Abstract: Improving the efficiency of energy consumption, conversion, and transmission on merchant ships has become a critical challenge due to rising fuel costs, increasingly stringent environmental regulations, and the introduction of operational efficiency requirements such as the IMO CII. Existing energy-efficiency metrics are predominantly based on abso-lute or design-oriented indicators and do not adequately capture the latent reserves of energy savings embedded in ship energy systems. This study addresses this gap by proposing a methodological framework for quantifying energy efficiency through the concept of relative energy-saving potential. The proposed approach integrates ship energy balance analysis with a hierarchical assessment of relative theoretical, technical, and economical-ly feasible energy-saving potentials. The methodology is demonstrated through an illustrative case study of a medium-size product tanker, focusing on the main engine, auxiliary generators, pumping systems, and HVAC loads. The results indicate that a significant share of energy losses can be systematically identified and progressively constrained by technical and economic feasibility considerations, providing a transparent basis for prior-itizing energy efficiency measures. The study concludes that relative energy-saving potentials offer an effective and scalable foundation for ship energy management, supporting SEEMP implementation, CII compliance strategies, and integration into digital twin and AI-based energy management systems.

Abstract: Background/Objectives: Anxiety and suicidal ideation are major mental health concerns during the perinatal period, impacting both mothers and newborns. While studies from South America address this issue, no prior systematic reviews have synthesized the findings. This review and meta-analysis aimed to explore the relationship between anxiety and suicidal ideation in perinatal women in South American countries. Methods: A systematic review and meta-analysis were registered in PROSPERO (CRD42025631849). Database searches were conducted in PubMed, Scopus, Web of Science, SciELO, and LILACS, including studies published up to December 2024, with no restrictions on year of publication. Study quality was evaluated using Joanna Briggs Institute tools. A random-effects model was used for the meta-analysis, following PRISMA guidelines. Results: Suicidal ideation was linked to 23 variables, such as age, race, depression, mother-infant bonding, violence, marital status, drug use, planned pregnancy, anxiety, low birth weight, and preterm birth. Anxiety was associated with 10 variables, including age, race, marital status, hyperglycemia, disabilities, eating habits, and mother-child relationship. The meta-analysis revealed a strong, significant association between suicidal ideation and violence, with affected women being 2.84 times more likely to report ideation. Conclusions: Violence and marital status emerged as key factors, reinforcing the need for screening and maternal mental health policies.

Abstract: This study is intended to examine how Human-AI collaboration-based identity threat appraisals in the form of loss of autonomy and loss of skill, triggers professional identity that fosters cyberloafing. Based on social identity theory, this study applied a three-wave survey design with 507 employees. The proposed research model was tested using partial least squares structural equation modeling (PLS-SEM) with SmartPLS 4, which enabled the assessment of both measurement and structural models Perceived loss of skill and loss of autonomy are positively associated with professional identity threat, which mediates their relationships with cyberloafing. AI-inclusive identity strengthens these associations for loss of autonomy suggesting employees high in AI-inclusive identity exhibit stronger professional identity threat and higher cyberloafing under autonomy loss. This study used self-reported data from a single cultural context, which may limit generalizability. The counterintuitive effect of AI-inclusive identity highlights the need for future research to examine when it serves as a protective versus a risk-enhancing factor. When integrating AI, organizations should mitigate autonomy and skill-erosion appraisals through participatory design, role redesign, and communication that emphasizes unique human contributions. Supporting healthy AI–human identity integration may reduce counterproductive behaviors such as cyberloafing. By positioning identity threat appraisals as Human-AI collaboration–driven antecedents of professional identity threat and cyberloafing, this study extends social identity theory to human–AI contexts. It further demonstrates that over-identification with AI may heighten professional identity threats by diminishing the value of uniquely human contributions.

Abstract: We address the control problem of a hybrid photovoltaic-grid water pumping system. This system includes a photovoltaic panel, an AC/DC PWM rectifier connected to an electric grid, a DC/AC PWM inverter paired with a Multiphase Induction Motor MPIM, and a centrifugal water pump. The control objectives are to ensure that the water pump flow rate follows a reference signal, regulate the norm of the rotor flux of the motor to its nominal value, maintain the DC link voltage at a reference value for optimal power point tracking (MPPT), and achieve satisfactory power factor correction (PFC). A nonlinear model of the controlled system is developed, followed by the synthesis of a multiloop nonlinear controller using the backstepping technique. This controller ensures global asymptotic stability in closed-loop operation and achieves all control objective.

Abstract: The objective of this study was to synthesize silver nanoparticles (AgNPs) utilizing an eco-friendly approach with Ocimum lamiifolium leaf extract as a biological reducing agent. The research focused on investigating how various experimental conditions influenced the stability and particle size of the AgNPs. The characterization of the synthesized nanoparticles involved multiple techniques, including X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), UV-visible spectroscopy, particle size analysis, Polydispersity Index (PDI), and zeta potential measurements. During the reduction process, a noticeable color change from colorless to grey indicated successful conversion of Ag+ to Ag°. The UV-vis spectra revealed a maximum absorption at 467 nm, confirming nanoparticle formation. The average particle size was found to be 65.37 nm with a PDI of 0.241, suggesting a relatively uniform size distribution. The zeta potential was measured at -29.85 mV, indicating good colloidal stability over time. FTIR analysis identified various functional groups associated with phytochemicals, supporting the role of plant compounds in reduction and stabilization. XRD patterns confirmed the face-centered cubic (FCC) crystalline structure of the AgNPs. Furthermore, antibacterial testing showed increased inhibitory zones with higher AgNP concentrations, with the minimum inhibitory zone of 4 mm and maximum of 15.45 mm against E. coli. The green synthesis mechanism involved reduction, stabilization, membrane disruption, reactive oxygen species (ROS) production, and bacterial cell death. Overall, AgNPs produced via Ocimum lamiifolium extract demonstrated enhanced stability and effective antibacterial activity, highlighting the potential of plant-based green synthesis methods for biomedical applications.

Abstract: Grassland state assessment is essential given their vital role in food security, carbon sequestration and other ecosystem services. Harvested aboveground biomass (HAB), aboveground net primary production (ANPP) and net primary production (NPP) are among the most important grassland state indicators. However, spatially explicit production estimates are largely lacking, and grassland area estimations also remain uncertain. This study addresses these gaps for drought-prone Central European grasslands over 2017-2024. We synthesized grassland extent data, collected extensive field measurements, and used remote sensing-based biophysical proxies to build an ensemble of eight linear models for spatial extrapolation at 10 m resolution. The proxies explained 11-41% of observed biomass (BM) variability. The ensemble mean ANPP was 325.7±56.6 gBM m2 year1 (median: 316 gBM m2 year1), with modest overall interannual variability. Upscaled country-wide ANPP averaged 35.1±9.2 Mt BM year-1 annually (range: 31.9-38.3; median: 35.9 Mt BM year-1). Uncertainty from grassland area estimation was roughly twice that from model choice. Using literature and local data, NPP was estimated at 421±110 gC m2 year-1 (median value), showing low interannual variability. Results highlight grassland area uncertainty as the dominant source of error in biomass estimation, rather than the remote sensing models themselves.

Abstract: An innovative pin-fin integrated thermoelectric device (iTED) is numerically modeled to quantify the effect of thermal flow conditions, namely inlet temperature (\( T_{\textrm{in}} \)) and Reynolds number (Re), as well as electrical load resistance (\( R_{\textrm{load}} \)), on the thermal-electric-fluid coupled performance. Quantification of the simultaneous thermal-fluid-electric behavior of the iTED was pursued through the implementation of an explicitly-coupled solution algorithm developed in ANSYS Fluent's User Defined Scalar (UDS) environment. The effects of \( T_{\textrm{in}} \), Re, and \( R_{\textrm{load}} \), which were varied between 350 and 650 K, 3,000 and 15,000, and 0.01 and \( 10^{6} \% \) of the internal device resistance (\( R_{\textrm{int}} \)), respectively, on the open-circuit voltage, current, power output (\( \dot{W} \)), heat input, pumping power, device thermal conversion efficiency (\( \eta \)), and dimensionless performance index (\( \zeta \)), were evaluated for a fixed cold-side temperature of 300 K. The performance of the iTED was then compared to that of a conventional TEG. At a \( T_{\textrm{in}} \) of 650 K, the iTED achieved a maximum \( \dot{W} \) of 23.9 W when \( R_{\textrm{load}}=R_{\textrm{int}} \) at a Re of 15,000, a maximum \( \eta \) of \( 8.1\% \) when \( R_{\textrm{load}} \) < \( R_{\textrm{int}} \) at a Re of 10,000, and a maximum \( \zeta \) of 7.8 at a Re of 3,000. In comparison, the conventional device achieved a \( \dot{W} \) of 5.2 W, an \( \eta \) of 2.9%, and a \( \zeta \) of 1.6 at the same \( T_{\textrm{in}} \) and Re of 15,000, 6,000, and 3,000, respectively. The proposed multiphysics numerical modeling illustrates marked improvements via device restructuring.

Abstract: Fish are essential for global nutrition and food security, particularly for coastal communities. The European sardine Sardina pilchardus, a key marine resource in Portugal and Spain, experienced severe population declines during the 2000s. Although management measures have helped its recovery, ensuring compliance and verifying product origin, especially given its high reliance on imports, requires robust traceability tools. This study examines the spatial and temporal variability of elemental fingerprints (EF) of S. pilchardus scales collected from fishing harbors in Galicia (Spain) and mainland Portugal to assess their effectiveness in confirming the place and time of capture. Moreover, to improve cost-efficiency, it also evaluates how temporal variability influences the accuracy of predictive models to confirm capture location when samples from different years are used for model development and testing. Random Forest models developed with samples from 2018 and 2019 correctly classified over 95% of the specimens by location within each year. Capture time classification achieved 95.3% accuracy. However, applying the 2018 model to samples from 2019 reduced accuracy to 24.4%. Despite this constraint, the EF of fish scales provides a practical and reliable method for verifying capture time and origin, thereby reducing mislabeling and promoting the sustainable management of S. pilchardus stocks.

Abstract: This study presents the Structural–Typological–Value Sensitivity Model (STVSM), a multi-dimensional framework for evaluating vulnerability in historic buildings where fragility cannot be explained by structural indicators alone. Existing models prioritise load-bearing behaviour but overlook typological discontinuity, spatial fragmentation and erosion of cultural or architectural value. STVSM addresses this through three weighted sub-indices—structural vulnerability (SV), typological degradation (TV) and heritage value (HV)—each calibrated using expert-derived micro–macro coefficients. Field-based deterioration scores (0–1) are multiplied by these final weights to produce SV, TV and HV values, then merged into a Conservation Priority Index (CPI).The model is applied to twenty-five buildings in three heritage contexts: Cumalıkızık traditional houses, vernacular dwellings in Balıkesir–Karesi and nineteenth-century Greek Orthodox churches in Bursa. The churches yield the highest CPI values due to roof loss, wall deformation and spatial discontinuity, reinforced by cultural significance. Vernacular houses show moderate structural deterioration but marked typological distortion linked to later additions and façade alterations. Cumalıkızık houses present heterogeneous conditions, combining preserved structures with material decay.By quantifying structural behaviour, typological integrity and heritage value within a single analytical system, STVSM offers a transparent and repeatable basis for conservation prioritisation across diverse historic building stocks.

Abstract: The Migdal effect has traditionally been viewed as a quantum phenomenon, with its explanation relying on assumptions such as non-adiabatic transitions and quantum coupling. This paper, based on the Great Tao Model, constructs a complete explanatory framework for this effect within classical physics: following an impact, the nucleus undergoes classical accelerated motion, inducing a dynamic distortion in its charge existence field, and transfers energy continuously to the extranuclear electrons via classical electrostatic interaction, leading to their excitation or ionization. Through quantitative derivation of the existence field distortion intensity, electron energy gain, and the geometric relationship of the double-track feature, all predictions of this theory are in complete agreement with the quantitative observational results of the direct measurement experiment, including the "co-vertex double-track" characteristic and the electron energy range.. Further analysis indicates that neutrinos, due to their extremely small mass, impart recoil energies far below the effect's threshold, while Subtrons, lacking charge interaction, cannot trigger the effect at all. This paper systematically analyzes the fundamental differences between the classical and quantum mechanical explanations regarding physical reality, energy transfer mechanisms, and theoretical self-consistency, and clarifies the underlying reason why the Migdal effect cannot be used to detect Subtrons (dark matter). This study not only confirms the universality of classical physical laws at the microscopic scale, providing a novel, physically real, and logically self-consistent paradigm for understanding the Migdal effect, but also offers clear guidance for the strategic direction of frontier experiments such as dark matter detection.

Abstract: The increasing adoption of artificial intelligence (AI) in cybersecurity has introduced new opportunities to enhance detection, response, and automation capabilities; however, applying AI within cybersecurity auditing remains constrained by traditional compliance-oriented approaches that rely profoundly on binary, checklist-based evaluations. Such approaches often reinforce a policing or “sheriff-style” perception of auditing, emphasizing enforcement rather than enablement, risk insight, and organizational improvement. This study proposes an Anti-Sherif AI-driven cybersecurity audit model that integrates AI-based analytics with human expert judgment to support a more adaptive, risk-informed auditing process. Grounded in design science research, the model combines conventional binary compliance checks with AI-derived intelligence and governance-based maturity assessments to evaluate cybersecurity controls across technical, operational, and organizational dimensions. The approach aligns with established standards and frameworks, including ISO/IEC 27001, the National Institute of Standards and Technology (NIST), and the Center for Internet Security (CIS) benchmarks, while extending their application beyond static compliance. A fictional case study is used to demonstrate the model’s applicability and to illustrate how hybrid scoring can reveal residual risk not captured by conventional audits. The results indicate that combining AI-driven insights with structured human judgment enhances audit depth, interpretability, and business relevance. The proposed model provides a foundation for evolving cybersecurity auditing from periodic compliance assessments toward continuous, intelligence-supported assurance.

Abstract: We show that the GBFA method of Elgindy (2025), originally developed for 0 < α < 1, extends to all α > 0 without altering its algorithmic structure. Elgindy's transformation τ = t(1 − y1/α) remains valid for all α > 0 and preserves the numerical framework, ensuring that interpolation, quadrature, and error analysis carry over unchanged. For α > 1, the mapping induces only Hölder regularity at y = 0, which a ects quadrature accuracy. We quantify this e ect and show that the interpolation error retains its original convergence properties. To restore higher-order endpoint smoothness, we introduce a ϕ(α)-generalized transformation that enforces Cr regularity for any prescribed r ≥ 0, accelerating quadrature convergence while preserving the GBFA structure. Numerical experiments con rm high accuracy and robustness across all α > 0, demonstrating that the uni ed GBFA formulation provides an e cient, non-adaptive, xed-node approach for arbitrary-order RLFIs.

Abstract: Traveling wave ultrasonic motors (TWUMs) are critical components in precision systems, their performance is susceptible to degradation under dynamic disturbances in harsh operating environments. This paper presents a monolithic U-shaped rotor designed to intrinsically achieve quasi-zero stiffness (QZS). Unlike conventional QZS systems that rely on assembling discrete positive and negative stiffness elements, the proposed design generates the target mechanical characteristic through the tailored nonlinear response of a unified U-shaped structure, thereby improving preload stability. Through exploring the critical parameters of the rotor cross-section, the finite element method (FEM) is employed to optimize the geometry configuration and characterize the mechanical performances. Simulation results show that the QZS behavior, demonstrating a stable force plateau of 320 ± 10 N across a 0.7 mm displacement range. A maximum von Mises stress of 788 MPa is obtained, well within the material's safety margin, thereby ensuring the structural integrity. Experimental tests validate the effectiveness of the proposed design. This compact, monolithic U-shaped rotor provides a robust and reliable QZS solution, demonstrating significant potential for enhancing the stability of TWUMs in applications prone to harsh environments such as extreme high and low temperatures, thermal cycling conditions, shock environments.

Abstract: Based on the latest cosmological observational and theoretical advancements, this paper proposes a unified systems theory framework, conceptualizing the universe as an adaptive ecosystem with a fundamental architecture of "spacetime-dark matter-dark energy". Through the metaphor of a "computer operating system", it elaborates on the roles of spacetime as a structural entity, dark matter as a gravitational framework, and dark energy as a functional core. It also explores the implications of this framework for understanding physical laws, cosmic evolution, and the nature of information, attempting to establish a unified theory for comprehending the universe. It is important to emphasize that all physical theories are essentially sets of "models" or "metaphorical systems" used to explain and predict observational phenomena. The "operating system" paradigm presented herein is also an approximate description of the complex cosmic reality, not the ultimate answer.

Abstract: The advent of autonomous systems has propelled the integration of artificial intelligence (AI) and machine learning (ML) techniques, particularly deep reinforcement learning (DRL), to enhance decision-making capabilities. This research paper conducts an exhaustive survey of state-of-the-art DRL algorithms, focusing on their applicability and performance within the realm of autonomous systems. To find out how flexible and useful DRL algorithms are in real life, our research covers a lot of different areas, such as robots, self-driving cars, and unmanned flying vehicles. The study takes a close look at the most important parts of these algorithms, like neural network designs, exploration-exploitation strategies, and payment processes, to see how they affect how well independent systems work. Additionally, the study goes into detail about the problems and restrictions that come with using DRL in self-driving systems, covering everything from sample waste to safety concerns. Wealso look at newdevelopments andimprovements in DRLthatmightbeable to get around current problems and make way for future innovations in driverless technology. As a valuable resource for researchers, engineers, and practitioners working on the development and deployment of autonomous systems, this brief survey shows the pros, cons, and opportunities that come with different DRL algorithms in this ever-changing field.

Abstract: This review highlights the pathophysiology and pathogenesis of diabetic retinopathy, the main complication of diabetes. The Neurovascular Unit (NVU) is brought to the surface for its importance to retinal physiological function. Diabetes impairs the NVU leading to the production of causative factors, such as ischemia, oxidative stress and excitotoxicity. The interplay between members of the above triad leads to the main pathological factors of diabetic retinopathy, namely neurodegeneration, neuroinflammation and vasculopathy. Emphasis is given to the pathology of the early stage of diabetic retinopathy (ESDR) and the putative new therapeutic treatments that will prevent/delay the development of the advanced stage of DR in which vision is compromised. NADPH Oxidases (NOX1-NOX5), whose main function is to produce reactive oxygen species (ROS) and induce oxidative/nitrative stress will be presented as novel therapeutic targets for the impaired neurovascular unit. The knowledge of the molecular mechanisms involved in the neuroprotection induced by novel specific inhibitors of NOX2 and NOX4 against the diabetic insults will confer the hope that therapeutic treatments for ESDR will evolve in the near future and be beneficial to the millions of patients who are in the early stage of diabetic retinopathy, as well as patients with other complications of diabetes.

Abstract: Plants exhibit complex internal dynamics in response to environmental conditions, yet whether these dynamics reflect structured affective regimes remains unclear. This study investigates whether internal plant signals encode information about affective states defined relationally by sustained environmental conditions. Valence and arousal were operationalised using temperature, humidity, and residualised light. Using only internal plant measurements—including bioelectrical activity and volatile gas emissions—we evaluated whether machine learning models could decode affective structure without access to environmental variables. Binary classification revealed that valence was reliably decoded over longer temporal windows, whereas arousal required shorter windows, suggesting distinct underlying timescales. Direct multiclass quadrant classification proved unstable, but an Echo State Network capturing temporal dependencies achieved improved performance. These results indicate that plant internal dynamics carry a learnable, temporally extended signature of environmentally defined affective regimes, supporting an interpretation of plant affect as embodied environmental engagement.

Abstract: Background/Objectives: Catalase (CAT) plays a pivotal role in cellular redox homeostasis by catalyzing the decomposition of hydrogen peroxide, thereby mitigating oxidative damage. Impaired CAT function has been linked to chronic inflammation, deregulated cell proliferation, and oncogenesis. While numerous CAT variants have been reported, their functional relevance in prostate cancer (PCa) remains unclear. This study investi-gated CAT genetic variants, enzymatic activity, and potential pathogenic significance in PCa. Methods: Peripheral blood samples from 17 patients with prostate cancer (PCa) and 17 patients with benign prostatic hyperplasia (BPH) were processed for genomic DNA extraction and quantification. Reported missense variants were screened using pub-lic genomic databases. Pathogenicity was predicted with SNPs&GO, PolyPhen-2, and I-Mutant 2.0. Protein modeling was performed with UCSF Chimera, and structural quality was assessed via Ramachandran plots. Two selected variants (L299F, A333T) were geno-typed using the rhAmp™ SNP assay. Serum CAT activity was quantified spectrophoto-metrically (240 nm) following Aebi’s protocol. Results: CAT activity was significantly re-duced in PCa compared to BPH (1.44 ± 0.24 vs. 1.91 ± 0.25 U/mg protein, p = 0.033), with no association to age, PSA levels, or comorbidities. In silico predictions identified variants K177T, G216V, and L351P as potentially deleterious, while N452S appeared benign. Gen-otyping revealed that all participants were wild-type homozygous for L299F and A333T. Conclusions: PCa is characterized by diminished CAT activity independent of the inves-tigated variants, suggesting an alternative regulatory mechanism, such as epigenetic reg-ulation or post-translational modifications, may drive CAT downregulation. These find-ings support further investigation into CAT as a potential biomarker and therapeutic tar-get in oxidative stress–mediated prostate carcinogenesis.

Abstract: Background/Objectives: Injuries caused by wild boar during hunting are repeatedly reported worldwide, yet the scientific literature is remarkably sparse and largely limited to isolated case reports, forensic analyses of fatal events or heterogeneous international compilations. Before this study, no structured nationwide data existed for Germany or comparable European hunting systems. This study aimed to systematically characterize wild boar–related hunting injuries and expand the empirical knowledge base. Methods: In this nationwide exploratory study, German hunters who had sustained at least one wild boar–related injury were recruited via hunting journals. Structured physician-led telephone interviews were conducted using a standardized questionnaire. Data were pseudonymized and analyzed descriptively, stratifying injuries into closed, outpatient open and inpatient open categories. Results: A total of 101 injured hunters were included, representing the largest systematically collected cohort of wild boar hunting injuries to date. Most were highly experienced male dog handlers injured during close-range tracking of wounded wild boar. Injuries predominantly involved the lower extremities. Open injuries—particularly those requiring inpatient treatment—were associated with extensive surgical management, higher complication rates, prolonged recovery and persistent functional impairment. Despite severe courses, all participants resumed hunting. Conclusions: This study substantially expands the previously limited evidence base on wild boar–related hunting injuries by providing structured nationwide data across the full spectrum of injury severity. The findings offer transferable insights relevant to hunting practices, prevention strategies and clinical management in international contexts where wild boar populations and hunting activity are increasing.

Abstract: Despite‍‍‍ Large Language Models (LLMs) exhibiting outstanding capabilities in various natural language processing tasks, they might still be unreliable. Actually, one of their main sources of unreliability is a phenomenon called hallucination, the creation of reasonable but false pieces of information. This work provides a comprehensive overview of advances in understanding, locating, and reducing hallucinations. We start by considering hallucination as the main obstacle in creating reliable AI, and define a taxonomy that follows the development of factual errors and the notion of unfaithfulness with respect to the model's accessible knowledge. Afterwards, we survey the detection methods that are classified depending on the degree of model access and also, and we also refer to the different cognitive processes used for their comparison, which comprise uncertainty estimation, consistency checking, and knowledge-grounding evaluation. In the end, we offer a well-organized representation of the interventions aimed at the abolition of the model hallucinations employed at various stages of the model lifecycle: (1) data-centric interventions exemplified by high-quality data curation, (2) model-centric alignment through preference optimization and knowledge editing, and (3) inference-time strategies such as retrieval-augmented generation (RAG) and self-correction. We affirm that the multilayer, defense-in-depth framework incorporating these non-overlapping strategies is crucial for robust hallucination abatement. Some of the ongoing difficulties are the scalable data curation, the trade-off between alignment and model capability, and the problem of editing the reasoning pathways instead of the surface ‍‍‍facts.