Search Results (56)

This article presents a comparative assessment of digital transformation pathways in ASEAN economies, taking into account both the level and the structural composition of digitalization within the region. The study draws on internationally comparable data and advances an original temporal–taxonomic analytical approach based on the composite CMG indicator, which integrates three core dimensions of digital transformation: connectivity, market, and governance–sustainability-trust areas. The focus on Southeast Asian economies is substantively justified by the region’s pronounced heterogeneity in economic development, institutional capacities, and digitalization outcomes, combined with its strategic importance as one of the most dynamic emerging regions where digital transformation plays a pivotal role in shaping long-term growth trajectories and structural convergence prospects. The novelty of the research lies in the construction and application of the research tool: the CMG indicator—as a transparent and replicable measurement framework designed to enable the simultaneous analysis of the intensity of digitalization and its internal structure. Such an approach addresses key limitations of existing digitalization indicators when employed in dynamic and structure-oriented comparative research. Moreover, the integration of synthetic measurement techniques with cluster analysis allows for the identification of distinct digitalization profiles among ASEAN countries and for tracing their differentiated development trajectories under conditions of accelerated digital transformation. The results reveal substantial heterogeneity in digital transformation processes across the region, arising from the combined influence of differences in economic development levels, institutional conditions, market structures, and regulatory frameworks, which jointly shape national digitalization trajectories. The analysis identifies both advanced and relatively stabilized digitalization profiles, as well as catch-up pathways characterized by rapid improvements in digital infrastructure and regulatory environments. The conducted research demonstrates a clear value added resulting from the proposed coherent measurement framework, which enables the analysis of digital transformation from both temporal and taxonomic perspectives and provides new empirical evidence on emerging economies. The findings also have practical relevance, offering insights for the design of context-sensitive digital strategies aimed at reducing structural disparities and supporting inclusive and sustainable digital development in Southeast Asia. Full article

Electrochemical impedance spectroscopy (EIS) is a powerful, non-destructive tool for evaluating ion-selective electrode (ISE) membrane condition. This work investigated EIS for identifying degradation mechanisms in all-solid-state Pb²⁺-selective electrodes. Graphene-containing PVC membranes deposited on glassy carbon electrodes were exposed to synthetic urine, river water, and seawater (24 h and 1 week) and to mechanical damage (cutting, needle puncture, or both). Degradation was assessed using EIS, potentiometric measurements, contact-angle analysis, profilometry, and SEM. River water and urine exposure decreased hydrophobicity, increased roughness, and produced fouling deposits. Seawater caused only minor morphological and wettability changes, though impedance data showed increased membrane hydration due to high ionic strength. Mechanical damage substantially disrupted membrane integrity, causing pronounced impedance changes, increased potential drift, and reduced analytical performance. Fouling and mechanical damage produced distinct electrochemical signatures: fouling mainly affected surface properties, while mechanical damage altered the membrane–transducer interface, increasing capacitance and reducing resistance. Notably, needle-punctured electrodes retained a near-Nernstian response despite clear impedance changes and reduced long-term stability, showing that EIS detects defects invisible to conventional calibration. These results confirm EIS as a sensitive method for distinguishing fouling from physical damage, useful for early degradation detection and lifetime monitoring of all-solid-state ISEs. Full article

Microbial α-L-rhamnosidases are increasingly recognised as selective biocatalysts in food biotechnology, nutraceutical production, and health-related applications. These glycoside hydrolases catalyse the hydrolysis of terminal alpha-L-rhamnose residues from flavonoids, terpenoids, saponins, and other glycosylated natural products, thereby modulating sensory properties, solubility, intestinal absorption, and biological activity. While their traditional uses include debittering citrus juice and enhancing wine aroma, recent evidence demonstrates their wider value in selective flavonoid biotransformation, production of rare mono-glycosylated derivatives, probiotic fermentations, and microbiome-associated metabolism. This review summarises microbial sources, catalytic mechanisms, CAZy classification, substrate specificity, structure–function relationships, analytical methods, industrial process engineering, and emerging applications in functional foods and targeted nutraceutical applications. Particular attention is given to the distinction between alpha-(1→2)- and alpha-(1→6)-linked substrates, the production of isoquercitrin and prunin, recombinant enzyme platforms, immobilised biocatalysts, and potential future opportunities arising from metagenomics, synthetic biology, and AI-assisted protein engineering. Full article

The present work introduces the Bulgarian Spectral Database for Painting Materials, a freely accessible web-based resource containing FTIR and Raman spectra, together with complementary analytical information, for materials commonly found in Bulgarian artworks. The database encompasses a collection of over 200 reference materials and more than 100 entries derived from authentic samples obtained from wall paintings, dating from the 5th century BC to the 20th century. The largest section of the database consists of inorganic reference materials, including natural and synthetic mineral pigments, fillers, and additives commonly identified in historical mural paintings, complemented by organic binders and natural dyes. Reference model mixtures simulating historical painting techniques are also included. The database provides interactive visualization and downloadable spectra in plain text formats (.txt) compatible with all spectroscopic software. The integration of spectral data obtained from artworks represents a distinctive feature of the resource. The database is a practical tool for material identification, comparative studies, and conservation research in the field of cultural heritage science. It also provides a robust foundation for comparative studies and facilitates interdisciplinary research across the Balkan region and beyond. Full article

Geoheritage is increasingly recognised as an integral component of the broader natural-cultural heritage of human societies. However, existing conceptual and methodological approaches often remain fragmented, relying either on spatial coincidence or on separate analytical treatments of geoheritage and cultural-historical values, which limits the understanding of their functional integration. This review paper advances the conceptualisation of geocultural heritage and the geocultural site by moving beyond simple spatial coincidence towards a functional integration of abiotic and cultural-historical values. In this context, geocultural heritage is defined as a hybrid form of natural and cultural heritage in which geological and cultural-historical components are mutually co-constitutive, generating value through their functional, historical, and symbolic integration rather than mere spatial co-occurrence. Within this framework, the primary aim is to develop a theoretical perspective that supports a holistic understanding of the integrative relationships between geoheritage and cultural-historical heritage. Its primary aim is to develop a theoretical perspective that supports a holistic understanding of the integrative relationships between geoheritage and cultural-historical heritage. The study identifies and demonstrates three fundamental levels of geocultural synergy, including spatio-material, causal, and symbolic-transcendental, through representative case examples from Slovakia. To bridge the gap between theoretical recognition and practical governance, the paper introduces a semi-quantitative assessment instrument, the Geocultural Management Matrix (GCMM). This framework aggregates assessment criteria into two synthetic dimensions: the Geocultural Value and Integrity Index (GVII) and the Management and Potential Index (MPI). Based on the interaction of these two dimensions, sites are assigned to four distinct management profiles, linking analytical assessment with differentiated management strategies. In this way, the matrix provides a methodologically consistent bridge between geocultural heritage assessment and site-specific decisions concerning conservation intensity, interpretative development, and management orientation. The proposed model strengthens the practical applicability of geocultural research by offering a transferable framework for geoparks, heritage conservation and management. Full article

Existing studies conduct general cost analyses for prefabricated components, yet structural heterogeneity results in distinct cost drivers. Most studies concentrate on the technical performance of prefabricated staircases, with insufficient investigation into dedicated cost-estimation methods. This study establishes a hybrid prediction framework integrating GAN-based data augmentation and SHAP-empowered Multilayer Perceptron (SHAP-MLP) modeling, using prefabricated straight staircases as empirical objects for multidimensional analysis. Total cost is classified into production, transportation, and on-site installation phases, followed by systematic screening of 33 influencing factors for predictive modeling. The Analytic Hierarchy Process (AHP), with a 1–9 scale, is adopted to quantify indicator weights and prioritize features. Triple verification (multi-expert consistency test, group opinion coordination test, and sensitivity analysis) removes five weakly correlated parameters to form a preliminary indicator system. Based on 240 original engineering data samples, the GAN generates 60 high-fidelity synthetic samples. Distribution consistency between synthetic and original data is validated via the Kolmogorov–Smirnov (KS) test, p-value verification, and kernel density estimation (KDE). SHAP interpretability analysis identifies four core determinants: prefabrication rate, total staircase area, standardization level, and number of floors. Eight low-impact parameters are excluded to optimize model input, leaving 20 validated indicators. The GAN-SHAP-MLP model maintains superior performance in testing, with a test-set RMSE of 49.538, representing improvements of 41.3%, 22.5%, and 25.7% over LSTM (89.33), CNN (67.59), and standard MLP (70.56), respectively. The difference between its test-set and overall R² is only 0.69%, significantly lower than 2.06% for LSTM and 5.47% for MLP. Empirical validation with real engineering cases from four different regions further confirms the model’s high prediction accuracy, with a minimum error of only 1.49%. The integration of data augmentation and interpretable deep learning provides a high-precision, interpretable cost prediction tool for prefabricated straight staircases, promoting methodological progress in construction economics. Full article

With the growing interest in personalized medicine, alternative biological matrices to blood are increasingly explored as sources of diagnostic information. Saliva has emerged as a promising diagnostic matrix due to its non-invasive collection, suitability for home sampling, and minimal requirements for personnel training. Numerous studies have demonstrated the presence of metabolites in saliva that enable disease diagnosis and monitoring. However, the influence of pre-analytical and analytical factors on salivary metabolomics outcomes remains insufficiently characterized. In this study, we investigated factors potentially affecting the number and abundance of detected metabolites in untargeted salivary metabolomics using liquid chromatography coupled with mass spectrometry (LC–MS). The impact of chromatographic column type, extraction protocol, and saliva type (stimulated versus resting) was evaluated. Additionally, the effect of swab type on analyte recovery was assessed. The use of a synthetic swab for saliva collection yielded results most comparable to those obtained without swabs, for both resting and stimulated saliva samples, indicating minimal pre-analytical interference. The greatest metabolite coverage was obtained using ACN:MeOH (1:1, v/v), with a ZIC-HILIC column for polar metabolites and a C18 column for non-polar metabolite separation. These findings demonstrate that swab type, chromatographic column, extraction solvent, and saliva type critically shape metabolite coverage in untargeted salivary metabolomics. Importantly, the distinct metabolic profiles of resting and stimulated saliva suggest that these matrices may provide complementary clinical insights, underscoring the need for saliva type selection tailored to specific diagnostic and biomarker discovery objectives. Full article

We present an analytical solution for the complex spectrum of a Creutz ladder subject to an imaginary Stark potential. By mapping the system to a momentum-space differential equation, we derive the closed-form solution for the momentum-space wavefunctions. We identify a distinct cross-shaped spectrum consisting of discrete localized sectors and a continuous branch of asymptotically real states. Our derivation reveals that the discrete sectors arise from a global phase winding condition, whereas the asymptotically real branch emerges when the energy magnitude is smaller than the inter-cell hopping strength, a regime in which the momentum-space wavefunction develops singularities. We demonstrate that these singularities prevent standard quantization; instead, the open boundary conditions are satisfied via a size-dependent imaginary energy component that regulates the wavefunction decay. To investigate the properties of this branch in the thermodynamic limit, we perform large-scale finite-size scaling analysis up to system sizes $L\sim {10}^9$. The numerical results confirm the power-law decay of the residual imaginary energy, supporting the asymptotic reality of these states. Furthermore, scaling of the inverse participation ratio and fractal dimension indicates that these states, while exhibiting size-dependent localization in finite systems, evolve into an extended phase in the thermodynamic limit. Our results establish a theoretical framework for understanding spectral transitions in systems with imaginary Stark potentials, with potential realizations in photonic frequency synthetic dimensions. Full article

Generative artificial intelligence increasingly mediates how individuals interpret information, perform cognitive tasks, and participate in economic and political life. While such systems promise efficiency and expanded access to knowledge, their societal effects are unevenly distributed. This article develops the concept of societal bifurcation to explain an emerging structural divergence between a cognitively resilient minority, capable of integrating AI reflectively, and a cognitively dependent majority, whose reliance on automated interpretation reduces interpretative autonomy. Drawing on contemporary empirical evidence from cognitive science, labour research, and human–AI interaction studies, the article shows how unstructured AI use diminishes metacognitive monitoring and inflates confidence, while labour-market restructuring amplifies differences in adaptability and resilience. These cognitive and economic dynamics interact with an increasingly fragile democratic information environment shaped by synthetic communication and declining epistemic trust. The article argues that these processes form a self-reinforcing sociotechnical mechanism through which cognitive dependency, economic inequality, and democratic vulnerability become mutually constitutive. By conceptualising societal bifurcation as a distinct analytical framework, the article contributes to sociological and science and technology studies debates on inequality, agency, and governance in AI-mediated societies, while highlighting the importance of sustaining interpretative autonomy in the age of generative AI. Full article

Employee turnover presents a significant challenge to modern organizations, often resulting in operational disruptions, substantial hiring costs, and a loss of institutional knowledge. While traditional human resource practices have historically been reactive, the emergence of machine learning has introduced a proactive capability to anticipate and mitigate attrition before it occurs. This research utilizes the IBM HR Analytics dataset, which contains 1470 employee records and 35 distinct features, to develop a hybrid machine learning model designed to enhance the accuracy of turnover predictions. To ensure the model’s effectiveness, the researchers employed a comprehensive preprocessing phase that included eliminating non-informative features, applying label encoding to categorical data, and using StandardScaler to normalize quantitative values. A critical component of the study addressed the common issue of class imbalance within HR data. To resolve this, a hybrid sampling strategy was implemented, combining Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) to create a more balanced learning environment for the algorithms. The core of the predictive engine is a soft voting ensemble that integrates three powerful algorithms: Random Forest, XGBoost, and logistic regression. Evaluated on an 80/20 train–test split, the tuned XGBoost model achieved an impressive 84% accuracy and an Area Under the Curve (AUC) of 0.80. Meanwhile, the logistic regression component contributed the highest F1-score, reinforcing the overall strength and balance of the ensemble approach. These metrics confirm that the hybrid model is both robust and reliable for identifying at-risk employees. Beyond simple prediction, the study prioritized interpretability by using SHapley Additive exPlanations (SHAP) to identify the primary drivers of attrition. The analysis revealed that the most significant variables influencing an employee’s decision to leave include the interaction between job level and experience, frequent overtime, monthly income, current job level, and total years spent at the company. By providing these data-driven insights, the model empowers HR teams to transition from reactive troubleshooting to proactive retention planning, ultimately securing the organization’s talent and stability. Full article

Background/Objectives: Oleosomes, plant-derived lipid nanostructures comprising a triacylglycerol core surrounded by a phospholipid monolayer and interfacial proteins, provide sustainable alternatives to synthetic lipid vesicles. This study compares solvent-free aqueous extractions of oleosomes from five nuts (almond, macadamia, walnut, hazelnut, pine) and five seeds (flaxseed, sunflower, hemp, sesame, canola/rapeseed) to understand how botanical origin influences composition and physicochemical behavior. Methods: Oleosomes were isolated using solvent-free aqueous extraction. Extraction yield, lipid content, protein content, particle size, polydispersity, and zeta potential were determined using standard analytical assays and dynamic light scattering techniques. SDS–PAGE was performed to evaluate interfacial protein profiles and oleosin abundance. Results: Extraction yields ranged from 8.4% (flaxseed) to 59.5% (walnut). Oleosome diameters spanned 424 nm to 3.9 µm, and all oleosome dispersions exhibited negative zeta potentials (–26 to –57 mV). SDS–PAGE revealed abundant 15–25 kDa oleosins in seed oleosomes but relatively sparse proteins in nut oleosomes. Seed oleosomes were smaller and exhibited stronger electrostatic stabilization, while nut oleosomes formed larger droplets stabilized primarily through steric interactions due to lower oleosin content. Conclusions: Variation in oleosin abundance and interfacial composition leads to distinct stabilization mechanisms in nut and seed oleosomes. These findings establish a predictive basis for tailoring oleosome size, stability, and functionality, and highlight their potential as natural nanocarriers for food, cosmetic, and pharmaceutical formulations. Full article

Per- and polyfluoroalkyl substances (PFAS) are a large family of synthetic chemicals known for their exceptional stability, strong surface activity, and ability to repel both water and oil. Due to these characteristics, PFAS have been widely used since the 1950s across multiple industries. However, over the decades, these substances have emerged as persistent and bioaccumulative contaminants. While it is evident that PFAS pose adverse effects on both ecosystems and human well-being, the mechanisms underlying their toxicities are yet to be fully understood. To better examine the thematic evolution of PFAS research, this review divides the literature into four distinct eras: before 2000s, from 2000 to 2010, from 2010 to 2020, and from 2020 onwards. Since the latter half of the 20th century, the rapid development and mass production of PFAS resulted in the manufacture of thousands of industrial and household products. After decades of concerns regarding their toxic impacts, major phase-outs in the early 2000s shifted attention towards environmental studies and biomonitoring. Throughout the 2010s, extensive studies were conducted to assess the PFAS toxicities, especially perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA), the two widely detected compounds on human populations. Since 2020, research efforts have increasingly progressed toward molecular-level studies, advancements in analytical detection methods, and remediation technologies. Additionally, this review examines regulatory changes, highlights current knowledge gaps, and outlines directions for future research. Full article

Funded pensions have become a key focus in strategies to ensure supplementary income during retirement. This paper explores two distinct approaches for estimating replacement rates: a deep learning model and a Mamdani Fuzzy Inference System (FIS). Using synthetic datasets for training, the deep learning model delivered accurate replacement rate predictions when benchmarked against exact solutions. On the other hand, the FIS approach, which leverages expert insights and practical experience, produced encouraging results but revealed opportunities for refining the definitions of intervals and linguistic categories. To bridge the strengths of both approaches, we introduce a conceptual integration using the Analytic Hierarchy Process (AHP), providing a multi-criteria decision-support framework that combines predictive accuracy from neural networks with the interpretability of fuzzy systems. The findings emphasize the potential of artificial intelligence (AI) methods, including neural networks and fuzzy logic, in advancing pension planning. While these techniques remain underutilized in this area, they hold significant promise for developing decision-support systems, particularly in big data contexts. Such systems can offer initial replacement rate estimates, serving as valuable inputs for experts during the decision-making process. Additionally, the paper suggests future research into multi-criteria decision analysis to improve decision-making within multi-pillar pension frameworks. Full article

Tarski’s first-order axiom system ${\mathcal{E}}_2$ for Euclidean geometry is notable for its completeness and decidability. However, the Pythagorean theorem—either in its modern algebraic form $a^2+b^2=c^2$ or in Euclid’s Elements—cannot be directly expressed in ${\mathcal{E}}_2$, since neither distance nor area is a primitive notion in the language of ${\mathcal{E}}_2$. In this paper, we introduce an alternative axiom system ${\mathcal{E}}_d$ in a two-sorted language, which takes a two-place distance function d as the only geometric primitive. We also present a conservative extension ${\mathcal{E}}_{da}$ of it, which also incorporates a three-place angle function a, both formulated strictly within first-order logic. The system ${\mathcal{E}}_d$ has two distinctive features: it is simple (with a single geometric primitive) and it is quantitative. Numerical distance can be directly expressed in this language. The Axiom of Similarity plays a central role in ${\mathcal{E}}_d$, effectively killing two birds with one stone: it provides a rigorous foundation for the theory of proportion and similarity, and it implies Euclid’s Parallel Postulate (EPP). The Axiom of Similarity can be viewed as a quantitative formulation of EPP. The Pythagorean theorem and other quantitative results from similarity theory can be directly expressed in the languages of ${\mathcal{E}}_d$ and ${\mathcal{E}}_{da}$, motivating the name Quantitative Euclidean Geometry. The traditional analytic geometry can be united under synthetic geometry in ${\mathcal{E}}_d$. Namely, analytic geometry is not treated as a model of ${\mathcal{E}}_d$, but rather, its statements can be expressed as first-order formal sentences in the language of ${\mathcal{E}}_d$. The system ${\mathcal{E}}_d$ is shown to be consistent, complete, and decidable. Finally, we extend the theories to hyperbolic geometry and Euclidean geometry in higher dimensions. Full article

Effluents from the textile industry, particularly those containing synthetic azo dyes, poses a significant environmental threat, necessitating the development of more effective and sustainable pollutant removal methods. Traditional dye removal techniques often fall short in efficiency and environmental impact, prompting the exploration of enzymatic degradation as a promising alternative. This study focuses on chloroperoxidase, a natural biocatalyst recognized for its ability to oxidize synthetic dyes into less harmful products. By exploring the mechanistic distinction between chlorination and oxidative processes, we investigate the enzyme’s specific degradation pathways for azo dyes and the resulting by-products. Utilizing analytical techniques, including liquid chromatography/mass spectrometry (LC/MS), and density functional theory (DFT), we gain insights into the decolorization mechanism, revealing that the enzyme preferentially generates oxidative products through C–N bond cleavage as its initial degradation step. These findings underscore not only the unique mechanistic properties of chloroperoxidase but also its potential as a biocatalyst for industrial applications. This study advocates further research into the optimization of enzyme-based systems, highlighting their relevance in advancing greener chemical practices in the textile industry, thus contributing to more sustainable manufacturing processes. Full article