System Requirements Research Articles

Encapsulating taurine into liposomes: A promising therapeutic for liver fibrosis

We summarize the mechanism by which taurine (Tau) inhibits autophagy and induces iron apoptosis in hepatic stellate cells. Tau interacts with autophagy regulates multifunctional proteins, microtubule-associated protein 1 light chain 3 Beta, and autophagy-related gene 5 to inhibit autophagy, binds to ferritin heavy chain 1 and nuclear receptor coactivator 4 to trigger ferritin autophagy, and interacts with glutathione peroxidase 4 to promote iron apoptosis. There is a solid rationale for developing Tau-based therapies targeting autophagy and ferroptosis regulation. From a pharmaceutical point of view, there are certain requirements for Tau protein delivery systems, such as loading efficiency, stability, and targeting. Nanomaterials should also contain a hydrophilic motif similar to Tau to optimize loading efficiency. Since Tau is a hydrophilic molecule with high water solubility, liposomes, micelles, and amphiphilic polymer nanoparticles may represent a superior choice. The nanostructure of the liposome includes a water region and a lipid membrane to sequester hydrophilic and hydrophobic drugs, respectively, whereas Tau is expected to be loaded into the water region. In addition, a representative method of actively targeting hematopoietic stem cells is introduced. A Tau-based method for the treatment of liver fibrosis is proposed based on the formulation of common liposomes (lecithin plus cholesterol).

Comparison of suckling and artificial rearing on calf growth and milk requirements in pastoral dairy systems

Context Dairy calf-rearing practises have the potential to influence profitability through milk requirements at rearing, and affect lifetime productivity and market access from an animal-welfare perspective. Aims The aim of this research was to compare calf growth and milk requirements by using conventional artificial rearing (AR) and restricted milk allocation with AR and high milk allocation or suckled calves. Methods Forty-five Friesian × Jersey calves were allocated to be either AR on 6 L/calf.day, (ARC) or 12 L/calf.day, (ARH), or suckled with dams in a cow–calf contact system using partial contact (15 h/day, CCC). AR calves were fed, and intake measured, using an automatic feeder, whereas CCC calves had access to their dams between 1500 hours and 0600 hours. Calves were transitioned off milk, once they reached at least 75 kg liveweight (LW), by gradually reducing their milk allocation (AR) or reducing access to their dam (CCC). Key results Milk consumption for ARH was greater than for ARC calves (382 vs 450 L/calf, P < 0.05), whereas for CCC calves milk-yield difference between dams for the control and suckling groups during the suckling period and over the full season was 706 and 1048 L/cow respectively. There was no difference in weaning weight of calves (87 ± 1.7 kg LW), but, owing to differences in pre-weaning growth rate, age to weaning was youngest (P < 0.05) for CCC (55 days), followed by ARH (62 days) and ARC (73 days). Respective growth rates between birth and weaning (P < 0.001) for CCC, ARH and ARC calves were 0.955, 0.873 and 0.755 ± 0.028 kg/day. Although there was a growth check among CCC calves during the weaning period, there were no post-weaning differences in growth rate among the groups. Conclusions Increasing milk allowance had the benefit of improving calf pre-weaning growth rate, giving the option of weaning calves either earlier or when heavier. However, milk yield losses under suckling systems may be too high to warrant the elevated pre-weaning growth of calves. Implications The costs–benefits of high milk allocation or suckling systems need to be ascertained over the lifetime of the animals to assess long-term survival and productivity outcomes.

Multi-scenario flexibility requirement analysis of high proportion of new energy access to power system

Abstract To address climate change, the proportion of renewable energy integration into the grid system is gradually increasing, leading to higher demands for flexibility. Current research typically employs methods such as dynamic system modeling, the construction of flexibility indicators, and scenario analysis to measure the flexibility requirements of the power system across different time scales. The use of frequency decomposition algorithms to explore flexibility requirements from the perspective of net load curves is relatively rare. This study utilizes net load data from the Western Inner Mongolia Power Grid to explore the distribution patterns of net load across different time scales under various seasonal and penetration scenarios using frequency decomposition algorithms. The results reveal that renewable energy output exhibits significant fluctuations and distinct diurnal variations, while net load also shows notable patterns and considerable volatility and seasonality. The analysis of flexibility requirements on typical seasonal days highlights the differences in demand distribution at various frequencies, with long-time-scale components primarily reflecting the overall trend of net load, while the medium-to-long time scale components characterize smaller, more frequent fluctuations. Additionally, the uncertainty associated with wind and solar output significantly affects the diurnal fluctuations of net load, with seasonal changes mainly represented in short-time-scale components. The study also emphasizes the impact of different penetration levels on flexibility requirements, indicating that as penetration decreases, the midnight requirement peak diminishes, suggesting differences in flexibility requirements based on renewable energy integration levels. Furthermore, the paper proposes corresponding solution technologies for “generation–grid–load–storage” across different time scales of flexibility requirements, ensuring the stable operation of the grid amidst climate change and rising electricity demand.

Energy-efficient design and CNFET implementation of GDI-based ternary prefix adders

Abstract Ternary adders have produced more benefits compared to binary adders i.e., the ternary adder occupies less amount of area as well as produces less interconnect complexity. However, the CMOS implementation of the ternary adders failed to perform the process when the channel length was taken as 32 nm. At 32 nm technology, the CMOS transistors exhibit undesired effects such as Short Channel Effects (SCEs), mobility degradation, high leakage current, etc. Multi-gate devices are preferred to overcome these issues. Carbon Nano-tube Field Effect Transistors (CNFETs) are one of the technologies to work efficiently when the channel length is 32 nm. In this paper, CNFET-based ternary prefix adders are designed. Power consumption is the most critical requirement for the VLSI system, as it enhances energy efficiency and reduces heat dissipation. One way to achieve this power reduction is by minimizing the number of transistors employed in the adder circuits. This study employed a reduction technique known as Gate Diffusion Input (GDI) logic included in the proposed prefix adder design. The overall experimental investigation is done with the help of the HSPICE supporting platform. The proposed adder improved by reducing the power by up to 83%, energy by up to 83%, current by up to 78%, and delay by up to 96%. Finally, the Power Delay product (PDP) was also reduced by 84% compared to existing ternary adders. The proposed design proves to be highly effective in implementing the neuron structure, with the corresponding parameters thoroughly analysed and well-documented in this study.

A Deep Cryptographic Framework for Securing the Healthcare Network from Penetration

Ensuring the security of picture data on a network presents considerable difficulties because of the requirement for conventional embedding systems, which ultimately leads to subpar performance. It poses a risk of unauthorized data acquisition and misuse. Moreover, the previous image security-based techniques faced several challenges, including high execution times. As a result, a novel framework called Graph Convolutional-Based Twofish Security (GCbTS) was introduced to secure the images used in healthcare. The medical data are gathered from the Kaggle site and included in the proposed architecture. Preprocessing is performed on the data inserted to remove noise, and the hash 1 value is computed. Using the generated key, these separated images are put through the encryption process to encrypt what they contain. Additionally, to verify the user’s identity, the encrypted data calculates the hash 2 values contrasted alongside the hash 1 value. Following completion of the verification procedure, the data are restored to their original condition and made accessible to authorized individuals by decrypting them with the collective key. Additionally, to determine the effectiveness, the calculated results of the suggested model are connected to the operational copy, which depends on picture privacy.

Knowledge-oriented management systems for complex objects

The article examines the issue of improving the efficiency of complex systems management through the use of knowledge-based methods and technologies. The article emphasizes the need to revise traditional approaches to scientific research and knowledge management by introducing a transdisciplinary paradigm and artificial intelligence to achieve sustainable development and knowledge evolution. The authors try to solve the problem of management in the face of uncertainty and incomplete information, which is typical for such systems. The methods of integrating knowledge from different fields to create an integrated management system are considered. The basis of management is the holarchic structure, which organizes the system in the form of a hierarchy of holons. Each holon has a specific role and knowledge, which ensures the flexibility and adaptability of the system. The study also uses self-organization and learning methods to ensure continuous improvement of the systemʼs functioning. The article discusses the requirements for knowledge-based management systems, such as flexibility, adaptability, data and knowledge integration, learning and self-organization, transparency of decisions, and resilience to errors and failures. The findings of the study emphasize the importance of implementing knowledge-based approaches in the management system of complex facilities. This allows to increase the level of adaptability and stability of management, which is critical in todayʼs environment of dynamic development of technology and knowledge. The proposed methods can be applied in various fields, which makes them universal and useful for a wide range of scientific and practical tasks, providing a high level of adaptability, efficiency and stability of complex objects management, taking into account modern requirements and challenges.

Development and Empirical Validation of an Information Technology Acceptance Framework

This paper explores the constructs affecting IT acceptance in modern organisations using theoretical frameworks such as the TAM and the UTAUT models. Realising the drawbacks of such dichotomous models for capturing the dynamics of contemporary IT enablers and use processes, the present study plans to offer and test new antecedent and moderating constructs that may provide a more accurate picture of today’s IT deployment and utilisation. The type of research in the study is the quantitative research method, which is adopted in the study where the researcher adopted a structured questionnaire to be administered to 200 participants across the different sectors of the economy, such as health, education, and business. Hypotheses of primary concern are those related to the construct of IT acceptance, including perceived usefulness, ease of use, trust, system transparency, perceived risk, and ethical concerns. The analysis of the data gathered is done using Structural Equation Modeling (SEM) because of the possibility of analysing the associations between several variables. Behavioural construct analysis revealed a significant positive correlation with IT acceptance behaviours, with the value of R for the new construct being 0.935 and the R square value being 0.874, which means that newly derived constructs explain a fair percentage of IT acceptance behaviours. The findings, therefore, point out the lack of conventional frameworks in addressing the complex nature of IT use in various organisational and cultural environments. Full-spectrum system requirements in newly developing domains, such as AI, machine learning, and blockchain, require new fundamental concepts regarding user trust, system openness and ethical considerations. This work extends the IT acceptance literature by providing a better view of the determinants of IT uptake in modern organisations. In totality, the validated constructs have practical implications for organisations desirous of adopting new IT systems, particularly highlighting the role of trust and perceived risk and the need for transparency or the adoption of transparency to guarantee user satisfaction rates and consequent adoption of new IT systems. In conclusion, the study points to the need to develop new models that address today’s diverse IT contexts to inform the next generation of IT acceptance and adoption research.

A Novel Light-Weight Machine Learning Classifier for Intrusion Detection in Controller Area Network in Smart Cars

The automotive industry has evolved enormously in recent years, marked by the proliferation of smart vehicles furnished with avant-garde technologies. These intelligent automobiles leverage cutting-edge innovations to deliver enhanced connectivity, automation, and convenience to drivers and passengers. Despite the myriad benefits of smart vehicles, their integration of digital systems has raised concerns regarding cybersecurity vulnerabilities. The primary components of smart cars within smart vehicles encompass in-vehicle communication and intricate computation, in addition to conventional control circuitry. In-vehicle communication is facilitated through a controller area network (CAN), whereby electronic control units communicate via message transmission across the CAN-bus, omitting explicit destination specifications. This broadcasting and non-delineating nature of CAN makes it susceptible to cyber attacks and intrusions, posing high-security risks to the passengers, ultimately prompting the requirement of an intrusion detection system (IDS) accepted for a wide range of cyber-attacks in CAN. To this end, this paper proposed a novel machine learning (ML)-based scheme employing a Pythagorean distance-based algorithm for IDS. This paper employs six real-time collected CAN datasets while studying several cyber attacks to simulate the IDS. The resilience of the proposed scheme is evaluated while comparing the results with the existing ML-based IDS schemes. The simulation results showed that the proposed scheme outperformed the existing studies and achieved 99.92% accuracy and 0.999 F1-score. The precision of the proposed scheme is 99.9%, while the area under the curve (AUC) is 0.9997. Additionally, the computational complexity of the proposed scheme is very low compared to the existing schemes, making it more suitable for the fast decision-making required for smart vehicles.

Dynamic analysis of two-stage steering reducer for electric vehicle based on ROMAX

Abstract. With the rapid development of electric vehicles in the modern market, the efficiency and performance requirements of electric vehicle transmission systems are getting higher and higher. As an important part of the electric vehicle reducer, the steering reducer can make full use of the advantages of the two-stage deceleration transmission, achieve more efficient power transmission, and improve the acceleration performance and driving performance of electric vehicles. This paper aims to design a two-stage steering reducer for an electric vehicle, which adopts planetary gear transmission type. Firstly, the working principle of the electric vehicle steering reducer is analyzed, and the overall design scheme of the reducer is drawn up. Secondly, the transmission system parameters of the reducer are designed and calculated, including transmission ratio distribution, tooth number ratio calculation, etc. Secondly, the transmission structure of the reducer is designed, including input shaft, output shaft, first planetary gear train, second planetary gear train, reducer shell, bearing, etc., and the reducer model is drawn by three-dimensional software Solidworks. Finally, the strength and deformation check analysis of the two-stage reducer is carried out by using the finite element software ROMAX to evaluate the performance of the designed reducer under this working condition. The full text design has a certain reference, and can be applied to other electric vehicle reducer design to provide theoretical guidance.

Ensuring the Correctness and Reliability of CBPS System Using Event‐B

ABSTRACTDuring the early phases of software system development, error detection can be challenging due to the complexity of both the requirements and the operating environments. This paper advocates for the utilization of formal modelling and verification throughout the first phases of systems development to promptly detect and correct errors. The formalism employed throughout is Event‐B, which is backed by the Rodin toolset. To conquer requirements complexity, the frameworks of set theory and first‐order logic are employed, which provide the necessary tools for formalizing and analysing the properties and behaviours associated with Event‐B. Also, we detail the way in which modelling may be used to achieve abstraction, as well as the way in which refinement can be used to manage complexity through layering. Furthermore, we emphasize the significance of model validation and verification in improving the precision of formal models and requirements in IoT communication systems. The model is exemplified using a Content‐Based Publish Subscribe System (CBPS), with a special emphasis on a fire alarm system as a motivating example.

How do ML practitioners perceive explainability? an interview study of practices and challenges

Explainable artificial intelligence (XAI) is a field of study that focuses on the development process of AI-based systems while making their decision-making processes understandable and transparent for users. Research already identified explainability as an emerging requirement for AI-based systems that use machine learning (ML) techniques. However, there is a notable absence of studies investigating how ML practitioners perceive the concept of explainability, the challenges they encounter, and the potential trade-offs with other quality attributes. In this study, we want to discover how practitioners define explainability for AI-based systems and what challenges they encounter in making them explainable. Furthermore, we explore how explainability interacts with other quality attributes. To this end, we conducted semi-structured interviews with 14 ML practitioners from 11 companies. Our study reveals diverse viewpoints on explainability and applied practices. Results suggest that the importance of explainability lies in enhancing transparency, refining models, and mitigating bias. Methods like SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanation (LIME) are frequently used by ML practitioners to understand how models work, while tailored approaches are typically adopted to meet the specific requirements of stakeholders. Moreover, we have discerned emerging challenges in eight categories. Issues such as effective communication with non-technical stakeholders and the absence of standardized approaches are frequently stated as recurring hurdles. We contextualize these findings in terms of requirements engineering and conclude that industry currently lacks a standardized framework to address arising explainability needs.

Study on Shutter Heating Performance of Gas Turbine Intake System

Abstract Aiming at the anti-icing and de-icing requirements of gas turbine intake system, study the heating performance of a typical shutter vane, and compares and analyzes the influence of different ambient temperature and different electrothermal pipe heat generation rate on vane temperature distribution and heat transfer characteristics. The results show that the air convection at different positions on the blade surface of the louver leads to the difference in the temperature distribution of the upper and lower surfaces of the blade, as well as the leading edge and trailing edge of the blade, and the leading edge of the blade, as the area with the lowest overall temperature distribution of the blade, should be focused on in the design of anti-icing and de-icing systems. Increasing the heat production rate of the electric heating tube, there is more heat flow near the midline of the blade, which makes the temperature rise at this position the highest, and it is necessary to pay attention to the energy waste and blade damage caused by local overheating of the blade. Therefore, when the ambient temperature increases from 245.15 K to 318.15 K, the leaf temperature increases from 248.73 K to 322.74 K, and the temperature rise also increases from 3.58 K to 4.59 K. The electrothermal technology can be used to the anti-icing and de-icing of the intake system shutters on the basis of choosing the heat generation rate of the electrothermal tube reasonably.

Liquid crystalline geometrical phase Alvarez-Lohmann lenses

Thin-film LC based geo-phase optics have previously been demonstrated to drastically reduce size, weight, and power requirements for large-aperture optical systems while providing non-mechanical functionality for discrete values – for example, digital switching of the beam steering angle. In this work, we present a series of geo-phase Alvarez-Lohmann lens systems capable of analog tuning of focal length through lateral translation or azimuthal rotation. For circularly polarized inputs, diffraction efficiencies greater than 90% were observed over the visible spectrum with some wavelengths exceeding 99% for lateral embodiments. Both cylindrical and spherical Alvarez-Lohman systems were fabricated with focal lengths varying from infinity to 110 mm with increasing lateral displacement. These LC-based geo-phase optical systems could enable a new generation of low-cost, high-performance, and ruggedized dynamic optical components.

Control System for a Heavy Ion Laser Source at Synchrotron Complex ICCR

The article describes the control system for a laser source of heavy ions. The control system has a hierarchical multilevel architecture, determined by the hierarchical structure of the laser ion source, and is a part of the control systems of the linear heavy ion injector and the entire synchrotron complex. The article describes the basic requirements for the control system and the general principles of its construction, and the circuitry and software solutions selected in accordance with these requirements and principles, such as: organization of data exchange between individual nodes in the automated control system; determination of algorithms for working with individual devices and subsystems of the ion source, and with the ion source as a unified system; selection of types and structural schemes of controllers of individual subsystems. The hierarchical structure of the automated control system and control subsystems for all the main devices of the laser source are also described.

Automatic control strategy of electronic and electrical engineering based on FPGA

Field Programmable Gate Array (FPGA) have demonstrated significant potential in the field of automation control due to their flexibility, programmability, and high performance. This study designs an FPGA-based automation control strategy for motor control systems, encompassing four core modules: signal acquisition, signal processing, control algorithm, and output drive. The signal acquisition module employs the AD9280 high-speed, high-precision ADC chip to achieve rapid and accurate signal acquisition. The signal processing module utilizes digital filtering and FFT analysis within the FPGA to extract key information such as motor speed. The control algorithm module adopts an adaptive fuzzy control strategy that fine-tunes control rules in real-time to ensure precise control. The output drive module generates PWM signals via the FPGA to drive the motor, guaranteeing efficient and stable operation. Experimental results indicate that the FPGA-based control strategy significantly outperforms traditional methods in terms of response speed and stability, capable of meeting the high-performance requirements of modern industrial motor control systems. This research provides new technical insights and practical references for the field of automation control in electrical and electronic engineering.

Evaluating potential landing sites for the Artemis III mission using a multi-criteria decision making approach

The selection of a landing site within the Artemis Exploration Zone (AEZ) involves multiple factors and presents a complex problem. This study evaluates potential landing sites for the Artemis III mission using a combination of Geographic Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) methodologies, specifically the TOPSIS algorithm. By integrating topographic, illumination, and mineralogy data of the Moon, we assess 1247 locations that meet the Human Landing System (HLS) requirements within 13 candidate regions and Site 004 near the lunar south pole. Criteria considered include surface visibility, HLS-astronaut line of sight, Permanently Shadowed Regions (PSRs), sunlight exposure, direct communication with Earth, geological units, and mafic mineral abundance. Site DM2 (Nobile Rim 2), particularly the point at latitude 84°12’5.61”S (−84.20156°) and longitude 60°41’59.61”E (60.69989°), is the optimal location for landing. Sensitivity analysis confirms the robustness of our approach, validating the suitability of the best location despite the MCDM method employed and variations in criteria weightings to prioritize illumination and PSRs. This research demonstrates the applicability of GIS-MCDM techniques for lunar exploration and the potential benefits they can bring to the Artemis program.

Experimental and numerical investigation of heat transfer characteristics of radiator using Graphene Amine-based nano coolant

This work presents experimental and numerical investigations on enhancing the heat transfer performance of automobile radiators with nano-coolant flowing with different mass flow rate and at different inlet temperatures conditions. Thermal management is critical as it dictates a system's overall power output, performance, efficiency and energy utilisation. Radiators and Thermal management systems for batteries are vital in keeping automobiles operating under optimal conditions. Conventional coolants lack the ability to keep up with the increasing performance requirements of thermal management systems. Nanocoolants represent a cutting-edge advancement in heat transfer fluid, boosting conductivity without compromising essential fluid dynamics of the cooling solutions. Several investigators have worked on various nanocoolants, but less work is done on radiators. In view of this, experimental and numerical investigations were done on radiator heat transfer performance using Graphene Amine based nanocoolant with varied coolant temperatures (50 °C–80 °C) and flow rates (3 l/min to 9 l/min). Thermophysical properties of nanocoolants, such as density, thermal conductivity, viscosity, specific heat, and Prandtl number, were determined by experimental and mathematical modelling techniques and were comparable with an average deviation of 5 %. Compared to the base fluids of De-ionised water and ethylene glycol combinations, Graphene Amine based nanocoolant has exhibited advancements in heat transfer properties, showing elevated Nusselt numbers, heat transfer coefficients and heat transfer rates. The investigation into the thermal transfer properties of nanocoolants utilizing theoretical and real-world experiments shows consistency with an average deviation of 20 %. Maximum heat transfer enhancement of 154.3 % and maximum heat transfer coefficient of 3209.52 W/m2K was found for Graphene Amine at 80 °C with coolant flowing at 9 l/min, which is 83.1 % higher when compared to De-ionised Water.

Sistem Pembiayaan Mitraguna Berkah pada PT Bank Syariah Indonesia KCP Medan Setia Budi

Financing and activity of a sharia bank in disbursing funds to parties other than banks based on sharia principles. Raise the money What the World Bank has to offer helps meet people's needs. Fund distribution in the form of financing is carried out in good faith. Transferred from the owner of the funds to the user. One of them is Doctor And retirement. The purpose of this discussion is to find out how the system works. Mitraguna Berkah Financing Funding, further requirements for Submission of Mitraguna Blessing Financing, and Constraints and solutions in the financing process Mitraguna Berkah. Syariah Indonesia is Mitraguna Berkah with components - Cooperation agreements and components to conclude my agreement to take action at Bank Syariah Indonesia Kcp. Medan Setia Budi Namely with Bank Syariah Indonesia The process of distributing funds to companies or agencies, then to employees. Furthermore, there are requirements for the financing system of Bank Syariah Indonesia Mitraguna Berkah. Keywords: disbursing funds, financing, sharia bank

RESPONSE OF LIQUID MANURES AS ALTERNATIVE NUTRIENT ON GROWTH AND YIELD OF TOMATO

This research explores the transformative role of advanced technologies, particularly AI, machine learning (ML) and block chain, in the development of smart e-commerce solutions that enhance user experiences and operational efficiencies. It also explores how customized algorithms can improve customer engagement, boost conversion rates and encourage sustainable shopping practices. Additionally, the research investigates AI-driven solutions to encourage sustainable shopping behaviors and optimize shopping post-purchase experiences. Specifically, it aims to assess the impact of AI-based personalization algorithms, examine the trade-offs between data privacy and user experience and analyze AI's influence on consumer trust and decision-making processes. Primary data from surveys and interviews are supplemented with secondary data on customer behavior and sales metrics to evaluate the effectiveness of technologies like AI, machine learning and block chain in driving personalized, ethical and efficient e-commerce. A mixed-method research design, combining qualitative and quantitative approaches, employed to capture insights from key stakeholders (e.g., consumers, developers and e-commerce managers) and assess the impact of smart e-commerce platforms on shopping behavior, trust and loyalty. Implementing the Agile Model, the research iteratively addresses system requirements, testing and deployment challenges to create a flexible, scalable solution. Key findings underscore the role of predictive modeling in inventory management, the ethical potential of sustainable product recommendations and the positive consumer responses to transparency initiatives. The study contributes valuable insights into the development of secure, personalized and ethical e-commerce platforms that align with modern consumer expectations for convenience and responsibility.

Leveraging recommendations using a multiplex graph database

PurposeBy applying targeted graph algorithms, the method used by the authors enables effective prediction of user interactions and thus fulfils the complex requirements of modern recommender systems. This study sets a new benchmark for multidimensional recommendation strategies and offers a path towards more advanced and user-centric models.Design/methodology/approachTo improve multidimensional data recommendation systems, multiplex graph structures are useful to capture various types of user interactions. This paper presents a novel framework that uses a graph database to compute and manipulate multiplex graphs. The approach enables flexible dimension management and increases expressive power through a specialised algebra designed for multiplex graph manipulation.FindingsThe authors compare the multiplex graph approach with traditional matrix methods, in particular random walk with restart, and show that the method not only provides deeper insights into user preferences by integrating scores from different layers of the multiplex graph, but also outperforming matrix-based approaches in most configurations. The results highlight the potential of multiplex graphs for developing sophisticated and customised recommender systems that significantly improve both performance and explainability.Originality/valueThe study provides a formal specification of a multiplex graph construction based on interaction and content-based information; and the study also developed an algebra dedicated to multiplex graphs, enabling robust and precise graph manipulations necessary for effective recommendation queries. The authors implement these algebraic operations within the Neo4j graph database system with a thorough analysis and experimentation with three different data sets, benchmarked against traditional matrix-based methods.