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The purpose of this paper is to highlight the key technical challenges that derive from the recently proposed European Artificial Intelligence Act and specifically, to investigate the applicability of the requirements that the AI Act mandates to high-risk AI systems from the perspective of AI security.

This paper presents the main points of the proposed AI Act, with emphasis on the compliance requirements of high-risk systems. It matches known AI security threats with the relevant technical requirements, it demonstrates the impact that these security threats can have to the AI Act technical requirements and evaluates the applicability of these requirements based on the effectiveness of the existing security protection measures. Finally, the paper highlights the necessity for an integrated framework for AI system evaluation.

The findings of the EU AI Act technical assessment highlight the gap between the proposed requirements and the available AI security countermeasures as well as the necessity for an AI security evaluation framework.

AI Act, high-risk AI systems, security threats, security countermeasures.

To develop and examine an efficient and reliable jujube grading model with reduced computational time, which could be utilized in the food processing and packaging industries to perform quick grading and pricing of jujube as well as for the other similar types of fruits.

The whole process begins with manual analysis and collection of four jujube grades from the jujube tree, in addition to this jujube image acquisition was performed utilizing MVS which is further followed by image pre-processing and augmentation tasks. Eventually, classification models (i.e. proposed model, from scratch and pre-trained VGG16 and AlexNet) were trained and validated over the original and augmented datasets to discriminate the jujube into maturity grades.

The highest success rates reported over the original and augmented datasets were 97.53% (i.e. error of 2.47%) and 99.44% (i.e. error of 0.56%) respectively using Adam optimizer and a learning rate of 0.003.

The investigation relies upon a single view of the jujube image and the outer appearance of the jujube. In the future, multi-view image capturing system could be employed for the model training/validation.

Due to the vast functional derivatives of jujube, the identification of maturity grades of jujube is paramount in the fruit industry, functional food production industries and pharmaceutical industry. Therefore, the proposed model which is practically feasible and easy to implement could be utilized in such industries.

This research examines the performance of proposed CNN models for selected optimizer and learning rates for the grading of jujube maturity into four classes and compares them with the classical models to depict the sublime model in terms of accuracy, the number of parameters, epochs and computational time. After a thorough investigation of the models, it was discovered that the proposed model transcends both classical models in all aspects for both the original and augmented datasets utilizing Adam optimizer with learning rate of 0.003.

This study aims to obtain methods to identify and find the place of damage, which is one of the topics that has always been discussed in structural engineering. The cost of repairing and rehabilitating massive bridges and buildings is very high, highlighting the need to monitor the structures continuously. One way to track the structure's health is to check the cracks in the concrete. Meanwhile, the current methods of concrete crack detection have complex and heavy calculations.

This paper presents a new lightweight architecture based on deep learning for crack classification in concrete structures. The proposed architecture was identified and classified in less time and with higher accuracy than other traditional and valid architectures in crack detection. This paper used a standard dataset to detect two-class and multi-class cracks.

Results show that two images were recognized with 99.53% accuracy based on the proposed method, and multi-class images were classified with 91% accuracy. The low execution time of the proposed architecture compared to other valid architectures in deep learning on the same hardware platform. The use of Adam's optimizer in this research had better performance than other optimizers.

This paper presents a framework based on a lightweight convolutional neural network for nondestructive monitoring of structural health to optimize the calculation costs and reduce execution time in processing.

Factors like bid price, submission time, and number of bidders influence the procurement process in public projects. These factors and the award criteria may impact the project’s financial compliance. Predicting budget compliance in construction projects has been traditionally challenging, but Machine Learning (ML) techniques have revolutionised estimations.

In this study, Portuguese Public Procurement Data (PPPData) was utilised as the model’s input. Notably, this dataset exhibited a substantial imbalance in the target feature. To address this issue, the study evaluated three distinct data balancing techniques: oversampling, undersampling, and the SMOTE method. Next, a comprehensive feature selection process was conducted, leading to the testing of five different algorithms for forecasting budget compliance. Finally, a secondary test was conducted, refining the features to include only those elements that procurement technicians can modify while also considering the two most accurate predictors identified in the previous test.

The findings indicate that employing the SMOTE method on the scraped data can achieve a balanced dataset. Furthermore, the results demonstrate that the Adam ANN algorithm outperformed others, boasting a precision rate of 68.1%.

The model can aid procurement technicians during the tendering phase by using historical data and analogous projects to predict performance.

Although the study reveals that ML algorithms cannot accurately predict budget compliance using procurement data, they can still provide project owners with insights into the most suitable criteria, aiding decision-making. Further research should assess the model’s impact and capacity within the procurement workflow.

Previous research predominantly focused on forecasting budgets by leveraging data from the private construction execution phase. While some investigations incorporated procurement data, this study distinguishes itself by using an imbalanced dataset and anticipating compliance rather than predicting budgetary figures. The model predicts budget compliance by analysing qualitative and quantitative characteristics of public project contracts. The research paper explores various model architectures and data treatment techniques to develop a model to assist the Client in tender definition.

Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise.

This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise.

Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36.

At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.

The build-up of large-scale COVID-19 testing required an unprecedented effort of coordination within decentralized healthcare systems around the world. The aim of the study was to elucidate the challenges of vertical policy coordination between non-political actors at the national and regional levels regarding this policy issue, using Sweden as our case.

Interviews with key actors at the national and regional levels were analyzed using an adapted version of a conceptualization by Adam et al. (2019), depicting barriers to vertical policy coordination.

Our results show that the main issues in the Swedish context were related to parallel sovereignty and a vagueness regarding responsibilities and mandates as well as complex governmental structures and that this was exacerbated by the unfamiliarity and uncertainty of the policy issue. We conclude that understanding the interaction between the comprehensiveness and complexity of the policy issue and the institutional context is crucial to achieving effective vertical policy coordination.

Many studies have focused on countries’ overall pandemic responses, but in order to improve the outcome of future pandemics, it is also important to learn from more specific response measures.

Because of the key role of joint torque in industrial robots (IRs) motion performance control and energy consumption calculation and efficiency optimization, the purpose of this paper is to propose a deep learning torque prediction method based on long short-term memory (LSTM) recurrent neural networks optimized by particle swarm optimization (PSO), which can accurately predict the the joint torque.

The proposed model optimized the LSTM with PSO algorithm to accurately predict the IRs joint torque. The authors design an excitation trajectory for ABB 1600–10/145 experimental robot and collect its relative dynamic data. The LSTM model was trained with the experimental data, and PSO was used to find optimal number of LSTM nodes and learning rate, then a torque prediction model is established based on PSO-LSTM deep learning method. The novel model is used to predict the robot’s six joint torque and the root mean error squares of the predicted data together with least squares (LS) method were comparably studied.

The predicted joint torque value by PSO-LSTM deep learning approach is highly overlapped with those from real experiment robot, and the error is quite small. The average square error between the predicted joint torque data and experiment data is 2.31 N.m smaller than that with the LS method. The accuracy of the novel PSO-LSTM learning method for joint torque prediction of IR is proved.

PSO and LSTM model are deeply integrated for the first time to predict the joint torque of IR and the prediction accuracy is verified.

This study investigates the impact of entrepreneurial orientation (EO) and knowledge management (KM) on firm performance (PERF), as well as the mediating role of KM in the EO–PERF (EO-PERF relationship). In particular, this study aims to explain the impact of KM on the relationship between the EO dimensions and PERF; dimensions are risk-taking (RT), innovativeness (IN) and proactiveness (PR).

This study uses structural equation modelling and fuzzy-set qualitative comparative analysis (fsQCA) methodologies to explore target relationships. The sample consists of 150 small furniture manufacturers operating in Poland (out of 1,480 in the population).

The study findings show that KM partially mediates the IN–PERF relationship. Furthermore, fsQCA reveals that KM accompanied by IN is a core condition that leads to PERF. Moreover, the absence of KM (accompanied by the absence of RT and IN) leads to the absence of PERF. In addition, the results show that all the variables examined (RT, IN, PR and KM) positively impact PERF.

This study explores the role of KM in the context of EO and its impact on PERF in the low-tech industry. The study uses simultaneously two methodologies that represent different approaches in the search for the expected relationships. The findings reveal that KM mediates the EO-PERF relationship.

The article explores the linkages between the type of environmental strategy (ES), the use of internal communication (IC), and the greening of organizational culture (OC). Moreover, the article empirically examines whether company size matters in the use of environmental IC practices in the green context. Additionally, the article considers differences between people employed at different organizational hierarchy levels. The basis for such a comparison is their opinions about the effectiveness of communication practices.

Empirical research employed a survey method done on 199 organizations in 2020. Statistical analyses used the chi-squared test, Kendall’s Tau-b correlation coefficient, and the Mann–Whitney U test.

The research showed that companies with a proactive green strategy more often use different communication practices related to ES and have a greener culture. The study proved that larger companies more often use the analyzed communication practices. However, we found no significant difference in opinion between middle managers and line employees about the effectiveness of these practices.

The main contribution to business practice is the exploratory model based on the empirical study, which allows organizations to successfully implement the ES.

Studies rarely combine the three organizational elements: IC, OC, and ES. This article provides new empirical evidence on relationships between features of OC, green strategy types, and communication practices.