Abstract: In the current paper, the nonlinear absorption characteristics and laser modulation performance of the ternary anisotropic semiconductor material ZrGeTe₄ were successfully explored. The recovery time of the ZrGeTe₄-PVA thin film was measured to be 5.74 ps by pump-probe technology. By employing ZrGeTe₄ as a saturable absorber, a passive mode-locked Yb-doped fiber laser was demonstrated for the first time. In the 1 µm mode-locked operation, the central wavelength is 1031.29 nm, the pulse repetition rate is 24.85 MHz, and the pulse width is 786.3 ps. In an Er-doped fiber laser operating at the wavelength of 1561.10 nm, the pulse width as short as 1.26 ps with a repetition rate of 4.38 MHz. The results show that ZrGeTe₄ has excellent broadband nonlinear optical characteristics.

Abstract: We introduce a divergence-based framework for structural normalization and constrained reconstruction in generative models for poetic translation. The central hypothesis is that a text admits a contextualized, language-independent structural representation capturing semantic, prosodic, rhetorical, cultural, and aective invariants independently of surface linguistic form. A normalization operator embeds each text into a domain-dependent structural manifold conditional on a contextual knowledge state Kt. Reconstruction in a target language is formulated as divergence-minimizing projection under explicit constraint functionals. Structural preservation is quantied through domain-dependent divergence between probability measures induced by structural representations. Cross-linguistic transfer is interpreted as analogical alignment between contextualized structural states. Because structural representation depends on the contextual knowledge state, epistemic updates modify the geometry of structural comparison and may induce time-indexed optimal realizations. The proposed formulation establishes a mathematical perspective on translation as constrained structural projection in contextualized measure spaces, separating relational invariants from surface realization and enabling controllable generative reconstruction under explicit structural validation.

Abstract: In-cylinder pressure is a critical parameter for assessing the combustion process and per-formance of spark ignition internal combustion engines. However, obtaining measured in-cylinder pressure data across the full operating range, particularly at various spark advance angles (SA), is costly and technically demanding, limiting its widespread application in engineering practice. This study proposes two artificial neural network (ANN) based in-cylinder pressure reconstruction methods utilizing in-cylinder pressure and heat release rate data from a three-cylinder motorcycle gasoline engine under varying spark advance angle conditions, aiming to achieve cost-effective, high-precision pressure prediction through machine learning technology. Both pressure reconstruction methods employ crank angle and spark advance angle as input features. Method 1 (ANN-P) directly predicts the in-cylinder pressure curve, achieving a coefficient of determination R² exceeding 0.99 on both training and validation sets with a root mean square error (RMSE) below 0.13 bar, accurately reproducing the pressure evolution throughout the compression, combustion, and expansion processes while achieving high-precision prediction of indicated mean effective pressure (IMEP). Method 2 (ANN-HRR) adopts an indirect strategy of "predicting heat release rate followed by integration to reconstruct pressure." This method first derives the apparent heat release rate (HRR) from measured in-cylinder pressure data, trains a machine learning algorithm with HRR data to predict the target operating condition's HRR curve, then reconstructs in-cylinder pressure through integral inversion based on a single-zone thermodynamic model, thereby avoiding error amplification caused by differential operations. This approach demonstrates superior performance in predicting combustion characteristic points (CA10, CA50). The results demonstrate that both methods accurately capture the influence of spark timing on combustion phasing and peak pressure. Method 1 achieves high accuracy in predicted pressure curves but exhibits lower accuracy in capturing combustion characteristics; Method 2 effectively compensates for the limitations of Method 1 in characterizing combustion features through heat release rate curve prediction. This study provides an economical and efficient technical approach for gasoline engine combustion diagnosis, performance calibration, and control optimization.

Abstract: This systematic literature review aims to first, explore the influence of remote work on occupational health and safety, in terms of mental and physical health and second, to shed light on best practices that can be adopted to improve the health and safety of employees working remotely. Twenty-four (24) peer-reviewed articles published from 2020 to 2024, were selected through the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. The review identified four themes, namely–positive impact of remote work; negative impact of remote work; challenges associated with remote work, and best practices for effective remote work practices. Findings from the study revealed that the impact of remote work on OHS was mixed. Eight (8) papers found that remote employees performed better in their OHS, whereas 18 papers found the opposite. The most dominant health disorders reported were depression, stress, anxiety and musculoskeletal pain. In contrast, the study identified that vertical trust levels and job design that considers physical and psychosocial aspects of the job can enhance safety while working remotely. Remote workers are encouraged to follow ergonomics best practices, take regular breaks during work to stretch and move around to reduce musculoskeletal disorders.

Abstract: The Pauli exclusion principle is traditionally introduced in quantum mechanics as a postulate encoded in the antisymmetry of the fermionic wavefunction. While extraordinarily successful, this formulation leaves open a deeper question: why must nature forbid the perfect overlap of identical fermions? In this work, we propose a reinterpretation of Pauli exclusion within the framework of Viscous Time Theory (VTT), where physical law emerges from the geometry of informational state space under constraints of memory, recoverability, and causal trace preservation. We propose that the coincidence of two identical fermionic states can be interpreted, in informational-geometric terms, as a loss of injectivity of the causal mapping, i.e., to an informational singularity where distinct histories become non-separable. To prevent this collapse of recoverability, the joint state manifold naturally develops a “diagonal barrier”: a forbidden submanifold where the informational cost diverges and admissible trajectories are repelled. Within this perspective, antisymmetry of the wavefunction appears not as the cause of exclusion, but as its mathematical symptom. Within this perspective, Pauli exclusion can be interpreted as a geometric and informational constraint rather than a primitive quantum axiom. The framework further suggests a unified interpretation of the difference between fermions and bosons: the former may be viewed as carriers of identity-bearing, non-overwritable informational structure, while the latter correspond to additive excitations that do not threaten causal injectivity. In this way, the exclusion principle appears as a consequence of informational geometry in a universe characterized by viscous time and memory.

Abstract: Monitoring equity drawdown risk requires real-time indicators that can be implemented without look-ahead bias and that may add information beyond standard volatility measures. This study develops a leakage-safe residual-stress indicator from cross-sectional PCA reconstruction errors in U.S. sector excess returns and examines whether it contributes to drawdown-risk monitoring. Using daily adjusted prices for SPY and 11 U.S. sector ETFs over 2020–2025, this study computes sector excess returns relative to SPY, estimate the common component with principal component analysis (PCA), and define residual stress as the cross-sectional root-mean-square magnitude of out-of-sample reconstruction residuals. The PCA mapping is estimated using information available only through t − 1, stress is computed at t, and high-stress regimes are defined using rolling train-only quantile thresholds shifted forward by one trading day. Performance is evaluated using drawdown- onset events and early-warning metrics including ROC-AUC, PR-AUC, and horizon-H precision and recall. The results indicate that although residual stress is not a superior standalone alternative to realized volatility, it remains the stronger benchmark in overall classification performance. Residual stress is most useful as a complementary indicator of cross-sectional market dislocation rather than as a replacement for volatility. In the baseline sample, residual-stress spikes cluster around drawdown onsets, and conditional regime analysis shows that when volatility is low, high residual stress is associated with a materially higher probability of a drawdown onset within the next H = 21 trading days than the low-stress/low-volatility regime. Event-overlap and lead-time diagnostics further suggest that residual stress can flag a subset of onset episodes not captured by a simple volatility- threshold rule, although its primary incremental value lies in conditional risk stratification rather than in systematically earlier triggering. A longer-history proxy-sector analysis yields similar evidence of conditional complementarity while also confirming the stronger standalone performance of volatility. The paper’s contribution is to develop a leakage- safe, interpretable, cross-sectional residual-stress diagnostic that improves conditional drawdown-risk stratification, especially in otherwise low-volatility states, rather than a standalone replacement for realized volatility. This interpretation is supported by both a modern ETF baseline sample and a longer-history proxy-sector sample, with the broader sample providing more stable evidence of complementarity across a larger set of drawdown onsets

Abstract: We study real-valued transition profiles on the real axis that admit holomorphic extension to a horizontal strip in the complex plane. The functions considered have a continuously differentiable and nondecreasing real trace and are normalized to take values strictly between zero and one. We assume that the associated conformal transform obtained by rescaling and shifting the profile extends holomorphically to the strip and maps it into the unit disk. Under these conditions strip analyticity imposes a sharp pointwise bound on the rate of change along the real axis. The bound depends only on the width of the analytic strip and is optimal. We further prove a rigidity result: if the bound is attained at any real point then the profile is uniquely determined up to translation and coincides with the logistic transition. The argument is purely analytic and follows from the Schwarz–Pick contraction principle applied to the strip geometry. No classification of non-saturating profiles is attempted.

Abstract: The prevalence of communicable diseases (also known as transmissible or infectious diseases) has been a significant health issue for sustainable development, hindering economic progress globally. Communicable diseases may affect economic development both directly (via the immediate effects of ill health on productive activities) and indirectly (through the effects of disease on intellectual capacity, mortality, morbidity, and fertility). This study examined the impact of communicable diseases on the economic growth of the SADC region between 2011 and 2024. The study utilised the two-step System-Generalised Method of Moments (GMM) to analyse the data. HIV prevalence and TB incidence served as proxies for communicable diseases, and GDP per capita served as a proxy for economic growth. The study took a comprehensive analytical approach by including variables such as current health expenditure and government expenditure on education as control variables. The findings reveal a significant positive correlation between HIV prevalence, TB incidence and economic growth of the SADC nations, and this is attributed to the fact that the economic growth of most SADC countries does not immediately translate to better healthcare access, as the poor often remain highly vulnerable. Current health expenditure and government expenditure on education negatively affected GDP per capita. This study therefore recommends harmonising the cross-border strategies, strengthening regional surveillance and integrating health into economic planning to ensure a productive workforce within the SADC region.

Abstract: Hilbert’s Sixth Problem challenges us to rigorously axiomatize physics, particularly the bridge between microscopic dynamics and macroscopic laws. Yet, a conceptual gap remains: probability is usually treated as a fundamental assumption rather than a derived consequence of physical evolution. To address this, we introduce a Viscous Time Theory (VTT) framework governing evolution through admissibility, coherence, and recoverability. Applying an informational action principle, probability naturally emerges as an induced statistical measure over bundles of admissible trajectories. We validate this approach by analyzing a viscous-time kinetic transport operator, mapping out its contraction semigroup structure, spectral gap, and hypocoercive convergence. We further extend the model to nonlinear interaction kernels and evaluate its hydrodynamic scaling limit. Our analysis proves this diffusion-driven operator achieves strict spectral stability, exponential entropy decay, and global nonlinear stability. Furthermore, the macroscopic scaling limit rigorously yields nonlinear diffusion dynamics for coherence density. Ultimately, this provides an analytically tractable layer connecting microscopic evolution to macroscopic behavior. It demonstrates that probability, irreversibility, and transport laws can cohesively emerge from informational geometry, advancing the structural program envisioned by Hilbert.

Abstract: Muscle stem cells (MuSC) are the cellular source for generation and regeneration of skeletal muscle. To ensure correct muscle growth, MuSC self-renewal and differentiation need to be tightly regulated. Several signaling systems have been implicated in the control of MuSCs, among them Bone Morphogenetic Proteins (BMPs) and Notch, both of which promote MuSC proliferation and suppress differentiation. To better understand the mechanisms of function and the target genes regulated by BMP signaling in myogenesis, we investigated the transcriptional responses of adult mouse MuSCs to BMP6/4 using RNA-sequencing. BMP6/4-stimulation of freshly isolated MuSCs for one hour rapidly increased the expression of classical BMP target genes like Id1 and strongly induced expression of genes of the Notch pathway (Hes1, Hey1, Lfng, Snai1). In parallel, using Cleavage Under Targets and Tagmentation (CUT&Tag), we generated whole-genome binding profiles for the BMP pathway effectors pSMAD1/5/9 and SMAD4 and detected binding in promoters and potential regulatory elements of BMP targets and Notch pathway genes (Hes1, Hey1, Lfng, Snai1) indicating that BMP signaling directly influences Notch and that crosstalk between the two pathways regulates myogenesis.

Abstract: Elucidating the pathophysiological mechanisms of mental disorders remains a critical challenge in psychiatric research. Recent studies have highlighted the potential involvement of cytoskeletal and molecular motor abnormalities in the development of mental disorders such as schizophrenia and autism spectrum disorder (ASD). This review synthesizes the latest findings on the relationship between cytoskeletal and molecular motor abnormalities and mental disorders. The cytoskeleton, composed of microtubules, actin filaments, and intermediate filaments, along with molecular motors such as kinesins, dyneins, and myosins, plays crucial roles in neurodevelopment, synapse formation, and neurotransmission. In schizophrenia, decreased expression of the microtubule-associated protein MAP2 and abnormalities in the DISC1 gene have been reported, potentially leading to dendritic morphological abnormalities and neurodevelopmental disorders. Additionally, abnormalities in molecular motors such as KIF17 and KIF1A have been implicated in disturbances of synaptic plasticity. In ASD, Myosin Id has been identified as a risk gene, and its localization in dendritic spines has recently been elucidated. Furthermore, abnormalities in actin-related proteins such as SHANK3 and CYFIP1 have been shown to cause synaptic dysfunction. These findings suggest that mental disorders arise from complex pathologies involving multiple cytoskeletal and molecular motor-related protein abnormalities. Future research should focus on elucidating the functions of individual proteins and adopting a comprehensive approach that includes glial cells. Advances in this field may deepen our understanding of the pathophysiological mechanisms of mental disorders and potentially lead to the development of novel therapeutic strategies.

Abstract: In classical mechanics, force is the physical entity mediating interactions between physical objects. Such objects consist of point masses, or appear as continuous bodies formed by a continuum of point masses. Force is defined as the sole entity capable of altering a point mass's state of motion (velocity) and is mathematically represented as a bound vector. However, this description of the physical world no longer holds at the atomic or subatomic level, where matter is discretized into quanta and interactions occur through the exchange of quanta of linear momentum and energy. While this dichotomy is currently accepted as the status quo, efforts to harmonize these frameworks into a more coherent formulation remain highly desirable. This paper investigates the extent to which interactions in classical mechanics can be reinterpreted as an exchange of linear momentum quanta. This investigation leads to a coherent reformulation of Newton’s laws, in which forces are treated as flow rates of these quanta. Therefore, classical mechanics admits a discretized description of the physical world even at the macroscopic level.

Abstract: Organizations operating in regulated industries must submit periodic reports to supervisory authorities. These reports often include financial disclosures, operational records, and compliance declarations. Traditional reporting methods involve manual document preparation and fragmented data sources. This research studies information systems designed for regulatory reporting and compliance documentation management. The proposed framework integrates structured reporting templates, data validation procedures, and submission tracking interfaces. System analysis demonstrates that centralized reporting platforms assist institutions in maintaining reporting accuracy and meeting regulatory submission deadlines.

Abstract: Tuberculosis (TB) is a subacute to chronic respiratory infection with insidious onset and protean symptoms. Treatment is complex and requires a multi-drug, multi-month regimen in which adherence is critical. Artificial intelligence (AI) offers promising solutions to challenges across the TB care cascade including screening, diagnosis and treatment. We conduct a scoping review of the literature published from 2017 to 2025 on the use of AI in TB screening, diagnosis, drug resistance diagnosis, treatment monitoring, and regimen design. We then extract data on study characteristics, AI methodology, input data, and sample size and describe AI tool performance and technology readiness level (TRL). Ninety studies are included, representing 803,383 study participants across 24 countries. Most studies (n=46) focus on radiological imaging for TB screening or diagnosis, but a burgeoning number of studies address drug resistance diagnosis (n=11), regimen design (n=4), treatment monitoring (n=12), and treatment adherence (n=8). Reported accuracy of AI interpretation of chest imaging for TB diagnosis was high at a median Area Under the receiver operating Curve (AUC) of 0.94 [IQR 0.12, range 0.81-0.99] for internal validation and 0.89 [IQR 0.14, range 0.66-0.98] for external validation, and a median TRL of 5 (IQR 1, range 4-7). AI demonstrates promise for advancing TB care towards World Health Organization (WHO) End TB targets, but several gaps remain, including AI-ready fit-for-task data availability, limited external validation and challenges in clinical integration. Closing these gaps will be critical for realizing the full potential of AI in TB care towards WHO End TB targets.

Abstract: Since the features of cross-seeding of alpha-synuclein forms may affect sensitivity and specificity of the test systems, we developed a modified approach to obtain alpha-synuclein amyloid seeds with particle sizes from 20 to 50 nm prepared from either the wild-type protein (α-synWT) or its more fibrillation-prone form A53T (α-synA53T). These seeds had optimal properties for subsequent initiation of fibrillation. Our data showed that the elevated efficiency of alpha-synuclein A53T monomers transformation was hardly affected by the type of used seeds, whereas the addition of the seeds obtained from the alpha-synuclein mutant form to wild-type protein monomers had a significantly less effect than α-synWT seeds. TEM data revealed that in the presence of α-synWT seeds the wild-type alpha-synuclein formed long and wide fibrils, while the addition of α-synA53T seeds led to the formation of long, but thin fibrils. The application of α-synA53T monomers significantly reduced the fibrillation lag period, making it a promising candidate for use in medical test systems. In the future, a set of alpha-synuclein mutant forms could be used for the differential diagnosis of synucleinopathies caused by the different mutations of this protein.

Abstract: This work explores the key capabilities of emerging sensing technologies in the context of Structural Health Monitoring (SHM) of civil infrastructures, aiming at contributing to the research on integrated and intelligent systems for more accessible and efficient monitoring solutions. As a case study, this study focuses on analysing the static and dynamic behaviour of the Edgar Cardoso stay-cable Bridge during its rehabilitation, recurring to a fully customized transducers and equipment. The developed system integrates sensors capable of measuring accelerations, displacements and temperature, which are connected to an autonomous data acquisition and transmission network. A digital interface was also developed to store, process and visualize the collected data, allowing remote access for later interpretation and analysis. The results confirmed the effectiveness of the developed system, which enabled the identification of the dynamic properties of the structure in terms of natural frequencies and vibration modes. The effects of traffic loads, as well as the correlations between temperature and structural displacements were also identified. Furthermore, the estimation of the axial forces in the stay cables permitted to study the influence of wind actions and traffic loads in these elements. The results demonstrate the potentialities of customized sensing solutions as effective tools for the management, maintenance, and long-term preservation of strategic infrastructures.

Abstract: This study examines the role of volunteers in the formation of social initiatives that utilize local resources. Previous research on volunteering has typically explained participation in terms of altruistic motives and a desire to contribute to society. However, this study focuses on intellectual curiosity—specifically, an interest in observing on-site situations and analyzing problem structures, as a factor that supports the continuity of volunteer activities.The study analyzes a local resource project that utilizes camellia leaves naturally growing on a remote island in Miyagi Prefecture, Japan. Using qualitative analysis, we examine the activities of a central practitioner, referred to as Practitioner A. Practitioner A plays a bridging role by connecting multiple actors, including local residents, companies, welfare facilities, tourism stakeholders, and researchers. This study conceptualizes such practitioners as “analytical volunteers.” Analytical volunteers are participants who are motivated not only by altruistic intentions but also by an intrinsic interest in observing real-world situations and constructing activities through problem analysis.The case analysis reveals that while the camellia project has succeeded in forming a network among diverse actors, it has not yet achieved stable commercialization. This stage is interpreted as the “growing pains” of social innovation in local communities.This study contributes to volunteer research by highlighting the presence of participants motivated by analytical thinking and by demonstrating the importance of network intermediaries in the formation of regional innovation processes.

Abstract: This research work deals with the challenges in software fault prediction (SFP) such as class imbalance in benchmark datasets, noisy features, and high-dimensional feature spaces. To overcome the above limitations, we propose a novel hybrid feature selection framework, FS-BWOA–COA, which incorporates Coati Optimization Algorithm (COA) for local exploitation and Beluga Whale Optimization Algorithm (BWOA) for global exploration. The two-phase optimization approach helps to avoid duplication and improves the stability of the classifier and also helps in maintaining the balance between exploration and exploitation. The framework was tested using several classifiers such as Decision Tree, SVM, KNN, and Naïve Bayes on eleven NASA PROMISE datasets. The hybrid outperforms single BWOA and COA, with an average accuracy of 0.9033 and peak values of 0.95 on the MC1 and JM1 datasets. The results of the statistical validation using the Friedman test, Wilcoxon signed-rank test, and paired t-tests confirm the same.

Abstract: Additive manufacturing is increasingly promoted as a pathway toward sustainable production; however, its Environmental, Social, and Governance (ESG) implications remain insufficiently defined at the operational level. This study addresses this gap by developing a governance implications assessment framework to interpret empirically validated life cycle sustainability assessment (LCSA) evidence for post-consumer recycled polylactic acid (PC-PLA)-based fused filament fabrication (FFF) within the Australian manufacturing context. The central contribution lies in demonstrating how LCSA evidence can be translated into decision-relevant ESG information without reliance on product declaration frameworks or simplified circularity claims. While LCAs and EPDs do not directly optimise production systems, sustainability-oriented governance can guide engineering and operational decisions by managing whole-system trade-offs and future risk. The framework integrates mechanical validation, service-life-normalised LCSA results, circularity metrics, and material flow modelling through a consequences analysis lens. Results indicate that a V50:R50 (vPLA:PC-PLA) blend achieves balanced performance, delivering a 57% reduction in global warming potential while maintaining functional durability. However, these benefits are governance-contingent; managing an 11.7% increase in service-life-normalised costs requires senior-level oversight, formalised material traceability, and structured workforce training. Waste diversion analysis further demonstrates that scalability is constrained primarily by upstream collection and sorting efficiency rather than fabrication performance. The study concludes with a strategic roadmap advocating regional recycling hubs, certified micro-credentials, and adoption of decision-relevant governance metrics aligned with ASRS S1 and S2 disclosure standards. The proposed framework offers a transferable approach for translating engineering-level sustainability evidence into credible governance insights, strengthening accountability and supporting Australia’s transition toward resilient, circular manufacturing.

Abstract: Motivation: Gene regulatory network (GRN) inference from single-cell RNA-seq (scRNA-seq) data remains hampered by technical noise, high false-positive rates, and extreme computational costs. Existing methods often require hours or days to process developmental datasets yet fail to capture the physical and topological constraints of regulatory interactions, essential for accurate regulatory mapping. Results: We developed AENetMoX, a multimodal autoencoder that integrates transcriptomic correlations with transcription factor (TF) binding motifs and protein-protein interaction (PPI) networks. We evaluated AENetMoX against SCENIC, GRNBoost2, and CLR across 48 independent configurations using three human brain organoid lineages. At K=100, AENetMoX achieved 7.7±5.1% precision (95.7% relative improvement over SCENIC; p=1.039×10^(-3), one-sided Wilcoxon test; Vargha-Delaney Â=0.635). ChIP-seq validation showed 51.6% precision (+9.5% over SCENIC, p=0.141, Â=0.500) and 168.4% improvement in F1 over SCENIC (p=2.883×10^(-9), Â=0.854). Ablation studies revealed PPI integration as the primary driver of performance, increasing precision by 492% (~5.9×) and ChIP-seq recovery precision by 198% (~3×) over its expression-only variant. Crucially, AENetMoX completes inference in under 5 minutes, a 24-fold speedup over SCENIC (~2 hours) and significantly outperforming CLR (>12 hours). Analysis of novel predictions identified 334 unique regulatory edges, including temporally persistent SMAD3 and SOX2 hubs that remain stable across multiple developmental stages. Availability: Source code is available at https://github.com/Shirshak52/AENetMoX. All datasets and databases used are available at OSF (Project DOI: https://doi.org/10.17605/OSF.IO/K6EHW).