Dynamic Methodology Research Articles

Postupak određivanja energetske karakteristike zgrade prema ISO 52016-1

With the idea of harmonizing with current EU regulation regarding methodology used for energy certification of buildings, and with aim of achieving energy savings in the building sector, the dynamic hourly calculation methodology for determining energy needs of the building for heating and cooling was presented and analyzed. The presented method is based on the new, currently valid standard SRPS EN ISO 52016-1. Unlike the previous so-called three-node method, determined by the now extracted standard SRPS EN ISO 13790, this method does not group layers of the construction envelope, but treats separately each layer of each element of the construction envelope. Accordingly, the new calculation method is based on forming energy balance equations not only for the internal and external boundary surface, but also for each layer of the building element, and their coupled solution for boundary conditions that change on an hourly basis. Due to complexity of this methodology, which in addition to the hourly temperatures of the external air, hourly values of solar irradiance, radiation to the sky, but also the own radiation of all solid surfaces, also takes into account dynamic behavior of all elements of the building's thermal envelope (resistance to heat conduction and heat accumulation of each layer of the envelope), a special software was developed, verification of which, as well as verification of methodology, was performed on the example of determining energy needs of a model object for heating and cooling.

Carbon emission scenarios of China's construction industry using a system dynamics methodology – Based on life cycle thinking

China is currently one of the world's major energy consumers and CO2 emitters. To save energy, and reduce consumption and carbon emissions, China proposed (at the 2020 United Nations General Assembly and Climate Summit) the introduction of stronger policies and measures for CO2 emissions to peak in 2030 and to achieve carbon neutrality by 2060. The construction industry is a major contributor to China's carbon emissions, thus research on energy saving and carbon reduction in this industry is essential. The construction industry is characterized by complex upstream and downstream industrial chains, with different energy consumption at each stage, and a long overall life cycle. We used life-cycle thinking (LCT) to analyze the carbon emissions of the whole life cycle of the construction industry and built a model by using a system dynamics method, which analyzes the carbon emission process of the construction industry at different stages. Combined with the planning and policies implemented by the construction-related departments, we identified the main indicators of policy regulation and control. We used sensitivity analysis to examine the impacts of factors of regulation and control. We also adjusted key indicators and set up different scenarios to simulate the carbon emissions based on the effort to achieve “peak carbon.” The results show that the carbon emissions of the construction industry will be reduced by 2060, achieving the goals of “peak carbon” and “carbon neutrality.” Although the construction and operation stages individually can achieve peak carbon by 2030, the whole process of the construction industry will reach peak carbon by 2045, 2038, or 2036, depending on specific aspects of the scenario considered. However, the indicators, such as the green building ratio can realize carbon emission reduction at building operation stage for 4%∼6%, but cause greater carbon emission for 5%∼7% at construction material production and transportation stage—a phenomenon called “policy island.” Therefore, the coupling of policies should be a key concern in policy formulation and implementation.

Structurally realistic carbide-derived carbon model in annealing molecular dynamics methodology with analytic bond-order potential

A realistic carbide-derived carbon (CDC) model is produced with ABOP at a low computational cost. The simulated CDC agrees well with experiments over a broad temperature range, facilitating the production of atomistic models of experimental samples.

An advanced product inspection and sorting system using artificial intelligence

The manufacturing sector is experiencing a notable transformation due to the incorporation of Industry 4.0 and the emerging concepts of Industry 5.0. Artificial intelligence (AI) plays a significant role in driving this transformation, particularly in the domain of product inspection and sorting systems. Incorporating digital computer vision, high accuracy and resolution sensors, and bigdata-driven simulations into manufacturing processes, the vision of smart manufacturing becomes tangible. These technologies offer practical solutions for automating product inspection and sorting processes, providing non-destructive and cost-effective alternatives. This ongoing research aims to develop a real-time product inspection and sorting system utilising artificial intelligence, specifically focusing on convolutional neural networks (CNNs) and machine learning algorithms. The proposed approach adopts a dynamic methodology, leveraging the synergistic capabilities of CNNs and machine learning algorithms. To extract features from images, CNN is trained on datasets containing both none-defective and defective product samples. These features are then further refined and classified by machine learning algorithms. Through rigorous training on diverse datasets, the system developed a robust ability to distinguish between none-detective and defective products and achieving an accuracy close to 98.10%

"سيّد قطب ومنهجه في التعامل مع الآيات المتعلقة بالسيرة النبوية: دراسة تحليلية في تفسيره "في ظلال القرآن

Knowing the method of a particular scholar in any book is important to understand his approach to dealing with any issue and to make his way to becoming the subject of attention and emulation by later researchers. This research discovers the methodology of Sayyid Quṭb, the dynamic preacher and leading exegete, regarding his dealing with the verses related to prophetic history through Fī Ẓilāl al-Qur’ān. This research deals with three aspects: interpreting the verses related to prophetic history, presenting the events, and extracting the highlights and lessons from the events. The researchers employed the descriptive method to explain the definition of Prophetic history and Sayyid Quṭb’s biography and the analytical method to extract Sayyid Quṭb’s methodology in his exegesis in dealing with verses related to Prophetic history. The study primarily showed that Sayyid Quṭb combined exegesis based on traditions (al-tafsīr bi al-ma’thūr) and opinions (al-tafsīr bi al-ra’y) in explaining the verses related to prophetic history. Sayyid Quṭb has two ways of presenting the events: detailed and overall views. Sayyid Quṭb’s presentation has three objectives: referring to the events as the reasons for the revelation of the verses, inferring events in the interpretation of the verses, and depicting the atmosphere in which the verses were revealed and reviving them. The most important Sayyid Quṭb’s trends in extracting highlights and lessons are employing Prophetic history for his dynamic methodology, clarifying the lessons in the Islamic faith, addressing facts and perceptions about Islam, and contextualizing Prophetic history’s highlights and lessons into current situations.

Artificial Intelligence for Hybrid Modeling in Fluid Catalytic Cracking (FCC)

This study reports a novel hybrid model for the prediction of six critical process variables of importance in an industrial-scale FCC (fluid catalytic cracking) riser reactor: vacuum gas oil (VGO) conversion, outlet riser temperature, light cycle oil (LCO), gasoline, light gases, and coke yields. The proposed model is developed via the integration of a computational particle-fluid dynamics (CPFD) methodology with artificial intelligence (AI). The adopted methodology solves the first principle model (FPM) equations numerically using the CPFD Barracuda Virtual Reactor 22.0® software. Based on 216 of these CPFD simulations, the performance of an industrial-scale FCC riser reactor unit was assessed using VGO catalytic cracking kinetics developed at CREC-UWO. The dataset obtained with CPFD is employed for the training and testing of a machine learning (ML) algorithm. This algorithm is based on a multiple output feedforward neural network (FNN) selected to allow one to establish correlations between the riser reactor feeding conditions and its outcoming parameters, with a 0.83 averaged regression coefficient and an overall RMSE of 1.93 being obtained. This research underscores the value of integrating CPFD simulations with ML to optimize industrial processes and enhance their predictive accuracy, offering significant advancements in FCC riser reactor unit operations.

The nexus between cash conversion cycle and operating performance: novel evidence from MENA region

PurposeThis article examines the relationship between the cash conversion cycle (CCC) and profitability in countries in the Middle East and North Africa (MENA) region.Design/methodology/approachThe authors used dynamic panel methodology to analyze a dataset consisting of 395 firms from nonfinancial sectors in the MENA countries from 2013 to 2018. The authors developed several models consisting of different measures of profitability, CCC and the components of CCC. The control variables were used in the models at different levels.FindingsThe results bring out a significantly positive relationship between the CCC and profitability. However, mixed results have been obtained for industry and country details. The quadratic model revealed an inverted U-shaped relationship and the presence of an optimal point of CCC. The robustness checks have confirmed the results of the main models.Practical implicationsThe results of the study imply that corporate managers and policymakers ought to pay equal attention to the components of CCC when developing working capital policies and be aware of the optimal level of CCC.Originality/valueThis paper handles the endogeneity problem that is inherent in the relationship. It investigates and confirms the nonlinear characteristics of the relationship. To the best of the authors' knowledge, this study is the first to use dynamic panel models to examine the CCC and its relationship with profitability in the MENA context.

Analisis dan Prediksi Pemanfaatan DAS Sebangau Secara Berkelanjutan di Desa Garung Provinsi Kalimantan Tengah

People who have lived in watersheds for a long time rely on fish and wood for their livelihoods, but these resources are still getting worse because of people. On the one hand, growth keeps going, and the population keeps growing yearly without anyone stopping it. This means that market demand keeps increasing, which makes people using the watershed do too much fishing and logging.
 Along the Sebangau watershed, social and environmental problems are now, such as the loss of peat swamp forest land cover and fewer fish. There are also social and cultural problems, such as the incomes of the people who really do sustainable fishing are still decreasing. To solve these complicated and interconnected problems, we need methods from different fields that work together.
 The system dynamics methodology is one way to figure out policy scenarios for how watersheds will be used in the future in a complicated, dynamic way that involves many parts and pieces. System dynamics can make the behaviour of the environmental management system easier to understand and model. This helps area managers and the government devise other ways to make decisions about the future.
 Palangka Raya University's academics did this study to give TNS managers and local governments information about climate change and the global environment and to predict symptoms or events that are bad for the people, nation, and state of RI. This study is about the University of Palangka Raya's Principal Scientific Pattern (PIP), the Development of Science and Technology in Tropical Peat Swamp Areas and River Streams, also known as Science and Technology Innovation.

A universal hydraulic-mechanical diagnostic framework based on feature extraction of abnormal on-field measurements: Application in micro pumped storage system

How to extract the running feature information and realize multi-type faults diagnosis is the key to carry out intelligent operation and maintenance of energy conversion machinery. The pumped storage unit (PSU) has various operating conditions, both energy storage and power generation. It may lead to diversified types of failures under the joint influence of hydraulic and mechanical factors. The existing data-driven models often show excellent diagnostic performance with laboratory-specific fault types or single subsystem but are not satisfactory when applying real operating scenarios or migrating to other devices. Therefore, a universal hydraulic-mechanical diagnostic framework integrating signal acquisition, feature extraction and fault recognition is proposed in this paper. In Stage 1, eight types of datasets caused by the hydraulic-mechanical coupling characteristics via abnormal on-field measurements form an on-site benchmark; for Stages 2 and 3, a novel refined composite multiscale cosine similarity Lempel-Ziv complexity method is proposed to quantify the various fault features based on multiscale signal processing and the nonlinear dynamics methodology, and random forests model is introduced to realize the efficient recognition of different status signals. Its core advantage is versatility, which is not limited to specific components but can be applied to different subsystems of pumped storage, such as hydraulic system (identification of vortex conditions, detection of hydraulic imbalances) and mechanical system (wear of shaft, bearing and runner). This framework is applied in the micro PSUs, the comprehensive experiments show that all evaluation indexes are above 92%. Various comparative analysis indicates that the framework is not only applicable to the detection and analysis of hydraulic anomalies but also has a competitive advantage in the diagnosis of hydraulic-mechanical faults. It is suitable for fault detection of different subsystems in real power stations, and also could be flexibly extended to other rotating energy storage systems, as a helpful tool to improve energy conversion efficiency and reduce maintenance cost.

A novel approach for failure and consequence assessment considering multi-hazard coupling scenario in chemical industrial parks

As a typical high-risk area, nature hazards acting on chemical industrial parks (CIPs) can result in catastrophic multi-hazard coupling scenarios (MHCSs). Existing research mainly adopts a linear superposition method based on simplified assumptions to analyze the coupling amplified effects of multi-hazard. Therefore, the complicated nonlinearity of consequence superposition has not been intensively studied. In this study, a new dynamic graph-based methodology is established to investigate the nonlinear superposition mechanism, by addressing the failure consequence assessment of multi-hazard. A typical MHCS of the earthquake-fire sequence is adopted to demonstrate the proposed methodology, and the failure prediction model of earthquake-damaged tanks subjected to the earthquake-fire sequence is numerically developed to calculate the failure consequence more accurately. The results indicate that the seismic damage significantly reduces the fire resistance of steel cylinder tanks. Compared to intact tanks, the time to failure of seismic damaged tanks is reduced by up to 8.48 min at most, exacerbating the escalation of domino effects. The consequence modeling without the consideration of fire resistance attenuation may be severely underestimated by a factor of 1.35 to 2.13. This method can be used to predict the dynamic consequence of the typical MHCSs and mitigate such accidents via more flexible adjustment of emergency decision-making at each evolution stage.

Project-based Language Learning and Its Implications for English Teaching in Chinese Universities

Project-Based Language Learning (PBLL) is a novel teaching method that focuses on the student and seeks to transform the model of second and foreign-language education. This technique can dramatically transform students learning experiences. Students are at the center of their own learning experience in PBLL. In comparison to traditional teacher-led instruction, PBLL emphasizes students active engagement and in-depth comprehension. Students will study language in PBLL by taking part in a range of projects that are typically related to their interests, professional domains, or social activities. This article dives into the unique characteristics, benefits, and limitations of PBLL, providing a complete assessment of its applicability in Chinese college English classes. By examining its essential components, useful insights into how PBLL might be used to maximize language acquisition in higher education contexts are gained. Furthermore, this analysis illuminates the current stage of PBLL implementation and forecasts its future trajectory in the field of Chinese college English instruction. This article seeks to serve as a beneficial resource for educators looking to innovate and improve their approaches to college-level English language instruction by chronicling the course of this dynamic teaching methodology.

Methodology for the Identification of Vehicle Congestion Based on Dynamic Clustering

Addressing sustainable mobility in urban areas has become a priority in today’s society, given the growing population and increasing vehicular flow in these areas. Intelligent Transportation Systems have emerged as innovative and effective technological solutions for addressing these challenges. Research in this area has become crucial, as it contributes not only to improving mobility in urban areas but also to positively impacting the quality of life of their inhabitants. To address this, a dynamic clustering methodology for vehicular trajectory data is proposed which can provide an accurate representation of the traffic state. Data were collected for the city of San Francisco, a dynamic clustering algorithm was applied and then an indicator was applied to identify areas with traffic congestion. Several experiments were also conducted with different parameterizations of the forgetting factor of the clustering algorithm. We observed that there is an inverse relationship between forgetting and accuracy, and the tolerance allows for a flexible margin of error that allows for better results in precision. The results showed in terms of precision that the dynamic clustering methodology achieved high match rates compared to the congestion indicator applied to static cells.

Do Multinational Enterprises Matter for Economic Development?

The aim of the paper is to determine the effect of Multinational Enterprises on economic development, measured by the Human Development Index, in a group of 58 high and upper-middle income countries where they operated from 2005-2016, while considering the role of free trade agreements. We follow a robust dynamic panel data econometric methodology. For our analysis, we considered the total sample and sub-samples of countries based on their trade status. The results indicate that Multinational Enterprises drive economic development by favoring the expansion of people’s capabilities. Therefore, measured by added value, the economic activity of this type of company tends to enrich the life of society as well as the fundamental freedoms in the foreign country in which they operate. The effects of Multinational Enterprises on economic development are higher in countries with a large number of Free Trade Agreements, which means that intensification of free trade favors the MNE’s role in social goals.

Numerical and experimental study on the dynamic equivalent methodology of a membrane antenna structure and a grid membrane structure

The dynamic equivalent methodology utilizing a grid membrane substitute for a planar membrane for vibration test verification on the ground has been demonstrated in small sizes with simple constraints. The method lacks accuracy and validity for a real complex membrane antenna. This paper thus focuses on the applicability of the equivalence approach to a membrane antenna and the factors affecting its dynamic equivalence to verify whether the grid membrane structure can replace the membrane antenna structure for modal identification on the ground. To this end, an experimental system is developed to conduct vibration tests on the grid membrane structure in air, using the grid membrane model with unidirectional tension as a substitute for the membrane array. The connecting strips attach the grid membrane to thin-walled lenticular booms (TWLBs) to allow for unidirectional tension. A laser displacement sensor array is employed to obtain the vibration data. A combination of the Fast Fourier Transfer (FFT) and Stochastic Subspace Identification (SSI) methods is applied to identify the structural frequencies and mode shapes. Vibration tests on the grid membranes with different added masses and tensile forces are conducted. Meanwhile, the numerical results for the rectangular membrane are chosen as a reference for comparison with the experimental results for the grid membrane. In conclusion, the numerical results for the membrane antenna structure and the experimental results for the grid membrane structure show good agreement in terms of frequency and mode shape. Additionally, both the added masses in the intersection areas of the grid membrane and the tensile force have a significant effect on the dynamic equivalence.

Additive manufacture of ultrasoft bioinspired metamaterials

The dynamic loading behavior of materials plays a vital role in various engineering applications, such as aerospace protective components, armor, marine infrastructures, and automotive crash safety. The advent of additive manufacturing technologies has enabled the design of metamaterials that exhibit exceptional mechanical performance and artificially engineered properties not found in nature. However, fabricating ideal materials that resist dynamic loading is challenging because of the complexity of dynamic mechanical processes and varying requirements across different applications. In this study, a novel hierarchical design is proposed that combines natural fiber-inspired frameworks with graphene-inspired parent structures. This design aims to produce metamaterials, with characteristics such as reduced dynamic compressive strength, high energy absorption, and programmable dynamic loading, via advanced manufacturing technologies. An additive-manufacturing-oriented digital design approach and machine learning techniques were employed to engineer the dynamic loading performance of graphene-inspired metamaterials using the bonding principles inspired by natural fibers, to facilitate the design of next-generation metamaterial for advanced manufacturing. Experimental results illustrate the significant improvements achieved with our metamaterials compared to their existing counterparts. These improvements include a decrease in dynamic compressive strength of up to 86 %, while maintaining a remarkable 682 % enhancement in energy absorption during dynamic compressions, with a 42 % reduction in the energy decay rate. A compositional design strategy and programmable dynamic compression curve methodology is proposed that enable the tailored optimization of dynamic loading behaviors without modifying the base topology of metamaterials. This research offers a promising pathway for the development of next-generation materials, engineered to withstand dynamic loadings with intelligent and programmable performances suitable for aerospace, defense, and other high-value applications. By leveraging the advantages of natural fiber-inspired structures and graphene-inspired metamaterials, this work contributes to the advancement of materials with tailored resistance to dynamic loading and opens new possibilities for intelligent dynamic loading performance.

Numerical Studies on Effects of Blade Number Variations on Performance of Sludge Pumps

The sedimentation of sludge in pangasius catfish ponds constitutes a notable challenge for the economic advancement of Vietnamese farmers. Sludge formation is the result of a composite process involving residual food, waste material, and algae, which undergo decomposition, thereby releasing hazardous gases such as hydrogen sulfide (H2S), ammonia (NH3), and nitrogen dioxide (NO2). These emissions subsequently diminish oxygen levels within the ponds, adversely affecting the growth of catfish. Removal of the accumulated sludge is deemed necessary every two months throughout the catfish cultivation cycle. A proficient sludge pump, designed to function effectively within the Vietnamese environmental context, becomes imperative to address this predicament. The primary objective of this research paper is to explore the influence of blade count on the performance metrics of a sludge pump. Given that pangasius ponds typically exhibit depths ranging from 2 to 4 meters, this study is predominantly centered on assessing pump performance within the specified head range of 2 to 4 meters. Employing the computational fluid dynamics (CFD) methodology, the research delves into the fluid dynamics of sludge pumps featuring impeller blades numbering 7, 8, 9, and 10. These pumps are modeled as semi-open impeller centrifugal pumps operating at a speed of 2200 revolutions per minute (rpm), with the k-e turbulence model and the time-averaged Navier-Stokes equation serving as the fundamental analytical framework.

Environmental Urban Morphology: A Multidisciplinary Methodology for the Analysis of Public Spaces in Dense Urban Fabrics

A city is an organism made of social, economic, cultural, and environmental fabrics, the interactions of which determine the form and functioning of city life. Different disciplines are then involved in analyzing the complex processes of the 21st-century city. The aim of this study was to explore the use of an analytical method that can act as a catalyst for the main players involved in the environmental urban morphology (EUM). This multidisciplinary methodology focuses on the study of public space in dense urban fabrics as a key context for understanding a city. Operationally, the work shows the potential of integrating morphological analysis, pedestrian flow analysis, and environmental analysis and applying them in dense and compact urban fabrics. The first of these analyses methods was carried out using urban survey tools and the geographic information system (GIS) in order to detect the physical forms of the city and develop a number of morphological maps. The second, using the global positioning system (GPS) and on-site detectors, maps pedestrian movement within public spaces. The latter mainly focuses on the microclimatic analysis of public spaces and outdoor comfort, carried out using environmental software such as ENVI-met (4.4 version). The ultimate goal of this study was to achieve the definition of a dynamic, multidisciplinary, and multilayer methodology for the analysis of dense urban fabrics which we believe could be very useful for addressing the regenerative processes of the contemporary city.

Energy Efficiency Improvement Using Simplified Dynamic Direct Voltage Maximum Torque Per Ampere Control for Interior PMSMs

An energy efficiency strategy is developed in this article using a simplified dynamic direct voltage MTPA speed control method for interior permanent magnet synchronous motors (IPMSMs). Finding an exclusive pair of the motor voltage angle and amplitude attains the MTPA trajectory tracking for each reference speed and/or torque condition, maintaining minimum current/power consumption and higher energy efficiency. This improvement is realized by taking into account the dynamic model of the motor, which makes the technology more precise in various operating conditions as contrary to existing literature. Further, the simplified dynamic strategy needs tuning of only couple of parameters compared to three times as much with the classical MTPA FOC technique. Moreover, a comparative study of the simplified dynamic direct voltage MTPA control methodology and the FOC strategy is carried out. Experimental outcomes and quantitative assessment prove that the simplified dynamic direct voltage MTPA control strategy is a worthy approach. Also, it is a substitution to the current MTPA methods and delivers higher efficiency for the IPMSMs' drive in modern industrial applications.

Models and numerical methods for XVA pricing under mean reversion spreads in a multicurrency framework

In this article we make some new relevant contributions to the computation of total valuation adjustments (XVA) for financial derivatives involving several currencies. From the modelling point of view, for the credit spreads we consider the more realistic exponential Vasicek and CIR positive mean reversion processes. Moreover, the derivative is partially collateralized in cash in a foreign currency and the collateral value is a percentage of the derivative prices. Under this modelling assumptions and using appropriate dynamic hedging methodologies, we obtain formulations in terms of linear and nonlinear partial differential equations, which are solved with Lagrange-Galerkin methods in low dimension. For higher dimensions, we use the Monte Carlo techniques for the equivalent formulations in terms of expectations. These techniques include a multilevel Picard iteration method for the nonlinear case. Finally, the methodologies are applied to several European options with different payoffs and the numerical results are discussed.

Business Cycle Downturn Likelihood Estimation for Ciudad Juarez

A monthly frequency metropolitan business cycle downturn likelihood equation is estimated for Ciudad Juarez. The binary index of economic conditions is based upon monthly IMMEX export oriented manufacturing employment. A dynamic probit methodology is used for parameter estimation. Continuous explanatory variables include a 1-year minus 1-month Mexico interest rate spread, a 2015 = 100 weighted real exchange rate index, and a 10-year minus 3-month USA interest rate spread. Parameter estimation results confirm the various hypotheses examined. However, model simulation outcomes are less favorable with the results indicating that accurate forecasting of the post-2010 business cycles may require additional refinement to the index.