bluuebunny/crossref_metadata_2025 huggingface dataset:
Lane Detection Based on ECBAM_ASPP Model
Authors: Gu Xiang , Huang Qiwei , Du Chaonan
Published: December 2024

With the growing prominence of autonomous driving, the demand for accurate and efficient lane detection has increased significantly. Beyond ensuring accuracy, achieving high detection speed is crucial to maintaining real-time performance, stability, and safety. To address this challenge, this study proposes the ECBAM_ASPP model, which integrates the Efficient Convolutional Block Attention Module (ECBAM) with the Atrous Spatial Pyramid Pooling (ASPP) module. Building on traditional attention mechanisms, the ECBAM module employs dynamic convolution kernels to eliminate dimensionality reduction, enhancing the efficiency of feature channel learning and local interactions while preserving computational efficiency. The ECBAM_ASPP model incorporates the ECBAM attention mechanism into the feature extraction network, effectively directing the network to focus on salient features while suppressing irrelevant ones. Additionally, through variable sampling of the input, the model achieves multi-scale feature extraction, enabling it to capture richer lane-related feature information. Experimental results on the TuSimple and CULane datasets demonstrate that the ECBAM_ASPP model significantly improves real-time performance while maintaining high detection accuracy. Compared with baseline methods, the proposed model delivers superior overall performance, showcasing greater robustness and practicality.
A Framework for Optimizing Deep Learning-Based Lane Detection and Steering for Autonomous Driving
Authors: Yordanov Daniel , Chakraborty Ashim , Hasan Md Mahmudul , Cirstea Silvia
Published: December 2024

Improving the ability of autonomous vehicles to accurately identify and follow lanes in various contexts is crucial. This project aims to provide a novel framework for optimizing a self-driving vehicle that can detect lanes and steer accordingly. A virtual sandbox environment was developed in Unity3D that provides a semi-automated procedural road and driving generation framework for a variety of road scenarios. Four types of segments replicate actual driving situations by directing the car using strategically positioned waypoints. A training dataset thus generated was used to train a behavioral driving model that employs a convolutional neural network to detect the lane and ensure that the car steers autonomously to remain within lane boundaries. The model was evaluated on real-world driving footage from Comma.ai, exhibiting an autonomy of 77% in low challenge road conditions and of 66% on roads with sharper turns.
Gas Sensor for Efficient Acetone Detection and Application Based on Au-Modified ZnO Porous Nanofoam
Authors: Sun Zhenchao , Sun Shanfu , Hao Xidong , Wang Yinglin , Gong Caili , Cheng Pengfei
Published: December 2024

Toxic acetone gas emissions and leakage are a potential threat to the environment and human health. Gas sensors founded on metal oxide semiconductors (MOS) have become an effective strategy for toxic gas detection with their mature process. In the present work, an efficient acetone gas sensor based on Au-modified ZnO porous nanofoam (Au/ZnO) is synthesized by polyvinylpyrrolidone-blowing followed by a calcination method. XRD and XPS spectra were utilized to investigate its structure, while SEM and TEM characterized its morphology. The gas sensitivity of the Au/ZnO sensors was investigated in a static test system. The results reveal that the gas-sensitive performance of porous ZnO toward the acetone can be enhanced by adjusting the loading ratio of noble Au nanoparticles. Specifically, the Au/ZnO sensor prepared by the Au loading ratio of 3.0% (Au/ZnO-3.0%) achieved a 100 ppm acetone gas response of 20.02 at the optimum working temperature of 275 °C. Additionally, a portable electronic device used a STM32 primary control chip to integrate the Au/ZnO-3.0% gas sensor with other modules to achieve the function of detecting and alarming toxic acetone gas. This work is of great significance for efficiently detecting and reducing acetone emissions.
Safety After Dark: A Privacy Compliant and Real-Time Edge Computing Intelligent Video Analytics for Safer Public Transportation
Authors: Barthelemy Johan , Iqbal Umair , Qian Yan , Amirghasemi Mehrdad , Perez Pascal
Published: December 2024

Public transportation systems play a vital role in modern cities, but they face growing security challenges, particularly related to incidents of violence. Detecting and responding to violence in real time is crucial for ensuring passenger safety and the smooth operation of these transport networks. To address this issue, we propose an advanced artificial intelligence (AI) solution for identifying unsafe behaviours in public transport. The proposed approach employs deep learning action recognition models and utilises technologies like NVIDIA DeepStream SDK, Amazon Web Services (AWS) DirectConnect, local edge computing server, ONNXRuntime and MQTT to accelerate the end-to-end pipeline. The solution captures video streams from remote train stations closed circuit television (CCTV) networks, processes the data in the cloud, applies the action recognition model, and transmits the results to a live web application. A temporal pyramid network (TPN) action recognition model was trained on a newly curated video dataset mixing open-source resources and live simulated trials to identify the unsafe behaviours. The base model was able to achieve a validation accuracy of 93% when trained using open-source dataset samples and was improved to 97% when live simulated dataset was included during the training. The developed AI system was deployed at Wollongong Train Station (NSW, Australia) and showcased impressive accuracy in detecting violence incidents during an 8-week test period, achieving a reliable false-positive (FP) rate of 23%. While the AI correctly identified 30 true-positive incidents, there were 6 cases of false negatives (FNs) where violence incidents were missed during the rainy weather suggesting more data in the training dataset related to bad weather. The AI model’s continuous retraining capability ensures its adaptability to various real-world scenarios, making it a valuable tool for enhancing safety and the overall passenger experience in public transport settings.
Integrating Wi-Fi, Li-Fi, and BPL Technologies for a Secure Indoor Communication System
Authors: Eltokhy Mostafa , El-Rifaie Ali M. , Gamal Heba Allah , Haggag Ayman , Ali Hisham , Youssef Ahmed A. F. , Aboshosha Ashraf
Published: December 2024

In today’s digital age, there is an increasing demand for integrated wireless and wired technologies; however, there is a difficulty in achieving secure and reliable communications within buildings and facilities. This paper presents a proposal for maintaining the infrastructure while expanding it to implement communication technologies with high transmission and reception speeds and high levels of data confidentiality to enhance the operational efficiency of organizations. Three main technologies have emerged as promising solutions for this purpose: Wi-Fi, Li-Fi, and BPL. Despite the advantages that each technology offers, some drawbacks appear in these technologies that affect data transmission. Wi-Fi, Li-Fi, and BPL can be combined to achieve maximum security and reduce noise and interference via the ESP8266 module. This combination could be an important step toward achieving an integrated and secure indoor communication system. The paper provides a comprehensive overview of the three techniques and how they are applied in practice. In addition, OSE, ABPF, and ASBF filters are used to detect and eliminate interference and attack secure internal networks.
Research on Tool Wear Monitoring Technology Based on Variational Mode Decomposition and Back Propagation Neural Network Model
Authors: Wang Kang , Wang Aimin , Wu Long
Published: December 2024

Accurately predicting tool wear during the machining process not only saves machining time and improves efficiency but also ensures the production of good-quality parts and automation. This paper proposes a combined variational mode decomposition (VMD) and back propagation (BP) neural network model (VMD-BP), which maps spindle power to tool wear. The model is trained using both historical and real-time data. To improve accuracy, the internal power data from the machine tool are used to calibrate the model’s input data. Data collected from milling experiments are used to test the model, with sensor-collected power being compared to the model’s predicted power. The average error was 1.1256%, which confirms the reliability of the model. In practical applications, the model enables the real-time monitoring of spindle power, helping prevent excessive tool wear during machining. This offers significant guidance for actual production processes.
Personalized Clustering for Emotion Recognition Improvement
Authors: Gutiérrez-Martín Laura , López-Ongil Celia , Lanza-Gutiérrez Jose M. , Miranda Calero Jose A.
Published: December 2024

Emotion recognition through artificial intelligence and smart sensing of physical and physiological signals (affective computing) is achieving very interesting results in terms of accuracy, inference times, and user-independent models. In this sense, there are applications related to the safety and well-being of people (sexual assaults, gender-based violence, children and elderly abuse, mental health, etc.) that require even more improvements. Emotion detection should be done with fast, discrete, and non-luxurious systems working in real time and real life (wearable devices, wireless communications, battery-powered). Furthermore, emotional reactions to violence are not equal in all people. Then, large general models cannot be applied to a multi-user system for people protection, and health and social workers and law enforcement agents would welcome customized and lightweight AI models. These semi-personalized models will be applicable to clusters of subjects sharing similarities in their emotional reactions to external stimuli. This customization requires several steps: creating clusters of subjects with similar behaviors, creating AI models for every cluster, continually updating these models with new data, and enrolling new subjects in clusters when required. An initial approach for clustering labeled data compiled (physiological data, together with emotional labels) is presented in this work, as well as the method to ensure the enrollment of new users with unlabeled data once the AI models are generated. The idea is that this complete methodology can be exportable to any other expert systems where unlabeled data are added during in-field operation and different profiles exist in terms of data. Experimental results demonstrate an improvement of 5% in accuracy and 4% in F1 score with respect to our baseline general model, along with a 32% to 58% reduction in variability, respectively.
Determination of the Bentonite Content in Molding Sands Using AI-Enhanced Electrical Impedance Spectroscopy
Authors: Ma Xiaohu , Fischerauer Alice , Haacke Sebastian , Fischerauer Gerhard
Published: December 2024

Molding sand mixtures in the foundry industry are typically composed of fresh and reclaimed sands, water, and additives such as bentonite. Optimizing the control of these mixtures and the recycling of used sand after casting requires an efficient in-line monitoring method, which is currently unavailable. This study explores the potential of an AI-enhanced electrical impedance spectroscopy (EIS) system as a solution. To establish a fundamental dataset, we characterized various sand mixtures containing quartz sand, bentonite, and deionized water using EIS in the frequency range from 20 Hz to 1 MHz under laboratory conditions and also measured the water content and density of samples. Principal component analysis was applied to the EIS data to extract relevant features as input data for machine learning models. These features, combined with water content and density, were used to train regression models based on fully connected neural networks to estimate the bentonite content in the mixtures. This led to a high prediction accuracy (R2 = 0.94). These results demonstrate that AI-enhanced EIS has promising potential for the in-line monitoring of bulk material in the foundry industry, paving the way for optimized process control and efficient sand recycling.
Confidence-Guided Frame Skipping to Enhance Object Tracking Speed
Authors: Lee Yun Gu
Published: December 2024

Object tracking is a challenging task in computer vision. While simple tracking methods offer fast speeds, they often fail to track targets. To address this issue, traditional methods typically rely on complex algorithms. This study presents a novel approach to enhance object tracking speed via confidence-guided frame skipping. The proposed method is strategically designed to complement existing methods. Initially, lightweight tracking is used to track a target. Only in scenarios where it fails to track is an existing, robust but complex algorithm used. The contribution of this study lies in the proposed confidence assessment of the lightweight tracking’s results. The proposed method determines the need for intervention by the robust algorithm based on the predicted confidence level. This two-tiered approach significantly enhances tracking speed by leveraging the lightweight method for straightforward situations and the robust algorithm for challenging scenarios. Experimental results demonstrate the effectiveness of the proposed approach in enhancing tracking speed.
Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data
Authors: Fergus Paul , Chalmers Carl , Matthews Naomi , Nixon Stuart , Burger André , Hartley Oliver , Sutherland Chris , Lambin Xavier , Longmore Steven , Wich Serge
Published: December 2024

Camera traps offer enormous new opportunities in ecological studies, but current automated image analysis methods often lack the contextual richness needed to support impactful conservation outcomes. Integrating vision–language models into these workflows could address this gap by providing enhanced contextual understanding and enabling advanced queries across temporal and spatial dimensions. Here, we present an integrated approach that combines deep learning-based vision and language models to improve ecological reporting using data from camera traps. We introduce a two-stage system: YOLOv10-X to localise and classify species (mammals and birds) within images and a Phi-3.5-vision-instruct model to read YOLOv10-X bounding box labels to identify species, overcoming its limitation with hard-to-classify objects in images. Additionally, Phi-3.5 detects broader variables, such as vegetation type and time of day, providing rich ecological and environmental context to YOLO’s species detection output. When combined, this output is processed by the model’s natural language system to answer complex queries, and retrieval-augmented generation (RAG) is employed to enrich responses with external information, like species weight and IUCN status (information that cannot be obtained through direct visual analysis). Combined, this information is used to automatically generate structured reports, providing biodiversity stakeholders with deeper insights into, for example, species abundance, distribution, animal behaviour, and habitat selection. Our approach delivers contextually rich narratives that aid in wildlife management decisions. By providing contextually rich insights, our approach not only reduces manual effort but also supports timely decision making in conservation, potentially shifting efforts from reactive to proactive.
YOLO-BOS: An Emerging Approach for Vehicle Detection with a Novel BRSA Mechanism
Authors: Zhao Liang , Fu Lulu , Jia Xin , Cui Beibei , Zhu Xianchao , Jin Junwei
Published: December 2024

In intelligent transportation systems, accurate vehicle target recognition within road scenarios is crucial for achieving intelligent traffic management. Addressing the challenges posed by complex environments and severe vehicle occlusion in such scenarios, this paper proposes a novel vehicle-detection method, YOLO-BOS. First, to bolster the feature-extraction capabilities of the backbone network, we propose a novel Bi-level Routing Spatial Attention (BRSA) mechanism, which selectively filters features based on task requirements and adjusts the importance of spatial locations to more accurately enhance relevant features. Second, we incorporate Omni-directional Dynamic Convolution (ODConv) into the head network, which is capable of simultaneously learning complementary attention across the four dimensions of the kernel space, therefore facilitating the capture of multifaceted features from the input data. Lastly, we introduce Shape-IOU, a new loss function that significantly enhances the accuracy and robustness of detection results for vehicles of varying sizes. Experimental evaluations conducted on the UA-DETRAC dataset demonstrate that our model achieves improvements of 4.7 and 4.4 percentage points in mAP@0.5 and mAP@0.5:0.95, respectively, compared to the baseline model. Furthermore, comparative experiments on the SODA10M dataset corroborate the superiority of our method in terms of precision and accuracy.
Enhancing Deep-Learning Classification for Remote Motor Imagery Rehabilitation Using Multi-Subject Transfer Learning in IoT Environment
Authors: Khabti Joharah , AlAhmadi Saad , Soudani Adel
Published: December 2024

One of the most promising applications for electroencephalogram (EEG)-based brain–computer interfaces (BCIs) is motor rehabilitation through motor imagery (MI) tasks. However, current MI training requires physical attendance, while remote MI training can be applied anywhere, facilitating flexible rehabilitation. Providing remote MI training raises challenges to ensuring an accurate recognition of MI tasks by healthcare providers, in addition to managing computation and communication costs. The MI tasks are recognized through EEG signal processing and classification, which can drain sensor energy due to the complexity of the data and the presence of redundant information, often influenced by subject-dependent factors. To address these challenges, we propose in this paper a multi-subject transfer-learning approach for an efficient MI training framework in remote rehabilitation within an IoT environment. For efficient implementation, we propose an IoT architecture that includes cloud/edge computing as a solution to enhance the system’s efficiency and reduce the use of network resources. Furthermore, deep-learning classification with and without channel selection is applied in the cloud, while multi-subject transfer-learning classification is utilized at the edge node. Various transfer-learning strategies, including different epochs, freezing layers, and data divisions, were employed to improve accuracy and efficiency. To validate this framework, we used the BCI IV 2a dataset, focusing on subjects 7, 8, and 9 as targets. The results demonstrated that our approach significantly enhanced the average accuracy in both multi-subject and single-subject transfer-learning classification. In three-subject transfer-learning classification, the FCNNA model achieved up to 79.77% accuracy without channel selection and 76.90% with channel selection. For two-subject and single-subject transfer learning, the application of transfer learning improved the average accuracy by up to 6.55% and 12.19%, respectively, compared to classification without transfer learning. This framework offers a promising solution for remote MI rehabilitation, providing both accurate task recognition and efficient resource usage.
Fractal Analysis of Electrodermal Activity for Emotion Recognition: A Novel Approach Using Detrended Fluctuation Analysis and Wavelet Entropy
Authors: Mercado-Diaz Luis R. , Veeranki Yedukondala Rao , Large Edward W. , Posada-Quintero Hugo F.
Published: December 2024

The field of emotion recognition from physiological signals is a growing area of research with significant implications for both mental health monitoring and human–computer interaction. This study introduces a novel approach to detecting emotional states based on fractal analysis of electrodermal activity (EDA) signals. We employed detrended fluctuation analysis (DFA), Hurst exponent estimation, and wavelet entropy calculation to extract fractal features from EDA signals obtained from the CASE dataset, which contains physiological recordings and continuous emotion annotations from 30 participants. The analysis revealed significant differences in fractal features across five emotional states (neutral, amused, bored, relaxed, and scared), particularly those derived from wavelet entropy. A cross-correlation analysis showed robust correlations between fractal features and both the arousal and valence dimensions of emotion, challenging the conventional view of EDA as a predominantly arousal-indicating measure. The application of machine learning for emotion classification using fractal features achieved a leave-one-subject-out accuracy of 84.3% and an F1 score of 0.802, surpassing the performance of previous methods on the same dataset. This study demonstrates the potential of fractal analysis in capturing the intricate, multi-scale dynamics of EDA signals for emotion recognition, opening new avenues for advancing emotion-aware systems and affective computing applications.
The Design of a Low-Noise CMOS Image Sensor Using a Hybrid Single-Slope Analog-to-Digital Converter
Authors: Choo Hyun Seon , Youn Da-Hyeon , Choi Hyunggyu , Kim Gi Yeol , Kim Soo Youn
Published: December 2024

In this study, we describe a low-noise complementary metal-oxide semiconductor (CMOS) image sensor (CIS) with a 10/11-bit hybrid single-slope analog-to-digital converter (SS-ADC). The proposed hybrid SS-ADC provides a resolution of 11 bits in low-light and 10 bits in high-light. To this end, in the low-light section, the digital-correlated double sampling method using a double data rate structure was used to obtain a noise performance similar to that of the 11-bit SS-ADC under low-light conditions, while maintaining linear in-out characteristics. The CIS with the proposed 10/11-bit hybrid SS-ADC was fabricated using a 110 nm 1-poly 4-metal CIS process. The measurement results showed that dark random noise was reduced by 8% in low light when using the proposed hybrid SS-ADC, compared with the existing 10-bit ADC. Additionally, in the case of high brightness, when using a 10-bit resolution, the dynamic power consumption decreased by approximately 31%, compared to the 11-bit ADC. The total power consumption is 3.9 mW at 15 fps when the analog, pixel, and digital supply voltages are 3.3 V, 3.3 V, and 1.5 V, respectively.
Data-Driven Koopman Based System Identification for Partially Observed Dynamical Systems with Input and Disturbance
Authors: Ketthong Patinya , Samkunta Jirayu , Mai Nghia Thi , Kamal Md Abdus Samad , Murakami Iwanori , Yamada Kou
Published: December 2024

The identification of dynamical systems from data is essential in control theory, enabling the creation of mathematical models that accurately represent the behavior of complex systems. However, real-world applications often present challenges such as the unknown dimensionality of the system and limited access to measurements, particularly in partially observed systems. The Hankel alternative view of Koopman (HAVOK) method offers a data-driven approach to identify linear representations of nonlinear systems, but it often overlooks the influence of external control signals (inputs) and disturbances. This paper introduces a novel input-aware modeling method for unstable linear systems using data-driven Koopman analysis. By explicitly incorporating the impact of inputs and disturbances, our method enhances the accuracy and robustness of system identification, even in the face of incomplete observations. The proposed approach leverages Koopman operator theory on augmented state-input data to capture both the intrinsic dynamics and the system’s sensitivity to external control. Through extensive numerical examples, we demonstrate the effectiveness of our method in accurately identifying and predicting the behavior of various dynamical systems, including real-world nonlinear systems and simulated unstable linear systems with and without disturbances. The results highlight the potential of our approach to advance the field of system identification and control, offering a powerful tool for modeling and analyzing complex systems in diverse applications.
Optimization of Extraction of Luteolin from Schisandra chinensis by Ionic Liquid–Enzyme Complex System and Antioxidant Study Analysis
Authors: Hao Jingwei , Dong Nan , Sun Yifan , Lu Xiaoxia , Pei Yingying , Zhou Yi , Zhou Xiangkun , Liu Heming
Published: December 2024

The luteolin in Schisandra chinensis [Schisandraceae Schisandra (Turcz.) Baill.] were extracted by ultrasonic extraction assisted by an ionic liquid–enzyme composite system, and the content of luteolins was determined using high-performance liquid chromatography (HPLC). This process was initially conducted through a one-factor experiment and a Box–Behnken combinatorial design of response surface method. The extraction process was optimized, and the results demonstrated that the optimal extraction conditions were 13.31% enzyme addition, 0.53 mol/L ionic liquid concentration, 173.47 min ultrasonic shaking, and 0.2266 mg/g, which was 4.88 times higher than that of the traditional reflux extraction. Secondly, the antioxidant function of luteolins was studied based on network pharmacology. For the study of the antioxidant mechanism of luteolin, the herb group identification database, SwissTargetPrediction on luteolins target prediction, and GeneCards database to achieve the antioxidant target were used. For the analysis of the intersection of the target protein interactions, GO bioanalysis and KEGG signaling pathway enrichment analysis were used. There were 57 overlapping targets of luteolin and antioxidants, including AKT1, MMP9, ESR1, EGFR, and SRC. GO function and KEGG pathway enrichment analysis showed that luteolin antioxidants were related to zoerythromycin metabolic process, adriamycin metabolic process, negative regulation of apoptotic process, endocrine resistance and oxidoreductase. The key targets in the pathways, such as luteolin AKT1 and MMP9, exert antioxidant effects. The antioxidant activity of luteolins was investigated by determining the scavenging ability of luteolins against two types of free radicals: 2,2-bipyridine-bis(3-ethyl-benzothiazole-6-sulfonic acid) diammonium salt (ABTS+) free radicals and 1,1-diphenyl-2-trinitrophenylhydrazine free radicals (DPPH-). The results of the antioxidant test demonstrated that the ABTS radical scavenging rate was 87.26%, and the DPPH radical scavenging rate was 93.85% when the quality concentration of Schisandra luteolins was 0.1 mg/g, indicating the potential of this natural antioxidant. This method of extracting Schisandra chinensis luteolins is highly productive, environmentally friendly, and practical, and it facilitates the development and utilization of industrial Schisandra chinensis.
Adolescence in the Italian Labour Market: In Search of an Equilibrium Among Instability, Uncertainty, and AI Challenges
Authors: Mancaniello Maria Rita , Lavanga Francesco
Published: December 2024

The school-to-work transition (STWT) represents a pivotal challenge in individual development, necessitating a global and interdisciplinary approach to address its inherent complexity. This article examines some key characteristics of the current labour market, focusing on the Italian context, which is undergoing profound transformations that disrupt traditional production systems. In the contemporary landscape, labour market instability has become a norm rather than an exception, often resulting in unclear future prospects and leaving individuals—particularly adolescents—grappling with uncertainty and a lack of direction. For younger generations, this sustained uncertainty exacerbates the challenges of an already delicate and transformative phase of life, generating scepticism toward both present realities and future possibilities. The evolving skills required to navigate the STWT present significant challenges for educational planning, particularly within institutional, organizational, and pedagogical dimensions. Emerging technologies, such as artificial intelligence (AI), are reshaping lifestyles and work environments, further complicating the dynamics of STWT transition. It is essential to undertake comprehensive pedagogical reflection and rigorous research to critically evaluate and redefine educational strategies and policies. Such efforts are crucial for equipping adolescents with the resilience and adaptability required to navigate instability and uncertainty, fostering preparedness for an unpredictable future.
Disclosure Dances in Doctoral Education
Authors: Brown Nicole
Published: December 2024

This article reports on a qualitative embodied inquiry that was undertaken to explore the lived experience of disclosure amongst disabled, chronically ill, and/or neurodivergent doctoral researchers. Previous research has shown that disabled, chronically ill, and/or neurodivergent academics apply a cost–benefit analysis to help them decide whether to disclose their needs. This study specifically focused on doctoral researchers and how doctoral researchers navigate the process of disclosure. Twelve participants from the United Kingdom took part. Data collection was via interviews and additional creative submissions. Data was analyzed using reflexive, thematic analysis. The themes generated were disclosure experiences, career considerations, and navigating spaces and academic buildings. The findings show that doctoral researchers’ decisions of whether to disclose depends on their individual situations and factors rather than it being a linear, either-or matter.
Research on Takeaway Delivery Route Optimization Problem Considering Merchant Priority
Authors: Xu Hanrong , Qian Wuyong , Guo Kaiyi , Wang Xuan
Published: December 2024

To address deficiencies in the delivery process of O2O takeaway delivery platforms, the concept of priority levels for merchants within the platform is introduced. Focusing on three core objectives—reducing the proportion of late deliveries, lowering delivery costs, and reducing average order fulfillment times—a model is established that considers merchant priorities for multi-objective pickup and delivery vehicle routing. A two-stage optimization model is proposed for efficient management of real-time order generation and dynamic changes in delivery personnel locations. An improved variable neighborhood search algorithm is used to obtain the initial distribution plan; meanwhile, in the subsequent dynamic adjustment phase, a periodic optimization strategy is adopted. Finally, a test case was constructed using partial data provided by the food delivery platform, and the effectiveness of the proposed model and algorithm was verified through numerical experiments. The experimental results show that as the weight of cost increases, costs decrease, which leads to an increase in the proportion of late deliveries and delivery times, which is detrimental to improving customer satisfaction with deliveries. The research findings not only enrich the theoretical study of the pickup and delivery vehicle routing problem (PDVRP) but also provide scientific optimization methods for food delivery platforms, facilitating their sustainable operations and enhancing the efficiency and sustainability of urban transportation networks.
Enhancing Urban Sustainability: How Spatial and Height Variability of Roadside Plants Improves Pollution Capture for Greener Cities
Authors: Popek Robert , Roy Anamika , Mandal Mamun , Przybysz Arkadiusz , Drążkiewicz Katarzyna , Romanowska Patrycja , Sarkar Abhijit
Published: December 2024

Air pollution, particularly particulate matter (PM), poses a significant threat to urban environments and public health. This study aims to explore the impact of small-scale spatial and height variations on the ability of different roadside tree species: Tilia cordata Mill., Platanus × hispanica Mill. ex Münchh., and Sorbus intermedia (Ehrh.) Pers., to accumulate PM, providing insights for enhancing urban sustainability. Conducted along a high-traffic road in Warsaw, Poland, the research examines PM accumulation at varying heights and locations within tree canopies while also evaluating the influence of PM on photosynthetic efficiency. Results indicate substantial differences in PM accumulation between tree species and height ranges, with S. intermedia accumulating the highest PM levels. PM accumulation was greatest near the roadside and at lower canopy heights (1–1.5 m), while higher canopies and areas distant from the road showed reduced PM concentrations. T. cordata exhibited the highest PM accumulation on the side facing traffic, averaging 12% at 1–1.5 m height, while the interior recorded 5% at 2–2.5 m height. In S. intermedia, the roadside crown contributed the highest accumulation (14%) at 1–1.5 m height and only 6% on the side facing departing traffic at 2–2.5 m height. P. hispanica displayed higher efficiency in PM accumulation, reaching 11% at the roadside (1–1.5 m) and 7% at the top of the crown (3–3.5 m). Additionally, PM accumulation negatively impacted photosynthetic efficiency and chlorophyll content, with the highest PM levels correlating with reduced plant vitality. PCA analysis showed a stronger association between leaf-deposited PM and total chlorophyll content and that the presence of accumulated PM may significantly influence the chlorophyll content of the plants. These findings provide valuable guidance for urban planners in strategically planting roadside vegetation to maximize air quality improvement, offering a cost-effective and sustainable approach to mitigate urban pollution.
Fostering Education for Sustainable Development in Higher Education: A Case Study on Sustainability Competences in Research, Development and Innovation (RDI)
Authors: Muhonen Tiina , Timonen Liisa , Väänänen Kristiina
Published: December 2024

This study explores how higher education institutions (HEIs) can foster the relevance of education for sustainable development (ESD) and strategic human resource development for a holistic sustainability transformation. Sharing a case example, it discusses how sustainable and responsible research, development and innovation (RDI) competences can be recognised, described and acknowledged to support the contextualised application of ESD and human resource development in one HEI of the INVEST university alliance and its stakeholders. It compares education professionals’ and stakeholders’ views on current and future competence needs and investigates inter-organisational competence development practices. The methodology encompasses a qualitative and interpretive case study based on focus group interviews. The results on the current sustainable competences indicated that HEI and stakeholder respondents identified disciplinary competences as the most important competence cluster followed by systems-thinking, strategies-thinking and integration competences. Although HEIs and stakeholders jointly regard disciplinary competences as most critical for the future, they have considerably different perspectives on the megatrends influencing these needs and the overall spectrum of the required competences. This study provides methodological means for contextualising sustainability competences and enhancing stakeholder-informed competence development. The results may serve as a point of reference for aligning higher education curricula and human resource development with ESD for more sustainable higher education.
Performance Analysis of Thermal Energy Storage Tanks and Chillers for Optimizing Cooling Efficiency in Smart Greenhouses in Hot and Arid Climates
Authors: Choi Sul-Geon , Park Doo-Yong , Choi Doo-Sung , Jung Yong-Ho
Published: December 2024

This study analyzes the performance of thermal energy storage tanks and chillers in efficiently operating cooling systems for smart greenhouses in hot, arid climates such as the United Arab Emirates (UAE). The performance of chillers is heavily influenced by outdoor air temperatures, with the coefficient of performance (COP) of chillers decreasing and energy consumption increasing as daytime temperatures rise. This study found that when the outdoor air temperature reached 46.6 °C, the COP of the chiller dropped to 2.18, representing a 24.6% decrease compared to the COP of 2.89 at 35 °C. Conversely, at night, when the outdoor air temperature dropped to 28.3 °C, the chiller’s performance recovered, with the COP rising to 3.67. To address this, a strategy utilizing thermal energy storage tanks to store chilled water at night for use during the day was proposed, compensating for the decline in chiller performance. The results showed that when the thermal energy storage capacity was set at 40%, energy consumption decreased by up to 15%. However, while increasing the thermal energy storage capacity beyond 50% effectively reduced the peak load on the chiller, further increasing it beyond 60% led to a rise in chiller capacity and energy consumption. Therefore, this study concludes that setting the thermal energy storage capacity to no more than 50% is the most effective strategy to maximize chiller performance and reduce energy consumption. These findings provide crucial guidelines for the design and operation of cooling systems, offering valuable strategies to optimize the operation of smart greenhouses in hot climates.
Urban Functional Zone Classification via Advanced Multi-Modal Data Fusion
Authors: Liu Tianyu , Chen Hongbing , Ren Junfeng , Zhang Long , Chen Hongrui , Hong Rundong , Li Chenshuang , Cui Wenlong , Guo Wenhua , Wen Changji
Published: December 2024

The classification of urban functional zones is crucial for improving land use efficiency and promoting balanced development across urban areas. Existing methods for classifying urban functional zones using mobile signaling data face challenges primarily due to the limitations of single data sources, insufficient utilization of multidimensional data, and inherent inaccuracies in mobile signaling data. To address these issues, this study proposes an innovative classification method that employs advanced multimodal data fusion techniques to enhance the accuracy and reliability of functional zone classification. Mobile signaling data are mapped into image data using timestamp and geographic location information and combined with point of interest (POI) data to construct a comprehensive multimodal dataset. Deep learning techniques are then applied to fuse the multimodal data features, enabling precise and reliable classification of functional zones. The experimental results demonstrate that this method achieves an accuracy of 95.128% in classifying urban functional zones, significantly outperforming methods that use single-modal data.
Assessing Zero-Emission Vehicles from the Customer’s Perspective by Using a Multi-Criteria Framework
Authors: Fabianek Paul , Madlener Reinhard
Published: December 2024

In this article, we propose an assessment framework for zero-emission vehicles (ZEVs) in Germany using economic and customer-relevant criteria, with a focus on the mobility needs of individuals. Developing this framework required data obtained from four different sources: (1) literature, (2) semi-structured interviews, (3) a survey, and (4) market research. First, we derived the criteria relevant to assessing ZEVs from the literature and from semi-structured interviews. These interviews were conducted with individuals who have driving experience with both battery and fuel cell electric vehicles. Seven criteria were found to be particularly relevant for assessing ZEVs: greenhouse gas emissions, infrastructure availability, charging/refueling time, range, spaciousness, total costs, and driving dynamics (in descending order of importance). Second, we conducted a survey among 569 ZEV drivers and ZEV-interested individuals in order to weight these seven criteria. This survey was based on the Analytic Hierarchy Process approach. We then used market research to assign value scores to each criterion, representing the extent to which a particular ZEV meets a given criterion. Finally, we combined the value scores with the criteria weights to create the assessment framework. This framework allows for a transparent assessment of different ZEVs from the perspective of (potential) customers, without the need to repeatedly involve the surveyed participants. Our study is primarily useful for mobility planners, policymakers, and car manufacturers to improve ZEV infrastructure and support transportation systems’ transition towards low-carbon mobility.
Adaptation Measures to Drought Risk Perceived by Smallholder Crop Farmers in the Eastern Cape Province, South Africa: Implications for Food and Nutrition Security
Authors: Mdoda Lelethu , Naidoo Denver , Ncoyini-Manciya Zoleka , Nontu Yanga , Govender Laurencia , Tamako Nthabeleng , Mdiya Lwandiso
Published: December 2024

The Eastern Cape Province of South Africa is largely rural, with agriculture as the main livelihood for many households. However, the province has a semi-arid climate and reliance on rain-fed agriculture, which makes the region highly vulnerable to climate-related risks, particularly droughts. Smallholder farmers, who play a crucial role in food production and local economies, face significant challenges due to limited access to resources like irrigation, modern technology, and financial support, exacerbating their vulnerability to climate variability. These droughts cause severe losses in agricultural productivity, threatening food security, increasing poverty, and driving rural migration. Despite the potential benefits of adaptation strategies, many farmers lack the tools and knowledge to effectively cope with the increasing frequency and severity of droughts. This study examines how smallholder farmers in South Africa’s Eastern Cape Province perceive, experience, and cope with drought risk and its food and nutrition security implications. Using structured questionnaires, the authors collected data from 160 smallholder farmers and employed statistical analysis techniques, including a multinomial logit model. The study found that smallholder farmers understand drought risk but continue to face high vulnerability to adverse effects such as water scarcity, crop failure, increased disease incidence, and rising temperatures. Socioeconomic and institutional factors significantly influenced the adaptation strategies chosen by smallholder farmers. This study suggests that a combination of localized and modern adaptation approaches, supported by various institutional, policy, and technological interventions, is essential to enhance the resilience of these farmers. Specifically, localized strategies such as using traditional water management systems and crop diversification were effective in addressing region-specific climate challenges. At the same time, modern approaches like climate-resilient crop varieties and early-warning systems provided broader, more scalable solutions. Institutional support measures, such as access to climate information and extension services, policy interventions promoting sustainable practices, and technological advancements in irrigation and seed technologies, were key factors in improving adaptive capacity and reducing vulnerability. There is an urgent need to provide comprehensive support and empower farmers to adapt effectively, protect their livelihoods, and contribute to broader food security and poverty reduction efforts. This study highlights the importance of understanding and addressing the perceptions and experiences of smallholder farmers facing drought risk to ensure sustainable food production, income generation, and poverty alleviation.
Research on Speed Planning and Energy Management Strategy for Fuel Cell Hybrid Bus in Green Wave Scenarios at Traffic Light Intersections Based on Deep Reinforcement Learning
Authors: Yi Fengyan , Guo Wei , Gong Hongtao , Shen Yang , Zhou Jiaming , Yu Wenhao , Lu Dagang , Jia Chunchun , Zhang Caizhi , Gong Farui
Published: December 2024

In the context of intelligent and connected transportation, obtaining the real-time vehicle status and comprehensive traffic data is crucial for addressing challenges related to speed optimization and energy regulation in intricate transportation situations. This paper introduces a control method for the speed optimization and energy management of a fuel cell hybrid bus (FCHB) based on the Deep Deterministic Policy Gradient (DDPG) algorithm. The strategy framework is built on a dual-objective optimization deep reinforcement learning (D-DRL) architecture, which integrates traffic signal information into the energy management framework, in addition to conventional state spaces to guide control decisions. The aim is to achieve “green wave” traffic while minimizing hydrogen consumption. To validate the effectiveness of the proposed strategy, simulation tests were conducted using the SUMO platform. The results show that in terms of speed planning, the difference between the maximum and minimum speeds of the FCHB was reduced by 21.66% compared with the traditional Intelligent Driver Model (IDM), while the acceleration and its variation were reduced by 8.89% and 13.21%, respectively. In terms of the hydrogen fuel efficiency, the proposed strategy achieved 95.71% of the performance level of the dynamic programming (DP) algorithm. The solution proposed in this paper is of great significance for improving passenger comfort and FCHB economy.
Determining Cost and Causes of Overruns in Infrastructure Projects in South Asia
Authors: Andrić Jelena M. , Lin Shuangyu , Cheng Yuan , Sun Bin
Published: December 2024

Sustainable development is supported by infrastructure projects that have a long-term impact on economic development, societies, and the environment. In this paper, the aim is to estimate the cost performance, investigate the best-fit function for modeling the correlations between cost overruns and three variables, and identify the root causes of overruns in South Asian infrastructure projects. In the past, linear regression analysis has been utilized to model the correlations between cost overruns and project size, implementation period, and time overruns. Modeling these correlations requires the study and application of other regression functions. A database of 138 infrastructure projects from the South Asian region is established from the collected data. A methodology based on mixed methods for qualitative and quantitative data analysis is developed to achieve the aims of the paper. A mixed method encompasses a probabilistic and statistical approach alongside machine learning as quantitative methods and employs content analysis facilitated by NVivo v. 11 software as a qualitative method. Based on the results, the average cost overrun in infrastructure projects in South Asia is 3.3%. The random forest regression function, a machine learning technique, is tested as the most suitable function for modeling the impact between cost overruns and other variables compared to the linear and quadratic regression functions. The practical application is to support project stakeholders in the process of cost estimation during the decision-making phase of the project, to predict overruns in future infrastructure projects using machine learning techniques such as random forest regression, and to contribute to overall sustainable development.