OpenAIRE

Permanent URI for this collectionhttp://hdl.handle.net/10084/89004

Kolekce určená pro sklízení infrastrukturou OpenAIRE; obsahuje otevřeně přístupné publikace, případně další publikace, které jsou výsledkem projektů rámcových programů Evropské komise (7. RP, H2020, Horizon Europe).

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Now showing 1 - 20 out of 5215 results

Demystifying sustainable innovation and governance in family firms: A critical review
(Elsevier, 2025) Gutuleac, Rada, RADA; Giachino, Chiara, Chiara; Vilamová, Šárka; Ferraris, Alberto
Family firms' governance, which is inherently risk-averse, frequently inhibits sustainable innovation. However, the same governance structure, driven by a strong desire for oversight, can enhance innovation outcomes by ensuring diligent monitoring and execution. This paper aims to conceptualise how governance bodies within family firms contribute to fostering sustainable innovation. To this end, we conducted a critical review to identify the state of the art of sustainable innovation and governance structure of family firms. Then, drawing on the dynamic capability theory, we developed a new theory combining literature with insights from an illustrative case study. The findings unveil that some family firm governance bodies champion sustainable innovation, yet their efforts are frequently confined to the same. Given the absence of a dedicated board to manage the innovation process end-to-end, we propose an emerging solution in managerial practice, known as a shadow board, to fill this gap. That is, a selected group of younger employees or external experts tasked with advising the executive team. The results shall guide scholars towards a better understanding of the underlying mechanisms that can drive sustainable innovation in family firms from a governance perspective. Moreover, by leveraging these insights, organisations can enhance their ability to overcome potential challenges and optimise sustainable innovation processes.
Study of the safety characteristics of different types of pepper powder (Capsicum L.)
(MDPI, 2024) Kosár, László; Szabová, Zuzana; Kuracina, Richard; Spitzer, Stefan H.; Mynarz, Miroslav; Filipi, Bohdan
This research was aimed at comparing the fire characteristics of different types of pepper in the context of explosion prevention. The following characteristics were studied: explosion pressure Pmax and Kst at selected concentrations, ignition temperature of the deposited dust layer from the hot surface, and minimum ignition energy. The comparison of the chemical properties of the used types of pepper was performed using TG/DSC. The results of the measurements suggest that different types of peppers exhibit different explosion characteristics. Each sample reached the maximum value of the explosion pressure and rate of pressure rise at different concentrations. The volume of the explosion chamber used also influenced the explosion characteristics. It is a consequence of the fact that the explosion characteristics strongly depend on the mechanism of action of a particular igniter. The minimum effect on the safety characteristics was observed when measuring the minimum ignition energy and the minimum ignition temperature of the dust layer from the hot surface. The results of the measurements suggest that different types of peppers exhibit different explosion characteristics. This information should then be considered in explosion prevention.
Methods for magnetic signature comparison evaluation in vehicle re-identification context
(MDPI, 2024) Balamutas, Juozas; Navikas, Dangirutis; Markevičius, Vytautas; Čepėnas, Mindaugas; Valinevičius, Algimantas; Žilys, Mindaugas; Prauzek, Michal; Konečný, Jaromír; Frivaldský, Michal; Li, Zhixiong; Andriukaitis, Darius
Intelligent transportation systems represent innovative solutions for traffic congestion minimization, mobility improvements and safety enhancement. These systems require various inputs about vehicles and traffic state. Vehicle re-identification systems based on video cameras are most popular; however, more strict privacy policy necessitates depersonalized vehicle re-identification systems. Promising research for depersonalized vehicle re-identification systems involves leveraging the captured unique distortions induced in the Earth's magnetic field by passing vehicles. Employing anisotropic magneto-resistive sensors embedded in the road surface system captures vehicle magnetic signatures for similarity evaluation. A novel vehicle re-identification algorithm utilizing Euclidean distances and Pearson correlation coefficients is analyzed, and performance is evaluated. Initial processing is applied on registered magnetic signatures, useful features for decision making are extracted, different classification algorithms are applied and prediction accuracy is checked. The results demonstrate the effectiveness of our approach, achieving 97% accuracy in vehicle re-identification for a subset of 300 different vehicles passing the sensor a few times.
Torrefied biomass quality prediction and optimization using machine learning algorithms
(Elsevier, 2024) Naveed, Muhammad Hamza; Gul, Jawad; Khan, Muhammad Nouman Aslam; Naqvi, Salman Raza; Štěpanec, Libor; Ali, Imtiaz
Torrefied biomass is a vital green energy source with applications in circular economies, addressing agricultural residue and rising energy demands. In this study, ML models were used to predict durability (%) and mass loss (%). Firstly, data was collected and preprocessed, and its distribution and correlation were analyzed. Gaussian Process Regression (GPR) and Ensemble Learning Trees (ELT) were then trained and tested on 80 % and 20 % of the data, respectively. Both machine learning models underwent optimization through Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for feature selection and hyperparameter tuning. GPR-PSO demonstrates excellent accuracy in predicting durability (%), achieving a training R2 score of 0.9469 and an RMSE value of 0.0785. GPR-GA exhibits exceptional performance in predicting mass loss (%), achieving a training R2 value of 1 and an RMSE value of 9.7373e-05. The temperature and duration during torrefaction are crucial variables that are in line with the conclusions drawn from previous studies. GPR and ELT models effectively predict and optimize torrefied biomass quality, leading to enhanced energy density, mechanical properties, grindability, and storage stability. Additionally, they contribute to sustainable agriculture by reducing carbon emissions, improving cost-effectiveness, and aiding in the design and development of pelletizers. This optimization not only increases energy density and grindability but also enhances nutrient delivery efficiency, water retention, and reduces the carbon footprint. Consequently, these outcomes support biodiversity and promote sustainable agricultural, ecosystem, and environmental practices.
Active microrobots for dual removal of biofilms via chemical and physical mechanisms
(American Chemical Society, 2025) Peng, Xia; Oral, Cagatay M.; Urso, Mario; Ussia, Martina; Pumera, Martin
Bacterial biofilms are complex multicellular communities that adhere firmly to solid surfaces. They are widely recognized as major threats to human health, contributing to issues such as persistent infections on medical implants and severe contamination in drinking water systems. As a potential treatment for biofilms, this work proposes two strategies: (i) light-driven ZnFe2O4 (ZFO)/Pt microrobots for photodegradation of biofilms and (ii) magnetically driven ZFO microrobots for mechanical removal of biofilms from surfaces. Magnetically driven ZFO microrobots were realized by synthesizing ZFO microspheres through a low-cost and large-scale hydrothermal synthesis, followed by a calcination process. Then, a Pt layer was deposited on the surface of the ZFO microspheres to break their symmetry, resulting in self-propelled light-driven Janus ZFO/Pt microrobots. Light-driven ZFO/Pt microrobots exhibited active locomotion under UV light irradiation and controllable motion in terms of “stop and go” features. Magnetically driven ZFO microrobots were capable of maneuvering precisely when subjected to an external rotating magnetic field. These microrobots could eliminate Gram-negative Escherichia coli (E. coli) biofilms through photogenerated reactive oxygen species (ROS)-related antibacterial properties in combination with their light-powered active locomotion, accelerating the mass transfer to remove biofilms more effectively in water. Moreover, the actuation of magnetically driven ZFO microrobots allowed for the physical disruption of biofilms, which represents a reliable alternative to photocatalysis for the removal of strongly anchored biofilms in confined spaces. With their versatile characteristics, the envisioned microrobots highlight a significant potential for biofilm removal with high efficacy in both open and confined spaces, such as the pipelines of industrial plants.
Nonlinear dynamics in a public good game
(Springer Nature, 2024) Gori, Luca; Sodini, Mauro
The present work aims to study the problem of individual voluntary anonymous contributions to the financing of public goods in a dynamic setting. To do this, the article departs from a textbook model & agrave; la Naimzada and Tramontana (2010) augmented with public goods. The article studies how bounded rationality and dependence on agents' past decisions combine with the problem of voluntary contributions. This favours the emergence of nonlinear dynamics in individual behaviour as well as in the aggregate contribution to the financing of a public good project. The Nash equilibrium can be destabilised through a flip bifurcation when the agent reactivity increases. In addition, some Neimark-Sacker bifurcations can also occur although not around the steady-state equilibrium. A sufficiently high agent reactivity level can also lead to chaotic dynamics with possible multiple attractors. When the chaotic regime prevails, synchronisation phenomena in agent behaviour may occur but are rare. Thus, usually, even if agents are homogeneous, they behave as if they were heterogeneous by making non-synchronised decisions. The work also explicitly deepens the case of a heterogeneous economy in terms of both consumer preferences and income.
Fire hazards caused by equipment used in offshore oil and gas operations: prescriptive vs. goal-oriented legislation
(MDPI, 2025) Brkic, Dejan
This article offers a concise overview of the best practices for safety in offshore oil and gas operations, focusing on the risks associated with various types of equipment, particularly on the risk of fire. It identifies specific machinery and systems that could pose hazards, assesses their potential impact on safety, and explores conditions that may lead to accidents. Some of the largest accidents were analyzed for their associations with fire hazards and specific equipment. Two primary regulatory approaches to offshore safety are examined: the prescriptive approach in the United States (US) and the goal-oriented approach in Europe. The prescriptive approach mandates strict compliance with specific regulations, while in the goal-oriented approach a failure to adhere to recognized best practices can result in legal accountability for negligence, especially concerning human life and environmental protection. This article also reviews achievements in safety through the efforts of regulatory authorities, industry collaborations, technical standards, and risk assessments, with particular attention given to the status of Mobile Offshore Drilling Units (MODUs). Contrary to common belief, the most frequent types of accidents are not those involving a fire/explosion caused by the failure of the Blowout Preventer (BOP) after a well problem has already started. Following analysis, it can be concluded that the most frequent type of accident typically occurs without fire and is due to material fatigue. This can result in the collapse of the facility, capsizing of the platform, and loss of buoyancy of mobile units, particularly in bad weather or during towing operations. It cannot be concluded that accidents can be more efficiently prevented under a specific type of safety regime, whether prescriptive or goal-oriented.
Determination of the brittleness of glass fibers on selected mechanical and rheological properties of the polymer composite
(Budapest University of Technology and Economics, Faculty of Mechanical Engineering, Department of Polymer Engineering, 2024) Miščík, Stanislav; Dobránsky, Jozef; Gombár, Miroslav; Čep, Robert
The paper deals with the influence of the brittleness of glass fibers on the selected performance properties of the fibrous polymer composite. Understanding the fatigue behavior of fiber-reinforced plastics is desirable for exploiting their features in safe, durable, and reliable industrial components. Based on the proposed methodology, it is possible to assess the impact of material reuse on selected mechanical and rheological properties. To verify the methodology by experimental analysis, homopolymer PP reinforced with chemically grafted glass fiber (30 wt%) was selected. The proposed methodology was subsequently verified by experimental analysis and evaluated statistically. The morphology of the fracture surfaces was evaluated, and the fiber-polymer matrix adhesion was monitored at the interface of the fracture surfaces. Based on the measured and evaluated values and fracture surfaces, we can say that the brittleness of the fibers significantly affects the performance properties of the tested polymer composite.
Emulation of quantum key distribution networks
(IEEE, 2025) Mehic, Miralem; Dervisevic, Emir; Burdiak, Patrik; Lipovac, Vlatko; Fazio, Peppino; Vozňák, Miroslav
Network emulators play an important role in testing network systems, applications, and protocols. Emulators bridge the gap between simulation setups that lack realism in results and real-world trials that are accurate but often expensive, non-reproducible, and uncontrollable. This article presents an extended model of the Quantum Key Distribution Network Simulation Module (QKDNetSim) with a model catalog of QKD components and functionalities. We explore emulations of point-to-point connections in QKD networks and the interaction of essential compo nents within QKD nodes. The presented tool will undoubtedly spur future development and teach ing, and it is critical for testing novel applications and protocols applied to QKD networks.
Shared entanglement for three-party causal order guessing game
(IOP Publishing, 2025) Kukulski, Ryszard; Lewandowska, Paulin; Zyczkowski, Karol
In a variant of communication tasks, players cooperate in choosing their local strategies to compute a given task later, working separately. Utilizing quantum bits for communication and sharing entanglement between parties is a recognized method to enhance performance in these situations. In this work, we introduce the game for which three parties, Alice, Bob and Charlie, would like to discover the hidden order in which they make the moves. We show the advantage of quantum strategies that use shared entanglement and local operations over classical setups for discriminating operations’ composition order. The role of quantum resources improving the probability of successful discrimination is also investigated. Our research provides a basis for examining computational model featuring a specific gate set while examining the diverse operations achievable through permutations of its elements.
Assessing the global dynamics of Nipah infection under vaccination and treatment: A novel computational modeling approach
(PLOS, 2025) Yu, Fang; Khan, Muhammad Younas; Riaz, Muhammad Bilal; Ullah, Saif
In biology and life sciences, fractal theory and fractional calculus have significant applications in simulating and understanding complex problems. In this paper, a compartmental model employing Caputo-type fractional and fractal-fractional operators is presented to analyze Nipah virus (NiV) dynamics and transmission. Initially, the model includes nine nonlinear ordinary differential equations that consider viral concentration, flying fox, and human populations simultaneously. The model is reconstructed using fractional calculus and fractal theory to better understand NiV transmission dynamics. We analyze the model’s existence and uniqueness in both contexts and instigate the equilibrium points. The clinical epidemiology of Bangladesh is used to estimate model parameters. The fractional model’s stability is examined using Ulam-Hyers and Ulam-Hyers-Rassias stabilities. Moreover, interpolation methods are used to construct computational techniques to simulate the NiV model in fractional and fractal-fractional cases. Simulations are performed to validate the stable behavior of the model for different fractal and fractional orders. The present findings will be beneficial in employing advanced computational approaches in modeling and control of NiV outbreaks.
Roman cement mortar prepared by a multi-stage mixing process
(Consejo Superior de Investigaciones Científicas, 2025) Daňková, Jana; Mec, Pavel; Gabor, Roman; Bujdoš, David; Majstríková, Tereza; Valentová, Adéla; Šafrata, Jiří
Roman cement is the predecessor to modern Portland cement. Nowadays, it is a very promising product with lower CO2 emissions, frequently used to restore historical objects. However, there are still many practical problems as a setting that can be affected in several ways. One possibility is the multistage mixing of fresh mortar, a practical historical method that has not yet been scientifically investigated. This article presents an experimental study investigating the effect of multistage mixing on the properties of fresh and hardened mortar. The properties and structure of the mortar were compared with a reference mortar (retardened by citric acid). Multistage mixing affects fresh mortars with optimal consistency and a workability time of 120 minutes. The influence of mixing on the hydration process and structural formation is characterized by isothermal calorimetry and SEM. Comparison of reference and modified mixing mortars exhibits differences in hydration process, structure, and initial strength, but no significant effect at 90 days strength.
Enhancing myocardial infarction detection with vectorcardiography: fusion-based comparative analysis of machine learning methods
(Frontiers Media S.A., 2026) Vondrák, Jaroslav; Penhaker, Marek
Background: Early detection and diagnosis of myocardial infarction (MI) help physicians save lives through timely treatment. Vectorcardiography (VCG) is an alternative to the 12-lead electrocardiography, providing not only characteristic changes in cardiac electrical activity in MI patients but also unique spatial information often overlooked by traditional methods. Despite its potential, comprehensive comparative studies applying machine learning (ML) techniques specifically to VCG data remain limited. Methods: This study proposes a novel VCG processing methodology using a comparative analysis of machine learning-based algorithms for the automated detection of MI patients from VCG recordings, utilizing extracted domain knowledge VCG features that monitor morphological changes in cardiac activity. For this purpose, records from the PTB Diagnostic dataset were used. The extracted domain knowledge dataset of morphological features was then fed into a diverse set of 210 machine learning configurations, including K-nearest neighbor, Support Vector Machine, Discriminant Analysis, Artificial Neural Network, Decision Tree, Random Forest, Naive Bayes, Logistic Regression, and Ensemble Methods. To further improve classification performance, we combined analyzed high-performing models using a stacking ensemble strategy, which integrates multiple base classifiers into a meta-classifier. Results: The stacking-based decision-level fusion achieved high accuracy of 95.55%, sensitivity of 97.70%, specificity of 86.25%, positive predictive value of 96.86%, negative predictive value of 89.61% and f1-score of 97.27%. Conclusion: The results demonstrate that decision-level fusion via stacking improves classification performance and enhances the reliability of MI detection from VCG recordings, supporting cardiologists in decision-making.
Proposes geometric accuracy and surface roughness estimation of anatomical models of the pelvic area manufactured using a material extrusion additive technique
(MDPI, 2025) Turek, Pawel; Snela, Slawomir; Budzik, Grzegorz; Bazan, Anna; Jablonski, Jaroslaw; Przeszlowski, Lukasz; Wojnarowski, Robert; Dziubek, Tomasz; Petrů, Jana
One of the main benefits of using 3D printing in orthopedics is the ability to create custom solutions tailored to a patient’s specific anatomical and functional needs. Conducting a reliable evaluation of the accuracy of the manufacture of anatomical structure models is essential. However, particular standards or procedures still need to be implemented to control the surface quality of anatomical models manufactured using additive manufacturing techniques. Models of pelvic parts made of polylactic acid (PLA) material were manufactured using the Material Extrusion (MEX) additive technique. Subsequently, guidelines were developed to reliably verify the geometric and surface roughness of the 3D printed models using Computer-Aided Inspection (CAI) systems. For this purpose, a measuring arm system (MCA-II) with a mounted laser head and Atos II Triple Scan was used. To inspect surface roughness parameters, procedures were developed for an Alicona InfiniteFocusG4 optical microscope. The results of the geometrical verification of the models are within the tolerance limits of ±0.22 mm to ±0.6 mm. In the case of surface roughness measurement, the highest values for the arithmetical mean height Sa were obtained on the side of the support material, while the smallest values were found along the applied layers. After the metrological control process, the models were used in the planning process for hip surgery.
How credit constrained are family-owned SMEs in Arab countries?
(Elsevier, 2025) Gourene, Grakolet; Schwidrowski, Zuzana Brixiova; Balcar, Jiří; Filipova, Lenka Johnson
Utilizing the World Bank Enterprise Surveys, this paper examines the links between family ownership and credit constraints of SMEs in Egypt, Jordan, Morocco, and Tunisia. We found that while family-owned firms have higher need for credit than nonfamily-owned firms, they are more likely to be discouraged from applying for it. Due to this self-selection out of credit markets, they end up more credit constrained even though their credit application rejection rates are below those of nonfamily firms. Stronger firm governance, formal business strategies and good mana gerial practices can ease access to credit for family-owned SMEs
Maximization of wear rates through effective configuration of standoff distance and hydraulic parameters in ultrasonic pulsating waterjet
(University of Niš, 2024) Nag, Akash; Dixit, Amit Rai; Petrů, Jana; Váňová, Petra; Konečná, Kateřina; Hloch, Sergej
A pulsating waterjet is a technological modification of a conventional waterjet that utilizes ultrasonic vibrations to generate a modulated jet, resulting in repetitive fatigue loading of the material. The erosion efficiency of the ultrasonic pulsating waterjet is majorly determined by the hydraulic factors and its interaction with standoff distance. However, the dependency of the wear rates on different hydraulic factors and formulation of an implicit prediction model for determining effective standoff distance is still not present to date. Therefore, in this study, the combined dependency of the supply pressure (20-40 MPa), nozzle diameter (0.3-1.0 mm), and standoff distance (1-121 mm) on wear rates of AW-6060 aluminum alloy are studied. Statistical analysis is used to determine the statistically significant factors and formulate regression equations to determine output responses within the experimental domain. The surface topography and sub-surface microhardness of the eroded grooves were studied. The results show that both the disintegration depth and the material removal increase with an increase in the nozzle diameter and supply pressure. However, the dependency of the output responses on nozzle diameter is statistically more evident than supply pressure and two-way interactions. Cross-sectional images of the grooves showed typical hydrodynamic erosion characteristics in erosion cavities, subsurface voids, and material upheaving. The results of microhardness analysis showed an approximately 15-20% increase in hardness values compared to the untreated samples.
Artificial neural networks learning for high-frequency data prediction—big data approach based on genetic and micro-genetic algorithms
(Springer Nature, 2026) Marček, Dušan; Maděra, Martin
This study investigates the use of state-of-the-art software tools available on contemporary desktop computing platforms to enhance predictive modeling with machine learning methods. Existing research has not sufficiently examined how efficient utilization of such tools-specifically state-space search reduction, operation parallelization, and mechanisms for escaping local optima-affects model performance when applied to large-scale high-frequency datasets. To address this gap, we introduce new predictive models that explicitly leverage these advanced software capabilities. We further propose strategies for overcoming local optima in neural-network training and for parameter tuning in population-based metaheuristic algorithms used for forecasting high-frequency financial data. Empirical evaluation is conducted on one-minute EUR/CZK exchange rate data from 2018 and on 17 high-frequency Amazon stock price datasets spanning 2005-2021. The results demonstrate that incorporating modern software optimization tools not only improves predictive accuracy but also significantly reduces computation time, making the approach well-suited for real-time forecasting of highly dynamic financial time series.
Submerged surface texturing of AISI 304L using the pulsating water jet method
(Springer Nature, 2024) Stolárik, Gabriel; Klichová, Dagmar; Poloprudský, Jakub; Chlupová, Alice; Nag, Akash; Hloch, Sergej
Submerged jets have a variety of practical applications due to their versatility in providing efficient and environmentally friendly options for treatment in various industries. The physical background is based on the continuous water jet (CWJ) application powered via stagnation pressure. However, it is known that impact pressure is much more effective than static pressure. When the impact pressure is repeated with a high frequency per time unit, the erosive effects of water can be used even at pressures below 100 MPa, which is attractive from the point of view of the low demands of the hydraulic system. Surface modification utilising impact pressure can be achieved by employing the pulsed water jet (PWJ) method. The combination of parameters such as the traverse speed and trajectory pattern can control the number of water clusters impacting the material surface. So far, the field of application of PWJ for surface treatment has mostly been investigated water atmospheric conditions. This article focuses on the possibility of the surface modification of AISI 304L stainless steel using the PWJ method under submerged conditions. The results are compared to those obtained under atmospheric conditions. The reference samples were treated by the same technological conditions using a continuous water jet (CWJ). The affected surfaces were characterised using areal surface roughness parameters Sa, Sz, Sp, and Sv, and the surface topography and mechanism of erosion wear were evaluated by scanning electron microscopy. A significant increase in all roughness parameters was confirmed using the PWJ compared to the CWJ method (both in atmospheric and submerged conditions), which confirms the importance of using impact pressure. The surface treatment by PWJ under submerged conditions resulted in a decrease of the surface roughness parameter Sa by approximately 97% compared to atmospheric conditions at a traverse speed of 2 mm/s for perpendicular interleaved trajectory, nevertheless, the homogeneity of treatment over a larger area was improved.
Proof of inherent intelligence consensus mechanism empowering blockchain-enabled transactive energy
(IEEE, 2025) Hussain, Imran; Hussain, Hafiz Ashiq; Ullah, Nasim; Mišák, Stanislav
An evolving energy system with a dispersed infrastructure may not be compatible with traditional centralized optimization and management techniques. Blockchain, a peer-to-peer immutable distributed ledger technology, has the potential to significantly contribute to the management of emerging trends of decentralized power networks. However, complex optimization problems associated with the decentralized power grid are poorly integrated into the existing blockchain applications. Here, we suggest Proof of Inherent Intelligence (PoII), a novel prosumer-centric consensus mechanism designed to assist multi-interest party optimization challenges of the distributed power grid. We demonstrate PoII’s operation and performance with comprehensive mathematical modeling of energy pool-market trading and scheduling optimization problems. The efficiency of the proposed framework is evaluated against the existing blockchain applications for peer-to-peer energy transactions in terms of latency, throughput, tolerance against adversaries, vulnerability, and optimization capabilities. A thorough case study of the power grid that includes thermal, wind, and intermittent generation sources is presented to assess the effectiveness of the proposed consensus mechanism. Power demand, reserves, trading, and scheduling scenarios in both the day-ahead and balancing markets are among the peer-to-peer energy transactional elements that are assessed to support the efficacy of the suggested consensus approach
Priority-based scheduling in residential energy management systems integrated with renewable sources using adaptive Salp swarm algorithm
(Elsevier, 2024) Panda, Subhasis; Samanta, Indu Sekhar; Rout, Pravat Kumar; Sahu, Binod Kumar; Bajaj, Mohit; Blažek, Vojtěch; Prokop, Lukáš; Mišák, Stanislav
With the remarkable growth and implementation of communication technology, sensors, and measurement equipment in the Smart Grid (SG) environment, demand side management (DSM) and demand response (DRs) can be easily implementable in residential energy systems integrated with renewable energy sources (RES). Looking at this perspective, this paper suggests an intelligent and dynamic load-priority-based scheduling optimal smart residential energy management system (REMS). The objectives to achieve through priority-based scheduling in the case of a residential energy management system are multi-focussed in terms of peak load reduction, consumer choice of consumption according to priority basis, and cost-effectiveness towards electricity price savings. The issues related to uncertainties with RES due to environmental dependency must be incorporated into the DSM. A single objective discrete formulation based on the Adaptive Salp Swarm Algorithm (ASSA) has been done on modelling and optimizing the crucial system parameters for scheduling, ideally the operation of residential appliances, along with the sources and prioritized-based loads available. System constraints, consumer priorities, energy source availability, uncertainties, and objectives are considered in the formulation to justify the approach that is feasible in real-time conditions. To enhance the search capabilities of SSA, the control parameters vary optimally in both the exploration and exploitation stages of searching. Comparative results with genetic algorithms (GA), particle swarm optimization (PSO), and conventional SSA are presented in different cases, such as (1) traditional homes without REMS, (ii) smart homes with REMS (iii) smart homes using REMS with RES.

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