Search Results (1,032)

This paper reports a statistical analysis of a database archiving information on the strengths of the materials in existing Italian bridges having pre- and post-tensioned concrete beams. Data were collected in anonymous form by analyzing a stock of about 170 bridges built between 1960 and 2000 and located in several Italian regions. To date, the database refers to steel reinforcing bars, concrete, and prestressing steel, whose strengths were gathered from design nominal values, acceptance certificates, and in situ test results, all derived by consulting the available documents for each examined bridge. At first, this paper describes how the available data were collected. Then, the results of a statistical analysis are presented and commented on. Moreover, goodness-of-fit tests are carried out to verify the assumption validity of a normal distribution for steel reinforcing bars and prestressing steel, and a log-normal distribution for concrete. The database represents a valuable resource for researchers and practitioners for the assessment of existing bridges. It may be applied for the use of prior knowledge within a framework where Bayesian methods are included for reducing uncertainties. The database provides essential information on the strengths of the materials to be used for a simulated design and/or for verification in the case of limited knowledge. Goodness-of-fit tests make the collected information very useful, even if probabilistic methods are applied. Full article

We introduce I-fp convergence (ideal convergence in fuzzy paranormed spaces) and develop its core theory, including stability results and an equivalence to $I^{*}$-fp convergence under the AP Property. Building on this foundation, we design an adaptive base-stock policy for a single-echelon inventory system in which weekly demand is expressed as triangular fuzzy numbers while holiday or promotion weeks are treated as ideal-small anomalies. The policy is updated by a simple learning rule that can be implemented in any spreadsheet, requires no optimisation software, and remains insensitive to tuning choices. Extensive simulation confirms that the method simultaneously lowers cost, reduces average inventory and raises service level relative to a crisp benchmark, all while filtering sparse demand spikes in a principled way. These findings position I-fp convergence as a lightweight yet rigorous tool for blending linguistic uncertainty with anomaly-aware decision making in supply-chain analytics. Full article

Recent years have seen a rise in combining deep learning and technical analysis for stock price prediction. However, technical indicators are often prioritized over technical charts due to quantification challenges. While some studies use closing price charts for predicting stock trends, they overlook charts from other indicators and their relationships, resulting in underutilized information for predicting stock. Therefore, we design a novel framework to address the underutilized information limitations within technical charts generated by different indicators. Specifically, different sequences of stock indicators are used to generate various technical charts, and an adaptive relationship graph learning layer is employed to learn the relationships among technical charts generated by different indicators. Finally, by applying a GNN model combined with the relationship graphs of diverse technical charts, temporal patterns of stock indicator sequences are captured, fully utilizing the information between various technical charts to achieve accurate stock price predictions. Additionally, we further tested our framework with real-world stock data, showing superior performance over advanced baselines in predicting stock prices, achieving the highest net value in trading simulations. Our research results not only complement the existing applications of non-singular technical charts in deep learning but also offer backing for investment applications in financial market decision-making. Full article

Renovating existing buildings is a key strategy for achieving the EU’s climate targets, as over 75% of the current building stock is energy inefficient. This study evaluates the environmental impacts of three façade renovation scenarios for an office building at the Technical University in Zvolen (Slovakia) using a life cycle assessment (LCA) approach. The aim is to quantify and compare these impacts based on material selection and its influence on sustainable construction. The analysis focuses on key environmental indicators, including global warming potential (GWP), abiotic depletion (ADE, ADF), ozone depletion (ODP), toxicity, acidification (AP), eutrophication potential (EP), and primary energy use (PERT, PENRT). The scenarios vary in the use of insulation materials (glass wool, wood fibre, mineral wool), façade finishes (cladding vs. render), and window types (aluminium vs. wood–aluminium). Uncertainty analysis identified GWP, AP, and ODP as robust decision-making categories, while toxicity-related results showed lower reliability. To support integrated and transparent comparison, a composite environmental index (CEI) was developed, aggregating characterisation, normalisation, and mass-based results into a single score. Scenario C–2, featuring an ETICS system with mineral wool insulation and wood–aluminium windows, achieved the lowest environmental impact across all categories. In contrast, scenarios with traditional cladding and aluminium windows showed significantly higher impacts, particularly in fossil fuel use and ecotoxicity. The findings underscore the decisive role of material selection in sustainable renovation and the need for a multi-criteria, context-sensitive approach aligned with architectural, functional, and regional priorities. Full article

The architectural, engineering, construction, and operation (AECO) sector is one of the main contributors to energy consumption and greenhouse gas emissions in Europe, making the renovation of the existing building stock a priority. However, defining effective and economically sustainable interventions remains a challenge, partly due to the variability of building characteristics and the lack of digital tools to support data-driven decision making. This research aims to identify the main factors influencing the energy consumption of buildings by analyzing a large database of building characteristics using machine learning algorithms. Based on the parameters that the analysis shows to have the greatest impact, the average cost of energy retrofitting measures will be used to elaborate a cost–benefit analysis model and the economic payback time for each measure, individually or in combination. The expected result is the creation of a tool that will allow the operator to evaluate the choice of interventions based on the energy efficiency that can be achieved and/or the economic sustainability. The proposed methodology aims to provide a digital approach that is replicable and adaptable to different territorial realities and useful for strategic planning of energy transformation in the building sector. Full article

Background: Greater tuberosity fracture–dislocations (GTFDs) represent a distinct subset of proximal humerus fractures, occurring in up to 57% of anterior glenohumeral dislocations. Malreduction may result in impingement, instability, and functional limitation. Treatment is influenced by the displacement magnitude and direction, bone quality, and patient activity level. Methods: This narrative review was based on a comprehensive search of PubMed, Scopus, and Web of Science for English-language articles published between January 2000 and March 2025. Studies on pathomechanics, classification, diagnosis, treatment, and outcomes of GTFDs in adult and pediatric populations were included. Data were analyzed to summarize the current evidence and identify clinical trends. Results: A displacement ≥ 5 mm is the standard surgical threshold, though superior or posterosuperior displacement ≥ 3 mm—and ≥2 mm in overhead athletes—may justify surgery. Conservative treatment remains appropriate for minimally displaced fractures but is associated with up to 48% subacromial impingement and 11% delayed surgery. Surgical options include arthroscopic repair for small or comminuted fragments and open reduction and internal fixation (ORIF) with screws or plates for larger, split-type fractures. Locking plates and double-row suture constructs demonstrate superior biomechanical performance compared with transosseous sutures. Reverse shoulder arthroplasty (RSA) is reserved for elderly patients with poor bone stock, cuff insufficiency, or severe comminution. Pediatric cases require physeal-sparing strategies. Conclusions: GTFDs management demands an individualized approach based on fragment displacement and direction, patient age and activity level, and bone quality. While 5 mm remains the common threshold, lower cutoffs are increasingly adopted in active patients. A tiered treatment algorithm integrating displacement thresholds, fracture morphology, and patient factors is proposed to support surgical decision making. The incorporation of fracture morphologic classifications further refines fixation strategy. Further prospective and pediatric-specific studies are needed to refine treatment algorithms and validate outcomes. Full article

Emerging digital technologies are transforming how consumers participate in financial markets, yet their benefits depend critically on the speed, reliability, and transparency of the underlying platforms. Online stock trading platforms must maintain high efficiency underload to ensure a good user experience. This paper presents performance analysis under various load conditions based on the containerized stock exchange system. A comprehensive data logging pipeline was implemented, capturing metrics such as API response times, database query times, and resource utilization. We analyze the collected data to identify performance patterns, using both statistical analysis and machine learning techniques. Preliminary analysis reveals correlations between application processing time and database load, as well as the impact of user behavior on system performance. Association rule mining is applied to uncover relationships among performance metrics, and multiple classification algorithms are evaluated for their ability to predict user activity class patterns from system metrics. The insights from this work can guide optimizations in similar distributed web applications to improve scalability and reliability under a heavy load. By framing performance not merely as a technical property but as a determinant of financial decision-making and well-being, the study contributes actionable insights for designers of consumer-facing fintech services seeking to meet sustainable development goals through trustworthy, resilient digital infrastructure. Full article

Material stocks (MS) serve as essential physical foundations for socio–economic systems, reflecting the accumulation, transformation, and consumption of resources over time and space. Positioned at the intersection of environmental and socio–economic systems, MS are increasingly recognized as leverage points for advancing sustainability. However, there is currently a lack of comprehensive overview, making it difficult to fully capture the latest developments and cutting–edge research. We adopt a systems perspective to conduct a comprehensive bibliometric and thematic review of 602 scholarly publications on MS research. The results showed that MS research encompasses has three development periods: preliminary exploration (before 2007), rapid development (2007–2016), and expansion and deepening (after 2016). MS research continues to deepen, gathering multiple teams and differentiating into diverse topics. MS research has evolved from simple accounting to intersection with socio–economic, resources, and environmental systems, and shifted from relying on statistical data to integrating high–spatio–temporal–resolution geographic big data. MS research is shifting from problem revelation to problem solving, constantly achieving new developments and improvements. In the future, it is still necessary to refine MS spatio–temporal distribution, reveal MS’s evolution mechanism, establish standardized databases, strengthen interaction with other systems, enhance problem–solving abilities, and provide powerful guidance for the formulation of dematerialization and decarbonization policies to achieve sustainable development. Full article

In response to the challenges of urbanization, digitalization, and the e-commerce surge intensified by the COVID-19 pandemic, Smart Urban Logistics (SUL) has become a key framework for addressing last-mile delivery issues, congestion, and environmental impacts. This study introduces a System Dynamics (SD)-based approach to investigate how urban logistics and access management policies may interact. At the center, there is a Causal Loop Diagram (CLD) that illustrates dynamic interdependencies among fleet composition, access regulations, logistics productivity, and environmental externalities. The CLD is a conceptual basis for future stock-and-flow simulations to support data-driven decision-making. The approach highlights the importance of route optimization, dynamic access control, and smart parking management systems as strategic tools, increasingly enabled by Industry 4.0 technologies, such as IoT, big data analytics, AI, and cyber-physical systems, which support real-time monitoring and adaptive planning. In alignment with the Industry 5.0 paradigm, this technological integration is paired with social and environmental sustainability goals. The study also emphasizes public–private collaboration in designing access policies and promoting alternative fuel vehicle adoption, supported by specific incentives. These coordinated efforts contribute to achieving the objectives of the 2030 Agenda, fostering a cleaner, more efficient, and inclusive urban logistics ecosystem. Full article

COVID-19 has caused global problems with complex ramifications. Vulnerable households worry about disruptions to food security. Mobility restrictions, decreased salaries, and supply chain disruptions have increased food insecurity. This study examined the best food security model and its determinants. The primary research data were collected from 257 respondents via cluster random sampling. Binary logistic regression, using R-Studio, was employed to analyze the data. The study showed that the Minimal Model (MM) was optimal in explaining food security status, with three predictors: the available food stock (AFS), education of the household head (EHH), and household income (HIc). This aligned with studies showing that food purchase ability depends on income and education. Male household heads demonstrated better food security than females, while women’s education influenced consumption through improved nutritional knowledge. Higher income provides more alternatives for meeting needs, while decreased income limits options. Food reserve storage influenced household food security during the pandemic. The Minimal Model effectively influenced food security through the AFS, EHH, and HIc. The findings underline the importance of available food stock, household head education, and household income in developing approaches to assist food-insecure households. The research makes a significant contribution to ensuring food availability and promoting sustainable development post-pandemic. Full article

This study explores the presence of hidden market regimes in a sector-specific stock index within the Thai equity market. The behavior of such indices often deviates from broader macroeconomic trends, making it difficult for conventional models to detect regime changes. To overcome this limitation, the study employs a multivariate Gaussian mixture hidden Markov model, which enables the identification of unobservable states based on daily and intraday return patterns. These patterns include open-to-close, open-to-high, and low-to-open returns. The model is estimated using various specifications, and the best-performing structure is chosen based on the Akaike Information Criterion and the Bayesian Information Criterion. The final model reveals three statistically distinct regimes that correspond to bullish, sideways, and bearish conditions. Statistical tests, particularly the Kruskal–Wallis method, confirm that return distributions, trading volume, and open interest differ significantly across these regimes. Additionally, the analysis incorporates risk measures, including expected shortfall, maximum drawdown, and the coefficient of variation. The results indicate that the bearish regime carries the highest risk, whereas the bullish regime is relatively stable. These findings offer practical insights for regime-aware portfolio management in sectoral equity markets. Full article

Italy supplies about one-seventh of the European Union’s total glass production, and the sector’s sizeable resource demands make it a linchpin of national industrial strategy. With growing environmental regulations and the push for resource efficiency, Material Flow Accounting has become essential for companies to stay compliant and advance sustainability. The investigation concentrates on Italy’s glass industry to clarify its material requirements, ecological footprint, and overall sustainability performance. STAN software v2, combined with an Economy-Wide Material Flow Accounting (EW-MFA) framework, models the national economy as a single integrated input–output system. By tracking each material stream from initial extraction to end-of-life, the analysis delivers a cradle-to-grave picture of the sector’s environmental impacts. During the 2021 production year, Italy’s glass makers drew on a total of 10.5 million tonnes (Mt) of material inputs, supplied 76% (7.9 Mt) from domestic quarries, and 24% (2.6 Mt) via imports. Outbound trade in finished glass removed 1.0 Mt, leaving 9.5 Mt recorded as Domestic Material Consumption (DMC). Within that balance, 6.6 Mt (63%) was locked into long-lived stock, whereas 2.9 Mt (28%) left the system as waste streams and airborne releases, including roughly 2.1 Mt of CO₂. At present, the post-consumer cult substitutes only one-third of the furnace batch, signalling considerable scope for improved circularity. When benchmarked against EU-27 aggregates for 2021, Italy registers a NAS/DMI ratio of 0.63 (EU median 0.55) and a DPO/DMI ratio of 0.28 (EU 0.31), indicating a higher share of material retained in stock and slightly lower waste generated per ton of input. A detailed analysis of glass production identifies critical stages, environmental challenges, and areas for improvement. Quantitative data on material use, waste generation, and recycling rates reveal the industry’s environmental footprint. The findings emphasise Economy-Wide Material Flow Accounting’s value in evaluating and improving sustainability efforts, offering insights for policymakers and industry leaders to drive resource efficiency and sustainable resource management. Results help scholars and policymakers in the analysis of the Italian glass industry context, supporting in the data gathering, while also in the use of this methodology for other sectors. Full article

The stock price of PT Indika Energy Tbk (INDY) reflects the dynamics of Indonesia’s energy sector, which is heavily influenced by global coal price fluctuations, national energy policies, and geopolitical conditions. This study aimed to develop an accurate forecasting model to predict the movement of INDY stock prices using a hybrid machine learning approach called CNN-BiGRU-AM. The objective was to generate future forecasts of INDY stock prices based on historical data from 28 August 2019 to 24 February 2025. The method applied a hybrid model combining a Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and an Attention Mechanism (AM) to address the nonlinear, volatile, and noisy characteristics of stock data. The results showed that the CNN-BiGRU-AM model achieved high accuracy with a Mean Absolute Percentage Error (MAPE) below 3%, indicating its effectiveness in capturing long-term patterns. The CNN helped extract local features and reduce noise, the BiGRU captured bidirectional temporal dependencies, and the Attention Mechanism allocated weights to the most relevant historical information. The model remained robust even when stock prices were sensitive to external factors such as global commodity trends and geopolitical events. This study contributes to providing more accurate forecasting solutions for companies, investors, and stakeholders in making strategic decisions. It also enriches the academic literature on the application of deep learning techniques in financial data analysis and stock market forecasting within a complex and dynamic environment. Full article

The urgent need for affordable and scalable building retrofit solutions has intensified due to stringent clean energy targets. Traditional building energy audits, which are essential in assessing energy performance, are often time-consuming and costly because of the extensive field analysis required. There has been a gradual shift towards the public use of drones, which present opportunities for effective remote procedures that could disrupt a variety of built environment disciplines. Drone-based approaches to data collection offer a great opportunity for the analysis and inspection of existing building stocks, enabling architects, engineers, energy auditors, and owners to document building performance, visualize heat transfer using infrared thermography, and create digital models using 3D photogrammetry. This study provides a review of the potential of a drone-based approach to integrated building envelope assessment, aiming to streamline the process. By evaluating various scanning techniques and their integration with drones, this research explores how drones can enhance data collection for defect identification, as well as digital model creation. A proposed drone-based workflow is tested through a case study in Syracuse, New York, demonstrating its feasibility and effectiveness in creating 3D models and conducting energy simulations. The study also discusses various challenges associated with drone-based approaches, including data accuracy, environmental conditions, operator training, and regulatory compliance, offering practical solutions and highlighting areas for further research. A discussion of the findings underscores the potential of drone technology to revolutionize building inspections, making them more efficient, accurate, and scalable, thus supporting the development of sustainable and energy-efficient buildings. Full article

Decarbonizing industrial manufacturing is a significant challenge in the effort to limit the impacts of global climate change. Additive manufacturing (AM) is one pathway for reducing the impacts of manufacturing as it creates parts layer-by-layer rather than by removing (i.e., subtracting) material from solid stock as with conventional techniques. This reduces material inputs and generates less waste, which can substantially lower life cycle energy consumption and greenhouse gas emissions. However, AM adoption in the manufacturing sector has been slow, partly due to challenges in making a strong business case compared with more traditional and widely available techniques. This paper highlights the need for the development of simple screening analysis tools to speed the adoption of AM in the manufacturing sector by providing decision-makers easy access to important production life cycle emissions, and cost information. Details on the development of two Microsoft Excel software tools are provided: upgrades to an existing tool on the energy and carbon impacts of AM and a new tool for analyzing the major cost components of AM. A case study applies these two tools to the production of a lightweight aerospace bracket, showing how the tools can be used to estimate the environmental benefits and production costs of AM. Full article