The shipping industry, responsible for transporting 90% of global goods, is a major source of pollution and greenhouse gas (GHGs) emissions. In response to the increasingly stricter global and regional emission control regulations, the maritime industry has adopted various operational and technical measures to improve vessel energy efficiency so as to reduce emissions. However, these measures might not be able to effectively address the core issue of emissions, which arises from a heavy reliance on carbon-intensive energy sources. To reduce the emissions from the whole shipping industry more fundamentally, this review evaluates the viability of five alternative marine fuels — liquefied natural gas (LNG), methanol, ammonia, biofuel, and hydrogen — as potential solutions for maritime decarbonization. This review adopts the systematic search flow (SSF) approach, using iterative search refinement and thematic analysis for a structured synthesis of maritime alternative fuel literature. It first introduces each type of alternative fuel with an emphasis on production methods and sources, which are distinctively categorized by “color.” Following this, a comprehensive comparison of the fuels is presented, focusing on technical feasibility, economic viability, emission reduction capabilities, availability, and safety considerations. The practical application of these fuels is further explored through an analysis of their adoption in operational fleets and new orders, as well as the readiness of port infrastructure to support these changes. This review also examines the role of alternative fuels in the development of green shipping corridors, underscored by an analysis of green shipping finance initiatives. The findings provide valuable insights into the viability of these fuels, supporting the International Maritime Organization (IMO)’s 2050 decarbonization goals and paving the way towards zero emissions in global shipping.
Systems engineering, Marketing. Distribution of products
The performance of Organic Solar Cells (OSCs) is intrinsically linked to the molecular, electronic, and structural properties of donor and acceptor materials. This study employs various machine learning techniques, namely the Generalized Regression Neural Network (GRNN), Support Vector Machine (SVM), and Tree Boost, to predict key performance metrics of OSCs, including power conversion efficiency (PCE), short-circuit current density (JSC), open-circuit voltage (VOC), and fill factor (FF). The models are trained and evaluated using an experimentally reported dataset compiled by Sahu et al. Correlation analysis demonstrates that material characteristics such as polarizability, bandgap, dipole moment, and charge transfer are statistically associated with OSC performance. The predictive performance of the GRNN model is compared with that of the SVM and Tree Boost models, showing consistently lower prediction errors within the considered dataset. In addition, sensitivity analysis is performed to assess the relative importance of the predictor variables and to examine the influence of kernel functions on GRNN performance. The results indicate that machine learning models, particularly GRNN, can serve as effective data-driven tools for predicting the performance of organic solar cells and for supporting computational screening studies.
Ahmed M. Zobaa, Hazem H. Abdelnabi, Rodan M. Reda
et al.
Abstract Corona discharge has been recognized for centuries, with sailors reporting the bluish glow of St. Elmo’s fire on ship masts during storms. In the early development of high-voltage engineering, researchers such as Townsend and Peek described the physical basis of this phenomenon as the ionization of air around a conductor when the electric field exceeds the strength of the surrounding medium. The result is a partial discharge that produces visible light, hissing sounds, ozone, and other reactive gases, while also creating radio interference and ultraviolet radiation. In modern transmission systems, these effects appear as wasted power, accelerated wear of insulators, shortened equipment lifetime, and environmental concerns. Although corona has been studied for decades, it continues to challenge the reliable and economical operation of high-voltage networks, particularly under changing weather conditions. This study investigates the phenomenon by analyzing its causes, effects, and mitigation strategies through a combination of theoretical modelling, simulation, and statistical analysis. Using MATLAB Simulink and Python, simulations were conducted under varying environmental conditions—including temperature, humidity, and pressure—as well as electrical parameters such as voltage and conductor design, using observed data to ensure practical relevance. Comparable data sources may be used in other national or regional contexts. Key statistical techniques, including linear and multiple regression, analysis of variance (ANOVA), t-tests, and Monte Carlo simulations, were applied to determine the most influential factors affecting corona discharge losses. Results confirmed that higher voltage levels and unfavorable environmental conditions significantly increase corona loss, while increased conductor spacing and the use of corona rings emerged as the most effective mitigation strategies. An economic analysis based on probabilistic modelling estimated potential annual savings of up to 455 million Egyptian pounds (EGP) for the Egyptian grid, serving as a representative case study. The analytical framework is general and can be applied to other national transmission systems with appropriate data. The findings offer data-driven insights for improving transmission efficiency, minimizing power losses, and enhancing the overall reliability and cost-effectiveness of high-voltage power systems.
Johandri Vosloo, Kenneth R. Uren, George van Schoor
The Tennessee Eastman process serves as a benchmark system for the evaluation of fault diagnosis techniques. Current simulator implementations are available in FORTRAN and in a C-mex S-function in MATLAB. The C-mex file is a conversion of the FORTRAN code to C for implementation in MATLAB. Both implementations have the limitation that not all the variables and parameters are directly accessible. Hence, a complete and open Tennessee Eastman process simulator was developed in Simulink to allow for total access to all parameters and variables and better Simulink integration. This implementation will give researchers more freedom towards the design of control and fault diagnosis techniques.
Silas M. Mbeche, Paul M. Wambua, David N. Githinji
Human hair (HH) is considered a waste material generated in salons and barbershops in most societies, especially highly populated cities, where it is produced in large quantities, thus rekindling the interests of academics. Several studies are ongoing on the possibility of utilizing it as a reinforcement in polymer composites, either in its raw form or as extracted keratin nanoparticles, due to its unique features and the current global emphasis on circular economy. The present review seeks to provide a synopsis of recent developments in the utilization of HH and keratin in polymer composites. Composites from different HH loading, length, and chemical treatments were made using hand lay-up and hot compression molding methods. HH has been investigated in diverse composite systems, encompassing HH/natural fiber composites, HH/synthetic fiber composites, and keratin-reinforced composites. Our study revealed that these innovative materials exhibit enhanced energy absorption capacity, mechanical strength, hardness, and thermal properties, positioning them as promising choices for a wide range of engineering applications. The review further revealed that keratin nano-particles can be extracted from waste HH using various methods such as reduction alkaline hydrolysis and can be used as reinforcement in polymer composites.
Science, Textile bleaching, dyeing, printing, etc.
Abstract The metaphor of technical debt (TD) is widely adopted in the software engineering field, referring to short‐term compromises in software artifacts in exchange for speed or to meet release schedules or other constraints. The implication is that TDs accumulate over time, and may eventually make rework or maintenance very expensive or even impossible. The analogy is generally applicable in the systems engineering field, particularly concerning numerous program cancellation and obsolescence challenges due to premature decisions made in early acquisition phases. This paper adapts this metaphor of TD to the systems engineering field, and proposes a TD taxonomy to support the early identification and assessment of TD items in engineering complex systems, especially in the early life cycle phases of engineering complex, distributed systems. The taxonomy identifies seven TD types: functionality, performance, interoperability, version conflicts, documentation and support, system evolution, and organic, based on systematic indicators and signs discoverable during early acquisition activities. We expect that the notion and the taxonomy of TD will offer an additional perspective for design decisions that will help mitigate challenging integration and obsolescence issues in the engineering of complex systems.
In this paper the objective is to force the outputs of nonlinear nonaffine multi-input multi-output (MIMO) systems to track those of a linear system with the desired properties. The approach is based on designing higher order sliding mode controller (HOSMC) with the definition of a new proportional-integral (PI) sliding surface. To this end, a linear state feedback with an adaptive switching gain (ASG) is applied to the nonlinear MIMO systems. Therefore, the switching gain can increase or decrease based on the system conditions. Then, the chattering is completely removed using a combination of HOSMC and ASG. Moreover, the proposed procedure is independent from the upper bound of the matched uncertainty, which is in the direction of system inputs. The finite time convergence to the sliding surface is also proved, which provides an invariance property in finite time. Note that invariance is the most important property of SMC. Finally, the general model of MIMO induction motors (IM) is used to address and to verify the proposed controller.
Credit card fraud is becoming a serious and growing problem as a result of the emergence of innovative technologies and communication methods, such as contactless payment. In this article, we present an in-depth review of cutting-edge research on detecting and predicting fraudulent credit card transactions conducted from 2015 to 2021 inclusive. The selection of 40 relevant articles is reviewed and categorized according to the topics covered (class imbalance problem, feature engineering, etc.) and the machine learning technology used (modelling traditional and deep learning). Our study shows a limited investigation to date into deep learning, revealing that more research is required to address the challenges associated with detecting credit card fraud through the use of new technologies such as big data analytics, large-scale machine learning and cloud computing. Raising current research issues and highlighting future research directions, our study provides a useful source to guide academic and industrial researchers in evaluating financial fraud detection systems and designing robust solutions.
Onur Çeli̇k, S. Ece Yilmaz, Hasan Yildizhan
et al.
Renewable energy sources are fundamental to a country’s economic growth. Solar energy is one of these resources that has a favorable effect on economic growth. Turkey’s solar energy industry is still in its early stages. Due to its location and degree of sunshine each year, the country has a great solar potential. Despite the huge potential, solar energy awareness and utilization are not widespread in all parts of Turkey. In order to identify the factors that affect consumers’ decisions to utilize water heating systems, which is a sort of solar energy system, the purpose of this research is to examine these systems. In this study, all factors influencing consumers’ decisions to acquire solar water heating systems were evaluated holistically for the first time. A questionnaire was used in the study, which is a quantitative research technique. The study identifies the variables that influence consumers’ attitudes toward solar collector purchases and assesses the consequences from an organizational point of view. The study’s results act as a guide for decision-makers.
Ibraheem Shayea, Abdulraqeb Alhammadi, Ayman A. El-Saleh
et al.
Mobile broadband (MBB) services are rapidly growing, causing a massive increase in mobile data traffic growth. This surge in data traffic is due to several factors (such as the massive increase of subscribers, mobile applications, etc.) which have led to the need for more bandwidth. Mobile service providers are constantly improving their network efficiency by upgrading current networks and investing in newer mobile network generations. However, these improvements will not be enough to accommodate the future spectrum demands. This paper proposes a time series forecasting model to analyze future spectrum demands based on the spectrum efficiency growth of MBB networks. This model depends on two key input data: the average spectrum efficiency per site and the number of sites per technology. The model is used to predict the spectrum efficiency growth of three countries (Turkey, Malaysia, and Oman) from 2015 to 2025. The proposed model is compared with various traditional statistical models such as the Moving Average (MA), Auto-Regression (AR), Autoregressive–Moving-Average (ARMA), and Autoregressive Integrated Moving Average (ARIMA). The forecasted results indicate that the average spectrum efficiency and growth will continue to rise multiple times by 2025 compared to 2015. The data from this prediction model can be used as input data to forecast the required spectrum needed in future for any specific country. This study further contributes to the network planning of future mobile networks for Fifth Generation (5G) and Sixth Generation (6G) technology. The proposed model obtains higher accuracy (by 90%) compared to other models. The proposed model is also applicable to any country, especially when new wireless communication technologies emerge in future. It is customizable and scalable since spectrum regulators can add additional metrics that positively contribute towards accurately estimating future spectrum efficiency growth.
Subhasis Panda, Sarthak Mohanty, Pravat Kumar Rout
et al.
Demand-side management (DSM) is a significant component of the smart grid. DSM without sufficient generation capabilities cannot be realized; taking that concern into account, the integration of distributed energy resources (solar, wind, waste-to-energy, EV, or storage systems) has brought effective transformation and challenges to the smart grid. In this review article, it is noted that to overcome these issues, it is crucial to analyze demand-side management from the generation point of view in considering various operational constraints and objectives and identifying multiple factors that affect better planning, scheduling, and management. In this paper, gaps in the research and possible prospects are discussed briefly to provide a proper insight into the current implementation of DSM using distributed energy resources and storage. With the expectation of an increase in the adoption of various types of distributed generation, it is estimated that DSM operations can offer a valuable opportunity for customers and utility aggregators to become active participants in the scheduling, dispatch, and market-oriented trading of energy. This review of DSM will help develop better energy management strategies and reduce system uncertainties, variations, and constraints.
F. S. Aghaei Maybodi, M. H. Heydari, F. M. Maalek Ghaeini
et al.
Introduction
Many mathematical formulations of physical phenomena contain integro-differential equations. These equations arise in fluid dynamics, biological models, chemical kinetics, ecology, control theory of financial mathematics, aerospace systems, industrial mathematics etc. It is worth mentioning that integro-differential equations are usually difficult to solve analytically, and so it is required to obtain an efficient approximate solution for them.
Fractional calculus deals with derivatives and integrals of arbitrary real or complex orders. This subject has attracted attention of many scientists in mathematics, physics and engineering. So, it has become a hot issue in recent years.
Fractional integro-differential equations arise in the mathematical modelling of various physical phenomena, such as heat conduction in materials with memory. Moreover, these equations are encountered in combined conduction, convection and radiation problems. There are only a few techniques for the solution of fractional integro-differential equations, since it is relatively a new subject in mathematics. Some of these methods are Legendre spectral tau method, Adomian decomposition method, piecewise polynomial collocation methods, spline collocation method, hybrid collocation method, hybrid functions approximation by block-pulse functions and Bernoulli polynomials, Taylor expansion approach, differential transform method and wavelet methods.
In recent years many problems in mathematics, physics and engineering have been numerically solved by radial basis functions (RBFs) methods. In this paper, we focus on the Gaussian and inverse multiquardic RBFs as two of the most important tools in engineering and sciences to solve a class of fractional parabolic integro-differential equations. This class of equations describes some phenomena in compression of viscoelastic media and nuclear reactor dynamics.
Material and methods
In the proposed method, first the fractional derivative operator is transformed into a non-singular equivalent. Then, the Gaussian and inverse multiquardic RBFs together with the collocation method and Gauss-Legendre quadrature formula are used to transform the problem under consideration into the corresponding system of linear algebraic equations, which can be simply solved to achieve an approximate solution of the problem.
Results and discussion
Some numerical examples are examined to demonstrate the efficiency and high accuracy of the present method. The obtained results demonstrate that there is a good agreement between the approximate solutions and the exact ones. Also we hope that the proposed method can provide numerical solutions with high accuracy for the problems under study for all fractional orders. Meanwhile, the best value for the shape parameter in the Gaussian and inverse multiquardic RBFs method can be obtained by employing an appropriate optimization method.
Conclusion
The following conclusions were extracted from this research.
The established method transforms such problems into equivalent systems of algebraic equations by expanding the solution of the problem in terms of the RBFs and applying Gauss-Legendre integration formula.
Only a few number of the RBFs is needed to obtain a high accurate numerical solution for such problems.
The presented method can easily be developed for other classes of fractional partial integro-differential equations../files/site1/files/71/2.pdf
Abstract This research paper focusses on designing a resilient fuzzy controller for a kind of singular stochastic biological economic fishery model with a variable economic profit. Initially, the singular stochastic biological economic fishery model is formulated as T‐S fuzzy singular systems. By using the ideas of continuous frequency distribution and Lyapunov approach, a fresh collection of linear matrix inequalities is developed which are sufficient to establish the asymptotic stability of the singular biological economic fishery model. Later these conditions are extended to obtain some novel sufficient finite‐time stability conditions for the addressed model. The ultimate objective of this research paper is to develop a more generalized version of the resilient controller against nonlinear actuator faults such that it makes the considered system stable within a finite interval of time. Moreover, the developed conditions are based on the solutions of LMIs which provides maximal estimation of region of attraction. Conclusively, simulation results are provided to embellish the usefulness of the obtained control design.
Control engineering systems. Automatic machinery (General)
Eugene Boon Kien Lee, Douglas L. Van Bossuyt, Jason F. Bickford
This article presents a Model-Based Systems Engineering (MBSE) methodology for the development of a Digital Twin (DT) for an Unmanned Aerial System (UAS) with the ability to demonstrate route selection capability with a Mission Engineering (ME) focus. It reviews the concept of ME and integrates ME with a MBSE framework for the development of the DT. The methodology is demonstrated through a case study where the UAS is deployed for a Last Mile Delivery (LMD) mission in a military context where adversaries are present, and a route optimization module recommends an optimal route to the user based on a variety of inputs including potential damage or destruction of the UAS by adversary action. The optimization module is based on Multiple Attribute Utility Theory (MAUT) which analyzes predefined criteria which the user assessed would enable the successful conduct of the UAS mission. The article demonstrates that the methodology can execute a ME analysis for route selection to support a user’s decision-making process. The discussion section highlights the key MBSE artifacts and also highlights the benefits of the methodology which standardizes the decision-making process thereby reducing the negative impact of human factors which may deviate from the predefined criteria.
Nicholas Embleton, Janet Berrington, Stephen Cummings
et al.
Background: Preterm infants have high rates of morbidity, especially from late-onset sepsis and necrotising enterocolitis. Lactoferrin is an anti-infective milk protein that may act through effects on gut bacteria, metabolites and epithelial cell function. The impact of supplemental lactoferrin in reducing late-onset sepsis was explored in the Enteral LactoFerrin In Neonates (ELFIN) trial. Objectives: The Mechanisms Affecting the Gut of Preterm Infants in Enteral feeding (MAGPIE) study was nested within the ELFIN trial and aimed to determine the impact of lactoferrin on gut microbiota and bacterial function, and changes preceding disease onset. We aimed to explore impacts on the stool bacteria and faecal/urinary metabolome using gas and liquid chromatography–mass spectrometry, and explore immunohistological pathways in resected tissue. Methods: Preterm infants from 12 NHS hospitals were enrolled in the study, and daily stool and urine samples were collected. Local sample collection data were combined with ELFIN trial data from the National Perinatal Epidemiology Unit, Oxford. The longitudinal impact of lactoferrin in healthy infants was determined, and samples that were collected before disease onset were matched with samples from healthy control infants. Established, quality-controlled 16S ribonucleic acid, gas chromatography–mass spectrometry and liquid chromatography–mass spectrometry analyses were conducted. Validated databases and standardised workflows were used to identify bacteria and metabolites. Tissue samples from infants undergoing surgery and matched controls were analysed. Results: We recruited 479 preterm infants (mean gestation of 28.4 ± 2.3 weeks) and collected > 33,000 usable samples from 467 infants. 16S ribonucleic acid bacterial analysis was conducted on samples from 201 infants, of whom 20 had necrotising enterocolitis and 51 had late-onset sepsis, along with samples from healthy matched controls to explore longitudinal changes. The greatest change in relative bacterial abundance over time was observed in Staphylococcus, which decreased from 42% at aged 7–9 days to only 2% at aged 30–60 days (p < 0.001). Small but significant differences in community composition were observed between samples in each ELFIN trial group (R2 = 0.005; p = 0.04). Staphylococcus (p < 0.01), Haemophilus (p < 0.01) and Lactobacillus (p = 0.01) showed greater mean relative abundance in the placebo group than in the lactoferrin group. Gas chromatography–mass spectrometry and liquid chromatography–mass spectrometry analyses showed that lactoferrin had limited impact on the metabolome. Liquid chromatography–mass spectrometry showed significant metabolite differences between necrotising enterocolitis or late-onset sepsis infants and healthy controls. The resected gut tissue analysis revealed 82 differentially expressed genes between healthy and necrotic tissue. Limitations: Although we recruited a large number of infants, collecting daily samples from every infant is challenging, especially in the few days immediately preceding disease onset. Conclusion: We conducted a large mechanistic study across multiple hospital sites and showed that, although lactoferrin significantly decreased the level of Staphylococcus and other key pathogens, the impact was smaller than those of other clinical variables. Immunohistochemistry identified multiple inflammatory pathways leading to necrotising enterocolitis and showed that the use of NHS pathology archive tissue is feasible in the context of a randomised controlled trial. Future work: We observed significant changes in the stool and urinary metabolome in cases preceding late-onset sepsis or necrotising enterocolitis, which provide metabolic targets for a future mechanistic and biomarker study. Trial registration: Current Controlled Trials ISRCTN12554594. Funding: This project was funded by the Efficacy and Mechanism Evaluation (EME) programme, a Medical Research Council (MRC) and National Institute for Health Research (NIHR) partnership. This will be published in full in Efficacy and Mechanism Evaluation; Vol. 8, No. 14. See the NIHR Journals Library website for further project information.
Deep geothermal energy systems employ electric submersible pumps (ESPs) in order to lift geothermal fluid from the production well to the surface. However, rough downhole conditions and high flow rates impose heavy strain on the components, leading to frequent failures of the pump system. As downhole sensor data is limited and often unrealible, a detailed and dynamical model system will serve as basis for deeper understanding and analysis of the overall system behavior. Furthermore, it allows to design model-based condition monitoring and fault detection systems, and to improve controls leading to a more robust and efficient operation. In this paper, a detailed state-space model of the complete ESP system is derived, covering the electrical, mechanical and hydraulic subsystems. Based on the derived model, the start-up phase of an exemplary yet realistic ESP system in the Megawatt range—located at a setting depth of 950 m and producing geothermal fluid of 140 ∘ C temperature at a rate of 0.145 m 3 s − 1 —is simulated in MATLAB/Simulink. The simulation results show that the system reaches a stable operating point with realistic values. Furthermore, the effect of self-excitation between the filter capacitor and the motor inductor can clearly be observed. A full set of parameters is provided, allowing for direct model implementation and reproduction of the presented results.
As Russian economy is still largely oriented on commodities production, there are a lot of cities where mining and commodity-oriented enterprises are the backbone of city economy. The mentioned enterprises mostly define the life quality of citizens in such cities, thus there are high requirements for engineering of city-forming enterprises. The paper describes the enterprise architecture approach for management system engineering of the mining enterprises. The paper contains the model of the mining enterprise architecture, the approach to the development and implementation of an integrated management system based on the concept of enterprise architecture and the structure of information systems and information technology infrastructure of the mining enterprise.
Noritaka Shimizu, Yutaka Utsuno, Yasunori Futamura
et al.
We introduce a novel method to obtain level densities in large-scale shell-model calculations. Our method is a stochastic estimation of eigenvalue count based on a shifted Krylov-subspace method, which enables us to obtain level densities of huge Hamiltonian matrices. This framework leads to a successful description of both low-lying spectroscopy and the experimentally observed equilibration of Jπ=2+ and 2− states in 58Ni in a unified manner.
Zhanna Petrova, Yuriy Snezhkіn, Vadym Pazyuk
et al.
Reduction in the cost of energy in the food industry is an important challenge today due to increased prices. Drying process of plant material refers to the complex energy-intensive processes. Solution of this problem is the development of drying methods that would reduce energy consumption by the process. In the drying of plant material, another major factor is the quality indicators of the finished product, which depend mainly on the drying technology. Therefore, in the Institute of Technical Thermal Physics, energy-efficient drying modes of plant material that made it possible to reduce energy consumption at the drying process and to preserve the biologically active substances of the feedstock were developed. Research is devoted to developing energy-efficient drying modes of antioxidant materials based on table beet that provide betanin saving by 95 % and reducing energy consumption by 49 %. Today in Ukraine, there are not enough companies processing plant materials in powder, thus preserving the quality of the feedstock.