Open Issues Research Articles

The trend of publication about Lumbricus rubellus for periodontitis treatment in the Scopus database: A bibliometric analysis

Periodontitis presents as a sustained deterioration of the periodontium, ultimately resulting in tooth loss. Lumbricus rubellus, a taxonomically recognized species commonly called the earthworm, is a significant component of natural constituents. This extraordinary organism exhibits intrinsic characteristics that confer antibacterial and anti-inflammatory capabilities. Nevertheless, it is essential to acknowledge that only a restricted subset of these pharmaceutical compounds has undergone meticulous scrutiny through clinical trials despite the potential effectiveness of L. rubellus in the treatment of periodontitis. The present investigation utilizes the Scopus database to conduct a bibliometric examination of the antioxidant characteristics of diverse strains of L. rubellus. This evaluation is performed through the utilization of the RStudio and VOSviewer applications. Consequently, 55 papers were encompassed within the ultimate bibliometric dataset. Moscow State University is widely acknowledged as a highly productive academic institution with significant global contributions. Mihara H is extensively acknowledged as the author with the highest level of productivity. The scholarly publication by Mihara et al. in 1991 significantly impacted the respective scientific discipline. These results provide insights to stimulate pharmaceutical research collaborations and reveal open issues about L. rubellus in the context of periodontitis therapy.

Security Issues and Defensive Approaches in Deep Learning Frameworks

Abstract: Deep learning frameworks are instrumental in advancing artificial intelligence, showcasing vast potential across diverse applications. Despite their transformative impact, security concerns pose significant risks, impeding widespread adoption. Malicious internal or external attacks on these frameworks can have far-reaching consequences for society. Our research delves into the intricacies of deep learning algorithms, conducting a thorough analysis of vulnerabilities and potential attacks. To address these challenges, we propose a comprehensive classification system for security issues and corresponding defensive approaches within deep learning frameworks. By establishing clear connections between specific attacks and their defenses, we aim to enhance the robustness of these frameworks. In addition to theoretical considerations, our study extends to real-world applications, exploring a case involving the practical implications of deep learning security issues. Looking ahead, we discuss future directions and open issues within the realm of deep learning frameworks, aspiring to inspire further developments. We anticipate that our research will not only contribute valuable insights but also attract attention from both academic and industrial domains, fostering a collective commitment to fortifying the security of deep learning frameworks.

Enhancing Human-Robot Collaboration in Industry 4.0 with AI-driven HRI

Human-robot interaction (HRI) is an important consideration in mechatronic design to ensure safe and intuitive operation of robotic systems. With advancements in artificial intelligence (AI), new opportunities have emerged to enhance HRI through learned models that can adapt to human behavior and preferences. This paper provides a comprehensive review of techniques to integrate AI into HRI for mechatronic systems. An overview is first provided of challenges and objectives in integrating intelligence into robotics for effective HRI. Modern approaches utilizing neural networks, reinforcement learning, and graph neural networks are then discussed for robotic perception, decision-making, motion control, and interaction adaptation. Additionally, hybrid approaches combining rule-based methods with learned models are highlighted. Guidelines are provided for collecting human interaction data, evaluating integrated system performance, and considering adjustability, explainability, and safety. Multiple tables summarize key studies on AI-enhanced user interfaces, interactive task learning, socially aware navigation, bio-inspired sensorimotor control, and personalized robots. Finally, open issues and future outlook are discussed. This paper aims to support mechatronic designers through an structured analysis of the emerging field of intelligent HRI with insights into current best practices for integration.

Survey on data science: its technique, tools and Open issues

Survey on data science: its technique, tools and Open issues

Environmental reclamation of limestone mining sites in Italy: Financial evaluation, challenges and proposals for sustainable development

Currently the environmental restoration of former extraction sites of limestone materials to be used in construction sector is still an open issue, challenging the opportunities for a more sustainable development of local communities.The present study evaluates the multiple causes underlying the abandonment of former limestone quarries and the lack of their reclamation as well as proposals to promote their environmental restoration.We primarily frame the issue in both the European and Italian regulatory framework. Then, the present study applies an innovative Cost Revenue Analysis to a case study of a generic environmental restoration project of a former extractive limestone site. Such analysis deepens on investment and operating costs, revenues, Net Present Value and Revenue/Costs ratio. Results show that restoration activities, performed in agreement with current regulations, involving the creation of terraces in embankments by means of excavation materials from external construction sites or even acting directly on the existing quarry, are currently discouraging for entrepreneurs, since they do not generate enough revenues to cover the costs of restoration. As a result, we suggest some solutions to strengthen the current regulatory and political framework, to make the restoration activities more convenient in financial terms. Such solutions would be also beneficial for the society and environment.

Međudržavno posvojenje u Republici Hrvatskoj kroz prizmu slučaja posvojenja djece iz DR Konga

Intercountry and interracial adoptions are forms of international adoption that imply the difference between adopter and adoptee in geographical, racial, and ethnic terms, which makes the adoption process very complex. The recent case of intercountry adoption of children from DR Congo by Croatian citizens confirmed the globally recognized controversy, questionability, and complexity of the intercountry adoption process. The key or additional problem in this case of intercountry adoption is the fact that DR Congo is not a party to the 1993 Convention on the Protection of Children and Co-operation in Respect of Intercountry Adoption. The case of intercountry adoption of children from DR Congo encouraged the authors to research and analyze international and national regulations and practices relevant to intercountry adoption. In this paper, the authors specifically analyze open issues related to intercountry adoptions in Croatian legislation, more precisely the recognition of foreign court decisions on adoption, minimum standards related to the establishment and recognition of intercountry adoptions, the possibility of termination of adoption, the citizenship status of adopted children, and the children's right to access information about adoption. In conclusion, the authors propose de lege ferenda solutions for the future regulation of intercountry adoptions.

An Enhanced Genetic Algorithm using Directional-Based Crossover and normal mutation For Global Optimization Problems

Global optimization has been employed in many practical modeling processes. Using gradient methods to solve optimization problems may be computationally inefficient and time-consuming, particularly when convexity or differentiability is not guaranteed. On the other hand, nature-inspired techniques offer an effective gradient-free approach for solving complex, non-convex, or non-differentiable problems. Genetic algorithms are one of the most effective and widely used nature-inspired techniques. However, canonical genetic algorithms do not always guarantee convergence to the optimum point owing to the stochastic nature of the genetic operators, and typically require more work to ensure convergence and increase performance. Improving the genetic operators remains an open issue and usually involves a trade-off between the speed of convergence and searchability. In this study, we propose an enhanced genetic algorithm that relies on directional-based crossover and normal mutation operators to increase the speed of convergence while preserving searchability. The proposed algorithm is evaluated using a set of 40 typical benchmark functions in two dimensions. In addition, to examine its performance at higher dimensions, 16 functions from the test set were tested at 10 and 100 dimensions. The evaluation results of the proposed algorithm are compared to the outcomes of three modern optimization algorithms, namely (Whale optimization algorithm, Teacher-Learner based algorithm, and Covariance matrix adaptation evolution strategy). The results revealed that the proposed algorithm outperformed the conventional algorithms at lower dimensions in all test functions and showed a relatively better performance than the other algorithms at higher dimensions.

Deep learning algorithms applied to computational chemistry.

Recently, there has been a significant increase in the use of deep learning techniques in the molecular sciences, which have shown high performance on datasets and the ability to generalize across data. However, no model has achieved perfect performance in solving all problems, and the pros and cons of each approach remain unclear to those new to the field. Therefore, this paper aims to review deep learning algorithms that have been applied to solve molecular challenges in computational chemistry. We proposed a comprehensive categorization that encompasses two primary approaches; conventional deep learning and geometric deep learning models. This classification takes into account the distinct techniques employed by the algorithms within each approach. We present an up-to-date analysis of these algorithms, emphasizing their key features and open issues. This includes details of input descriptors, datasets used, open-source code availability, task solutions, and actual research applications, focusing on general applications rather than specific ones such as drug discovery. Furthermore, our report discusses trends and future directions in molecular algorithm design, including the input descriptors used for each deep learning model, GPU usage, training and forward processing time, model parameters, the most commonly used datasets, libraries, and optimization schemes. This information aids in identifying the most suitable algorithms for a given task. It also serves as a reference for the datasets and input data frequently used for each algorithm technique. In addition, it provides insights into the benefits and open issues of each technique, and supports the development of novel computational chemistry systems.

An In Silico Investigation of the Molecular Interactions between Volatile Anesthetics and Actin.

Volatile anesthetics (VAs) are medicinal chemistry compounds commonly used to enable surgical procedures for patients who undergo painful treatments and can be partially or fully sedated, remaining in an unconscious state during the operation. The specific molecular mechanism of anesthesia is still an open issue, but scientific evidence supports the hypothesis of the involvement of both putative hydrophobic cavities in membrane receptors as binding pockets and interactions between anesthetics and cytoplasmic proteins. Previous studies demonstrated the binding of VAs to tubulin. Since actin is the other major component of the cytoskeleton, this study involves an investigation of its interactions with four major anesthetics: halothane, isoflurane, sevoflurane, and desflurane. Molecular docking was implemented using the Molecular Operating Environment (MOE) software (version 2022.02) and applied to a G-actin monomer, extrapolating the relative binding affinities and root-mean-square deviation (RMSD) values. A comparison with the F-actin was also made to assess if the generally accepted idea about the enhanced F-to-G-actin transformation during anesthesia is warranted. Overall, our results confirm the solvent-like behavior of anesthetics, as evidenced by Van der Waals interactions as well as the relevant hydrogen bonds formed in the case of isoflurane and sevoflurane. Also, a comparison of the interactions of anesthetics with tubulin was made. Finally, the short- and long-term effects of anesthetics are discussed for their possible impact on the occurrence of mental disorders.

A systematic review of deep learning approaches for surface defect detection in industrial applications

Detecting surface defects plays a crucial role in ensuring the quality, functionality, and security of the production process. Traditional image processing techniques and machine learning models rely on manual analysis and feature extraction for specific vision inspection tasks. Deep learning approaches, which can automatically extract features from images, have demonstrated outstanding performance in computer vision tasks, including detecting surface defects. Motivated by this consideration, a Systematic Literature Review (SLR) method is employed for the comprehensive analysis of studies published between 2020 and 2023 in the field of deep learning-based surface defect detection applications in industrial products. The study provides a technical taxonomy for deep learning models according to the content of current studies through the SLR process, including Convolutional Neural Networks (CNN), encoder–decoder models, pyramid network models, Generative Adversarial Networks (GAN), attention-based models, and other models for surface defect detection. Then, the commonly used datasets for surface defect detection are discussed, and a comparative analysis of deep learning models’ performance is provided. Our comparative analysis reveals that pyramid network models and CNN models are the most frequently used deep learning models for surface defect detection. These models yield reasonable results in surface defect detection due to their exceptional feature extraction capabilities. Finally, some hints for addressing future research directions and identifying open issues in surface defect detection applications are presented.

Metaheuristics Method for Computation Offloading In Mobile Edge Computing: Survey

In recent years, edge computing has emerged as a computing paradigm to support the computationally intensive and latency-critical applications for resource limited devices. The main feature of edge computing is to push computation, networking, and storage facilities closer to the network edge. This enables user equipment (UE) to profit from the edge computing paradigm by mainly offloading their intensive computation tasks to edge resources. Because of some rising problems such as inherent software and hardware heterogeneity, restrictions, dynamism, and stochastic behavior of the ecosystem, the computation offloading issues consider as the essential challenging problems in the MEC environment. However, to the best of the author's knowledge, in spite of its significance, in Metaheuristic -based computation offloading mechanisms, there is not any systematic, comprehensive, and detailed survey in the MEC environment. In this paper, we provide a review on the Metaheuristic -based computation offloading mechanisms in the MEC environment in the form of a classical taxonomy to identify the contemporary mechanisms on this crucial topic and to offer open issues as well. The proposed taxonomy is classified into three main fields: genetic algorithm (GA)-based mechanisms, Partical Swarm Optimization(PSO)-based mechanisms, and Hybrid GA with PSO -based mechanisms. Next, these classes are compared with each other based on the essential features such as performance metrics, case studies, utilized techniques, and evaluation tools, and their advantages and weaknesses are discussed, as well. Finally, open issues and uncovered or inadequately covered future research challenges are argued, and the survey is concluded.

A Survey of Learning-based Automated Program Repair

Automated program repair (APR) aims to fix software bugs automatically and plays a crucial role in software development and maintenance. With the recent advances in deep learning (DL), an increasing number of APR techniques have been proposed to leverage neural networks to learn bug-fixing patterns from massive open-source code repositories. Such learning-based techniques usually treat APR as a neural machine translation (NMT) task, where buggy code snippets (i.e., source language) are translated into fixed code snippets (i.e., target language) automatically. Benefiting from the powerful capability of DL to learn hidden relationships from previous bug-fixing datasets, learning-based APR techniques have achieved remarkable performance. In this article, we provide a systematic survey to summarize the current state-of-the-art research in the learning-based APR community. We illustrate the general workflow of learning-based APR techniques and detail the crucial components, including fault localization, patch generation, patch ranking, patch validation, and patch correctness phases. We then discuss the widely adopted datasets and evaluation metrics and outline existing empirical studies. We discuss several critical aspects of learning-based APR techniques, such as repair domains, industrial deployment, and the open science issue. We highlight several practical guidelines on applying DL techniques for future APR studies, such as exploring explainable patch generation and utilizing code features. Overall, our article can help researchers gain a comprehensive understanding about the achievements of the existing learning-based APR techniques and promote the practical application of these techniques. Our artifacts are publicly available at the repository: https://github.com/iSEngLab/AwesomeLearningAPR .

An approach to on-demand extension of multidimensional cubes in multi-model settings: Application to IoT-based agro-ecology

Managing unstructured and heterogeneous data, integrating them, and enabling their analysis are among the key challenges in data ecosystems, together with the need to accommodate a progressive growth in these systems by seamlessly supporting extensibility. This is particularly relevant for OLAP analyses on multidimensional cubes stored in data warehouses (DWs), which naturally span large portions of heterogeneous data, possibly relying on different data models (relational, document-based, graph-based). While the management of model heterogeneity in DWs, using for instance multi-model databases, has already been investigated, not much has been done to support extensibility. In a previous paper we have investigated a schema-on-read scenario aimed at granting the extensibility of multidimensional cubes by proposing an architecture to support it and discussing the main open issues associated. This paper takes a step further by presenting xCube, an approach to provide on-demand extensibility of multidimensional cubes in a supply-driven fashion. xCube lets users choose a multidimensional element to be extended, using additional data, possibly uploaded from a data lake. Then, the multidimensional schema is extended by considering the functional dependencies implied by these additional data, and the extended multidimensional schema is made available to users for OLAP analyses. After explaining our approach with reference to a motivating case study in agro-ecology, we propose a proof-of-concept implementation using AgensGraph and Mondrian.

Measurement of Dilational Modulus of an Adsorbed BSA Film Using Pendant Bubble Tensiometry: From a Clean Interface to Saturation

Two open issues on the measurement of the dilational modulus (E) for an adsorbed protein film during the adsorption process have been unacknowledged: how E varies during the adsorption and the length of time needed to attain a stable E value. A new approach for detecting the E variation from a clean air–water interface to saturated film and estimating the time needed to reach a saturated state was proposed. A pendant bubble tensiometer was utilized for measuring the relaxations of surface tension (ST) and surface area (SA), and the E was evaluated from the relaxation data of minute distinct perturbances. The data showed a clear variation in E during the BSA adsorption: E sharply decreased to a minimum at the early stage of BSA adsorption; then, it rose from this minimum and oscillated for a while before reaching an E corresponding to a saturated BSA film after a significant duration. The adsorbed BSA film took ~35 h to reach its saturated state, which was much longer than the reported lifetime of the adsorbed film in the literature. A rapid surface perturbation (forced bubble expansion/compression) could change the E, causing a significant drop in E followed by a slow increase to the original stable value.

Vision-Based Object Localization and Classification for Electric Vehicle Driving Assistance

The continuous advances in intelligent systems and cutting-edge technology have greatly influenced the development of intelligent vehicles. Recently, integrating multiple sensors in cars has improved and spread the advanced drive-assistance systems (ADAS) solutions for achieving the goal of total autonomy. Despite current self-driving approaches and systems, autonomous driving is still an open research issue that must guarantee the safety and reliability of drivers. This work employs images from two cameras and Global Positioning System (GPS) data to propose a 3D vision-based object localization and classification method for assisting a car during driving. The experimental platform is a prototype of a two-sitter electric vehicle designed and assembled for navigating the campus under controlled mobility conditions. Simultaneously, color and depth images from the primary camera are combined to extract 2D features, which are reprojected into 3D space. Road detection and depth features isolate point clouds representing the objects to construct the occupancy map of the environment. A convolutional neural network was trained to classify typical urban objects in the color images. Experimental tests validate car and object pose in the occupancy map for different scenarios, reinforcing the car position visually estimated with GPS measurements.

Possible remediation of impact-loading debris avalanches via fine long rooted grass: an experimental and material point method (MPM) analysis

Debris avalanches often originate along steep unsaturated slopes and have catastrophic consequences. However, their forecast and mitigation still pose relevant scientific challenges. This is also due to the variety of mechanisms observed near high sub-vertical bedrock outcrops, such as the impact loading of soil failed upslope the outcrop, the build-up of pore water pressures in the inception zone, and the bed entrainment along the landslide propagation path. At the University of Salerno, an experimental and numerical investigation campaign started some years ago to explore the feasibility of using long-root grass to mitigate or even inhibit the inception of debris avalanches. Previous laboratory results were achieved through two twin 2-m-long columns (one bare, one vegetated), where the change in soil retention curve and soil mechanical response was assessed. As follow-up, an experimental field setup was installed in 2020 first, and in an improved configuration in 2021. Here, three different species of long-root grass were grown. In situ soil suction and water content measurements were periodically collected in the vegetated and in the original soils. In both cases, soil specimens were also collected, and laboratory geotechnical tests were performed to individuate the changes in both the water retention and strength response. Increased values of soil suction and shear strength were outlined, despite some differences, for all the grown species compared to the original soil. Using these novel experimental data, advanced large-deformation stress–strain hydro-mechanically coupled analyses were recently performed through a material point method (MPM) approach. The original slope conditions were compared to various slope configurations engineered via long-root grass. The benefits and the open issues related to this novel green technology for landslide mitigation are discussed. Some insights are outlined for the possible reduction of the soil volumes mobilized inside the inception zone of debris avalanches.

A Survey on the Impact of Intelligent Surfaces in the Terahertz Communication Channel Models.

Terahertz (THz) band will play an important role in enabling sixth generation (6G) envisioned applications. Compared with lower frequency signals, THz waves are severely attenuated by the atmosphere temperature, pressure, and humidity. Thus, designing a THz communication system must take into account how to circumvent or diminish those issues to achieve a sufficient quality of service. Different solutions are being analyzed: intelligent communication environments, ubiquitous artificial intelligence, extensive network automation, and dynamic spectrum access, among others. This survey focuses on the benefits of integrating intelligent surfaces (ISs) and THz communication systems by providing an overview of IS in wireless communications with the scanning of the recent developments, a description of the architecture, and an explanation of the operation. The survey also covers THz channel models, differentiating them based on deterministic and statistical channel modeling. The IS-aided THz channels are elucidated at the end of the survey. Finally, discussions and research directions are given to help enrich the IS field of research and guide the reader through open issues.

Automated facial characterization and image retrieval by convolutional neural networks.

Developing efficient methods to infer relations among different faces consisting of numerous expressions or on the same face at different times (e.g., disease progression) is an open issue in imaging related research. In this study, we present a novel method for facial feature extraction, characterization, and identification based on classical computer vision coupled with deep learning and, more specifically, convolutional neural networks. We describe the hybrid face characterization system named FRetrAIval (FRAI), which is a hybrid of the GoogleNet and the AlexNet Neural Network (NN) models. Images analyzed by the FRAI network are preprocessed by computer vision techniques such as the oriented gradient-based algorithm that can extract only the face region from any kind of picture. The Aligned Face dataset (AFD) was used to train and test the FRAI solution for extracting image features. The Labeled Faces in the Wild (LFW) holdout dataset has been used for external validation. Overall, in comparison to previous techniques, our methodology has shown much better results on k-Nearest Neighbors (KNN) by yielding the maximum precision, recall, F1, and F2 score values (92.00, 92.66, 92.33, and 92.52%, respectively) for AFD and (95.00% for each variable) for LFW dataset, which were used as training and testing datasets. The FRAI model may be potentially used in healthcare and criminology as well as many other applications where it is important to quickly identify face features such as fingerprint for a specific identification target.

Ontologies in digital twins: A systematic literature review

Digital Twins (DT) facilitate monitoring and reasoning processes in cyber–physical systems. They have progressively gained popularity over the past years because of intense research activity and industrial advancements. Cognitive Twins is a novel concept, recently coined to refer to the involvement of Semantic Web technology in DTs. Recent studies address the relevance of ontologies and knowledge graphs in the context of DTs, in terms of knowledge representation, interoperability and automatic reasoning. However, there is no comprehensive analysis of how semantic technologies, and specifically ontologies, are utilized within DTs. This Systematic Literature Review (SLR) is based on the analysis of 82 research articles, that either propose or benefit from ontologies with respect to DT. The paper uses different analysis perspectives, including a structural analysis based on a reference DT architecture, and an application-specific analysis to specifically address the different domains, such as Manufacturing and Infrastructure. The review also identifies open issues and possible research directions on the usage of ontologies and knowledge graphs in DTs.

A feature-recombinant asynchronous deep reservoir computing for modeling time series data

Modeling time series data is an important issue in many areas. Reservoir computing (RC) is a promising tool to build time series model due to its dynamic characteristic and simple training way. Asynchronous deep reservoir computing (ADRC) is an improvement version of the traditional RC. It solves time-dependent tasks more efficiently than traditional RC because of its rich dynamics and flexible short-term memory (STM). Nevertheless, it has been an open issue to design RC’s or ADRC’s reservoir topology owing to large amounts of random factors. To promote the solution of this problem, the paper proposes a feature-recombinant ADRC (FR-ADRC) for modeling time series data. In the FR-ADRC scheme, the first sub-reservoir is designed as the feature-adaptive layer, and a trainable matrix C is introduced into this layer. By learning C, the singular value (SV) distribution of the first layer could be adjusted. Further, the principal components of the reservoir topology can be extracted by the principal component analysis (PCA). Then a new temporary reservoir is constructed by recombining these extracted components. The subsequent information processing is carried out based on the recombinant reservoir, which can be adaptive to the input signals. The validity of the FR-ADRC is tested by modeling some numerical and real-life time series data. Experimental results show that the proposed approach is better than the traditional ADRC in modeling precision, generalization ability and stability.