Linear Approach Research Articles

Relationship between the amplitudes of cerebral blood flow velocity and intracranial pressure using linear and non-linear approach.

Intracranial pressure (ICP), cerebral blood flow and volume are affected by craniospinal elasticity and cerebrospinal fluid dynamics, interacting in complex, nonlinear ways. Traumatic brain injury (TBI) may significantly alter this relationship. This retrospective study investigated the relationship between the vascular and parenchymal intracranial compartments by analysing two amplitudes: cerebral blood flow velocity (AmpCBFV) and ICP (AMP) during hypocapnia manoeuvre in TBI patients. Twenty-nine TBI patients hospitalised at Addenbrooke's Hospital, whose ICP and CBFV were monitored during mild hypocapnia, were included. A linear metric of the relationship was defined as a moving-window correlation (R) between AmpCBFV and AMP, named RAMP. Nonlinear metrics were based on the Joint Symbolical Analysis (JSA) algorithm, which transforms AmpCBFV and AMP into sequences of symbols ('words') using a binary scheme with word lengths of three. The mean AmpCBFV and AMP were not significantly correlated at baseline (r = -0.10) or during hypocapnia (r = -0.19). However, the RAMP index was significantly higher at baseline (0.64 ± 0.04) compared to hypocapnia (0.57 ± 0.04, p = 0.035). The relative frequency of symmetrical word types (JSAsym) describing the AmpCBFV-AMP interaction decreased during hypocapnia (0.35 ± 0.30) compared to baseline (0.44 ± 0.030; p = 0.004). Our results indicate that while the grouped-averaged AmpCBFV and AMP were not significantly correlated, either at baseline or during hypocapnia, significant changes were observed when using RAMP and JSA indices. Further validation of these new parameters, which reflect the association between the vascular and parenchymal intracranial compartments, is needed in a larger cohort.

Differential quantification of alternative splicing events on spliced pangenome graphs.

Pangenomes are becoming a powerful framework to perform many bioinformatics analyses taking into account the genetic variability of a population, thus reducing the bias introduced by a single reference genome. With the wider diffusion of pangenomes, integrating genetic variability with transcriptome diversity is becoming a natural extension that demands specific methods for its exploration. In this work, we extend the notion of spliced pangenomes to that of annotated spliced pangenomes; this allows us to introduce a formal definition of Alternative Splicing (AS) events on a graph structure. To investigate the usage of graph pangenomes for the quantification of AS events across conditions, we developed pantas, the first pangenomic method for the detection and differential analysis of AS events from short RNA-Seq reads. A comparison with state-of-the-art linear reference-based approaches proves that pantas achieves competitive accuracy, making spliced pangenomes effective for conducting AS events quantification and opening future directions for the analysis of population-based transcriptomes.

Key Drivers of Stock Price Movements: Financial and Economic Indicators in Unilever Indonesia (2014–2023)

Multinational corporations are critical in ensuring financial market stability and boosting corporate performance, mainly through job creation and increased productivity. These contributions positively influence investor confidence and significantly impact stock market performance. As one of the leading multinational companies, PT Unilever contributes substantially to Indonesia's economic development. However, in recent years, PT Unilever has faced a notable decline in its stock prices. Such a decline can undermine investor trust, affecting the company’s investment strategies. For this reason, examining factors that influence stock price movements, including financial ratios and macroeconomic variables, becomes essential. This research investigates the influence of financial ratios and macroeconomic indicators on PT Unilever Indonesia Tbk's stock price fluctuations in 2014–2023. The variables analyzed include the Current Ratio (CR), Earnings Per Share (EPS), inflation, interest rates, and Gross Domestic Product (GDP). Using secondary data and a multiple linear regression approach, the study reveals that EPS and inflation positively and significantly impact stock prices. At the same time, interest rates and GDP exhibit a negative and significant effect. On the other hand, the CR variable does not significantly affect stock price movements

Optimizing Multi-Echelon Delivery Routes for Perishable Goods with Time Constraints

As the logistics industry modernizes, living standards improve, and consumption patterns shift, the demand for fresh food continues to grow, making cold chain logistics for perishable goods a critical component in ensuring food quality and safety. However, the presence of both soft and hard time windows among demand nodes can complicate the single-network distribution of perishable goods. In response to these challenges, this paper proposes an optimization model for multi-distribution center perishable goods delivery, considering both one-echelon and two-echelon network joint distributions. The model aims to minimize total costs, including transportation, fixed, refrigeration, goods damage, and penalty costs, while measuring customer satisfaction by the start time of service at each demand node. A two-stage heuristic algorithm is designed to solve the model. In the first stage, an initial solution is constructed using a greedy approach based on the principles of the k-medoids clustering algorithm, which considers both spatial and temporal distances. In the second stage, the initial routing solution is optimized using a linear programming approach from the Ortools solver combined with an Improved Adaptive Large Neighborhood Search (IALNS) algorithm. The effectiveness of the proposed model and algorithm is validated through a case study analysis. The results demonstrate that the initial solutions obtained through the k-medoids clustering algorithm based on spatio-temporal distance improved the overall cost optimization by 1.85% and 4.74% compared to the other two algorithms. Among the three two-stage heuristic algorithms, the Ortools-IALNS proposed here showed enhancements in the overall cost optimization over the IALNS, with improvements of 3.24%, 1.12%, and 0.41%, respectively. The two-stage heuristic algorithm designed in this study also converged faster than the other two heuristic algorithms, with overall optimization improvements of 1.55% and 1.28%, further validating the superior performance of the proposed heuristic algorithm.

Softening index‐based equivalent linear modeling approach for bolt‐connected precast concrete frame

AbstractPrecast concrete structures with bolt‐connected joints have been widely constructed in the world, and their prospective application market is still considerable. Considering the clear load transferring path and damage mechanism, simplified analysis method is feasible for the bolt‐connected precast concrete frame. This paper proposes an equivalent linear modeling approach (ELMA) for this type of structure, which is convenient and high efficiency to help the preliminary design of structures and seismic assessment of existing buildings. In this method, a series of linear models are established to simulate the real structure in different seismic intensity levels. The softening indices summarized from the variation of modal frequencies are defined to represent the stiffness degradation of structures. In order to obtain specific softening indices, a shaking table test for a bolt‐connected precast concrete frame was implemented. Based on the test results, the recommended softening indices, applicable to bolt‐connected frames with similar design performance objectives, are 0.325, 0.713, and 0.816 for the minor, moderate, and major earthquake levels, respectively. Following the ELMA, the numerical model of the test frame was constructed and its equivalent seismic behaviors were analyzed. By comparison with the test results, the convenience and effectiveness of this method is validated, and the precision of simulation meets the requirement of engineering application. The ELMA is also applicable for precast frames with different joints or design requirements, if the softening indices are appropriately defined by shaking table test of the structure or cyclic loading test of its single‐connection model.

Using IT in Area Suitability Modeling To Plan Urban Areas: Case of Kabul City, Afghanistan

The goal of this work was to determine the optimal locations for urban growth in Kabul city using an integrated GIS-AHP model and Land Suitability Analysis (LSA). The study’s findings demonstrated that GIS-AHP is a useful approach for urban planning and ecological management. Maps were normalized using the pairwise comparison matrix technique using the generated criteria. Each model’s weights were created by comparing them according to their relative importance. Weight Age Average (OWA) and Weighted Linear Combination (WLC) were used to combine criteria weights and maps. To assess if a piece of land is suitable for urban development to determine which area was suitable for urban development, six thematic layers—slope, road, land use, river, settlement, and elevation—were used. AHP calculated their weighted scores based on the pairwise comparison. The linear combination approach was used to multiply each factor’s weight and score, and the outcome was then multiplied to make the urban development appropriate. 45.3 square kilometers of land were deemed highly suitable, 191.77 square kilometers were deemed fairly acceptable, 72.14 square kilometers were deemed marginally suitable, and 0.79 square kilometers were deemed unsuitable.

Unambiguous simulation of diffusive charge transport in disordered nanoribbons

Charge transport in disordered two-dimensional (2D) systems showcases a myriad of unique phenomenologies that highlight different aspects of the underlying quantum dynamics. Electrons in such systems undergo a crossover from ballistic propagation to Anderson localization, contingent on the system’s effective coherence length. Between the extended and localized phases lies a diffusive crossover in which the charge conductivity is properly defined. The numerical observation of this regime has remained elusive because it requires fully coherent transport to be simulated in systems whose dimensions are sufficiently large to meaningfully split the mean-free path and localization length scales. To address this challenge, we employed a novel linear scaling time-resolved approach that enabled us to derive the dc-transport characteristics and observe the three expected 2D transport regimes — ballistic, diffusive, and localized.

Mixed Marginal Rates of Substitution for Analyzing Banking Efficiency Using a Linear Programming Approach

This paper introduces a Mixed Marginal Rates of Substitution (MMRS) approach for analyzing banking efficiency within a linear programming framework. For this purpose, a linear programming model is proposed that efficiently calculates Marginal Rates, revealing how partial input changes affect multiple outputs simultaneously. Applying this methodology to data from the Taiwanese banking system spanning 2010 to 2020 reveals diverse efficiency patterns across financial institutions. The analysis demonstrates that while increased financial inputs generally improve outputs, individual banks exhibit varying sensitivity to these changes. Some institutions were resilient to input fluctuations, while others showed marked sensitivity, particularly in loans and investments. As a result, the proposed MMRS framework provides a robust tool for analyzing indicator relationships within efficient units, offering valuable insights for tailored management strategies and policy formulation in the banking sector. The findings emphasize the importance of institution-specific approaches to enhancing banking efficiency and managing risk effectively, contributing to a more nuanced understanding of economic outcomes in the financial industry. Additionally, symmetry concepts in input–output relationships are suggested as a potential area for future research, offering a possible framework for identifying balanced efficiency patterns among banks.

Machine learning and regression in the management of runoff in bauxite mines under rehabilitation.

Accurate and reliable forecasting of monthly runoff considering several years of rehabilitation helps in planning and managing the water resources system of bauxite mining areas. A combination of linear regression models and artificial intelligence was proposed and applied to predict runoff from mined areas. The ideal number of input variables was chosen considering a correction threshold (correlation above 95%), stepwise selection, and according to the recursive feature elimination (RFE). The linear regression equations were divided into explanatory and predictive, with the predictive ones being trained and validated with all possible combinations of annual data periods. Five machine learning models (linear model (LM), K-nearest neighbor regression (KNN), support vector machine (SVM), random forest (RF), and Cubist) were trained and used as a prediction instrument on the test set of each subsequent year. Precipitation, accumulated precipitation duration, maximum precipitation intensity, and number of events were the most explanatory variables in most linear regression and machine learning equations. The linear regression model approach presented satisfactory performance indices in predicting surface runoff in a mined bauxite area. The Cubist and RF models were the best predictors for the greatest number of years of rehabilitation, with a coefficient of determination varying with maximum values between 0.52 and 0.88 in validation. There was no best model that consistently presented the best result in all years of rehabilitation, and it is recommended to use the annual model for the respective year of rehabilitation. The linear regression equations and algorithms were useful tools in predicting runoff series and, therefore, promising monthly runoff prediction. The presented approach becomes a starting point in the search to optimize the hydrological planning of surface bauxite mines.

Changes in tree-ring wood density of European beech (Fagus sylvatica L.), silver fir (Abies alba Mill.), and Norway spruce (Picea abies (L.) H. Karst.) in European mountain forests between 1901 and 2016

Key messageWe found a significant increase in the latewood density of European beech, and a decrease in the latewood and mean wood density of silver fir and Norway spruce in European mountain forests over the period 1901–2016. In the past century, drought did not directly influence the wood density trend of the three studied species. However, for both fir and spruce, drought indirectly affected the mean wood density via changes in the latewood to earlywood ratio, i.e., in the case of extreme drought, trees with high values of latewood to earlywood ratio experienced a slight attenuation in the declining trend of their mean wood density.ContextCentury-long wood density measurements can provide novel information on tree response to climate change and the carbon sequestration potential of forest ecosystems. Still, the knowledge about long-term changes in wood density of European beech (Fagus sylvatica L.), silver fir (Abies alba Mill.), and Norway spruce (Picea abies (L.) H.Karst.) in European mountain forests needs to be further explored.AimsWe assessed long-term changes in tree-ring mean wood density, earlywood density, and latewood density in trees of the three species between 1901 and 2016. We investigated the influence of endogenous factors (i.e., tree-ring width, current tree diameter, and latewood to earlywood ratio) and drought events on wood density.MethodsIn total, 150 tree cores were sampled from mountain forests in Bulgaria, Bosnia and Herzegovina, Slovenia, Switzerland, and Germany. The mean, early, and latewood density of these samples were measured with the LIGNOSTATION™ system. To address our research aims, we applied a linear mixed-effect modelling approach using the data from 101 correctly cross-dated cores that spanned the entire period of analysis.ResultsIn the absence of drought, the latewood density of European beech increased by 7.1%, the late and mean wood density of silver fir decreased by 16.8% and 11.0%, respectively, and the late and mean wood density of Norway spruce decreased by 16.1% and 7.2%, respectively, between 1901–2016. In the past century, drought influenced the trends of wood density through an effect mediated by changes in the latewood to earlywood ratio. Specifically, in cases of extreme drought, silver fir and Norway spruce trees with a latewood to earlywood ratio value 50% higher than the median experience a slight attenuation in the declining trend of their mean wood density, making the negative impact of drought marginally less severe.ConclusionsOur findings have significant implications for the accuracy of carbon stock assessments, national greenhouse gas inventories, and the utilization of wood from the three species. Given the fact that changes in wood density follow species-specific patterns and the expectation of more frequent drought events in Europe, in the future, it is essential to build further tree-ring density time series for other species and sites to improve our understanding of how climate change alters wood density and carbon sequestration of forest ecosystems.

Design of Novel Acat Inhibitors as Potent Anti-Hyperlipidemic Agents Using Chemometric Approaches

Aim: A 2D QSAR study of acyl-coenzyme A (CoA): cholesterol acyltransferase (ACAT) inhibitors revealed that electronic, topological, and steric properties are important structural features required for activity against ACAT. Background: In order to interpret the evidence encrypted by the molecular structure of the compounds, a standard physicochemical descriptors-centered, and Quantitative Structure-Activity Relationship (QSAR) approach was implemented on a data set of Indoline derivatives were reported to be acyl-coenzyme A (CoA): cholesterol acyltransferase ACAT inhibitors. Objective: The ACAT enzyme plays an important role in the absorption of dietary cholesterol. Therefore, the inhibition of ACAT is a key strategy or primary objective for the treatment of hypercholesterolemia and atherosclerosis. Method: Chemo metric models were designed by inserting a battery of statistical techniques in the current study that demonstrate the linear approaches of analysis, including multiple linear regression (MLR), partial least square PLS, and non-linear methods such as artificial neural networks (ANN). Result: The activity contributions of these molecules were analyzed through regression equation, and the best QSAR model was created with an excellent correlative and predictive ability. Significant statistical values S = 0.35, F = 60.30, r = 0.92, r² = 0.85, r² (CV) = 0.82 of the designed models were obtained using stepwise MLR and a comparable PLS and FFNN model with r² (CV) = 0.82, 0.88 and 0.86 respectively and the relevant descriptors like inertia moment 1 size, Kier Chiv4 (cluster) index, Kier Chiv6(ring) index offered important information regarding this model. Conclusion: The model reveals that inertia moment 1 size, Kier Chiv4 (cluster) index, and Kier Chiv6 (ring) index are prerequisite descriptors to determine other promising ACAT antagonists with high and liable potency against the target. Therefore, these characteristics may be used efficiently for the design and evaluation of active compounds as new ACAT inhibitors thanks to their utilization.

Piecewise affine modeling of parallel boost converter in a DC microgrid and its control application by utilizing a Linear Matrix Inequality approach

Piecewise affine modeling of parallel boost converter in a DC microgrid and its control application by utilizing a Linear Matrix Inequality approach

A novel linear programming-based predictive control method for building battery operations with reduced cost and enhanced computational efficiency

Battery energy storage systems can be readily integrated with buildings to enhance renewable energy self-consumptions while leveraging time-variant electricity tariffs for possible operation cost reductions. The extensive variability in building operating conditions presents significant challenges in developing universally applicable methods for optimal controls. To ensure reliable and robust controls, this study integrates predictive control with efficient linear programming to effectively fine-tune battery controls for real-time operations. An adaptive time aggregation scheme has been proposed to streamline the optimization process by accounting for unique changes in energy balances and tariffs. Comprehensive data experiments, based on measurements from 95 unique building operation scenarios, have been conducted to quantify the control performance given different optimization formulations, varying types and levels of prediction uncertainties in building energy demands and PV generations. The results validate the value of the method proposed, leading to 11.75% to 34.63% operation cost reductions on average, while reducing computation steps by 87.75% to 92.60% compared with conventional linear programming approaches. The insights obtained are useful for developing flexible building control strategies with improved computation efficiency and robustness, while providing extensible optimization frameworks for buildings with various energy patterns and storage systems.

THE EFFECT OF PERFORMANCE EXPECTATIONS, EFFORT EXPECTATIONS, FACILITATING CONDITIONS AND SOCIAL INFLUENCE ON BEHAVIOR INTENTION TO PAY ZAKAT THROUGH THE QRIS DIGITAL PLATFORM IN BAITUL MAL ACEH

This study aims to determine the effect of performance expectations, business expectations, facilitating conditions and social influences on the behavioral intention to pay zakat through the QRIS digital platform at Baitul Mal Aceh. The sample used in the study was 100 respondents selected using the Slovin formula and using Non-Probability Sampling with the Purposive Sampling method. The methodology used is quantitative with the Multiple Linear Regression approach. The results of the study partially show that the performance expectation variable has a positive and significant effect on the behavioral intention to pay zakat through the QRIS digital platform, and business expectations have a negative and significant effect on the behavioral intention to pay zakat through the QRIS digital platform. While the facilitating conditions and social influences do not affect the behavioral intention to pay zakat through the QRIS digital platform. While simultaneously (together) the performance expectation variable, business expectations, facilitating conditions, and social influences have a effect on the behavioral intention to pay zakat through the QRIS digital platform.Keywords: Performance Expectation, Effort Expectation, Facilitating Conditions, Social Influence, Behavior Intention, QRIS, Zakat

Recursive Bayesian estimation of wind load on a monopile-supported offshore wind turbine using output-only measurements

Offshore wind turbine structures experience combined wind and wave loading during their lifetime, and the cyclic characteristics of these loads significantly impact the fatigue life of the support structure. Continuous monitoring of stress-related quantities, such as strain time histories at hotspot locations of the structure, can help to estimate the fatigue life. However, sparse measurements from the substructure necessitate using a digital twin as a tool for structural health monitoring by creating a virtual model. This virtual model relies on real-time input loads for virtual sensing and stress-related quantities prediction. However, the exact loading on these structural systems is often unknown or estimated with considerable uncertainty. This paper implements a recursive window-based Bayesian estimator for input load estimation and virtual sensing of acceleration and strain time history at unmeasured critical locations using output-only measurements. The estimator is numerically and experimentally validated in the context of input load estimation for an offshore wind turbine, and the results are compared with a traditional augmented Kalman filter and a linear regression approach for the quasi-static component of loading. The method is first demonstrated through a numerical study using a finite element model of a 6 MW offshore wind turbine, where input wind load time histories are accurately estimated. Then, the framework is applied to the real data measured from an offshore wind turbine in the North Sea. The study demonstrates the window-based Bayesian input estimator as a promising tool for the digital twinning of offshore wind turbines, demonstrating superior accuracy in input load estimations and full response predictions compared to the augmented Kalman filter and linear regression methods without the constraint of collocated measurements at input load locations.

Design of a Hybrid Excitation Controller for Multimachine Infinite Bus Power System

Abstract: A significant portion of contemporary power networks is still conventional power system that uses synchronous generators to produce electricity. Conventional power systems are operated under various operating situations by means of excitation systems and turbine-governor systems of synchronous generators. This paper proposes an excitation controller for synchronous generators (SGs) in a multi machine infinite bus (MMIB) power system through combining the idea of the nonlinear backstepping (BS) and partial feedback linearization methods. The model of third-order has been linearized into a second-order linearized system that is independent of the rotor angle which is unmeasurable, using the partial feedback linearization approach. In this case, the BS is applied to the partially linearized system. The idea is to find a control input that drives the system towards a desired behaviour while ensuring the transformed states converge to their target values. Lastly, simulation research is carried out on an MMIB system to analyse how the proposed control mechanism (BS-PFLC) performs in stabilizing the system under a three phase-to-ground fault conditions of power networks. To demonstrate the effectiveness of the BS-PFLC, an existing excitation controller has been used to compare the performance.

Integrating Energy-Efficient Train Control in railway Vertical Alignment Optimization: A novel Mixed-Integer Linear Programming approach

Incorporating train control into the railway design process enables a practical and comprehensive evaluation of the lifecycle utility of a track profile. This paper proposes a novel integrated approach, termed EETC-VAO, which combines railway track Vertical Alignment Optimization (VAO) and Energy-Efficient Train Control (EETC). Initially formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem, EETC-VAO aims to meet various geometric constraints and simultaneously minimize construction costs, traction energy consumption, and section running times in both directions. The model is subsequently reformulated into an equivalent Mixed-Integer Linear Programming (MILP) model using linearization methods and is further enhanced with valid inequalities, logic cuts, and a warm start algorithm with random velocity generation. The model has been extensively tested across a variety of case studies and train types, from synthetic small-scale scenarios to challenging real-world cases spanning from 3 to 71.2 km. Our findings demonstrate that operational costs can be significantly reduced with only marginal increases in construction costs. The integrated approach achieves reductions in total lifecycle costs of up to 40%, revealing a critical trade-off between construction and operational expenses. Notably, our results also indicate that lower construction costs do not inherently conflict with reduced operational costs, emphasizing the critical importance of integrating the train control scheme into the VAO problem.

Effect of Substrate Temperature on Bead Track Geometry of 316L in Directed Energy Deposition: Investigation and Regression Modeling

The optimization of process parameters in powder Directed Energy Deposition (DED) is essential for achieving consistent, high-quality bead geometries, which directly influence the performance and structural integrity of fabricated components. As a subset of additive manufacturing (AM), the DED process, also referred to as laser metal deposition (LMD), enables precise, layer-by-layer material deposition, making it highly suitable for complex geometries and part repair applications. Critical parameters, such as the laser power, feed rate, powder mass flow, and substrate temperature govern the deposition process, impacting the bead height, width, contact angle, and dilution. Inconsistent control over these variables can lead to defects, such as poor bonding, dimensional inaccuracies, and material weaknesses, ultimately compromising the final product. This paper investigates the effects of various process parameters, specifically the substrate temperature, on bead track geometry in DED processes for stainless steel (1.4404). A specialized experimental setup, integrated within a DED machine, facilitates the controlled thermal conditioning of sample sheets. Using Design of Experiments (DoE) methods, individual bead marks are generated and analyzed to assess geometric characteristics. Regression models, including both linear and quadratic approaches, are constructed to predict machine parameters for achieving the desired bead geometry at different substrate temperatures. Validation experiments confirm the accuracy and reliability of the models, particularly in predicting the bead height, bead width, and contact angle across a broad range of substrate temperatures. However, the models demonstrated limitations in accurately predicting dilution, indicating the need for further refinement. Despite some deviations in measured values, successful fabrication is achieved, demonstrating robust bonding between the bead and substrate. The developed models offer insights into optimizing DED process parameters to achieve desired bead characteristics, advancing the precision and reliability of additive manufacturing technology. Future work will focus on refining the regression models to improve predictions, particularly for dilution, and further investigate non-linear interactions between process variables.

Intelligent control system for technological complexes of aluminum industry enterprises

Relevance. Aluminum production is one of the most important industries all over the world. It has a high environmental load, resource and energy intensity. To reduce the negative impact of aluminum production, efforts are underway to improve its technological processes, aimed at increasing aluminum recovery rates, reducing waste volumes, and lowering energy consumption. The reduction of energy consumption can be achieved, among others, through implementing control systems that provide energy management. Due to the complexity and multiplicity of technological processes in aluminum production, the use of traditional linear approaches for such control systems is ineffective, and adequate mathematical models are required. At the same time, for the production level, the use of mathematical models based on a phenomenological description of the ongoing physical processes overly complicates the control task. As a result, the use of intelligent and adaptive approaches to both the mathematical description of technological processes and the optimal energy consumption management is relevant. Objects. Technological complexes of aluminum production, which have the properties of inertia, nonlinearity, and closedness; and the control system based on artificial intelligence methods. Alumina production is chosen as an illustration. Aim. To develop mathematical models capable of adequately describing the interrelated processes in the technological complexes under consideration, as well as the control system that allows optimal energy consumption management. Methods. For the mathematical model based on balance equations under uncertainty, the fuzzy-set theory was used along with the gradient descent method to identify the model parameters; for the optimization task, the genetic algorithm method was used. Results. The mass balance model and the process conditions changing model have been developed to determine the energy consumption for the technological complexes of aluminum production with continuous inertial nonlinear closed production. Based on these models, the dynamic characteristics of energy consumption and the parameters of technological processes were determined depending on the main controlled parameters, allowing us to predict emergencies. Considering technological parameters and cost factors, the optimization task for energy consumption management was solved.

Reuse of building materials—the perspective of Swedish clients

In the context of the circular economy, there is an urgent need for transformation into circular material flows by avoiding waste, reducing extraction of virgin raw materials, and extending product life cycles. Within the construction and real estate industry, the reuse of building materials stands out as a critical strategy for value retention. The objective of this paper is to localise the forefront of the practical implementation of reused materials within the Swedish construction and real estate industry. To achieve this, the current state of reuse practices through the perspective of property companies as clients in decision-making positions was conducted through semi-structured interviews to identify key barriers and enablers associated with reuse. The three most significant barriers that emerged were a lack of measurable economic incentives, the absence of a professional reuse market, and obsolete project management. Conversely, the three most significant enablers were new and improved project management methods, enhancing competence and learning within and beyond organisations, and introduce reuse at an early stage. The results emphasise the need for project management to develop and adopt circular economy principles. This is further understood due to responders highlighting the industry's linear approach as a major obstacle to circularity together with uncertainties related to product performance, responsibilities and economics characterising reuse efforts. However, an industry in transition is witnessed, e.g. by the emergence of new roles which suggests a continued need for focused research in organisational matters.