a significant and yet largely underexploited resource with huge potential not only for basic plant science but also for various biotechnological applications, including pharmaceutical and agrochemical development. bioactive plant peptides represent a significant and yet largely underexploited resource with huge potential not only for basic plant science but also for various biotechnological applications, including pharmaceutical and agrochemical development. This fastexpanding research area of plant peptidomics demands the creation and continuous updating of dedicated databases that facilitate data integration of heterogeneous nature and enable efficient knowledge discovery. The most representative databases, like PlantPepDB and PhytAMP, but also recent multi-purpose databases like MFPPDB, are reviewed here in terms of data sources used-literature and public repositories-manual curation extent, functional classification-such as therapeutic, defense, and inhibitory-and the availability of relevant metadata on physicochemical properties and structure. While databases focused on specific bioactivities of plant peptides offer high-quality, focused data, broader repositories are crucial for discovering multifunctional peptides and structure-activity relationships. The refinement and integration of these databases, alongside advanced bioinformatics tools, remain essential for overcoming these hurdles. These resources stand to facilitate innovation in ways that will continue to illuminate insights into the molecular function of plants and allow the successful harnessing of plant peptides toward human health improvements and sustainable agriculture. This review briefly introduces the progress of plant peptide research, presents an overview of plant peptide studies, and provides a comprehensive analysis of existing plant peptide databases, evaluating their scope, content, and utility. We anticipate that this work will bridge the gap between peptide discovery and the development of nextgeneration plant peptide databases.

Abstract: We introduce a new notion, that of a contextuality profile of a system of random variables. Rather than characterizing a system's contextuality by a single number, its overall degree of contextuality, we show how it can be characterized by a curve relating degree of contextuality to level at which the system is considered, \( \begin{array}{c|c|c|c|c|c|c|c} \textnormal{level} & 1 & \cdots & n-1 & n>1 & n+1 & \cdots & N\\ \hline \textnormal{degree} & 0 & \cdots & 0 & d_{n}>0 & d_{n+1}\geq d_{n} & \cdots & d_{N}\geq d_{N-1} \end{array} \), where N is the maximum number of variables per system's context. A system is represented at level n if one only considers the joint distributions with \( k\leq n \) variables, ignoring higher-order joint distributions. We show that the level-wise contextuality analysis can be used in conjunction with any well-constructed measure of contextuality. We present a method of concatenated systems to explore contextuality profiles systematically, and we apply it to the contextuality profiles for three major measures of contextuality proposed in the literature.

Abstract: Companion dogs are increasingly recognized as translational models for studying human physiology and disease. Unlike conventional or genetically engineered laboratory models, dogs are outbred, immunocompetent animals that spontaneously develop complex diseases whose pathogenesis and environmental exposures commonly overlap with those of humans. These distinctive features create opportunities to study mechanisms of disease, progression, and therapeutic responses under conditions that more closely resemble clinical reality. This review highlights evidence for the translational relevance of canine models across multiple therapeutic areas. We further discuss how advances in genomics, transcriptomics, spatial biology, in vitro, and in silico model systems are expanding the translational utility of canine models for applications in human medicine. Although important species differences must be carefully weighed, dogs represent a uniquely valuable comparative model for elucidating disease mechanisms, informing drug development, and accelerating the translation of scientific discoveries to human medicine.

Abstract: Oleoresins are complex natural lipophilic matrices traditionally analyzed using chromatographic techniques that require extensive sample preparation, derivatization, and authentic standards. Amazonian oleoresins from Copaifera and Eperua species (Fabaceae) represent valuable bioresources with recognized pharmacological potential, largely attributed to diterpenoids such as copalic and hardwickiic acids, as well as bioactive sesquiterpenes, including the cannabinoid b-caryophyllene. In this study, we present a proof-of-concept application of Direct Analysis in Real Time coupled with High-Resolution Mass Spectrometry (DART-HRMS) as a rapid, direct, and environmentally friendly approach for chemical fingerprinting and semi-targeted screening of the two most important amazonian oleoresins from these two genera: Eperua oleifera and Copaifera multijuga. Analyses were performed using a Q Exactive Orbitrap coupled to a DART ion source under after conditions optimization. Hardwickiic acid was used as a model compound for method optimization, with optimal performance achieved at 200 °C and 100 V, yielding stable signal intensities (CV &lt; 10%) and high mass accuracy (&lt; 1 ppm). The method enabled reproducible detection of diterpenic acids in both oleoresins, allowing differentiation of their chemical profiles and assessment of short-term stability under ambient conditions. In addition to diterpenes, free fatty acids were also detected, expanding the compositional characterization of these matrices. Compound annotation was performed based on accurate mass measurements and literature comparison, corresponding to Level 5 confidence according to established metabolomics criteria. Although the absence of chromatographic separation limits isomer discrimination and absolute quantification, DART-HRMS provides a rapid and solvent-free strategy for chemical fingerprinting and preliminary characterization of oleoresins. This approach aligns with Green Chemistry principles and shows strong potential as a screening and triage tool for quality control, chemotaxonomic studies, and sustainable valorization of Amazonian natural products.

Abstract: Ear biometrics has emerged as a promising alternative in biometric recognition systems, offering robustness in unconstrained environments where traditional modalities such as face recognition may fail on its own, but can be enhanced by ear. Ear segmentation, in particular, plays a crucial role in downstream recognition by isolating discriminative ear regions and reducing background interference. However, existing approaches to ear detection and segmentation are commonly susceptible to severe occlusions, ear accessories, and variable illumination, and their performance deteriorates on images captured in the wild. To address these limitations, we introduce a tailored ear-segmentation architecture based on a U-Net with a ResNet-50 encoder. Trained and validated on the Annotated Web Ears (AWE) dataset, our method achieves a mean Intersection over Union (IoU) of 77.1% and a pixel-wise accuracy of 99.7%, outperforming the Convolutional Encoder--Decoder (CED) baseline. We further evaluate on the EarSegDB-25 dataset, where our approach attains a test-set IoU of 94.76%, significantly surpassing previous ear segmentation methods based on the original U-Net architecture. High pixel-wise accuracy across methods is largely attributable to background dominance; in contrast, the improved IoU achieved by our approach more accurately reflects gains in ear region segmentation performance. Leveraging a ResNet-50 encoder, our model demonstrates robust performance under occlusion and illumination challenges, achieving state-of-the-art results on AWE and EarSegDB-25 and showing strong potential for biometric applications in unconstrained environments.

Abstract: Chili pepper (Capsicum spp.) plays a crucial cultural, nutritional, and economic role in Senegal, supporting smallholder livelihoods, domestic food systems, and regional export markets. However, post-harvest losses associated with traditional open-air drying and storage remain high, particularly under coastal conditions characterized by high relative humidity and variable weather. Improving drying assessment and storage practices is therefore essential to enhance chili quality, safety, and marketability. This study evaluated smallholder chili drying and storage practices in Senegal, with a particular focus on processor dryness judgment, drying surfaces, and storage materials, and assessed the potential role of a simple equilibrium relative humidity (ERH)-based decision-support tool. A participatory, on-farm study was conducted with six chili processors in the Niayes agroecological zone of western Senegal. Freshly harvested chilies were dried concurrently using black plastic sheeting and processors’ customary drying materials under farmer-managed conditions. Processor assessments of storage readiness were compared with ERH-based classifications using the DryCard tool. Drying duration and dry matter content were recorded for each treatment. Dried chilies were subsequently stored in either airtight glass jars or traditional gunny bags, and weight changes were monitored at 25, 45, and 70 days after storage. Data were analysed descriptively using within-processor comparisons to identify consistent patterns across sites. After 15 days of drying, all chili samples were classified as not dried for storage, with DryCard readings of approximately 75% ERH across sites. Final processor-determined drying durations ranged from 29 to 42 days. DryCard ERH values at this stage ranged from 30% to 70%, with three instances where processor judgement indicated storage readiness despite ERH values exceeding the 65% threshold. Dry matter content varied widely across treatments and sites, ranging from 7.4% to 18.3%. Chilies dried on black plastic sheeting showed higher and more consistent dry matter content (median ≈ 11.5%) compared with chillies dried on processor-preferred materials (median ≈ 9.1%). During storage, chilies kept in airtight glass jars maintained stable weights close to the initial 200 g over 70 days (199–201 g). In contrast, those stored in gunny bags showed progressive weight increases, reaching median values of approximately 208.5 g after 70 days. The study demonstrates that while experiential knowledge remains central to smallholder chili processing, it can be strengthened with simple, objective tools that support more reliable drying and storage decisions. Incremental improvements in drying surface selection, combined with ERH-based assessment and moisture-limiting storage, offer practical pathways for reducing post-harvest losses and quality risks. These context-appropriate interventions can enhance the safety, stability, and economic value of dried chilies without requiring costly infrastructure, making them well-suited to smallholder systems in humid environments.

Abstract: Side-scan sonar is a critical instrument for underwater cultural heritage preservation, as it allows large-scale detection of shipwrecks in turbid waters where optical methods fail. However, the automated segmentation of these targets remains a significant challenge, as severe speckle noise and complex seabed reverberations often obscure the distinct geometric features of submerge structures. To address this challenge, this paper proposes SW-Net, which utilizes a multi-scale input strategy and a novel Directional Filter Bank to inject physical priors into the feature extraction process. Furthermore, by coupling this with a Directional Attention Mechanism, the network dynamically modulates structural features to accurately segment targets despite intensity inversions and speckle noise. As demonstrated by the experimental results on the AI4Shipwrecks dataset, the SW-Net outperforms five state-of-the-art architectures, achieving the highest intersection over union of 39.26% and F1-score of 56.38%. In addition, the model exhibits superior robustness against complex seabed interference while maintaining the lowest computational complexity of 4.01 million parameters among the evaluated methods. Taken together, the SW-Net is proposed to offer a practical solution for shipwreck detection on resource-constrained autonomous underwater vehicles.

Abstract: The persistence of pharmaceutical contaminants such as carbamazepine (CBZ) in aquatic environments presents a growing challenge for conventional wastewater treatment processes. In this study, potato peel waste was valorised into carbonaceous adsorbents via hydrothermal carbonization (HTC) and conventional pyrolysis, and their performance for CBZ removal from water was systematically compared. Hydrochars were prepared at 200 °C under varying residence times and biomass-to-water ratios, while biochars were produced at 400 °C using KOH activation under different reaction times and impregnation ratios. The materials were characterised using BET surface area analysis, CHNS elemental analysis, and FTIR spectroscopy. Adsorption experiments revealed that HTC-derived hydrochars achieved outstanding CBZ removal efficiencies (up to ~100%) and high uptake capacities (~50 mg g⁻¹) within one minute of contact, despite relatively low surface areas (< 2 m² g⁻¹). In contrast, pyrolysis biochars exhibited significantly lower removal efficiencies (7–55%) and slower, less stable adsorption behaviour. Correlation analysis demonstrated that CBZ removal was strongly associated with surface chemistry—particularly carbon, hydrogen, and nitrogen content and N/C ratio—rather than BET surface area or pore diameter. FTIR analysis indicated that π–π interactions, hydrogen bonding, and pore filling collectively govern CBZ adsorption, with oxygen- and nitrogen-containing functional groups playing a dominant role in rapid uptake. These findings highlight hydrothermal carbonization as an effective, low-severity route for producing high-performance adsorbents from food waste and demonstrate the potential of potato peel–derived hydrochars for rapid pharmaceutical remediation in water treatment applications.

Abstract: Cell-derived microparticles with an equivalent diameter below ~1 μm have been observed in a wide spectrum of chronic inflammatory diseases, many of which also present with overlapping comorbidities. These same disorders consistently show microcirculatory disturbances—detectable by nailfold capillaroscopy, laser Doppler techniques, laser speckle imaging or thermal perfusion mapping, and, where measured, the presence of amyloid-containing fibrinaloid microclots ranging from 1–200 μm. Although both microparticles and fibrinaloid microclots have been independently described in numerous conditions, their systematic correspondence has not previously been synthesised. In this commentary, we highlight how the co-occurrence of microparticles and fibrinaloid microclots reflects a shared pathophysiological axis involving endothelial injury, thrombo-inflammation and persistent protein misfolding. We outline the conceptual links underlying this pattern and argue that combined measurement of microparticles and fibrinaloid microclots could offer new diagnostic and mechanistic insights across chronic inflammatory diseases.