Search Results (111)

Building Information Modelling (BIM) has increasingly transformed global architectural and construction practices by enhancing collaboration, design accuracy, and project efficiency. However, BIM adoption remains slow in several developing countries, including Nigeria, where architectural firms play a critical role in driving digital transformation across the wider construction sector. This study investigates the current level of BIM implementation within Nigerian architectural practices and identifies key factors that either enable or constrain its uptake. Survey findings (77 responses; 77% response rate), analysed using SPSS 26.0 and the Relative Importance Index (RII), reveal that although some firms have begun integrating BIM tools, many still rely heavily on traditional 2D CAD (Computer-Aided Design) workflows. Major barriers include high software acquisition and maintenance costs, limited technical expertise, and insufficient organisational readiness. The results highlight the urgent need for government incentives, targeted capacity-building programmes, and industry-wide digital skill development to accelerate BIM diffusion among architectural firms, whose early adoption is essential for sector-wide modernisation. Future research should explore how socio-technical alignment can reshape BIM-enabled workflows to generate measurable value for clients, contractors, and end users. Examining collaborative data environments, information exchange standards, and participatory design practices will be crucial for demonstrating BIM’s long-term return on investment and establishing sustainable digital transformation pathways within Nigeria’s architectural and construction industries. Full article

Forecasting stock prices remains a central challenge in financial modelling, as markets are influenced by market sentiment, firm-level fundamentals and complex interactions between macroeconomic and microeconomic factors, for example. This study evaluates the predictive performance of both classical statistical models and advanced attention-based deep learning architectures for daily stock price forecasting. Using a dataset of major U.S. equities and Exchange Traded Funds (ETFs) covering 2012–2024, we compare traditional statistical approaches, Seasonal Autoregressive Integrated Moving Average (SARIMA) and Exponential Smoothing (ES) in the Error, Trend, Seasonal (ETS) framework, with deep learning architectures such as the Temporal Fusion Transformer (TFT), and a novel hybrid model, the TFT-Graph Neural Network (TFT-GNN), which incorporates relational information between assets. All models are assessed under consistent experimental conditions in terms of forecast accuracy, computational efficiency, and interpretability. Our results indicate that while statistical models offer strong baselines with high stability and low computational cost, the TFT outperforms them in capturing short-term nonlinear dependencies. The hybrid TFT-GNN achieves the highest overall predictive accuracy, demonstrating that relational signals derived from inter-asset connections provide meaningful enhancements beyond traditional temporal and technical indicators. These findings highlight the advantages of integrating relational learning into temporal forecasting frameworks and emphasise the continued relevance of statistical models as interpretable and efficient benchmarks for evaluating deep learning approaches in high-frequency financial prediction. Full article

Blackberry (Rubus subg. Eubatus Focke) ranks among the four most commercially valuable berry crops globally, alongside raspberry, strawberry, and blueberry, owing to its high antioxidant content—particularly flavonoids, anthocyanins, and polyphenols. Compared to other berry crops, blackberry cultivation requires lower labor and financial inputs, with plantations remaining productive for 12–15 years. In Russia, total blackberry area is limited (~100 ha), and the Moscow Region is particularly suited for trailing and semi-trailing cultivars with early-to-mid-season ripening. This three-year study (2021–2023) conducted at the Tsitsin Main Botanical Garden (RAS) evaluated ten promising blackberry cultivars to (i) assess interrelationships among phenological, morphological, and fruit quality traits; and (ii) identify optimal market niches for each genotype. Cultivars were grouped by ripening time: early (‘Karaka Black’, ‘Loch Tay’, ‘Natchez’) and medium (‘Columbia Sunrise’, ‘Hall’s Beauty’, ‘Caddo’, ‘Columbia Giant’, ‘Victoria’, ‘Brzezina’). Morphologically, ‘Columbia Giant’, ‘Columbia Star’, ‘Columbia Sunrise’, ‘Hall’s Beauty’, and ‘Loch Tay’ exhibited the most balanced architecture. For fresh-market retail, ‘Hall’s Beauty’ (650.3 gf), ‘Loch Tay’ (632.0 gf), and ‘Victoria’ (882.2 gf) stood out for high fruit firmness, whereas ‘Columbia Giant’ (11.5 g fruit mass, 354.1 gf) is recommended for direct consumer sales due to its large fruit size and acceptable firmness. Key trait associations included flowering duration and drupelet number (r = −0.83); fruiting onset and lateral length (r = 0.75); central leaflet length and fruiting laterals per shoot (r = −0.86); fruit number per lateral and Soluble Solids Content (SSC, r = 0.83); and lateral length (r = 0.84). These findings indicate the importance of proper variety selection for establishing blackberry plantations in the specific climatic conditions of the Moscow Region. Full article

This article explores the foundational mechanisms of the Discrete Event System Specification (DEVS) theory—closure under coupling, universality, and uniqueness—and their critical role in enabling interoperability through modular, hierarchical simulation frameworks. Closure under coupling empowers modelers to compose interconnected models, both atomic and coupled, into unified systems without departing from the DEVS formalism. We show how this modular approach supports the scalable and flexible construction of complex simulation architectures on a firm system-theoretic foundation. Also, we show that facilitating the transformation from non-modular to modular and hierarchical structures endows a major benefit in that existing non-modular models can be accommodated by simply wrapping them in DEVS-compliant format. Therefore, DEVS theory simplifies model maintenance, integration, and extension, thereby promoting interoperability and reuse. Additionally, we demonstrate how DEVS universality and uniqueness guarantee that any system with discrete event interfaces can be structurally represented with the DEVS formalism, ensuring consistency across heterogeneous platforms. We propose that these mechanisms collectively can streamline simulator design and implementation for advancing simulation interoperability. Full article

Cerebral ischemia represents a major mortality and disability cause; oxidative stress is the main intensifier mechanism of excitotoxicity, neuroinflammation, blood–brain barrier failure, and neuronal loss; under these circumstances, firm, mechanism-anchored neuroprotection is an absolute necessity. The work includes a exhaustive, PRISMA (Preferred reporting items for systematic review and meta-analysis)-adherent presentation of the effects of antioxidant peptides and small molecules on tissues, unifying disparate readouts into a coherent tissue-level narrative. A systematic interrogation was performed across major databases over a prespecified interval, applying transparent eligibility criteria to studies that quantified canonical endpoints—infarct volume, neuronal integrity (NeuN/MAP2), apoptosis (TUNEL/cleaved caspase-3), gliosis (GFAP/Iba1), and ultrastructural preservation. The evidence coalesces around a strikingly consistent signal: antioxidant strategies converge on smaller infarcts, robust preservation of neuronal markers, attenuation of apoptotic burden, dampened astroglial–microglial reactivity, and stabilization of mitochondrial and axonal architecture—patterns that align with antioxidative, anti-apoptotic, anti-inflammatory, and ferroptosis-modulating mechanisms. While early clinical data echo these benefits, translation is tempered by heterogeneity in models, timing and dosing windows, and outcome batteries. By consolidating the histological landscape and pinpointing where effects are durable versus contingent, this work elevates antioxidant peptide and small-molecule neuroprotection from promising fragments to an integrated framework and sets an actionable agenda—standardized histological endpoints, protocol harmonization, head-to-head comparisons of peptide versus small-molecule strategies, and adequately powered randomized trials embedded with mechanistic biomarkers to decisively test efficacy and accelerate clinical adoption. Full article

This review surveys how contemporary machine learning is reshaping financial and economic forecasting across markets, macroeconomics, and corporate planning. We synthesize evidence on model families, such as regularized linear methods, tree ensembles, and deep neural architecture, and explain their optimization (with gradient-based training) and design choices (activation and loss functions). Across tasks, Random Forest and gradient-boosted trees emerge as robust baselines, offering strong out-of-sample accuracy and interpretable variable importance. For sequential signals, recurrent models, especially LSTM ensembles, consistently improve directional classification and volatility-aware predictions, while transformer-style attention is a promising direction for longer contexts. Practical performance hinges on aligning losses with business objectives (for example cross-entropy vs. RMSE/MAE), handling class imbalance, and avoiding data leakage through rigorous cross-validation. In high-dimensional settings, regularization (such as ridge/lasso/elastic-net) stabilizes estimation and enhances generalization. We compile task-specific feature sets for macro indicators, market microstructure, and firm-level data, and distill implementation guidance covering hyperparameter search, evaluation metrics, and reproducibility. We conclude in open challenges (accuracy–interpretability trade-off, limited causal insight) and outline a research agenda combining econometrics with representation learning and data-centric evaluation. Full article

High-bandwidth memory (HBM) has become a strategic bottleneck in AI-centric systems, shifting competitive advantage from computing power alone to a design that is orchestrated by memory and packaging. We investigate whether publicly available information about companies’ integration decisions—vertical integration by Samsung Electronics and horizontal partnerships by SK Hynix—is included in market-expected valuation. We create a Korean-language news corpus spanning January 2023 to September 2025 and use seed-guided topic models to obtain firms’ vertical and horizontal integration. We verify qualitative distinguishability with t-SNE embeddings and use firm-specific ordinary least squares specifications to link topic intensities to equity prices. According to research findings, for Samsung, consolidation-oriented vertical indicators (M&A and risk ring-fencing) positively correlate with valuation, whereas supplier-enablement or operational vertical topics are not reliably factored into their valuation. Vendor-assisted scale-up and joint development topics support positive valuation for SK Hynix. This study provides a scalable framework for text evaluation, which distinguishes between general sentiment and strategic architecture, as well as evidence that capital markets reward consolidation and alliance execution differently depending on the management of the HBM bottleneck. Full article

This study explores the role of social trust, a critical informal institution, in mitigating corporate brownwashing—the strategic concealment of positive environmental performance. Drawing on a panel of 15,081 firm-year observations from Chinese A-share listed firms between 2010 and 2022, we operationalize brownwashing as a strategy where firms demonstrate substantive environmental compliance (i.e., no environmental penalties) while simultaneously practicing symbolic verbal conservatism (below-median environmental disclosure). Our empirical analysis reveals that higher regional social trust significantly curbs the propensity for firms to engage in brownwashing. This effect is not only statistically significant but also economically meaningful: a one-standard-deviation increase in social trust is associated with a 1.85 percentage point decrease in the likelihood of brownwashing. This effect operates through two key channels: enhancing stakeholder monitoring and reinforcing internal governance for environmental accountability. The impact of trust is significantly amplified under specific conditions: its role is more pronounced where formal sustainability regulations are weaker, highlighting trust as a crucial informal pillar of the sustainability governance architecture, and its inhibitory effect is strengthened when firms face higher reputational risks tied to their environmental performance. This study makes several contributions: it provides broad, cross-industry evidence on a key challenge in sustainability reporting; offers empirical support for the “trust fidelity” theory in the context of environmental disclosure; and develops a ‘channel-amplifier’ framework that advances our understanding of the complex institutional interplay required to foster corporate environmental transparency. Full article

Artificial Intelligence (AI) has emerged as a transformative force in the field of architectural design. This study aims to systematically analyze the influence mechanisms of Artificial Intelligence-based Architectural Design (AIAD) by constructing a comprehensive hybrid model that integrates the Analytic Hierarchy Process (AHP), Decision-Making Trial and Evaluation Laboratory (DEMATEL), Interpretive Structural Modeling (ISM), and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC). Based on the previous quantitative literature review, 6 primary categories and 18 secondary influencing factors were identified. Data were collected from a panel of fifteen experts representing the architecture industry, academia, and computer science. Through weighting analysis, causal mapping, hierarchical structuring, and driving–dependence classification, the study clarifies the complex interrelationships among influencing factors and reveals the underlying drivers that accelerate or constrain AI adoption in architectural design. By quantifying the hierarchical and causal influence of factors, this research provides theoretical findings and practical insights for design firms undergoing digital transformation. The results extend previous meta-analytical studies, offering a decision-support system that bridges academic research and real-world applications, thereby guiding stakeholders toward informed adoption of artificial intelligence for future cultural tourism development and regional spatial innovation. Full article

Financial risk early warning systems provide critical corporate financial status information to stakeholders, including corporate managers, investors, regulatory agencies, and other interested parties, enabling informed decision-making. This study proposes a corporate financial risk early warning model based on a bagging–cascading–boosting architecture, which can be used to predict the financial risk of a firm. The model performance is improved by integrating the residual fitting characteristics of LightGBM, the variance suppression mechanism of bagging, and the adaptive expansion ability of the cascade framework. Evaluated on 46 financial indicators from 2826 A-share-listed companies, the model demonstrates superior performance in AUC and F1-score metrics, outperforming traditional statistical methods and standalone machine-learning models. The methodological innovation lies in its tripartite mechanism: LightGBM ensures low-bias prediction, bagging controls variance, and the cascading structure dynamically adapts to data complexity, maintaining 94.09% AUC robustness, even when training data is reduced to 50%. Empirical results confirm this “ensemble-of-ensembles” framework effectively identifies Special Treatment (ST) firms, delivering early risk alerts for management while supporting investment decisions and regulatory risk mitigation. Full article

Peptides are widely used in cosmetic formulations to stimulate extracellular matrix (ECM) synthesis, while silybin (a flavonolignan from Silybum marianum) offers retinol-like benefits through antioxidant and photoprotective activity. This study evaluated a novel anti-aging cream combining seven bioactive peptides with silybin to assess synergistic effects on ECM regeneration and clinical skin rejuvenation. In vitro assays in human dermal fibroblasts and keratinocytes revealed that the formulation rapidly upregulated gene and protein expression of collagen types I, III, IV, and XVII and lysyl oxidase (LOX) within 4–16 h. Ex-vivo, ultraviolet (UV)-damaged skin explants treated with the peptide–silybin complex showed enhanced recovery of collagen, elastic fibers, and LOX versus untreated controls. A 56-day clinical study (n = 31) demonstrated significant improvements in wrinkle area and volume, elasticity (+12.5%), firmness (+20.7%), and dermal density (+78%, all p < 0.001). No adverse effects were reported, and over 80% of participants noted improved skin texture and firmness. These findings highlight a novel synergy between peptides and silybin, with rapid ECM activation and clinical efficacy. To our knowledge, this is the first evidence of a cosmetic peptide formulation significantly upregulating LOX expression, suggesting a new mechanism for strengthening dermal architecture and improving skin resilience. Future studies should elucidate the mechanisms underlying these effects and assess whether other botanicals confer complementary benefits when combined with peptide blends. Full article

The global electric vehicle (EV) market has experienced sustained growth over the last decade; however, adoption within the commercial EV segment remains comparatively sluggish. This disparity is driven by three primary factors: the intrinsic limitations of lithium-ion battery chemistry, which imposes constraints on charge–discharge cycling, excessive charging durations for large battery packs used in long-haul semi-trucks, and diminished charging effectiveness under cold weather conditions, which further extends downtime and increases grid demand. To address these operational and infrastructural challenges, this article proposes a novel battery swapping station layout with ‘design-integrated safety’ features, enabling rapid battery replacement while ensuring compliance with safety codes and standards. Two complementary pricing strategies are developed for deployment under differing market structures. The first is a Cournot competition, applicable to deregulated environments, where firms strategically allocate battery inventory between EV swapping services and participation in a secondary energy market. As an extension of the Cournot competition model, the profit functions are analytically derived for a duopoly in which one firm engages in dual markets, enabling assessment of equilibrium outcomes under competitive conditions. The second strategy is a degradation-sensitive pricing framework, intended for regulated markets, which dynamically adjusts swap prices based on state-of-charge depletion, duty cycle intensity, environmental exposure, and nonlinear battery degradation effects. This formulation is evaluated for six representative operational cases, demonstrating its ability to incentivize shallow cycling, penalize deep discharges, and incorporate fair usage-based pricing. The proposed architectures and pricing models offer a viable pathway to accelerate commercial EV adoption while optimizing asset utilization and profitability for station operators. Full article

This study investigates how bank mergers and acquisitions (M&As) reshape the monitoring architecture of syndicated loans and, by extension, borrowers’ financing conditions. Using a global panel of 20,299 syndicated loan contracts, originating in 43 countries between 1982 and 2020, we link LPC DealScan data to Securities Data Company M&A records to trace each loan’s lead arrangers before and after consolidation events. Fixed-effects regressions, enriched with borrower- and loan-level controls, reveal three key patterns. First, post-merger loans exhibit significantly more concentrated syndicates: the Herfindahl–Hirschman Index rises by roughly 130 points and lead arrangers retain an additional 0.8–1.1 percentage points of the loan, consistent with heightened monitoring incentives. Second, these effects are amplified when information asymmetry is acute, i.e., for opaque or unrated firms, supporting moral hazard theory predictions that lenders internalize greater risk by holding larger stakes. Third, relational capital tempers the impact of consolidation: borrowers with repeated pre-merger relationships face smaller increases in syndicate concentration, while switchers experience the most significant jumps. Robustness checks using lead arranger market share, alternative spread measures, and lag structures confirm the findings. Overall, the results suggest that bank consolidation strengthens lead arrangers’ incentives to monitor but simultaneously reduces risk-sharing among participant lenders. For borrowers, the net effect is a trade-off between potentially tighter oversight and reduced syndicate diversification, with the balance hinging on transparency and prior ties to the lender. These insights refine our understanding of how structural shifts in the banking sector cascade into corporate credit markets and should inform both antitrust assessments and borrower funding strategies. Full article

Fueled by advances in cloud technologies and industrial platforms, networked collaborative manufacturing platforms (NCMPs) are reshaping how products are priced and customized. As decision rights increasingly shape value creation within these platforms, platform leadership—whether driven by the manufacturer or the designer—emerges as a critical determinant of product strategy. However, the effects of different leadership structures on pricing and customization remain unclear. To address this issue, we develop game models comparing manufacturer-led and designer-led platforms. Our analysis reveals that under manufacturer-led platforms, dual-product strategies remain viable across a wider range of customization conditions, ensuring pricing stability and broader demand coverage. In contrast, designer-led platforms are more sensitive to the commission rate—excessive commissions tend to crowd out standard product offerings and distort pricing incentives. Moreover, platform control does not always guarantee superior profit: while designers consistently outperform manufacturers under manufacturer-led platforms, profit dominance in designer-led settings shifts with commission rates. Notably, by jointly optimizing product strategy and pricing mechanisms, firms can achieve more balanced value distribution and sustain collaboration. These findings offer a strategic framework for manufacturers and designers to align platform governance with product architecture, contributing new insights into collaborative pricing, platform leadership, and dual-product innovation in industrial platform ecosystems. Full article

The construction sector, facing a persistent productivity gap compared to other industries, is hindered by fragmented value streams, inconsistent performance metrics, and the limited scalability of process improvements. We introduce a pioneering, four-tiered hierarchical productivity framework to respond to these challenges. This innovative approach integrates operational, tactical, strategic, and normative layers. At its core, the framework applies standardised, repeatable process steps—mapped using Value Stream Mapping (VSM)—to capture key indicators such as input efficiency, output effectiveness, and First-Time Quality (FTQ). These are then aggregated through takt time compliance, schedule reliability, and workload balance to evaluate trade synchronisation and flow stability. Higher-level metrics—flow efficiency, multi-resource utilisation, and ESG-linked performance—are integrated into an Overall Productivity Index (OPI). Building on a modular production model, the proposed framework supports real-time sensing, AI-driven monitoring, and intelligent process control, as demonstrated through an empirical case study of continuous process monitoring for Kelly drilling operations. This validation illustrates how sensor-equipped machinery and machine learning algorithms can automate data capture, map observed activities to standardised process steps, and detect productivity deviations in situ. This paper contributes to a multi-scalar measurement architecture that links micro-level execution with macro-level decision-making. It provides a foundation for real-time monitoring, performance-based coordination, and data-driven innovation. The framework is applicable across modular construction, digital twins, and platform-based delivery models, offering benefits beyond specialised foundation work to all construction trades. Grounded in over a century of productivity research, the approach demonstrates how emerging technologies can deliver measurable and scalable improvements. Framing productivity as an integrative, actionable metric enables sector-wide performance gains. The framework supports construction firms, technology providers, and policymakers in advancing robust, outcome-oriented innovation strategies. Full article