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AI for Sustainability and Innovation—2nd Edition

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 March 2026) | Viewed by 20451

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Special Issue Editors

Prof. Dr. Ah-Lian Kor

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Guest Editor

School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds LS6 3QS, UK
Interests: AI reasoning; sustainability; intelligent computing; AI agentic learning approaches
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Eric Rondeau

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Guest Editor

Faculté des Sciences et Technologies, University of Lorraine, Vandoeuvre Les Nancy, France
Interests: networks; green IT/neworking; industrial networks
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Prof. Dr. Karl Andersson

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Guest Editor

Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, SE-93187 Skellefteå, Sweden
Interests: pervasive and mobile computing
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Special Issue Information

Dear Colleagues,

“AI for Sustainability and Innovation” aims to support GESI’s Smarter2030 initiative and the UN’s Sustainable Development Goals in order to contribute to a sustainable future. This theme encompasses theoretical and applied research to address challenges relating to society and human needs (SDG2 Zero Hunger: autonomous machines, smart and precision agriculture to increase productivity and reduce waste; SDG3 Good Health and Well Being: smart and personalized health; SDG4 Quality Education: smart and personalized educational technologies), sustainable amenities and utilities for the environment (SDG7 Affordable and Clean Energy: smart grid, smart microgrid, and smart renewable energy management systems; SDG13 Climate Change via Low Carbon Growth: smart technologies to reduce energy, as well as resource consumption and waste emissions; SDG11 Sustainable Cities and Communities: smart sustainable cities and infrastructure), and sustainable industry (SDG9 Industry, Innovation, and Infrastructure: smart technologies to support Industry 4.0; SDG12 Responsible Consumption and Production: smart technologies for resource optimization, energy efficiency, and waste reduction). As such, this Special Issue calls for AI-enabled research (positional, theoretical, or applied) that addresses relevant SDGs across the following sectors (listed in GESI’s Smarter 2020 initiative): business, power, transportation, manufacturing, services (education and health), agriculture, and buildings.

We are honored to be given the opportunity to undertake this Special Issue. This is a cutting-edge research theme, considering the pervasiveness of AI in every sector (see the summary for details). We cordially invite you to submit any of the following: survey papers that encompass a relevant comprehensive and critical literature review; theoretical research papers that address underlying design concepts, theories, principles, or algorithms; or applied research papers that address the implementation, deployment, and evaluation of relevant smart technologies.

Thank you very much and we look forward to receiving your submissions.

Prof. Dr. Ah-Lian Kor
Prof. Dr. Eric Rondeau
Prof. Dr. Karl Andersson
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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Keywords

artificial intelligence
sustainable IT
innovation
smart and pervasive technologies
smart systems
cloud computing
green networking
mobile technologies
machine learning
Internet of Things

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Published Papers (6 papers)

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Research

16 pages, 649 KB

Open AccessCommunication

Scientific Impact Prediction via Virtual Geography Hawkes Process

by Babusurya Ganeshbabu, Xin Liu, Akiyoshi Matono, Kyoung-Sook Kim and Xun Shen

Appl. Sci. 2026, 16(4), 2085; https://doi.org/10.3390/app16042085 - 20 Feb 2026

Viewed by 252

Abstract

This brief proposes a novel Virtual-Geography Hawkes Process (VG-Hawkes) to model citation dynamics considering academic networks. The VG-Hawkes model incorporates academic relationships between authors as virtual distances by extending the conventional temporal Hawkes process, enabling a more detailed and realistic representation of citation behavior. Validation results on real-world datasets show that the VG-Hawkes model consistently achieves higher log-likelihood scores than temporal Hawkes models and effectively captures citation peaks and distributional patterns. While this study focuses on selected datasets and pairwise interactions, the model is general and readily extensible. Future work includes scaling to broader datasets and incorporating more complex author relationships. The VG-Hawkes model provides a novel and flexible framework for academic network analysis and scientific impact prediction. Full article

(This article belongs to the Special Issue AI for Sustainability and Innovation—2nd Edition)

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19 pages, 8340 KB

Open AccessArticle

Open-Vocabulary Multi-Object Tracking Based on Multi-Cue Fusion

by Liangfeng Xu, Jinqi Bai, Lin Nai and Chang Liu

Appl. Sci. 2025, 15(24), 13151; https://doi.org/10.3390/app152413151 - 15 Dec 2025

Viewed by 773

Abstract

Multi-object tracking (MOT) technology integrates multiple fields such as pattern recognition, machine learning, and object detection, demonstrating broad application potential in scenarios like low-altitude logistics delivery, urban security, autonomous driving, and intelligent navigation. However, in open-world scenarios, existing MOT methods often face challenges of imprecise target category identification and insufficient tracking accuracy, especially when dealing with numerous target types affected by occlusion and deformation. To address this, we propose a multi-object tracking strategy based on multi-cue fusion. This strategy combines appearance features and spatial feature information, employing BYTE and weighted Intersection over Union (IoU) modules to handle target association, thereby improving tracking accuracy. Furthermore, to tackle the challenge of large vocabularies in open-world scenarios, we introduce an open-vocabulary prompting strategy. By incorporating diverse sentence structures, emotional elements, and image quality descriptions, the expressiveness of text descriptions is enhanced. Combined with the CLIP model, this strategy significantly improves the recognition capability for novel category targets without requiring model retraining. Experimental results on the public TAO benchmark show that our method yields consistent TETA improvements over existing open-vocabulary trackers, with gains of 10% and 16% on base and novel categories, respectively. The results demonstrate that the proposed framework offers a more robust solution for open-vocabulary multi-object tracking in complex environments. Full article

(This article belongs to the Special Issue AI for Sustainability and Innovation—2nd Edition)

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20 pages, 2057 KB

Open AccessArticle

Applying Deep Learning to Bathymetric LiDAR Point Cloud Data for Classifying Submerged Environments

by Nabila Tabassum, Henri Giudici, Vimala Nunavath and Ivar Oveland

Appl. Sci. 2025, 15(24), 12914; https://doi.org/10.3390/app152412914 - 8 Dec 2025

Viewed by 815

Abstract

Subsea environments are vital for global biodiversity, climate regulation, and human activities such as fishing, transport, and resource extraction. Accurate mapping and monitoring of these ecosystems are essential for sustainable management. Airborne LiDAR bathymetry (ALB) provides high-resolution underwater data but produces large and complex datasets that make efficient analysis challenging. This study employs deep learning (DL) models for the multi-class classification of ALB waveform data, comparing two recurrent neural networks, i.e., Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM). A preprocessing pipeline was developed to extract and label waveform peaks corresponding to five classes: sea surface, water, vegetation, seabed, and noise. Experimental results from two datasets demonstrated high classification accuracy for both models, with LSTM achieving 95.22% and 94.85%, and BiLSTM obtaining 94.37% and 84.18% on Dataset 1 and Dataset 2, respectively. Results show that the LSTM exhibited robustness and generalization, confirming its suitability for modeling causal, time-of-flight ALB signals. Overall, the findings highlight the potential of DL-based ALB data processing to improve underwater classification accuracy, thereby supporting safe navigation, resource management, and marine environmental monitoring. Full article

(This article belongs to the Special Issue AI for Sustainability and Innovation—2nd Edition)

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11 pages, 1727 KB

Open AccessArticle

JLMS25 and Jiao-Liao Mandarin Speech Recognition Based on Multi-Dialect Knowledge Transfer

by Xuchen Li, Yiqun Wang, Xiaoyang Liu, Kun Su, Zhaochen Li, Yitian Wang, Bin Jiang, Kang Xie and Jie Liu

Appl. Sci. 2025, 15(3), 1670; https://doi.org/10.3390/app15031670 - 6 Feb 2025

Cited by 1 | Viewed by 2535

Abstract

Jiao-Liao Mandarin, a distinguished dialect in China, reflects the linguistic features and cultural heritage of the Jiao-Liao region. However, the labor-intensive and costly nature of manual transcription limits the scale of transcribed corpora, posing challenges for speech recognition. We present JLMS25, a transcribed corpus for Jiao-Liao Mandarin, alongside a novel multi-dialect knowledge transfer (MDKT) framework for low-resource speech recognition. By leveraging phonetic and linguistic knowledge from neighboring dialects, the MDKT framework improves recognition in resource-constrained settings. It comprises an acoustic feature extractor, a dialect feature extractor, and two modules—WFAdapter (weight decomposition adapter) and AttAdapter (attention-based adapter)—to enhance adaptability and mitigate overfitting. The training involves a three-phase strategy: multi-dialect AID-ASR multi-task learning in phase one, freezing the dialect feature extractor in phase two, and fine-tuning only the adapters in phase three. Experiments on the Jiao-Liao Mandarin subset of the KeSpeech dataset and JLMS25 dataset show that MDKT outperforms full-parameter fine-tuning, reducing Character Error Rate (CER) by 5.4% and 7.7% and Word Error Rate (WER) by 6.1% and 10.8%, respectively. Full article

(This article belongs to the Special Issue AI for Sustainability and Innovation—2nd Edition)

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30 pages, 2386 KB

Open AccessArticle

Sustainable Development in the Digital Age: Harnessing Emerging Digital Technologies to Catalyze Global SDG Achievement

by Claudiu George Bocean

Appl. Sci. 2025, 15(2), 816; https://doi.org/10.3390/app15020816 - 15 Jan 2025

Cited by 18 | Viewed by 6192

Abstract

The digital revolution, characterized by rapid technological advancements, presents a unique opportunity to accelerate progress towards the United Nations’ Sustainable Development Goals (SDGs). This research explores the transformative potential of cutting-edge digital technologies—including artificial intelligence, big data analytics, cloud computing, and the Internet of Things—in fostering sustainable development across economic, social, and environmental dimensions. Our study employs a rigorous empirical approach to quantify the impact of digital innovation on SDG achievement within the European Union. Utilizing the Digital Economy and Society Index (DESI) as a comprehensive measure of technological progress, we apply structural equation modeling to emphasize the complex interplay between digital advancement and sustainable development indicators. A key focus of our analysis is the mediating role of economic performance, measured by GDP per capita, in the relationship between digital technology adoption and SDG progress. This nuanced examination provides insights into how economic factors influence the effectiveness of digital solutions in addressing global challenges. Our findings underscore the need for adaptive policies that harness the power of digital technologies while addressing potential challenges and ensuring inclusive growth. Full article

(This article belongs to the Special Issue AI for Sustainability and Innovation—2nd Edition)

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20 pages, 2007 KB

Open AccessArticle

Hybrid Intelligence: Design for Sustainable Multiverse via Integrative Cognitive Creation Model through Human–Computer Collaboration

by Yuqi Liu and Zhiyong Fu

Appl. Sci. 2024, 14(11), 4662; https://doi.org/10.3390/app14114662 - 29 May 2024

Cited by 12 | Viewed by 8022

Abstract

The unprecedented development of artificial intelligence (AI) makes it possible for computers to imitate and surpass human intelligence (HI). Hybrid intelligence is the result of the co-evolution of AI and HI and has huge application potential in promoting the sustainable development of human society. This study starts from the similarities and differences between biological neural networks and artificial neural networks, compares the cognitive foundations of human intelligence and artificial intelligence, highlights the difference and connection between AI and HI, and puts forward the necessity and inevitability of their co-evolution to achieve hybrid intelligence with complementary advantages. Hybrid intelligence stands to become the pivotal force driving purposeful and planned sustainable creative behavior in the artificial intelligence era. This study proposes a design cognitive creation model based on human–computer collaboration that considers computational design thinking as the central concept. Moreover, the paradigm shift of design under hybrid intelligence intervention are explored from five aspects: “tool evolution”, “response mode”, “output result”, “iterative optimization” and “system innovation”. Finally, this article constructs a creative intervention mechanism of design creation driven by hybrid intelligence and discusses its role playing in the design activities of sustainable multiverse construction in the future. The proposal of the multiverse model transcends the confines of the metaverse’s virtual worldview and embraces sustainable development for value guidance. It advocates a future trajectory for humanity that hinges on technological progress, fostering a prosperous, balanced, and harmonious coexistence between the natureverse, socialverse, and digitalverse. This approach is not only rational and scientific, but also inherently sustainable. Full article

(This article belongs to the Special Issue AI for Sustainability and Innovation—2nd Edition)

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