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The paper presents experimental investigations and numerical analysis of bearing type lap joint behaviour with one bolt and two shear planes joining thin-walled steel parts. The aim of the study is to describe the actual force-displacement F-Δ relationship for the tested joints and to represent it numerically by validated models with varying degrees of geometry detailing in the finite element analysis. Validation involves determining properties of plates, bolts and the entire shear joint based on individual experimental testing. The obtained shear characteristic include all main stages of F-Δ connection behaviour, as elastic phase, friction, slip and bearing. Although bearing joints are analysed, friction-slip mechanism resulting from tightening of the bolts is also examined. Analyses also take into account existence of threaded portion of the bolt shank in bearing. FEA model with bolt thread confirmed its validity in terms of predicting load-bearing capacity with an overestimation of 1% with respect to the experimental average. However, simplified cylindrical model of the bolt shank appeared to be more reliable in terms of initial bearing stiffness, where the value was 20% higher comparing to the empirical results. An analytical characteristic of the considered joint was also derived based on the component method, which required certain assumptions and adjustments to include the actual phases of connection behaviour. This approach provided a satisfactory match to the experimental results, characterised by underestimations of maximum force and initial bearing stiffness of 2.5 and 7.6% respectively.

In the paper an example of an integrated Software-Defined Network (SDN) system with heterogeneous technological instances based on the Linux platform will be shown. For this purpose, two research testing stands with a POX controller and OVS (Open vSwitch) switches were used. In the first testing stand, the research based on the ICMP traffic was done while in the second one, MQTT traffic was analysed. The capabilities of these systems were examined in terms of responding to detected incidents and traffic anomalies. In particular, their appropriate responses to anomalies were tested, as well as the possibility of continuous monitoring of packet transfer between separate network components. The aim of the paper is to investigate the effectiveness of SDN in enhancing the security and adaptability of critical infrastructure systems. For isolation and optimised resource management, some components, such as POX or the MQTT broker, were run in Docker containers. The test environment used both hardware cases and prepared software, enabling comprehensive design and testing of networks based on the OpenFlow protocol used in SDN architecture, enabling the separation of control from traffic in computer networks. The results of this research make it possible to implement anomaly detection solutions in critical infrastructure systems that will adapt on the fly to changing conditions that arise, for example, in the case of an attack on such infrastructure or physical damage to it at a selected node.

This study examines the application of BPMN 2.0 notation within the implementation process of modern Industry 5.0 solution – cobots (collaborative robots). The research aims to compare the operational efficiencies of traditional human-only production processes with those augmented by cobots. The authors analyze two variants of furniture production processes: one involving only human workers and another combining cobots and human workers. The efficiency of these processes was assessed in terms of production time and output quantity. The investigation revealed that the production process incorporating cobots was more efficient, with a reduction in production time by 4.76% and an increase in the amount of products produced by 35.5%. The study concludes that integrating cobots into production processes can significantly enhance efficiency, reducing time and increasing output. BPMN 2.0 notation is a critical tool for modeling, automating, and monitoring these improved processes, aiding organizations in making strategic decisions towards adopting robotic solutions to boost productivity and competitive edge in the marketplace.

This study assessed the effects of vehicle mileage on the floor panel geometry and the upper mounting of the McPherson struts, as well as on the wheel alignment parameters. Geometry changes were determined in cars with the same design parameters, differing only in the type of engine supply system. The cars were van-bodied vehicles with compression ignition engines fuelled with diesel fuel and spark-ignition engines fuelled with LPG. The vehicles differed significantly in the weight distribution on their axles. The study is distinguished by its comprehensive approach to identifying in-service body wear. A detailed analysis covered not only the main causes of changes in body geometry, namely the influence of the vehicles’ mileage and design parameters, but also the effect of identifying the values of changes in the suspension and steering system geometry parameters. The body geometry and wheel alignment testing was carried out at a mileage of approximately 300,000 km. The study determined the relationships between changes in body geometry and wheel alignment. Changes in body geometry were determined by the type of basis point and the type of vehicle under analysis. Major changes in body geometry and wheel alignment were found in diesel-fuelled cars at the front of the vehicles and in LPG-fuelled cars at the rear of the vehicles. The greatest changes in body geometry in both vehicle types were noted within the area of the points associated with the mounting of the front and rear axle suspension system components.

Electro-hydraulic actuators offer advantages over traditional hydraulic actuators, particularly for high-precision force or position control, but their nonlinear dynamics complicate modelling and control. This study proposes a predictor-integrated adaptive controller to address these challenges. By combining a proportional-integral-derivative controller with a sliding surface, the system tracks desired trajectories, while a parameter optimization rule minimizes errors. Additionally, a smart grey prediction model with dynamic step size adjusts parameters and generates control signals to reduce noise and disturbances. Simulations show improved accuracy and control performance, with a root mean square error reduction of over 50% compared to traditional control algorithms. This adaptive approach ensures precise control in varying conditions, making it suitable for aerospace, automotive, and robotics applications.

The purpose of this work was to determine the essence of the influence of the impact velocity (5, 7, and 9 m/s) on Hertzian stresses and the erosion mechanism of ferritic-pearlitic S355J2 steel. The investigations were carried out using a slurry pot tester. S355J2 steel showed a strong sensitivity to changes in impact velocity. A significant increase in erosion rate was observed at a velocity of 9 m/s. This increase was 5-fold and over 15-fold compared to velocities of 7 m/s and 5 m/s, respectively. The study of Hertzian stress is crucial in erosion research because it helps understand how impact energy is absorbed by the eroded material and the mechanisms that cause surface wear. A linear increase in mean contact pressure and maximum shear stress was observed with increasing impact velocity. The mean contact pressure increased from 4.3 GPa to 5.5 GPa and the maximum shear stress increased from 2.0 GPa to 2.5 GPa. The kinetic energy of the solid particles that hits the eroded steel is distributed in the contact area, which leads to various deformations and wear mechanisms. The primary type of deformation was fatigue degradation of the surface layers of the eroded steel. The high kinetic energy of solid particles contributed to the formation of plastic deformations and strongly deformed steel flakes. Higher impact velocities generally result in greater forces and contact stresses on the material surface. This led to the intensification of plastic deformation in the contact areas and increased the Hertzian stresses.

The purpose of this article is to propose a fuzzy logic system as a tool for automated risk identification of potential technical challenges and social barriers during the implementation of artificial intelligence-based co-bots on workstations in manufacturing enterprises. On the basis of an extensive literature review, as well as industry reports and expert consultations, the basic challenges and enterprise barriers occurring during the implementation of changes in enterprises, especially during the implementation of the latest technologies, were selected. A fuzzy logic model was then developed that, based on the values of the input factors, generates an answer as to whether there is a risk of technical or social challenges in an enterprise when implementing the latest technologies. The results generated by the developed model, when confronted with expert knowledge, experience and subjective assessments, showed that the model works as expected. The results of the study suggest that the use of fuzzy logic can effectively support companies in detecting challenges and obstacles, thereby facilitating decision-making in reducing the risk of their occurrence. Adaptation to the conditions currently prevailing in the company allows for dynamic adjustment of co-bot deployment strategies, which in turn can lead to more effective management of technological changes and minimization of potential operational disruptions.

The dimensional accuracy of injection molded parts plays an increasingly significant role in the plastics processing industry, as is the utilization of recycled raw materials. To obtain the desired dimensions and properties of injection moldings, various material modification methods are used simultaneously. The conducted research aimed to assess the impact of hybrid modification of non-nucleated heterogeneously isotactic polypropylene using recovered phosphogypsum and a chemical blowing agent on the shrinkage value and mechanical properties of injection molded parts. Additionally, changes in dimensions and properties of composites with solid and porous structures that occur within 1000 hours of their removal from the injection mold were determined. The research showed that the filler used acts as a nucleating agent causing an increase in the shrinkage of the parts, up to 10 wt%. Similar changes were observed in the case of tensile strength. The increase in the value of this parameter at the lowest phosphogypsum contents used was most likely the result of changes in the crystalline structure of polypropylene. Changes in the mechanical properties of the molded parts that occurred during conditioning are correlated with shrinkage changes that occur from the moment the molded parts are removed from the injection mold. Young's modulus and tensile strength increased linearly for both solid and porous moldings. However, the rate of stiffness increases as a function of shrinkage changes with the filler content. Nevertheless, the opposite tendency was observed in the case of changes in impact strength, the values of which decreased as a function of shrinkage to the greatest extent in the case of unfilled polypropylene.

This article presents examples of the use of complex sheet metal in everyday life and the course of modeling a sheet bent simultaneously in two planes. The element selected to describe the modelling process is a fragment of the side wall of a lever hydroelectric power plant that operates on the Bystrzyca River in Lublin. The shape of the element is conditioned by the need to achieve a gentle change in the cross-section of the water stream during the flow between the steering system and the outflow area. The segment through which the water flows is clearly lower and wider than the other modules of the power plant, which is due to the average water level in the Bystrzyca River. Such a shape is necessary because the inflow and outflow parts of the power plant must be submerged under water all the time. This is due to the principle of operation of the power plant, which works by obtaining a vacuum created by pumping air from the inside. The article also presents the results of the strength analysis, which was performed for the segment exposed to the highest loads. The analysis and the entire design were performed in Autodesk Inventor, and the results obtained were as expected and even in the most critical places no values were reached that would require changes to the design. The Von Mises stresses reached a maximum of 193.4MPa, the largest displacement was 1.7mm, and the minimum safety factor was 1.07, and although it was slightly higher than the minimum allowable value, it occurred in the contact area of the 4 sheets, i.e. in the place where the weld was laid, which made it the most acceptable value.

This publication presents a comprehensive method for assessing the homogeneity of the composition of a large batch of polymer materials, with a mass of 1.5 tons, consisting of 60 transport packages, using the example of rubber granulate from recycled car tires. The presented method combines statistical analysis tools used to determine the samples number to be taken from the batch and the pyrolytic thermal desorption technique coupled with gas chromatography-mass spectrometry (Py/TD-GC-MS), used for material identification. Directions for improving the method for the quantitative identification of batch components are also indicated. The developed method can be applied to assess the purity of batches of recycled rubber granulate used in the construction of sports field surfaces and to determine the content of substances harmful to the environment and human health, such as polycyclic aromatic hydrocarbons. The direction for improving the developed spot-check control method using an alternative approach is to supplement it with a quantitative assessment of batch parameters in relation to the threshold values, such as tolerance limits.

This study focuses on improving the mechanical properties and microstructure of cement-based materials, which are crucial for the durability and safety of construction projects. Conventional cement, although commonly used, has certain limitations in terms of its mechanical strength and durability. Therefore, there is a requirement for innovative methods to enhance these properties. This study investigates the potential of nano-TiO2 (titanium dioxide) as an additive to overcome these limitations. The objective of this research is to perform a thorough experimental analysis to examine how different concentrations of nano-TiO2 impact the mechanical performance and microstructural changes in cement paste and mortar. The study examines the influence of nano-TiO2 on the compressive and flexural strengths of cementitious materials. It also explores how nano-TiO2 modifies the microstructure to enhance compactness and resilience. The results suggest that incorporating nano-TiO2 into cement leads to a substantial improvement in both compressive and flexural strengths. This enhancement is particularly notable when the nano-TiO2 concentration is at an optimal level of 1.0% by weight. The SEM and XRD analyses demonstrate that this concentration enhances the microstructure by decreasing voids and facilitating the development of C-S-H crystals. However, excessive concentration may have negative consequences, such as the creation of extra empty spaces. The results indicate that nano-TiO2 has considerable promise in enhancing cement-based materials, thereby aiding the advancement of construction materials that are more long-lasting and effective. This study contributes to the knowledge of how nano-TiO2 improves cement and emphasizes its potential uses in the construction sector.

Concrete is a constantly evolving building material whose demand is increasing due to population growth and urban development. This calls for more research on this composite material to improve its performance. However, concrete has some disadvantages, including that it is a brittle material and cannot withstand tensile stress. Therefore, rebars and fibers are incorporated into concrete to improve this property. Although previous works investigated the properties of concrete containing steel fibers, most of them were concerned with mechanical properties, while the durability properties still require further investigation to understand them. Thus, the purpose of this study is to ascertain how adding steel fibers to concrete in varying proportions (0.5, 1 and 1.5%) affects its mechanical and durability properties, including compressive strength, flexural strength, tensile strength, bulk density, water absorption, mode of failure, ultrasonic pulse velocity, dynamic modulus of elasticity and electrical resistance. Statistical relationships between the compressive strength and other characteristics were also established. The results indicated that all mechanical and durability characteristics significantly improved after adding steel fibers for all addition ratios, except for electrical resistivity, which showed lower values than the reference mixture for the 0.5 and 1% steel fiber proportions. Moreover, it was found that the best addition rate of steel fibers was 1.5%. At this percentage, the recorded increasing rates over the control sample were 29.3% in compressive strength, 83.7% in tensile strength, 27.9% in flexural strength, 50.1 in water absorption resistance, and 11.2% in electrical resistivity.

This paper focuses on the development of a short training curriculum for tertiary level education that can horizontally cover the Electric Vehicle technology in a manner understandable and attractive to students of appropriate background. The training program is divided into four groups of technical topics, the extend of which is balanced with regards to the learning outcomes and the teaching time provided. The blended teaching nature is supported by theoretical online lectures, synchronous and asynchronous, a great number of laboratory courses to enhance knowledge assimilation and a medium size project divided into two parts for the students to delve into the engineering way of thinking through a problem based learning approach. In the first part, the students exploited the Augmented Reality technology in order to describe the electric powertrain of small scale electric vehicles after having troubleshooting them. Their work and findings were utilized for the preparation of a scientific paper in the second part of the project. The training program was completed by a short industrial internship. The transferability of the training program is discussed in the last section of the paper.

This paper presents new method for traceability assurance of measurements performed using hybrid measuring systems built using one system that is based on tactile point measurement method (for example: coordinate measuring machine, articulated arm coordinate measuring machine, laser tracker system) and the second one that is based on optical field measurement method (for example: structured light scanners, digital image correlation systems). Within works described in this paper a series of tests aimed at determining task-specific errors for measurements performed using such composed systems were run. Measurement tasks for which such errors were determined include length measurements and measurements of form deviations (roundness, flatness, etc). Measurements were performed using material standards representing various shapes, dimensions and geometric relations. Measurements were run in different orientations and positions of the standards. Types of standards along with orientations and positions used were chosen basing on the guidelines of the ISO 10360 standard, parts 2, 5, 7, 8, 9, 10, 12, VDI/VDE 2634 and longtime experience of authors of this paper. At the end, results of performed measurements were checked for consistency with results of material standards calibration and values of task- specific maximum permissible errors were established. Guidelines for using developed method in other hybrid systems were also presented in the paper.

The article analyzes the environmental costs of Diesel Multiple Unit and Diesel Railcars operated in Poland. It consisted in determining the annual cost of gases and particulate matter emitted into the ambient air by the analyzed rolling stock. For this purpose, tests were carried out on the emission of harmful exhaust gases from a railbus equipped with the most commonly used drive system. The vehicle had two identical compression ignition engines meeting the Stage IIIB standard with a displacement of 12.8 dm3. Maximum power of the engine was 390 kW at a rotational speed of 1,800 rpm and a maximum torque of 2,200 Nm at 1,300 rpm. In order to measure harmful exhaust compounds, tests were carried out in real operating conditions using Portable Emission Measurement System (PEMS) equipment. The tests carried out showed that the rail vehicle did not meet the approval limits for nitrogen oxides (real value – 2.43 g/kWh, emission standard – 2.0 g/kWh). In the next stage, the annual emissions of rail buses operating in Poland were estimated. For this purpose, data from the Office of Rail Transport on the number of vehicles registered in the country and the average distance traveled by the vehicles were used. The data obtained indicate that the vast majority (over 98%) of environmental costs are costs related to carbon dioxide emitted into the atmosphere.

The article describes design of a solar dryer with a heat pump, which is used to increase heat output of the unit twice to solve the problem of using environmentally clean sources of thermal energy for fruit drying. The authors of the research have developed methodology of substantiation of the solar dryer parameters, which is applied to design and justify the optimal technological modes and parameters of the heat carrier in the unit, to describe the heat transfer characteristics of the unit operation, to assess the impact of physical environmental parameters on technological indicators of the process. The novelty described in the article is developed design of a solar dryer with a heat pump and to substantiate its constructive and technological structure. The work supplies scientifically substantiated methodic recommendations on composing and forecasting a parametric series of solar dryers for the conditions of private households and farms, and grounds their design parameters. Designed solar dryer with a heat pump affects solving environmental problems of power engineering due to substitution of the electric and thermal energy by the one obtained from solar energy, and mitigation of social problems by creating new job places needed when producing, installing and exploiting such units. The obtained results can be used for designing and improving technical aspects of fruit drying, to increase technological and energy efficiency of the process.

The development of new composite and structural materials in various areas of industry has led to an ever-growing interest in research results regarding the applications of these materials. Combining these materials allows for the modification of the strength characteristics of structures and components manufactured from them. The article presents the results of bending test of Carbon Fibre Reinforced Polymers (CFRP) strips bonded with a two-component epoxy adhesive. Additionally, the reinforcement effect resulting from gluing composite strips (multilayer reinforcement) was compared with shortened strips. The positive and negative influences of shortening strips unidirectionally reinforced with long fibre have been discussed. Differences in the stiffness of the tested belt systems indicate that adding another belt increases the load capacity by approximately 100% of the bending force compared to the previous test of fixed-length belts. TSP tapes also show an increase in the ability to transfer the bending force by gluing subsequent strips, but not necessarily along the entire length, as shown in tests. Simulation tests showed smaller displacement compared to physical samples. The smallest differences between measurements and simulation results were recorded for two-layer configurations, while the largest differences were observed for four-layer strips.

Abstract This article examined the utilization of digestate as an organic fertilizer to enhance crop yields. In contemporary agriculture, applying digestate as a fertilizer is gaining traction as a viable method for boosting crop productivity. The paper delved into recent studies on the impact of digestate fertilization on crop quality and yield, explaining the production process of digestate, its application techniques, and its effects on plant health. The rise in environmental pollution has underscored the importance of sustainable agricultural practices, increasing the adoption of natural fertilizers. Digestate, a by-product of the methane fermentation process in biogas production, is rich in essential nutrients vital for plant growth and development. This article explored various aspects of digestate fertilization, including its chemical composition and effects on soil and plants. Additionally, it presented scientific findings on the use of digestate as an alternative fertilizer for field crops. The review aimed to provide a thorough understanding of digestate fertilization as well as highlight its potential advantages and challenges in agricultural applications.

The importance of microfluidics research is growing, especially in the fields of chemistry, biology or medicine. This is coupled with a growing demand for specialized capillary equipment that allows advanced research at the microscale. Conventional methods of manufacturing such devices are expensive, time-consuming and do not guarantee good results. An alternative to these methods is the use of 3D printing technology. Despite the existence of numerous works presenting the possibilities of 3D printing in the context of creating microfluidic devices, there is a lack of comprehensive works presenting qualitative analysis of printed objects. This paper presents a method of producing microcapillary structures for microfluidics research with the help of 3D printing using stereolithography technology. The quality requirements that the printed objects should meet are defined and all stages of production are characterized. A qualitative analysis of the obtained objects was carried out, taking into account both the influence of individual printing parameters and print processing methods. The results of microfluidic tests using printed objects are also presented. This work is aimed at providing specific knowledge that allows the manufacture of precision devices for microfluidics purposes at low cost.

In the article, the problem of multi-criteria optimization of quality control mechanisms is analyzed. The presented method assumes the use of the NPGA genetic algorithm to simultaneously manage costs and the level of detecting non-conformities. The main assumption of the presented approach is to treat individual quality control procedures as vectors, whose elements are probability generating functions of defect detection. Each of these procedures generates certain operational costs and covers specific types of defects within its scope. The task of the presented algorithm is to indicate which procedure and to what extent should operate to ensure an appropriate level of non-conformity detection while minimizing costs. The article presents the theoretical foundations of the developed algorithm and the results of its implementation. The software has been developed in C++ with a particular focus on performance aspects. Its essence lies in the implementation of data structures introduced in the theoretical part, as well as methods for their rapid processing. Thanks to this approach, the entire program is scalable and can be used to solve multidimensional optimization problems. The presented approach may also find application in other areas of enterprise management. This will be possible primarily in cases where the effectiveness of procedures or devices is primarily evaluated based on probability. Therefore, the presented methods can provide effective optimization of other areas related to enterprise management.

Management decisions about store atmosphere, such as temperature, or light intensity in retail establishments can be made based on solutions from machine learning methods. These conditions determine whether the customer will stay in the store longer and whether his shopping cart will reach the desired high value. Previous literature research associates certain atmospheric factors with customers' propensity to make purchasing decisions and allows us to identify what influences the customer during shopping and to what extent. The article aims to reveal the feasibility of using machine learning methods to make management decisions based on store atmosphere parameters. When deciding on the conditions in a retail establishment, applicable health and safety regulations should also be considered. This was used to set limits on the input parameters for the model. The authors identified 3 atmospheric factors and, based on them, proposed two types of models: regression and classification models, predicting how long customers stay in an establishment and can classify it into categories: short, medium and long. These models can then be used to create a model that optimizes the parameters in the facility to achieve a minimum given time a customer stays in the facility.

Objective: The design of a low-noise low-power 4-channel neural amplifier with variable bandwidth and gain for multi-channel electrode arrays is presented with application in recording of neural signals. Methods: To amplify neural signals with low amplitudes and frequencies, pseudo-resistors are used in the neural amplifier, creating a small low cut-off frequency. The neural amplifier is designed to amplify wide range of neural signals, including Local Field Potential (LFP) and Active Potential (AP) signals, with sufficient gain. In the design process of the proposed amplifier, important parameters such as noise, power consumption, and linearity have been considered and improved to make the proposed structure efficient. The suggested amplifier utilizes an OTA sharing structure where the common parts of amplifiers in distinct channels are shared. The advantage of this structure is that it minimizes the die area and power dissipation of the neural recording system to a significant extent. In addition, the attenuator circuit used in the feedback path can increase the mid-band gain for amplifying low-amplitude neural signals such as LFPs. This avoids the need to increase the value of the input capacitor in order to enhance the mid-band gain, preventing the increase in die area and the decrease in the input impedance of the amplifier. The 4-channel neural amplifier is designed and simulated in 180 nm TSMC CMOS technology. Results: The neural amplifier has a mid-band gain of 47 dB with 3 Hz to 10.8 kHz bandwidth without using an attenuator circuit, and by using the attenuator, the mid-band gain is increased to 55 dB and the bandwidth is decreased to 4 kHz. The power dissipation of each channel is 2.4 µW from 1-V supply voltage. The input-referred noise is 1.9 µVrms over the 3 Hz-10.8 kHz frequency range, and it is 1.45 µVrms over the 11 Hz-4 kHz frequency range when the attenuator circuit is active in the feedback path.

With the rise in load demand, improving the voltage profile and reducing line loss is vital to ensure reliable power delivery to the customer. However, increasing power plant generation capacity is limited by environmental and economic factors, requiring a careful assessment of locally optimal options. Distributed Generation (DG) installation into the electricity grid is one of the reliable remedial actions to ensure a smooth power delivery. Installation of DG requires an optimization process to identify the appropriate placement and sizing. Inaccurate sizing and placement of the DG installation may result to over-compensation or under-compensation phenomena. This paper proposes a novel approach termed the Integrated Immune Moth Flame Evolutionary Programming technique (IIMFEP) for optimizing the installation of DG sources in distribution systems. It handles various scenarios, including multi-DG single-type and multi-DG multi-type installations, with the main objective of minimizing power loss in the system. This technique employs a hybrid approach that combines elements of immune algorithms, moth flame optimization, and evolutionary programming to achieve more accurate and efficient results. Two cases were considered in this study termed Case 1 and Case 2. Results in Case 1 discovered that the optimal sizing and placement of four Type III DGs exhibit the lowest power loss worth 2.74 kW (98.78 % reduction) for the 69-Bus RDS, while for the 118-Bus RDS the power loss is 319.89 kW (75.36 % reduction). In Case Study 2, the combination of DG Types I and III provided the highest power loss reduction. With one DG Type I and two DG Type III units installed, power loss was reduced by 97.15 % to 6.41 kW for the 69-Bus RDS and by 62.01 % to 493.21 kW for the 118-Bus RDS. The proposed IIMFEP managed to alleviate the setback experienced in the traditional EP, AIS and MFO which found to be stuck at local optimum. The IIMFEP method is compared to Moth Flame Optimization, Artificial Immune System and Evolutionary Programming and validated using the IEEE 69-Bus and 118-Bus Radial Distribution Systems, resulting in outstanding performance.

In the context of Industry 4.0, the most important trend in the shipbuilding industry is to efficiently manufacture intelligent and more environmentally friendly ships. This requires the shipbuilding industry to achieve technological innovations in all aspects of the ship's life cycle and accelerate the digital transformation of the industry. Thanks to the development of new-generation information technology, digital twins have received widespread attention and application in the manufacturing industry. The excellent performance of digital twins in monitoring, diagnosis and decision support also provides strong support for the digital transformation of the manufacturing industry. However, the application and development of digital twin technology are constrained when faced with the complex and discrete manufacturing processes in the shipbuilding industry. To address the constraints in the shipbuilding industry, promote the application of digital twin technology, and support the digital transformation of the shipbuilding industry, a statistical analysis of publications linked to the shipbuilding industry and digital twin using the Web of Science and the Scopus are done in this paper. The application and development of digital twin in the shipbuilding industry was systematically analyzed, the problems restricting the development of digital twin in the shipbuilding industry was summarized. Finally, the development trend of digital twin in shipbuilding industry prospects.

In intelligent transportation systems, traditional object detection algorithms struggle to handle complex environments and varying lighting conditions, particularly when detecting small targets and processing multimodal data. Furthermore, existing IoT frameworks are limited in their efficiency for real-time data collection and processing, leading to data transmission delays and increased resource consumption, which constrains the overall performance of intelligent transportation systems. To address these issues, this paper proposes a novel deep learning model, YOLO-Fusion. Based on the YOLOv8 architecture, this model innovatively integrates infrared and visible-light images, utilizing FusionAttention and Dynamic Fusion modules to optimize the fusion of multimodal information. To further enhance detection performance, this paper designs a Fusion-Dynamic Loss, improving the model’s performance in complex intelligent transportation scenarios. To support the efficient operation of YOLO-Fusion, this paper also introduces an IoT framework that uses intelligent sensors and edge computing technology to achieve real-time collection, transmission and processing of traffic data, significantly improving data timeliness and accuracy. Experimental results demonstrate that YOLO-Fusion significantly outperforms traditional methods on the DroneVehicle and FLIR datasets, showcasing its broad application potential in intelligent traffic monitoring and management.

This study focused on enhancing the electrical conductivity of metal oxide nanosheet films by increasing the size of the nanosheets to promote the electron flow in the desired in-plane direction. We developed a method for increasing the size of RuO2 metal oxide nanosheets, a 2D material, by controlling the heat-treatment process. The size of the RuO2 nanosheets increased from 0.7 to 3.5 μm by modifying the heat-treatment conditions of KxRuO2, which was used as the precursor material. As a consequence, the electrical conductivity of the large-sized-RuO2 nanosheet-coated films was eight times higher than that of their small-sized counterparts. This lower resistance was attributed to the minimal number of contacts required for electron flow; the contact resistance between the nanosheets being higher than the intrinsic resistance. The proposed approach for enhancing the conductivity of metal oxide 2D materials is very promising and is expected to significantly contribute to the development of advanced flexible electronic devices.

Sensors and wearable technology are used to accumulate health-related data from the visually impaired (VI) and other special persons. This health-related data is disseminated via an open network channel, which poses numerous threats and requires rigorous attention for its protection. An appropriate security framework must be implemented on both the sender and the recipient sides to ensure the privacy and security of sensitive medical information for VI and disabled persons. The telehealth system will then benefit the VI and disabled people by improving their quality of life, telemonitoring, saving time and money, providing online support, and increasing the degree of trust and reliance on the system and the treatment of doctors. These practical benefits are not just theoretical but potentially life-changing for the VI and disabled community. Strong and flawless mutual authentication is necessary for the telehealth system to secure and preserve the privacy of VI and disabled people. Therefore, this article proposes a privacy-preserving asymmetric-based, lightweight, and robust security framework for the telehealth system operationalized for VI and disabled people. The proposed security framework alleviates all threats to sensitive information and preserves the privacy and security of the whole telehealth system. The security of the proposed framework has been verified through a well-known Real-Or-Random (RoR) model and simulated through a ProVerif software verification toolkit, while the performance analysis has been tackled by measuring computation, storage, and communication costs. The comparative analysis result revealed that the proposed framework outperforms its competitors, further reinforcing the hope about its potential impact.

The conventional logistics’ paradigm is being replaced with smarter ways of supply chain management to readily serve the customers with quick modes of delivery in affordable operational costs. The weak infrastructure in rural terrain and congested roads in urban cities act as a big impediment in the successful accomplishment of last mile delivery down the lane across the complete chain of logistics. The traditional logistics has been evolved and improved in many respects, the last mile delivery is still plagued by security and privacy risks in logistics. The use of drones for package deliveries may offer promising gains in terms of efficiency. Nonetheless, drones undergo security and privacy risks besides physical threats for being deployed in public places communicating on open channels. Thus, more efforts are needed to secure the communication of inducted drones in delivery assignments. We propose a novel PUF-enabled drone access control mechanism PDAC-SL for smart logistics. By the careful employment of symmetric crypto-primitives, the contributed protocol not only supports low end devices but stands resistant to physical drone and other known threats besides preserving anonymity and untraceability features. The RoR model-based formal security analysis and performance analysis demonstrate distinct security properties and applicability of the PDAC-SL.

Magnetic levitation systems are complex and nonlinear, requiring sophisticated control methods to maintain the stability and position of the levitated object. This research presents an optimized fractional order PID (FOPID) control approach for position control of a freely-suspended ferromagnetic object. The dynamic system model is mathematically modeled in MATLAB using first-principle modeling and the grey box method. The FOPID controller has five degrees of freedom (DOFs) that allow for fine-tuning of the control gains and fractional orders, enabling the system to handle the nonlinearity inherent in the magnetic levitation system. The DOFs of FOPID and integer order PID controllers are optimized using the Artificial Bee Colony (ABC) algorithm and results are compared with state-of-the-art optimization methods. The results showed that the FOPID controller can effectively control the magnetic levitation system with constraints and outperforms other methods by up to 92.14% in terms of settling time with negligible steady-state error.

This study addresses the challenges of data scarcity and class imbalance in structural health monitoring (SHM) of composite structures. Data-driven SHM techniques that benefit from non-destructive evaluation (NDE) are used in various composite structures. However, the lack of damaged state data causes data scarcity and class imbalance problems that prevent robust diagnostics of composite structures. This study introduces a novel data-driven multi-class data augmentation method for composite structures, employing a multi-class generative adversarial network (MC-GAN) for the first time to generate synthetic data for multiple classes without the need for excessive experimentation or simulation. Additionally, the MC-GAN model is integrated with the convolutional neural network (CNN) to develop the MC-GAN-CNN model for autonomous data augmentation and delamination detection. The approach has been validated using experimentally obtained vibrational data for laminated composites. The damage detection using manual feature extraction showed overfitting and very high standard deviation during 10-fold cross-validation for various machine learning models. However, the proposed method suggested a more rigorous assessment with a mean accuracy of 99.72 ± 0.08 %. In addition, the proposed framework assists in handling the delamination detection problem autonomously without requiring hand-crafted statistical features with a good generalization capability.

Edge–cloud coordination offers the chance to mitigate the enormous storage and processing load brought on by a massive increase in traffic at the network’s edge. Though this paradigm has benefits on a large scale, outsourcing the sensitive data from the smart devices deployed in an Internet of Things (IoT) application may lead to privacy leakage. With an attribute-based keyword search (ABKS), the search over ciphertext can be achieved; this reduces the risk of sensitive data explosion. However, ABKS has several issues, like huge computational overhead to perform multi-keyword searches and tracing malicious users. To address these issues and enhance the performance of ABKS, we propose a novel traceable and lightweight attribute-based keyword search technique in an Edge–cloud-assisted IoT, named TL-ABKS, using edge–cloud coordination. With TL-ABKS, it is possible to do effective multi-keyword searches and implement fine-grained access control. Further, TL-ABKS outsources the encryption and decryption computation to edge nodes to enable its usage to resource-limited IoT smart devices. In addition, TL-ABKS achieves tracing user identity who misuse their secret keys. TL-ABKS is secure against modified secret keys, chosen plaintext, and chosen keyword attacks. By comparing the proposed TL-ABKS with the current state-of-the-art schemes, and conducting a theoretical and experimental evaluation of its performance and credibility, TL-ABKS is efficient.

This study investigates the biomass and carbon fixation rate of Chlorella sp. by the main valuable elements in converter steel slag in F/2 seawater medium. The results indicate that Ca, Mg, P, Si, Fe, and Mn can increase the biomass and carbon fixation rate of Chlorella sp., while Cu, Zn, Cr, and Al can decrease the biomass and carbon fixation rate of Chlorella sp.. Three machine learning methods known as Back Propagation neural network (BPNN), decision tree (DT), and random forest (RF) were applied to construct the prediction model for the carbon fixation rate of Chlorella sp. based on real-life experimental data obtained from single factor experiments. The overall results exhibited that the BPNN model is better than the DT model and RF model to predict the carbon fixation rate of Chlorella sp.. Finally, the maximum carbon fixation rate for Chlorella sp. predicted by BPNN model is 50.86 mg/(L·d), which was 2.46 times that of the control group, under the optimum conditions of Ca 5.77 g/L, Mg 4.74 g/L, P 1.27 g/L, Si 6.31×10−1 g/L, Fe 6.50×10−4 g/L, Mn 5.00×10−5 g/L, Cu 2.51×10−6 g/L, Zn 4.98×10−6 g/L, Cr 0 g/L, and Al 0 g/L in F/2 seawater medium.

Wheat yellow rust disease poses a significant threat to global wheat yield and grain quality. Early detection of this disease will help to minimize the loss caused by its effects. Existing models work well on images taken in a controlled environment, whereas a uniform background is placed behind the leaf, but these models fail to produce good results in natural settings. Previous research also involves manual interventions in the pipeline to achieve good classification results such as cropping the images, using uniform backgrounds, etc. These systems are not practical to use in natural environments where there will be a lot of background noise to the image and manual cropping becomes an extra step for the farmer. Moreover, the unavailability of the dataset in which images of leaves are taken in a natural setting became another challenge. In this research, a dataset is curated and leaves are annotated for object detection, object segmentation further the leaves are classified into 3 classes ie healthy, resistant, and susceptible. A novel unsupervised image rotation algorithm is proposed that takes input from YOLOv8 to align the leave in such a way that maximum background can be removed by a rectangular bounding box . Then the comparison between multiple state-of-the-art segmentation models ie. UNET, Segment-Anything (SAM), Segnet, LinkNet, PSPNet, FPN, Deep-Labv3+ (Xception), and DeepLabv3+ (Mo-bileNet) has shown that UNET has outperformed all the other segmentation models with an IOU score of 0.9563. Lastly for classification, the performance of multiple convolution neural networks ie. VGG16, Resnet 101(v2), Xception, Mo-bileNetV2, and Transformer-based models ie. Swin trans-former and MobileVit have been compared. Swin transformer has outperformed the state-of-the-art CNN models with an accuracy of 95.8%. This paper proposes a complete robust pipeline that can be deployed in natural environment and does not need any manual intervention to produce good results. This research shows that good localization of leaves and removal of unwanted background noise at the earliest stage of the pipeline will assist the segmentation model to effectively segment the leaf from the background which will enable classification models to achieve high classification accuracy, even when dealing with very small datasets.

The use of nanocarbon materials to enhance the toughness of fragile cementitious materials has attracted increasing attention for investigation. This study examines the effects of incorporating graphene oxide (GO) into NaOH-activated slag (AAS) pastes and mortars, focusing on workability, phase composition, mechanical properties, and the local mechanical properties of the interfacial transition zone (ITZ) between sand and paste. The experimental results indicate that the addition of GO decreases the flowability of NaOH-AAS pastes, with noticeable aggregation at concentrations above 0.03 wt%. Furthermore, GO enhances the hydration degree of ground granulated blast furnace slag (GGBS), leading to an increased amount of C-A-S-H gel without introducing new phases. In terms of macro performance, incorporating 0.03 wt% GO significantly improves the compressive and flexural strengths of NaOH-AAS mortars by 10.6 % and 17.9 %, respectively. Additionally, the presence of GO reduces the ITZ thickness from 30 to 15 micrometres in NaOH-AAS mortars, which likely contributes to the enhanced mechanical performance.

Traditional predictive modeling techniques have significantly influenced the analysis of survival times and predictive markers in oncology. However, these models often do not fully address the complexities of cancer data, leading to a noticeable gap in accurately modeling both cancer survival and mortality. The aim of this study is to show a model that offers the best fit for cancer mortality and survival data, thereby improving treatment strategies and patient prognoses. I propose the a New Exponential-Weibull (NEWE) distribution within the New Exponential-Generated (NE-G) class of distributions to enhance oncological analytics with greater accuracy and comprehensiveness. An empirical analysis of Brazilian cancer mortality data is conducted., This data set covers children, head and neck, and cervical cancer. Concurrently, survival time-to-event data for bladder and advanced lung cancers patients are assessed to evaluate the model’s effectiveness. Based on standard metrics, extensive simulation experiments show the Maximum Product of Spacing Estimator as the best of seven-point estimation techniques. The NEWE distribution shows superior modeling capabilities, surpassing traditional models with lower values of Log-likelihood, Cramer-von Mises, and Anderson-Darling and higher Kolmogorov-Smirnov (KS) p-values. The study also discerns the most fitting estimators for distinct types of cancer mortality and survival data. This includes the Right Tail Anderson-Darling for child cancer deaths, the Maximum Product Spacing for head and neck cancer deaths, the Least Squares for cervical cancer deaths, the Weighted Least Squares for bladder cancer survival, and the Anderson-Darling for advanced lung cancer deaths. This shows that the NEWE distribution can be used in a number of different cancers settings. The development and implementation of the NEWE distribution marks a significant advancement in oncological modeling. By analyzing both cancer mortality and survival data with enhanced accuracy and flexibility, this novel approach surpasses traditional models, offering deeper insights into cancer progression and treatment outcomes. As a result, the NEWE distribution equips healthcare professionals with a powerful tool for improving clinical decisions, leading to better prognostic assessments and patient care in cancer treatment.

This paper evaluates the nonlinear dynamics of a melanoma cancer model through the iterative technique of finding compact invariant sets (LMCIS). The objective is to discover equilibrium points, ascertain their stability qualities, and determine the presence of compact invariant sets within the model. For instance, this approach is assessed in a model demonstrating the interplay between four cellular populations and administrating interleukin-12 (IL-12) immunotherapy. Nevertheless, the mechanism by which this anti-cancer therapy operates has yet to be completely defined. The approach is evaluated using severe treatment situations to provide evidence for the presence of equilibrium points inside the iteratively confined zone and to assess its effectiveness. The outcomes derived from using this approach were then compared with a conventional iteration of Newton’s method. Newton’s approach extends to the resolution of the system of equations inside the model, facilitating the identification of the equilibrium points with a small tolerance error. While the iterations of the localization algorithm were set close to the results obtained by this method. Finally, the simulation outcomes are shown via the utilization of MATLAB R2020b. The findings demonstrate the temporal progression of the model under the effect of immunotherapy and without it.

This study presented the electrochemical deposition of potassium nitrate-doped polypyrrole (Ppy:KNO3) on stainless steel (SS), graphite sheet (GRs), and carbon fiber (CF) substrates, Ppy:KNO3/SS, Ppy:KNO3/GRs, and Ppy:KNO3/CF supercapacitor electrodes. Structural, morphological, and molecular analyses were conducted using X-ray diffraction (XRD), scanning electronic microscope (SEM), and Fourier transform infrared spectroscopy (FTIR). SEM images exhibited distinct morphologies of Ppy:KNO3 films, varying with cycles, scan rates, and Ppy:KNO3 concentrations. Ppy:KNO3/CF film displayed a unique structure with minimal particle aggregation. Electrochemical performance was assessed through Cyclic voltammetry (CV), galvanostatic charge-discharge (GCD) measurements and electrochemical impedance spectroscopy (EIS). Notably, Ppy:KNO3/CF demonstrated a specific capacitance of 1020 F/g at 1 A/g. It exhibited 92 % capacitance retention after 1000 cycles, along with impressive power and energy densities of 310 W/kg and 71.2 Wh/kg, respectively. These results represented a significant progress in the development of high-performance, durable supercapacitors.

This study focuses on the development and characterization of a novel composite material based on flue gas desulfurization (FGD) gypsum modified with functionalized nanocellulose. The incorporation of 0.1 wt% nanocellulose into the FGD gypsum matrix resulted in significant improvements in mechanical strength, water resistance, and thermal insulation properties. The flexural and compressive strength of the optimized composite increased by 32 % and 27 %, respectively, compared to the control sample. The water absorption decreased by 22 %, and the softening coefficient increased from 0.72 to 0.85, indicating enhanced water resistance. The thermal conductivity of the composite was reduced by 31 %, showcasing its potential as an energy-efficient building material. SEM analysis revealed a refined microstructure with improved interfacial bonding between the nanocellulose and gypsum matrix, while FTIR spectroscopy confirmed the presence of chemical interactions between the components. The moisture buffering capacity of the composite was also superior, with a 38 % higher moisture buffering value than the control sample. These findings highlight the potential of functionalized nanocellulose as a reinforcing agent in FGD gypsum-based composites, offering a promising solution for the development of high-performance, eco-friendly building materials.

With the widespread application of Internet of Things technology in industrial control systems, abnormal behavior detection has become a key task to ensure the safety and stable operation of the system. We propose a multi-branch convolutional fusion neural network method to improve the accuracy and efficiency of abnormal behavior detection in Internet of Things industrial control systems. This method achieves efficient detection of abnormal behavior in video data by combining ResNet152 for spatial feature extraction and GRU for temporal feature extraction. Unlike traditional methods, this method adopts a multi-branch structure, which can simultaneously capture multi-scale feature information, significantly enhancing the richness of feature expression and the accuracy of detection. Experimental results show that on the UCF-Crime dataset, the accuracy of this method reaches 85.76%, which is significantly better than that of traditional methods. In addition, on the larger UCF-101 dataset, the accuracy of this method reaches 92.21%, further verifying its excellent generalization performance. Compared with the C3D network, this method improves the accuracy by nearly 6% while maintaining a high processing speed. These results show that the proposed method has great potential in practical applications.

As urbanization accelerates and buildings become more complex, fire emergency evacuation has become increasingly challenging. Traditional evacuation plans often struggle with slow response times and suboptimal path planning in real-time dynamic and complex fire scenarios. To address these issues, this study proposes the IoT-based DWM-Evac model for fire emergency evacuation path planning. The model leverages IoT technology by using various sensors placed inside buildings to monitor fire incidents and spread in real-time, collecting critical data such as temperature, smoke concentration, and flame location. It integrates Dynamic Graph Neural Networks (DGNN), Whale Optimization Algorithm (WOA), and Markov Decision Process (MDP) to enhance path efficiency and safety. Experimental results indicate that the DWM-Evac model achieves an average evacuation time of 315 s in a virtual mall environment, 25 s shorter than traditional plans, with an average path length of 255 meters and a path safety score of 0.92, higher than the traditional plan’s 0.88. The application of IoT in fire emergency management not only improves response speed but also optimizes path planning, significantly enhancing personnel safety.

Currently, the rapid development of the industrial Internet has led to the creation of a massive number of intelligent agents that are widely and distributively applied in various edge scenarios. The work conditions in these edge scenarios are complex, uncertain, and random. Traditional manual updates or human judgments are used for task decision-making in large-scale distributive intelligent agent edge work scenarios, which lack dynamic perception and autonomous recognition capabilities for edge work conditions. This inevitably leads to low decision-making accuracy, poor reliability, and ultimately, task failure. To address this issue, this study proposes an adaptive task identification strategy based on cloud-edge collaboration. This method utilizes a cloud-edge collaborative industrial intelligent application architecture to achieve cloud-based training and encapsulation of the task model, with online calling at the edge-end. Then, edge-end intelligent agents identify edge work conditions through multi-source data fusion, enabling accurate task decision-making. Finally, the edge-end requests the cloud for task model matching. The effectiveness of the proposed method is validated in an industrial safety situation virtual detection system.

In this paper, we introduce a novel approach to enhance the accuracy and convergence behavior of Self-Organizing Maps (SOM) by incorporating a reweighted zero-attracting term into the loss function. We evaluated two SOM versions: conventional SOM and robust adaptive SOM (RASOM). The enhanced versions, reweighted zero-attracting SOM (RZA-SOM) and reweighted zero-attracting RASOM (RZA-RASOM), include an l1 norm in the error function to add a zero-attractor term, which improves weight coefficient adjustments while preserving topology. The models were assessed for convergence speed and misadjustment under sparsity assumptions of the true coefficient matrix, and their robustness was tested under conditions of increased non-zero taps. Using six different datasets, we compared the performance of RZA-SOM and RZA-RASOM against conventional SOM and RA-SOM in terms of accuracy, quantization error, and topology preservation. Experimental results consistently demonstrated that RZA-SOM and RZA-RASOM surpassed the performance of conventional SOM and RA-SOM.

This paper explores the impact of MHD couple stress and slip velocity over a conical bearing in the presence of magnetic field. The flow is considered as incompressible. A uniform magnetic field is imposed perpendicular to the bearing alongy-axis. The modified Reynolds equation is solved using appropriate boundary conditions in dimensionless form to find the pressure distribution which is then used to obtain the expression for load carrying capacity paving the way for the calculation of response time. Numerical computations of the results show that the MHD couple stress on the conical bearings indicates that enhances the pressure distribution, load carrying capacity and squeeze film time of the conical bearings. Further, decrease in these characteristics was observed for the increased values of slip velocity and cone angle.

Nanofluids have captured the attention of scientists due to their superior thermophysical properties compared to ordinary liquids. Consequently, nanofluids serve as suitable cooling agents applicable to systems requiring swift response to thermal changes, such as vehicle engines. Therefore, the current article scrutinizes the characteristics of heat transfer on the pressure-driven flow of magnetized nanofluid between two curved corrugated surfaces in the presence of heat generation/absorption. Copper oxide nanoparticles (Cu) have been combined with pure water to form a nanofluid called Cu/H2O. The geometry of the channel is represented mathematically in an orthogonal curvilinear coordinate system. The corrugation grooves are described by sinusoidal functions with phase differences between the corrugated curved walls. The boundary perturbation method is used to find the analytical solution for the velocity field, temperature field, and volumetric flow rate taking the corrugation amplitude as the perturbation parameter. The impact of dissimilar parameters such as the curvature parameter (0.5≤k≤10), wave number (1≤α≤3), magnetic parameter (1≤M≤5), and wave amplitude (0.1≤ε≤0.8) on the flow fields are analyzed through graphs and discussed in detail. The results show that the peak of the velocity increases with the radius of curvature and the width of the channel for a constant pressure gradient. If we increase the magnetic parameter from 1 to 4, the velocity profile at the specified point decreases by 30 %. If we increase the heat source/sink parameter from 2 to 5, the temperature profile at the specified point increases by approximately 17 %. The flow rate is increased by the corrugations for any phase difference between the corrugated curved walls depending on the corrugation wavenumber and the channel radius of curvature.

Reservoir heterogeneity, water-oil mobility ratio, cementation degree, and strong injection and production significantly influence long-term waterflooding and polymer flooding processes in oil reservoir development, leading to the formation of low-resistance dominant seepage channels in the lower part of thick oil layers without internal laminations. Understanding the spatial distribution and evolution of dominant seepage channels in oil reservoirs during waterflooding and polymer flooding is crucial for improving reservoir development and recovery rates. It uses a streamlined numerical simulation method to quantitatively show the spatial and temporal distribution of dominant seepage channels in the oil reservoir. A model and chart were created to demonstrate the relationship between permeability and throat radius, capturing changes in reservoir permeabilities over time. The study developed a comprehensive mathematical model to identify dominant seepage channels, considering parameters such as inter-well flow ratio, injection efficiency, permeability change value, and water saturation. Research findings indicate a 14.8 % increase in reservoir permeability after polymer flooding, with slight changes in porosity and a 20.3 % decrease in clay content. The established mathematical model and identification criteria were used to comprehensively quantitatively evaluate flow channels, describing the distribution characteristics and evolution process of dominant seepage channels in the experimental area over four periods: pre-polymer flooding, low water-saturation period during polymer flooding, post-polymer injection, and subsequent 10 years of water flooding. Both polymer flooding and subsequent water flooding have significantly altered the distribution pattern of predominant fluid pathways: 254 well layers now demonstrate robust predominant fluid pathways (19 %); another 272 well layers feature moderately predominant fluid pathways (22 %); while an additional 440 well layers display weaker predominant fluid pathways (approximately 35 %). There has been a noticeable increase in both weak and moderate predominant fluid pathways, which are now more widespread in the area. However, localized occurrences of strong predominant fluid pathways persist with no significant change in their distribution characteristics. Analysis at four different time points shows a clear trend of increasing intensity as we move from polymer to subsequent water flooding in this decade. Additionally, examination along an axis from upper left to lower right reveals a linear progression that indicates clear connections among different categories of dominant seepage channel. The verification results show a strong correlation between the calculated dominant seepage channel distribution and actual water-injection profile test results, indicating that the method effectively identifies dominant seepage channels in oil reservoirs after polymer flooding and has the potential to enhance oil recovery further.

Investigating Drug-Target Interactions (DTI) is crucial for drug repositioning and discovery tasks. However, discovering DTIs through experimental approaches is time-consuming and requires substantial financial resources. To address these challenges, machine learning-based methodologies have been adopted to reduce costs and save time. Unfortunately, the effectiveness of these methods has been limited due to the binary classification approach and the lack of empirically validated negative samples. The availability of abundant DTI datasets and protein structure data has enabled the development of new approaches, such as redefining the DTI problem as a regression task. Given this context, we propose an innovative deep-learning approach to predict binding affinities between drugs and targets. Our model, named the Convolution Self-Attention Network with Attention-based Bidirectional Long Short-Term Memory Network (CSAN-BiLSTM-Att), integrates convolutional neural network (CNN) blocks with self-attention mechanisms to create an attention-based bidirectional long short-term memory (BiLSTM) model, followed by fully connected layers. Due to the model's complexity, proper hyperparameter tuning is essential. To optimize this, we employ the Differential Evolution (DE) technique to select the most suitable hyperparameters. Experimental results demonstrate that the DE-based CSAN-BiLSTM-Att model outperforms previous approaches. Specifically, the model achieved a concordance index of 0.898 and a mean square error of 0.228 on the DAVIS dataset, and a concordance value of 0.971 with a mean square error of 0.014 on the KIBA dataset.

To achieve carbon neutrality for China, the transmission mechanism, path, diffusion effect,and allocation of Emission Reduction Targets (ERT) among industries must be investigated. Setting short-term ERT will create a reverse pressure mechanism for the industry's carbon reduction, ensuring the achievement of carbon emission control target. However, few studies have explored how to influence the entire system by controlling key industries from the perspective of network control. Therefore, this study investigates Inter-industry Carbon Emission Transfer (ICET) in China from 1997 to 2017,and found that ICET can achieve fair distribution of ERT. Inspired by the controllability theory of complex networks to integrate the driving factors, the transmission paths, diffusion effects, and allocation of ERT into a unified analytical framework. Then, breaking the traditional top-down hierarchical decomposition mechanism and transforming it into a bottom-up target forcing mechanism, a decomposition algorithm for the total control target was proposed.The results reveal that ICET accounts for the majority of carbon emission. The characteristics of network structure have a significant impact on the ICET.Different industries have varying control capabilities. Compared with industry-wide control, target control on the Minimum Control Industry Set (MCMCS) has a slightly worse emission reduction effect, but the economic loss is smaller, making it an effective policy choice.

This study explores inventory management strategies specifically designed for non-instantaneous deteriorating goods under inflationary conditions, utilizing a dual-warehouse system—one owned and the other rented with limited storage capacity. We examine the effects of advertising frequency and product selling price on demand rates, taking into account the gradual decline in customer patience, which leads to partial backlogging of shortages. The primary objective is to determine optimal replenishment policies for retailers that effectively minimize total costs per unit time. In real life, managing oil inventory is crucial for industries where factors like gradual deterioration, demand fluctuations due to advertising and pricing, and sensitivity to inflation require sophisticated inventory models to optimize replenishment policies and minimize costs. To validate our proposed inventory model, we provide a numerical. A sensitivity study using MATLAB R2024a software highlights the impact of parameter changes, providing significant information for decision-makers across various industries.

Probability-based methodologies have gained widespread recognition for their pivotal role in steering decision-making in contexts marked by uncertainty or vagueness. In order to guarantee that decisions made in these circumstances are both significant and impactful, various methodologies focused on probability have been devised and utilized. This study seeks to significantly enrich the existing literature by proposing a new probability model termed the weighted sine exponentiated Weibull distribution. Certain characteristics are obtained for the suggested model. Additionally, the parameter estimation method and simulation studies related to the suggested model are also provided. Two distinct data sets, acquired from the disciplines of physical education and reliability, were effectively implemented using the proposed model, resulting in a successful outcome. Through the utilization of the p-value and various tests, it becomes apparent that the weighted sine exponentiated Weibull model consistently surpasses its competitor distributions in terms of statistical significance. The proposed model’s practical illustration highlights its effectiveness and potential for extensive use in both physical education and reliability research and practice.

This study aims to investigate a financial system consisting of four ordinary differential equations associated with the rate of interest, investment demand, price index, and the density of financial information gained by the population. The equilibrium and local stability of the system are investigated numerically. The impact of saving amounts and the rate of investment demand increases after getting financial information on the system are discussed. The findings of the study are verified graphically. It is found that the system becomes stable if the rate of investment demand increases after getting financial information kept at a certain level, such that the savings amount is maintained at a higher level. Also, the bifurcation diagrams of the system for various significant parameters that affect the system’s stability have been depicted.