bluuebunny/crossref_metadata_2025 huggingface dataset:
A Systematic Approach to Identify Shipping Emissions Using Spatio-Temporally Resolved TROPOMI Data
Authors: Kim Juhuhn , Emmerich Michael T. M. , Voors Robert , Ording Barend , Lee Jong-Seok
Published: July 2023

Stringent global regulations aim to reduce nitrogen dioxide (NO2) emissions from maritime shipping. However, the lack of a global monitoring system makes compliance verification challenging. To address this issue, we propose a systematic approach to monitor shipping emissions using unsupervised clustering techniques on spatio-temporal georeferenced data, specifically NO2 measurements obtained from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Copernicus Sentinel-5 Precursor satellite. Our method involves partitioning spatio-temporally resolved measurements based on the similarity of NO2 column levels. We demonstrate the reproducibility of our approach through rigorous testing and validation using data collected from multiple regions and time periods. Our approach improves the spatial correlation coefficients between NO2 column clusters and shipping traffic frequency. Additionally, we identify a temporal correlation between NO2 column levels along shipping routes and the global container throughput index. We expect that our approach may serve as a prototype for a tool to identify anthropogenic maritime emissions, distinguishing them from background sources.
Exploring Multisource Feature Fusion and Stacking Ensemble Learning for Accurate Estimation of Maize Chlorophyll Content Using Unmanned Aerial Vehicle Remote Sensing
Authors: Zhai Weiguang , Li Changchun , Cheng Qian , Ding Fan , Chen Zhen
Published: July 2023

Crop chlorophyll content measuring plays a vital role in monitoring crop growth and optimizing agricultural inputs such as water and fertilizer. However, traditional methods for measuring chlorophyll content primarily rely on labor-intensive chemical analysis. These methods not only involve destructive sampling but also are time-consuming, often resulting in obtaining monitoring results after the optimal growth period of crops. Unmanned aerial vehicle (UAV) remote sensing technology offers the potential for rapidly acquiring chlorophyll content estimations over large areas. Currently, most studies only utilize single features from UAV data and employ traditional machine learning algorithms to estimate chlorophyll content, while the potential of multisource feature fusion and stacking ensemble learning in chlorophyll content estimation research remains largely unexplored. Therefore, this study collected UAV spectral features, thermal features, structural features, as well as chlorophyll content data during maize jointing, trumpet, and big trumpet stages, creating a multisource feature dataset. Subsequently, chlorophyll content estimation models were built based on four machine learning algorithms, namely, ridge regression (RR), light gradient boosting machine (LightGBM), random forest regression (RFR), and stacking ensemble learning. The research results demonstrate that (1) the multisource feature fusion approach achieves higher estimation accuracy compared to the single-feature method, with R2 ranging from 0.699 to 0.754 and rRMSE ranging from 8.36% to 9.47%; and (2) the stacking ensemble learning outperforms traditional machine learning algorithms in chlorophyll content estimation accuracy, particularly when combined with multisource feature fusion, resulting in the best estimation results. In summary, this study proves the effective improvement in chlorophyll content estimation accuracy through multisource feature fusion and stacking ensemble learning. The combination of these methods provides reliable estimation of chlorophyll content using UAV remote sensing technology and brings new insights to precision agriculture management in this field.
A New Remote Hyperspectral Imaging System Embedded on an Unmanned Aquatic Drone for the Detection and Identification of Floating Plastic Litter Using Machine Learning
Authors: Alboody Ahed , Vandenbroucke Nicolas , Porebski Alice , Sawan Rosa , Viudes Florence , Doyen Perine , Amara Rachid
Published: July 2023

This paper presents a new Remote Hyperspectral Imaging System (RHIS) embedded on an Unmanned Aquatic Drone (UAD) for plastic detection and identification in coastal and freshwater environments. This original system, namely the Remotely Operated Vehicle of the University of Littoral Côte d’Opale (ROV-ULCO), works in a near-field of view, where the distance between the hyperspectral camera and the water surface is about 45 cm. In this paper, the new ROV-ULCO system with all its components is firstly presented. Then, a hyperspectral image database of plastic litter acquired with this system is described. This database contains hyperspectral data cubes of different plastic types and polymers corresponding to the most-common plastic litter items found in aquatic environments. An in situ spectral analysis was conducted from this benchmark database to characterize the hyperspectral reflectance of these items in order to identify the absorption feature wavelengths for each type of plastic. Finally, the ability of our original system RHIS to automatically recognize different types of plastic litter was assessed by applying different supervised machine learning methods on a set of representative image patches of marine litter. The obtained results highlighted the plastic litter classification capability with an overall accuracy close to 90%. This paper showed that the newly presented RHIS coupled with the UAD is a promising approach to identify plastic waste in aquatic environments.
Corn Phenology Detection Using the Derivative Dynamic Time Warping Method and Sentinel-2 Time Series
Authors: Ye Junyan , Bao Wenhao , Liao Chunhua , Chen Dairong , Hu Haoxuan
Published: July 2023

Accurate determination of crop phenology information is essential for effective field management and decision-making processes. Remote sensing time series analyses are widely employed to extract the phenological stages. Each crop’s phenological stage has its unique characteristic on the crop plant, while the satellite-derived crop phenology refers to some key transition dates in time series satellite observations. Current techniques primarily estimate specific phenological stages by detecting points with distinctive features on the remote sensing time series curve. But these stages may be different from the Biologische Bundesanstalt, Bundessortenamt and CHemical Industry (BBCH) scale, which is commonly used to identify the phenological development stages of crops. Moreover, when aiming to extract various phenological stages concurrently, it becomes necessary to adjust the extraction strategy for each unique feature. This need for distinct strategies at each stage heightens the complexity of simultaneous extraction. In this study, we utilize the Sentinel-2 Normalized Difference Vegetation Index (NDVI) time series data and propose a phenology extraction framework based on the Derivative Dynamic Time Warping (DDTW) algorithm. This method is capable of simultaneously extracting complete phenological stages, and the results demonstrate that the Root Mean Square Errors (RMSEs, days) of detected phenology on the BBCH scale for corn were less than 6 days overall.
A Quantification of Heat Storage Change-Based Evaporation Behavior in Middle–Large-Sized Lakes in the Inland of the Tibetan Plateau and Their Temporal and Spatial Variations
Authors: Du Baolong , Zhu Liping , Ju Jianting , Wang Junbo , Ma Qingfeng , Kou Qiangqiang
Published: July 2023

A large number of different-sized lakes exist in the inland area of the Tibetan Plateau (TP), which are examples of the important connection between the atmosphere and hydrosphere through the analysis of lake surface convergence and evaporation processes. The evaporation level changes that occur in middle–large-sized lakes (surface area > 50 km2) in the area directly influence the regional mass and energy balance values, atmospheric boundary layer heat and humidity structures, and weather processes occurring in the lower-reach areas. The studies conducted in the literature at present, concerning lake evaporation processes, generally overlook the differences in lake heat storage behavior due to the reduced amount of data in the literature concerning lake bathymetry. According to the in situ bathymetric data obtained for 68 middle–large-sized lakes in the inner basin of the TP, in this study, we calculated their heat storage (G) change values by using the different vertical-depth water-temperature-change integral method, and we established a regression equation for the heat storage and lake surface net radiation values for 68 lakes. The evaporation rates of 134 middle–large-sized lakes larger than 50 km2 in the inland are of the TP were calculated by obtaining the G regression result and adopting it into the Penman model, as well as estimating the evaporation losses of theses 134 lakes from 2002 to 2018. The result shows that the annual average evaporation rate for these lakes is 927.39 mm/year, with an insignificant upward trend (0.10 mm/year). This method achieved good accuracy compared with the Bowen ratio method, which estimates the evaporation rate during the ice-free season, with a high correlation coefficient (R) value of 0.95 and least root mean square error (RMSE) value of 61 mm. The annual mean evaporation rate can be divided into the southern and northern lake groups along a 34°N line with a difference of 314.41 mm/year. The annual average evaporation volume of these lakes was 25.02 km3 and showed an upward trend of 0.35 km3/year. Among them, the annual average evaporation volume contribution ratio of level-1 lakes (50 km2 ≤ lake’s area < 100 km2, 61 lakes) was 14.04%, showing an upward trend, and the contribution of level-3 lakes (lake’s area ≥ 500 km2, 10 lakes) was 41.50%, showing a downward trend. There were no obvious changes in the level-2 lakes (100 km2 ≤ lake’s area < 500 km2, 63 lakes), which maintained at the same level in approximately 44.46%. Air temperature is the most important factor affecting the evaporation rate of lakes, while the lake surface area is the main factor affecting lake evaporation volume. Our study, considering the actual lake heat storage value, provides a useful reference for further improving lake water budget balance values and watershed hydrologic features in the inland closed lakes located in the TP.
Few-Shot Object Detection in Remote Sensing Imagery via Fuse Context Dependencies and Global Features
Authors: Wang Bin , Ma Guorui , Sui Haigang , Zhang Yongxian , Zhang Haiming , Zhou Yuan
Published: July 2023

The rapid development of Earth observation technology has promoted the continuous accumulation of images in the field of remote sensing. However, a large number of remote sensing images still lack manual annotations of objects, which makes the strongly supervised deep learning object detection method not widely used, as it lacks generalization ability for unseen object categories. Considering the above problems, this study proposes a few-shot remote sensing image object detection method that integrates context dependencies and global features. The method can be used to fine-tune the model with a small number of sample annotations based on the model trained in the base class, as a way to enhance the detection capability of new object classes. The method proposed in this study consists of three main modules, namely, the meta-feature extractor (ME), reweighting module (RM), and feature fusion module (FFM). These three modules are respectively used to enhance the context dependencies of the query set features, improve the global features of the support set that contains annotations, and finally fuse the query set features and support set features. The baseline of the meta-feature extractor of the entire framework is based on the optimized YOLOv5 framework. The reweighting module of the support set feature extraction is based on a simple convolutional neural network (CNN) framework, and the foreground feature enhancement of the support sets was made in the preprocessing stage. This study achieved beneficial results in the two benchmark datasets NWPU VHR-10 and DIOR. Compared with the comparison methods, the proposed method achieved the best performance in the object detection of the base class and the novel class.
Remote Sensing for Soil Organic Carbon Mapping and Monitoring
Authors: van Wesemael Bas , Chabrillat Sabine , Sanz Dias Adrian , Berger Michael , Szantoi Zoltan
Published: July 2023

Remote sensing soil properties in a coherent manner is now feasible from regional to global scales [...]
SRTPN: Scale and Rotation Transform Prediction Net for Multimodal Remote Sensing Image Registration
Authors: Liu Xiangzeng , Xu Xueling , Zhang Xiaodong , Miao Qiguang , Wang Lei , Chang Liang , Liu Ruyi
Published: July 2023

How to recover geometric transformations is one of the most challenging issues in image registration. To alleviate the effect of large geometric distortion in multimodal remote sensing image registration, a scale and rotate transform prediction net is proposed in this paper. First, to reduce the scale between the reference and sensed images, the image scale regression module is constructed via CNN feature extraction and FFT correlation, and the scale of sensed image can be recovered roughly. Second, the rotation estimate module is developed for predicting the rotation angles between the reference and the scale-recovered images. Finally, to obtain the accurate registration results, LoFTR is employed to match the geometric-recovered images. The proposed registration network was evaluated on GoogleEarth, HRMS, VIS-NIR and UAV datasets with contrast differences and geometric distortions. The experimental results show that the number of correct matches of our model reached 74.6%, and the RMSE of the registration results achieved 1.236, which is superior to the related methods.
RFE-UNet: Remote Feature Exploration with Local Learning for Medical Image Segmentation
Authors: Zhong Xiuxian , Xu Lianghui , Li Chaoqun , An Lijing , Wang Liejun
Published: July 2023

Although convolutional neural networks (CNNs) have produced great achievements in various fields, many scholars are still exploring better network models, since CNNs have an inherent limitation—that is, the remote modeling ability of convolutional kernels is limited. On the contrary, the transformer has been applied by many scholars to the field of vision, and although it has a strong global modeling capability, its close-range modeling capability is mediocre. While the foreground information to be segmented in medical images is usually clustered in a small interval in the image, the distance between different categories of foreground information is uncertain. Therefore, in order to obtain a perfect medical segmentation prediction graph, the network should not only have a strong learning ability for local details, but also have a certain distance modeling ability. To solve these problems, a remote feature exploration (RFE) module is proposed in this paper. The most important feature of this module is that remote elements can be used to assist in the generation of local features. In addition, in order to better verify the feasibility of the innovation in this paper, a new multi-organ segmentation dataset (MOD) was manually created. While both the MOD and Synapse datasets label eight categories of organs, there are some images in the Synapse dataset that label only a few categories of organs. The proposed method achieved 79.77% and 75.12% DSC on the Synapse and MOD datasets, respectively. Meanwhile, the HD95 (mm) scores were 21.75 on Synapse and 7.43 on the MOD dataset.
Applications of Nanosatellites in Constellation: Overview and Feasibility Study for a Space Mission Based on Internet of Space Things Applications Used for AIS and Fire Detection
Authors: Kerrouche Kamel Djamel Eddine , Wang Lina , Seddjar Abderrahmane , Rastinasab Vahid , Oukil Souad , Ghaffour Yassine Mohammed , Nouar Larbi
Published: July 2023

In some geographically challenging areas (such as deserts, seas, and forests) where direct connectivity to a terrestrial network is difficult, space communication is the only option. In these remote locations, Internet of Space Things (IoST) applications can also be used successfully. In this paper, the proposed payload for IoST applications demonstrates how an Automatic Identification System (AIS) and a fire detection system can be used effectively. A space mission based on efficient and low-cost communication can use a constellation of nanosatellites to better meet this need. These two applications, which use a constellation of nanosatellites, can provide relevant university-level data in several countries as an effective policy for the transfer of space technology in an educational initiative project. To enhance educational participation and interest in space technology, this paper shares the lessons learned from the project feasibility study based on an in-depth design of a nanosatellite with several analyses (data budget, link budget, power budget, and lifetime estimation). Lastly, this paper highlights by experiments the development and application of a cost-effective sensor node for fire detection and the use of GPS to enable AIS capabilities in the IoST framework.
Automated Micro-Crack Detection within Photovoltaic Manufacturing Facility via Ground Modelling for a Regularized Convolutional Network
Authors: Animashaun Damilola , Hussain Muhammad
Published: July 2023

The manufacturing of photovoltaic cells is a complex and intensive process involving the exposure of the cell surface to high temperature differentials and external pressure, which can lead to the development of surface defects, such as micro-cracks. Currently, domain experts manually inspect the cell surface to detect micro-cracks, a process that is subject to human bias, high error rates, fatigue, and labor costs. To overcome the need for domain experts, this research proposes modelling cell surfaces via representative augmentations grounded in production floor conditions. The modelled dataset is then used as input for a custom ‘lightweight’ convolutional neural network architecture for training a robust, noninvasive classifier, essentially presenting an automated micro-crack detector. In addition to data modelling, the proposed architecture is further regularized using several regularization strategies to enhance performance, achieving an overall F1-score of 85%.
Research on Road Scene Understanding of Autonomous Vehicles Based on Multi-Task Learning
Authors: Guo Jinghua , Wang Jingyao , Wang Huinian , Xiao Baoping , He Zhifei , Li Lubin
Published: July 2023

Road scene understanding is crucial to the safe driving of autonomous vehicles. Comprehensive road scene understanding requires a visual perception system to deal with a large number of tasks at the same time, which needs a perception model with a small size, fast speed, and high accuracy. As multi-task learning has evident advantages in performance and computational resources, in this paper, a multi-task model YOLO-Object, Drivable Area, and Lane Line Detection (YOLO-ODL) based on hard parameter sharing is proposed to realize joint and efficient detection of traffic objects, drivable areas, and lane lines. In order to balance tasks of YOLO-ODL, a weight balancing strategy is introduced so that the weight parameters of the model can be automatically adjusted during training, and a Mosaic migration optimization scheme is adopted to improve the evaluation indicators of the model. Our YOLO-ODL model performs well on the challenging BDD100K dataset, achieving the state of the art in terms of accuracy and computational efficiency.
Identification of Geometric Features of the Corrugated Board Using Images and Genetic Algorithm
Authors: Rogalka Maciej , Grabski Jakub Krzysztof , Garbowski Tomasz
Published: July 2023

The corrugated board is a versatile and durable material that is widely used in the packaging industry. Its unique structure provides strength and cushioning, while its recyclability and bio-degradability make it an environmentally friendly option. The strength of the corrugated board depends on many factors, including the type of individual papers on flat and corrugated layers, the geometry of the flute, temperature, humidity, etc. This paper presents a new approach to the analysis of the geometric features of corrugated boards. The experimental set used in the work and the created software are characterized by high reliability and precision of measurement thanks to the use of an identification procedure based on image analysis and a genetic algorithm. In the applied procedure, the thickness of each layer, corrugated cardboard thickness, flute height and center line are calculated. In most cases, the proposed algorithm successfully approximated these parameters.
Wheel Defect Detection Using a Hybrid Deep Learning Approach
Authors: Shaikh Khurram , Hussain Imtiaz , Chowdhry Bhawani Shankar
Published: July 2023

Defective wheels pose a significant challenge in railway transportation, impacting operational performance and safety. Excessive traction and braking forces give rise to deviations from the intended conical tread shape, resulting in amplified vibrations and noise. Moreover, these deviations contribute to the accelerated damage of track components. Detecting wheel defects at an early stage is crucial to ensure safe and comfortable operation, as well as to minimize maintenance costs. However, the presence of various vibrations, such as those induced by the track, traction motors, and other rolling stock subsystems, poses a significant challenge for onboard detection techniques. These vibrations create difficulties in accurately identifying wheel defects in real-time during operational activities, often resulting in false alarms. This research paper aims to address this issue by using a hybrid deep learning-based approach for the accurate detection of various types of wheel defects using accelerometer data. The proposed approach aims to enhance wheel defect detection accuracy while considering onboard techniques’ cost-effectiveness and efficiency. A realistic simulation model of the railway wheelset is developed to generate a comprehensive dataset. To generate vibration data in various scenarios, the model is simulated for 20 s under different conditions, including one non-faulty scenario and six faulty scenarios. The simulations are conducted at different speeds and track conditions to capture a wide range of operating conditions. Within each simulation iteration, a total of 200,000 data points are generated, providing a comprehensive dataset for analysis and evaluation. The generated data are then utilized to train and evaluate a hybrid deep learning model, employing a multi-layer perceptron (MLP) as a feature extractor and multiple machine learning models (support vector machine, random forest, decision tree, and k-nearest neighbors) for performance comparison. The results demonstrate that the MLP-RF (multi-layer perceptron with random forest) model achieved an accuracy of 99%, while the MLP-DT (multi-layer perceptron with decision tree) model achieved an accuracy of 98%. These high accuracy values indicate the effectiveness of the models in accurately classifying and predicting the outcomes. The contributions of this research work include the development of a realistic simulation model, the evaluation of sensor layout effectiveness, and the application of deep learning techniques for improved wheel flat detections.
Phase-Locked Synthetic Wavelength Interferometer Using a Femtosecond Laser for Absolute Distance Measurement without Cyclic Error
Authors: Kang Hyeokin , Lee Joohyung , Kim Young-Jin , Kim Seung-Woo
Published: July 2023

We present a phase-locked synthetic wavelength interferometer that enables a complete elimination of cyclic errors in absolute distance measurements. With this method, the phase difference between the reference and measurement paths is fed back into a phase lock-in system, which is then used to control the synthetic wavelength and set the phase difference to zero using an external cavity acousto-optic modulator. We validated the cyclic error removal of the proposed phase-locked method by comparing it with the conventional phase-measuring method of the synthetic wavelength interferometer. By analyzing the locked error signal, we achieved a precision of 0.6 mrad in phase without any observed cyclic errors.
Unsupervised Video Anomaly Detection Based on Similarity with Predefined Text Descriptions
Authors: Kim Jaehyun , Yoon Seongwook , Choi Taehyeon , Sull Sanghoon
Published: July 2023

Research on video anomaly detection has mainly been based on video data. However, many real-world cases involve users who can conceive potential normal and abnormal situations within the anomaly detection domain. This domain knowledge can be conveniently expressed as text descriptions, such as “walking” or “people fighting”, which can be easily obtained, customized for specific applications, and applied to unseen abnormal videos not included in the training dataset. We explore the potential of using these text descriptions with unlabeled video datasets. We use large language models to obtain text descriptions and leverage them to detect abnormal frames by calculating the cosine similarity between the input frame and text descriptions using the CLIP visual language model. To enhance the performance, we refined the CLIP-derived cosine similarity using an unlabeled dataset and the proposed text-conditional similarity, which is a similarity measure between two vectors based on additional learnable parameters and a triplet loss. The proposed method has a simple training and inference process that avoids the computationally intensive analyses of optical flow or multiple frames. The experimental results demonstrate that the proposed method outperforms unsupervised methods by showing 8% and 13% better AUC scores for the ShanghaiTech and UCFcrime datasets, respectively. Although the proposed method shows −6% and −5% than weakly supervised methods for those datasets, in abnormal videos, the proposed method shows 17% and 5% better AUC scores, which means that the proposed method shows comparable results with weakly supervised methods that require resource-intensive dataset labeling. These outcomes validate the potential of using text descriptions in unsupervised video anomaly detection.
Knowledge-Fusion-Based Iterative Graph Structure Learning Framework for Implicit Sentiment Identification
Authors: Zhao Yuxia , Mamat Mahpirat , Aysa Alimjan , Ubul Kurban
Published: July 2023

Implicit sentiment identification is a significant classical task in text analysis. Graph neural networks (GNNs) have recently been successful in implicit sentiment identification, but the current approaches still suffer from two problems. On the one hand, there is a lack of structural information carried by the single-view graph structure of implicit sentiment texts to accurately capture obscure sentiment expressions. On the other hand, the predefined fixed graph structure may contain some noisy edges that cannot represent semantic information using an accurate topology, which can seriously impair the performance of implicit sentiment analysis. To address these problems, we introduce a knowledge-fusion-based iterative graph structure learning framework (KIG). Specifically, for the first problem, KIG constructs graph structures based on three views, namely, co-occurrence statistics, cosine similarity, and syntactic dependency trees through prior knowledge, which provides rich multi-source information for implicit sentiment analysis and facilitates the capture of implicit obscure sentiment expressions. To address the second problem, KIG innovatively iterates the three original graph structures and searches for their implicit graph structures to better fit the data themselves to optimize the downstream implicit sentiment analysis task. We compared our method with the mainstream implicit sentiment identification methods on two publicly available datasets, and ours outperformed both benchmark models. The accuracy, recall, and F1 values of KIG on the Pun of the Day dataset reached 89.2%, 93.7%, and 91.1%, respectively. Extensive experimental results demonstrate the superiority of our proposed method for the implicit sentiment identification task.
Exploring Teslasuit’s Potential in Detecting Sequential Slip-Induced Kinematic Changes among Healthy Young Adults
Authors: Hepp Jacob , Shiraishi Michael , Tran Michelle , Henson Emmy , Ananthanarayanan Mira , Soangra Rahul
Published: July 2023

This study aimed to assess whether the Teslasuit, a wearable motion-sensing technology, could detect subtle changes in gait following slip perturbations comparable to an infrared motion capture system. A total of 12 participants wore Teslasuits equipped with inertial measurement units (IMUs) and reflective markers. The experiments were conducted using the Motek GRAIL system, which allowed for accurate timing of slip perturbations during heel strikes. The data from Teslasuit and camera systems were analyzed using statistical parameter mapping (SPM) to compare gait patterns from the two systems and before and after slip. We found significant changes in ankle angles and moments before and after slip perturbations. We also found that step width significantly increased after slip perturbations (p = 0.03) and total double support time significantly decreased after slip (p = 0.01). However, we found that initial double support time significantly increased after slip (p = 0.01). However, there were no significant differences observed between the Teslasuit and motion capture systems in terms of kinematic curves for ankle, knee, and hip movements. The Teslasuit showed promise as an alternative to camera-based motion capture systems for assessing ankle, knee, and hip kinematics during slips. However, some limitations were noted, including kinematics magnitude differences between the two systems. The findings of this study contribute to the understanding of gait adaptations due to sequential slips and potential use of Teslasuit for fall prevention strategies, such as perturbation training.
Structure, Luminescence and Temperature Detection Capability of [C(NH2)3]M(HCOO)3 (M = Mg2+, Mn2+, Zn2+) Hybrid Organic–Inorganic Formate Perovskites Containing Cr3+ Ions
Authors: Stefańska Dagmara , Kabański Adam , Vu Thi Hong Quan , Adaszyński Marek , Ptak Maciej
Published: July 2023

Metal-organic frameworks are of great interest to scientists from various fields. This group also includes organic–inorganic hybrids with a perovskite structure. Recently their structural, phonon, and luminescent properties have been paid much attention. However, a new way of characterization of these materials has become luminescence thermometry. Herein, we report the structure, luminescence, and temperature detection ability of formate organic–inorganic perovskite [C(NH2)3]M(HCOO)3 (Mg2+, Mn2+, Zn2+) doped with Cr3+ ions. Crystal field strength (Dq/B) and Racah parameters were determined based on diffuse reflectance spectra. It was shown that Cr3+ ions are positioned in the intermediate crystal field or close to it with a Dq/B range of 2.29–2.41. The co-existence of the spin-forbidden and spin-allowed transitions of Cr3+ ions enable the proposal of an approach for remote readout of the temperature. The relative sensitivity (Sr) can be easily modified by sample composition and Cr3+ ions concentration. The luminescent thermometer based on the 2E/4T2g transitions has the relative sensitivity Sr of 2.08%K−1 at 90 K for [C(NH2)3]Mg(HCOO)3: 1% Cr3+ and decrease to 1.20%K−1 at 100 K and 1.08%K−1 at 90 K for Mn2+ and Zn2+ analogs, respectively.
EU−Africa: Digital and Social Questions in a Multicultural Agroecological Transition for the Cocoa Production in Africa
Authors: Pinardi Stefano , Salis Matteo , Sartor Gabriele , Meo Rosa
Published: July 2023

The challenge of this century is without a doubt to counter global warming. Land management, agriculture, and forests are responsible for 23% of total greenhouse gases (GHGs). In developing countries, such as those in African territories, where economic capacities are sometimes small and socio-cultural and linguistic perceptions are different, this requires a transition that is just and respectful of local culture and language, while at the same time helping to create labor conditions that respect gender and minors. This article describes a socio-technical ecological transition in the cocoa chain production in Côte d’Ivoire, which is not only the world’s leading producer of cocoa beans (45%) but also one of the African countries most prone to deforestation. Linguistic and multicultural aspects come together in Côte d’Ivoire, where more than 70 local languages are spoken. Intelligent digital approaches, agroecological issues, new methods, and sociocultural questions are embedded in a context of collaboration and co-creation, a living lab approach, to ensures sharing and co-creation among NGOs, farmers, companies, and researchers. A framework of socio-technical transition is presented, and this research aims to not only achieve the goals of a just ecological transition and reduce carbon footprint and deforestations but also to create diverse labour conditions, gender respect and inclusion.
Understanding the Spatial Distribution and Behavior of Elderly Residents in Age-Friendly Communities: An Analysis of Outdoor Space Features in Hangzhou, China
Authors: Lu Chenchong , Wu Wenting , Han Dan
Published: July 2023

In the context of an aging population and the rise of age-friendly communities, the creation of outdoor spaces that cater to the daily needs of elderly residents, promote their physical and mental well-being, and strengthen their sense of community is a pressing matter requiring immediate attention and solutions. This study focuses on boosting the outdoor space of communities that are age-friendly. The research sample consists of four age-friendly communities in the representative city of Hangzhou. Aerial and ground surveys were carried out to examine the spatial distribution and behavioral tendencies of elderly citizens in outdoor community gathering locations. A GoPro MAX panoramic camera and a weather meter were utilized to evaluate the outside space’s landscape components, spatial attributes, and microclimate. Using Pearson correlation and multiple linear regression analyses, we discovered the key parameters impacting the spatial gathering and behaviors of elderly residents in age-friendly communities’ outdoor spaces. When constructing outdoor gathering spaces for the elderly, our findings indicate that weatherproof amenities, a greater variety of pavers, and greenery should be prioritized. Community fitness facilities should be considered throughout the development and refurbishment phases for activity areas. Consideration should also be given to the construction of weatherproof facilities with less walls and increased hard surface areas for chess spaces.
Practical Approach for Defining the Sustainable Yield of Wells in Low-Permeability Fractured Rocks
Authors: Piscopo Vincenzo , Sbarbati Chiara , Dinagde Tolosa Dabi , Lotti Francesca
Published: July 2023

Groundwater sustainability is still an open question. Theoretical and practical approaches to the definition of groundwater sustainability were mainly developed on large scales, while few studies are available for its definition at a single well scale, especially in fractured aquifers. This study examines how much the sustainable yield of a well in fractured aquifers depends on the trend of drawdown over time. For this aim, pumping tests conducted in fractured rocks were considered and analytical models were applied to examine the long-term response of groundwater levels in some wells. To define the operational pumping flow of a well in these aquifers, results show that long constant flow rate pumping tests are preferred to step-drawdown tests. The late drawdown–time curve and residual drawdown segment-curves during the recovery, approximated by power, exponential or polynomial equations, represent the first step to extrapolating the long-term drawdown in the well. This prediction combined with the assessment of a drawdown limit in the well (as a function of the saturated thickness of the aquifer) are essential to plan the first operation of the well (flow rate and time of pumping). Subsequently, through the monitoring of this first operation step, the following operation phases can be updated and improved.
Motorcycle-Riding Experience: Friend or Foe? Understanding Its Effects on Driving Behavior and Accident Risk
Authors: Yousaf Adnan , Wu Jianping
Published: July 2023

For those who cannot afford vehicles, motorcycles are a common mode of transportation in Pakistan. Although many motorcycle riders also drive vehicles, they continue to engage in dangerous behaviors such as speeding, weaving through traffic, and disobeying traffic laws, which can result in accidents. The purpose of this study was to investigate the relationship between prior motorcycle-riding experience, dangerous driving behaviors, and traffic accidents. A total of 623 drivers in Pakistan participated in a study in which questions on their demographics, involvement in accidents, and dangerous driving practices were posed. Two statistical models were employed to analyze the data and ascertain how motorcycle-riding experience affected dangerous driving behaviors and accidents. Drivers with past motorcycle-riding experience were found to be more likely to exhibit aggressive and risky driving behaviors, as validated by principal component analysis. Certain demographic characteristics were also linked to dangerous driving behaviors, and prior motorcycle experience was identified as a crucial factor in anticipating traffic collisions. The probability of a traffic accident increased by 67% for each unit rise in motorcycle-riding experience. To lower the incidence of accidents, the study suggests that the government and road safety regulatory authorities impose strict rules and regulations for motorcycle riders.
Exploring the Effects of DEE Pilot Injection on a Biogas-Fueled HCCI Engine at Different Injection Locations
Authors: Mishra Nihal , Mitra Shubham , Thapliyal Abhishek , Mahajan Aniket , Khan T. M. Yunus , Manavalla Sreekanth , Baig Rahmath Ulla , Janvekar Ayub Ahmed , M Feroskhan
Published: July 2023

One of the popular ways to minimise the impact of emissions produced by engines is by enabling alternative fuels. Out of the many trending options for alternative fuels, biogas provides some unique advantages, such as being considered to be environmentally friendly, obeying the laws of renewable energy and generating the smallest carbon footprints. The two major drawbacks of traditional diesel engines are their high rate of NOx and significant amount of soot. The best candidates for overcoming these issues are HCCI engines; HCCI engines can provide better control over NOx generation and overall thermal efficiency can be improved to a greater level. These types of engines are compatible with both SI and CI. Now, to understand and analyse the behaviour of HCCI, the present work was focused on a modified single-cylinder CI engine. It was made to operate in HCCI mode by enabling the combination of biogas, along with diethyl ether (DEE), as a fuel mixture. To achieve better combustion, biogas was combined with air, while DEE acted as an ignition source, which can be introduced at three different locations. In total, the experiment was performed sixty times so as to achieve the best injection position. To obtain this information, other parameters, such as biogas flow rate, torque, methane fraction and DEE injection position, were also incorporated. The main results were consolidated by warping the output parameters such as brake thermal efficiency, equivalence ratio, air–fuel ratio, and brake-specific fuel consumption. Emission such as CO, HC, NOx, and smoke were taken into account. The results indicate that port injection provides higher thermal efficiency than manifold injections, while lower emissions were observed in manifold injections.
Defining Drivers and Barriers of Sustainable Fashion Manufacturing: Perceptions in the Global South
Authors: Monyaki Nthabeleng Caroline , Cilliers Ryna
Published: July 2023

The practices of reuse and repurposing as sustainability measures are on the rise, particularly in the Global North, emphasizing environmental sustainability. In the Global South, however, the approach to circularity is more inclined towards livelihoods, employment, and education. In the North, remanufacturing and upcycling have been instrumental in promoting sustainable design and manufacturing. Research on circular fashion in Africa, however, tends to be limited to the second-hand clothing market, with a lack of focus on remanufacturing and upcycling as part of circular fashion practice. Thus, this study aimed to explore perspectives on the challenges and enablers of sustainable fashion manufacturing, specifically focusing on remanufacturing and upcycling, within four Cape Town-based fashion businesses. This study also aimed to build upon a previously established conceptual framework, identifying the key barriers and drivers using a tool from the Theory of Constraints. The findings suggested that funding is the primary driving force behind sustainable manufacturing practices, whereas a lack of education on remanufacturing and upcycling was identified as the main barrier.
Stability of Unsaturated Soil Slope Considering Stratigraphic Uncertainty
Authors: Cao Wei , Wan Zheng , Li Wenjing
Published: July 2023

Stratigraphic uncertainty is widely present in nature, but it has not been well considered in the stability analysis of unsaturated soil slopes in the past. In this study, the stability of the unsaturated soil slope is evaluated based on borehole data considering stratigraphic uncertainty. Firstly, an enhanced coupled Markov chain model is used to simulate stratigraphic uncertainty. Then, a finite element algorithm for automatically calculating the safety factor (FS) and the average groundwater table (AGT) of the unsaturated soil slope is developed. At last, a hypothetical slope located in the stratum from Perth, West Australia is analyzed using the proposed algorithm under different borehole schemes. The results show that with the increase in the borehole number, the statistics of FS and AGT will not monotonically increase or decrease. But the trend is that the mean values of FS and AGT gradually approach and eventually converge to the real values, and the standard deviations of FS and AGT decrease. There is a linear relationship between the standard deviation of FS (or AGT) and the average information entropy. The FS and AGT are negatively correlated considering stratigraphic uncertainty.
Societal Impacts of Higher Education Research: From ‘Publish or Perish’ to ‘Publish and Prosper’ in Business School Scholarship
Authors: Steingard David , Rodenburg Kathleen
Published: July 2023

This paper introduces a transformative systems-level framework for understanding the interplay of institutional, cultural, and systemic dynamics influencing the societal impacts of academic research. We introduce and apply the Societal Impacts of Research Institutional Ecosystem (SIRIE) framework to business school scholarship and academic research in higher education. The United Nations Sustainable Development Goals (SDGs) serve as SIRIE’s normative ethical framework to benchmark: institutional mission; accreditation bodies’ compliance requirements; faculty tenure and promotion research expectations; the influence of rankings and ratings; and journal quality metrics. Our framework acknowledges the role the Anthropocene Epoch plays in contributing to contemporary social and environmental problems. We argue that recalcitrant institutional forces in academia neutralize the promise of academic scholarship to galvanize meaningful societal impacts. We assert that the contemporary state of higher education research is unfortunately dominated by a “publish or perish” mentality. This narrative produces academic research that is decontextualized from today’s exigent “grand challenges” related to poverty, climate, equity, health, peace, environment, etc., as well as transformative solutions for a sustainable future. By exploring an alternative paradigm for academic research through SIRIE and the SDGs—“publish and prosper”—we detail how academic research can meaningfully contribute to change the world for the better.
Definition of Corporate Social Responsibility as a Management Philosophy Oriented towards the Management of Externalities: Proposal and Argumentation
Authors: Licandro Oscar , Vázquez-Burguete José Luis , Ortigueira Luis , Correa Patricia
Published: July 2023

The purpose of this paper is to contribute to the theoretical discussion on the best way to define Corporate Social Responsibility. The methodology used is a bibliographic review through a search for articles in different catalogues (Redalyc, Ebsco, JStor, SAGE, Science Direct, and Springer), focusing on articles that addressed the study of definitions of this concept. The different ways of defining it are identified and analysed in order to find a definition can encompass all the others. The paper also proposes that locating this concept at the level of management philosophy (and not simply as a desirable or expected behaviour) facilitates the construction of agreements around a consensual definition, unlike under the predominant behavioural approach of its conceptualisation as corporate behaviour. The paper concludes by proposing to define Corporate Social Responsibility as a management philosophy based on the responsible management of the company’s operational externalities on stakeholders, society, and the environment. It is concluded that this definition contributes to the theoretical discussion, strengthens empirical research, and improves the application of Corporate Social Responsibility in the corporate world.
Point Cloud-Based Historical Building Information Modeling (H-BIM) in Urban Heritage Documentation Studies
Authors: Karasaka Lutfiye , Ulutas Neslisah
Published: July 2023

Integrating building information modeling (BIM) technology into historical and cultural heritage structures has resulted in historic building information modeling (H-BIM), an effective method for managing and documenting invaluable historical artifacts. H-BIM has the potential to aid with the design, restoration, and maintenance activities of historical buildings by properly integrating parametric data and deliberately assessing semantic information, thus progressing the 3D model’s performance. H-BIM also aims to conduct cultural heritage (CH) studies in a systematic manner by combining 3D modeling techniques and information management. The first step in this process is to conduct a suitable geomatic survey to build accurate 3D models. The most efficient modeling method is to employ 3D point cloud data that are acquired using the terrestrial laser scanning method. This study proposes a manual modeling strategy for urban historical buildings that falls within the scope of the H-BIM methodology. The proposed modeling strategy follows a workflow that inserts point cloud data into the BIM environment in order to create a 3D urban cultural heritage model using ontologies applied with the H-BIM methodology. With this objective in mind, the H-BIM model development platform Revit was used to model the object elements of the inheritance structure in 3D and save them in the BIM library. It is very important to model building elements separately in 3D modeling studies. The resulting BIM product sheds light on fundamental data for the future planning, design, operation, and maintenance processes of the building. Users can obtain data suitable for H-BIM by adding graphical and non-graphical data.