Abstract: The Standard Model describes particle phenomena through continuous gauge structures, chiral assignments, color, generations, and Yukawa masses, but it does not derive these labels from a deeper structural principle. This paper proposes a carrier-resolution interpretation in which particle species are not separate primitive objects, but different carrier-readable manifestations of one loop-detectable codimension-two archetype defect. The carrier supplies Lorentzian propagation and globally available \(U(1)\) phase closure, while particle labels arise through holonomy, embedding, closure, and saturation conditions. The framework argues that local \(U(1)\) closure favors a neutral-parent starting point, whose persistent asymmetric resolution is modeled as \[P_0\longrightarrow Z_2\oplus (Z_2\!\rightarrow\!Z_3).\] The \(Z_2\) branch is interpreted as lepton-like after Lorentz embedding, whereas the \(Z_2\!\rightarrow\!Z_3\) branch supports a nested confined \(Z_3\) monodromy interpreted as hadronic structure. Incomplete \(Z_3\) sectors are not carrier-readable as isolated hadrons; the usual \(SU(3)_C\) QCD description is retained as the effective high-energy continuum envelope of temporarily resolved \(Z_3\) sectorality. The paper further gives a conditional interpretation of the three observed generations as leading saturation modes of the dominant \(U(1)/Z_2/Z_3\) closure backbone, while higher \(Z_n\) refinements appear as suppressed response corrections rather than ordinary additional generations. As a concrete test, the neutron--proton magnetic-moment ratio is derived from an ideal \(Z_3\)-complete baseline and a rule-generated interface sequence through \(Z_7\). The successive predictions improve from the \(10^{-4}\) level to the few-ppm level and then to below one ppm of observation, without introducing new particles, new fundamental interactions, or fitted coefficients.

Abstract: A quantitative risk assessment of human loss of control over advanced AI used a Bowtie diagram extended with fault tree and event tree analysis. Six primary threats were identified (recursive self‑improvement, power seeking, deceptive alignment, loss of corrigibility, off‑switch subversion, malicious misuse) and six consequences (systemic infrastructure collapse, economic breakdown, resource shortages, non‑human value lock‑in, human marginalization, global supply cascade failures). Preventive and mitigative barriers were assigned per pathway from expert literature. Input probabilities (threat base rates and barrier failure‑on‑demand values) were sourced from experts and modeled with triangular uncertainty distributions. A 1,000‑iteration Monte Carlo simulation propagated epistemic uncertainty, yielding a median probability of the top event (loss of human control) of 12.8% (90% CI: 11.3%–14.4%), roughly 1 in 8. The distribution is approximately symmetric with slight positive skew, indicating modest tail risk if barrier failures interact. Conditional on the top event, Expected Severity is 1.85 on a 1–10 scale (90% CI: 1.75–1.96), suggesting mitigation is effective in most scenarios. Results align with expert estimates and demonstrate barrier effects; narrow CIs reflect model consistency. Remaining tail risks support precautionary governance, increased alignment research, iterative risk modeling, and investment in international coordination with robust safety measures to reduce the existential risk of AI loss of control.

Abstract: To solve the drawbacks of conventional long-cycle wear tests for miniature standing- wave linear ultrasonic motors, an accelerated equivalent wear model and test system were proposed in this work. After primary screening of multiple friction pair materials, graphite and Al2O3 were adopted to modify epoxy films. The optimal friction pair is composed of 6061 hard anodic oxidation film and ECA105 composite film. The matched pair exhibits excellent driving stability and low wear loss, with fatigue wear as the main wear form. Graphite and Al₂O₃ exert synergistic anti-wear and load-bearing effects via forming a stable transfer film on the friction interface. Experimental results confirm that the accelerated test is equivalent to full-life durability test. The presented method and optimized friction pair can effectively guide the development of high-performance ultrasonic motors.

Abstract: Background/Objectives: Diffuse large B-cell lymphoma (DLBCL) is an aggressive lymphoma and one of the most common hematological neoplasia. Entropy is a statistical measure of randomness that can be used to characterize the texture of an input image and measure tissue complexity. Methods: Image processing and computer vision analysis were performed on a series of 114 diagnostic DLBCL cases and 44 reactive lymphoid tissues stained with hematoxylin & eosin (H&E). Histological entropy was measured to differentiate between reactive lymphoid tissue and DLBCL and predict clinical evolution. Gene expression analysis using the NanoString nCounter PanCancer Immune Profiling Panel was performed in 29 cases. Results: Comparison with reactive lymphoid tissue, DLBCL was characterized by lower entropy (7.3 ± 0.2 vs. 6.8 ± 0.6; P < 0.001, respectively). Within the DLBCL diagnostic category and at patient-level analysis, higher entropy was associated with poor overall survival and death events within the first 2 years (hazard-risk = 2.4, P = 0.004) and lower entropy with a moderate and more favorable outcome (hazard-risk = 0.4, P = 0.004). High entropy was also correlated with ECOG performance status ≥ 2, lower protein expression of apoptosis markers of cPARP and cCASP3, and upregulation and downregulation of specific immuno-oncology genes. Conclusion: The histological evaluation of entropy is useful for both the differential diagnosis of reactive lymphoid tissue and DLBCL and can be used as a predictor factor of DLBCL prognosis.

Abstract: The charged-lepton mass hierarchy remains unexplained in the Standard Model: the Higgs mechanism relates \(m_e\), \(m_\mu\), and \(m_\tau\) to three Yukawa couplings, but does not determine their ratios. This paper applies a minimal effective subset of a previously developed reconstruction framework, including carrier embedding, finite-action phase behavior, and codimension-two defect structure, to the charged-lepton mass problem. Charged leptons are modeled as carrier-embedded defect channels arising from a common neutral parent, and their masses are interpreted as quadratic residual responses. The natural variables are therefore root residuals rather than masses themselves. The main algebraic result is that Koide's relation is equivalent to equality between the democratic parent component and the orthogonal channel-splitting component of the charged-lepton root vector. This reduces the hierarchy to a one-angle problem on the Koide cone. The remaining angle is fixed by an effective weak-closure selector involving the neutron--proton mass difference, the democratic electron projection, the beta-continuum scale, the fine-structure constant as a \(U(1)\) carrier-dressing factor, and a finite positive-end recoil term. No continuous parameter is fitted. Using only \(m_e\), \(m_p\), \(m_n\), and \(\alpha\) as physical boundary inputs, the weak-closure selector gives \[m_\mu^{\rm pred}\simeq 105.6565~{\rm MeV}, \qquad m_\tau^{\rm pred}\simeq 1776.94~{\rm MeV}, \] with relative errors of approximately \(-0.0017\%\) and \(+0.0045\%\), respectively. The Koide-cone reduction is algebraic, while the weak-closure selector is presented as an effective boundary condition requiring future microscopic derivation. The result provides a falsifiable route by which the charged-lepton hierarchy may arise from neutral-parent root balance and weak closure rather than from three independent Yukawa parameters.

Abstract: Our first objective was to quantify the associations between health-related events (HRE) before insemination, the relative increase in estrus intensity (REI) at insemination, and the probability of pregnancy per artificial insemination (P/AI) in organic dairy cows. Quanti-fying these associations may aid on-farm decision-making, such as setting the voluntary waiting period, choice of type of semen, do-not-breed and culling decisions. A second ob-jective was to develop predictive models to estimate P/AI based on readily available data, and present common goodness-of-fit results also used in the machine learning community. All data were collected from a certified organic dairy farm in the western USA from 2019 to 2021. Health-related and reproduction data were obtained through DRMS (Raleigh, NC, USA). Activity data were collected using pedometers (IceRobotics, Stirling, UK) mounted on the rear legs. The REI, defined as walking steps per hour before insemination divided by the cow’s baseline steps per hour, was available for 17,238 inseminations from 4,759 cows. The REI was categorized as ≤200% (6,999 inseminations), >200-400% (4,685), >400-600% (2,929), or >600% (2,625). The HRE were available for 65,684 inseminations from 13,365 cows. The HRE was categorized as mastitis (prior to 9,114 inseminations), metabolic (displaced abomasum, ketosis, milk fever; 1,941), reproductive disease (metritis, endometritis, pyometra, retained fetal membranes; 4,907), lameness (4,058), 2 different diseases (4,022), ≥3 different diseases (813), or as healthy (none of these diseases prior to insemination; 40,829). Combinations (COMBO) between REI categories and 0, 1, or ≥2 HRE were also created for 16,415 inseminations in 4,647 cows. Data were split into training and test sets. The training data were used to fit 3 logistic regression models that included either HRE, or REI, or COMBO. Each of the 3 models also included the covariates of prior 3-mo herd P/AI and days in milk (DIM), and the fixed effects of parity, insemination season, days after the previous insemination or days to 1st insemination. A random effect ac-counted for repeated inseminations within cow. Parameter estimates, odds ratios, and the least-square means of the estimated P/AI of the fixed effects were obtained from the logistic regression models. The models’ estimates were applied to the test datasets, and discrimi-nation and calibration statistics were calculated to judge goodness-of-fit. Unadjusted mean P/AI were 31%, 28% and 28% for the HRE, REI and COMBO training datasets. For the HRE model, estimated P/AI ranged from 20% (≥3 different HRE) to 30% (healthy). The estimated P/AI associated with the 4 REI categories were not different from 27% in the REI model. The estimated P/AI associated with the combinations of HRE and REI in the COMBO model varied from 18% after ≥2 HRE and >200-400% REI, to 30% when insemina-tions were in healthy cows with REI >600%. Inseminations in older cows, in the spring, and outside 18-24 d after the previous insemination were also associated with lower esti-mated P/AI. The area underneath the Receiver Operating Characteristic curve ranged from 0.57 (COMBO) to 0.60 (HRE) for the test data, indicating fair discrimination ability of the models. The Brier score ranged from 0.19 to 0.21, indicating moderate performance of the prediction models. Calibration plots showed that the prediction models produced unbi-ased estimated P/AI. In conclusion, the results showed no conclusive evidence of greater estimated P/AI related to greater REI as a measure of estrus activity. More health-related events were associated with lower estimated P/AI. Combinations of low REI and more HRE were associated with notably decreased estimated P/AI. The logistic regression mod-els produce unbiased estimated P/AI. These predictive models may inform insemination and culling decisions in organic dairy cows. A variety of goodness of fit statistics were calculated to allow comparisons of the current logistic regression analyses with future analyses made by other machines learning techniques.

Abstract: The effectiveness of machine learning and deep learning methods for network anomaly detection depends strongly on the quality and representativeness of the datasets used for training and evaluation. However, many publicly available benchmarks rely on synthetic traffic, outdated attack scenarios, or limited representation of encrypted communications. This work presents a network traffic dataset derived from operational firewall logs collected in a heterogeneous institutional environment dominated by HTTPS/TLS traffic. A structured data-centric pipeline was implemented, including preprocessing, behavioral feature engineering, unsupervised pseudo-labeling through the EFMS-KMeans algorithm, class balancing using SMOTE, and temporal sequence generation for sequential analysis. The resulting dataset contains large-scale flow-level records describing volumetric, behavioral, and temporal traffic characteristics while preserving privacy through anonymization procedures. Technical validation was conducted using statistical analysis, entropy-based measurements, clustering quality metrics, and dimensionality reduction techniques, confirming data consistency, diversity, and class separability. The dataset is publicly available through the Mendeley Data repository together with metadata and documentation supporting anomaly detection research, encrypted traffic analysis, and the evaluation of machine learning and deep learning approaches in realistic cybersecurity environments.

Abstract: The PPR Global Research and Expertise Network (GREN) plays a central role in identifying research priorities supporting the Global Peste des petits ruminants (PPR) Eradication Programme (GEP). A scoping review of PPR epidemiological research published between 2015 and 2025 was conducted to assess progress and remain gaps across research domains relevant to GREN priorities. A total of 670 PPR-related publications were retrieved. Epidemiological research constituted 46.9% of the PPR literature, pathogenesis 19.1%, diagnostics 15.6%, then vaccinology 9.5%, and immunology 7.9%. From the 273 epidemiological studies, most originated from Africa (53.1%) and Asia (37.7%). The epidemiological studies were categorized into seven domains. Molecular epidemiology and strain analysis represented the largest proportion of published studies (40.0%), followed by surveillance and disease monitoring (23.8%), transmission dynamics and risk modelling (12.5%), vaccination epidemiology (10.6%), analytical and risk factor epidemiology (8.4%), control strategy evaluation (2.9%), and socioeconomic research (1.8%). Progress towards GREN research priorities was variable. Molecular epidemiology and surveillance demonstrated the greatest methodological advancement, supported by widespread application of phylogenetics, serological surveillance, and expanding sequencing capacity across endemic settings. Transmission dynamics and risk modelling also showed increasing analytical sophistication through spatial modelling, network analysis, ecological suitability modelling, and dynamic simulation approaches, although applications remained geographically concentrated and only partially integrated into operational decision-support systems. In contrast, vaccination epidemiology, comparative evaluation of control strategies, and socioeconomic research remained limited in volume and operational integration. Across domains, important gaps persisted in implementation-focused evaluation, integrated surveillance systems, and translation of research evidence into adaptive eradication planning and policy development. Overall, the findings demonstrate substantial growth in the global PPR epidemiological evidence base over the past decade but also reveal a persistent imbalance across research domains and geographies. While major advances have been achieved in molecular characterization and descriptive surveillance, greater integration of epidemiological, operational, modelling, and socioeconomic evidence is needed to strengthen adaptive eradication planning and improve alignment between research priorities and policy implementation. Research and policy need to be better integrated, with policy needs driving research focus, with research findings then informing policy.

Abstract: Community-based organisations (CBOs) play a critical role in implementing HIV programmes, many of which have historically relied on United States (U.S) donor funding. Recent U.S funding cuts have disrupted community-based HIV programmes, underscoring the need to understand implementers’ experiences to support sustainable service delivery. This study explored how funding instability affected community-based HIV programmes in rural KwaZulu-Natal, South Africa. A qualitative, exploratory design was employed using interviews and focus group discussions with purposively selected youth frontline workers and programme managers (n = 26). The Health Systems Building Blocks framework guided thematic analysis. Participants described disruptions in outreach and prevention services, contract terminations and reduced working hours among frontline personnel, weakened data and follow-up systems, shortages or reduced local availability of HIV prevention commodities, lack of transition planning, and abrupt program closure without sustainability measures. Community-based HIV programmes are a critical component of the local health system, and funding-related disruptions may weaken progress across the HIV care cascade by undermining testing, linkage to treatment, retention, and viral suppression. Protecting these community-based functions is therefore essential for sustaining progress toward epidemic control, while future research should examine the longer-term effects of donor funding reductions on service continuity and health outcomes.

Abstract: This article reviews pharmacological strategies targeting key metabolic pathways in cancer cells and highlights their inherent limitations, including metabolic plasticity and lack of selectivity. It is proposed that these vulnerabilities can be addressed through a global redox-based approach using high-dose vitamin C. Evidence suggests that the anticancer activity of vitamin C is mediated by its oxidation to dehydroascorbic acid (DHA). Although DHA cannot be administered directly due to its instability, it can be generated in situ in the circulation. Once taken up by cancer cells, DHA perturbs multiple redox-sensitive processes, leading to depletion of NADPH and collapse of cellular redox homeostasis. We present a mechanistic framework outlining how controlled generation of DHA may enable a more robust and clinically effective anticancer strategy.

Abstract: Aberrant signalling by leucine-rich repeat kinase 2 (LRRK2) is a major driver of Parkinson’s disease (PD) biology, linking Rab phosphorylation to vesicular trafficking, endolysosomal dysfunction and immune-cell regulation. However, how LRRK2-linked trafficking programs intersect with metabolic and mitochondrial remodeling within vulnerable human substantia nigra cell states remains poorly defined. Here, phosphatome-wide systems analysis, single-nucleus transcriptomics, structural modelling and deep-phosphoproteomic validation were integrated to identify the glycolytic regulator PFKFB2 as a phosphorylation-regulated metabolic node in the Parkinsonian substantia nigra. Sample-level pseudobulk profiling of the GSE184950 human substantia nigra single-nucleus RNA-seq dataset revealed distributed phosphatome remodeling across the PD spectrum, with PFKFB2 emerging as a transcriptionally reduced and dynamically rewired systems hub. Full-atlas analysis across 390,360 nuclei showed that this tissue-level decrease resolved into disease-stage-specific cellular redistribution: PFKFB2 was reduced across oligodendrocyte-lineage and neuronal compartments in PD, whereas Parkinson’s disease dementia (PDD) showed microglial induction alongside astrocytic, astrocyte/glial-intermediate and neuronal loss. Stratification by PFKFB2 expression revealed marked transcriptional polarity, with PFKFB2-positive cellular states enriched for PD-associated genes, LRRK2–Rab trafficking, lysosomal/autophagy and glycolytic programs, while PFKFB2-negative states retained stronger mitochondrial/OXPHOS-associated signatures. Mechanistically, sequence-based phosphosite prioritization and AlphaFold-guided peptide docking identified the flexible, putatively disordered C-terminal regulatory tail of PFKFB2 as a kinase-accessible region, prioritizing the conserved Ser483 locus as the strongest LRRK2-compatible structural candidate. Independent phosphoproteomic interrogation of human and mouse LRRK2 perturbation datasets further supported the orthologous PFKFB2/Pfkfb2 Ser483/Ser486 region as a conserved LRRK2-responsive phosphosite candidate, including inhibitor-sensitive reduction and inhibitor-resistant retention under A2016T LRRK2 conditions. Together, these findings position the PFKFB2 signalling axis as a cell-state-resolved metabolic bridge linking LRRK2–Rab trafficking programs with the mitochondrial–endolysosomal disease architecture of the Parkinsonian substantia nigra.

Abstract: Buildings account for a substantial share of global energy consumption and greenhouse gas emissions, creating an urgent need for design strategies that simultaneously address operational performance, occupant comfort, and life-cycle environmental impacts. While simulation-based optimization has become increasingly common in building performance research, relatively few studies evaluate energy use, thermal comfort, and embodied carbon within a unified tri-objective framework. This study presents a simulation-based tri-objective Pareto optimization of residential buildings in Riyadh, Saudi Arabia, and Dubai, United Arab Emirates, using DesignBuilder, EnergyPlus, and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). A standardized four-story residential apartment prototype comprising 16 thermal zones and 2239.82 m² of conditioned floor area was developed and simulated under identical geometric, operational, and HVAC assumptions. Window-to-wall ratio, glazing type, external shading depth, and cooling setpoint temperature were optimized to minimize annual site energy consumption, ASHRAE 55 thermal discomfort hours, and embodied carbon emissions. Baseline simulations revealed substantially higher operational demand in Dubai, with annual energy consumption reaching 272,077 kWh compared with 196,478 kWh in Riyadh, while discomfort hours increased from 2,530 h/year to 3,262 h/year. Optimization reduced annual energy demand by 72.9% in Riyadh and 74.5% in Dubai, while thermal discomfort was reduced to 776 h/year in the best-performing comfort solution. Pareto-optimal solutions consistently favored low window-to-wall ratios (10–16%), high-performance glazing, and external overhangs between 1.5 and 2.0 m. The findings demonstrate the effectiveness of tri-objective optimization for balancing operational efficiency, occupant comfort, and embodied carbon while providing climate-responsive façade design guidance for residential buildings in hot-arid Gulf environments.

Abstract: Prebiotic Earth builds with zero entropy while dissipating with maximum entropy. This is demonstrated by a new physical model presented in this paper, the type of physical model that is needed to determine a theoretically grounded statistical probability of abiogenesis. The thermodynamic mechanism behind this capacity has not been explained by existing frameworks, none of which capture all properties of dissipative structures that would form a coherent, internally consistent set derived from a single physical model. A close reading of Clausius's foundational papers reveals a convention and properties for far-from-equilibrium systems that modern thermodynamics has overlooked, pointing to a framework of an Earth that builds with zero entropy while dissipating with maximum entropy. This paper introduces the gravitational dissipative structure (GDS) and provides that complete quantitative framework. Grounded in Clausius's interior/exterior work distinction, his force-balance reversibility criterion, and his dynamic equivalence values of compensated and uncompensated transformations, the GDS model derives six thermodynamic properties, including complexity yield, specific heat quality, and heat transformation effectivity, and proves two theorems. The first theorem establishes that dissipative structures are more effective at transforming heat into stored energy at greater local heat sink temperatures. The second theorem proves that the ratio of real efficiency to Carnot efficiency is constant regardless of boundary temperatures. Applied to Earth's tropospheric water and air cycles, the model yields auto-powering capacities of 82 W/m² and 345 W/m², with greater than 98% of initial heat quality retained. Their combined heat quality outputs estimate jet stream velocity to within the same order of magnitude, cross-validating the GDS model and the concept of heat quality networking. When GDSs network at planetary scale, the result is the delivery of heat at temperature to the molecular scale and a massive mixing that is a geological-timescale concentration of dissolved salts, acids, bases, and minerals that delivers the physical preconditions for prebiotic chemistry on Earth. This is Part I of a four-part series. Parts II through IV build on these preconditions. The objective is to conceptually and quantitatively describe the physical systems that are the context for the emergence of life.

Abstract: Background/Objectives: Birt-Hogg-Dubé (BHD) syndrome is a rare autosomal dominant disorder caused by germline pathogenic variants in the FLCN gene. Although it carries a substantial lifetime risk of renal cell carcinoma, its earliest manifestations are typically pulmonary cysts and spontaneous pneumothorax, which are frequently misclassified as primary spontaneous pneumothorax, resulting in diagnostic delay and inadequate oncological surveillance. We aimed to characterise the real-world phenotypic spectrum of BHD encountered in a respiratory referral setting. Methods: We retrospectively describe seven consecutive patients with genetically confirmed BHD syndrome diagnosed at our tertiary referral centre between 2022 and 2024. Demographic data, smoking history, FLCN variants, pneumothorax episodes, HRCT findings, pulmonary function tests and extrapulmonary neoplasms were collected. Reporting followed the PROCESS 2020 guideline. Results: Mean age at genetic diagnosis was 53.1 years (range 41–64). All seven patients had multiple thin-walled pulmonary cysts on HRCT, with the typical basal, subpleural and paramediastinal distribution; three had a pneumothorax history. Despite largely preserved spirometry (mean FEV1 82.4% predicted), DLCO was reduced in five patients (mean 67.4% predicted) and was the most frequently affected functional parameter, although the overall functional picture was heterogeneous. Five patients had solid neoplasms (one renal, one colorectal, one thyroid/parathyroid, one ovarian, one lung adenocarcinoma). Conclusions: In this referral-based case series, pulmonary cysts were a constant finding and DLCO was the most frequently reduced functional parameter, although the functional picture varied across patients. These descriptive observations are hypothesis-generating and require prospective, controlled validation—including comparison with other diffuse cystic lung diseases—before any diagnostic algorithm can be proposed.

Abstract: The present study aimed to evaluate the stability of volatile organic compounds (VOCs) in exhaled breath samples under different storage conditions (refrigeration at -20 °C vs. room temperature) and analysis times (0 h, 3 h, 6 h, 12 h). Alveolar exhaled breath samples were collected from 30 volunteers in 500 mL Tedlar® bags, followed by analysis using headspace solid-phase microextraction (HS-SPME) coupled to gas chromatography-mass spectrometry (GC-MS). The results showed the putative identification of 73 metabolites, 41 of which were common to both conditions. Pre-analytical storage of the samples at -20 °C significantly altered (p < 0.05) the stability of 33 of the 41 common VOCs analyzed. Specifically, refrigeration improved stability by reducing the coefficient of variation (CV) in 23 of these metabolites compared to samples kept at room temperature. Highly stable metabolites with a CV < 15% were found. A 90% loss of the analytical signal was observed 12 hours after sample collection, in contrast to the stability maintained in refrigerated samples. These findings highlight the influence of pre-analytical conditions on the integrity of volatile profiles, establishing immediate refrigeration as a fundamental step for the study of potential biomarkers present in breath. These results provide key criteria for the standardization of breathomics protocols.

Abstract: The paper examines the impact of gas turbine engine component manufacturing quality on the efficiency criteria of its life test. Known methods for selecting test parameters apply maximum damageability equivalence and minimum test time as test efficiency criteria. This study also proposes taking into account the maximization of engine lifecycle profits through the proper selection of test parameters. Engine components that determine its life were selected: the turbine blade, rotor bearing, reducer driving gear, fan bearing, and DC and AC generators. Both the mathematical expectation and variance of the quality parameters were varied during the study. The manufacturing quality of engine components and assemblies is characterized by geometric, mechanical, and physical parameters. These parameters include bearing fit diameters, initial radial clearance, turbine blade geometry, mechanical properties and gear shape, generator insulation quality, and others. Parameters selection was based on the life cycle simulation model. The following results were obtained in the course of the study: (1) Manufacturing accuracy has a more significant impact on test results than deviations from mean values of initial state parameters; (2) Despite the fact that variation in production parameters from the standard values do not affect the comparability of test results, they lead to an acceleration of the testing process. At the same time, this entails a decrease in overall economic efficiency throughout the entire life cycle of the product; (3) The overall profitability of a production run of engines is primarily determined by the quality characteristics of the turbine blades, and least on fan bearing quality parameters; (4) in the case where there is a full guarantee of engine component manufacturing quality, only short-term (acceptance, control) tests can be carried out.

Abstract: Increasing high-impact, low-probability (HILP) disruptions require a paradigm shift in emergency power for critical infrastructure (CI), moving away from traditional cost-driven assessments toward physical resilience. To address this gap, this article develops a resilience-oriented screening framework to prequalify energy technologies (including CHP and CCHP) for CI facing prolonged outages. Diverging from pure economic optimization, the methodology prioritizes survivability criteria: islanding readiness, black-start capability, fuel autonomy, multivector energy coverage, implementation feasibility, and operational safety. A hospital serves as the reference CI due to its rigorous demand for simultaneous electricity, heat, cooling, and process loads. The framework employs a two-stage procedure: a Stage I go/no-go boundary filter and a Stage II weighted scoring matrix. This methodology evaluates a broad technology basket encompassing gas, biogas, and biomass CHP, CCHP with absorption cooling, hybrid CHP/BESS, RES+BESS, and diesel generators. Rather than providing a definitive techno-economic ranking, this study establishes a transparent, replicable front-end engineering tool. Ultimately, the results define boundary conditions for prequalifying multivector energy architectures, creating a foundation for future modeling and dynamic simulations of CI microgrids.

Abstract: The educational portfolio is in line with the principles of OBE (Outcome-Based Education) as a tool enabling the documentation and assessment of learning outcomes in a continuous, individualized manner, embedded in a real clinical context. Its use allows for a shift away from point-based assessment of achievements in favor of monitoring the progression of competencies over time, which is one of the key postulates of competency-based education (programmatic assessment). The educational portfolio is an important tool supporting the process of medical education, combining the perspectives of the teaching physician and the medical student. From the clinical teacher’s point of view, the portfolio enables systematic assessment of the student’s progress, clinical competencies, reflective skills, and professional development over time. It also facilitates the individualization of the teaching process, supports constructive feedback, and promotes the development of professional attitudes consistent with the principles of evidence-based medicine and medical ethics. For medical students, the portfolio is a space for active learning, integrating theoretical knowledge with clinical experience. It allows them to document the skills they have acquired, analyze their strengths and areas for further development, and develop self-reflection and responsibility for their own learning process. Regular portfolio maintenance promotes awareness of the role of a future physician and prepares students for lifelong learning. The joint use of the portfolio by the teacher and student strengthens the partnership model of education, increases the transparency of requirements, and improves the quality of medical education, making it more focused on the development of competencies and patient needs.

Abstract: The rapid expansion of unmanned aerial vehicles (UAVs) applications in logistics, surveillance, and defense highlights the need for scalable and reliable energy delivery solutions. Conventional charging approaches constrain operational endurance and scalability, requiring frequent returns to base. This paper presents a laser-based wireless power transmission system designed to enable safe, contactless and efficient power transfer from ground to air. The proposed architecture integrates a high-power optical source, a hierarchical beam-pointing system combining coarse and fine steering, and a receiver-side sensing and energy-conversion module. The system is designed to be adaptable across different UAV classes, from lightweight platforms to larger aerial systems. An experimental campaign is conducted to validate the main system functions under representative operating conditions. Beam propagation, pointing accuracy, and control response are characterized through laboratory and outdoor tests, including long-range spot measurements and closed-loop steering validation. Overall, the study demonstrates the feasibility of laser-based wireless energy transfer for UAV applications and provides an experimental foundation for the development of persistent aerial operations in both civil and defense scenarios.

Abstract: Raman spectroscopy combined with machine learning offers a rapid, label-free approach for bacterial identification, but robust translation remains challenged by spectral variability, biological heterogeneity, and limited model interpretability. Here, we present an integrated evaluation of an optimized Spectral Transformer (ST) framework for Raman-based bacterial classification benchmarked against a systematically optimized one-dimensional convolutional neural network (1D-CNN). The comparison was performed using a curated 36-class dataset comprising 15 Gram-negative bacterial entries, 15 Gram-positive bacterial entries, one non-bacterial microorganism, and five background/reference classes, enabling evaluation of both species-level and fine-grained bacterial classification. Under 15 dB noise-augmented evaluation, the ST achieved 80.6% ± 0.3% accuracy and a Matthews correlation coefficient (MCC) of 0.801 ± 0.003, outperforming the 1D-CNN baseline with 72.9% ± 0.3% accuracy andanMCCof0.721±0.003. Integrated Gradients analysis combined with attention map visualization enabled multi-level model interpretation, revealing that the ST’s improved robustness correlates with more bounded attribution patterns during misclassification, whereas the 1D-CNN’s feature attribution becomes scattered under noise perturbation. Importantly, this interpretability-driven analysis identified model-specific failure modes in the baseline architecture, including an over-reliance on non-specific spectral regions under noise, which can inform future data collection strategies and guide refinements to experimental protocols. These results demonstrate that attention-based spectral modeling improves Raman-based bacterial classification under noise-perturbed conditions while enabling multi-level interpretability that bridges model understanding with actionable feedback on experimental design and data quality requirements.