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In recent years, in the world as well as in Iran, there is little interest of engineering and basic sciences, and the number of high school students who choose mathematics and sciences is decreasing. Despite the fact that the number of engineering colleges in the country has increased in recent years, the number of applicants for education has decreased sharply. This decrease in the long term will cause a shortage of efficient specialists in the country’s industries because weaker students will be attracted to engineering fields, which will be rare talented among them even if the number of graduates does not decrease. There are many reasons for this decreases, which are discussed in this article. It is tried to find ways to increase the interest of students in the mentioned fields in the future and reduce the severity of the problems that the country will face in the coming years. Students’ lack of knowledge about engineering fields and the factors that lead to choosing other fields have been studied. It is found that investment in engineering education is the basis of economic development, and it is the existence of expert and skilled forces that makes the investment of large companies possible.

The maize leaf diseases create severe yield reductions and critical problems. The maize leaf disease should be discovered early, perfectly identified, and precisely diagnosed to make greater yield. This work studies three main leaf diseases: common rust, blight, and grey leaf spot. This approach involves pre-processing, including sampling and labelling, while ensuring class balance and preventing overfitting via the SMOTE algorithm. The maize leaf dataset with augmentation was used to classify these diseases using several deep-learning pre-trained networks, including VGG16, Resnet34, Resnet50, and SqueezeNet. The model was evaluated using a maize leaf dataset that included various leaf classes, mini-batch sizes, and input sizes. Performance measures, recall, precision, accuracy, F1-score, and confusion matrix were computed for each network. The SqueezeNet learning model produces an accuracy of 97% in classifying four different classes of plant leaf datasets. Comparatively, the SqueezeNet learning model has improved accuracy by 2–5% and reduced the mean square error by 4–11% over VGG16, Resnet34, and Resnet50 deep learning models.

Federated Learning (FL) is a promising form of distributed machine learning that preserves privacy by training models locally without sharing raw data. While FL ensures data privacy through collaborative learning, it faces several critical challenges. These include vulnerabilities to reverse engineering, risks to model architecture privacy, susceptibility to model poisoning attacks, threats to data integrity, and the high costs associated with communication and connectivity. This paper presents a comprehensive review of FL, categorizing data partitioning formats into horizontal federated learning, vertical federated learning, and federated transfer learning. Furthermore, it explores the integration of FL with blockchain, leveraging blockchain’s decentralized nature to enhance FL’s security, reliability, and performance. The study reviews existing FL models, identifying key challenges such as privacy risks, communication overhead, model poisoning vulnerabilities, and ethical dilemmas. It evaluates privacy-preserving mechanisms and security strategies in FL, particularly those enabled by blockchain, such as cryptographic methods, decentralized consensus protocols, and tamper-proof data logging. Additionally, the research analyzes regulatory and ethical considerations for adopting blockchain-based FL solutions. Key findings highlight the effectiveness of blockchain in addressing FL challenges, particularly in mitigating model poisoning, ensuring data integrity, and reducing communication costs. The paper concludes with future directions for integrating blockchain and FL, emphasizing areas such as interoperability, lightweight consensus mechanisms, and regulatory compliance.

One of the significant challenges facing the higher education system in recent years has been the declining social demand for engineering studies. This downward trend is also observed in other countries, even those with labor markets potentially capable of absorbing competent engineering graduates. This article examines the reasons behind the declining appeal of engineering education in several selected countries and then focuses on the state of social demand for this field of study in Iran. The study’s findings indicate that the proportion of engineering students in the overall student population, as well as in the youth population of the country, has been declining since 2010. This downward trend is observed across all educational levels in engineering, including associate, bachelor’s, master’s, and doctoral degrees, sequentially over time. Moreover, this trend is not confined to a single engineering discipline but is evident in six selected major engineering fields. A comparison of the situation of engineering graduates in Iran with those in other countries also points to ineffective policymaking in the country’s higher education system over the past two decades. The article also discusses the causes, consequences, and youth strategies in response to this phenomenon, using thematic analysis of the conducted studies.

With the mining depth increases, under the condition of high stress and intensive disturbance, large deformation problem of roadway surrounding rock is more serious. Therefore, energy design criteria of support materials to control large deformation of roadway surrounding rock are established. Based on energy design criteria, the method of pressure relief by roof cutting and absorbing energy is proposed, which can be noted that the stress transfer path of overburden roof is cut off by using the technique of directional roof cutting to make roadway in the low stress state, and constant-resistance anchor cable with extraordinary characteristics of absorbing energy can effectively control the roof subsidence and deformation of roadway. A field engineering case using this method was performed in Guotun coal mine with burial depth of 890 m; field test results are as follows: (1) compared with the influence zone without roof cutting, support stress, advancing abutment stress, and lateral abutment stress in the influence zone of roof cutting is significantly reduced; (2) constant-resistance anchor cables have large deformation ability and extraordinary absorbing energy characteristic to meet energy design criteria; (3) compared with the influence zone without roof cutting, micro-seismic events, maximum energy, and total energy are obviously reduced in the influence zone of roof cutting; (4) the displacement of the gob-side roadway by using this method meets the engineering requirements. The field engineering case proves that this method can make the gob-side roadway in the state of low stress condition and improve the ability of absorbing energy of support system to effectively control large deformation of roadway surrounding rock.

This paper presents an analytical and finite element modeling (FEM) investigation on the carbon fiber reinforced polymer concrete-filled polyvinyl chloride tube (CCFPT) concrete columns under axial eccentric compression. The study involved collecting experimental data from 32 CFPT columns confined with CFRP from literature and modeling them using FEM in ABAQUS. A parametric study was conducted on 260 CCFRP concrete columns, examining various parameters such as eccentricity, number of CFRP layers, thickness of PVC tube, column slenderness ratio, CFRP spacing, thickness of CFRP strips, confined concrete strength, and concrete core diameter. The effects of these parameters on the ultimate load and strain capacity were analyzed. Analytical models were developed to express the confined concrete strength and strain as functions of the constituent properties and dimensionless confinement parameters. The findings revealed that increasing eccentricity significantly reduced the ultimate load (up to 45%) and strain (up to 67%) capacities. Adding more layers of CFRP increased strength and strain capacities by 25% when going from 2 to 3 layers at a 20 mm eccentricity. Thicker PVC tubes increased load capacity by preventing buckling, but had inconsistent effects on strain. Higher slenderness ratios decreased both capacities, particularly strain. Six machine learning models were employed to predict the load-carrying capacity and confined ultimate strain. Various performance metrics, data visualization techniques, SHAP analysis, sensitivity analysis, and error characteristic curves were used to evaluate the prediction performance and analyze the impact of input parameters. The findings revealed that increasing eccentricity and CFRP layers lead to reduced ultimate load and strain capacities, while higher slenderness ratios result in increased ultimate loads. The study concluded that CCFPT columns with optimized CFRP wrapping can offer superior performance for eccentrically loaded columns.

Machine learning (ML) models have gained significant attention in a variety of applications, from computer vision to natural language processing, and are almost always based on big data. There are a growing number of applications and products with built-in machine learning models, and this is the area where software engineering, artificial intelligence and data science meet. The requirement for a system to operate in a real-world environment poses many challenges, such as how to design for wrong predictions the model may make; How to assure safety and security despite possible mistakes; which qualities matter beyond a model’s prediction accuracy; How can we identify and measure important quality requirements, including learning and inference latency, scalability, explainability, fairness, privacy, robustness, and safety. It has become crucial to test thoroughly these models to assess their capabilities and potential errors. Existing software testing methods have been adapted and refined to discover faults in machine learning and deep learning models. This paper covers a taxonomy, a methodologically uniform presentation of all presented solutions to the aforementioned issues, as well as conclusions about possible future development trends. The main contributions of this paper are a classification that closely follows the structure of the ML-pipeline, a precisely defined role of each team member within that pipeline, an overview of trends and challenges in the combination of ML and big data analytics, with uses in the domains of industry and education.

This study compares seismic losses considering initial construction costs and direct-repair costs for New Zealand steel moment-resisting frame buildings with friction connections and those with extended bolted-end-plate connections. A total of 12 buildings have been designed and analysed considering both connection types, two building heights (4-storey and 12-storey), and three locations around New Zealand (Auckland, Christchurch, and Wellington). It was found that buildings with friction connections required design to a higher design ductility, yet are generally stiffer due to larger beams being required to satisfy higher connection overstrength requirements. This resulted in the frames with friction connections experiencing lower interstorey drifts on most floors but similar peak total floor accelerations, and subsequently incurring lower drift-related seismic repair losses. Frames with friction connections tended to have lower expected net-present-costs within 50 years of the building being in service for shorter buildings and/or if located in regions of high seismicity. None of the frames with friction connections in Auckland showed any benefits due to the low seismicity of the region.

Since the invention of lasers, spatial-light-modulated laser processing has become a powerful tool for various applications. It enables multidimensional and dynamic modulation of the laser beam, which significantly improves the processing efficiency, accuracy, and flexibility, and presents wider prospects over traditional mechanical technologies for machining three-dimensional, hard, brittle, or transparent materials. In this review, we introduce: (1) The role of spatial light modulation technology in the development of femtosecond laser manufacturing; (2) the structured light generated by spatial light modulation and its generation methods; and (3) representative applications of spatial-light-modulated femtosecond laser manufacturing, including aberration correction, parallel processing, focal field engineering, and polarization control. Finally, we summarize the present challenges in the field and possible future research.

The scientific community has proposed diversified set of parameters to rank researchers, including publications, citations, h-index, different variants and extensions of h-index. However, there is a debate in the scientific Community which index ranks authors in a better way. Current state-of-the-art depicts that these indices are evaluated on imaginary case scenarios and small datasets. Furthermore, these indices are evaluated on different datasets, making it difficult to grasp the contribution and importance of each index over the others. To analyze the individual behavior of each index, these indices should comprehensively be evaluated on some extensive data set. This study emphasizes on the scrutiny of h-index, some of its variants and extensions to rank authors. These indices are evaluated using a comprehensive data set of Civil Engineering field. For the evaluation of results obtained from these indices, first correlation was calculated among indices. There exists weak correlation between various indices, which demonstrates that the author’s rankings acquired from these indices are not identical. Secondly, occurrences of awardees are checked in all ranked lists. The prestigious award winners of four Civil Engineering societies are considered as a benchmark. In top 10% of ranked list, maximum 47% of the awardees were brought by Wu-index. Overall, none of the index succeeded in bringing 100% awardees to the top rankings. Highest number of awardees on top of all ranked lists are found to be from ACI (American Concrete Institute), which shows ACI might be dependent on these indices for its criterion to honor awards.