Scenario Approach Research Articles

Optimizing operations of flexible assembly systems: demonstration of a digital twin concept with optimized planning and control, sensors and visualization

AbstractThis paper presents the development of an optimized planning and control method for flexible manufacturing and assembly systems. While the significant potential of flexible manufacturing concepts to help producers adapt to market developments is recognized, the complexity of the flexible systems and the need to optimally plan and control them is a major obstacle in their practical implementation. Thus, this paper aims to develop a comprehensive digital planning method, based on a digital twin and to demonstrate the feasibility of the approach for practical application scenarios. The approach consists of four modules: (1) a simulation-based optimization module that applies reinforcement learning and genetic algorithms to optimize the module configuration and job routing in cellular reconfigurable manufacturing systems; (2) a synchronization module that links the physical and virtual systems via sensors and event handling; (3) a sensor module that enables a continuous status update for the digital twin; and (4) a visualization module that communicates the optimized plans and control measures to the shop floor staff. The demonstrator implementation and evaluation are implemented in a learning factory. The results include solutions for the method components and demonstrate their successful interaction in a digital twin, while also pointing towards the current technology readiness and future work required to transfer this demonstrator implementation to a full-scale industrial implementation.

Accuracy Assessment of Advanced Laser Scanner Technologies for Forest Survey Based on Three-Dimensional Point Cloud Data

Forests play a crucial role in carbon sequestration and climate change mitigation, offering ecosystem services, biodiversity conservation, and water resource management. As global efforts to reduce greenhouse gas emissions intensify, the demand for accurate spatial information to monitor forest conditions and assess carbon absorption capacity has grown. LiDAR (Light Detection and Ranging) has emerged as a transformative tool, providing high-resolution 3D spatial data for detailed analysis of forest attributes, including tree height, canopy structure, and biomass distribution. Unlike traditional manpower-intensive forest surveys, which are time-consuming and often limited in accuracy, LiDAR offers a more efficient and reliable solution. This study evaluates the accuracy and applicability of advanced LiDAR technologies—drone-mounted, terrestrial, and mobile scanners—for generating 3D forest spatial data. The results show that the terrestrial LiDAR achieved the highest precision for diameter at breast height (DBH) and tree height measurements, with RMSE values of 0.66 cm and 0.91 m, respectively. Drone-mounted LiDAR demonstrated excellent efficiency for large-scale surveys, while mobile LiDAR offered portability and speed but required further improvement in accuracy (e.g., RMSE: DBH 0.76 cm, tree height 1.83 m). By comparing these technologies, this study identifies their strengths, limitations, and optimal application scenarios, contributing to more accurate forest management practices and carbon absorption assessments.

Innovative management of the production risks of agricultural enterprises

The article examines the innovative infrastructure in the management of production risks of agricultural enterprises, which is related to the rules for evaluating alternative scenarios for reducing threats in the production system of agricultural production entities in order to obtain the desired result. A methodical approach to the assessment of factors-tools in the innovative management of production risks, which activate the process of economic development of agricultural enterprises in an institutional environment, is presented. It is proved that the institutional determinants that manage the production risks of agricultural enterprises form a system whose effectiveness depends on the implementation of the relevant directions of economic development of agricultural institutes focused on the introduction of innovations into the production cycle. The structural dialectic connection of the concept of innovative development of agricultural enterprises with the cyclical development of the production system is presented. A structural and logical diagram of a methodical approach to the implementation of the mechanism of innovative management of production risks of agricultural enterprises has been built. A mathematical toolkit for evaluating scenarios of innovative management of production risks of agricultural enterprises is defined. Stimulating and disincentive factors-tools in the innovative management of production risks and their impact on the economic development of agricultural enterprises are determined. In order to determine the optimal scenarios for neutralizing threats to the economic development of agricultural enterprises, models of acceleration (deceleration) of the action of stimulating and disincentive factors-tools in the innovative management of production risks were built. The integral index of the economic development of agricultural enterprises of the agro-climatic regions of Ukraine in the pre-war, war-conflict and post-war periods was calculated. It has been established that due to the accumulation of a significant amount of production, financial, material, technical and innovative potential, the level of economic development of agricultural enterprises of the agro-climatic zones of the Forest-Steppe and Polissia increases, which characterizes their ability to reproduce the production system of agriculture in Ukraine.

Stochastic population models to identify optimal and cost‐effective harvest strategies for feral pig eradication

AbstractEradicating feral pigs from island ecosystems can assist in restoring damaged biodiversity values and protect commercial industries such as agriculture. Although many feral pig eradications have been attempted, management decisions are often led by practitioner experience rather than empirical evidence. Few interventions have been guided by population models to identify harvest intensity necessary to achieve eradication within a specified time frame, nor have they applied data on control effort and costs to evaluate the relative cost‐effectiveness of proposed control strategies. We used effort and cost data from a feral pig‐control program on Kangaroo Island, South Australia, over 17 months to derive functional‐response relationships between control effort (in hours per pig) and pig abundance for four control methods: (1) ground‐based shooting, (2) trapping with remote triggers, (3) poison baiting, and (4) thermal‐assisted aerial culling. We developed a stochastic Leslie matrix with compensatory density feedback on survival to project population trajectories from an initial population (N0) of 250 female pigs with an estimated island‐wide carrying capacity (K) of 2500 over 3 and 10 years for populations subjected to an annual harvest of 35%–95%. We built functional‐response models to calculate annual effort and cost for six cull scenarios across all harvest rates. We derived total cost and effort over 3‐ and 10‐year projections from the sum of annual cost and effort within the projection intervals. Pig populations were reduced to <10% N0 based on harvest rates >80% and 60% for culls of 3‐ and 10‐year durations, respectively. In all scenarios above, the minimum required harvest rate and the total cost to reduce population to ≤10% of N0 decreased with increasing harvest proportion, with lower total costs incurred over 3 years compared to 10 years. The simulations suggest that the most cost‐effective approach for most scenarios is to maximize annual harvest and complete eradication effort over the shortest periods.

Sigmoidal learning rate optimizer for deep neural network training using a two-phase adaptation approach

The optimization of machine learning models is an open research question since an optimal procedure to change the learning rate throughout training is still unknown. Manually defining a learning rate schedule involves troublesome, time-consuming try-and-error procedures to determine hyperparameters, such as learning rate decay epochs and learning rate decay rates, in order to navigate the intricate landscape of loss functions. Although adaptive learning rate optimizers automatize this process, recent studies suggest they may produce overfitting and reduce performance compared to fine-tuned learning rate schedules. Considering that the new machine learning loss function approaches present landscapes with much more saddle points than local minima, we proposed the Training Aware Sigmoidal Optimizer (TASO). This automated two-phase learning rate adaptation mechanism significantly reduces the need for manual hyperparameter tuning. The first phase uses a high learning rate to quickly traverse the numerous saddle points in the error surface, while the second phase uses a low learning rate to gradually approach the center of the local minimum previously found. We compared the proposed approach with commonly used adaptive learning rate schedules such as Adam, RMSProp, and Adagrad. The validation experiments were performed in image and text datasets and showed that TASO outperformed all competing methods in optimal (i.e., performing hyperparameter validation) and suboptimal (i.e., using default hyperparameters) scenarios. In our benchmark tests, TASO demonstrated promising performance, achieving an 8.32% increase in accuracy and a significant 46.62% decrease in training loss on average across various datasets and models. This remarkable performance positions TASO ahead of well-established adaptive optimizers, suggesting higher effectiveness and consistency in performance.

Development of a Cloud Service for Comprehensive Research of Polymer Synthesis Processes

The issue of digitalization in chemical technology production is currently quite pressing, and the available computational infrastructure is insufficient for assessing the technological properties of the products obtained through mathematical modeling tools. This problem is particularly relevant for polymer synthesis processes, where standard empirical evaluations require enormous computational resources, and existing methods and algorithms prove ineffective when organizing multiple computational trials to select optimal production scenarios. The aim of this study is to develop a cloud-based digital service that enables comprehensive research into complex physicochemical processes occurring via polymerization mechanisms. The implementation of all algorithms is based on the use of kinetic and statistical approaches to modeling, and the embedded calculation methods are adapted to the specifics of polymerization processes. The conceptual framework of the developed cloud service is represented by a three-tier network architecture, and the established mechanism of network interaction allows the service to operate in a 24-hour multi-user mode. Task execution in the remote environment and the distribution of computational resources are handled using Docker containerization technology, which provides software-level virtualization within the operating system. The storage subsystem is managed by the MongoDB database management system, which supports distributed information storage functions. The organization of test computational experiments in evaluating the detailed properties of polymer products allowed for the assessment of the system’s core logic in web interface mode and the adequacy of the obtained calculation results. Doi: 10.28991/ESJ-2024-08-06-023 Full Text: PDF

Quantum Ant Colony Algorithm for Solving the Traveling Salesman Problem: A Theoretical and Practical Analysis

The quantum ant colony algorithm (QACO) is explored as a solution to the traveling salesman problem (TSP), targeting inefficiencies such as slow convergence and local optima entrapment found in traditional ant colony optimization (ACO) methods. By integrating quantum computing elements, specifically quantum rotation gates and qubits, into the ACO framework, QACO demonstrates enhanced efficiency and faster convergence. This paper delves into the foundational principles of quantum computing and their application to refine ACO, effectively solving the TSP. An in-depth analysis is provided, showcasing QACO's advantages over traditional ACO through empirical examples. These examples illustrate QACO's potential to improve path planning for applications within the rapidly growing e-commerce and logistics sectors. The integration of quantum mechanisms facilitates a more dynamic pheromone update process, which significantly reduces the likelihood of premature convergence and enhances solution quality in complex optimization scenarios. This advancement indicates a promising direction for future research in algorithmic optimization in both theoretical and practical applications.

Eco-efficient cadmium(II) removal from water using alcohol distillate waste: A study of life cycle assessment

This study investigates the use of exhausted grape pomace, a byproduct of alcohol distillation, as a biosorbent for use in removing cadmium(II) from wastewater, in line with principles of a circular economy. Cadmium in water presents substantial environmental and health risks. Conventional treatment methods are often costly and environmentally damaging. This research proposes the use of agro-industrial waste as a sustainable and economical alternative. The biosorption process was optimized with Box-Behnken Design and Response Surface Methodology an adjusting factor such as the cadmium concentration, biosorbent dosage, solution pH, and stirring time. The findings reveal that cadmium(II) removal improves with increased stirring time, reaching a 99 % removal rate at 134 min. Optimal conditions include a biosorbent dosage of 2.4 g, pH 4.7, and initial cadmium concentration of 55 mg/L. The biosorption process also increased the solution’s pH, ensuring compliance with wastewater discharge regulations. Two optimization scenarios were evaluated. One achieved a 99.99 % removal efficiency for maximum cadmium reduction, The second focused on cost-effectiveness and environmental impact, attaining 77.35 % removal. This scenario highlights reduced the environmental impact, thereby supporting the role of exhausted grape pomace as a viable and eco-friendly option for wastewater treatment. Life cycle analysis, using CML-IA mid-point and ReCiPe end-point indicators, further underscores the sustainability of this approach, affirming the potential of grape pomace to contribute to circular, resource-efficient wastewater management.

Flight trajectory optimization study of a variable-cycle turbine-based combined cycle engine hypersonic vehicle based on airframe/engine integration

Abstract To investigate the optimal flight trajectory for hypersonic vehicle and Turbine-Based Combined Cycle (TBCC) engine to enhance mission efficacy, a performance calculation model of a variable-cycle TBCC engine is developed utilizing the method of airframe/engine integrated performance analysis. Employing the Radau pseudospectral method for trajectory optimization, the study of the hypersonic vehicle involves the angle of attack, the scramjet engine equivalence ratio, and the core driven fan stage (CDFS) stator vane angle as control variables. Two optimization scenarios were considered: minimizing climb time and maximizing range. The findings indicate that by optimally adjusting the angle of attack and the scramjet engine’s equivalence ratio, the vehicle’s climb time can be reduced by up to 33.68 %, and the total flight duration by 8.07 %. Moreover, optimizing these variables, along with CDFS stator vane angle, can decrease fuel consumption during climb and potentially extend cruising distance by 8.56 %, increasing overall range by 3.63 %.

Bootstrap-based inference for multiple mean-variance changepoint models

Identifying multiple change points in the mean and/or variance is crucial across various fields, including finance and quality control. We introduce a novel technique that detects change points for the mean and/or variance of a noisy sequence and constructs confidence intervals for both the mean and variance of the sequence. This method integrates the weighted bootstrap with the Sequential Binary Segmentation (SBS) algorithm. Not only does our technique pinpoint the location and number of change points, but it also determines the type of change for each estimated point, specifying whether the change occurred in the mean, variance, or both. Our simulations show that our method outperforms other approaches in most scenarios, clearly demonstrating its superiority. Finally, we apply our technique to three datasets, including DNA copy number variation, stock volume, and traffic flow data, further validating its practical utility and wide-ranging applicability.

Efficient Tuning of an Isotope Separation Online System Through Safe Bayesian Optimization with Simulation-Informed Gaussian Process for the Constraints

Optimizing process outcomes by tuning parameters through an automated system is common in industry. Ideally, this optimization is performed as efficiently as possible, using the minimum number of steps to achieve an optimal configuration. However, care must often be taken to ensure that, in pursuing the optimal solution, the process does not enter an “unsafe” state (for the process itself or its surroundings). Safe Bayesian optimization is a viable method in such contexts, as it guarantees constraint fulfillment during the optimization process, ensuring the system remains safe. This method assumes the constraints are real-valued and continuous functions. However, in some cases, the constraints are binary (true/false) or classification-based (safe/unsafe), limiting the direct application of safe Bayesian optimization. Therefore, a slight modification of safe Bayesian optimization allows for applying the method using a probabilistic classifier for learning classification constraints. However, violation of constraints may occur during the optimization process, as the theoretical guarantees of safe Bayesian optimization do not apply to discontinuous functions. This paper addresses this limitation by introducing an enhanced version of safe Bayesian optimization incorporating a simulation-informed Gaussian process (GP) for handling classification constraints. The simulation-informed GP transforms the classification constraint into a piece-wise function, enabling the application of safe Bayesian optimization. We applied this approach to optimize the parameters of a computational model for the isotope separator online (ISOL) at the MYRRHA facility (Multipurpose Hybrid Research Reactor for High-Tech Applications). The results revealed a significant reduction in constraint violations—approximately 80%—compared to safe Bayesian optimization methods that directly learn the classification constraints using Laplace approximation and expectation propagation. The sensitivity to the accuracy of the simulation model was analyzed to determine the extent to which it is advantageous to use the proposed method. These findings suggest that incorporating available information into the optimization process is valuable for reducing the number of unsafe outcomes in constrained optimization scenarios.

Identifying future challenges for climate change adaptation through insights from participatory scenario-downscaling in Mumbai

Populations in many coastal urban areas are increasingly exposed to climate-related hazards. At the same time, the number of people residing in coastal cities is growing, and, especially in the Global South, these cities are characterised by rapid urbanisation and social inequality. However, the progress of adaptation is lagging, and there is a limited understanding of how future socioeconomic urban developments will affect cities’ social vulnerability and challenges to adaptation. We use the case study of Mumbai to apply a participatory scenario approach, in which we downscale the global Shared Socioeconomic Pathway (SSP) narratives to the local level. Our results stress the relevance of addressing social inequality in urban change processes across different sectors, including labour, housing, transport, and health and streamlining urban planning across different governance scales. Our study lays the ground for integrated modelling of future vulnerability and exposure scenarios and the development of local adaptation pathways.

An Optimization Approach for Biosurveillance in Wastewater Networks

Biosurveillance is the ongoing systematic collection, analysis, and interpretation of biospheric data essential to planning, implementing, and evaluating national security interests. Biosurveillance efforts often focus on disease activity and threats to human, animal, or plant health to improve situational awareness of and early warnings of disease emergence or activity. Wastewater-based epidemiology (WBE) is an important subdomain of biosurveillance that monitors the consumption of, or exposure to, chemicals and pathogens at the community/population level. However, ecological, environmental, economic, cultural, and political variability yields an asymmetric landscape of threats, subsequent exposure(s), and community burden. This work presents an exact approach based on mixed-integer programming to optimize sensor placement in a wastewater network. The primary goal is to maximize the amount of information captured from the network while narrowing down the potential geographical sources of chemical or biological markers (i.e., signals). We use the wastewater systems of Los Angeles (US) and Regina (CAN) to demonstrate the scalability of our approach in realistic scenarios and how it can enhance stakeholders’ ability to identify emerging threats or assess the effectiveness of strategic interventions post-deployment.

Where to forestation in dry-wet transition areas of China?

As global warming intensifies, forestation has been recognized as an effective measure for mitigating climate change. However, current research often overlooks the potential for forestation in dry-wet transition areas and lacks scientific quantitative methods for assessing the suitability of forestation in these regions. This study offers a detailed analysis aimed at accurately predicting the potential for forestation within China's dry-wet transition areas. The findings indicate that these areas, spanning 71 million hectares across North, Northeast, and Northwest China, align closely with the Heihe-Tengchong Line and the Three-North Shelter Forest project. Despite the need for soil quality improvement, these regions exhibit the fundamental conditions required for forestation. Climatically, the mid-temperate zone, characterized by a mild climate and moderate rainfall, holds the greatest forestation potential (52.7 Mha), followed by the southern temperate and plateau climate zones, which, despite lower rainfall and higher altitudes, still provide substantial opportunities for tree planting. At the provincial level, Inner Mongolia offers the highest potential with 32.7 Mha, followed by Gansu, Shaanxi, Shanxi, and Xinjiang, which possess vast arid and semi-arid landscapes conducive to ecological restoration and desertification control. In contrast, highly urbanized regions such as Beijing, Tianjin, and Jiangsu show limited potential due to land scarcity. The study highlights optimal forestation scenarios while acknowledging the substantial costs associated with land modification, irrigation, and forest management. These economic factors must be carefully weighed against the ecological benefits of forestation to guide effective policy decisions in these regions.

Optimum Geometry of Double-skin Self-Shading Facade of Classrooms with the Aim of Creating Energy Saving and Visual Comfort in Isfahan Province, Iran

The significant energy consumption in educational spaces worldwide and its environmental impact greatly influence the quality of space, learning levels, and student comfort. Despite offering free school energy costs, developing countries like Iran have not established specific design principles to ensure student comfort. Additionally, the poor design of school building exteriors, such as the common installation of large, unshaded windows in Iranian schools, causes glare issues. The primary objective of this study is to control direct sunlight and increase shading, thereby reducing its impact on energy consumption and enhancing visual comfort. This paper proposes a novel solution that combines a self-shading facade with a double-skin facade for classroom spaces. The study variables, involving the modification of the geometry of the double-skin self-shading facade via DesignBuilder software and the Daysim plugin, were compared to a simple double-layer facade. Based on the results, the optimal scenario for the self-shading double-skin façade with the specifications of a triangular pyramid module shape, ridge position fold 3/2 the module height, cavity depth 7.0, and number of module 2×2 exhibited 40% lower cooling load, 25% lower heating load, and 95% lower lighting load than a simple double-skin facade. At the same time, all scenarios of the new solution provided better visual comfort and daylighting criteria compared to the simple double-skin facade. The modularity and use of indigenous brick materials in the double-skin self-shading facade design reduce construction costs.

Comparison of the Effectiveness of Different Assisted Reproductive Techniques (ART) in Couples with Unexplained Infertility

Introduction Unexplained infertility, impacting 10-30% of couples, remains a significant challenge due to the absence of identifiable etiologies, complicating treatment decisions. Assisted reproductive technologies (ART) like IVF, IUI, and ICSI have become primary interventions, though their efficacy varies across patient subgroups. Objective of this StudyThis study aims to evaluate the clinical efficacy of IVF, IUI, and ICSI in managing unexplained infertility, considering patient demographics and prior ART outcomes, to guide evidence-based clinical decision-making. Matherials and methods A detailed literature search was undertaken across PubMed, Web of Science, and Embase to locate research on the adverse effects of monoclonal antibodies used in asthma management. The search covered English-language studies from the start of each database up to 2024. To ensure comprehensiveness, additional relevant studies were found by examining the reference lists of selected articles. Aim of the KnowledgeThe research seeks to clarify ART effectiveness in unexplained infertility, identifying optimal scenarios for each treatment type and assessing advancements that enhance reproductive outcomes while minimizing risks. ConclusionsThe analysis suggests IVF as the most effective option for complex cases, especially in advanced maternal age, though it involves higher risks. IUI is a lower-risk, cost-effective initial treatment, while ICSI is indicated for male factor infertility. Tailored ART strategies based on individual reproductive profiles are essential. SummaryThis study underscores the need for individualized ART approaches in unexplained infertility. IVF remains highly effective for challenging cases, while IUI and ICSI provide viable alternatives depending on patient-specific factors. Future research should aim to refine ART protocols to optimize success rates and minimize associated risks.

Affordability of healthy and water-saving dietary patterns in The Gambia

Abstract Background: Dietary modification has the potential to improve nutritional status and reduce environmental impacts of the food system. However, for many countries, the optimal composition of locally contextualised healthy and sustainable diets is unknown. The Gambia is vulnerable to climate-change-induced future water scarcity which may affect crop yields and the ability to supply healthy diets. This study identifies potential shifts in Gambian diets that could make diets healthier and reduce the associated agricultural water footprint, and assesses the cost and affordability implications of such dietary changes. 
Methods: Gambian Integrated Household Survey (IHS) food consumption data was combined with market prices, food expenditure and agricultural water footprint data. Current dietary patterns were compared with WHO dietary guidelines and optimised using linear programming to identify least-cost diets that met nutrition recommendations and reduced agricultural water use. Optimisation scenarios explored the maximum reduction in green water use that could be achieved with “culturally-acceptable” dietary shifts, and the magnitude of shifts required to maintain green water use at current levels. 
Results: On average, current diets provide adequate energy and have appropriate macronutrient composition. However, only 14% of households consume enough fruit and vegetables, and consumption of added sugars exceeds recommendations. With ‘culturally-acceptable’ changes in consumption, agricultural water use could decrease by 10–13% or increase by 9%, depending on baseline dietary pattern. Extreme dietary shifts will be required to maintain water use at 2015 levels with projected population growth. To meet WHO recommendations dietary costs would increase by 43% compared to the current baseline. Healthy and green water-saving diets would require 48–63% of average household expenditure to purchase, which is unaffordable for almost half of the population. Fruit and vegetables alone account for 31–40% of the cost of optimised diets compared to 12% of current diets.
Conclusion: Dietary modification has the potential to improve the nutritional quality of Gambian diets while reducing agricultural water use, but the required changes are likely to be unaffordable for a large proportion of the population. Improving availability and affordability of nutritious foods—particularly fruit and vegetables—will be crucial for accessibility of healthy and sustainable diets in the Gambian population.

Debt-deflation theory and middle-income trap: system dynamics approach

The article analyzes the theoretical and practical aspects of the theory of the relationship between national debt and deflationary processes using the example of the impact of the economic recession in China on the decline in global economic growth, and determines the systemic effects of the national economy falling into the middle income debt trap and exiting it. With the help of simulation models, which are based on the methodology of system dynamics, the interrelationship of economic processes of global and national economies is presented. It was found that deflation leads to an increase in the price of the existing external and internal debt and inhibits economic growth and economic development of the national economy and is one of the forms of the country’s middle income trap. The use of the scenario approach made it possible to identify the basic options for the development of the world economy under the influence of negative economic phenomena at the national level and to develop appropriate recommendations for middle-income countries to achieve the goals of macroeconomic and financial stabilization with the appropriate access to the trajectory of new economic development.The study found that the factors of education, social and innovative development are of decisive importance in the country’s achievement of the goal of leaving the middle income trap. The economies of such countries as China are unable to independently overcome this trap without further involvement in global integration and trade processes, and at the same time they themselves form deflationary processes at the global level. In addition, the policy of economic and political semi-isolation leads to the destabilization of the existing world economic and financial systems.

Estimating the potential for thermal energy efficiency in the industrial sector: A hybrid model integrating exergy analysis and long-term technology diffusion scenarios

Thermal energy for heating and cooling accounts for a significant portion of the total energy consumed in the industrial sector, and is predominantly provided by fossil fuels. Thus, effective thermal energy management is essential for reducing overall energy consumption, operational costs, and emissions in the industrial sector. While previous studies have explored energy efficiency in this sector, there is a lack of research specifically addressing the challenges of modeling thermal energy efficiency in industry. Therefore, this study proposes a novel hybrid methodological approach that integrates macroeconomic perspectives with detailed end-use analysis and exergy considerations, allowing for a more accurate assessment of recoverable energy in industrial processes in alternative technology diffusion scenarios over a long-term horizon. An empirical study focusing on the pulp and paper subsector in Brazil demonstrated the applicability of the proposed model and revealed significant potential for thermal energy efficiency improvements. The model identified that through optimal technology diffusion scenarios, the sector could achieve up to 25 % reduction in thermal energy consumption by 2050, with heat recovery systems accounting for approximately 40 % of these savings. Cost-effective technology adoption curves indicated that 60 % of this potential could be realized through economically viable investments with payback periods under 3 years. Overall, this study contributes to the field of industrial energy efficiency by offering a hybrid methodological approach that can be adapted to different industrial settings, and can help policymakers and industry stakeholders develop effective strategies to improve thermal energy efficiency and reduce emissions in the industrial sector.

Revolutionizing Facial Recognition: A Dolphin Glowworm Hybrid Approach for Masked and Unmasked Scenarios

Machine learning has several essential applications, including classification and recognition. Both people and objects may be identified using the Machine learning technique. It is particularly important in the verification process since it recognizes the characteristics of human eyes, fingerprints, and facial patterns. With the advanced technology developments, nowadays, Facial recognition is used as one of the authentication processes by utilizing machine learning and deep learning algorithms and it has been the subject of several academic studies. These algorithms performed well on faces without masks, but not well on faces with masks. since the masks obscured the preponderance of the facial features. As a result, an improved algorithm for facial identification with and without masks is required. After the Covid-19 breakout, deep learning algorithms were utilized in research to recognize faces wearing masks. Those algorithms, however, were trained on both mask- and mask-free faces. Hence, in this, the cropped region for the faces is only used for facial recognition. Here, the features were extracted using the texture features, and the best-optimized features from the glow worm optimization algorithm are used in this paper. With these features set, the hybrid Dolphin glow worm optimization is used for finding the optimal features and spread function value for the neural network. The regression neural network is trained with the optimized feature set and spread function for the face recognition task. The performance of the suggested method will be compared to that of known approaches such as CNN-GSO and CNN for face recognition with and without masks using accuracy, sensitivity, and specificity will next be examined.