Abstract: Can artificial systems form internal representations that resemble conscious perception? Here we introduce eXCube1, a brain-inspired spiking neural network (BI-SNN) framework that learns evolving spatio-temporal associative memories (ESTAMs) from fMRI data. ESTAMs provide an interpretable, causal account of how neural activity propagates across space and time, bridging statistical neuroimaging analysis and mechanistic modelling. We show that eXCube1 learns discriminative ESTAMs that separate meaningful from meaningless visual and auditory stimuli without access to semantic content. Across two fMRI case studies, the learned models achieve high classification accuracy while revealing structured, modality-specific spatio-temporal dynamics consistent with known neurobiological pathways. Notably, ESTAMs can be robustly recalled from partial temporal input, demonstrating emergent associative memory properties. By explicitly modelling directed spike-based dynamics, eXCube1 moves beyond correlation-based analysis toward causal, interpretable representations of neural computation. These results position ESTAMs as a candidate computational substrate for aspects of machine consciousness grounded in spatio-temporal neural dynamics.

Abstract: Rapid advancements in digital payment technologies have completely changed how customers perceive spending, frequently making money less psychologically visible and promoting impulsive buying. This study creates a dual-mechanism framework to explain how digital spending environments both facilitate and regulate consumer purchasing decisions, building on the idea of Spendception. Spendception has been break into two dimensions: Perceived Spendception Control (PSC), which measures consumers' perceived capacity to keep an eye on and control their financial behavior in extremely stimulating online environments, and Perceived Spendception Ease (PSE), which describes the seamless and cognitively effortless character of digital spending. The study investigates how Digital Twin Environment (DTE) affects impulsive purchasing behavior through these two mechanisms, using survey data from Generation Z consumers involved in social commerce. PSE mediates the relationship between DTE and impulse buying by lowering psychological spending barriers, while PSC moderates this relationship by enhancing consumers' financial self-regulation and decreasing impulsive purchasing tendencies, according to the results of a study that used structural equation modeling in conjunction with machine learning techniques. These results show that the degree to which emotional stimuli result in impulsive consumption is determined by two opposing psychological forces produced by digital payment systems: spending facilitation and spending control. By viewing Spendception as a multifaceted concept, the study advances Spendception theory and offers crucial insights into how digital financial technologies affect consumer behavior in social commerce settings. The results emphasize the significance of creating digital payment systems that strike a balance between transactional ease and features that promote responsible consumption and financial awareness from a sustainability standpoint.

Abstract: Software aging and the corresponding need for system rejuvenation are well‑established concepts in computer science. As virtualization technologies are increasingly adopted within electric power utility infrastructures, early investigation into Software Aging and Rejuvenation (SAR) models, aging indicators, and empirical data collection becomes essential. Given the critical role of the electric power grid and the high dependability requirements of the protection and control systems that support its operation, proactive research in this area is timely and necessary. Motivated by this need, this work proposes a hierarchical framework that integrates an SAR model into the Reliability Block Diagram (RBD) representation of a Digital Substation Automation System (DSAS). The analysis shows that, for the selected parameter set, incorporating SAR into the VPAC reliability model results in higher estimated failure rates and increased annual downtime relative to hardware‑only models. When combined with substation primary system indices, however, the overall reliability indices remain largely unchanged, aside from reduced outage duration attributed to improved switching performance enabled by the DSAS architecture. Further examination reveals that the limited influence of SAR is primarily due to the lack of historical failure‑mode data for the secondary system. Availability of such empirical data is expected to significantly affect combined reliability indices and improve the accuracy of reliability evaluations. This highlights the importance of systematic data collection and aging‑indicator analysis as utility infrastructures transition toward virtualized and software‑dependent architectures.

Abstract: The rapid expansion of 5G/6G technologies and the burgeoning demand for low-latency, high-reliability services at the network edge challenge traditional centralized cloud architectures. Edge computing offers a promising solution, yet its dynamic, resource-constrained, and heterogeneous nature necessitates decentralized intelligence for effective resource management. This paper addresses these challenges by synergistically integrating Federated Learning (FL), Multi-Agent Reinforcement Learning (MARL), and Non-Orthogonal Multiple Access (NOMA) resource management. While Federated Multi-Agent Reinforcement Learning (FMARL) is a critical direction, existing methods struggle with extreme heterogeneity and privacy concerns. To overcome these limitations, we propose FedHeRM: Federated Heterogeneity-aware Reinforcement Learning for Secure NOMA Resource Management in Vehicular Edge Networks. FedHeRM models the vehicular edge environment as a Partially Observable Stochastic Game, where RSU agents learn optimal resource allocation policies. Our core innovation lies in a novel Heterogeneity-aware Federated Multi-Agent Reinforcement Learning (HeRA-FMARL) framework, which introduces a Dynamic Heterogeneity Measure (DHM) for adaptive weighted aggregation of model updates, significantly accelerating convergence and enhancing generalization across diverse agents. Furthermore, FedHeRM integrates robust privacy-preserving mechanisms, including Differential Privacy for local updates and Secure Aggregation protocols. Comprehensive experiments in a simulated vehicular edge environment demonstrate that FedHeRM significantly outperforms state-of-the-art baselines across critical metrics, achieving superior system throughput, lower task latency, reduced energy consumption, and enhanced user fairness, while maintaining excellent scalability and strong privacy guarantees. An ablation study confirms the crucial roles of its key components, further validating FedHeRM's efficacy in highly dynamic and heterogeneous edge networks.

Abstract: This paper presents a certification-oriented, system-level argument that Linux is unsuitable for safety-critical avionics. Because Linux is a feature-rich, high-performance general-purpose OS, it exhibits open and dynamic execution semantics that cannot be finitely bounded or frozen at integration time. Two consequences follow. First, airworthiness infeasibility: an oversized TCB, prohibitive DO-330 toolchain qualification burden, and continuous patch churn that prevents stable, certifiable baselines. Second, semantic complexity: temporal non-isolation and spatial non-isolation, materializing as mutable logical-to-physical mappings, driver-induced contamination of global kernel state, and lack of fault containment. We consolidate these observations into an avionics-oriented OS evaluation framework that makes certification implications explicit—closed-world timing analysis at the partition level, provable spatial and fault isolation, TCB minimization, and lifecycle-stable evidence under DO-178C/DO-330. The framework turns architectural properties into concrete certification risks and provides actionable guidance for OS selection and governance in integrated modular avionics [3,6].

Abstract: While complex numbers form the foundational number system of quantum mechanics, the theoretical possibility of higher-dimensional hypercomplex alternatives persists. In this paper, we present a test protocol designed to distinguish between complex and quaternionic quantum mechanics. The protocol is founded upon the quantum superdense coding scheme and functions by detecting the presence of multiple distinct quantum phase gates. We also provide the corresponding quantum circuit required to perform this test.

Abstract: Background: Cardiovascular risk (CVR) prediction using SCORE2 may underestimate subclinical atherosclerosis in patients with chronic inflammatory conditions. Identify-ing simple, accessible markers to refine risk stratification remains an unmet need. Objective: To evaluate whether serum urate improves detection of subclinical athero-sclerosis beyond SCORE2 in a high-risk inflammatory cohort. Methods: We conducted a cross-sectional study including 250 patients with psoriatic arthritis fulfilling CASPAR criteria. Vascular assessment comprised carotid and femoral ultrasound and ab-dominal radiography. Atherosclerotic plaque was defined according to Mannheim criteria. The main outcomes were global plaque (≥1 vascular territory) and extended plaque (≥2 territories). Multivariable logistic regression adjusted for SCORE2 categories assessed independent associations. Incremental value was evaluated using decision curve analysis (DCA), category-free net reclassification improvement (cfNRI), and in-tegrated discrimination improvement (IDI). Results: Hyperuricemia prevalence was 21.6%. Patients with hyperuricemia showed a higher prevalence of global plaque (88.9% vs. 62.8%, p< 0.001). After adjustment for SCORE2, serum urate independently predicted global plaque (OR 4.23, 95% CI 1.26–14.2). Notably, 64.3% of patients classi-fied as low–moderate risk already exhibited plaque. In the 50–69-year subgroup, adding serum urate improved reclassification (cfNRI +0.60; IDI +0.031) and was asso-ciated with higher net clinical benefit across decision thresholds. The combined model (SCORE2+HU+cIMT) achieved the highest curves, although with limited incremental gain over HU alone. Conclusions: SCORE2 substantially underestimates subclinical atherosclerosis in this population. Serum urate, an inexpensive and widely available marker, may help refine cardiovascular risk stratification and identify patients who could benefit from further vascular assessment.

Abstract: Background: Despite its negative impact on systemic and oral health, tobacco smoking in the military remains a long-standing problem. It contributes to the high prevalence of tobacco smoking worldwide, while it serves as a stress-reliever in military life. Purpose: This survey aimed to identify the tobacco use, impact on oral health, and knowledge among Marines military personnel. Materials and methods: An online survey verified for validity and reliability, was employed to collect socio-demographic information of Marine smokers by age, education level, and length of military service, as well as their smoking behaviors, impact on oral health, and knowledge. Data were presented descriptively, and the associated factors were analyzed using multivariate analysis. Results: A total of 475 naval marine unit personnel completed the study, with 44.8% reported being current smokers, 25.7% former smokers, and 29.5% never smokers. Seventy-one percent of the participants had a good knowledge of smoking. The most commonly reported impacts on oral health are bad breath, tooth staining, and taste impairment. Smoking habits were not significantly different according to age (p = 0.095 > 0.05), nor by education level (p = 0.610 > 0.05), but were significantly different by length of service (p = 0.00 < 0.05) and level of knowledge (p = 0.00 < 0.05). The results of multivariate analysis utilizing multinomial logistic regression based on p-value (p = 0.005 < 0.05) and 95% CI (0.282-0.8) demonstrated that the 1-5 years length of service is the most significant factor influencing smoking habits. According to OR (odds ratio) values, smoking habits increase the risk by 1.8 times among those with 11-15 years of military service. Conclusion: Despite a good level of knowledge of smoking-related health risks, the present study indicates a high prevalence of tobacco smoking. The most common impacts on oral health among smokers include stained teeth, bad breath, and taste impairment. It was also found that the length of military service and knowledge significantly influence smoking behavior.

Abstract: Image restoration aims to recover a high-quality image from its degraded counterpart by mitigating distortions introduced during acquisition, transmission, or environmental interaction. Despite the remarkable progress of deep learning–based restoration models, most conventional approaches remain tightly coupled to predefined degradation assumptions and pixel-level supervision, limiting their capability to handle complex and diverse scenarios or user-dependent restoration targets. Recent advances in multimodal large language models (MLLMs) and vision–language models (VLMs) have introduced a new paradigm in which restoration systems incorporate semantic reasoning, language-driven interaction, and cross-modal knowledge. By integrating language models, restoration is extended beyond low-level reconstruction toward degradation interpretation, perceptual alignment, and high-level controllability. In this survey, we provide a systematic review of language-driven image restoration, organized through an interaction-centric taxonomy that characterizes how language models are coupled with restoration pipelines. We analyze representative frameworks from the perspectives of semantic conditioning, perceptual supervision, and execution-level interaction, and discuss how these mechanisms influence restoration objectives and system design. In addition, we review emerging language-driven image quality assessment (IQA) approaches, highlighting their complementary role to conventional fidelity-based metrics. Finally, we identify unresolved challenges and outline potential research directions toward more robust, efficient, and trustworthy restoration techniques. https://github.com/MingyuLiu1/Language-Driven-IR-and-IQA

Abstract: Traditional time-series analysis methods, such as Fourier and wavelet transforms, excel at identifying frequency components and their temporal localization. While powerful for spectral analysis, these methods do not explicitly capture the global geometric structure of state transitions or the emergence of cyclic (non-conservative) dynamics within the signal. In this paper, we propose a novel geometric framework that encodes the local complexity dynamics of a time series as a simplicial complex. Using a sliding Hann window, we map the signal into a sequence of local power spectral density (PSD) distributions. We construct a Vietoris-Rips complex using the Wasserstein distance to preserve the physical metric of frequency shifts, and define a directed edge flow based on the asymmetry of Kullback-Leibler (KL) divergence. Applying discrete Hodge decomposition to this flow separates the dynamics into gradient, curl, and harmonic components.Baseline experiments with synthetic signals demonstrate that our method robustly discriminates commensurable signals (gradient-dominant), incommensurable quasi-periodic signals (emergence of curl flow), and stochastic noise (curl-dominant decomposition). An exploratory application to empirical photoplethysmography (PPG) data demonstrates the framework's capability to characterize real-world biological fluctuations, showing that PPG trajectory patterns are structurally similar to those of incommensurable quasi-periodic signals. In a pilot study with 53 PPG recordings, the harmonic component showed a statistically significant correlation with heart rate that is not explained by standard heart rate variability (HRV) features, suggesting the framework extracts genuinely novel information from physiological signals. This framework offers a potential new mathematical lens for quantifying and classifying the hidden topological structures of time-series data, laying a foundation for future empirical applications and explorations across diverse scientific domains.

Abstract: Lysosomes are acidic organelles central to cellular degradation, nutrient sensing, and regulated cell death. The stability of the lysosomal membrane is essential for cell viability, yet it is highly vulnerable to disruption by diverse environmental and endogenous stressors. This review synthesizes current understanding of the molecular mechanisms driving lysosomal membrane permeabilization (LMP) and lysosomal membrane rupture (LMR) in response to xenobiotics, including cationic amphiphilic drugs, perfluorinated alkyl substances (PFAS), and redox‑active heavy metals. We review lysosomal injury, namely intralysosomal ion trapping, metal‑catalyzed Fenton chemistry, and lipid peroxidation, which converges to generate oxidative membrane pores. We also examine cellular membrane‑repair systems, particularly the Endosomal Sorting Complex Required for Transport (ESCRT)-III machinery and ER–lysosome lipid transfer pathways, that act to restore lysosomal integrity. Failure of these protective mechanisms initiates distinct regulated cell death programs, including apoptosis, ferroptosis, and pyroptosis. Finally, we discuss the dual role of LMP in human health: both as a mediator of environmental toxicant‑induced injury and as a promising therapeutic target for overcoming multidrug resistance in cancer. By integrating findings from emerging non‑mammalian model systems and advanced imaging modalities, this review provides a unified framework for understanding lysosomal membrane dynamics under chemical stress.

Abstract: By integrating the Norm Activation Model (NAM) with cognitive and behavioral variables, the study reveals mechanisms that translate into increased waste reduction intention. Data from 382 domestic tourists in Vientiane, Laos were analyzed using ordinary least squares regression. The results reveal that ascription of responsibility (AR) is the strongest predictor of intention, followed by personal norms (PN) and actual waste management behavior. Environmental knowledge and awareness of consequences show no significant influence. The findings confirm that fostering internalized moral sentiments, such as AR and PN, is more crucial in enhancing tourists’ waste reduction intention than mere cogni-tive awareness. Environmental campaigns and education to increase knowledge and heighten awareness of the negative impacts caused by poorly managed waste at popular destinations cannot guarantee an increase in tourists’ waste reduction intention.

Abstract: In recent years, increasing consumer demand for healthier and more natural foods has driven the food industry to replace artificial additives. Among these, colorants play a crucial role, as they influence the sensory perception and acceptance of food products. However, the widespread use of synthetic colorants has raised growing concerns due to their potential association with adverse health effects. In addition, several regula-tory agencies have restricted or banned the use of certain synthetic colorants, requiring their replacement with natural alternatives. In this context, anthocyanins have emerged as a promising substitute for artificial colorants, owing to their similar color properties. Despite their potential, their use as food colorant still faces several challenges, particularly regarding stability, incorporation into food matrices, and regula-tory constraints. Therefore, this review examines the challenges and current trends in natural colorants, highlighting the potential of anthocyanins as substitutes for syn-thetic red colorants in food products.

Abstract: This study investigated the records management systems employed in the education and health sectors in Zambia, with a focus on their role in improving service delivery and decision-making. Using a mixed-methods triangulation design, data was collected through surveys, interviews, observations from registry users and key informants with a sample size of 150. 100 teachers where purposively sampled from the Education department specifically at the District Education Secretary registry with 5 key informants and 40 from the Livingstone Teaching Hospital registry with 5 key informants. Chi-square t-test and descriptive analysis was used in quantitative data, while thematic analysis was used for qualitative data. The research demonstrated that Electronic Health Record (EHR) systems are extensively utilised within the healthcare domain with 100% (n=40) response whereas responses at 100% (n=100) in the Education indicated a manual records management system was used. The study further explored the speed and efficacy of the two systems in record retrieval. Majority of records were accessed in a matter of minutes (70%, n=28) or seconds (20%, n=8) in the Health Sector, contrary to the Education domain where it was discovered majority took minutes (51%, n=51), 17% (n=17) took seconds and some records took days to be retrieved (8%, n=8). Furthermore, the study determined that records retrieval systems were generally viewed as effective, especially within the healthcare sector, despite the ongoing presence of challenges such as insufficient IT infrastructure, data integration difficulties, and deficient records management practices. Overall, while digital transformation has improved records management, the study highlights the need for continued investment in technology, staff training, and system integration to enhance efficiency and reliability across sectors.

Abstract: We study Wilf’s conjecture for a numerical semigroup S using only the first Kunz layer S ∩ (m, 2m). Let η = |S ∩ (m, 2m)| and write the conductor as c = qm − ρ with 0 ≤ ρ < m. We prove the staircase bound |L| ≥ q + (q − 2)η + ηρ, where L = S ∩ [0, c) and ηρ = |S ∩ (m, 2m − ρ)|. This yields a lower bound for Wilf’s number and the criterion e(η + 2) ≥ 2m, hence also (η + 1)(η + 2) ≥ 2m, implying Wilf’s conjecture. When m | c, we obtain the stronger condition e(3η + 4) ≥ 4m, and in particular 3η² + 7η + 4 ≥ 4m. We also derive an exact cumulative-layer formula for |L| and apply it to interval-generated semigroups, where the second cumulative layer gives a strictly stronger infinite family.

Abstract: Background: Colorectal cancer (CRC) caused over 1.9 million new cases and 930,000 deaths globally in 2020. There is an urgent need for novel biomarkers capable of predicting disease progression and therapeutic response. The gut microbiome has emerged as a promising source of diagnostic and prognostic indicators. Objective: This narrative review summarizes current evidences on gut microbiota and their metabolites as potential biomarkers for CRC diagnosis and prognosis. Main Content: Gut microbiomes influence CRC development through metabolism, immune modulation, inflammation, proliferation/apoptosis regulation, genotoxicity, and mucosal barrier disruption. Pathogenic species including Fusobacterium nucleatum and enterotoxigenic Bacteroides fragilis promote tumorigenesis via FadA-mediated signaling and Th17/IL-17 responses. Beneficial bacteria such as Faecalibacterium prausnitzii and Akkermansia muciniphila exert protective effects through short-chain fatty acid production. Macrophages phenotype physiological equilibrium is interrupted or maintained by different floras and inflammatory status fluctuates under the former. Metabolically, hydrogen sulfide damages mitochondrial DNA and secondary bile acids stimulate proliferation. Common detection methods include 16S rRNA sequencing and shotgun metagenomics, while organoids and gene arrays as innovate carriers are in exploratory stage so far. Clinical studies show F. nucleatum abundance correlates with advanced tumor stage, with combined F. nucleatum and colibactin-producing E. coli detection achieving 84.6% sensitivity for early CRC. A. muciniphila levels also predict response to PD-1 blockade immunotherapy. These microbiomes or metabolites support predictions in diagnosis, prognosis, therapeutic efficacy and even locations in earlier stages. Conclusion: Microbiome-based biomarkers represent a promising frontier in CRC management. Future research should focus on standardizing detection protocols, validating multi-marker panels, and exploring metabolite-based approaches to enhance clinical translation.

Abstract: We prove that the set \( T=\Bigl\{n\in\mathbb{N}: \exists p,q\in\mathbb{N}\;\Bigl((2n=(p+q)(p+q+1)+2q)\;\wedge\ \) \( \forall (x_0,\ldots,x_p)\in\mathbb{N}^{p+1}\;\exists (y_0,\ldots,y_p)\in\{0,\ldots,q\}^{p+1}\ \) \( \bigl((\forall k\in\{0,\ldots,p\}\;(1=x_k \Rightarrow 1=y_k))\;\wedge\ \) \( (\forall i,j,k\in\{0,\ldots,p\}\;(x_i+x_j=x_k \Rightarrow y_i+y_j=y_k))\;\wedge\ \) \( (\forall i,j,k\in\{0,\ldots,p\}\;(x_i\cdot x_j=x_k \Rightarrow y_i\cdot y_j=y_k))\bigr)\Bigr)\Bigr\}\ \) is not recursively enumerable. By using Gödel's \( \beta \) function, we prove that the formula that defines the set T can be easily translated into a first-order formula which uses only + and \( \cdot \). The same properties has the set \( \Bigl\{n\in\mathbb{N} : \exists p,q\in\mathbb{N}\;\Bigl((2n=(p+q)(p+q+1)+2q)\;\wedge\ \) \( \forall (x_0,\ldots,x_p)\in\mathbb{N}^{p+1}\;\exists (y_0,\ldots,y_p)\in\{0,\ldots,q\}^{p+1}\ \) \( \bigl((\forall j,k\in\{0,\ldots,p\}\;(x_j+1=x_k \Rightarrow y_j+1=y_k))\;\wedge\ \) \( (\forall i,j,k\in\{0,\ldots,p\}\;(x_i\cdot x_j=x_k \Rightarrow y_i\cdot y_j=y_k))\bigr)\Bigr)\Bigr\}\ \).

Abstract: Background: Non-communicable diseases (NCDs) account for the majority of global mortality, yet healthcare systems remain largely oriented toward the treatment of acute conditions. This study examines the structural mismatch between contemporary disease patterns and healthcare system organization. Methods: A narrative analytical review was conducted using secondary data from the Global Burden of Disease (GBD) study and World Health Organization (WHO) reports, supplemented by literature from PubMed, Scopus, and Google Scholar (2000–2026). Findings were interpreted using epidemiological transition theory, health systems analysis, and political economy frameworks. Results: The analysis identifies multiple structural drivers of treatment-oriented healthcare systems, including economic incentives favoring curative services, short-term political decision-making cycles, and the historical dominance of the biomedical model. These factors contribute to systematic underinvestment in prevention, rising healthcare expenditures, and persistent global inequalities in access to medical technologies, as demonstrated during the COVID-19 pandemic. The current model is associated with increasing economic burden and projected losses in global productivity by 2030–2050. Conclusions: The findings indicate that the current healthcare model is structurally misaligned with population health needs. Improving health outcomes and system sustainability requires a reorientation toward prevention, long-term health metrics, and the evidence-based integration of complementary approaches within healthcare systems.

Abstract: This position paper argues that remote-capable knowledge work should default to AI-enabled flexibility because the workflow-integrated foundation-model stack changes the coordination economics that once favored daily co-presence. By foundation-model stack, we mean systems that combine natural-language interaction, multimodal capture, long context, retrieval, transcription, translation, and increasingly bounded tool use inside everyday workflows. Their organizational significance is not generic automation but the accumulation of artifact capital: durable, queryable, reusable traces such as transcripts, summaries, decisions, tickets, code comments, and retrieval layers. The argument rests primarily on capabilities that are already widely deployed---transcription, summarization, retrieval, translation, drafting, and code assistance---with bounded agents treated as an amplifying but not necessary extension. Rather than eliminating the office, this shift supports selective co-presence, reserving in-person time for tasks with high tacitness, high coupling, or high relational stakes, including apprenticeship, conflict repair, trust formation, and early-stage synthesis. Because the same systems can also intensify surveillance, skill atrophy, and compute-related emissions, we outline a machine-learning research agenda centered on team-level evaluation, privacy-preserving memory layers, scaffolded AI for learning, carbon-aware routing, and pro-agency workflow design.

Abstract: Artificial Intelligence (AI) and its subset, Machine Learning (ML), play transformative roles in the manufacturing sector, forming the foundation of the “Industry 4.0 and 5.0” frameworks. This research contributes to that evolution by developing AI-based advisory systems that utilize advanced data models to optimize casting processes. These systems exemplify the principles of smart manufacturing, where machines and processes are interconnected, adaptive, and driven by data. They support key objectives such as automation, seamless connectivity, real-time data exchange, human-centric innovation, operational resilience, and sustainability. The models developed in this work enable manufacturers to fine-tune product quality, minimize waste, and accelerate time-to-market through predictive analytics and dynamic process control. By integrating AI-based advisory systems, hybrid modeling, and reduced-order modeling techniques, the systems facilitate real-time decision-making and continuous improvement—essential for achieving flexible, efficient, and customized production environments. A real-world case study further demonstrates the effectiveness of these AI-based advisory systems in casting applications, detailing the steps involved in database construction, data training, and predictive modeling.