Future Challenges Research Articles

Artificial intelligence and sustainability in higher education: a bibliometric analysis and its relations with the UN SDGs

This research aimed to analyze the importance of artificial intelligence and sustainability in higher education according to the literature in the field and to present the relationships of this context with the United Nations Sustainable Development Goals (SDGs). The adopted research strategies included bibliometric analysis using VOSviewer software and literature review, considering the Web of Science scientific database. The bibliometric analysis resulted in the clustering of four groups. The blue cluster highlighted the emergence of interest in studies on AI and sustainability in higher education following the Covid-19 pandemic. The green cluster emphasized the importance of more efficient teaching methods adapted to the demands of higher education, as well as the need to empower teachers to use artificial intelligence in developing students' skills and competencies, emphasizing sustainability. The yellow cluster indicated the presence of artificial intelligence in higher education based on the triad of sustainable education and innovation, aiming to prepare students for future challenges. The red cluster emphasized the impact of artificial intelligence in higher education, focusing on student learning, efficiency, and sustainable performance. Finally, the literature analysis identified the main AI technologies in higher education and their relationship with the United Nations SDGs. The reflections presented here can contribute to expanding discussions on the relationship between artificial intelligence and sustainability in higher education. From a practical standpoint, it can serve as a foundation for university managers aiming to promote the integration of AI into their teaching processes, considering the context of sustainability.

Developing students’ chemical literacy through the integration of dilemma stories into a STEAM project on petroleum topic

The aim of this study was to develop students’ chemical literacy by integrating dilemma stories into a Science, Technology, Engineering, Arts, and Mathematics (STEAM) project on petroleum. Chemical literacy can help students face future challenges by applying what they learn at school to solve problems in their daily lives. A qualitative research method was employed that included interviews, observations, reflective journals, chemical literacy tests, and student activity sheets, as data collection tools. 106 secondary school students participated in the research using a project-based approach consisting of five steps: value reflection, problem-solving, project monitoring and evaluation, project development, and transformation. The five indicators of chemical literacy by Shwartz, Ben-Zvi, and Hofstein (2006); general scientific ideas, characteristics of chemistry, chemistry in context, higher-order learning skills, and affective aspect, the research found that the students’ chemical literacy abilities were significantly developed. This study uses a dilemma story to understand chemistry concepts whereby students can develop a project by integrating chemistry and STEAM principles. The STEAM Project involved making alternative fuel to find a solution to a problem presented in the dilemma story. The researchers were challenged by the need to find a suitable chemistry-based dilemma story to integrate with a STEAM project while also empowering students and managing time resources.

A performance study of algorithms and frameworks for federated learning

Federated learning is a type of distributed machine learning that focuses on solutions for the properties of training data on edge devices as well as the privacy of the training set. Federated learning is a discipline highly relevant to real-world applications, and thus the emphasis on different perspectives requires an adaptation of the federated learning framework. Although almost every newly proposed federated learning algorithm is compared with some existing algorithms, current research on testing comparisons between commonly used federated learning algorithms remains vague and complex. Therefore, the purpose of this paper is to test and compare several federated learning frameworks, including the representative FedAvg, MOON, FedProx, and MOON. Based on revisiting the theory and key steps of these algorithms, an analysis of the performance performance will be conducted, evaluating their advantages for applications. Furthermore, a summary based on the test results will be provided, pointing out possible future challenges as well as research directions.

Resorbable conductive materials for optimally interfacing medical devices with the living.

Implantable and wearable bioelectronic systems are arising growing interest in the medical field. Linking the microelectronic (electronic conductivity) and biological (ionic conductivity) worlds, the biocompatible conductive materials at the electrode/tissue interface are key components in these systems. We herein focus more particularly on resorbable bioelectronic systems, which can safely degrade in the biological environment once they have completed their purpose, namely, stimulating or sensing biological activity in the tissues. Resorbable conductive materials are also explored in the fields of tissue engineering and 3D cell culture. After a short description of polymer-based substrates and scaffolds, and resorbable electrical conductors, we review how they can be combined to design resorbable conductive materials. Although these materials are still emerging, various medical and biomedical applications are already taking shape that can profoundly modify post-operative and wound healing follow-up. Future challenges and perspectives in the field are proposed.

The Role of Artificial Intelligence in Shaping the Future of Education at Higher Secondary Level

This study examines the implications of Artificial Intelligence (AI) in enhancing educational methodologies at the higher secondary level, focusing on its impact on student engagement and learning outcomes, the experiences and challenges faced by educators in integrating AI tools, and the broader ethical and privacy considerations. Utilizing a qualitative methodology based on interviews with educators and students, the research aims to explore the nuanced perspectives on the role of AI in education, identifying both the potential benefits and drawbacks. Initial findings highlight the positive effects of AI on personalized learning experiences alongside significant challenges related to resource limitations and the need for comprehensive support for educators. Moreover, concerns about ethical implications and data privacy emerge as critical issues requiring careful management. The study concludes that while AI offers transformative potential for educational practices, ensuring effective integration, addressing ethical concerns, and preparing students for future technological challenges is essential for leveraging AI's full benefits in the educational sector.

Toward a Brain-Neuromorphics Interface.

Brain-computer interfaces (BCIs) that enable human-machine interaction have immense potential in restoring or augmenting human capabilities. Traditional BCIs are realized based on complementary metal-oxide-semiconductor (CMOS) technologies with complex, bulky, and low biocompatible circuits, and suffer with the low energy efficiency of the von Neumann architecture. The brain-neuromorphics interface (BNI) would offer a promising solution to advance the BCI technologies and shape the interactions with machineries. Neuromorphic devices and systems are able to provide substantial computation power with extremely high energy-efficiency by implementing in-materia computing such as in situ vector-matrix multiplication (VMM) and physical reservoir computing. Recent progresses on integrating neuromorphic components with sensing and/or actuating modules, give birth to the neuromorphic afferent nerve, efferent nerve, sensorimotor loop, and so on, which has advanced the technologies for future neurorobotics by achieving sophisticated sensorimotor capabilities as the biological system. With the development on the compact artificial spiking neuron and bioelectronic interfaces, the seamless communication between a BNI and a bioentity is reasonably expectable. In this review, the upcoming BNIs are profiled by introducing the brief history of neuromorphics, reviewing the recent progresses on related areas, and discussing the future advances and challenges that lie ahead.

‘What a funny looking video’: Using allegorical representations of technological change to reflect on future digital communication and design challenges

This paper presents a reflective examination of challenges to design and communication from the current digital revolution, using the prism of a 1980s television advertisement for the Yellow Pages. Originating at the same time as the height of the desktop publishing revolution, the advert illuminates a transitional period in the evolution of digital technology and media communications, marked by changing user practices and experiences. The advert’s storyline follows a young man’s quest to convert an old cine film (of his father) to videotape for his mother’s birthday, in the process showcasing the impending shift from analogue to digital technologies and encapsulating the multifaceted implications and transitional challenges of the period. The hybrid setting of technologies, alongside the tensions, confusion and ambiguities of different stakeholders metaphorically symbolises and can be contrasted alongside, the challenges impacting the design, media and communications industry of the period and can inform reflection on contemporary challenges. Engaging with the narrative, the Yellow Pages advert is used as a creative device which functions as a cultural, historical, narrative lens with which to contrast against contemporary (and future) transitional challenges within the digital revolution.

Trends of regenerative tissue engineering for oral and maxillofacial reconstruction in veterinary medicine.

Oral and maxillofacial (OMF) defects are not limited to humans and are often encountered in other species. Reconstructing significant tissue defects requires an excellent strategy for efficient and cost-effective treatment. In this regard, tissue engineering comprising stem cells, scaffolds, and signaling molecules is emerging as an innovative approach to treating OMF defects in veterinary patients. This review presents a comprehensive overview of OMF defects and tissue engineering principles to establish proper treatment and achieve both hard and soft tissue regeneration in veterinary practice. Moreover, bench-to-bedside future opportunities and challenges of tissue engineering usage are also addressed in this literature review.

Geographies of inland waterscapes: Thinking with watery places

AbstractHumans and water have a complex relationship that includes various dimensions such as sociocultural, political, legal and ecological. Considering the ubiquity of water, we need a more holistic perspective to help us see water not as a static entity but rather as one in constant movement, physically and conceptually; acknowledging the interplay between water and humans is essential to understanding societal narratives deeply embedded in places. In this special section, an interdisciplinary group of scholars explore inland waters, taking a water‐centric view instead of a land‐centric one. The special section delves into the emerging hydro‐social connections, diverse forms of expertise, governance models, collective and spontaneous actions, and resilience strategies within the context of inland water bodies, exploring how canals, rivers and wetlands are experienced and represented as places. The papers in this collection show that any form of placemaking should take responsible stewardship of water, embrace its dynamic nature, and present a realistic pathway towards sustainable solutions for present and future water challenges.

Epigenetic regulation and factors that influence the effect of iPSCs-derived neural stem/progenitor cells (NS/PCs) in the treatment of spinal cord injury.

Spinal cord injury (SCI) is a severe neurological disorder that causes neurological impairment and disability. Neural stem/progenitor cells (NS/PCs) derived from induced pluripotent stem cells (iPSCs) represent a promising cell therapy strategy for spinal cord regeneration and repair. However, iPSC-derived NS/PCs face many challenges and issues in SCI therapy; one of the most significant challenges is epigenetic regulation and that factors that influence this mechanism. Epigenetics refers to the regulation of gene expression and function by DNA methylation, histone modification, and chromatin structure without changing the DNA sequence. Previous research has shown that epigenetics plays a crucial role in the generation, differentiation, and transplantation of iPSCs, and can influence the quality, safety, and outcome of transplanted cells. In this study, we review the effects of epigenetic regulation and various influencing factors on the role of iPSC-derived NS/PCs in SCI therapy at multiple levels, including epigenetic reprogramming, regulation, and the adaptation of iPSCs during generation, differentiation, and transplantation, as well as the impact of other therapeutic tools (e.g., drugs, electrical stimulation, and scaffolds) on the epigenetic status of transplanted cells. We summarize our main findings and insights in this field and identify future challenges and directions that need to be addressed and explored.

A Review of Deep Learning Applications in Tunneling and Underground Engineering in China

With the advent of the era of big data and information technology, deep learning (DL) has become a hot trend in the research field of artificial intelligence (AI). The use of deep learning methods for parameter inversion, disease identification, detection, surrounding rock classification, disaster prediction, and other tunnel engineering problems has also become a new trend in recent years, both domestically and internationally. This paper briefly introduces the development process of deep learning. By reviewing a number of published papers on the application of deep learning in tunnel engineering over the past 20 years, this paper discusses the intelligent application of deep learning algorithms in tunnel engineering, including collapse risk assessment, water inrush prediction, crack identification, structural stability evaluation, and seepage erosion in mountain tunnels, urban subway tunnels, and subsea tunnels. Finally, it explores the future challenges and development prospects of deep learning in tunnel engineering.

Degradation Models and Maintenance Strategies for Reinforced Concrete Structures in Coastal Environments under Climate Change: A Review

Modern engineering faces challenges in ensuring technical standards for service, durability, and sustainability. Political, administrative, and budgetary factors, coupled with climate change, pose tasks to structural integrity, affecting industries and economies. Marine infrastructures represent a strategic asset of a country as they handle a large part of the economic exchanges. This article analyzes five essential factors that play a fundamental role in the performance analysis of coastal structures: chloride-induced corrosion, degradation models, maintenance strategies, monitoring, and climate change. We start with reinforcement corrosion, which is considered as the main cause of distress, particularly in coastal zones, for the long-term behavior of structures. Additional pressure from the influences of climate change is becoming evident and extreme, leading to a reduction in capacity. To guarantee the lifespan of infrastructures, degradation models contribute by estimating the long-term performance of the asset as a strategic piece to the development of effective maintenance solutions. Artificial Neural Networks (ANNs) have gained recent prominence in this field due to their ability to learn intricate patterns from historical data, making them valuable instruments for predicting structural deterioration. Additionally, quantifying the condition of the structure from monitoring data plays a crucial part in providing information on the current situation of the structure. Finally, this review summarizes the challenges associated with the maintenance of aging marine structures considering aspects such as corrosion, monitoring, and the future challenges this area will face due to climate change.

Development of a Decommissioning Robot with a Simple Structure Capable of Traversing Steps Using Two Different Drive Systems

In response to the imperative need for robotic involvement in the decommissioning of the Fukushima Daiichi Nuclear Power Plant, a proactive initiative was undertaken. This initiative led to the organization of the “7th Decommissioning Creative Robot Contest,” in which pivotal challenges related to nuclear plant decommissioning were addressed. Recognizing inaccessible areas within a decommissioned plant, the primary objective of this study was to develop a remotely operated robot capable of executing decontamination tasks in such environments. Two distinct locomotion systems were designed: mecanum wheels and crawlers. The mecanum wheels, characterized by their ability to execute approximately 100 mm vertical movements, facilitated the robot to surmount obstacles, particularly by negotiating 100 mm steps by engaging the tracks. Simultaneously, to address decontamination tasks at elevated locations, a threefold expandable ladder truck system was integrated, augmenting the operational range of the robot. Participation in the contest provided a platform for showcasing the developed technologies and validating their practicality. Despite encountering challenges during competition, such as damage to one locomotion system, the other locomotion system can be used to reach the destination. Experience serves as a crucial means of verifying technological advancements. Issues such as malfunctions in the range of motion control due to faulty limit switch contacts and unexpected loads on the tire elevation motors were noted, which hindered the achievement of the anticipated results. Despite these drawbacks, the adopted structural design demonstrates notable advantages, particularly in terms of maneuverability. The combination of mecanum wheels and crawlers along with a ladder truck system demonstrated adaptability to various scenarios. This adaptability holds significant promise for future nuclear power plant decontamination applications. While the competition may not have yielded the desired outcome, the lessons learned and the verified practicality of the developed technologies reinforce their potential utility in addressing the future challenges of nuclear plant decommissioning.

Tiny but mighty: metal nanoparticles as effective antimicrobial agents for plant pathogen control.

Metal nanoparticles (MNPs) have gained significant attention in recent years for their potential use as effective antimicrobial agents for controlling plant pathogens. This review article summarizes the recent advances in the role of MNPs in the control of plant pathogens, focusing on their mechanisms of action, applications, and limitations. MNPs can act as a broad-spectrum antimicrobial agent against various plant pathogens, including bacteria, fungi, and viruses. Different types of MNPs, such as silver, copper, zinc, iron, and gold, have been studied for their antimicrobial properties. The unique physicochemical properties of MNPs, such as their small size, large surface area, and high reactivity, allow them to interact with plant pathogens at the molecular level, leading to disruption of the cell membrane, inhibition of cellular respiration, and generation of reactive oxygen species. The use of MNPs in plant pathogen control has several advantages, including their low toxicity, selectivity, and biodegradability. However, their effectiveness can be influenced by several factors, including the type of MNP, concentration, and mode of application. This review highlights the current state of knowledge on the use of MNPs in plant pathogen control and discusses the future prospects and challenges in the field. Overall, the review provides insight into the potential of MNPs as a promising alternative to conventional chemical agents for controlling plant pathogens.

Evaluation of the intra-varietal diversity of ‘Tempranillo Tinto’ clones prospected in the demarcated winemaking region of Rioja (Spain)

The sustainability of traditional viticulture systems is compromised by different threats, like those derived from climate change. The use of alternative grapevine varieties is suggested as a possible solution, but different regulations limit this practice in many well-known winemaking regions. In these cases, the exploitation of clonal diversity emerges as a useful alternative of adaptation. Here, 30 ‘Tempranillo Tinto’ clones selected from a grapevine collection of 729 clones initially prospected in the demarcated winemaking region of Rioja (Spain) have been studied. The characterization of 27 traits related to phenology, agronomic performance, and oenological potential during three consecutive seasons revealed a high intra-varietal phenotypic spectrum, of interest to adapt to different growing conditions and needs. Interestingly, this diversity was markedly higher to that observed in a series of commercial clones used as controls. Further analyses revealed their clustering into four groups of clones with significant phenotypic differences. We identified a group of clones with late ripening and long veraison-to-harvest periods, of special interest to respond to projected warming conditions. In general, we proved that old vineyards in traditional winemaking regions are reservoirs of clonal diversity for ancient grapevine varieties like ‘Tempranillo Tinto’, which store useful traits to address current and future viticulture challenges.

Risk assessment methods for process safety, process security and resilience in the chemical process industry: A thorough literature review

This paper presents a systematic literature review of risk assessment methods in the chemical process industry (CPI), focusing on process safety, process security, and resilience. We analyzed peer-reviewed articles from 2000 to 2022 using the PRISMA methodology and identified twelve predominant methods. Our findings reveal a shift towards dynamic, systemic-based assessments like the Functional Resonance Analysis Method (FRAM) and System-Theoretic Accident Model and Processes (STAMP). These methods are particularly effective at capturing the complexities of sociotechnical systems in the CPI. However, a significant observation from our review is the limited emphasis on the resilience paradigm within many existing methods when addressing both process safety and process security risks, which is crucial for preventing and recovering from disruptions. Given the evolving challenges in system safety and security threats, there is an urgent need for holistic methods that integrate process safety, process security, and resilience. Our review highlights the opportunity for further research to better prepare the industry for future challenges, ensuring safer, more secure, reliable, and resilient operations.

Review on Sensors for Sustainable and Safe Maritime Mobility

The increasingly stringent requirements—in terms of limiting pollutants and the constant need to make maritime transport safer—generated the necessity to foresee different solutions that are original. According to the European Maritime Safety Agency, the total number of reported marine casualties and incidents is 21.173 over the period 2014–2021, with a yearly average of 2.647 casualties and incidents. According to the same source, 495 cases of pollution were reported in the period from 2014 to 2021. Marine pollution by ships’ fuel and other pollutants is linked to 64.2% of all pollution. It is mandatory to offer a new kind of ship that can exploit new technologies to increase safety for people and onboard goods. It has been found that existing marine structures for maritime mobility do not have essential sensors for avoiding emergency situations such as flooding, oil spills, or health situations requiring efficient monitoring. In addition, there is a lack of legislation defining the insertion of unmanned vehicles into the actual marine infrastructure. This review highlighted the strengths and weaknesses of sensors in the maritime sector, intensifying areas of improvement for future challenges, such as sensor energy efficiency, data processing, sensor fusion methodologies, and accurate sea state description with environmental monitoring by means of unmanned vehicles.

Creativity and Mirror Effect: Teaching Creative Skills Through Non-traditional Pedagogies

Design pedagogy is crucial for transferring project methodologies and fostering creative skills. However, the technological and social transformations of this century have challenged traditional educational canons and the very definition of creativity. This article proposes that this challenge can be analyzed through non-traditional educational paradigms to address this challenge from a design methodology perspective. It first reviews the existing literature on creativity training and presents a case study on training methodologies. This case was incorporated into an undergraduate design course, providing insight into design pedagogy itself. Then, the article discusses the future challenges and projections of Design education and the cultivation of creative skills for the 21st century. Finally, by examining alternative educational approaches, this research contributes to the current discourse on design pedagogy and its adaptation to the changing needs of contemporary society.

Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges.

Drug discovery plays a critical role in advancing human health by developing new medications and treatments to combat diseases. How to accelerate the pace and reduce the costs of new drug discovery has long been a key concern for the pharmaceutical industry. Fortunately, by leveraging advanced algorithms, computational power and biological big data, artificial intelligence (AI) technology, especially machine learning (ML), holds the promise of making the hunt for new drugs more efficient. Recently, the Transformer-based models that have achieved revolutionary breakthroughs in natural language processing have sparked a new era of their applications in drug discovery. Herein, we introduce the latest applications of ML in drug discovery, highlight the potential of advanced Transformer-based ML models, and discuss the future prospects and challenges in the field.

From Pixels to Precision: A Survey of Monocular Visual Odometry in Digital Twin Applications.

This survey provides a comprehensive overview of traditional techniques and deep learning-based methodologies for monocular visual odometry (VO), with a focus on displacement measurement applications. This paper outlines the fundamental concepts and general procedures for VO implementation, including feature detection, tracking, motion estimation, triangulation, and trajectory estimation. This paper also explores the research challenges inherent in VO implementation, including scale estimation and ground plane considerations. The scientific literature is rife with diverse methodologies aiming to overcome these challenges, particularly focusing on the problem of accurate scale estimation. This issue has been typically addressed through the reliance on knowledge regarding the height of the camera from the ground plane and the evaluation of feature movements on that plane. Alternatively, some approaches have utilized additional tools, such as LiDAR or depth sensors. This survey of approaches concludes with a discussion of future research challenges and opportunities in the field of monocular visual odometry.