Abstract: Rising complexity in cyber-physical systems development exposes challenges in the consistent and reusable specification of graphical domain-specific languages (DSLs). Despite the benefits of model-based systems engineering (MBSE), the absence of a standardized, lifecycle-wide specification process results in semantic inconsistencies, tool dependence, and limited interoperability. While our previous work has addressed individual stages of DSL definition, a comprehensive, standards-based process integrating these stages remains missing. Building on these foundations, this paper introduces a unified language specification process for graphical DSLs grounded in established standards---the Meta-Object Facility (MOF), Unified Modeling Language (UML), Web Ontology Language (OWL), and Resource Description Framework (RDF). The process integrates three core artifacts: a tool-independent ontology capturing domain semantics, a MOF-conformant metamodel unifying abstract syntax, semantics, and concrete syntax, and a UML-profile-based implementation. To support and exemplify this process, a prototypical toolchain is introduced that enables automated transformations between these artifacts, thereby facilitating the consistent propagation of semantics from ontology to implementation. The applicability of the proposed process is demonstrated through both a top-down automotive case and a bottom-up cybersecurity DSL, illustrating its cross-domain generalizability. By explicitly structuring and connecting ontology, metamodel, and implementation, this work contributes a semantically consistent, machine-interpretable, and tool-independent specification process for graphical DSLs in MBSE.

Abstract: Retroviruses induce immunosuppression in their infected hosts. This phenomenon is well described for the immunodeficiency viruses, with human immunodeficiency virus type 1 (HIV-1) representing the best-studied example, but it also occurs in other retroviral infec-tions. Immunosuppressive properties were first characterized in murine leukemia viruses (MuLV). Additional well-studied examples include feline leukemia virus (FeLV) and koala retrovirus (KoRV). Investigations into the mechanisms underlying retrovirus-induced immunosuppression revealed that not only inactivated viral particles, but also their purified transmembrane envelope (TM) proteins exhibit immunosuppressive activity. However, in certain retroviral infections, additional viral proteins contribute to the immunosup-pression in vivo. Within the TM proteins, a highly conserved region, designated the immunosuppressive (isu) domain, was identified. Synthetic peptides corresponding to this domain suppress a wide range of in vitro immune responses by regulating Ras-Raf-MEK-MAPK and PI3K-AKT-mTOR pathways. They modulate cytokine release and alter gene expression in immune cells, mirroring the activity of the corresponding TM protein. Mutations in the sequence abrogate the effect. Numerous TM proteins have demonstrated immunosuppressive activity in vivo, in a tumor rejection model, and muta-tions within the isu domain also abrogate this function. These studies have important implications for reproduction, particularly through the immunosuppressive syncytins in the placenta; for tumor development, where similar mechanisms may protect cancer cells from the host immune system; as well as for vaccine development and xenotransplantation. Notably, immunization with TM proteins carrying mutations in the isu domain elic-its stronger immune responses compared with the wild-type proteins. Finally, the potential of retroviral TM proteins to protect xenotransplants from immune rejection will be discussed.

Abstract: The flotation of oxidized lead–zinc ores presents a significant challenge due to the low floatability of oxidized minerals and their weak interaction with conventional reagents. This study investigates the influence of the electrochemical parameters of the pulp (redox potential, Eh, and pH) on the flotation kinetics of oxidized lead–zinc ore from the Koskuduk deposit. It was established that the use of sodium sulfide Na₂S leads to the selective activation of lead-bearing minerals (Pb recovery up to 40.74%) with low zinc recovery (~12%). The use of a polysulfide-lime system S:CaO:H₂O is proposed, providing more uniform and stable sulfidization of the mineral surface. It is shown that the application of this reagent increases recovery to 65.10% for lead and 56.89% for zinc. It was established that the maximum recoveries are achieved within an Eh range of -120 to -180 mV at pH 11-12. Kinetic studies demonstrated that the main contribution to metal recovery occurs within the first 2-6 minutes of flotation. The obtained results indicate that flotation efficiency is determined both by the type of reagent and by the electrochemical state of the pulp, and that the use of polysulfide systems represents a promising approach for the processing of oxidized lead-zinc ores.

Abstract: In a previous work the authors initiated a study on mutation semigroups, where elementary mutation operations were encoded as total maps on finite sets and analyzed through structural, algebraic, and computational methods. Here we address several of the open problems raised therein. First, we investigate the algebraic characterization of generator sets that force the existence of constant or low-rank maps, linking these conditions to classical results on synchronizing automata. Second, we analyze the computational complexity of contraction-based heuristics, identifying cases where polynomial-time criteria are achievable and others where hardness results emerge. Finally, we extend the finite theory to parameterized and infinite families of mutations, drawing connections with quasispecies models in biology and interpreting image contractions as mechanisms of error suppression and genomic stability. By combining algebraic definitions, structural theorems, and algorithmic analyses, we provide a refined toolkit for understanding mutation collapse and its theoretical and biomedical implications.

Abstract: Background/Objectives: To investigate the effects of a 12-week multimodal intervention (MMI [resistance + motor-cognitive dual-task training]) versus a unimodal intervention (UMI [resistance training]) on gait and cognitive performance in people with glaucoma during single-task (ST) and dual-task (DT) walking. Methods: In this randomized controlled pilot study, fifteen glaucoma patients (MMI: n=8, UMI: n=7) completed 24 supervised intervention sessions over 12-weeks. Spatio-temporal gait parameters (stride length, gait velocity, minimum toe clearance [MTC], and their respective coefficient of variance [CoV]) were assessed using inertial measurement units (sampling frequency 100Hz) during ST and DT walking. During DT walking, the participants performed three cognitive tasks: reaction time task, N-Back task, and letter fluency task. Each cognitive task was performed with two levels of difficulty. Repeated measures analysis of covariance (TIME x INTERVENION x CONDITION) was conducted to analyze the data. Results: No significant group or time effects were observed for ST gait or cognitive performance. Independent of intervention, dual-task costs (DTC) improved for MTC (p=0.188,η_p²=0.205) and MTCCoV (p=0.021, η_p²=0.713) over time. UMI showed greater improvements over time than MMI for DTC MTC (p=0.168,η_p²=0.223) and DTC MTCCoV (p=0.047,η_p²=0.407).Conclusion: This pilot study revealed that both MMI and UMI decreased DTC MTC and DTC MTCCoV in glaucoma patients. However, UMI appeared more effective than MMI to improve gait performance in glaucoma patients. Thus, resistance training might be a promising intervention to improve gait performance in glaucoma patients, with the ultimate aim to reduce the risk of falls.

Abstract: Highway–rail grade crossing (HRGC) safety analysis is often based on raw incident counts or site-level models that do not control for exposure and ignore spatial dependence. This limits the ability to identify where risk is structurally concentrated across the rail network. The problem is important because misidentifying high-risk environments leads to inefficient allocation of limited safety resources and weakens corridor-level intervention strategies. This study introduces accumulated incidents per crossing (AIPX), an exposure-normalized metric that measured cumulative incident burden at the county level over a 51-year period (1975–2025). The study developed an algorithmic framework that integrates data reconciliation with spatial autocorrelation analysis, distributional modeling, and nonparametric machine learning to identify and interpret high-intensity risk environments. Global Moran’s I indicates statistically significant positive spatial autocorrelation (I = 0.359, p = 0.001), confirming that incident intensity is spatially clustered rather than random. Local indicators identify coherent high and low intensity county clusters. Distributional analysis shows that AIPX in high intensity clusters follows heavy-tailed behavior best represented by lognormal and Johnson SU distributions, indicating concentrated risk in a small subset of counties. Machine learning models achieve strong classification performance (AUC ≈ 0.85), with explainability methods consistently identifying temperature, train direction, crossing warning configuration, train composition, and track class as dominant associated features. These variables function as proxies for exposure intensity and network structure rather than causal drivers. The findings demonstrate that HRGC risk is a regional, network-driven phenomenon concentrated along freight-intensive corridors. The study provides a transparent and transferable framework that supports corridor-level prioritization of safety interventions and more effective allocation of infrastructure investments.

Abstract: The article analyzes the linear-quadratic optimal stabilization problem in the so-called "critical case", namely, the situation is considered when the spectrum of the system matrix contains purely imaginary eigenvalues or when the standard conditions of positive definiteness of the weight matrices of the quality functional are violated. Methods for regularizing critical problems by perturbing the system matrices and the functional are investigated, and algorithms for decomposing multidimensional problems into a set of one-dimensional canonical systems are proposed. The results are of practical importance for constructing optimal synthesis in various engineering and economic systems, in particular, the results can be used for stabilizing unmanned aerial vehicles, robotic complexes and intelligent power grids.

Abstract: This study examines the perspectives of coffee industry stakeholders on blockchain traceability systems in Colombia and Switzerland. Adopting the Unified Theory of Acceptance and Use of Technology (UTAUT) as an interpretive framework, the study analyzes these perceptions through the constructs of performance expectancy, effort expectancy, social influence, and facilitating conditions. Using a quantitative cross-sectional design, we survey 360 participants, comprising 60 coffee supply chain companies and 300 consumers. Results reveal that 78.3% of stakeholders consider traceability essential, yet only 46.7% are familiar with blockchain. Stakeholders identify three primary benefits: improved transparency (91.7%), fraud prevention (88.3%), and enhanced security (86.7%). However, significant barriers persist: high implementation costs (95%), limited expertise (91.7%), and lack of awareness (93.3%). Geographic differences emerge: Colombian stakeholders prioritize cost reduction and fraud prevention, while Swiss participants focus on data management and privacy protection. Among consumers, 62.7% express interest in provenance information, 56.7% indicate willingness to pay for blockchain systems, and 59% show interest in tipping farmers. The study classifies benefits and drawbacks across nine dimensions, providing a comprehensive framework for understanding the multidimensional impacts of blockchain on coffee supply chains.

Abstract: Urban mobility planning in smart cities requires sophisticated simulation tools, yet their complexity often creates a technical barrier for non-expert stakeholders. This paper presents a novel architecture that integrates generative artificial intelligence with digital twin technology to create an accessible and robust decision-support system. The framework employs a conversational AI agent based on Gemini 2.5 Flash Lite to interpret natural language intentions and translate them into validated simulation parameters. A critical safety layer, built using Pydantic, ensures that the agent’s stochastic outputs adhere to strict technical schemas and urban logic before execution. The underlying digital twin, developed with SimPy, NetworkX, and OSMnx, features a multi-source data integration strategy that includes demographic density (INE), tourism activity (ISTAC), and high-resolution traffic statistics (TomTom) to calibrate vehicle behavior. The architecture was validated through a Technology Readiness Level (TRL) 4 proof-of-concept in Las Palmas de Gran Canaria, simulating multimodal scenarios including buses, the future MetroGuagua (BRT), and pedestrian flows. Results demonstrate a 95.99% success rate in intent recognition and configuration mapping, with end-to-end execution times under 20 minutes for a 19-hour simulated day. This study demonstrates that LLM-driven orchestration, coupled with automated data pipelines and a decoupled microservice architecture, can democratize access to urban simulation, fostering more inclusive, agile, and evidence-based smart city governance.

Abstract: We propose a Generalized Multivariate Functional Additive Mixed Model (GMFAMM) for the simultaneous bias correction of five hydroclimatic variables derived from the NASA POWER satellite product: minimum temperature (Tmin), maximum temperature (Tmax), relative humidity (HR), solar radiation (Rad), and precipitation occurrence (Pbin). The GMFAMM extends the univariate functional framework by incorporating a shared latent Gaussian process Λ0i(t) that captures cross-variable thermodynamic dependence. A systematic experimental grid of more than 200 model configurations across four distributional families (Gaussian, Gamma, Poisson, Binomial), two effect structures (linear and smooth P-splines), and four nested covariate sets is evaluated on a strict chronological 70/30 hold-out – seven training years (2016–2022) and three hold-out years (2023–2025) – to identify the optimal marginal specification for each variable. The value of joint modelling is quantified through a two-stage cross-residual approximation to the GMFAMM shared latent process, which constitutes a conservative lower bound on the gains achievable by the full simultaneous model: out-of-sample RMSE is reduced by 53% for Tmin, 38% for Tmax, and 51% for relative humidity relative to the independent GAMM baseline. These gains are physically interpretable through the Clausius-Clapeyron thermodynamic coupling documented in the residual cross-correlation analysis. The trained model artefacts are deployed in ColClim, an open-access R Shiny web application that queries the NASA POWER API and the Open-Meteo forecast service for any user-selected location in Colombia, applies the GMFAMM correction pipeline, and delivers both historical bias-corrected time series and short-range (1–16 day) forecasts across the five variables.

Abstract: As Intelligent Transportation Systems (ITS) transition towards automated ecosystems, the deployment of advanced wireless charging technologies becomes a critical infrastructure requirement. Central to the management of these networks is the Open Charge Point Protocol (OCPP), which ensures interoperability across diverse hardware vendors. However, the reliance on digital communication for power transfer introduces significant cybersecurity vulnerabilities. This paper presents a methodology for evaluating the impact of cyber-threats on urban transport services, with a specific focus on the communication layers that support these Advanced Wireless Power Transfer (WPT) environments. Utilising Stochastic Petri net (SPN) ontology, we model the operational states of an Electric Vehicle (EV) service—including the activation and the arrival phases—to quantify how protocol-level vulnerabilities affect service reliability. We introduce an Extended Vulnerability List (EVL) and analyse two distinct scenarios: a public transport service and a weather forecasting integration. Our results demonstrate that as wireless charging moves towards standardization, the security of the OCPP-based backbone is a fundamental necessity for preventing service disruption. The proposed assessment framework provides a roadmap for securing the next generation of dynamic wireless charging infrastructures against evolving cyber-physical threats.

Abstract: Forecasting crash severity is critical for emergency response, infrastructure spending & risk communication. Although machine learning has been widely applied to this problem, three shortcomings prevent its practical application: poorly calibrated probability scores, SHAP-based explanations whose faithfulness has not been verified, and models never tested in different regions. The proposed framework, termed SAE-XCrash (Safety-Aware and Explainable Crash Severity Prediction), considers all three using two public datasets - US-Accidents (7.0 million records, 2016-2023) and UK STATS19 (approximately 1,010,000 records, 2016-2022). Notably, the US-Accidents severity label refers to traffic disruption duration, not injury outcome, and results should be interpreted accordingly. Previously unknown label-schema drift led to a revised binary target with Severity 4 as only the positive class; strict temporal splits are used throughout. FIVE classifiers are compared. Post hoc isotonic Calibration reduces expected calibration. Error by 97.3 percent while maintaining negligible discrimination loss. A four-step SHAP audit confirms that explanations genuinely reflect model behavior: deletion-based per-budget faithfulness gaps exceed the 0.05 threshold at every feature budget (min gap=0.066, p<0.0001), though the aggregate trapezoidal AUC is borderline due to scale compression at AUPRC≈0.13, and insertion gaps are statistically significant at more than ten percent of features. Explanation stability holds under conservative noise levels but degrades at realistic perturbation magnitudes mainly in spatially sparse geohash cells. In a three-tier cross-dataset transfer experiment - zero-shot, recalibration and full retraining - spatial memorization is the major generalization barrier while temporal features transfer smoothly between jurisdictions.

Abstract: Objective: To synthesise the pathobiology of cortical spreading depolarization (CSD) and critically appraise current and emerging pharmacological strategies specifically targeting migraine aura prevention. Background: Migraine with aura affects 25–30% of patients, and the aura phenomenon remains a substantial unmet preventive need. Calcitonin gene-related peptide (CGRP) monoclonal antibodies do not readily cross the blood–brain barrier and frequently fail to suppress CSD, the neurophysiological substrate of aura.Methods: A literature search of PubMed, Embase, and the Cochrane Library (inception through January 2026) identified studies on CSD pathophysiology, preclinical CSD suppression, and clinical efficacy of candidate agents. Evidence quality was assessed with GRADE; risk of bias with Cochrane RoB 2 (RCTs) and ROBINS-I (observational); narrative synthesis followed SWiM. De novo quantitative estimations (post-hoc power analyses, sample-size projections, worst-case sensitivity analyses) were used as methodological tools, not as original empirical data. Results: CSD pathogenesis is organised into four phases: pre-CSD vulnerability, initiation, glial propagation, and neuro-inflammatory transduction. Lamotrigine and memantine target initiation and have the most advanced clinical evidence; both lack aura-specific RCTs. A 2024 network meta-analysis ranked memantine favourably (50% responder rate OR 5.58, 95% CI 2.41–12.92) but no contributing trial stratified by aura. An a priori sample-size calculation indicates 214 enrolled patients (170 evaluable; NNT≈4.9; n/(1−d) for 20% attrition) for a definitive aura-specific memantine RCT. Tonabersat—a Cx36/Cx43 gap-junction modulator—reduced aura attacks from 3.2 to 1.0 per 12 weeks in Phase 2; a worst-case intention-to-treat sensitivity analysis confirms that this signal survives even 16.6% unaccounted attrition. Spironolactone (pannexin-1 inhibition) and amiloride (ASIC1a) remain preclinical or pilot-stage. Tissue-selective KATP antagonists (Kir6.1/SUR2B) and the anti-PACAP-38 antibody Lu AG09222 represent the most promising pipeline agents. Conclusion: The therapeutic gap for migraine aura prevention reflects correctable methodological choices, not a lack of biological tractability. Four mechanism-based drug classes—NMDA-receptor antagonists, pannexin-1 inhibitors, gap-junction modulators, and KATP antagonists—offer entry points for aura-specific prevention. Adequately powered, aura-enriched RCTs with validated CSD biomarkers (DC-EEG co-registered against electrocorticography; neuron-derived extracellular vesicles) and pre-specified falsifiability thresholds are now the rate-limiting step. Seven testable methodological predictions are proposed.

Abstract: We present a systematic experimental investigation of the primary breakup of a planar liquid film subjected to high-speed co-flowing gas streams. A water film of thickness D_ℓ ≈ 150 μm is produced from a symmetric airfoil lip and sheared on both sides by compressed air. Interfacial dynamics were recorded with a high-speed camera and analyzed to extract transverse wavelengths, rupture modes, and their dependence on operating conditions. We find that the transverse wavelength λ_tra decreases strongly with increasing gas speed and that, for a given dynamic pressure ratio M = (ρ_gV²_g )/(ρ_ℓV²_ℓ ), different absolute combinations of V_g and V_l produce markedly different λ_tra. These observations indicate that gas-shear intensity and the gas flow instability modes (vortex shedding) control the breakup of the liquid film; the liquid inflow plays a secondary role under our conditions. The results provide experimental benchmarks for model validation and suggest routes to tune atomizer performance via gas-side control.

Abstract: Anemia management in chronic kidney disease patients is a significant challenge for modern healthcare professionals. Anemia in chronic kidney disease patients has multiple causes which include erythropoietin deficiency, abnormal iron metabolism, resistance to erythropoietin signaling, bone marrow suppression, blood loss, inflammation, nutrition deficiencies and oxidative stress. Vadadustat (a stabilizer of hypoxia-inducible factor (HIF) is indicated for the treatment of symptomatic anemia associated with chronic kidney disease (CKD) in adults on chronic maintenance dialysis. Evidence-based pharmaceutical care services are of great importance for chronic kidney disease patients because they provide safe, effective and economic care for patients. In the present article, we outline the most important pharmaceutical aspects that may affect the efficacy and safety of drug therapy with vadadustat and other HIF stabilizers. We conclude that evidence-based pharmaceutical care is one of the criteria that promotes management of vadadustat therapeutic efficacy and safety. Such an approach will contribute to improving patient adherence to treatment and, consequently, quality of life. Special attention is paid to structure-derived side effects of widely used HIF stabilizers, including their advantages and disadvantages. Based on all available safety and efficacy data for vadadustat, the overall risk-benefit profile remains positive for the approved indications for use.

Abstract: Whether the vibrational picture of elementary particles; now nearly half a century old; determines the spectrum we observe has remained without a settled answer. We close the question. At a fixed compactification of the heterotic string, the entire low-energy content follows from the geometry of the chosen Calabi–Yau manifold. Across compactifications, no internal rule singles out the one nature uses: the choice depends on input from outside the framework, of a kind we make explicit. Once that input is supplied, masses and mixings are computable; without it, no derivation is possible. Background. The picture of elementary particles as vibrational modes of a string is forty years old, and a steady catalogue of explicit heterotic constructions has shown that the observed gauge group and three-family chiral content can be reproduced. What no construction has settled is the converse question: does the resonance–particle correspondence, taken as a classification programme, determine the observed spectrum, or merely admit it? The literature has answered case by case; a framework-level resolution has been absent. Methods. We separate the framework into an unconditional mathematical layer (index theorem, Dolbeault cohomology, slope-polystability, Calabi–Yau metric existence, unobstructedness, BRST classification, anomaly cancellation) and a selection layer (choice of compactification datum). The achievable range of topological and cohomological selectors over the resulting landscape is computed by Kodaira–Spencer deformation theory and the special geometry of the complex-structure moduli space. Results. At fixed admissible datum the perturbative massless spectrum is fully determined by bundle cohomology and representation branching (positive direction). No topological or cohomological rule singles out the observed vacuum; the obstruction is a nontrivial class on a positive-dimensional moduli component (negative direction). A closure-completeness theorem unifies the two directions; universality, maximality, rigidity, stable-obstruction, categorical-impossibility, and quantitative-dimension theorems show the result holds for every framework whose predictive data is cohomological and is not improvable from within. A corrected audit lemma, with its converse, identifies the singleton condition any external selector must satisfy. Five residual closure theorems — selector completion, internal no-go, fixed-vacuum Yukawa computability, algebraic exotic lifting with post-stabilisation threshold, and ensemble finiteness — reduce the residual problems to one external axiom.

Abstract: Reliable soil moisture information is essential for agricultural drought warning, but tropical smallholder regions often lack ground networks for validating satellite products. This study evaluates CYGNSS Level 3 soil moisture in Guinea savanna agriculture over Benue State, Nigeria, from 2021 to 2023. Extended Triple Collocation was applied to CYGNSS, SMAP Enhanced Level 3, and ERA5-Land anomalies. Quadruple Collocation then used ESA CCI ACTIVE as a fourth product to quantify CYGNSS-SMAP error dependence. The standard SMAP-inclusive configuration gives CYGNSS a correlation with unknown true soil moisture of r = 0.425, an error standard deviation of 0.036m³m⁻³, and a signal-to-noise ratio of −6.56 dB. Quadruple Collocation identifies a CYGNSS-SMAP cross-error correlation of 0.325 and reduces the SMAP-independent CYGNSS estimate to r = 0.386, indicating that SMAP-inclusive validation overstates retrieval skill. Performance is weakest under dry soils (r = 0.331), where drought detection is most important, and location-level ETC convergence fails during Harmattan conditions as anomaly variance collapses. Skill is higher over cropland (r = 0.447), shrubland or grassland (r = 0.455), and moderate precipitation conditions (r = 0.630), but lower over tree cover (r = 0.342). These findings show that uncorrected CYGNSS Level 3 soil moisture is not sufficient for standalone year-round drought monitoring in Guinea savanna agriculture. Its value is strongest in bias-corrected, multi-sensor systems that account for vegetation, soil moisture state, precipitation history, land cover, and seasonality.

Abstract: Smart cities are being advocated to solve the excessive urbanization. They are supposed to be more efficient, more connected and more sustainable. However, long-term sustainability cannot be achieved in a real situation unless we seal the loopholes that still prevail in our current efforts. The main issues are: integrating renewable-energy solutions to reduce carbon; applying the principles of the circular economy to consume fewer resources and produce less waste; and ensuring that every resident of urban areas is able to access available urban resources and services that are usually not evenly distributed. In this article, the sustainability challenges are gathered together and demonstrate how they can be addressed by use of technology, provided that the technology is supported by good governance and accepted standards. A city that strategically connects its smart-city strategies to the United Nations Sustainable Development Goals, as well as to the appropriate national, industry, and IEEE standards, has left its technology-centered viewpoint, where people, the environment, and long-term resilience are prioritized. Ultimately, to head in the right direction and create the sustainable urban futures, we need to combine the latest technology with equitable policies and plans that would respond to climate change. This is when smart cities will bring a sustainable advantage to the lives of people and the planet.

Abstract: We report the first successful synthesis of millimeter-sized single crystals of high-entropy perovskite manganites with the composition (La_0.25Pr_0.25Sm_0.25Gd_0.25)_1-xCa_xMnO₃ (x = 0.3, 0.4, 0.5). Single crystals exceeding 2 mm in size were grown via a flux method using a PbF₂/PbO/B₂O₃ system. The X-ray diffraction patterns exhibit only (0k0) series reflections, indicating strong preferred orientation and high-quality single-crystal character. Scanning electron microscopy reveals dense, grain-boundary-free surfaces. Energy-dispersive X-ray spectroscopy elemental mapping shows uniform distribution of all constituent elements without detectable segregation or secondary phases, confirming the formation of a true high-entropy solid solution in single-crystal form. To the best of our knowledge, based on the publicly searchable literature, this is the first report of bulk single-crystalline high-entropy perovskite oxide. This breakthrough provides a much-needed single crystal experimental platform for systematically investigating the intrinsic magnetotransport mechanisms of compositionally complex strongly correlated oxides, free from grain-boundary interference.

Abstract: A green and facile hydrothermal method for synthesizing of copper-doped hydroxyapatite (Cu-HA) nanowires was reported. In this research, oleic acid was completely replaced by food-grade peanut oil as both the solvent and template agent for the synthesis of hydroxyapatite nanowires (HAW). Results confirm that a uniform one-dimensional nanowire morphology Cu-doped HAW was successfully synthesized. In vitro cytotoxicity tests confirm that the material exhibits good biocompatibility and supports normal cell growth. This study presents a viable route for the green and multifunctional fabrication of HA nanowires.