Abstract: Land degradation caused by soil erosion is a major challenge in Mediterranean sloped agroecosystems, where extreme weather events and conventional land management practices accelerate soil loss and threaten long-term sustainability. This study evaluates and compares three complementary approaches to estimate soil erosion in an olive orchard in Messenia, Greece. Field-based runoff plots provided direct measurements of sediment yield, drone-based LiDAR surveys enabled soil surface change detection through the Difference of Digital Elevation Models (DoD) method, and the Revised Universal Soil Loss Equation (RUSLE) was applied to model erosion risk using site-specific parameters. Results indicate that field measurements and RUSLE estimates are broadly consistent, particularly when the model is calibrated with empirical data, offering reliable insights into soil loss dynamics. In contrast, the LiDAR–DoD approach was used to characterize soil surface displacement rather than to directly quantify soil erosion. Due to methodological and technical limitations, LiDAR–DoD results are presented primarily as a framework for future research rather than as a definitive erosion assessment tool. Overall, the integration of field monitoring, remote sensing, and modeling highlights the strengths and limitations of each method and demonstrates the value of multi-method approaches for improving erosion assessment and supporting sustainable land management in vulnerable Mediterranean landscapes.

Abstract: We present a complete, parameter-free derivation of the bandlimited Green's function G = \sin (\sqrt{- \sigma^2}) and the celestial conformal weights \Delta_l = l + 1 from a single input: four-dimensional Minkowski spacetime (M,\eta_{\mu \nu}). The derivation proceeds in three steps. First, the exactness of the exponential map \exp_p: T_p M \to M in flat spacetime, combined with the requirement that discrete sampling be isometrically equivalent to the continuous field, uniquely determines—via the Whittaker interpolation theorem—the reproducing kernel G = \sin (\Omega \sqrt{- \sigma^2}). Second, the null geodesic locus \sigma^2 = 0 emerges as the natural boundary through the reproducing kernel normalisation condition K(x,x) = 1; restriction to this null hypersurface induces a signature flip from Lorentzian (- , + , + , +) to Euclidean (+, +) on the transverse S^2. Third, the SL(2,\mathbb{C}) principal-series representation on the Euclidean celestial sphere, combined with the spherical Bessel decomposition of G, yields \Delta_l = l + 1 as a pure spectral theorem with no free parameters. The result is cross-validated by five independent routes: Kempf's operator-theoretic reconstruction, the present geometric construction, a boundary RKHS derivation, Pasterski-Shao-Strominger from scattering amplitudes, and Gover-Shaukat-Waldron tractor calculus providing the SO(4,2) group-theoretic skeleton explaining why all five routes converge. The scale \Omega is structurally irrelevant: all physical conclusions depend only on the Minkowski metric. We identify the null-geodesic data set as a natural basis for geometric consistency checks, and note that if the universe is a quantum state, the multi-path convergence in principle circumvents the classical cosmic variance bound.

Abstract: AI-enabled tourism platform services across East Asia often generate privacy concern while continuing to attract user participation. Rather than treating this pattern as a simple contradiction, this study interprets it as privacy satisficing, in which users remain willing to participate once platform conditions are perceived as sufficiently acceptable. Using a symmetric adult survey from Fujian, China (N = 185) and Busan–Gyeongnam, Korea (N = 187), the study examines how privacy concern and accountability visibility are associated with willingness to use AI-enabled tourism platform services. Diagnostic heat maps and bootstrap checks show generally high willingness to use (typically 0.70 or higher) across most conditions, with stronger accountability sensitivity in Busan–Gyeongnam and more stable participation in Fujian. The findings suggest that continued participation is shaped not only by privacy concern itself but also by perceived accountability visibility and operational reliability.

Abstract: Genome editing is widely used and conceptually simple, yet in practice, it is hindered by laborious workflows and high costs. These challenges stem from the difficulty of identifying and isolating cells that contain the desired user‑defined modifications, a problem compounded by the wide variability in editing efficiencies across cell types. While homology-directed repair (HDR) provides a mechanism for precise genome modification following nuclease-induced double-strand breaks (DSBs), it is frequently outcompeted by the dominant mutagenic non-homologous end-joining (NHEJ) pathway in mammalian cells. Therefore, we developed a novel enrichment method, Essential HDRescue, to increase the frequency of HDR events at a target site by co-targeting an essential genomic locus. Using both intrinsic positive and negative selection at a common essential gene, we enabled enrichment of precise editing events at a second, unlinked target site. We demonstrated that co-targeting essential genes in cancer cell lines and iPSCs increased HDR rates without the need for an exogenous reporter or selective drug. Analysis of resulting clones revealed that Essential HDRescue produced up to a 6‑fold increase in single‑allele edits and almost a four‑fold increase in homozygous edits relative to single‑targeted controls. By harnessing the intrinsic cellular dependencies that arise from DSB repair at essential loci, Essential HDRescue offers a widely applicable method to improve precise genome editing outcomes in mammalian cells, leaving only a minimal, protein-silent scar at the essential gene.

Abstract: In this paper, we investigate the feasibility of using Large Language Models (LLMs) for the structural analysis of flow-based network data, considering the fundamental onto-logical difference between the multidimensional numerical space of IoT data and the symbolic space in which these models operate. The primary objective was the development of a formal framework that enables the controlled transformation of numerical data into linguistically analyzable semantic representations, without resorting to classification or machine-learning mechanisms. We propose the SFE mechanism, a deterministic method for robust discretization and behavioral abstraction that converts the numerical characteristics of IoT flows into structural semantic descriptions, based on the CIC IoT-DIAD 2024 [1] dataset. Through formal informational measures, we demonstrate the existence of an intrinsic structural difference between benign and DDoS traffic in the analyzed dataset. In the validation stage, we evaluated whether these informational differences are reflected at the level of linguistic abstraction through controlled inference experiments in IBM WatsonX [2]. The paper demonstrates that LLMs can work as mechanisms for semantic auditing of distributional structure when supported by a formal encoding layer, offering a reproducible framework for integrating numerical security data into language-model-based analysis.

Abstract: A method for analyzing long-term (1997-2025) continuous records of low-frequency global seismic noise measured at a network of 229 broadband seismic stations distributed across the Earth's surface is proposed. The method is based on the use of nonlinear multifractal and entropy statistics, evaluated daily in successive time intervals. The method is based on the use of first-principal component analysis, correlation analysis, and parametric models of point process intensity. The relationships between changes in seismic noise properties and the response of noise properties to the irregularity of the Earth's rotation with the sequence of strong earthquakes, including those of a predictive nature, are investigated.

Abstract: The ongoing gap between the local expansion rate of the universe (H0) and the global rate inferred from the Cosmic Microwave Background has triggered a genuine crisis for the standard ΛCDM model. Most attempts to patch this Hubble Tension rely on early dark energy or modifications to General Relativity—approaches that usually require injecting unconstrained variables into the math. We propose a strictly thermodynamic resolution instead. By modeling the universe as a closed system governed by information conservation, we can redefine Dark Energy. It is not a uniform, static vacuum energy; it is an emergent, dynamic osmotic pressure. When we apply standard fluid dynamics to the local KBC Void (δ≈−0.46), the elevated local Hubble constant (Hlocal≈73.0km/s/Mpc) mathematically drops out of the global baseline (Hglobal≈67.4km/s/Mpc) as a direct consequence of osmotic decompression. Beyond expansion rates, this closed-system boundary establishes a foundational cosmic noise floor. This allows us to derive the MOND acceleration threshold (a0≈1.1×10−10m/s2) from first principles, resolving the Bullet Cluster paradox without the need for collisionless dark matter.

Abstract: To address the issue of low accuracy of faulty feeder detection methods based on single fault characteristics, a faulty feeder detection method for resonant grounding systems based on multiple transient characteristics fusion was proposed. First, the transient zero-sequence current fault characteristics of both faulty and healthy feeders during single-phase-to-ground (SPG) faults were analyzed. Then, the transient zero-sequence current of each feeder was decomposed into intrinsic mode functions (IMFs) using variational mode decomposition (VMD), and a new signal was constructed by combining IMF1 and IMF2. Subsequently, transient energy and waveform similarity fault characteristics were extracted from the constructed signal, and a faulty feeder detection criterion based on multiple transient characteristics fusion was developed. Finally, extensive simulations and field data were used to verify the proposed faulty feeder detection method. The results demonstrated that the method was robust against fault resistance, fault inception angle, fault location, and noise, achieving high accuracy in faulty feeder detection. This method can be widely applied to detect faulty feeders in resonant grounding systems.

Abstract: The dextran sulfate sodium (DSS) colitis model is the most widely used experimental model of inflammatory bowel disease (IBD) due to its simplicity and versatility, with over 7,000 PubMed entries in the last decade and an exponential rise in recent years. Since its initial description in 1985, DSS colitis has been extensively evaluated across species, most notably in mice and rats, and has yielded substantial insights into IBD pathogenesis. However, the model’s multifactorial nature poses a dual challenge: it offers an opportunity but complicates study design, interpretation, and translational relevance. This complexity is worsened by inconsistent reporting, which hampers reproducibility and comparability across studies. The broad use of the DSS-induced colitis model yields numerous insights about the model, which help better understand its complexity, characteristics and limitations. Although DSS colitis is induced locally, inflammation in the colon and the gut barrier destruction may also affect other organs (such as the liver and brain) and their metabolism and molecular responses, which, in turn, influence colitis development, drug response, and the interpretation of results. These intrinsic (intra-experimental) characteristics of the DSS colitis are summarised in the paper (colitis, gut-brain axis, gut-liver axis). In addition, the DSS model is heavily influenced by numerous ex-trinsic (inter-experimental) factors (environmental, microbiological, genetic), which may further complicate the colitis model, the study outcomes, and data interpretation and are also discussed in the paper. As science advances and new data accumulate, understanding the intricate interplay among internal mechanisms, external factors, and technical variables becomes increasingly essential for accurate interpretation of DSS outcomes. This review synthesizes the complexity and interdependence of factors shaping the DSS model, emphasizing the need for meticulous reporting and consideration of methodological nuances to enhance reproducibility, interpretation, and translational value in DSS colitis research. In addition, the review provides practical guidance through a “traps & tricks” subsections and a checklist table designed to provide a framework and practical recommendations to better understand, apply, and interpret DSS model results in the context of broader systemic and methodological considerations.

Abstract: This work introduces a novel conceptual framework that integrates crystallographic visualization techniques with cosmological geometry. Specifically, we reinterpret the crystallo-graphic holography of three-dimensional crystal structures onto a two-dimensional plane within the three-dimensional spatial sector of the Friedmann–Lemaître–Robertson–Walker (FLRW) metric, formulated following the Landau–Lifshitz approach. Within this framework, the surface of a four-dimensional hypermanifold (a 4D sphere) is conceptually interpreted as exhibiting topological features analogous to the “inside–outside” structure of a Klein bottle. This geometrical perspective provides a foundation for analyzing the mass–energy budget of the Universe as determined by the Planck's mission. We examine the present mass–energy composition—including the relative contributions of visible matter (baryonic), and dark energy identified with the zero-point field (ZPF)—within a differential geometric setting. These components are ultimately represented through a crystallographic holography–based formulation of the Planck observational mass–energy budget.

Abstract: With the rapid advancement of deep learning, differential privacy has become a key technique for protecting sensitive data with a formal guarantee of privacy. By injecting noise and enforcing privacy budgets, differentially private deep learning (DP-DL) systems are able to protect individual data points yet still maintain a model’s utility. However, recent studies reveal that DP-DL systems can be vulnerable to different types of attacks throughout their lifecycle. Naturally, this has attracted the attention of both academia and industry. Critically, these risks are not the same as those associated with traditional deep learning. This is because the differential privacy mechanism itself introduces new attack surfaces that adversaries can exploit. Our work focuses on the distinct vulnerabilities that can arise at the data, algorithm, and architecture levels. By analyzing representative attacks and corresponding defenses, this survey highlights emerging challenges and outlines promising research directions. Overall, our aim is to make differential privacy more robust and deployable in real-world deep learning systems.

Abstract: Crop simulation models and irrigation decision support systems (IDSS) are essen-tial tools for improving water-use efficiency in agriculture, particularly in Mediterra-nean and semi-arid regions where water scarcity is a major constraint. However, many operational platforms are either too complex and data-demanding for widespread adoption or too simplified to adequately simulate crop responses to the combined ef-fects of temperature, water stress, and elevated CO2. This paper presents the Easy Sim-ulator Crop Model (EaSiCroM), a modular, low-parameterisation decision support system designed to simulate daily crop growth, soil water dynamics, and irrigation requirements. EaSiCroM simulates canopy development through a be-ta-function-derived leaf area index (LAI) trajectory and Beer–Lambert canopy cover (CC), with growth progressively constrained by temperature (Tlim) and water stress (Kstress and KScc). Biomass accumulation is estimated through a water-productivity (WP) approach, optionally complemented by a radiation-use efficiency (RUE) path-way. A Michaelis–Menten sub-model accounts for the CO2 fertilisation effect on WP and RUE. The soil water balance includes a two-stage bare-soil evaporation formula-tion and supports multiple irrigation triggering strategies. EaSiCroM is implemented as a Docker-containerised web application supporting single-crop, multi-plot, and near-real-time irrigation modes. The model requires a limited parameter set, operates at daily time steps, and integrates user-provided canopy observations (field or remote sensing) for adaptive irrigation scheduling. Its modular architecture and accessible in-terface make it suitable for both research and operational irrigation management in water-scarce agricultural systems.

Abstract: Background: The clinical management of refractory Small Intestinal Bacterial Overgrowth (SIBO) and persistent gastrointestinal dysmotility represents a significant challenge, as these symptoms are often resistant to standard antibiotic treatments. While frequently categorized as functional disorders, the chronicity and systemic nature of these presentations suggest a possible underlying involvement of the autonomic nervous system. We explore the hypothesis that systemic iron dysregulation, rather than isolated dysbiosis, may contribute to these neuro and gastrointestinal manifestations. Hypothesis: We propose that iron overload mediated by HFE mutations, potentially exacerbated by low ferroxidase activity (ceruloplasmin), may lead to the accumulation of non-transferrin-bound iron (NTBI) in its reactive ferrous state (Fe2+). In this framework, we examine whether a lack of efficient iron chaperoning creates a pro-oxidative environment that could interfere with normal autonomic function. Mechanism: The suggested mechanism involves the Fenton reaction, where excess Fe2+ facilitates the generation of hydroxyl radicals. It is hypothesized that this localized oxidative stress may affect the unmyelinated neurons of the myenteric plexus, potentially leading to autonomic dysregulation. Such an environment could impair intestinal motility, thereby creating a substrate for recurrent and refractory SIBO. Furthermore, this iron dysregulation may act as a nutrient for pathogenic microbiota. This availability supports bacterial proliferation and biofilm formation, further contributing to the refractory nature of SIBO. Clinical Relevance: This model suggests that in patients with overlapping HFE variants and low ceruloplasmin, refractory SIBO may be a symptom of a broader metabolic dysregulation. Consequently, therapeutic strategies could consider the management of the systemic iron burden. Therapeutic phlebotomy is discussed as a potential intervention to reduce reactive iron levels, which might mitigate oxidative stress and support the stabilization of autonomic gastrointestinal function.

Abstract: The substantial computational and memory demands of Large Language Models (LLMs) during fine-tuning are partially addressed by Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA. However, their static low-rank configurations overlook heterogeneous learning sensitivity across layers, leading to suboptimal capacity allocation. We propose Adaptive-PEFT (AP-PEFT), a novel dynamic PEFT framework that introduces a real-time, layer-specific rank adjustment mechanism. This is accomplished via a lightweight module that assesses layer contributions using gradient information, combined with a dynamic rank strategy involving growth and shrink thresholds and a smooth transition for stability. Comprehensive experiments on diverse LLMs (from 3B to 8B parameters) and datasets show AP-PEFT achieves superior task performance and enhanced resource efficiency. AP-PEFT consistently demonstrates competitive or improved metrics in memory usage, compute utilization, latency, throughput, and energy consumption compared to state-of-the-art PEFT baselines and full fine-tuning. This work underscores the importance of dynamic parameter allocation for achieving an optimal balance between performance and efficiency in LLM fine-tuning.

Abstract: Item response theory (IRT) models are widely used in the social sciences to analyze multivariate discrete data that include cognitive test items. In many applications, the performance of two groups is compared using IRT modeling. The assessment of differential item functioning (DIF) plays a central role in this context, as it evaluates whether specific items function differently across groups; that is, whether their item parameters differ between groups. DIF detection is commonly based on statistical inference using item fit statistics. The mean deviation (MD) and root mean square deviation (RMSD) statistics are two widely used item fit measures. However, in the literature and in empirical research, these statistics are typically treated only as effect size measures (i.e., point estimates), and formal statistical inference for them is largely lacking. To address this gap, this article proposes confidence interval (CI) estimation for the MD and RMSD statistics based on asymptotic theory and a computationally efficient parametric bootstrap method. A simulation study was conducted to evaluate the proposed CI estimation approaches and demonstrated their validity. Across both item fit statistics, for DIF and non-DIF items, and across all simulation conditions, the results indicate that CI estimation based on the parametric bootstrap using empirical percentiles performed best and outperformed both the parametric bootstrap with normal distribution-based CIs and the asymptotic theory-based approach. It is therefore recommended that CI estimation for MD and RMSD statistics be routinely reported in addition to point estimates in empirical research.

Abstract: The large‑scale deployment of photovoltaic (PV) inverters in distributed energy resource (DER) ecosystems has created a highly connected environment where telemetry, remote access, and cloud platforms play a central operational role. Unlike smart meters, however, PV inverters have not been systematically examined from a privacy perspective, despite continuously generating fine‑grained data that can reveal sensitive information about users and installations. This preprint presents the first comprehensive analysis of privacy leakage vectors in modern PV inverter ecosystems, covering device‑level measurements, local interfaces, fieldbus protocols, cloud platforms, and external actors such as installers, aggregators, and utilities. Through a technical examination of inverter telemetry and widely adopted DER communication protocols (SunSpec Modbus, Modbus TCP, IEEE 2030.5), we identify structural risks including telemetry oversharing, metadata exposure, behavioural inference, cloud retention leakage, and installer‑side overprivilege. Our findings show that inverter telemetry can reveal occupancy patterns, behavioural routines, consumption habits, and installation characteristics with high fidelity. We conclude by outlining initial recommendations for telemetry minimization, metadata reduction, and cloud governance, establishing the foundation for a dedicated privacy‑by‑design framework for PV inverters and DER systems.This work establishes that PV inverters represent a first-order privacy threat in the modern home, demanding immediate attention from manufacturers, standard-setting bodies, and policymakers.

Abstract: This study explores the sensitivity differences between behavioral experiments and verbal reports in translation quality evaluation. Results indicate that behavioral metrics (e.g., response times) are significantly more sensitive to syntactic-pragmatic manipulations (phrase order) than verbal reports. Translations with congruent phrase order received higher ratings and faster response times compared to those with incongruent order. However, most participants explicitly denied phrase order's influence in verbal reports. Lexical equivalence showed no significant impact on explicit ratings but increased cognitive effort, as indicated by slower response times for approximate lexical matches. These findings reveal a critical dissociation between implicit cognitive processes and explicit awareness in translation evaluation. The study highlights that translation assessment involves both implicit System 1 processes and explicit System 2 reasoning, offering new cognitive insights for translation research and practical implications for translator education and machine translation assessment. By bridging cognitive science and translation studies, this research contributes to a paradigm shift: translation quality is not merely what evaluators say it is, but what their cognitive behavior reveals it to be.

Abstract: Coffee is a very important world commodity because of the countries involved in its production, along with the total cultivated area, production volume, consumption and economic impact. In Mexico, the coffee producing area locates mainly in hilly terrain of southern Mexico under agroforestry systems predominantly owned by smallholders. Low productivity is faced specially in the state of Oaxaca as a result of inadequate management practices such as aged plantations and deficient practices on pruning and plant nutrition. In order to evaluate the effect of N-P-K inorganic fertilizer application an experiment was carried out at three plantations located in the coastal coffee producing region of the state of Oaxaca, Mexico. Three levels of Nitrogen, Phosphorus and Potassium were evaluated using a randomized complete block design with four replications. The experiments initiated on plantations with three and four years since planted with the objective of having at least one harvest for yield evaluation. The results showed that Nitrogen application increased coffee yield on both varieties of Arabica coffee: Typica and Oro Azteca.

Abstract: Persistent and systemic disruptions—such as pandemics, geopolitical crises, and climate-related events—have exposed critical vulnerabilities in global supply chains, highlighting the urgent need for dynamic and adaptive resilience strategies. This paper proposes a novel immune system-inspired dynamic model for designing resilient, adaptive, and financially viable supply chains under severe disruptions. The model integrates innate and adaptive response mechanisms, including organizational memory as a dynamic capability that enables supply chains to learn from past disruptions and improve future responses. Unlike traditional models focused solely on structural redundancy or flexibility, this framework combines operational, financial, and learning dimensions within a unified system modeled through nonlinear differential equations. To validate the model, we conducted a scenario-based analysis, simulating three configurations: (1) a Total System Collapse without adaptation or learning, (2) a Baseline Resilience scenario with innate responses only, and (3) an advanced scenario with active organizational memory and adaptive mechanisms. Results demonstrate that the presence of learning and adaptive capacities significantly enhances both operational and financial resilience, reducing disruption intensity and accelerating recovery. Furthermore, a comprehensive sensitivity analysis was performed on three critical parameters: rate of active adaptation, organizational memory accumulation rate, and supply chain vulnerability. Findings reveal that higher adaptation rates and stronger organizational memory dramatically improve supply chain resilience, while higher structural vulnerability leads to systemic failures that cannot be mitigated by reactive measures alone. This study offers a quantitative and interdisciplinary contribution to supply chain resilience theory and provides practical guidelines for managers and policymakers to invest in adaptive capabilities, institutionalize learning processes, and reinforce structural robustness. The proposed model serves as a foundation for designing next-generation resilient supply chains, capable of surviving and thriving under persistent global uncertainty.

Abstract: Background: Patients described as high-need, high-cost (HNHC) represent a subset of individuals with complex multimorbidity whose healthcare trajectories are char-acterised by recurrent instability and intensive use of acute care services. Concepts such as trajectory disruption, resilience, and complex adaptive behaviour are widely discussed in health systems research, yet empirical evidence linking these ideas to longitudinal patient monitoring remains limited. The PaJR (Patient Journey Record) monitoring system was designed using principles from complex adaptive systems theory, enabling longitudinal observation of patient trajectories in real-world care. Objective: This study develops a complex adaptive system–informed theory of instability phases within patient trajectories using longitudinal monitoring data generated by the PaJR system. Methods: Analyses draw on two PaJR monitoring datasets used for complementary purposes: a MonashWatch cohort dataset comprising 100 patients and 1,137 monitoring calls used to illustrate trajectory dynamics, and an Irish monitoring dataset comprising 286 patients and 11,108 monitoring calls over 18 months used to examine signal distributions and instability patterns. Monitoring calls capture structured signals across multiple domains including illness, medication, healthcare utilisation, social support, environmental factors, and self-care. Results: Instability signals were concentrated within a minority of monitoring observations, producing a long-tail distribution of alert intensity. Alerts frequently occurred in clusters across consecutive monitoring calls, with approximately 63% of alert calls occurring immediately after a previous alert. Alerts were also commonly multi-domain, with approximately 42% involving disturbances across more than one domain simultaneously. These observations support an instability–plasticity framework that integrates empirical monitoring data with concepts from complex adaptive systems and resilience theory, interpreting clusters of patient-reported signals preceding hospital admission as indicators of declining resilience and increasing trajectory plasticity. Conclusions: Longitudinal relational monitoring can reveal instability patterns within patient journeys that are not visible through episodic health system data. These findings help empirically ground emerging theories of complex healthcare trajectories and suggest that recognising instability phases may support earlier and more adaptive responses for patients with complex healthcare needs.