Abstract:

Nature is often understood as a purely physical or biological entity governed by scientific laws and economic utility. In contrast, perspectives associated with dark green religion draw attention to how nature itself can be regarded as sacred and morally significant, revealing the cultural and ethical dimensions through which humans can relate to the environment. In this context, this paper examines religion as a symbolic and narrative system through which nature is socially constructed as a moral domain. Focusing on Indigenous Ijaw communities in the Niger Delta, this paper explains how rivers, creeks and wetlands are embedded within religious value systems that emphasize moral responsibility, respect and restraint in human-environment relations. Within this worldview environmental harm is understood not only as ecological degradation but also as a moral and spiritual transgression with consequences for communal well-being.

Abstract: The standard mathematical framework of differential topology reveals a profound peculiarity: smooth structures on 4-manifolds are wild, unclassifiable, and form an uncountably infinite set, while in all other dimensions they are either unique or finitely classifiable. This presents a fundamental obstacle to constructing a quantum theory of gravity via a path integral sum over geometries as the configuration space becomes intractably complex. Crucially, this mathematical wildness is not a mere curiosity but a diagnostic signal that our dimension-agnostic mathematical framework is fundamentally inadequate for describing quantum spacetime in our 4-dimensional universe. We argue that this impasse signals not a pathology of 4-dimensional spacetime, but a critical flaw in our mathematical starting point. We propose a radical inversion of priorities: instead of seeking to tame 4D wildness within a dimension-agnostic formalism, we should construct a new mathematical framework whose axioms are explicitly designed so that 4-dimensional spacetime emerges as its unique, natural, and tame solution. The price for this 4D simplicity is that other dimensionalities may appear ill-defined or trivial within the new framework, a price we argue is not only acceptable but necessary for a physical theory of our universe. We outline the philosophical and formal principles of such a 4D-native approach and discuss its embodiment in existing pre-geometric quantum gravity programs where smooth geometry emerges from more fundamental substrates.

Abstract: Accurate, large-scale building-use information at very high spatial resolution is critical for urban, economic, and risk-related applications. We present a nationwide framework for building-use mapping at 50~cm resolution in China by fusing very-high-resolution RGB imagery with points-of-interest (POI) data. A multi-task U-Net with a ResNet-34 backbone jointly predicts building footprints and three building-use classes (residential, commercial, industrial), using POI-based probability maps as additional input channels. We construct a labeled dataset of approximately 100{,}000 buildings from 90 cities and a 351-million-tile inference dataset covering about 54\% of China's land area. Experiments show that POI fusion and multi-task learning significantly improve performance over imagery-only baselines. In the final nationwide product, the residential, commercial, and industrial classes achieve per-class accuracies of 0.9711, 0.9664, and 0.9854, with F1-scores of 0.8416, 0.5828, and 0.8143, respectively. The resulting building-use database can support a wide range of downstream applications, including catastrophe risk assessment, exposure mapping, and urban analytics.

Abstract: The precise determination of the rheological properties of road bitumen types is essen-tial for the reliable prediction of long-term pavement behaviour. The aim of this study is to compare different viscosity determination methods – approximations, capillary vis-cosity, Brookfield measurement, and complex viscosity determined by dynamic shear rheometer (DSR) – and to analyse their relationships with each other. Furthermore, the European and Australian bitumen classification standards are compared in terms of dynamic viscosity and penetration, according to which Australian bitumen types show more stable results. The statistical evaluation of the results obtained with the different methods was based on Pearson correlation analysis and relative deviation analysis. The results obtained show that the DSR measurements at 1.6 Hz are most closely related to capillary viscosity and best correlated with the other measurements, while the Heuke-lom equation relationship overestimates the dynamic viscosity. The Brookfield method provided higher viscosities for all tests. The study highlights that the results of different measurement methods can only be compared under shear conditions, and that the DSR-based approach can be more suitable for the introduction of a new European bi-tumen classification.

Abstract: In this article, we prove that for every integer \(n \geq 2\), there exist positive integers \(t\) and \(x\) such that the expression \( E = t^2(4x - n)^2 - 2xtn \) is always a perfect square.

Abstract: Background: Schizophrenia is driven by many common variants, and two biological themes—excessive synaptic pruning and reduced glutamatergic transmission—feature prominently in current models. Yet these mechanisms do not fully account for the early-emerging, severe cognitive difficulties seen in affected individuals. To examine how pruning and plasticity signals diverge or overlap with cognition, we contrasted their genetic footprints in schizophrenia and in general intelligence.Methods: Using identical analytic steps, we processed summary statistics from the Psychiatric Genomics Consortium Wave 3 schizophrenia genome-wide association study and a large-scale intelligence study. The pipeline combined three approaches: (1) MAGMA for competitive gene-set enrichment, (2) stratified LD-score regression to partition heritability, and (3) S-PrediXcan to infer transcriptome-wide associations. Seven predefined gene panels anchored the work: three capturing pruning biology, two plasticity and two controls.Results: All three methods converged on a robust enrichment of pruning genes in schizophrenia. For the shortened pruning panel, MAGMA yielded a Bonferroni-corrected p = 1.3 × 10⁻⁵ and LD-score regression indicated extreme enrichment (p ≈ 10⁻¹⁷⁹). Signal persisted after glutamatergic genes were removed, and S-PrediXcan suggested up-regulated expression of key complement components such as C4A. In contrast, glutamatergic pathways showed only modest schizophrenia involvement.Conclusions: A double dissociation emerges. Schizophrenia risk aligns mainly with overactive pruning, whereas successful cognitive performance depends more on balanced glutamatergic-driven plasticity. We outline a “prune-without-repair” model in which unchecked complement activity, combined with weak glutamatergic stabilization, progressively undermines cognitive circuits in schizophrenia.

Abstract: In a sentinel health event investigation of a back disorder claim, the vibration exposure and ergonomic function of a modified suspension seat were assessed. (1) Background: In a forensic occupational injury investigation an after-market altered operator seat in a railroad rail-track tamper machine was evaluated. (2) Methods: Detailed whole-body vibration (WBV) exposure measurements were conducted according to current applicable technical standards and guidelines (i.e., ISO 2631-1; 1997) on a 09-16 DYNACAT Continuous Action Tamper with Stabilizer during routine track repair services. The modified Grammer Mfg. suspension operator seat was evaluated for performance and ergonomic features (i.e., adjustability, posture, and suspension quality); (3) Results: The tested seat appeared to underperform and was overloaded with the aftermarket control devices, attachments and modifications. The suspension system's end-stopper was damaged. The seat system had excessive play and wobbles; it was not firmly braced and attached. The vector sum (av) results ranged from 0.26 m/s² (no tamping) to maximal 0.55 m/s² (tamping). The seat transfer (SEAT) analysis showed magnification of vibration input and variable performance of the suspension depending on operational tasks. (4) Conclusions: The modified suspension seat underperformed and seemed to magnify and worsen the vibration, jolts and shock exposures of the seated operator. The heavy and bulky seat modifications likely limited the suspension function. The malfunctioning seat was more likely than not a contributing factor in the pathogenesis of the spinal disorders for the injured machine operator.

Abstract: This systematic review on intelligent HVAC systems for residential buildings focuses on advanced control techniques and AI applications. Model Predictive Control (MPC) is the most common method (≈40% of studies), achieving 15–20% energy savings and 10–30% peak demand reduction. Deep reinforcement learning (DRL) offers a model-free alter-native, reducing energy costs by 15% and comfort violations by up to 98%. Neural networks (LSTM, CNN-BiLSTM, attention mechanisms) significantly improve load pre-diction and thermal comfort modelling, with fusion models boosting accuracy by 66–85%. Comprehensive AI-based systems deliver 22–44% energy savings and 22–86% comfort improvements. Performance varies by climate, building type, and baseline; field trials show lower but more reliable savings than simulations. Hybrid MPC–ML approaches are emerging as best practice. Barriers include model complexity, computational demands, limited training data, and integration with legacy systems. Occupancy-aware strategies save 19–45% energy, while intelligent thermal storage can raise solar fractions from 11% to 61%. Overall, intelligent HVAC control is technically feasible and economically advantageous, but success depends on accurate modelling, tailored control strategies, and robust han-dling of occupancy uncertainty.

Abstract: Accurate distance measurement in outdoor environments remains a challenging problem for mobile augmented reality (AR) systems due to sensor noise, environmental variability, and the limitations of single-modality approaches. Existing consumer AR solutions often prioritize usability over metric robustness, leading to performance degradation in large-scale or heterogeneous outdoor scenarios. This work presents EfMAR, an adaptive framework for outdoor mobile AR-based geospatial measurements that integrates multiple sensing modalities through a structured sensor fusion architecture. EfMAR combines visual SLAM, inertial sensing, depth information, and global positioning cues to improve robustness and consistency in distance estimation across diverse outdoor conditions. Beyond implementation, the framework formalizes a reusable architectural model for adaptive multi-sensor fusion, supporting reproducibility and future comparative research. A dedicated dataset is described, comprising a combination of real-world field measurements collected in representative outdoor scenarios and synthetic data informed by existing literature, enabling structured performance analysis while maintaining methodological transparency. Performance evaluation focuses on analyzing relative behavior, stability, and variability across sensing approaches rather than establishing absolute accuracy benchmarks. Comparative results across multiple distance ranges and environments indicate that hybrid sensor fusion strategies exhibit more stable and consistent performance trends compared to single-modality solutions, particularly in challenging urban contexts. Dispersion analysis further highlights the influence of environmental factors such as lighting conditions and spatial scale on measurement variability. Overall, the results position EfMAR as a flexible and adaptive framework designed to enhance robustness in outdoor AR-based geospatial measurement tasks. By emphasizing consistency, transparency, and architectural generalization, this work contributes a practical foundation for future research and development in mobile AR sensing for real-world outdoor applications.

Abstract: Newton published his mechanics in the form of an axiomatic system just as the Euclidean geometry. The Newton’s three laws are three axioms, from which, together with the necessary definitions of physical concepts and propositions, all contents of classical mechanics can be derived. Non-Euclidean geometries tell us that one of the axioms in an axiomatic system may take different forms. Modified axioms can lead to new axiomatic systems that are logically rigorous and self-consistent. The fifth axiom in the Euclidian geometry was modified to be two other different form, and consequently, two non-Euclidean geometries were developed. We think that Newton’s second law can be modified. The second law can be simply stated as: force is the cause of acceleration. It can be modified as: force is the cause of deceleration. This results in a new axiomatic system called new classical mechanics. This paper presents the fundamental formulas of the new classical mechanics. The most distinctive feature of the new mechanics is that the direction of momentum is opposite to that of velocity, and the kinetic energy is negative, i.e., a negative sign is attached to usual positive kinetic energy (PKE). Therefore, the new classical mechanics can be called negative kinetic energy (NKE) one, while the existing classical mechanics can be called PKE one. These two parts can be collectively referred to as a whole classical mechanics, which includes both PKE and NKE parts. The formulas of these two parts have symmetry with respect to positive and negative kinetic energy. The PKE classical mechanics describes the motion of macroscopic matter that we can observe, while the NKE classical mechanics describes the motion of macroscopic matter that we cannot observe, i.e., the motion of dark matter. Our universe has symmetry with respect to PKE and NKE, which is also the symmetry with respect to matter and dark matter. The basic equations of quantum mechanics describing the motion of micro-particles also have symmetry with respect to PKW and NKE, which has been elaborated in the author’s previous work. The theory presented in this paper describe the motion of macroscopic NKE matter.

Abstract: It has long been recognized that the oxygen reduction reaction occurs more readily on Pt(111) surfaces that include steps, both (111) and (100), than on near-perfect Pt(111). Theoretical models were developed involving the water structure in the electric double layer and its interactions with adsorbed OH, with the actual O₂ reduction occurring on the (111) terraces adjacent to the steps. However, the present density functional theory (DFT) calculations confirms that O₂ adsorbs strongly at the steps and can undergo dissociation aided by adjacent water molecules to produce adsorbed OH. OH produced at the steps can move to the (111) terraces, where it can be more readily reduced to H₂O and desorbed. This model avoids the scaling relation, which predicts that all oxygen-containing reactants and intermediates are proportional to each other on any given surface. Efforts to develop new O₂ reduction catalysts have been hampered by this assumption, which supposes that the reaction rate can be increased by decreasing OH adsorption strength, even though decreased OH adsorption strength is accompanied by decreased O₂ adsorption strength. This proposed model can explain the experimental results on stepped surfaces and may also be important for the development of Pt nanoparticle catalysts.

Abstract: Based on the dilemma of analyzing the resonance stability of AC power grids using im-pedance models, it is demonstrated that the "negative resistance" mechanism of wide-band oscillation is untenable. A general method for describing power electronic devices using wideband voltage source converter models and wideband current source convert-er models is proposed, thereby representing the nonlinear characteristics of power elec-tronic devices with harmonic voltage sources and harmonic current sources. This allows the new energy power system to still be described by a linear system, and interprets the mechanism of wideband oscillation as a "harmonic amplification" phenomenon caused by network resonance, thus establishing a new framework for explaining the mechanism of wideband oscillation in new energy power systems. Through the analysis of two basic resonant circuits, the relationship between the damping ratio of resonant modes and the harmonic amplification factor is derived, laying a theoretical foundation for the analysis and suppression of wideband oscillation based on the s-domain nodal admittance matrix method and C-type damping filters. Based on the maximum damping criterion, a design method for C-type damping filters is proposed. The designed C-type damping filters ex-hibit strong broadband damping effects.

Abstract: This tutorial presents a coherent overview of reinforcement learning (RL), tracing its evolution from theoretical foundations to advanced deep learning algorithms. We begin with the mathematical formalization of sequential decision-making via Markov Decision Processes (MDPs). Central to RL theory is the Bellman equation for policy evaluation and its extension, the Bellman optimality equation, which provides the fundamental condition for optimal behavior. The journey from these equations to practical algorithms is explored, starting with model-based dynamic programming and progressing to model-free temporal-difference learning. We highlight Q-learning as a pivotal model-free algorithm that directly implements the Bellman optimality equation through sampling. To handle high-dimensional state spaces, the paradigm shifts to function approximation and deep reinforcement learning, exemplified by Deep Q-Networks (DQN). A significant challenge arises in continuous action spaces, addressed by actor-critic methods. We examine the Deep Deterministic Policy Gradient (DDPG) algorithm in detail, explaining how it adapts the principles of optimality to continuous control by maintaining separate actor and critic networks. The tutorial concludes with a unified perspective, framing RL's development as a logical progression from defining optimality conditions to developing scalable solution algorithms, and briefly surveys subsequent improvements and future directions, all underpinned by the enduring framework of the Bellman equations.

Abstract: Despite the Minamata Convention’s targeted reductions in mercury consumption, global trade data exhibits a ‘Compliance Paradox’ where reported flows vanish while artisanal gold mining output remains stable. This research proposes a ‘Mineral Intelligence’ pipeline utilizing unsupervised machine learning to detect illicit mercury trafficking disguised as Electronic Waste (HS 8549). By applying Gaussian Mixture Models (GMM) and Isolation Forest algorithms to UN Comtrade data (2020–2024), we identify a systemic ‘Balloon Effect’: as elemental mercury bans took effect in 2022, illicit volumes were structurally displaced into ‘fake waste’ classifications. Forensic analysis reveals a statistically significant ‘Smuggler’s Signature’ within these flows, characterized by a price anomaly of $24–$80/kg (mirroring liquid mercury markets) and a Net to-Gross weight ratio exceeding 90%, physically corresponding to standard 34.5 kg steel mercury flasks. Furthermore, Node2Vec and spectral embedding analysis exposes a ‘Decoupling Chasm’ (Manifold Distance: 2.06) that topologically separates financial gold hubs from mercury-intensive mining zones. Finally, Recursive LSTM forecasts predict a ‘burnout’ of the current HS 8549 smuggling vector (-618M kg/yr), warning of an imminent regime shift toward chemically masked commodities.

Abstract: The aim of this study was the validation of the published method for the estimation of the personal maximum manual handling weight limit (PMHWLmax) [1] which is based on the personal body weight, as a reference to general population percentiles anthropometrics. For this purpose, an experiment was designed, were a repetitive process of transferring progressively increasing loads, was performed, with the aim of identifying through EMG recording of the erector spinae muscle, the load that brings about the first distinct quantity change in muscle activation and fatigue, compared to the preceding trials and afterwards the matching of the specific load value with the body weight of the participant

Abstract: Exporting food products from the European Union (EU) to the United States of America (USA) involves navigating complex regulations and procedural barriers that hinder market access. Italian food businesses (FBs), particularly small and medium-sized enter-prises (SMEs), often face difficulties accessing clear guidance, as national procedures are scattered across multiple sources. This paper proposes a structured four-step analytical framework to support EU FBs: product-specific analysis, identification of relevant EU and USA legislation, comparative legislative analysis via concordance tables, and identifica-tion of procedures to integrate into the Food Safety Management System. The framework was applied to an Italian medium-sized FB exporting pork-based pasta sauce to the USA. Beyond the specific case study, the proposed framework was designed to be replicable and adaptable to different food products and third-country destinations. As such, it can support both FBs and Competent Authorities in conducting risk-based assessments of regulatory equivalence and export compliance. Results indicated the need for Sanitation Standard Operating Procedures, thermal process validation, direct verification activities, and pre-shipment review. Findings emphasize that operational and procedural barriers disproportionately affect SMEs, highlighting the importance of targeted support to facil-itate market access and strengthen certification systems.

Abstract: As an emerging threat to global food security, wheat blast necessitates the development of a rapid and field-deployable detection system to facilitate early diagnosis, enable effective management, and prevent its further spread to new regions. In this study, we aimed to validate and improve an Recombinase Polymerase Amplification coupled with PCRD lateral flow detection (RPA-PCRD strip assay) kit for the rapid and specific identification of Magnaporthe oryzae pathotype Triticum (MoT) in field samples. The assay demonstrated exceptional sensitivity, detecting as low as 10 pg/µL of target DNA, and exhibited no cross-reactivity with M. oryzae Oryzae (MoO) isolates and other major fungal phytopathogens under the genera of Fusarium, Bipolaris, Colletotrichum and Botrydiplodia. The method successfully detected MoT in wheat leaves as early as 4 days post-infection (DPI) (asymptomatic plants), as well as in infected spikes, seeds, and alternate hosts. Furthermore, by combining a simplified polyethylene glycol-NaOH method for extracting DNA from plant samples, the entire RPA-PCRD strip assay enabled the detection of MoT within 30 min with no specialized equipment and high technical skills at ambient temperature (37-39 °C). When applied to field samples, it successfully detected MoT in naturally infected diseased wheat plants from seven different fields in wheat blast hotspot district, Meherpur in Bangladesh. This method offers a practical, low-cost, and portable point-of-care diagnostic tool suitable for on-site surveillance, integrated management, seed health testing, and quarantine screening of wheat blast in resource-limited settings. Furthermore, the RPA-PCRD platform serves as a modular diagnostic template that can be readily adapted to detect a wide array of phytopathogens by integrating target-specific genomic primers.

Abstract: We present a virtual reality (VR) framework for controlling dual-arm robotic manipulators through immersive interfaces, integrating both simulated and real-world platforms. The system combines the Webots robotics simulator with Unreal Engine 5.6.1 to provide real-time visualization and interaction, enabling users to manipulate each arm’s tool point via VR controllers with natural depth perception and motion tracking. The same control interface is seamlessly extended to a physical dual-arm robot, allowing for teleoperation using the same VR environment. Our architecture supports real-time bidirectional communication between the VR layer and both the simulator and hardware, enabling responsive control and feedback. We describe the system design, and performance evaluation in both domains, demonstrating the viability of immersive VR as a unified interface for simulation and physical robot control.

Abstract: With the increasing adoption of EMV-based digital payment systems, ensuring compliance with privacy regulations (GDPR, PSD2, PCI DSS) has become essential. A critical challenge in regulatory-compliant payment transactions is the risk of transaction linkability, which can expose sensitive user data and violate privacy mandates. In this paper, I analyze the privacy vulnerabilities of EMV 2nd Gen payment protocols and propose an improved key agreement mechanism to enhance unlinkability and transaction security. The approach builds on the Blinded Diffie-Hellman (BDH) key establishment protocol, integrating cryptographic enhancements to mitigate active and passive tracking threats. I introduce a stronger unlinkability definition, accommodating active attackers and ensuring compliance with EMVCo security requirements. The proposed scheme uses anonymous credential techniques to prevent transaction tracing while preserving authentication integrity. Experimental results show that the method significantly improves transaction unlinkability, reducing privacy leakage risks and aligning with regulatory standards in secure payment processing. This research highlights the role of privacy-preserving cryptographic techniques in ensuring regulatory compliance for modern digital payment ecosystems.

Abstract: The vaginal microbiome plays a crucial role in protecting its host from bacterial, viral, and fungal pathogens and serves as a first line of immune system defense for the lower reproductive tract. The composition of the microbiome is relatively understudied despite its integral role in women’s reproductive health. Maintenance of the correct abundances of bacteria can help prevent many different infections. Additionally, the microbiota also serve to initiate and support the immune system of a newborn in the case of vaginal birth. The introduction of these organisms can mediate many interactions in the developing brain and nervous system of the infant (Günther, V. et al. 2022). Similarly, research on biological causes of sexual orientation is a new field that severely lacks extensive research. There are a few theories related to the development of homosexuality that have been backed by research. Synthesizing what we know from previous research on these two fields, we aim to bridge the gap between these two areas of study by postulating a relationship between the composition of the female vaginal microbiome during pregnancy and the fraternal birth order effect leading to male homosexuality. We propose that during pregnancy, changes in the maternal vaginal microbiome result in a change in the vaginal microbiome that makes them more susceptible to the development of anti-NLGN4Y antibodies responsible for the immune attack on the Y-linked NLGN4Y protein responsible for male brain development (Bogaert, A. et al. 2017), specifically in the anterior hypothalamus. We propose that mothers who have already given birth to males are especially susceptible to this antibody development, providing a possible explanation for the Fraternal Birth Order effect.