bluuebunny/crossref_metadata_2025 huggingface dataset:
High-Quality Object Detection Method for UAV Images Based on Improved DINO and Masked Image Modeling
Authors: Lu Wanjie , Niu Chaoyang , Lan Chaozhen , Liu Wei , Wang Shiju , Yu Junming , Hu Tao
Published: September 2023

The extensive application of unmanned aerial vehicle (UAV) technology has increased academic interest in object detection algorithms for UAV images. Nevertheless, these algorithms present issues such as low accuracy, inadequate stability, and insufficient pre-training model utilization. Therefore, a high-quality object detection method based on a performance-improved object detection baseline and pretraining algorithm is proposed. To fully extract global and local feature information, a hybrid backbone based on the combination of convolutional neural network (CNN) and vision transformer (ViT) is constructed using an excellent object detection method as the baseline network for feature extraction. This backbone is then combined with a more stable and generalizable optimizer to obtain high-quality object detection results. Because the domain gap between natural and UAV aerial photography scenes hinders the application of mainstream pre-training models to downstream UAV image object detection tasks, this study applies the masked image modeling (MIM) method to aerospace remote sensing datasets with a lower volume than mainstream natural scene datasets to produce a pre-training model for the proposed method and further improve UAV image object detection accuracy. Experimental results for two UAV imagery datasets show that the proposed method achieves better object detection performance compared to state-of-the-art (SOTA) methods with fewer pre-training datasets and parameters.
Investigating Spatial Variations of Compound Heat–Precipitation Events in Guangdong, China through a Convection-Permitting Model
Authors: Zhu Tingan , Zhang Wei , Wang Jun , Chen Yuanpeng , Xin Shuhao , Zhu Jinxin
Published: September 2023

Compound heat–precipitation events exert significant impacts on severe weather occurrences. Intense vertical air movement, driving vigorous convection, primarily contributes to the formation of extreme precipitation. Nevertheless, such compound events’ temporal and spatial variation patterns at convection-permitting resolutions remain inadequately explored. This study assesses the performance of the Convection-Permitting Model (CPM) against a model of convection parameterization while investigating the spatial dynamics of compound heat–precipitation events in Guangdong, China. Our findings indicate that the CPM exhibits heightened reliability and precision in simulating temperature and precipitation patterns, especially in extreme precipitation simulation, which would be highly underestimated without a convection-permitting process. Projections from the CPM reveal that, across historical and future periods, the occurrence frequency and fraction of T-P events (instances of extreme heat followed by extreme precipitation) surpass those of P-T events (occurrences of extreme precipitation followed by extreme heat). For T-P events, the CPM exhibits better capability in capturing high-frequency occurrence areas, whereas the results of the relatively low-resolution model show less distinct spatial variations. Both types of events exhibit noticeable upward trends yearly within each period. By the close of this century, the provincial average frequency of P-T events is anticipated to decrease from 20.32 times to 14.55 times. In contrast, the frequency of T-P events is projected to increase from 87.7 times to 101.38 times. These projected changes underscore the shifting dynamics of compound heat–precipitation events in the study region.
GammaGAN: Gamma-Scaled Class Embeddings for Conditional Video Generation
Authors: Kang Minjae , Heo Yong Seok
Published: September 2023

In this paper, we propose a new model for conditional video generation (GammaGAN). Generally, it is challenging to generate a plausible video from a single image with a class label as a condition. Traditional methods based on conditional generative adversarial networks (cGANs) often encounter difficulties in effectively utilizing a class label, typically by concatenating a class label to the input or hidden layer. In contrast, the proposed GammaGAN adopts the projection method to effectively utilize a class label and proposes scaling class embeddings and normalizing outputs. Concretely, our proposed architecture consists of two streams: a class embedding stream and a data stream. In the class embedding stream, class embeddings are scaled to effectively emphasize class-specific differences. Meanwhile, the outputs in the data stream are normalized. Our normalization technique balances the outputs of both streams, ensuring a balance between the importance of feature vectors and class embeddings during training. This results in enhanced video quality. We evaluated the proposed method using the MUG facial expression dataset, which consists of six facial expressions. Compared with the prior conditional video generation model, ImaGINator, our model yielded relative improvements of 1.61%, 1.66%, and 0.36% in terms of PSNR, SSIM, and LPIPS, respectively. These results suggest potential for further advancements in conditional video generation.
Integrated Smart Gas Tracking Device with Artificially Tailored Selectivity for Real-Time Monitoring Food Freshness
Authors: Xu Yuli , Liu Zicheng , Lin Jingren , Zhao Jintao , Hoa Nguyen Duc , Hieu Nguyen Van , Ganeev Alexander A. , Chuchina Victoria , Jouyban Abolghasem , Cui Daxiang , Wang Ying , Jin Han
Published: September 2023

The real-time monitoring of food freshness in refrigerators is of significant importance in detecting potential food spoiling and preventing serious health issues. One method that is commonly reported and has received substantial attention is the discrimination of food freshness via the tracking of volatile molecules. Nevertheless, the ambient environment of low temperature (normally below 4 °C) and high humidity (90% R.H.), as well as poor selectivity in sensing gas species remain the challenge. In this research, an integrated smart gas-tracking device is designed and fabricated. By applying pump voltage on the yttria-stabilized zirconia (YSZ) membrane, the oxygen concentration in the testing chamber can be manually tailored. Due to the working principle of the sensor following the mixed potential behavior, distinct differences in sensitivity and selectivity are observed for the sensor that operated at different oxygen concentrations. Typically, the sensor gives satisfactory selectivity to H2S, NH3, and C2H5OH at the oxygen concentrations of 10%, 30%, and 40%, respectively. In addition, an acceptable response/recovery rate (within 24 s) is also confirmed. Finally, a refrigerator prototype that includes the smart gas sensor is built, and satisfactory performance in discriminating food freshness status of fresh or semi-fresh is verified for the proposed refrigerator prototype. In conclusion, these aforementioned promising results suggest that the proposed integrated smart gas sensor could be a potential candidate for alarming food spoilage.
Impact of the Mining Process on the Near-Seabed Environment of a Polymetallic Nodule Area: A Field Simulation Experiment in a Western Pacific Area
Authors: Li Bowen , Jia Yonggang , Fan Zhihan , Li Kai , Shi Xuefa
Published: September 2023

With the consumption of terrestrial metal resources, deep-sea polymetallic nodule minerals have been widely exploited around the world. Therefore, the environmental impact of deep-sea polymetallic nodule mining cannot be ignored. In this study, for the first time, a field disturbance and observation device, integrated with multiple sensors, is used to simulate the disturbance process of mining on seabed sediments in the polymetallic nodule area of the western Pacific Ocean at a depth of 5700 m. The impact of the process of stroking and lifting on the bottom sediment in the polymetallic nodule area is 30 times higher than that caused by the waves or the current. The time for turbidity to return to normal after the increase is about 30 min, and the influence distance of a disturbance to the bottom bed on turbidity is about 126 m. The time it takes for density to return to normal is about four hours, and the influence is about 1000 m. At the same time, the resuspension of the bottom sediment leads to an increase in density anomaly and salinity. Moreover, suspended sediments rich in metal ions may react with dissolved oxygen in water, resulting in a decrease in the dissolved oxygen content and an increase in ORP. During the observation period, the phenomenon of a deep-sea reciprocating current is found, which may cause the suspended sediment generated by the continuous operation of the mining vehicle to produce suspended sediment clouds in the water near the bottom of the mining area. This could lead to the continuous increase in nutrients in the water near the bottom of the mining area and the continuous reduction in dissolved oxygen, which will have a significant impact on the local ecological environment. Therefore, the way mining vehicles dig and wash in water bodies could have a marked impact on the marine environment. We suggest adopting the technology of suction and ore separation on mining ships, as well as bringing the separated sediment back to the land for comprehensive utilization.
Cost-Efficient Network Planning for the Cross-Border Baltic Corridor—A Study
Authors: Elgarhy Osama , Alam Muhammad Mahtab , Tammets Anet , Roosipuu Priit , Ancāns Guntis , Saidāns Guntars , Tutovs Jurijs , Saliņš Klāvs , Vērdiņš Aleksandrs , Aleksandrovs Māris , Peršēvics Arnis , Zariņš Dainis , Uusmaa Mart , Uhtlik Ove , Soom Priit
Published: September 2023

Performing 5G coverage planning across borders introduces real-life challenges related to legalities, intercountry agreements, and binding documents. This work provides RF network modelling exercise results to provide uninterrupted 5G coverage to the Via Baltica and Rail Baltica transport corridors crossing Estonia and Latvia and on the border with Lithuania, as well as the Tallinn–Tartu–Valga and Valka–Valga roads (Latvia–Estonia), capable of cross-border 5G services. The study starts by identifying and interviewing stakeholders from different sectors of operation in the Baltic states and Europe and then provides an overview of some of the main legal acts and documents regulating the electronic communications sector in the Baltic states and Europe. Furthermore, 5G network requirements are proposed. In addition, the necessary and existing passive and active infrastructure is described, including spectrum management-related analysis, where the RF bands 700 MHz and 3500 MHz are analysed. Finally, coverage planning is performed. The network modelling results aim to foresee the number of new sites that need to be built on the said transport corridors, also examining the existing infrastructure for such purposes. Additionally, an estimated timeline for building the new sites is provided.
Car Bumper Effects in ADAS Sensors at Automotive Radar Frequencies
Authors: Expósito Isabel , Chin Ingo , García Sánchez Manuel , Cuiñas Iñigo , Verhaevert Jo
Published: September 2023

Radars in the W-band are being integrated into car bumpers for functionalities such as adaptive cruise control, collision avoidance, or lane-keeping. These Advanced Driving Assistance Systems (ADAS) enhance traffic security in coordination with Intelligent Transport Systems (ITS). This paper analyzes the attenuation effect that car bumpers cause on the signals passing through them. Using the free-space transmission technique inside an anechoic chamber, we measured the attenuation caused by car bumper samples with different material compositions. The results show level drops lower than 1.25 dB in all the samples analyzed. The signal attenuation triggered by the bumpers decreases with the frequency, with differences ranging from 0.55 dB to 0.86 dB when comparing the end frequencies within the radar band. Among the analyzed bumper samples, those with a thicker varnish layer or with talc in the composition seem to attenuate more. We also provide an estimation of the measurement uncertainty for the validation of the obtained results. Uncertainty analysis yields values below 0.21 dB with a 95% coverage interval in the measured frequency band. When comparing the measured value with its uncertainty, i.e., the relative uncertainty, the lower the frequency in the measured band, the more accurate the measurements seem to be.
SenseLite: A YOLO-Based Lightweight Model for Small Object Detection in Aerial Imagery
Authors: Han Tianxin , Dong Qing , Sun Lina
Published: September 2023

In the field of aerial remote sensing, detecting small objects in aerial images is challenging. Their subtle presence against broad backgrounds, combined with environmental complexities and low image resolution, complicates identification. While their detection is crucial for urban planning, traffic monitoring, and military reconnaissance, many deep learning approaches demand significant computational resources, hindering real-time applications. To elevate the accuracy of small object detection in aerial imagery and cater to real-time requirements, we introduce SenseLite, a lightweight and efficient model tailored for aerial image object detection. First, we innovatively structured the YOLOv5 model for a more streamlined structure. In the backbone, we replaced the original structure with cutting-edge lightweight neural operator Involution, enhancing contextual semantics and weight distribution. For the neck, we incorporated GSConv and slim-Neck, striking a balance between reduced computational complexity and performance, which is ideal for rapid predictions. Additionally, to enhance detection accuracy, we integrated a squeeze-and-excitation (SE) mechanism to amplify channel communication and improve detection accuracy. Finally, the Soft-NMS strategy was employed to manage overlapping targets, ensuring precise concurrent detections. Performance-wise, SenseLite reduces parameters by 30.5%, from 7.05 M to 4.9 M, as well as computational demands, with GFLOPs decreasing from 15.9 to 11.2. It surpasses the original YOLOv5, showing a 5.5% mAP0.5 improvement, 0.9% higher precision, and 1.4% better recall on the DOTA dataset. Compared to other leading methods, SenseLite stands out in terms of performance.
Influence of Tilting Angle on Temperature Measurements of Different Object Sizes Using Fiber-Optic Pyrometers
Authors: Vargas Salvador , Tapetado Alberto , Vázquez Carmen
Published: September 2023

This article presents a new model of optical power gathered by a fiber-optic pyrometer when there is a tilting angle between the fiber longitudinal axis and the vector perpendicular to the tangent plane of the emitted surface. This optical power depends on the fiber specifications, such as the diameter and the numerical aperture (NA), as well as the object parameters, including its diameter, emissivity, and tilting angle. Some simulations are carried out using other pyrometers from the literature without tilting to validate the model. Additional simulations with different optical fibers, object sizes, and distances at different tilting angles allow us to describe the behavior of the pyrometer when the object is smaller than the optical fiber field of view (the light cone defined by its NA). The results show that for a finite surface object, the power collected by the optical fiber is affected by changes in the tilting angle, greater tilting lesser gathered power, and reaching the maximum power when the field of view of the fiber covers up the entire object, as expected. On the other hand, additional equations are presented to describe the maximum tilting angle, and distance that allow the maximum power gathered for a determined object diameter and fiber, avoiding temperature measurement errors.
Towards Building a Trustworthy Deep Learning Framework for Medical Image Analysis
Authors: Ma Kai , He Siyuan , Sinha Grant , Ebadi Ashkan , Florea Adrian , Tremblay Stéphane , Wong Alexander , Xi Pengcheng
Published: September 2023

Computer vision and deep learning have the potential to improve medical artificial intelligence (AI) by assisting in diagnosis, prediction, and prognosis. However, the application of deep learning to medical image analysis is challenging due to limited data availability and imbalanced data. While model performance is undoubtedly essential for medical image analysis, model trust is equally important. To address these challenges, we propose TRUDLMIA, a trustworthy deep learning framework for medical image analysis, which leverages image features learned through self-supervised learning and utilizes a novel surrogate loss function to build trustworthy models with optimal performance. The framework is validated on three benchmark data sets for detecting pneumonia, COVID-19, and melanoma, and the created models prove to be highly competitive, even outperforming those designed specifically for the tasks. Furthermore, we conduct ablation studies, cross-validation, and result visualization and demonstrate the contribution of proposed modules to both model performance (up to 21%) and model trust (up to 5%). We expect that the proposed framework will support researchers and clinicians in advancing the use of deep learning for dealing with public health crises, improving patient outcomes, increasing diagnostic accuracy, and enhancing the overall quality of healthcare delivery.
A New Semantic Segmentation Framework Based on UNet
Authors: Fu Leiyang , Li Shaowen
Published: September 2023

This paper discusses a semantic segmentation framework and shows its application in agricultural intelligence, such as providing environmental awareness for agricultural robots to work autonomously and efficiently. We propose an ensemble framework based on the bagging strategy and the UNet network, using RGB and HSV color spaces. We evaluated the framework on our self-built dataset (Maize) and a public dataset (Sugar Beets). Then, we compared it with UNet-based methods (single RGB and single HSV), DeepLab V3+, and SegNet. Experimental results show that our ensemble framework can synthesize the advantages of each color space and obtain the best IoUs (0.8276 and 0.6972) on the datasets (Maize and Sugar Beets), respectively. In addition, including our framework, the UNet-based methods have faster speed and a smaller parameter space than DeepLab V3+ and SegNet, which are more suitable for deployment in resource-constrained environments such as mobile robots.
Special Traffic Event Detection: Framework, Dataset Generation, and Deep Neural Network Perspectives
Authors: Lee Soomok , Lee Sanghyun , Noh Jongmin , Kim Jinyoung , Jeong Harim
Published: September 2023

Identifying early special traffic events is crucial for efficient traffic control management. If there are a sufficient number of vehicles equipped with automatic event detection and report gadgets, this enables a more rapid response to special events, including road debris, unexpected pedestrians, accidents, and malfunctioning vehicles. To address the needs of such a system and service, we propose a framework for an in-vehicle module-based special traffic event and emergency detection and safe driving monitoring service, which utilizes the modified ResNet classification algorithm to improve the efficiency of traffic management on highways. Due to the fact that this type of classification problem has scarcely been proposed, we have adapted various classification algorithms and corresponding datasets specifically designed for detecting special traffic events. By utilizing datasets containing data on road debris and malfunctioning or crashed vehicles obtained from Korean highways, we demonstrate the feasibility of our algorithms. Our main contributions encompass a thorough adaptation of various deep-learning algorithms and class definitions aimed at detecting actual emergencies on highways. We have also developed a dataset and detection algorithm specifically tailored for this task. Furthermore, our final end-to-end algorithm showcases a notable 9.2% improvement in performance compared to the object accident detection-based algorithm.
Application of Adaptive Robust Kalman Filter Base on MCC for SINS/GPS Integrated Navigation
Authors: Li Linfeng , Wang Jian , Chen Zhiming , Yu Teng
Published: September 2023

In this paper, an adaptive and robust Kalman filter algorithm based on the maximum correntropy criterion (MCC) is proposed to solve the problem of integrated navigation accuracy reduction, which is caused by the non-Gaussian noise and time-varying noise of GPS measurement in complex environment. Firstly, the Grubbs criterion was used to remove outliers, which are contained in the GPS measurement. Then, a fixed-length sliding window was used to estimate the decay factor adaptively. Based on the fixed-length sliding window method, the time-varying noises, which are considered in integrated navigation system, are addressed. Moreover, a MCC method is used to suppress the non-Gaussian noises, which are generated with external corruption. Finally, the method, which is proposed in this paper, is verified by the designed simulation and field tests. The results show that the influence of the non-Gaussian noise and time-varying noise of the GPS measurement is detected and isolated by the proposed algorithm, effectively. The navigation accuracy and stability are improved.
A Deep Learning Approach for Arabic Manuscripts Classification
Authors: Al-homed Lutfieh S. , Jambi Kamal M. , Al-Barhamtoshy Hassanin M.
Published: September 2023

For centuries, libraries worldwide have preserved ancient manuscripts due to their immense historical and cultural value. However, over time, both natural and human-made factors have led to the degradation of many ancient Arabic manuscripts, causing the loss of significant information, such as authorship, titles, or subjects, rendering them as unknown manuscripts. Although catalog cards attached to these manuscripts might contain some of the missing details, these cards have degraded significantly in quality over the decades within libraries. This paper presents a framework for identifying these unknown ancient Arabic manuscripts by processing the catalog cards associated with them. Given the challenges posed by the degradation of these cards, simple optical character recognition (OCR) is often insufficient. The proposed framework uses deep learning architecture to identify unknown manuscripts within a collection of ancient Arabic documents. This involves locating, extracting, and classifying the text from these catalog cards, along with implementing processes for region-of-interest identification, rotation correction, feature extraction, and classification. The results demonstrate the effectiveness of the proposed method, achieving an accuracy rate of 92.5%, compared to 83.5% with classical image classification and 81.5% with OCR alone.
IDAF: Iterative Dual-Scale Attentional Fusion Network for Automatic Modulation Recognition
Authors: Liu Bohan , Ge Ruixing , Zhu Yuxuan , Zhang Bolin , Zhang Xiaokai , Bao Yanfei
Published: September 2023

Recently, deep learning models have been widely applied to modulation recognition, and they have become a hot topic due to their excellent end-to-end learning capabilities. However, current methods are mostly based on uni-modal inputs, which suffer from incomplete information and local optimization. To complement the advantages of different modalities, we focus on the multi-modal fusion method. Therefore, we introduce an iterative dual-scale attentional fusion (iDAF) method to integrate multimodal data. Firstly, two feature maps with different receptive field sizes are constructed using local and global embedding layers. Secondly, the feature inputs are iterated into the iterative dual-channel attention module (iDCAM), where the two branches capture the details of high-level features and the global weights of each modal channel, respectively. The iDAF not only extracts the recognition characteristics of each of the specific domains, but also complements the strengths of different modalities to obtain a fruitful view. Our iDAF achieves a recognition accuracy of 93.5% at 10 dB and 0.6232 at full signal-to-noise ratio (SNR). The comparative experiments and ablation studies effectively demonstrate the effectiveness and superiority of the iDAF.
Modeling the Visual Landscape: A Review on Approaches, Methods and Techniques
Authors: Misthos Loukas-Moysis , Krassanakis Vassilios , Merlemis Nikolaos , Kesidis Anastasios L.
Published: September 2023

Modeling the perception and evaluation of landscapes from the human perspective is a desirable goal for several scientific domains and applications. Human vision is the dominant sense, and human eyes are the sensors for apperceiving the environmental stimuli of our surroundings. Therefore, exploring the experimental recording and measurement of the visual landscape can reveal crucial aspects about human visual perception responses while viewing the natural or man-made landscapes. Landscape evaluation (or assessment) is another dimension that refers mainly to preferences of the visual landscape, involving human cognition as well, in ways that are often unpredictable. Yet, landscape can be approached by both egocentric (i.e., human view) and exocentric (i.e., bird’s eye view) perspectives. The overarching approach of this review article lies in systematically presenting the different ways for modeling and quantifying the two ‘modalities’ of human perception and evaluation, under the two geometric perspectives, suggesting integrative approaches on these two ‘diverging’ dualities. To this end, several pertinent traditions/approaches, sensor-based experimental methods and techniques (e.g., eye tracking, fMRI, and EEG), and metrics are adduced and described. Essentially, this review article acts as a ‘guide-map’ for the delineation of the different activities related to landscape experience and/or management and to the valid or potentially suitable types of stimuli, sensors techniques, and metrics for each activity. Throughout our work, two main research directions are identified: (1) one that attempts to transfer the visual landscape experience/management from the one perspective to the other (and vice versa); (2) another one that aims to anticipate the visual perception of different landscapes and establish connections between perceptual processes and landscape preferences. As it appears, the research in the field is rapidly growing. In our opinion, it can be greatly advanced and enriched using integrative, interdisciplinary approaches in order to better understand the concepts and the mechanisms by which the visual landscape, as a complex set of stimuli, influences visual perception, potentially leading to more elaborate outcomes such as the anticipation of landscape preferences. As an effect, such approaches can support a rigorous, evidence-based, and socially just framework towards landscape management, protection, and decision making, based on a wide spectrum of well-suited and advanced sensor-based technologies.
Person Re-Identification Using Local Relation-Aware Graph Convolutional Network
Authors: Lian Yu , Huang Wenmin , Liu Shuang , Guo Peng , Zhang Zhong , Durrani Tariq S.
Published: September 2023

Local feature extractions have been verified to be effective for person re-identification (re-ID) in recent literature. However, existing methods usually rely on extracting local features from single part of a pedestrian while neglecting the relationship of local features among different pedestrian images. As a result, local features contain limited information from one pedestrian image, and cannot benefit from other pedestrian images. In this paper, we propose a novel approach named Local Relation-Aware Graph Convolutional Network (LRGCN) to learn the relationship of local features among different pedestrian images. In order to completely describe the relationship of local features among different pedestrian images, we propose overlap graph and similarity graph. The overlap graph formulates the edge weight as the overlap node number in the node’s neighborhoods so as to learn robust local features, and the similarity graph defines the edge weight as the similarity between the nodes to learn discriminative local features. To propagate the information for different kinds of nodes effectively, we propose the Structural Graph Convolution (SGConv) operation. Different from traditional graph convolution operations where all nodes share the same parameter matrix, SGConv learns different parameter matrices for the node itself and its neighbor nodes to improve the expressive power. We conduct comprehensive experiments to verify our method on four large-scale person re-ID databases, and the overall results show LRGCN exceeds the state-of-the-art methods.
Hypoglycemic and Antihyperglycemic Potential of Flavonoid Fraction from Citrus sinensis (L.) Osbeck in Normoglycemic and Diabetic Rats
Authors: Araujo-León Jesús Alfredo , Segura-Campos Maira Rubi , Ortiz-Andrade Rolffy , Vazquez-Garcia Priscila , Carvajal-Sánchez Daniela , Cabañas-Wuan Ángel , González-Sánchez Avel Adolfo , Uuh-Narvaez Jonatan , Sánchez-Salgado Juan Carlos , Fuentes-Noriega Inés , Martín-Quintal Zhelmy
Published: September 2023

Diabetes is one of the most prevalent diseases worldwide, and the search for therapeutic alternatives in developing countries has been focused on natural products, primarily from plants. This study evaluated the antihyperglycemic and hypoglycemic activities of the albedo (FA) and flavedo (FF) flavonoid fractions obtained from orange peels (often discarded) in normoglycemic Wistar rats. The flavonoid fractions were identified and quantified using HPLC-UV-DAD and compared with glibenclamide, repaglinide, saxagliptin, and acarbose. Additionally, both fractions were tested in a streptozotocin (65 mg/kg)/nicotinamide (100 mg/kg)-induced diabetic model. In normoglycemic rats, the highest glucose variation (%VG) occurred during the first hour after FA (112.8%) and FF (105.30%) administration at 100 mg/kg, indicating a hypoglycemic effect. In diabetic rats, FF at 100 mg/kg showed the highest %VG (140.41%) during the first hour after administration. HPLC-UV-DAD analysis revealed the presence of hesperidin (HSP) and naringenin (NGN), with the highest concentrations found in FA (HSP: 41.41%; NGN: 10.75%). These findings suggest potential antihyperglycemic effects of FA and FF fractions, possibly attributed to the presence of HSP and NGN. The results obtained in this work lay the foundations to explore the therapeutic applications of orange peels for controlling hyperglycemia in diabetes. In conclusion, our results suggest a reevaluation and revalorization of orange peels, as they contain pharmaceutically relevant flavonoids.
Employing Dissonance-Based Interventions to Promote Health Equity Utilizing a Community-Based Participatory Research Approach and Social Network Analysis
Authors: Bell Sherry , Van den Berg Martin , Liboro Renato M.
Published: September 2023

The purpose of this paper is to examine and advocate for the consideration of relevant approaches that can be utilized to increase the effectiveness of cognitive dissonance-based interventions (DBIs) designed to promote health equity. Although DBIs informed by different paradigms have been reported to be effective in creating behavior change, particularly among at-risk populations, their long-term impacts on behavior change have apparently been difficult to sustain. We argue that a community-based participatory research (CBPR) approach could considerably improve the effectiveness and long-term impacts of DBIs by harnessing community strengths, increasing stakeholder participation, and facilitating collaborations and partnerships in the planning, implementation, and evaluation of such interventions. Then, we argue that the benefits of employing a CBPR approach in DBIs can be further enhanced when combined with an approach that intentionally utilizes Social Network Analysis (SNA). SNA applies powerful techniques to recognize the type of connections that hold a specific network together and identify that network’s key and influential stakeholders. We conclude by providing recommendations for the use of CBPR and SNA in DBIs and demonstrating the benefits of our recommendations, especially in the context of promoting health equity.
Investigation of Smart Sustainable City Indicators of Sustainable Development—A Case Study of the City of Suwon
Authors: Pinem Robetmi Jumpakita , Kustedjo Ancilla Katherina , Iskandar Yelita Anggiane , Yahya Bernardo Nugroho
Published: September 2023

This paper aims to investigate the interlink between city sustainability development indicators and smart sustainable city indicators (SSCIs). While the sustainability development indicators of a city mostly rely on its sustainable development goals (SDGs), no investigations into whether these indicators imply SSCIs have been conducted. SSCIs are critical during the current unprecedented climate crisis and are necessary to facilitate a livable future. This study considers the city of Suwon as a case study. Suwon’s sustainable development policy adopts 10 SDGs among the 17 SDGs from the United Nations, later called Suwon SDGs (SSDGs). By conducting a content analysis, this study found that the indicators adopted from the SDGs require further investigation to evaluate the core SSCIs. Using text analytics, we found that Suwon’s policies focus on indicators in the local environment, such as the employment rates for every age category, even if the SSDG indicators incorporate the SDGs. The purpose of this analysis is to gain more attention from policymakers about the necessity of reconstructing and considering SSCIs as a part of a smart city’s sustainable development. Lessons and practical implications are discussed for future studies.
Performance of Alkali-Activated Slag Concrete Masonry Blocks Subjected to Accelerated Carbonation Curing
Authors: Hwalla Joud , Al-Mazrouei Mahra , Al-Karbi Khalood , Al-Hebsi Afraa , Al-Ameri Mariam , Al-Hadrami Fatima , El-Hassan Hilal
Published: September 2023

This study investigates the effect of accelerated carbonation curing on the carbon sequestration potential, performance, and microstructure of alkali-activated slag mixes representing concrete masonry blocks (CMBs). The carbonation curing process parameters varied, including initial curing duration, carbonation curing duration, and carbonation pressure. Research findings showed that a maximum CO2 uptake of 12.8%, by binder mass, was attained upon exposing concrete to 4 h initial curing and 20 h carbonation curing at a pressure of 5 bars. The compressive strength and water absorption capacity improved with longer initial and carbonation curing durations and higher pressure. Upon subjecting to salt attack, the mass and strength of 28-day concrete samples increased, owing to the formation of Friedel’s salt and Halite. All mixes could be used as non-load-bearing CMB, with a 1-day strength greater than 4.1 MPa. Based on the global warming potential index, the carbon footprint of carbonation-cured, alkali-activated slag concrete masonry units was up to 46% lower than non-carbonation-cured counterparts. Research findings offer valuable information on the production of carbonation-cured, cement-free concrete masonry blocks to replenish natural resources, recycle industrial waste, and mitigate CO2 emissions.
The Diffusion Rule of Demand-Oriented Biogas Supply in Distributed Renewable Energy System: An Evolutionary Game-Based Approach
Authors: Liu Yiyun , Wu Jun , Li Jianjun , Huang Jingjing
Published: September 2023

Biogas can be regarded as a dispatchable renewable source when changing into the demand-oriented operation mode (DO), thus could be used for complementing with solar and wind power in distributed energy system (DES) as a substitute for chemical energy storage. However, if the DO is implemented in regional DES, uncertainties are emerged caused by the complex interest interaction between the seller and the buyer groups formed by the biogas plant and the DES’s dispatching center, thus making the development trend of DO unknown. In this context, this study explored the diffusion law of DO in regional DES by establishing a mathematical model based on an evolutionary game between the two major stakeholders, during which the evolutionarily stable strategy (ESS) was deduced for understanding their strategy selections, and then the dynamic diffusion trend was simulated by the system dynamics via a case example. Finally, the sensitivity analysis of parameters is carried out and the optimal policy instruments are proposed according to the main influencing factors. The study revealed that when the DES can realize monetized returns from socio-environmental benefits, the adoption of DO becomes more feasible. Importantly, the revenue generated from electricity sales, by the dispatching center when they do not utilize biogas, emerged as the most critical parameter influencing the ultimate outcomes. The limitations of this research and modeling are discussed to lay a foundation for further improvement.
Global Energy Transformation and the Impacts of Systematic Energy Change Policy on Climate Change Mitigation
Authors: Güneş Hakan , Simba Hamis Miraji Ally , Karadağ Haydar , Şit Mustafa
Published: September 2023

This study aims to evaluate the effect of global energy transformation and systematic energy change on climate change. The model is constructed from dynamic panel data which comprises 26 world regions from the World Database Indicators (WDIs), International Energy Atomic (IEA), and International Monetary Fund (IMF), with a span from 2005 to 2022. The Generalized system Method of Moment (sys-GMM) and pooled OLS and random effect models have been used to empirically evaluate the linked effect of global transformation and systematic change on climate change. The sys-GMM approach is used to control the endogeneity of the lagged dependent variable when there is an association between the exogenous variable and the error term. Furthermore, it omits variable bias, measurement errors in the estimation, and unobserved panel heterogeneity. The econometric applications allow us to quantify the direct effect of global transformation and systematic change on climate change. The empirical analysis revealed that renewable energy, alternative energy, technology and innovation, and financial climate have a negative effect on climate change. It means that increasing consumption of the transformation energies leads to reducing the effect of climate change. However, fossil energy is statistically significant and positively affects climate change. Increasing the consumption of fossil energy raises the effect of climate change. There is a global need for massive decarbonization infrastructure that will help minimize the global warming that leads to climate change. Policies that take an endogenous approach through global transformation and systematic change should be implemented to reduce the effect of climate change. The policy should reduce the consumption of non-renewable energy and increase the consumption of renewable energy.
Road Intersection Extraction Based on Low-Frequency Vehicle Trajectory Data
Authors: Du Jiusheng , Liu Xingwang , Meng Chengyang
Published: September 2023

Global navigation satellite system (GNSS) vehicle trajectory data play an important role in obtaining timely urban road information. However, most models cannot effectively extract road information from low-frequency trajectory data. In this study, we aimed to accurately extract urban road network intersections and central locations from low-frequency GNSS trajectory data, and we developed a method for accurate road intersection identification based on filtered trajectory sequences and multiple clustering algorithms. Our approach was founded on the following principles. (1) We put in place a rigorous filtering rule to account for the offset characteristics of low-frequency trajectory data. (2) To overcome the low density and weak connection features of vehicle turning points, we adopted the CDC clustering algorithm. (3) By combining the projection features of orientation values in 2D coordinates, a mean solving method based on the DBSCAN algorithm was devised to obtain intersection center coordinates with greater accuracy. Our method could effectively identify urban road intersections and determine the center position and more effectively apply low-frequency trajectory data. Compared with remote sensing images, the intersection identification accuracy was 96.4%, the recall rate was 89.6%, and the F-value was 92.88% for our method; the intersection center position’s root mean square error (RMSE) was 10.39 m, which was 14.9% higher than that of the mean value method.
Adding Value to Reclaimed Water from Wastewater Treatment Plants: The Environmental Feasibility of a Minimal Liquid Discharge System for the Case Study of Larnaca
Authors: Avramidi Maria , Spyropoulou Christina , Loizou Constantinos , Kyriazi Maria , Novakovic Jelica , Moustakas Konstantinos , Malamis Dimitris , Loizidou Maria
Published: September 2023

The escalating water demand in Cyprus has necessitated the exploration of alternative water resources. The available water, which relies on rainfall and dam storage supplemented by methods such as desalination and aquifer enrichment, is inadequate to meet the current water demand. As a solution, Cyprus is utilizing reclaimed water for irrigation, in full compliance with both local and EU regulations. To address sustainable water management in Cyprus, a minimal liquid discharge (MLD) system is assessed for its environmental feasibility. A system incorporating reverse osmosis (RO), a multi-effect distillation (MED) evaporator, and a vacuum crystallizer (VC) is proposed for treating reclaimed water from the wastewater treatment plant (WWTP) in Larnaca. The proposed system aims to control the salinity (2500 mg/L) that limits the use of recovered water to the irrigation of non-sensitive types of crops, while recovering salt (sodium chloride). A life cycle assessment (LCA) was conducted, comparing the proposed MLD system with a reference system based on RO technology, where water is recovered, and brine is rejected back into the sea. The environmental feasibility was assessed via comparing 16 different environmental impact categories. Based on the analysis, the reference study provided a positive numeric value for most of the impact categories that were examined. Thus, it was concluded that the reference study has an overall negative impact on the environment, whereas the proposed MLD system demonstrated an overall positive impact, mainly due to low ecotoxicity.
Modeling the Transformation of Configuration Management Processes in a Multi-Project Environment
Authors: Dotsenko Nataliia , Chumachenko Igor , Galkin Andrii , Kuchuk Heorhii , Chumachenko Dmytro
Published: September 2023

Human resource management during project implementation in a multi-project environment requires addressing the resource-constrained project scheduling problem. Agile methodologies allow for greater management flexibility, necessitating an agile transformation of human resource management processes. Changes occurring in human resource management lead to modifications in the initial project team and alterations in the state of the resource pool in a multi-project environment. To ensure controllable changes in the project team and address the task of allocating (reallocating) limited resources among project tasks in a multi-project environment with subsequent optimization based on a selected criterion, it is proposed to use configuration management of human resources. Depending on the chosen level of detail, project specifics, and the implementation environment, configuration elements can be an executor, project team, or intact team. Types of equivalence applied to the set of configuration elements have been classified. A model of the configuration management process for human resources has been considered. Using the proposed model will allow for formalizing the process of implementing human resource configuration management in a multi-project environment. Constructive enumeration of configuration elements in a multi-project environment has been examined. Identifying a typical representative of the configuration and considering the given equivalence, followed by selecting a resource allocation/reallocation option that meets the specified constraints, enhances team adaptability. An example of configuration management in addressing team composition management tasks has been discussed. The proposed approach can be applied in managing human resources for agile transformation projects of critical infrastructure, particularly in the healthcare sector, during the establishment of hospital clusters and supercluster medical institutions. This is because implementing such projects necessitates continuous monitoring of changes and requirements for resource provisioning.
Addressing Challenges in Kazakh Education for Sustainable Development
Authors: Yelubayeva Perizat , Tashkyn Erkan , Berkinbayeva Gulzat
Published: September 2023

Sustainability education equips individuals with the knowledge, skills, and values to address complex environmental, social, and economic challenges. Like many other nations, Kazakhstan recognizes the importance of integrating sustainable development principles and values into education to foster a sustainable and resilient future. However, several hurdles, such as the COVID-19 lockdown, have impeded the country’s effective implementation of Education for Sustainable Development. The study explores these barriers and constraints and proposes potential strategies for advancing sustainability education practices in the Kazakh educational scene. This study used a qualitative methodology involving interviews with first-year students from four Kazakh universities. The study results show that preserving the role of education in fostering a sustainable society will pave the way for a brighter and greener future for the nation and its citizens beyond lockdown. The findings revealed that using the transformative power of education, Kazakhstan, with its more sustainable and inclusive education system, can nurture a generation equipped with the knowledge, skills, and values necessary to advance the country towards a sustainable future. The findings and conclusions of this study can guide policymakers, educators, and stakeholders in better addressing the challenges of sustainable development in Kazakhstan and enhancing ESD practices in Kazakh schools and institutions.
Algae Cultivation as Measure for the Sanitation of Organic Waste—A Case Study Based on the Alga Galdieria sulphuraria Grown on Food Waste Hydrolysate in a Continuous Flow Culture
Authors: Pleissner Daniel , Händel Nicole
Published: September 2023

Due to its growth under harsh acidic conditions, the microalga Galdieria sulphuraria may offer the opportunity to combine sanitation and the utilization of organic waste streams. To further deepen the knowledge of alternative waste treatment strategies that allow for holistic utilization, the control and removal of microbial contaminants via non-sterile heterotrophic G. sulphuraria on food waste hydrolysate were investigated in a continuous flow bioreactor culture. Furthermore, a substrate reservoir and harvested biomass were stored under non-sterile conditions over a period of 12 days. Despite the non-sterile conditions, the microbial load of the biomass could be kept under control. Neither the pathogen Salmonella sp. nor the coliform bacteria Escherichia coli could be found. Only nine counts per g of biomass were found for species belonging to Enterococcus spp., Enterobacteriacae, and moulds. Aerobic spore formers were counted with 2700 counts per g of biomass. Most of the aerobic mesophilic counts were formed by yeasts (1.5 × 106 vs. 1.3 × 106 counts per g biomass). The results revealed that, when using acidic growth conditions, contamination will not take over the culture; thus, the sterilization of waste materials can be skipped. It is assumed that such an approach can result in efficient processes for future waste-based bioeconomy strategies.