Compared with traditional technology, bonding technology is more suitable for civil structure reinforcement because of its cost-efficiency and superior mechanical properties. As one of the simplest forms of adhesive joints, numerous studies have been conducted on the performance of single-lap joints (SLJs). However, research on the long-term performance of SLJs requires better organization and comprehension. This paper aims to investigate the long-term performance and optimization design of SLJs. The main factors influencing the long-term performance of SLJs from both material and component levels are discussed. The moisture diffusion mechanisms of bulk adhesives and the degradation mechanisms of SLJs are explored. Moreover, the optimization design of SLJs focuses on evaluating the overlap length, adhesive layer thicknesses, and changes in adhesives along the overlap length based on available literature. This paper can be employed to improve the shear strength and long-term performance of SLJs and to provide insights into their challenges and prospects.

Engineering systems have been designed to facilitate society. These systems can be seen everywhere in our daily lives ranging from electrical systems to mechanical systems, and from bio-medical systems to industrial systems. With tight coupling with information and communication technology (ICT), these engineering systems can be even controlled and monitored remotely. These systems are supported massively with sensors through which they capture enormous data, which is then used to improve the performance of the systems. Moreover, complex processes are involved in the overall functioning of these engineering systems. The management of data and processes within these engineering systems has been done through traditional ways such as database management systems or spread sheets, however, involvement of multiple parties makes these engineering systems more complex to operate, track, and audit. Blockchain technology has the potential to replace traditional database systems and offers a level of trust in an untrusted environment. With features of immutability, traceability, transparency, availability, and decentralization, blockchain technology is a good match for engineering systems. Blockchain technology can help in supply chain in these engineering systems, but it can also be used to facilitate data, process, and parties. Considering enormous applications of blockchain technology in engineering systems, this Special Issue in Wiley Engineering Reports invited for the original scientific and technical contributions.

Current study examined the magnetohydrodynamic (MHD) Prandtl nanofluid of a thermal double-diffusive flow through an exponentially vertical surface in association with heat generation, and thermophoresis effect. The novelty of this study is due to the analysis of Prandtl nanofluid model with Soret mechanism and chemically responding fluids. The fluid flow phenomenon is characterized by nonlinear coupled differential equations involving two or more independent variables. A suitable numerical technique is used to handle the set of governing equations along with a stability and convergence analysis. According to recent study, the fluid velocity increases since all the parameters are set to higher levels. For the various parametric values, isotherms and streamlines have been explored. This suggested model is beneficial since it can significantly advance the domains of thermal and industrial engineering. For instance, thermal radiation is crucial in designing sophisticated energy-transformed systems that operate at high temperatures. On the other hand, the phenomenon of Soret is useful in separating isotopes in chemical engineering. These studies have several applications in the manufacturing and biomedical fields, petrochemical industries, automobiles, medical sciences, and various production processes in industries.

Bilge and oily water (BOW) during vessel’s operation are the most large-tonnage type of waste and for their treatment all ships, in accordance with regulatory requirements [14], have to be equipped with special equipment – oily water separators. At sea vessel’s operating conditions three main directions of BOW cleaning are now used: physical, chemical and biological. The analysis of BOW separation methods based on these three directions has shown that it is very difficult to obtain secondary petrochemical products. In the article authors offer a new method for BOW separation which is based on the use of a hydrodynamic process of supercavitation with artificial ventilation of the cavitational cavern. With local origin in the flow of a supercavitating cavern, there will always be saturated water vapor inside of it. The process of permanent water vapor selection from the cavern will ultimately contribute to the production of highly concentrated mixture of secondary petroleum products from initial mixture of BOW. During the study of BOW separation process it was found that decreasing of the working pressure inside the working chamber of the cavitation separator have to be always compensated by an increase in the temperature of the processed multiphase flow.

To ensure that the crane can smoothly calibrate and align the lifting rod with the beam body lifting hole, it is necessary to use image processing technology to locate and detect the corner coordinates of the crane’s lifting rod. Traditional corner detection methods are not suitable for this scene. This article proposes a new idea for corner positioning, which locates corner coordinates through the intersection of straight lines. Firstly, using the R and G channels of the RGB color space to construct a grayscale difference map is beneficial for Otsu’s threshold segmentation; Secondly, this article proposes an optimal adaptive threshold determination method to filter the number of votes in the clustering results, eliminate interfering straight lines, and improve the clustering centroid calculation method based on the weight calculation formula of different voting proportion, replacing the original clustering centroid as the basis for line fitting; Finally, calculate the corner coordinates of the crane’s grab boom based on the straight line fitting results, and compare the recognition accuracy under different lighting conditions. This method is significantly superior to traditional corner detection methods, providing a method basis for solving the algorithm accuracy and robustness problems of port cranes under multiple environmental variables.

This paper presents a framework combining Monte Carlo Simulation (MCS) and the Newmark sliding block model with Representative slip surfaces (RSS) (model II) and Multiple response surfaces method (MRSM) to conduct seismic reliability analysis and risk assessment of soil slopes. An empirical threshold is introduced to define the limit state function to identify the failure samples in MCS and the sliding area and Newmark sliding displacement are multiplied to quantify the failure consequence. The proposed methodology is illustrated through a soil slope with multiple layers. The calculation results demonstrate that traditional Newmark sliding block model (model I) tend to underestimate the variations of yield acceleration. Both the failure probability and landslide risk exhibit decreasing trends with the increase of threshold. Significant discrepancy in failure probability and landslide risk between two models is found even for a small threshold. It is therefore, the proposed methodology is highly recommended in seismic reliability analysis and risk assessment. The contributions of RSSs to the failure probability and landslide risk are insensitive to the variation of displacement thresholds.

Aiming at the problem of crosstalk between microstrip lines, a method of reducing crosstalk by using Cross-Shape Resonators (CSR) structures is proposed. On the premise of not changing the spacing of microstrip lines, this method adds CSR structures between the coupled microstrip lines to increase the capacitive coupling and thus to suppress the far-end crosstalk. Based on the analysis of the equivalent circuit of CSR structure, the parameters simulation and verification are carried out by ADS and HFSS software. Through HFSS simulation and physical test of the designed CSR structure, the results show that: the CSR structure can significantly reduce the far-end crosstalk by about 15 dB in the frequency of 0~10GHz, and the maximum can reach 43 dB. Compared with 3W crosstalk reduction method and RectangularShape Resonators (RSR) crosstalk reduction method, the crosstalk reduction effect is improved.

The study’s foundation is a scenario analysis of a textile mill’s weaving department, with the goal of determining the necessity of a reliable and comprehensive plan for scheduling maintenance time. According to the background information and problem statement, incidents of Run failure maintenance and lengthy downtime (up to 60 days) undermine the machines’ availability (Schmidt, Galar & Wang, 2016).The desired efficiency and production are 90% and 194.76 m, respectively, however, the preliminary result indicate less. This indicates a gap that must be closed by implementing regular and appropriate maintenance plans. Additionally, the the incoherent and inconsistencies points at a lack of an efficient maintenance plan. It was established that the current strategy is not optimized and does not ensure machine availability because there are disparities and irregularities in the maintenance of crucial equipment. The objective of the study was to map out the critical equipment and collect data on the number and time between failures encountered in the weaving section of the textile manufacturing processes. Failure mode and effect analysis and fish-bone diagram were used in the analysis of the data. Mapping results indicates downtime up to 60 days, the productivity was estimated at 194.76 meters, and efficiency was 90%. The results showed that weaving looms were the essential piece of machinery.

The failure mechanisms caused by electrostatic discharge (ESD) effects at ambient temperatures ranging from -75℃ to 125℃ are investigated by Silvaco TCAD simulator. The devices are NMOS transistors fabricated with 28nm fully depleted silicon-on-insulator (FDSOI) technology. Results indicate that with an increase in temperature, the first breakdown voltage of the device decreased by 27.32%, while the holding voltage decreased by approximately 8.49%. The total current density, lattice temperature, and potential etc. were extracted for a detailed insight into the failure process. These findings provide valuable references for the design and development of ESD protection devices applied at different temperature ranges.

Background: Students’ academic achievement is regarded as the scholastic standing of students at the end of a given study period that is expressed in terms of grades. The key to bridging the attainment gap at the end of their study period is through their cumulative grade points over the duration of the study. Predictive validity study on students first-year GPA as a predictor of their final-year CGPA was carried out to predict the students’ academic performance in Chemical, Civil, Electrical, and Mechanical Engineering. Purpose/Hypothesis: This study examined the relationship between first-year GPA and final-year CGPA, as well as the relationship between Age, Gender and Geopolitical zones on first-year GPA and CGPA of Engineering students in the Faculty of Engineering students University of Abuja, Nigeria. The data obtained from the four Departments; Chemical, Civil, Electrical and Mechanical were analyzed. Two hypotheses were formulated to guide the study. Design/Method: An ex-post factor research approach was adopted, and Pearson’s correlation and Regression Analysis were fitted with the data using Minitab software. Results: The results of the study highlighted that first-year GPA had a strong positive relationship with final-year CGPA. Age, Gender and Geopolitical zones have no correlation with students’ final-year CGPA. The regression equations can be used to predict students’ CGPA to bridge the attainment gap at the end of their studies. Conclusions: Finally, the study emphasized the need to admit more female students in Engineering studies as they constitute 12.9% of the population.

As an important part of the construction industry, rural residential buildings are characterized by low energy utilization, unreasonable structures and low consumption levels, and it is particularly important to study their low-carbon transformation and evaluation system. In view of the many low-carbon transformation needs of rural residential buildings, the existing research results were analyzed in depth, and the coefficient of variation method was used to identify the important factors affecting the low-carbon transformation of rural residential buildings, and the evaluation system of rural residential buildings’ low-carbon transformation was determined by Analytic Hierarchy Process (APH), and the system was used in a rural residential building low-carbon evaluation study. The results show that the influence of “energy use”, “envelope structure” and “economic factors” on the decarbonization of buildings is obvious, with the weights of 36.4%, 24.5% and 19.5% respectively. Among the secondary indicators, “clean energy utilization”, “electricity consumption”, “external wall insulation system” and “window performance” are the most important factors in reducing carbon emissions in rural areas. The most critical influencing factors for the low carbonization level of clean energy in rural residential buildings are “window performance”. Finally, based on the constructed low carbonization evaluation system, we propose a targeted solution strategy to provide a theoretical basis for the establishment of an effective low carbonization evaluation system for clean energy in rural residential buildings.

The article discusses the interconnected fields of computing and machine learning, and their impact on various areas such as energy, economics, indoor positioning, and business. Computing provides the foundation for data processing and storage, while machine learning enables algorithms and models to learn from data and make predictions. These advancements have revolutionized how we approach complex problems and opened up new avenues for research and innovation. The article highlights the potential of computing and data science to solve complex problems and the importance of staying up-to-date with the latest developments.

A document by Gengrui Chen. Click on the document to view its contents.

Model ensemble is widely used in deep learning since it can balance the variance and bias of complex models. The mainstream model ensemble methods can be divided into “implicit” and “explicit”. The “implicit” method obtains different models by randomly inactivating the internal parameters in the complex structure of the deep learning model, and these models are integrated by sharing parameters. However, these methods lack flexibility because they can only ensemble homogeneous models with the similar structure. While the “explicit” ensemble method can fuse completely different heterogeneous model structures, which significantly enhances the flexibility of model selection and makes it possible to integrate more models with entirely different perspectives. However, the explicit ensemble will face the challenge of averaging the outputs, leading to a chaotic result. To this end, researchers further proposed using knowledge distillation and adversarial learning technologies to perform a nonlinear combination of multiple heterogeneous models to obtain better ensemble performance, however these require significant modifications to the training or testing procedure and are computationally expensive compared to simply averaging. In this paper, based on the linear combination assumption, we propose an interpretable ensemble method for averaging model results which is simple to implement, and conducting experiments on the representation learning tasks of Computer Vision(CV) and Natural Language Processing(NLP). The results show that our method is superior to direct averaging results while retaining the practicality of direct averaging.

Anemia is one of the global public health challenges that particularly affect children and pregnant women. A study by WHO indicates that 42% of children below 6 years and 40% of pregnant women worldwide are anemic. This affects the world’s total population by 33%, due to the cause of iron deficiency. The non-invasive technique, such as the use of machine learning algorithms, is one of the methods used in the diagnosing or detection of clinical diseases, which anemia detection cannot be overlooked in recent days. In this study, machine learning algorithms were used to detect iron-deficiency anemia with the application of Naïve Bayes, CNN, SVM, k-NN, and Decision Tree. This enabled us to compare the conjunctiva of the eyes, the palpable palm, and the colour of the fingernail images to justify which of them has a higher accuracy for detecting anemia in children. The technique utilized in this study was categorized into three different stages: collecting of datasets (conjunctiva of the eyes, fingernails and the palpable palm images), preprocessing the images; image extraction, segmentation of the Region of Interest of the images, obtained each component of the CIE L*a*b* colour space (CIELAB). The models were then developed for the detection of anemia using various algorithms. The CNN had an accuracy of 99.12% in the detection of anemia, followed by the Naïve Bayes with an accuracy of 98.96%, while Decision Tree and k-NN had 98.29% and 98.92% accuracy respectively. However, the SVM had the least accuracy of 95.4% on the palpable palm. The performance of the models justifies that the non-invasive approach is an effective mechanism for anemia detection. Keywords: Iron deficiency, anemia, non-invasive, machine learning, data augmentation, algorithms, region of interest.

Error Augmentation training using a robotic interface is thought to promote motor recovery by enhancing proprioceptive feedback, which motivates and challenges patients to optimize their performance during training. Here, we investigated the effectiveness of robotic Error Augmentation training on motor recovery after a stroke, compared to standard robotic training in a null field. Post-stroke patients were randomly assigned to one of two groups: a study group (n=9) that was trained on a 3D robotic system applying Error Augmentation forces, and a control group (n=7) that carried out the same protocol in null field conditions. The robotic rehabilitation intervention was applied in addition to the standard rehabilitation protocol of the rehabilitation center. Error Augmentation training increased clinical scores compared to standard robotic training by 266% on the Motor Assessment Scale, and 88% on the Fugl-Meyer scale. The Motor Assessment Scale scores were significantly correlated with the Fugl-Meyer scores (p=0.03, r=0.541). There were more movement errors on the initial trials of the game sequence using the DeXtreme robotic device with Error Augmentation compared to trials with no force field. This difference vanished however after 10 trials. Error Augmentation training decreased the number of movement units and jerkiness compared to the control treatment. These findings suggest that Error Augmentation training may enhance motor performance possibly through motor adaptation.

A complete shape factor investigation of water-based mixture type hybrid nano-fluid in a permeable boundary with the impact of magnetic field, thick dissemination, and warm radiation is presented in this article. A computational convection analysis of an inverted semi vertical cone with a porous surface in the form of S i O 2 / w a t e r nano-fluid and M O S 2 − S i O 2 / w a t e r hybrid nano-fluid transport is developed. The system of differential equations is presented and resolved numerically by the Lobatto IIIA method. The temperature distributions and fluid velocity are studied along with the coefficient of skin friction and the nusselt number, taking into account the form of distinct nano-particles. The flow problem’s results are approximated by using several embedding variables. Tables and graphs are constructed for a variety of scenarios including maximum residual error, mesh points, and nusselt numbers. We conclude that boundary film thickness reduces and the fluid flow is resisted by magnetic field presence. Fluid flow slows down as λ increases, and this reduction is more evident in nanofluids than in hybrid nanofluids. With an increment in S, velocity drops. A detailed analysis of the proposed ordinary differential equations, boundary conditions, and numerical data of skin friction is given both in tabular and graphical forms. Additionally, it is observed that the fluid flow slows down more for the hybrid nanofluid than for the SiO2 /water nanofluid. Additionally, it is clear that the temperature increase for the SiO2 /water nanofluid is substantially greater. The authors deduce that the existence of a magnetic field resists fluid flow for hybrid nanofluid forms and decreases the thickness of the viscous boundary layer.

Internet of Things (IOT) related and IOT enabled education and research are becoming more prevalent in today’s academic environment. One of the challenges faced by educators and researchers is the availability of resources to support lab based, hands on learning and research projects. Existing resources are often highly customized, balkanized, and difficult to manage. For new technology areas such as IOT, these environments rarely provide opportunities for resource sharing and interdisciplinary collaboration. In this paper we describe the development of a multi-tenant, IOT centric platform, the Environmental Sensing Data Network (ESDN) meant to support plug and play sensor deployment and the tools necessary for data management. The platform turns the University campus into an open platform that can be used to easily set up IOT based labs for education, research, campus infrastructure management and community applications innovation. The paper covers the development of a sensor augmentation module facilitating easy deployment and management. The operational tools and services integration are also described. We believe the platform is an important step in facilitating distance and in person education and research. ESDN is rapidly expanding beyond the multi-site University campus supporting a growing number of research and community projects.