bluuebunny/crossref_metadata_2025 huggingface dataset:
Audio-Based Engine Fault Diagnosis with Wavelet, Markov Blanket, ROCKET, and Optimized Machine Learning Classifiers
Authors: Tuleski Bernardo Luis , Yamaguchi Cristina Keiko , Stefenon Stefano Frizzo , Coelho Leandro dos Santos , Mariani Viviana Cocco
Published: November 2024

Engine fault diagnosis is a critical task in automotive aftermarket management. Developing appropriate fault-labeled datasets can be challenging due to nonlinearity variations and divergence in feature distribution among different engine kinds or operating scenarios. To solve this task, this study experimentally measures audio emission signals from compression ignition engines in different vehicles, simulating injector failures, intake hose failures, and absence of failures. Based on these faults, a hybrid approach is applied to classify different conditions that help the planning and decision-making of the automobile industry. The proposed hybrid approach combines the wavelet packet transform (WPT), Markov blanket feature selection, random convolutional kernel transform (ROCKET), tree-structured Parzen estimator (TPE) for hyperparameters tuning, and ten machine learning (ML) classifiers, such as ridge regression, quadratic discriminant analysis (QDA), naive Bayes, k-nearest neighbors (k-NN), support vector machine (SVM), multilayer perceptron (MLP), random forest (RF), extra trees (ET), gradient boosting machine (GBM), and LightGBM. The audio data are broken down into sub-time series with various frequencies and resolutions using the WPT. These data are subsequently utilized as input for obtaining an informative feature subset using a Markov blanket-based selection method. This feature subset is then fed into the ROCKET method, which is paired with ML classifiers, and tuned using Optuna using the TPE approach. The generalization performance applying the proposed hybrid approach outperforms other standard ML classifiers.
Multi-Intelligent Reflecting Surfaces and Artificial Noise-Assisted Cell-Free Massive MIMO Against Simultaneous Jamming and Eavesdropping
Authors: Hu Huazhi , Xie Wei , Xu Kui , Xia Xiaochen , Li Na , Wu Huaiwu
Published: November 2024

In a cell-free massive multiple-input multiple-output (MIMO) system without cells, it is assumed that there are smart jammers and disrupters (SJDs) that attempt to interfere with and eavesdrop on the downlink communications of legitimate users. A secure transmission scheme based on multiple intelligent reflecting surfaces (IRSs) and artificial noise (AN) is proposed. First, an access point (AP) selection strategy based on user location information is designed, which aims to determine the set of APs serving users. Then, a joint optimization framework based on the block coordinate descent (BCD) algorithm is constructed, and a non-convex optimization solution based on the univariate function optimization and semi-definite relaxation (SDR) is proposed with the aim of maximising the minimum achievable secrecy rate for users. By solving the univariate function maximisation problem, the multi-variable coupled non-convex problem is transformed into a solvable convex problem, obtaining the optimal AP beamforming, AN matrix and IRS phase shift matrix. Specifically, in a single-user scenario, the scheme of multiple intelligent reflecting surfaces combined with artificial noise can improve the user’s achievable secrecy rate by about 11% compared to the existing method (single intelligent reflective surface combined with artificial noise) and about 2% compared to the scheme assisted by multiple intelligent reflecting surfaces without artificial noise assistance.
A Microfluidic-Based Sensing Platform for Rapid Quality Control on Target Cells from Bioreactors
Authors: Foscarini Alessia , Romano Fabio , Garzarelli Valeria , Turco Antonio , Bramanti Alessandro Paolo , Tarantini Iolena , Ferrara Francesco , Visconti Paolo , Gigli Giuseppe , Chiriacò Maria Serena
Published: November 2024

We investigated the design and characterization of a Lab-On-a-Chip (LoC) cell detection system primarily designed to support immunotherapy in cancer treatment. Immunotherapy uses Chimeric Antigen Receptors (CARs) and T Cell Receptors (TCRs) to fight cancer, engineering the response of the immune system. In recent years, it has emerged as a promising strategy for personalized cancer treatment. However, it requires bioreactor-based cell culture expansion and manual quality control (QC) of the modified cells, which is time-consuming, labour-intensive, and prone to errors. The miniaturized LoC device for automated QC demonstrated here is simple, has a low cost, and is reliable. Its final target is to become one of the building blocks of an LoC for immunotherapy, which would take the place of present labs and manual procedures to the benefit of throughput and affordability. The core of the system is a commercial, on-chip-integrated capacitive sensor managed by a microcontroller capable of sensing cells as accurately measured charge variations. The hardware is based on standardized components, which makes it suitable for mass manufacturing. Moreover, unlike in other cell detection solutions, no external AC source is required. The device has been characterized with a cell line model selectively labelled with gold nanoparticles to simulate its future use in bioreactors in which labelling can apply to successfully engineered CAR-T-cells. Experiments were run both in the air—free drop with no microfluidics—and in the channel, where the fluid volume was considerably lower than in the drop. The device showed good sensitivity even with a low number of cells—around 120, compared with the 107 to 108 needed per kilogram of body weight—which is desirable for a good outcome of the expansion process. Since cell detection is needed in several contexts other than immunotherapy, the usefulness of this LoC goes potentially beyond the scope considered here.
Integration of FTIR Spectroscopy, Volatile Compound Profiling, and Chemometric Techniques for Advanced Geographical and Varietal Analysis of Moroccan Eucalyptus Essential Oils
Authors: El Orche Aimen , El Mrabet Abdennacer , Said Amal Ait Haj , Mousannif Soumaya , Elhamdaoui Omar , Ansari Siddique Akber , Alkahtani Hamad M. , Ansari Shoeb Anwar , Sbai El Otmani Ibrahim , Bouatia Mustapha
Published: November 2024

Eucalyptus essential oil is widely valued for its therapeutic properties and extensive commercial applications, with its chemical composition significantly influenced by species variety, geographical origin, and environmental conditions. This study aims to develop a reliable method for identifying the geographical origin and variety of eucalyptus oil samples through the application of advanced analytical techniques combined with chemometric methods. Essential oils from Eucalyptus globulus and Eucalyptus camaldulensis were analyzed using Gas Chromatography–Flame Ionization Detection (GC–FID) and Fourier Transform Infrared (FTIR) Spectroscopy. Chemometric analyses, including Orthogonal Partial Least Squares-Discriminant Analysis (O2PLS-DA) and Hierarchical Cluster Analysis (HCA), were utilized to classify the oils based on their volatile compound profiles. Notably, O2PLS-DA was applied directly to the raw FTIR data without additional spectral processing, showcasing its robustness in handling unprocessed data. For geographical origin determination, the GC–FID model achieved a Correct Classification Rate (CCR) of 100%, with 100% specificity and 100% sensitivity for both calibration and validation sets. FTIR spectroscopy achieved a CCR of 100%, specificity of 100%, and sensitivity of 100% for the calibration set, while the validation set yielded a CCR of 95.83%, specificity of 99.02%, and sensitivity of 94.44%. In contrast, the analysis based on species variety demonstrated 100% accuracy across all metrics CCR, specificity, and sensitivity—for both calibration and validation using both techniques. These findings underscore the effectiveness of volatile and infrared spectroscopy profiling for quality control and authentication, providing robust tools for ensuring the consistency and reliability of eucalyptus essential oils in various industrial and therapeutic applications.
Explicit and Implicit Feature Contrastive Learning Model for Knowledge Graph Link Prediction
Authors: Yuan Xu , Wang Weihe , Gao Buyun , Zhao Liang , Ma Ruixin , Ding Feng
Published: November 2024

Knowledge graph link prediction is crucial for constructing triples in knowledge graphs, which aim to infer whether there is a relation between the entities. Recently, graph neural networks and contrastive learning have demonstrated superior performance compared with traditional translation-based models; they successfully extracted common features through explicit linking between entities. However, the implicit associations between entities without a linking relationship are ignored, which impedes the model from capturing distant but semantically rich entities. In addition, directly applying contrastive learning based on random node dropout to link prediction tasks, or limiting it to triplet-level, leads to constrained model performance. To address these challenges, we design an implicit feature extraction module that utilizes the clustering characteristics of latent vector space to find entities with potential associations and enrich entity representations by mining similar semantic features from the conceptual level. Meanwhile, the subgraph mechanism is introduced to preserve the structural information of explicitly connected entities. Implicit semantic features and explicit structural features serve as complementary information to provide high-quality self-supervised signals. Experiments are conducted on three benchmark knowledge graph datasets. The results validate that our model outperforms the state-of-the-art baselines in link prediction tasks.
MLUG: Bootstrapping Language-Motion Pre-Training for Unified Motion-Language Understanding and Generation
Authors: Luo Hongliang , Xi Wei , Tang Daniel
Published: November 2024

In the realm of computer vision and animation, the generation of human motion from textual descriptions represents a frontier of significant challenge and potential. This paper introduces MLUG, a groundbreaking framework poised to transform motion synthesis by harnessing the power of vision–language pre-training techniques. MLUG addresses the nuanced challenge of creating semantically rich, physically plausible, and emotionally expressive human motions through a novel integration of a unimodal encoder with motion–text contrastive loss, a motion-grounded text encoder, a motion-grounded motion decoder, and a motion length predictor. These components work in concert to align textual descriptions with dynamic motion sequences, offering an innovative solution to the limitations of existing models in open-vocabulary motion generation and emotional expressiveness. Through extensive evaluations, MLUG demonstrates unparalleled effectiveness in generating realistic and diverse motions from a broad spectrum of textual inputs, setting a new benchmark in the field.
Array Radar Three-Dimensional Forward-Looking Imaging Algorithm Based on Two-Dimensional Super-Resolution
Authors: Dai Jinke , Sun Weijie , Jiang Xinrui , Wu Di
Published: November 2024

Radar imaging is a technology that uses radar systems to generate target images. It transmits radio waves, receives the signal reflected back by the target, and realizes imaging by analyzing the target’s position, shape, and motion information. The three-dimensional (3D) forward-looking imaging of missile-borne radar is a branch of radar imaging. However, owing to the limitation of antenna aperture, the imaging resolution of real aperture radar is restricted. By implementing the super-resolution techniques in array signal processing into missile-borne radar 3D forward-looking imaging, the resolution can be further improved. In this paper, a 3D forward-looking imaging algorithm based on the two-dimensional (2D) super-resolution algorithm is proposed for missile-borne planar array radars. In the proposed algorithm, a forward-looking planar array with scanning beams is considered, and each range-pulse cell in the received data is processed one by one using a 2D super-resolution method with the error function constructed according to the weighted least squares (WLS) criterion to generate a group of 2D spectra in the azimuth-pitch domain. Considering the lack of training samples, the super-resolution spectrum of each range-pulse cell is estimated via adaptive iteration processing only with one sample, i.e., the cell under process. After that, all the 2D super-resolution spectra in azimuth-pitch are accumulated according to the changes in instantaneous beam centers of the beam scanning. As is verified by simulation results, the proposed algorithm outperforms the real aperture imaging method in terms of azimuth-pitch resolution and can obtain 3D forward-looking images that are of a higher quality.
Piezoresistive, Piezocapacitive and Memcapacitive Silk Fibroin-Based Cement Mortars
Authors: Triana-Camacho Daniel A. , D’Alessandro Antonella , Bittolo Bon Silvia , Malaspina Rocco , Ubertini Filippo , Valentini Luca
Published: November 2024

Water-stable proteins may offer a new field of applications in smart materials for buildings and infrastructures where hydraulic reactions are involved. In this study, cement mortars modified through water-soluble silk fibroin (SF) are proposed. Water-soluble SF obtained by redissolving SF films in phosphate buffer solution (PBS) showed the formation of a gel with the β sheet features of silk II. Electrical measurements of SF indicate that calcium ions are primarily involved in the conductivity mechanism. By exploiting the water solubility properties of silk II and Ca2+ ion transport phenomena as well as their trapping effect on water molecules, SF provides piezoresistive and piezocapacitive properties to cement mortars, thus enabling self-sensing of mechanical strain, which is quite attractive in structural health monitoring applications. The SF/cement-based composite introduces a capacitive gauge factor which surpasses the traditional resistive gauge factor reported in the literature by threefold. Cyclic voltammetry measurements demonstrated that the SF/cement mortars possessed memcapacitive behavior for positive potentials near +5 V, which was attributed to an interfacial charge build-up modulated by the SF concentration and the working electrode. Electrical square-biphasic excitation combined with cyclic compressive loads revealed memristive behavior during the unloading stages. These findings, along with the availability and sustainability of SF, pave the way for the design of novel multifunctional materials, particularly for applications in masonry and concrete structures.
A YOLO-Based Method for Head Detection in Complex Scenes
Authors: Xie Ming , Yang Xiaobing , Li Boxu , Fan Yingjie
Published: November 2024

Detecting objects in intricate scenes has always presented a significant challenge in the field of machine vision. Complex scenes typically refer to situations in images or videos where there are numerous densely distributed and mutually occluded objects, making the object detection task even more difficult. This paper introduces a novel head detection algorithm, YOLO-Based Head Detection in Complex Scenes (YOLO-HDCS). Firstly, in complex scenes, head detection typically involves a large number of small objects that are randomly distributed. Traditional object detection algorithms struggle to address the challenge of small object detection. For this purpose, two new modules have been constructed: one is a feature fusion module based on context enhancement with scale adjustment, and the other is an attention-based convolutional module. These modules are characterized by high detection efficiency and high accuracy. They significantly improve the model’s multi-scale detection capabilities, thus enhancing the detection ability of the system. Secondly, it was found in practical operations that the original IoU function has a serious problem with overlapping detection in complex scenes. There is an IoU function that can ensure that the final selection boxes cover the object as accurately as possible without overlapping. This not only improves the detection performance but also greatly aids in enhancing the detection efficiency and accuracy. Our method achieves impressive results for head detection in complex scenarios, with average accuracy of 82.2%, and has the advantage of rapid loss convergence during training.
Transportation Mode Detection Using Learning Methods and Self-Contained Sensors: Review
Authors: Gharbi Ilhem , Taia-Alaoui Fadoua , Fourati Hassen , Vuillerme Nicolas , Zhou Zebo
Published: November 2024

Due to increasing traffic congestion, travel modeling has gained importance in the development of transportion mode detection (TMD) strategies over the past decade. Nowadays, recent smartphones, equipped with integrated inertial measurement units (IMUs) and embedded algorithms, can play a crucial role in such development. In particular, obtaining much more information on the transportation modes used by users through smartphones is very challenging due to the variety of the data (accelerometers, magnetometers, gyroscopes, proximity sensors, etc.), the standardization issue of datasets and the pertinence of learning methods for that purpose. Reviewing the latest progress on TMD systems is important to inform readers about recent datasets used in detection, best practices for classification issues and the remaining challenges that still impact the detection performances. Existing TMD review papers until now offer overviews of applications and algorithms without tackling the specific issues faced with real-world data collection and classification. Compared to these works, the proposed review provides some novelties such as an in-depth analysis of the current state-of-the-art techniques in TMD systems, relying on recent references and focusing particularly on the major existing problems, and an evaluation of existing methodologies for detecting travel modes using smartphone IMUs (including dataset structures, sensor data types, feature extraction, etc.). This review paper can help researchers to focus their efforts on the main problems and challenges identified.
CLIP-Llama: A New Approach for Scene Text Recognition with a Pre-Trained Vision-Language Model and a Pre-Trained Language Model
Authors: Zhao Xiaoqing , Xu Miaomiao , Silamu Wushour , Li Yanbing
Published: November 2024

This study focuses on Scene Text Recognition (STR), which plays a crucial role in various applications of artificial intelligence such as image retrieval, office automation, and intelligent transportation systems. Currently, pre-trained vision-language models have become the foundation for various downstream tasks. CLIP exhibits robustness in recognizing both regular (horizontal) and irregular (rotated, curved, blurred, or occluded) text in natural images. As research in scene text recognition requires substantial linguistic knowledge, we introduce the pre-trained vision-language model CLIP and the pre-trained language model Llama. Our approach builds upon CLIP’s image and text encoders, featuring two encoder–decoder branches: one visual branch and one cross-modal branch. The visual branch provides initial predictions based on image features, while the cross-modal branch refines these predictions by addressing the differences between image features and textual semantics. We incorporate the large language model Llama2-7B in the cross-modal branch to assist in correcting erroneous predictions generated by the decoder. To fully leverage the potential of both branches, we employ a dual prediction and refinement decoding scheme during inference, resulting in improved accuracy. Experimental results demonstrate that CLIP-Llama achieves state-of-the-art performance on 11 STR benchmark tests, showcasing its robust capabilities. We firmly believe that CLIP-Llama lays a solid and straightforward foundation for future research in scene text recognition based on vision-language models.
Predictors of Anxiety in Middle-Aged and Older European Adults: A Machine Learning Comparative Study
Authors: Aichele Stephen R.
Published: November 2024

Anxiety in older adults is a prevalent yet under-recognized condition associated with significant societal and individual burdens. This study used a machine learning approach to compare the relative importance of 57 risk and protective factors for anxiety symptoms in a population-representative sample of middle-aged and older European adults (N = 65,684; ages 45–103 years; 55.7% women; 15 countries represented). The results revealed loneliness and self-rated poor health as primary risk factors (Nagelkerke R2 = 0.272), with additional predictive contributions from country of residence, functional limitations, financial distress, and family care burden. Notably, follow-up analysis showed that none of the 16 social network variables were associated with loneliness; rather, cohabitating with a partner/spouse was most strongly associated with reduced loneliness. Further research is needed to elucidate directional associations between loneliness and anxiety (both general and sub-types). These findings underscore the imperative of addressing loneliness for mitigating anxiety and related mental health conditions among aging populations.
The Role of the Visitor Center in Destination Development
Authors: Iannaccone Barbora , Šoltésová Marieta , Molokáč Mário , Taušová Marcela
Published: November 2024

This study examines sustainability in tourism, with a particular focus on enhancing destination management through comprehensive visitor monitoring. Precise data on visitor volumes constitute an essential prerequisite for the sustainable planning and management of tourism-related services and infrastructure. This research investigates the correlation between destination attendance and the utilization of visitor centers, employing regression analysis and the Kolmogorov–Smirnov test to quantify these relationships. The study specifically addresses the distribution of domestic and international visitors in the city of Prague and their engagement with municipal information centers, enabling the identification of key visitor behavior patterns. The results offer a robust foundation for forecasting future trends in international visitor flows, which is critical for strategic planning and the sustainable development of tourism destinations.
Experimental Investigation on the Permeability and Fine Particle Migration of Debris-Flow Deposits with Discontinuous Gradation: Implications for the Sustainable Development of Debris-Flow Fans in Jiangjia Ravine, China
Authors: Li Pu , Hu Kaiheng , Yu Jie
Published: November 2024

The particle size distribution (PSD) is a crucial parameter used to characterize the material composition of debris-flow deposits which determines their hydraulic permeability, affecting the mobility of debris flows and, hence, the sustainable development of debris-flow fans. Three types of graded bedding structures—normal, reverse, and mixed graded bedding structures—are characterized by discontinuous gradation within a specific deposit thickness. A series of permeability tests were conducted to study the effects of bed sediment composition, particularly coarse grain sizes and fine particle contents, on the permeability and migration of fine particles in discontinuous debris-flow deposits. An increase in fine particles within the discontinuously graded bed sediment led to a power-law decrease in the average permeability coefficient. With fine particle contents of 10% and 15% in the bed sediments, the final permeability coefficient consistently exceeded the initial value. However, this trend reversed when the fine particle contents were increased to 20%, 25%, and 30%. Lower fine particle contents indicated enhanced permeability efficiency due to more interconnected voids within the coarse particle skeleton. Conversely, an increase in fine particle content reduced the permeability efficiency, as fine particles tended to aggregate at the lower section of the seepage channel. An increase in coarse particle size decreased the formation of flow channels at the coarse–fine particle interface, causing fine particles to move slowly along adjacent or clustered slow flow channels formed by fine particles, resulting in decreased permeability efficiency. Three formulae are proposed to calculate the permeability coefficients of discontinuously graded bed sediments, which may aid in understanding the initiation mechanism of channel deposits. Based on experimental studies and field investigations, it is proposed that achieving sustainable development of debris-flow fans requires a practical approach that integrates three key components: spatial land-use planning, in situ monitoring of debris flows and the environment, and land-use adjustment and management. This comprehensive and integrated approach is essential for effectively managing and mitigating the risks associated with debris flows, ensuring sustainable development in vulnerable areas.
The Interplay of Happiness and Sustainability: A Multidimensional Scaling and K-Means Cluster Approach
Authors: Detrinidad Emmanuel , López-Ruiz Víctor-Raúl
Published: November 2024

This study examines the intricate relationship between national happiness and sustainability by analyzing data from the 2024 World Happiness Report (WHR) and the Sustainable Development Goal (SDG) Index across 74 countries. Employing multidimensional scaling (MDS) and K-means clustering, we investigate the interplay of economic, social, and environmental factors that contribute to subjective well-being. Our results indicate that high-income countries, particularly those characterized by secular–rational values, tend to achieve elevated scores in both happiness and sustainability. In contrast, lower-income nations exhibit diverse cultural value systems that significantly influence their happiness and sustainability outcomes. This analysis identifies distinct clusters of countries with unique governance and cultural profiles, underscoring the inadequacies of traditional economic indicators in measuring societal well-being. By integrating sustainability metrics into happiness assessments, we propose a comprehensive framework for understanding and promoting human happiness. These findings offer critical insights for policymakers aiming to address pressing global challenges, including inequality and health disparities.
The Financial Gap Arising from the Local Government’s Educational Tasks and the Sustainable Development of Communes in Poland
Authors: Wojarska Magdalena
Published: November 2024

In Poland, responsibility for educational tasks has mainly been delegated to local governments, although the state still plays a decisive role. Municipalities finance these tasks using educational subsidies and targeted grants from the central budget. Because transfers from the state budget cover an ever-decreasing share of the costs of educational tasks, municipalities increasingly need to engage their own resources. Such a situation (due to the limited funds of these units) poses a threat to the proper implementation of other tasks, which may ultimately negatively affect their sustainable development. This study aimed to determine the impact of the financial gap from implementing educational tasks on the sustainable development of communes and cities with county rights. The research primarily employed linear ordering methods and Spearman’s rho correlation. It also employed the nonparametric Kruskal–Wallis test and the Page test for trends. This study utilized two types of data: secondary data from Statistics Poland covering 2477 municipalities and primary data collected through a survey of 482 local government representatives. The correlation analysis revealed that sustainable development indicators decreased as the financial gap widened between 2012 and 2022. The highest correlation coefficients were observed in all communes combined and in cities with county rights. The weakest correlations between the financial gap and sustainable development were found in urban municipalities. Representatives of local governments had similar observations. According to 63.1% of respondents, the imbalance between funds transferred from the state budget for educational tasks and the expenditure incurred by the municipality for this purpose negatively affects the sustainable development of local governments.
Prehistoric Cultural Migration in the Middle–Lower Lishui Catchment of Central China in Response to Environmental Changes
Authors: Yang Guifang , Yao Changhong
Published: November 2024

Climate change and geological shifts were pivotal in the survival and development of ancient human societies, especially in densely populated regions like the middle and lower Lishui River Basin. This study explored the dynamic interactions between ancient human cultures and the region’s natural environment, using field research alongside geological, geomorphological, and archaeological data spanning from the late Paleolithic to the Neolithic periods. Our findings showed that prehistoric sites in the middle and lower Lishui River Basin were primarily located in the low hilly areas surrounding the lower basin during the Paleolithic era, a pattern shaped by the region’s geomorphology. Early human settlements were strategically positioned near the Lishui River, offering access to vital resources while minimizing flood risk. These locations provided flat terrain, abundant vegetation, and materials for tool-making, factors that supported a hunting and gathering lifestyle. As the coldest phase of the last glacial period approached, the transition from the Paleolithic to the Neolithic eras marked a period of significant behavioral adaptation. In response to the harsher environment, humans began settling on lower terraces and miniaturizing their stone tools, signaling a shift to more specialized hunting techniques. This adaptability and resilience marked the refinement of hunting economies during this period. With the onset of the Holocene epoch and a warmer climate, conditions for human habitation became more favorable. Societies began migrating from the hills to the fertile lower Lishui River Basin, heralding the Neolithic period. This era saw the emergence of settlements and the onset of early rice cultivation, marking the transition from a hunting–gathering economy to one centered on agriculture. By the Daxi period, these settlements had expanded, extending their influence throughout the region. In conclusion, this study underscores the critical roles of climate change and geological features in shaping human settlement patterns, economic activities, and cultural evolution in the middle and lower Lishui River Basin. Our findings offer valuable insights into the evolutionary processes of ancient human cultures in the region and provide a foundation to understand future challenges in sustainable development.
Identification of Determinants That Reduce Women’s Safety and Comfort in Urban Public Spaces (UPS)
Authors: Zysk Elżbieta
Published: November 2024

Urban development and population growth present new challenges for developing urban public spaces (UPS) in cities. The significance of cities as centers of integration of different socio-cultural groups is growing. Taking into account the diversity of needs and expectations of sensory-sensitive groups (women) as residents and users of urban public spaces is a key task facing modern city managers. Women’s public participation is relevant and important, because recognizing their needs and taking them into account in urban spatial policy contributes to creating cities tailored to the needs and expectations of residents and users, according to the principle of “everyone-important”. This article has goals for the identification of factors that reduce the safety and comfort of women’s activities and the most important architectural features UPS. The results of the research indicate that the determinants that reduce the friendliness of safety and comfort in UPS are a lack of lighting, lack of esthetics of space development, lack of benches and toilets, and street noise. A space tailored to women’s needs and expectations should be well-lit with a level walking and sidewalk surface and include architectural infrastructure elements such as adequate municipal sanitation (toilets), benches and urban furniture, with landscaped green space (squares, flowerbeds, trees), which is in line with the principles of universal design. This study’s results found that such factors can help create safe, egalitarian, and inclusive cities. They complete the research gap in the field of sustainable urban development and are key to developing gender-equitable urban planning and urban development policy practices.
Utilizing Marble Waste for Soil Acidity Correction in Colombian Caribbean Agriculture: A Sustainability Assessment
Authors: Puche Johnny Oliver Corcho , Bodah Brian William , Salas Karen Esther Muñoz , Palma Hugo Hernández , Theodoro Suzi Huff , Neckel Alcindo , Moreno-Ríos Andrea Liliana , Mores Giana , Silva Caliane Christie Oliveira de Almeida , Dal Moro Leila , Cardoso Grace Tibério , Ramos Claudete Gindri
Published: November 2024

Agricultural industrial waste has demonstrated potential as a soil acidity corrector and fertilizer, in addition to reducing environmental impacts caused by inadequate waste disposal. Ornamental rock waste is a sustainable alternative as it contains essential elements for plant growth. (1) Background: this study aims to evaluate using marble waste in SENA and the Gallo Crudo Quarry in Colombia as an acidity mitigator in soils cultivated with maize (Zea mays) in a greenhouse. (2) Method: four treatments were applied: T0: without marble dust—MD; three doses of MD (T1: 1.1 Mg of MD ha−1; T2: 2.2 Mg of MD ha−1; and T3: 3.3 Mg of MD ha−1). After 70 days, soil fertility analyses were carried out. (3) Results: The results show that the chemical properties of the soil improved with all treatments, mainly with T2, influencing the calcium (Ca), carbon (C), sulfur (S), and magnesium (Mg) contents. MD’s pH and Al + H values were higher than conventional treatments. The T2 treatment reduced soil acidity from 0.2 cmol + kg−1 to 0.0 cmol + kg−1 and increased pH to 7.91 compared to the control (5.4). The maize plants in the T2 treatment developed better, indicating that the dose of 2.2 Mg of MD ha−1 can replace commercial limestone. (4) Conclusions: This agroecological technique is an innovative alternative in Colombia, replicable in areas with ornamental rock reserves, benefiting the agricultural economy and contributing to target the Sustainable Development Goals, which promote sustainability, responsible management of natural resources, and a reduction in environmental impacts.
Cost Performance Comparison of Road Construction Projects Considering Bidding Condition and Project Characteristics
Authors: Lee Kang-Wook
Published: November 2024

Developing road infrastructure facilities is important because it directly affects national competitiveness and has significant socio-economic ripple effects throughout the country. However, road construction projects are vulnerable to various risks and prone to cost overruns because they are funded by large-scale national budgets and conducted over long time periods. Therefore, tracking the changes in construction costs from planning to completion and providing insights for similar future projects is essential for successful project planning and management. Considering the bidding conditions and project characteristics of road construction projects, this study comparatively analyzed the construction cost growth of 170 road construction projects based on the bid award rate and facility-specific project characteristics, such as construction type, contract method, facility capacity, and construction cost components. This study analyzed the differences in cost performance between various sample groups using non-parametric tests (Mann–Whitney U test and Kruskal–Wallis test), considering the non-normality of the collected sample. In addition, this study conducted multiple regression analysis to examine the causal relationship between all variables used in the analysis and cost performance and to identify critical factors. The results indicated that the construction cost growth of the group with a bid award rate of less than 70% was higher compared to that of other groups. Furthermore, the differences in construction cost growth based on project characteristics were more pronounced in the following groups: construction types (expansion/renovation and complex), contract method (long-term continuous), road length (≥7.5 km), % of bridge/tunnel length (<10%), construction cost (≤USD 100 mil.), % of direct construction cost (≥70%), and % of bridge/tunnel cost (both less and more than 50%). Furthermore, the regression model showed that % of direct construction cost, bid award rate, total road length, and contract method were shown to be critical factors in construction cost growth, which implies the importance of indirect cost management, PDS selection decisions that affect bid award rates, and risk management for complex projects from the perspective of construction cost growth management. These results empirically demonstrate that bidding conditions and project characteristics are key to understanding the pattern of construction cost growth in road construction projects. Hence, this study provides significant references that can be used by policy makers and clients to estimate cost buffers for future road projects.
A Methodological Approach for Enriching Activity–Travel Schedules with In-Home Activities
Authors: Liu Feng , Bellemans Tom , Janssens Davy , Wets Geert , Adnan Muhammad
Published: November 2024

In-home activities are inevitably important parts of individuals’ daily schedules, as people spend more time working and doing various other activities (e.g., online shopping or banking) at home. However, conventional activity-based travel demand models (ABMs) only consider travel and travel-related out-of-home activities, ignoring the interaction between in-home and out-of-home activities. To fill in this gap and increase the understanding of what people do at home and how in-home and out-of-home activities affect each other, a new method is proposed in this study. The approach predicts the types and durations of in-home activities of daily schedules generated by ABMs. In model building, statistical methods such as multinomial logit, log-linear regression, and activity sequential information are utilized, while in calibration, the Simultaneous Perturbation Stochastic Approximation (SPSA) method is employed. The proposed method was tested using training data and by applying the approach to the schedules of 6.3 million people in the Flemish region of Belgium generated by a representative ABM. Based on the statistical methods, the mean absolute errors were 0.36 and 0.21 for predicting the number and sum of the durations of in-home activities (over all types) per schedule, respectively. The prediction obtained a 10% and 8% improvement using sequential information. After calibration, an additional 60% and 68% were gained regarding activity participation rates and time spent per day. The experimental results demonstrate the potential and practical ability of the proposed method for the incorporation of in-home activities in activity–travel schedules, contributing towards the extension of ABMs to a wide range of applications that are associated with individuals’ in-home activities (e.g., the appropriate evaluation of energy consumption and carbon emission estimation as well as sustainable policy designs for telecommuting).
Natural Hazard Assessment in the Southeastern Margin of the Ría de Arosa (Pontevedra, Spain) Using GIS Techniques
Authors: Nieto Carlos E. , Martínez-Graña Antonio Miguel , Merchán Leticia
Published: November 2024

The characterization of natural hazards in coastal environments is of great necessity, especially in the current context of global climate change and increasing population concentrations. This research focuses on a multi-hazard analysis of the main geotechnical, geomorphological, hydrological, and lithological risks in the southeastern margin of the Ría de Arosa using Geographic Information System techniques. The integration of geotechnical characterization maps and natural hazard maps has allowed for the identification of areas with a high susceptibility to natural disasters, which is crucial for territorial planning and management in the context of growing urban pressure and global climate change. The results indicate that poorly consolidated surface formations, especially in transitional areas such as dunes and marshes, are particularly vulnerable. Additionally, areas with higher lithological competence have been identified, where slope changes contribute to ground instability. This analysis provides valuable tools for decision-making and the implementation of risk management policies, promoting sustainable development, the protection of coastal ecosystems, and the prevention of risks from urban planning and civil engineering activities in the Ría de Arosa.
GRMD: A Two-Stage Design Space Exploration Strategy for Customized RNN Accelerators
Authors: Li Qingpeng , Xiao Jian , Wei Jizeng
Published: November 2024

Recurrent neural networks (RNNs) have produced significant results in many fields, such as natural language processing and speech recognition. Owing to their computational complexity and sequence dependencies, RNNs need to be deployed on customized hardware accelerators to satisfy performance and energy-efficiency constraints. However, designing hardware accelerators for RNNs is challenged by the vast design space and the reliance on ineffective optimization. An efficient automated design space exploration (DSE) strategy that can balance conflicting objectives is wanted. To address the low efficiency and insufficient universality of the resource allocation process employed for hardware accelerators, we propose an automated two-stage design space exploration (DSE) strategy for customized RNN accelerators. The strategy combines a genetic algorithm (GA) and a reinforcement learning (RL) algorithm, and it utilizes symmetrical exploration and exploitation to find the optimal solutions. In the first stage, the area of the hardware accelerator is taken as the optimization objective, and the GA is used for partial exploration purposes to narrow the design space while maintaining diversity. Then, the latency and power of the hardware accelerator are taken as the optimization objectives, and the RL algorithm is used in the second stage to find the corresponding Pareto solutions. To verify the effectiveness of the developed strategy, it is compared with other algorithms. We use three different network models as benchmarks: a vanilla RNN, LSTM, and a GRU. The results demonstrate that the strategy proposed in this paper can provide better solutions and can achieve latency, power, and area reductions of 9.35%, 5.34%, and 11.95%, respectively. The HV of GRMD is reduced by averages of 6.33%, 6.32%, and 0.67%, and the runtime is reduced by averages of 18.11%, 14.94%, and 10.28%, respectively. Additionally, given different weights, it can make reasonable trade-offs between multiple objectives.
A General Mollification Regularization Method to Solve a Cauchy Problem for the Multi-Dimensional Modified Helmholtz Equation
Authors: Xu Huilin , Wang Baoxia , Zhou Duanmei
Published: November 2024

This paper considers a Cauchy problem for the multi-dimensional modified Helmholtz equation with inhomogeneous Dirichlet and Neumann data. The Cauchy problem is severely ill-posed, and a general mollification method is introduced to solve the problem. Both the a priori and a posteriori choice strategies of the regularization parameter are proposed, and error estimations of the corresponding regularization solutions are also presented. Finally, two numerical examples are introduced to show the effectiveness of the general mollification regularization method.
The Antecedents of Courier Continuance Participation Intention: A Dyadic Analysis of Courier–Customer Interactions in Crowdsourcing Delivery
Authors: Wang Wenjie , Yin Yulu
Published: November 2024

Crowdsourcing delivery has emerged as an innovative solution for last-mile delivery in the sharing economy era. However, enhancing courier continuance participation intention is an increasing challenge for crowdsourcing delivery platforms due to the independence of crowdsourced couriers. Given that couriers and customers are subject to interdependencies and may influence each other in delivery service systems, this paper utilizes a dyadic analysis to examine how courier–customer interactions may influence the continuance participation intention of couriers. Specifically, we investigate the influence mechanism of customer satisfaction and courier job satisfaction, as well as the mediating role of courier pay satisfaction, drawing upon the balance theory and distributive justice theory. The empirical results of 261 courier–customer dyads indicate that the customer side impacts the courier side in both direct and indirect ways. There is a direct satisfaction transmission in courier–customer delivery service interactions. Meanwhile, satisfied customers indirectly enhance courier job satisfaction via the mediator of courier pay satisfaction, which in turn strengthens courier continuance participation intention. Our study offers novel insights for administrators on the influence of courier–customer interactions and pay satisfaction on courier continuance participation intention, contributing to decreasing couriers’ turnover in the fluid crowdsourcing labor market.
The Impact of Artificial Intelligence on ESG Performance of Manufacturing Firms: The Mediating Role of Ambidextrous Green Innovation
Authors: Jing Hao , Zhang Shiyu
Published: November 2024

In the context of the worldwide quest for green and sustainable development, there is a growing importance in enhancing the environmental, social, and governance (ESG) performance of manufacturing companies. With the rise of digital transformation and pressing environmental challenges, artificial intelligence (AI) has emerged as a crucial tool for manufacturing organizations to gain a competitive edge in sustainability. While the role of digital technologies in driving ESG improvements has been widely discussed, there is limited scholarly exploration of the specific impact of AI on the ESG performance of manufacturing firms, as well as the underlying mechanisms at play from an AI perspective. Addressing this research gap, this study constructs a theoretical model of AI affecting manufacturing firms’ ESG performance using a sample of Chinese-listed manufacturing firms from 2012–2022. Additionally, this study examines the role of mediating mechanisms of ambidextrous green innovation as well as differences in the intrinsic mechanisms triggered by the equilibrium of ambidextrous green innovation and firm size. The findings indicate that the application of AI technology effectively promotes improvements in the ESG performance of manufacturing firms, with ambidextrous green innovation playing a positive mediating role. Furthermore, manufacturing companies with a strong balance of ambidextrous green innovation and larger scale exhibit enhanced effects of AI on ESG performance. This study serves to supplement existing literature on ESG performance enhancement, elucidate the theoretical rationale behind the non-economic performance of AI-enabled firms, and extend the application of organizational dualism theory to new contexts.