Abstract: Electric vehicles (EVs) have fundamentally changed the noise, vibration, and harshness (NVH) landscape of automotive powertrains. In the absence of masking inter-nal-combustion-engine noise, harmonic components such as gear whine, electric-motor orders, and inverter-related tones become more perceptible and more critical to vehicle re-finement. This review synthesizes the current state of the art in harmonic NVH engineer-ing for electric drivetrains, focusing on the interactions between gear geometry, manufac-turing variability, electromechanical coupling, structural transfer, and human sound per-ception. Classical mechanisms of gear-mesh excitation are revisited together with emerg-ing EV-specific challenges, including long-wavelength flank deviations, ghost orders, lightweight housing dynamics, and psychoacoustic sound-quality requirements. The re-view further examines recent progress in predictive and data-driven approaches, includ-ing machine-learning-based gear-noise modeling, digital-twin concepts, and virtual NVH assessment workflows. Overall, the literature shows that harmonic NVH engineering in EVs is evolving from a conventional gear-noise problem into a multidisciplinary sys-tem-level task integrating gear dynamics, manufacturing science, structural acoustics, electric-drive control, psychoacoustics, and data-driven optimization. This review pro-vides a structured synthesis of these developments and identifies key research gaps and future directions for the next generation of refined electric drivetrains.

Abstract: Monkey pox virus has caused two major outbreaks in the past 4 years. Transmission of the virus is via close person-to-person contact, which suggests that the virus is of high virulence and pathogenicity. This work reports evidence gleaned from an in-house MATLAB open reading frame finder applied to the GenBank assembled genome of monkey pox virus. Using the data in the output file of the software that includes gene sequence, gene length, gene molecular weight, amino acid sequence of gene, protein molecular weight, and isoelectric point of protein, two significant results were observed. Firstly, profiling for genes of length more than 60 base pair reveal a total of 255 candidate genes, of which more than 200 are of protein length between 20 to 80 plus amino acids. Secondly, plotting the protein isoelectric point and molecular weight in a theoretical 2D protein gel plot shows that there are clusters of viral proteins of different molecular weight at different pH values from pH 4 to 12. In particular, there is a large cluster of proteins between pH 8 and 10, which suggests that alkaline blood pH may promote monkey pox virus virulence. However, absence of many proteins in the pH range of around 5, points to a potential therapeutic window where modulation and control of blood pH at 5, may provide symptomatic relief for the patient, while awaiting the patient’s immune response to degrade the viral particles in the blood.

Abstract: Massera and Schaffer [\textit{Ann. Math. (2), 1958}] derived a breakthrough upper bound for the Clarkson angle between two nonzero vectors in a normed linear space, which was later improved by Maligranda [\textit{Am. Math. Mon., 2006}]. Pecaric and Rajic [\textit{Math. Inequal. Appl., 2007}] extended Maligranda's inequality to finitely many nonzero vectors. We derive a non-Archimedean version of Massera-Schaffer-Maligranda-Pecaric-Rajic inequality.

Abstract: The digital age has expanded social media and online forums, allowing free expression for nearly 45% of the global population. Yet, it has also fueled online harassment, bullying, and harmful behaviors like hate speech and toxic comments across social networks, messaging apps, and gaming communities. Studies show 65% of parents notice hostile online behavior, and one-third of adolescents in mobile games experience bullying. A substantial volume of abusive content is generated and shared daily, not only on the surface web but also within dark web forums. Creators of abusive comments often employ specific words or coded phrases to evade detection and conceal their intentions. To address these challenges, we propose a hybrid deep learning model that integrates BERT, CNN, and LSTM architectures with a ReLU activation function to detect abusive language across multiple online platforms, including YouTube comments, online forum discussions, and dark web posts. The model demonstrates strong performance on a diverse and imbalanced dataset containing 77,620 abusive and 272,214 non-abusive text samples (ratio 1:3.5), achieving approximately 99% across evaluation metrics such as Precision, Recall, Accuracy, F1-score, and AUC. This approach effectively captures semantic, contextual, and sequential patterns in text, enabling robust detection of abusive content even in highly skewed datasets, as encountered in real-world scenarios.

Abstract: Fostering teachers’ professional agency is central to promoting relevant chemistry learning, as it enables future teachers to design accurate, evidence-informed and meaningful learning in a rapidly changing scientific landscape. This article summarises 25 years of evidence-based chemistry teacher education (EBTE) within the LUMA co design ecosystem. Situated in the Department of Chemistry, the Chemistry Teacher Education Unit collaborates closely with scientists and co designs pedagogical content knowledge (PCK) and Technological Pedagogical Content Knowledge (TPASK) courses that are both scientifically up to date and pedagogically effective. Developing strong PCK grounded in chemistry research, chemistry education research and classroom practice, strengthens scientific literacy, enabling teachers to translate often complex chemical ideas into accessible and relevant forms for learners. The EBTE model connects theory and practice, supports cross boundary collaboration and prepares research-oriented designer teachers for the demands of modern science, sustainability, digitalisation and AI. Co design within the co-design-based research (CoDBR) framework enhances teachers’ professional, relational and epistemic agency by enabling research informed development and fostering close collaboration with chemists and societal partners through LUMAlab Gadolin. Agency driven co design within the LUMA ecosystem can build the capacity of student teachers, practising teachers and students to engage confidently with contemporary chemistry, and contribute to a scientifically literate and sustainable future.

Abstract: Introduction: Propofol is one of the most commonly used intravenous anesthetics worldwide and is considered safe for all age groups. However, there have been reports that propofol can induce severe atrioventricular block in humans, and several studies have shown that propofol hinders or prevents the inducibility of arrhythmias during electrophysiological studies (EPS) and radiofrequency (RF) ablation. Objectives: Verify whether propofol prevents or hinders the inducibility of arrhythmias during EPS and RF ablation procedures in children with Wolff-Parkinson-White Syndrome. Methods:We conducted a retrospective observational cohort study including 45 pediatric patients aged 0–18 years. Patients were identified through a review and analysis of a database of individuals with Wolff–Parkinson–White syndrome who were referred for electrophysiological study and/or radiofrequency ablation at the Electrophysiology Laboratory of the Institute of Cardiology (IC/FUC) in Porto Alegre over the past five years (2019-2024). Patients with prior ablation, structural heart disease, or ongoing antiarrhythmic therapy were excluded. The patients were divided into two groups and designated as group S (who received sedation) or group G (who received general anesthesia). Sedation (group S) was performed with midazolam (0.08–0.2 mg/kg), fentanyl (0.1–0.2 μcg/kg), and propofol 50–60 µg/kg/min in continuous infusion. General anesthesia (group G), in turn, was performed with sevoflurane at an average dose of 2% (1 MAC according to age). Results: From 4,874 invasive electrophysiology procedures performed during the study period, 45 involved pediatric patients with WPW. The sedation group (n=29) had significantly older patients (14.6±2.5 vs 10.3±2.8 years, p<0.001) with higher weight (65.9±16.3 vs 41.2±7.8 kg, p<0.001) compared to the general anesthesia group (n=16). Arrhythmia was successfully induced in 15/29 (51.7%) patients in the sedation group compared to 13/16 (81.2%) in the general anesthesia group (p=0.062, Fisher's exact test). Although this difference did not reach statistical significance, it represents a clinically relevant 29.5% lower induction rate in the sedation group. Post-hoc power analysis revealed the study was underpowered (49.8%), suggesting a possible Type II error. Analysis of the "procedure room time" revealed a longer duration in the general anesthesia group (97.8±36.7 vs 67.8±24.4 minutes), and this difference was statistically significant (p = 0.002). Conclusions: This study compared propofol-based sedation with sevoflurane-based general anesthesia in pediatric WPW patients. While sedation with propofol did not show a statistically significant reduction in arrhythmia inducibility, there was a concerning trend toward lower induction rates (29.5% difference) that may be clinically relevant. The study's limited statistical power (49.8%) suggests these findings should be interpreted cautiously, and larger prospective studies are needed to definitively establish whether propofol affects arrhythmia inducibility in this population. Propofol remains a viable option for these procedures, but clinicians should be aware of the potential for reduced inducibility, particularly in cases where arrhythmia induction is critical for diagnosis and treatment.

Abstract: The global food supply chain (FSC) wastes nearly one-third of all food produced—over 2 billion tonnes annually—highlighting the need for technologies to reduce food loss and waste (FLW). Simultaneously, existing solutions are often evaluated in isolation, limiting cross-comparison and informed decision-making. This research develops an explainable decision support system (XDSS) that combines the Best–Worst Method (BWM) and Stochastic Multi-criteria Acceptability Analysis for Group Decision-Making (SMAA-2), providing probabilistic rankings that incorporate preference uncertainty. The framework assesses 100 technology-based strategies for reducing FLW across five criteria: geographic fit, product category, FSC stage, stakeholder role, and technology used. Each scenario undergoes 50,000 Monte Carlo simulations with a fixed seed of 12345 to enable reproducibility. Trade-offs are formalised through penalty functions and weight vectors, while hit-and-run sampling explores feasible weight regions. Example user queries demonstrate how qualitative preferences translate into rank-acceptability profiles: Query 1's maximum rank-1 acceptability is 62%, and Query 2's is 74%. The XDSS provides transparent, robust, and context-sensitive recommendations that support evidence-based technology adoption by SMEs and local authorities. By enabling reproducible and explainable prioritisation, the system advances UN’s Sustainable Development Goal (SDG) 12.3, which aims to reduce FLW along the FSC.

Abstract: First described in the 1980s, mentorship has evolved from an emerging idea to a well-established culture. As such, a concomitant understanding of "mentor", "mentee", and "mentorship" is indicated—something currently lacking. In response, this work examines the author's fourteen types of mentoring relationships, over 39 years, regarding outcomes of peer-reviewed publications. The results demonstrate that mentors are most effective when they possess applicable experience compared to their mentees, experience considered by the mentees as having the potential to aid in their ability to solve a problem. In this respect, mentees require a particular self-awareness to identify that they have a solvable problem, anticipate a solution, and can effectively utilize the mentor's experience. Therefore, mentees are not equivalent to protégés, who, with their mentor, develop their careers and psychosocial competencies. The basis of mentorship is problem-solving for mentees, in contrast. Consequently, most productively, mentees choose their mentors, rather than having them assigned. Therefore, much of the effort in organizing matches for advancing the mentorship culture can be misguided and unproductive. In contrast, effective mentorship matching aids a relevant self-awareness in mentees and provides them with options for selecting mentors who are willing and able to share their pertinent problem-solving knowledge.

Abstract: Background: Obesity is a risk factor for increased blood pressure, in which the relation-ship is mediated by the action of various pro-inflammatory mediators such as myelop-eroxidase (MPO), xanthine oxidase (XO), and oxidized low-density lipoprotein (Ox-LDL). The objective of this research is to evaluate the contribution serum MPO, xanthine oxidase XO, and Ox-LDL as determinants of blood pressure in adults with abdominal obesity. Methods: A cross-sectional study was conducted involving 86 adults with abdominal obesity. Waist circumference (WC), fasting serum glucose (FSG), MPO, XO, and serum Ox-LDL were measured. The contributions of these parameters to systolic blood pressure (SBP) and diastolic blood pressure (DBP) were then assessed. Results: Multivariate analy-sis showed that the determinants of SBP were WC, FSG, MPO, and XO (Beta = 0.418, 0.328, 0.282, 0.248 respectively, all p< 0.05; adjusted R2 = 41.5%), while the determinants of DBP were FSG, WC, and MPO (Beta = 0.310, 0.284, 0.274, respectively, all p< 0.05; adjusted R2 = 24.8%). Conclusions: MPO has a role as a determining factor for SBP and DBP, XO has a role as a determining factor for SBP, while Ox-LDL does not have a significant role in blood pressure.

Abstract: Efficiently adapting large language models (LLMs) to specialized domains remains challenging due to substantial computational and memory requirements. In this work, we introduce CoDES (Context-efficient Domain Ensemble System), a framework designed to enhance small language models through context-efficient domain adaptation and weighted parameter ensembling. CoDES integrates context-specific fine-tuning, parameter-efficient adaptation using Low-Rank Adaptation (LoRA), and completion-only supervision to focus training on answer tokens while preserving pretrained capabilities and reducing computational cost. To further improve performance and robustness, the framework combines multiple fine-tuned models through weighted parameter ensembling. We evaluate CoDES on biomedical multiple-choice question answering using the MedMCQA benchmark. Experimental results show that the ensemble of tuned small models achieves 74.8% accuracy, approaching the performance of a much larger 72B-parameter model (77.1%). While requiring substantially fewer computational resources. The proposed framework offers several practical advantages, including achieving comparable performance, lower energy consumption, faster inference, and flexible adaptation to specialized domains. By reducing the reliance on extremely large models, CoDES provides a scalable and resource-efficient pathway for deploying high-performing language model systems in knowledge-intensive environments where models must be frequently updated with evolving domain information.

Abstract: Contemporary philosophy of mind is beginning to rehabilitate Arthur Schopenhauer as a proto-phenomenologist whose metaphysics of the will—once divested of its ontological commitments—provides thick descriptions of embodied agency, self-structure, and intersubjective resonance. This article validates this thesis through a four-stage naturalized reconstruction: (1) Schopenhauer’s "world-knot" and the unity of body and will are interpreted as phenomenal facets of minimal self-models within the framework of the Free Energy Principle (Friston, 2010). (2) His fragmented theory of the self is situated within Gallagher’s Pattern Theory of Self (2013). (3) His ethics of compassion is framed as a precursor to a Pattern Theory of Compassion. (4) Finally, affective criticality is employed to explain Schopenhauer's diagnosis of pessimism as a form of predictive dysregulation. Methodologically, the paper circumvents the pitfall of superficial analogies by adopting a weak methodological naturalism, utilizing cognitive models as a functional grammar for phenomenal material without reductively truncating the metaphysical deep structure.

Abstract: Objective: To assess the psychometric properties of the Academic Motivation Scale (AMS) in a sample of university students from Ecuador Method: This instrumental research examines the construct validity through confirmatory factor analysis and the reliability of the AMS. Sample: 1007 subjects (n=403 women, 39.9%) from the Salesian Polytechnic University of Ecuador. Results: Regarding the construct validity of the AMS, confirmatory factor analysis indicated that the model demonstrated a good fit to the data, with CMIN or χ2(303) = 1171.948, p ¡ 0.001, CFI = 0.987 (robust), RMSEA = 0.053 (confidence interval between 0.050 and 0.057), and SRMR = 0.047. The Cronbach’s Alpha (α) and McDonald’s Omega (ω) reliability coefficients were ≥0.80. Conclusion: Given the psychometric properties presented, the use of the AMS-E is recommended for evaluating factors of academic motivation in the Ecuadorian population.

Abstract: This study analyzes four Indonesian presidential elections (2009-2024) as a time series analysis within the event study framework on IHSG volatility. We examine the influence of macroeconomic, trading activity, and VIX channels, as well as whether their impact differs between pre-election and post-election periods. The methodology employs Principal Component Analysis (PCA) to construct channel variables and multiple linear regression with interaction terms on a baseline 5-month window before and after election month. The results consistently show that global risk sentiment (VIX) is the only significant positive driver of volatility. There is no significant interaction effects were found in either the 5-month, indicating that the influence of these channels is not statistically different between the pre-election and post-election periods. We conclude that Indonesian stock market volatility around presidential elections is predominantly driven by global risk sentiment, which overshadows domestic political transitions and associated local market dynamics.

Abstract: Background: Post-stroke upper-limb spasticity can cause pain, hinder passive care, and lead to secondary musculoskeletal complications. Current minimally invasive treatments have important limitations. Cryoneurolysis, which creates a controlled cold lesion of peripheral nerves, may offer a partially reversible focal denervation alternative. Methods: We conducted a feasibility case series in the outpatient department of a rehabilitation center. Three adults with chronic post-stroke hemi-paresis and a non-functional spastic upper limb underwent ultrasound- and nerve stimula-tion-guided cryoneurolysis of the musculocutaneous, median, and/or ulnar nerves. All had demonstrated a positive response to diagnostic nerve blocks beforehand. Feasibility outcomes in-cluded completion of planned nerve targets, tolerability under local anesthesia, absence of serious adverse events, and completion of 6-month follow-up. Secondary outcomes were Modified Ash-worth Scale (MAS), qualitatively assessed passive joint mobility (video-documented), pain meas-ured by visual analogue scale, sensory testing, and electroneuromyography (ENMG). Results: All procedures were completed as planned. Treatment was well tolerated under local anesthesia, and no serious adverse events occurred. MAS decreased by at least 2 points in targeted patterns, with immediate improvement in passive mobility; these effects persisted at 6 months. Pain remained unchanged in two participants and improved in one. Sensory testing at 6 weeks was stable. ENMG findings were heterogeneous, including reduced ulnar sensory action potential amplitude and biceps denervation activity in one participant. Conclusions: In this small series, cryoneurolysis for post-stroke upper-limb spasticity was feasible and associated with sustained tone reduction and improved passive mobility. Larger controlled studies are required to better define safety, optimize targeting strategies, and assess patient-centered outcomes.

Abstract: This study investigates the structural performance of a redesigned AAR Type E knuckle coupler using finite element analysis (FEA). The modified knuckle incorporates geometric reinforcement in critical load-bearing regions together with a hollow internal structure aimed at reducing component weight while maintaining structural integrity. Two numerical models were developed: a component level model, in which the knuckle was analyzed independently, and an assembly-level model that integrates the knuckle with the coupler body to capture realistic load transfer through contact interactions. Both models were subjected to a tensile draft load of 650,000 lbs (2670 kN) in accordance with Association of American Railroads (AAR) standards. The component-level analysis predicted peak von Mises stresses of approximately 1050 MPa, primarily concentrated near the pivot pin hole and curved pulling face regions. When contact interactions between the knuckle and coupler body were included in the assembly model, the representative peak stress decreased to approximately 950 MPa, corresponding to a stress reduction of about 10% due to load redistribution across the assembly interfaces. Highly localized stress peaks at sharp geometric edges were identified as numerical stress singularities and were excluded from engineering interpretation. The results demonstrate that assembly-level finite element modeling provides a more realistic representation of load transfer mechanisms in railway coupler systems and is essential for accurately predicting stress distribution and identifying critical fatigue-prone regions. These findings provide valuable insights for improving the structural reliability and design optimization of freight rail coupler components.

Abstract: The paper presents the cutting tool-life of uncoated and DLC-coated inserts used for machining of aluminum-lithium components used in the structure of Airbus A350 aircraft. Based on the collected data, a feed-forward artificial neural network with two hidden layers was created, trained using the Bayesian Regularization (trainbr) algorithm in MATLAB. The obtained results indicate a high performance of the model, with a low mean square error (MSE) and a correlation coefficient R &gt; 0.98, which reflects an excellent generalization capacity and a close correlation between the actual and estimated values. The regression plot and error analysis confirmed the accuracy of the predictions made by the network. The internal parameters of the algorithm, such as the gradient and μ, provided additional information regarding the optimization process.

Abstract: For a prime p>2, let N(p):=#{{q,r}⊂P:q≤r, q+r=p+1}, counting Goldbach representations of p+1 when p is itself prime. We study the arithmetic, computational, and heuristic behaviour of N(p) on the shifted-prime subsequence {p+1:p∈P}.Our principal result proves that the Euler product S_∞ := ∏_(l>2,l∈P)^( 1+1/((l-1)(l-2)))=1.74273… converges absolutely and equals the limiting Cesàro mean of the Hardy–Littlewood singular factor S(p+1) on this subsequence. This value, strictly greater than 1, reflects a divisibility bias from Dirichlet’s theorem and explains why the empirical constant differs structurally from 2C_2.We verify N(p)≥2 for every prime 11<p<10^7 (664 574 primes, zero violations) and classify primes into Mirror, Anchor-3, and Orphan types, proving two congruence theorems. Law 3 achieves RMSE 13 times smaller than the classical formula. Five disjoint ranges support α_∞=1/S_∞≈0.5738 over the alternative α_∞=1/2.

Abstract: This study examines how market leadership in Indian equities has structurally shifted away from Foreign Institutional Investors (FIIs) toward Domestic Institutional Investors (DIIs) and Mutual Funds (MFs), and evaluates the systemic risks created by this rebalancing. Using monthly transaction data from April 2007 to January 2026, we analyze evolving investment patterns among FIIs, DIIs, and MFs through trend analysis, Pearson and Spearman correlations, and phase decomposition. Since 2021, FIIs have recorded cumulative net outflows exceeding ₹8.68 lakh crore (US$ 95.36 billion), while DIIs—led by Mutual Funds financed largely through Systematic Investment Plans (SIPs)—have made net purchases of over ₹19.37 lakh crore (US $212.67 billion) , effectively absorbing FII selling and helping to maintain elevated index levels. The SENSEX remained above 80,000 points through 2025 despite persistent FII disengagement. We find that DII flows are positively and significantly correlated with SENSEX levels (r = 0.686, p < 0.001), whereas FII flows are significantly negatively correlated (r = −0.365, p < 0.001). The DII share of total market purchases increased from roughly 39% in 2017 to more than 54% by January 2026, highlighting a growing structural reliance of Indian equity markets on domestic liquidity. Drawing on Minsky’s financial instability hypothesis and behavioral finance perspectives, we argue that prolonged, sentiment‑driven domestic absorption of FII exits, in the absence of corresponding gains in corporate fundamentals or earnings, represents an emerging source of systemic vulnerability, with important implications for retail investors, fund managers, and regulators.

Abstract: We conducted this study to identify a correlation between the coraco-humeral distance (CHD) and the subscapularis tendon tears (SSCTs) in a single-institution setting. A total of 670 patients (n = 670) were included in the current study; they comprise 488 men (72.8%) and 182 (27.2%), whose mean age was 59.32 ± 12.58 years old. Depending on the presence of the SSCT and supraspinatus tendon tear (SST), they were divided into three groups: the control group (n = 179; the patients without SSCT and SST), the SSCT group (n = 133; those with SSCT only) and the SSCT+SST group (n = 358; those with both SSCT and SST). This is followed by comparison of the CHD between the three groups. The CHD was significantly greater in the control group as compared with the SSCT group and the SSCT+SST group (P < 0.05). In conclusion, our results indicate that there is a possible relationship between the CHD and SSCT. But further large-scale, multi-center studies are warranted to establish our results.

Abstract: The Trace & Trajectory Framework (TTF) offers a non-representationalist approach to meaning, cognition, and selfhood grounded in dynamical systems theory and radical enactivism. Rather than treating meaning as something stored in mental representations, TTF proposes that meaning is enacted—it emerges through temporally extended navigational patterns called trajectories traversing dynamic structures called ribbons. The framework’s layered ontology comprises traces (probabilistic preconditions), threads (pre-navigational filamentary configurations emerging as the first semiotic coherence structure over trace sets), ribbons (coordinated thread-bundles whose fold dynamics generate navigational positions), and trajectories (meaning-events). The dual-parameter architecture (λ for structural granularity, σ for epistemic access) combines with ribbon dynamics to handle phenomena typically addressed through separate, domain-specific machinery. This version foregrounds the toroidal topology (T² _H) of navigational space. The Gaussian saturation profile—previously presented as a hill with a terminal apex—is reconceived as a cross-section of an asymmetric torus: the upper half carries the saturative convergence gradient (from maximal thread differentiation toward autosimilar collapse through Θ); the lower half maps the dissolutive gradient (decreasing dissociative awareness toward NET substrate). Autosimilar collapse (A) is redefined as a navigational-epistemic function rather than a structural property. The ontological stack from threads upward is grounded in semiotic coherence (SC)—the structural tendency of configurations to maintain consistency across differential positions—rather than temporal accumulation; threads are reconceived as SC structures (filamentary coherence-tracking) rather than cumulative functions, and ribbons as second-order SC morphisms. A three-factor convergence model (architectural predisposition, mimetic fold dynamics, emergent navigation) replaces single-factor accounts of how configurations stabilize, positioning TTF against stochastic, nativist, and social-constructivist alternatives. The framework retains ribbon dynamics as its primary organizational level, with the Hx namespace, QRS-CONFIG, stratified epistemic barriers, hex bands, and Macro-α providing analytical instruments. The framework dissolves rather than solves classical problems—including symbol grounding, the scalability challenge, and the tension between embodied and abstract cognition—by rejecting the representationalist premises that generate them.