bluuebunny/crossref_metadata_2025 huggingface dataset:
Artificial Intelligence Techniques in Grapevine Research: A Comparative Study with an Extensive Review of Datasets, Diseases, and Techniques Evaluation
Authors: Gatou Paraskevi , Tsiara Xanthi , Spitalas Alexandros , Sioutas Spyros , Vonitsanos Gerasimos
Published: September 2024

In the last few years, the agricultural field has undergone a digital transformation, incorporating artificial intelligence systems to make good employment of the growing volume of data from various sources and derive value from it. Within artificial intelligence, Machine Learning is a powerful tool for confronting the numerous challenges of developing knowledge-based farming systems. This study aims to comprehensively review the current scientific literature from 2017 to 2023, emphasizing Machine Learning in agriculture, especially viticulture, to detect and predict grape infections. Most of these studies (88%) were conducted within the last five years. A variety of Machine Learning algorithms were used, with those belonging to the Neural Networks (especially Convolutional Neural Networks) standing out as having the best results most of the time. Out of the list of diseases, the ones most researched were Grapevine Yellow, Flavescence Dorée, Esca, Downy mildew, Leafroll, Pierce’s, and Root Rot. Also, some other fields were studied, namely Water Management, plant deficiencies, and classification. Because of the difficulty of the topic, we collected all datasets that were available about grapevines, and we described each dataset with the type of data (e.g., statistical, images, type of images), along with the number of images where they were mentioned. This work provides a unique source of information for a general audience comprising AI researchers, agricultural scientists, wine grape growers, and policymakers. Among others, its outcomes could be effective in curbing diseases in viticulture, which in turn will drive sustainable gains and boost success. Additionally, it could help build resilience in related farming industries such as winemaking.
Virtual Obstacle Avoidance Strategy: Navigating through a Complex Environment While Interacting with Virtual and Physical Elements
Authors: Machado Fabiana , Loureiro Matheus , Bezerra Marcio , Zimerer Carla , Mello Ricardo , Frizera Anselmo
Published: September 2024

Robotic walking devices can be used for intensive exercises to enhance gait rehabilitation therapies. Mixed Reality (MR) techniques may improve engagement through immersive and interactive environments. This article introduces an MR-based multimodal human–robot interaction strategy designed to enable shared control with a Smart Walker. The MR system integrates virtual and physical sensors to (i) enhance safe navigation and (ii) facilitate intuitive mobility training in personalized virtual scenarios by using an interface with three elements: an arrow to indicate where to go, laser lines to indicate nearby obstacles, and an ellipse to show the activation zone. The multimodal interaction is context-based; the presence of nearby individuals and obstacles modulates the robot’s behavior during navigation to simplify collision avoidance while allowing for proper social navigation. An experiment was conducted to evaluate the proposed strategy and the self-explanatory nature of the interface. The volunteers were divided into four groups, with each navigating under different conditions. Three evaluation methods were employed: task performance, self-assessment, and observational measurement. Analysis revealed that participants enjoyed the MR system and understood most of the interface elements without prior explanation. Regarding the interface, volunteers who did not receive any introductory explanation about the interface elements were mostly able to guess their purpose. Volunteers that interacted with the interface in the first session provided more correct answers. In future research, virtual elements will be integrated with the physical environment to enhance user safety during navigation, and the control strategy will be improved to consider both physical and virtual obstacles.
A Multimodal Bracelet to Acquire Muscular Activity and Gyroscopic Data to Study Sensor Fusion for Intent Detection
Authors: Andreas Daniel , Hou Zhongshi , Tabak Mohamad Obada , Dwivedi Anany , Beckerle Philipp
Published: September 2024

Researchers have attempted to control robotic hands and prostheses through biosignals but could not match the human hand. Surface electromyography records electrical muscle activity using non-invasive electrodes and has been the primary method in most studies. While surface electromyography-based hand motion decoding shows promise, it has not yet met the requirements for reliable use. Combining different sensing modalities has been shown to improve hand gesture classification accuracy. This work introduces a multimodal bracelet that integrates a 24-channel force myography system with six commercial surface electromyography sensors, each containing a six-axis inertial measurement unit. The device’s functionality was tested by acquiring muscular activity with the proposed device from five participants performing five different gestures in a random order. A random forest model was then used to classify the performed gestures from the acquired signal. The results confirmed the device’s functionality, making it suitable to study sensor fusion for intent detection in future studies. The results showed that combining all modalities yielded the highest classification accuracies across all participants, reaching 92.3±2.6% on average, effectively reducing misclassifications by 37% and 22% compared to using surface electromyography and force myography individually as input signals, respectively. This demonstrates the potential benefits of sensor fusion for more robust and accurate hand gesture classification and paves the way for advanced control of robotic and prosthetic hands.
Experimental Study of Sinkhole Propagation Induced by a Leaking Pipe Using Fibre Bragg Grating Sensors
Authors: Yumba Josué , Ferentinou Maria , Grobler Michael
Published: September 2024

Sinkhole formation caused by leaking pipes in karst soluble rocks is a significant concern, leading to infrastructure damage and safety risks. In this paper, an experiment was conducted to investigate sinkhole formation in dense sand induced by a leaking pipe. Fibre Bragg grating (FBG) sensors were used to record the strain. A balloon was gradually deflated within a bed of wet silica sand to create an underground cavity. Eighteen FBG sensors, with a wavelength range between 1550 nm and 1560 nm, were embedded horizontally and vertically in the physical model at different levels to monitor deformation at various locations. A leaking pipe was installed to induce the collapse of the formed arch above the cavity. The strain measurements suggested the following four phases in the sinkhole formation process: (1) cavity formation, (2) progressive weathering and erosion, (3) catastrophic collapse, and (4) subsequent equilibrium conditions. The results showed differences in the strain signatures and distributions between the horizontal and vertical measurements. During the critical phase of the sinkhole collapse, the horizontal measurements primarily showed tension, while the vertical measurements indicated compression. This investigation demonstrates the effectiveness of FBGs as advanced monitoring tools for sinkhole precursor identification. The study also suggests using FBGs in geotechnical monitoring applications to improve the understanding and mitigation of sinkholes and related geohazards.
FloatingBlue: A Delay Tolerant Networks-Enabled Internet of Things Architecture for Remote Areas Combining Data Mules and Low Power Communications
Authors: Teixeira Ruan C. M. , Carvalho Celso B. , Calafate Carlos T. , Mota Edjair , Fernandes Rubens A. , Printes Andre L. , Nascimento Lennon B. F.
Published: September 2024

Monitoring vast and remote areas like forests using Wireless Sensor Networks (WSNs) presents significant challenges, such as limited energy resources and signal attenuation over long distances due to natural obstacles. Traditional solutions often require extensive infrastructure, which is impractical in such environments. To address these limitations, we introduce the “FloatingBlue” architecture. This architecture, known for its superior energy efficiency, combines Bluetooth Low Energy (BLE) technology with Delay Tolerant Networks (DTN) and data mules. It leverages BLE’s low power consumption for energy-efficient sensor broadcasts while utilizing DTN-enabled data mules to collect data from dispersed sensors without constant network connectivity. Deployed in a remote agricultural area in the Amazon region, “FloatingBlue” demonstrated significant improvements in energy efficiency and communication range, with a high Packet Delivery Ratio (PDR). The developed BLE beacon sensor achieved state-of-the-art energy consumption levels, using only 2.25 µJ in sleep mode and 11.8 µJ in transmission mode. Our results highlight “FloatingBlue” as a robust, low-power solution for remote monitoring in challenging environments, offering an energy-efficient and scalable alternative to traditional WSN approaches.
Research on Subsurface Electrical Structure Based on a Dense Geomagnetic Array in Southern Yunnan
Authors: Shen Xiaoyu , Cao Yujia
Published: September 2024

The electrical resistivity of subsurface rocks is one of the important sensitive parameters characterizing the internal physics of the Earth. Currently, research on subsurface electrical structures using geomagnetic sounding methods primarily focuses on two approaches: the first is based on observations from a few geomagnetic stations, which have low spatial resolution and cannot effectively describe the distribution of anomalies; the second is based on mobile geomagnetic observations, which have low temporal resolution and cannot promptly reflect anomalies. To address these issues, this study deployed a dense geomagnetic array for long-term observation in the southern segment of the Xiaojiang Fault Zone in the Yuxi area of southern Yunnan. This setup aims to promptly capture seismic magnetic anomalies, providing more data support and fundamental information for short-term earthquake prediction. Based on the long-term observation data from the dense array, the study of the subsurface electrical structure is carried out. The results indicate that during the observation period, which was seismically quiet, the regional subsurface electrical structure remained stable. A large-scale subsurface low-resistivity body was observed in the region, and the electrical structures at the two ends of the southern segment of the Xiaojiang Fault Zone were found to be completely different.
The Design and Validation of a High-Precision Angular Vibration Calibration System Based on a Laser Vibrometer
Authors: Lin Xinghan , Huang Zhigang , Guo Keyou , Li Gang
Published: September 2024

This paper presents the design and validation of a high-precision angular vibration calibration system based on a laser vibrometer, aimed at meeting the high-precision requirements for measuring small angular vibrations. The system primarily consists of a self-driving angular vibration platform and a laser vibrometer. The platform is isolated from ground interference via an air-floating platform and uses a split-type motor to control the platform, generating specific angular vibrations. Detailed simulations of the platform’s modal characteristics and the stability of the spring plates were conducted using the finite element analysis software ANSYS 11. Moreover, fundamental frequency testing and measurement accuracy testing were conducted on the system. Experimental results demonstrate that the system has a fundamental frequency of 2.69 Hz and a maximum measurement error of 0.00172″, confirming the system’s effectiveness in dynamic characteristics, stability, and measurement accuracy. This research provides essential technical support for high-precision angular vibration control in spacecraft.
Validation of a Textile-Based Wearable Measuring Electrocardiogram and Breathing Frequency for Sleep Apnea Monitoring
Authors: Baty Florent , Cvetkovic Dragan , Boesch Maximilian , Bauer Frederik , Adão Martins Neusa R. , Rossi René M. , Schoch Otto D. , Annaheim Simon , Brutsche Martin H.
Published: September 2024

Sleep apnea (SA) is a prevalent disorder characterized by recurrent events of nocturnal apnea. Polysomnography (PSG) represents the gold standard for SA diagnosis. This laboratory-based procedure is complex and costly, and less cumbersome wearable devices have been proposed for SA detection and monitoring. A novel textile multi-sensor monitoring belt recording electrocardiogram (ECG) and breathing frequency (BF) measured by thorax excursion was developed and tested in a sleep laboratory for validation purposes. The aim of the current study was to evaluate the diagnostic performance of ECG-derived heart rate variability and BF-derived breathing rate variability and their combination for the detection of sleep apnea in a population of patients with a suspicion of SA. Fifty-one patients with a suspicion of SA were recruited in the sleep laboratory of the Cantonal Hospital St. Gallen. Patients were equipped with the monitoring belt and underwent a single overnight laboratory-based PSG. In addition, some patients further tested the monitoring belt at home. The ECG and BF signals from the belt were compared to PSG signals using the Bland-Altman methodology. Heart rate and breathing rate variability analyses were performed. Features derived from these analyses were used to build a support vector machine (SVM) classifier for the prediction of SA severity. Model performance was assessed using receiver operating characteristics (ROC) curves. Patients included 35 males and 16 females with a median age of 49 years (range: 21 to 65) and a median apnea-hypopnea index (AHI) of 33 (IQR: 16 to 58). Belt-derived data provided ECG and BF signals with a low bias and in good agreement with PSG-derived signals. The combined ECG and BF signals improved the classification accuracy for SA (area under the ROC curve: 0.98; sensitivity and specificity greater than 90%) compared to single parameter classification based on either ECG or BF alone. This novel wearable device combining ECG and BF provided accurate signals in good agreement with the gold standard PSG. Due to its unobtrusive nature, it is potentially interesting for multi-night assessments and home-based patient follow-up.
Towards a Unified Management Interface for 5G Sensor Networks: Interoperability between Yet Another Next Generation and Open Platform Communication Unified Architecture
Authors: Sambandan Devaraj , Thirupathi Devi
Published: September 2024

Fifth-generation (5G) sensor networks are critical enablers of Industry 4.0, facilitating real-time monitoring and control of industrial processes. However, significant challenges to their deployment in industrial settings remain, such as a lack of support for interoperability and manageability with existing industrial applications and the specialized technical expertise required for the management of private 5G sensor networks. This research proposes a solution to achieve interoperability between private 5G sensor networks and industrial applications by mapping Yet Another Next Generation (YANG) models to Open Platform Communication Unified Architecture (OPC UA) models. An OPC UA pyang plugin, developed to convert YANG models into OPC UA design model files, has been made available on GitHub for open access. The key finding of this research is that the proposed solution enables seamless interoperability without requiring modifications to the private 5G sensor network components, thus enhancing the efficiency and reliability of industrial automation systems. By leveraging existing industrial applications, the management and monitoring of private 5G networks are streamlined. Unlike prior studies that explored OPC UA’s integration with other protocols, this work is the first to focus on the YANG–OPC UA integration, filling a critical gap in Industry 4.0 enablement research.
A Comparative Analysis of Performance Simulation for PUI Detectors Based on Traditional Probability Model and the Vasyliunas and Siscoe Model
Authors: Cao Yu , Zhang Yuzhu , Peng Xiaodong , Xue Changbin , Su Bin
Published: September 2024

In recent years, the enthusiasm for deep space missions has remained unabated, resulting in continuous advancements in the research field of space environment and particles. Many instruments carried on these missions have conducted detection of pickup ions (PUIs) in the solar system. For those instruments, simulation is an effective means and a crucial step for their performance optimization and future operation in-orbit. It holds great significance for the instrument’s in-orbit performance assessment, science operation optimization, and detection efficiency enhancement. In this paper, the traditional probability model and the Vasyliunas and Siscoe (V–S) model are used to generate the PUIs, which are the input for the simulation of the PUI detector. For further analysis, the numerical results of the simulation are processed to calculate the instrument’s geometric factor, mass resolution, and count rates. Then, two sets of experiments are carried out for the comparison of the traditional probability model and the V–S model. The results show that, for the simulation of the instrument in the design stage, the simulation results of the traditional probability model and the V–S model are not much different. However, for the simulation of the instrument performance in-orbit, the PUI data generated based on the V–S model gave a better result than those of the traditional probability model. This conclusion is of great significance for evaluating the detection ability of the PUI detector in future deep space explorations.
Experimental Study of Long Short-Term Memory and Transformer Models for Fall Detection on Smartwatches
Authors: Haque Syed Tousiful , Debnath Minakshi , Yasmin Awatif , Mahmud Tarek , Ngu Anne Hee Hiong
Published: September 2024

Falls are the second leading cause of unintentional injury deaths worldwide. While numerous wearable fall detection devices incorporating AI models have been developed, none of them are used successfully in a fall detection application running on commodity-based smartwatches in real time. The system misses some falls, and generates an annoying amount of False Positives for practical use. We have investigated and experimented with an LSTM model for fall detection on a smartwatch. Even though the LSTM model has high accuracy during offline testing, the good performance of offline LSTM models cannot be translated to the equivalence of real-time performance. Transformers, on the other hand, can learn long-sequence data and patterns intrinsic to the data due to their self-attention mechanism. This paper compares three variants of LSTM and two variants of Transformer models for learning fall patterns. We trained all models using fall and activity data from three datasets, and the real-time testing of the model was performed using the SmartFall App. Our findings showed that in the offline training, the CNN-LSTM model was better than the Transformer model for all the datasets. However, the Transformer is a preferable choice for deployment in real-time fall detection applications.
SSG-Net: A Multi-Branch Fault Diagnosis Method for Scroll Compressors Using Swin Transformer Sliding Window, Shallow ResNet, and Global Attention Mechanism (GAM)
Authors: Xu Zhiwei , Liu Tao , Xia Zezhou , Fan Yanan , Yan Min , Dang Xu
Published: September 2024

The reliable operation of scroll compressors is crucial for the efficiency of rotating machinery and refrigeration systems. To address the need for efficient and accurate fault diagnosis in scroll compressor technology under varying operating states, diverse failure modes, and different operating conditions, a multi-branch convolutional neural network fault diagnosis method (SSG-Net) has been developed. This method is based on the Swin Transformer, the Global Attention Mechanism (GAM), and the ResNet architecture. Initially, the one-dimensional time-series signal is converted into a two-dimensional image using the Short-Time Fourier Transform, thereby enriching the feature set for deep learning analysis. Subsequently, the method integrates the window attention mechanism of the Swin Transformer, the 2D convolution of GAM attention, and the shallow ResNet’s two-dimensional convolution feature extraction branch network. This integration further optimizes the feature extraction process, enhancing the accuracy of fault feature recognition and sensitivity to data variability. Consequently, by combining the global and local features extracted from these three branch networks, the model significantly improves feature representation capability and robustness. Finally, experimental results on scroll compressor datasets and the CWRU dataset demonstrate diagnostic accuracies of 97.44% and 99.78%, respectively. These results surpass existing comparative models and confirm the model’s superior recognition precision and rapid convergence capabilities in complex fault environments.
Large Dynamic Range Spectral Measurement in Terahertz Region Based on Frequency Up-Conversion Detection via OH1 Crystal
Authors: Yuan Jiasheng , Guo Quanxin , Zhang Xingyu , Liu Naichang , Yin Xiaoqin , Ming Na , Guo Liyuan , Jiao Binzhe , Wang Kaiyu , Fan Shuzhen
Published: September 2024

Terahertz spectroscopy systems, which integrate terahertz sources and detectors, have important applications in many fields such as materials science and security checking. Based on highly sensitive frequency up-conversion detection, large dynamic range spectral measurements in a terahertz region are reported. Our system realized the detection sensitivity at a 10 aJ level with a 2-(3-(4-hydroxystyryl)-5,5-dime-thylcyclohex-2-enylidene) malononitrile (OH1) crystal and a dynamic range up to seven orders. Based on this system, we verified the validity of the spectral measurement with tests which were conducted on monohydrate glucose, anhydrous glucose and mixed tablet samples with a thickness of 0.8 mm in 1~3 THz, respectively. Also, a mini coppery elbow tube with an inner diameter of 1 mm was used for the transmission of a terahertz wave to simulate some strip biological tissue samples. By allowing terahertz to transmit through this tube filled with 0.5 g glucose powder, we successfully obtained the absorption spectrum with a minimum transmittance at the absorption peak in the order of 10−4.
Comparative Assessment of Multimodal Sensor Data Quality Collected Using Android and iOS Smartphones in Real-World Settings
Authors: Halabi Ramzi , Selvarajan Rahavi , Lin Zixiong , Herd Calvin , Li Xueying , Kabrit Jana , Tummalacherla Meghasyam , Chaibub Neto Elias , Pratap Abhishek
Published: September 2024

Healthcare researchers are increasingly utilizing smartphone sensor data as a scalable and cost-effective approach to studying individualized health-related behaviors in real-world settings. However, to develop reliable and robust digital behavioral signatures that may help in the early prediction of the individualized disease trajectory and future prognosis, there is a critical need to quantify the potential variability that may be present in the underlying sensor data due to variations in the smartphone hardware and software used by large population. Using sensor data collected in real-world settings from 3000 participants’ smartphones for up to 84 days, we compared differences in the completeness, correctness, and consistency of the three most common smartphone sensors—the accelerometer, gyroscope, and GPS— within and across Android and iOS devices. Our findings show considerable variation in sensor data quality within and across Android and iOS devices. Sensor data from iOS devices showed significantly lower levels of anomalous point density (APD) compared to Android across all sensors (p  <  1 × 10−4). iOS devices showed a considerably lower missing data ratio (MDR) for the accelerometer compared to the GPS data (p  <  1 × 10−4). Notably, the quality features derived from raw sensor data across devices alone could predict the device type (Android vs. iOS) with an up to 0.98 accuracy 95% CI [0.977, 0.982]. Such significant differences in sensor data quantity and quality gathered from iOS and Android platforms could lead to considerable variation in health-related inference derived from heterogenous consumer-owned smartphones. Our research highlights the importance of assessing, measuring, and adjusting for such critical differences in smartphone sensor-based assessments. Understanding the factors contributing to the variation in sensor data based on daily device usage will help develop reliable, standardized, inclusive, and practically applicable digital behavioral patterns that may be linked to health outcomes in real-world settings.
EEG-TCNTransformer: A Temporal Convolutional Transformer for Motor Imagery Brain–Computer Interfaces
Authors: Nguyen Anh Hoang Phuc , Oyefisayo Oluwabunmi , Pfeffer Maximilian Achim , Ling Sai Ho
Published: September 2024

In brain–computer interface motor imagery (BCI-MI) systems, convolutional neural networks (CNNs) have traditionally dominated as the deep learning method of choice, demonstrating significant advancements in state-of-the-art studies. Recently, Transformer models with attention mechanisms have emerged as a sophisticated technique, enhancing the capture of long-term dependencies and intricate feature relationships in BCI-MI. This research investigates the performance of EEG-TCNet and EEG-Conformer models, which are trained and validated using various hyperparameters and bandpass filters during preprocessing to assess improvements in model accuracy. Additionally, this study introduces EEG-TCNTransformer, a novel model that integrates the convolutional architecture of EEG-TCNet with a series of self-attention blocks employing a multi-head structure. EEG-TCNTransformer achieves an accuracy of 83.41% without the application of bandpass filtering.
Can Industrial Spatial Configuration Catalyze the Transition and Advancement of Resource-Dependent Regions? An Empirical Analysis from Heilongjiang Province, China
Authors: Huang Yingli , Lou Guoyi , Ren Yue
Published: September 2024

Resource-based regions are built upon the endowment of abundant natural resources; however, they often fall into development dilemmas due to the depletion of natural resources and ecological environmental regulations. How to achieve transformative development relying on the original industrial base is an important choice for the sustainable development of resource-based regions. This paper takes Heilongjiang Province, a resource-based province in China, as the research area and analyzes its process and strategies of transformative development from the perspective of industrial spatial patterns. The results show that: (1) There is spatial convergence in the development of secondary industry and industry in Heilongjiang Province from 2011 to 2020. The construction industry does not have spatial convergence, and the development of tertiary industry and its sub-industry does not have spatial convergence on the whole. (2) From 2011 to 2022, the development of secondary and tertiary industries in Heilongjiang Province formed a relatively stable spatial correlation network with good accessibility, but the hierarchy of network structure is not obvious, and the correlation strength and stability of the network need to be improved. (3) Harbin, Hegang, Qitaihe and other regions occupy a relatively central position in the spatial association network of the secondary industry; Harbin, Jiamusi, Suihua and other regions are in a leading position in the spatial association network of the tertiary industry which plays an important role as an intermediary bridge; other regions are in a relatively marginal position in the spatial association network of the industrial industry. (4) The increase in network density can effectively promote the development of the secondary and tertiary industries, and the network level and network efficiency will inhibit the development of the secondary and tertiary industries. The increase in network density will narrow the spatial difference of the secondary and tertiary industries, and the decrease in network level and network efficiency can effectively promote the spatial balance of the development of the secondary and tertiary industries. (5) The closer the spatial correlation between each region and other regions, the more benefits from the overall network, the more conducive to the development of local secondary and tertiary industries. The aforementioned results indicate that Heilongjiang Province is constructing a spatial pattern characterized by the complementarity of the primary, secondary and tertiary industries, which serves as a strategy for the transformative development of resource-based regions.
What Type of Public Library Best Supports Agricultural Economic Development? An Empirical Study Based on Rural China
Authors: Zhang Dimeng , Li Jiayao , Ye Yingchi , Zhang Rong , Zou Yuntao
Published: September 2024

Modernizing agricultural economies requires the infusion of knowledge and industrialization, necessitating the bridging of the “digital divide” and “talent gap” between urban and rural areas. Public libraries, as key knowledge dissemination institutions, play a crucial role in this process. This study aimed to explore how the development of such institutions can align with agricultural economic growth. Using China as a case study, where the Rural Revitalization Strategy has been implemented in recent years, including the extensive construction of rural public libraries and other infrastructure, we empirically analyzed the correlation between county-level public libraries and agricultural economic development from 2012 to 2019. The results show that the number of county-level public libraries and their assets, collection sizes, e-books, and professional staff have a significant positive impact on agricultural economics, while non-professional staff and facilities have a negative impact. It is recommended that future rural public library development should focus on enhancing professional standards and advancing digitalization and mobile internet integration, while being cautious about expanding the physical scale and staffing. This study fills a gap in the research on the correlation between rural public libraries and agricultural economics, and the methodology employed has a certain degree of general applicability. However, the applicability of the conclusions may be limited by China’s unique national conditions.
Sustainable Utilization of Hemp Press Cake Flour in Ice Cream Production: Physicochemical, Rheological, Textural, and Sensorial Properties
Authors: Lučan Čolić Mirela , Jukić Marko , Nakov Gjore , Lukinac Jasmina , Antunović Martina
Published: September 2024

The aim of this study was to investigate the influence of replacing skim milk powder with hempseed press cake on the quality properties of ice cream. Four ice cream mix formulations were developed, three with hemp press cake (25.0%, 37.5% and 50.0% milk powder replacement) and one control sample. The physicochemical (basic composition, pH, titratable acidity, water activity) and rheological properties of the ice cream mixes and the fat destabilization index, overrun, texture profile, colour, and sensory attributes of the ice cream were analysed. The results showed that the partial replacement of milk powder with hemp flour had no significant effect on the total values of the main components of the ice cream; only the origin of the nutrients was changed, which affected the properties of the samples. In the enriched samples, a decrease in acidity (from 0.146% to 0.133% LA) and fat destabilization (43.70 to 26.84%); an increase in viscosity (from 1.319 to 1.908 Pa sn), thixotropy (from 1682.00 to 2120.50 Pa/s), overrun (from 26.83 to 35.00%) and hardness (from 6833.12 to 14,660.06 g); as well as a change in colour to darker shades of red were observed. Although the incorporation of hempseed cake led to a decrease in sensory scores (from 7.57 to 6.47–5.63 on the hedonic scale), all samples were rated as acceptable. This study demonstrated that hemp press cake can be utilized as a functional and sustainable ingredient in ice cream production, providing additional nutritional benefits and creating a novel sensory experience for consumers.
Environmental Simulation Model Using System Dynamics to Estimate Air Pollution: A Case Study of Mexico City Metropolitan Area
Authors: Godínez Cárdenas Héctor Manuel , Miranda Pérez Argelia Fabiola , Ramírez Portilla Andrés , Lezama León Myrna Hortencia
Published: September 2024

Air pollution in megacities worldwide has been a severe public health and environmental problem; it contributes to climate change and threatens life. Among all services, the transport sector accounts for most of these pollutants. However, despite the strategies implemented to reduce these pollutants, mitigate their effects, and promote prosperity and sustainability, emission reduction targets remain unmet, causing the average global temperatures to keep increasing. In this study, the air pollution in the Mexico City Metropolitan Area (MCMA) is estimated through the design of an environmental simulation model using system dynamics, which constitutes a possibility for authorities to foresee the evolution of air quality in MCMA by assessing the emissions from the transport sector from a holistic perspective, based on the region DESTEP analysis factors. Simulation results estimate a more significant reduction than predicted by the local government’s current forecast; this emission reduction would be up to 106% lower for PM10, 176% for PM2.5, 34% for NOx, and 17% for VOC. The conclusion demonstrated that one of the main factors with the most significant impact on the control and reduction of emissions is the use and promotion of public transportation, along with the improvement of its road infrastructure.
Assessment of the Spatio-Temporal Dynamics in Urban Green Space via Intensity Analysis and Landscape Pattern Indices: A Case Study of Taiyuan, China
Authors: Liu Yang , Yusof Mohd Johari Mohd , Rehan Balqis Mohamed , Kasim Junainah Abu
Published: September 2024

Urban green space (UGS) is a crucial physical area that supports the functioning of urban ecosystems, and its changes affect urban ecological balance. In order to accurately analyze the dynamic processes and transfer targets of UGS during urbanization, this study proposes a new method of UGS assessment based on multi-temporal Landsat remote sensing data. This method is integrated with intensity analysis and landscape pattern indices so as to explore the spatio-temporal dynamics of the evolution process, landscape pattern, and driving forces of UGS from 2000 to 2022 in the resource-based city of Taiyuan in central China. The results of the case study show that rapid urbanization brought about a continuous reduction in UGS in the study area, but the trend of decreasing gradually slowed down; UGS patches have become more dispersed and isolated, bare land has been targeted for both gains and losses of UGS, and ecological restoration of bare land mitigated the rapid reduction of UGS. The results of this study not only confirm the applicability of this methodology for monitoring and assessing the evolution of UGS, but also reveal the identification of the targeting or avoidance of other categories during the conversion of UGS. Thus, the potential factors influencing changes in UGS can be analyzed to guide and safeguard sustainable development.
Navigating Transitions: How Electric Vehicle Sharing Is Shaping Sustainable Mobility in Chinese Cities
Authors: Yang Tiansheng , Peattie Ken , Skeete Jean-Paul , Koenig-Lewis Nicole
Published: September 2024

Urban mobility has a relatively high potential for radical change and plays an important role in building more sustainable cities. This paper investigates the role of business models and local conditions in the transitions towards sustainable urban mobility through a study set in the context of Chinese cities that focuses on the emergence of electric vehicle-sharing services (EVSSs) as a sustainable mobility innovation. Transitions theory and its multi-level perspective has been adopted as a theoretical framework for this research. A case study methodology was employed comprising semi-structured interviews with 26 respondents. These comprised EVSS providers, other mobility service providers, and other stakeholders including local and national governments bodies. It is based in the Yangtze River Delta Economic Zone (YRDEZ), which includes the major cities of Shanghai, Hangzhou, and Suzhou that have been at the forefront of EVSS adoption and innovation. The findings highlight the dynamic interplay between business models, regulatory environments, and urban settings, providing a comprehensive framework for understanding the socio-technical shifts necessary for fostering more sustainable urban mobility. The study also demonstrates the importance of a range of research disciplines for understanding the processes of sustainability transitions, whilst also revealing some limitations of transitions theory and the multi-level perspective for analysing and understanding transitions.
Impact of Corporate Social Responsibility on Organizational Resilience in Construction Firms—A Study from China
Authors: Ma Teng , Wang Huiling , Qu Ying
Published: September 2024

Construction industry enterprises involve many stakeholders and are highly vulnerable to systemic risk. Whether managers can pay attention to the implementation of social responsibility in construction enterprises and achieve the synergistic development of stakeholders and organizational resilience is crucial to the future of their enterprises. However, how construction companies can achieve a resilient organization by fulfilling social responsibility is still a “black box problem”. Based on a fixed effects model that eliminates time trends from influencing the results, this study explores the role of the mechanism in the relationships among CSR performance, disclosure, and organizational resilience in the construction industry. This study finds that the disclosure of CSR reports enhances organizational resilience in the construction industry, making companies more resistant and resilient. Subdividing the different dimensions of CSR reveals that the fulfillment of social responsibility to shareholders, society, and employees has a significant effect on the organizational resilience of construction firms. This study deepens the understanding of the relationship between CSR activities and the organizational resilience of construction firms, contributing to the theoretical foundations and managerial references for achieving sustainable corporate development.
Floating Photovoltaic Plant Monitoring: A Review of Requirements and Feasible Technologies
Authors: Bossi Silvia , Blasi Luciano , Cupertino Giacomo , dell’Erba Ramiro , Cipollini Angelo , De Vito Saverio , Santoro Marco , Di Francia Girolamo , Tina Giuseppe Marco
Published: September 2024

Photovoltaic energy (PV) is considered one of the pillars of the energy transition. However, this energy source is limited by a power density per unit surface lower than 200 W/m2, depending on the latitude of the installation site. Compared to fossil fuels, such low power density opens a sustainability issue for this type of renewable energy in terms of its competition with other land uses, and forces us to consider areas suitable for the installation of photovoltaic arrays other than farmlands. In this frame, floating PV plants, installed in internal water basins or even offshore, are receiving increasing interest. On the other hand, this kind of installation might significantly affect the water ecosystem environment in various ways, such as by the effects of solar shading or of anchorage installation. As a result, monitoring of floating PV (FPV) plants, both during the ex ante site evaluation phase and during the operation of the PV plant itself, is therefore necessary to keep such effects under control. This review aims to examine the technical and academic literature on FPV plant monitoring, focusing on the measurement and discussion of key physico-chemical parameters. This paper also aims to identify the additional monitoring features required for energy assessment of a floating PV system compared to a ground-based PV system. Moreover, due to the intrinsic difficulty in the maintenance operations of PV structures not installed on land, novel approaches have introduced autonomous solutions for monitoring the environmental impacts of FPV systems. Technologies for autonomous mapping and monitoring of water bodies are reviewed and discussed. The extensive technical literature analyzed in this review highlights the current lack of a cohesive framework for monitoring these impacts. This paper concludes that there is a need to establish general guidelines and criteria for standardized water quality monitoring (WQM) and management in relation to FPV systems.
Sustainability Assessment of Harvesting Rainwater and Air-Conditioning Condensate Water in Multi-Family Residential Buildings under Various Conditions in Israel—A Simulation Study
Authors: Opher Tamar , Friedler Eran
Published: September 2024

The environmental impacts and water savings of different configurations of non-potable domestic water use (toilet flushing and laundry), sourced from rainwater harvesting (RWH) and air-conditioning condensate water (ACWH), in multi-family buildings in Israel are examined. Two building types differing in specific roof areas, and three climatic sub-regions were modeled. RWH satisfied 23 and 46% of the water demand for toilet flushing and laundry in high-rise and low-rise buildings, respectively. Air conditioning is used almost daily during Israel’s hot and dry summers. Hence, the combined RWH-ACWH system saved 42 and 64% in high- and low-rise buildings, respectively. Displacing desalinated seawater, a significant water source in Israel, with alternative water sources lowered the environmental impacts with an increase in storage, up to a certain volume, beyond which impacts started rising. The same infrastructure is used during winter for RWH and for ACWH during summer; thus, combining the two exhibits significant water savings, with marginal extra costs while lowering the environmental impacts.
Effects of Dairy Cattle Slurry Application on Alfalfa Biomass: Photosynthetic Characteristics and Nitrogen and Phosphorus Use Efficiency
Authors: Shi Huixian , Huang Yanqin , Zhu Jinghua , Du Huiying , Zhai Zhongwei
Published: September 2024

With the rapid development of the animal farming industry in China, the large amount of manure has caused a systematic environmental problem, while the demand for high-quality feed continues to increase. The application of dairy cattle slurry to alfalfa fields is a simple and inexpensive solution to the problems above. A repacked soil column study was conducted to investigate the effect of slurry nitrogen (N) on alfalfa biomass, as well as its photosynthetic characteristics. Dairy cattle slurry N or mineral fertilizer N was applied in two dressings at the first cut, with a target amount of 90 kg ha−1. A non-fertilization control (CK), a single mineral fertilizer N (MIN), and a slurry substitution for mineral N fertilizers (with equivalent N rate: FPS, 50% N from dairy cattle slurry; SLU, 100% N from dairy cattle slurry) were used. The results show that the slurry N increased the alfalfa biomass by 16.40–36.36% and the SPAD value by 30.27–61.34% with FPS and SLU treatments, respectively. Compared to the CK treatment, the FPS and SLU treatments meaningfully increased the net photosynthetic rate by 19.97–60.04% and 3.03–89.48%, the stomatal conductance by 10.53–57.14% and 15.38–88.89%, the intercellular CO2 concentration by 5.78–24.92% and 7.21–32.53%, and the transpiration rate by 13.16–103.50% and 16.44–111.19%. More specifically, compared with the CK treatment, the N absorption of the SLU treatment increased by 6.78–12.30%, and the use efficiency increased by 30.98–46.60% in the SLU treatment. Similarly, phosphorus (P) absorption of the SLU treatment increased by 36.73–52.57%, and the use efficiency increased by 30.98–46.60%. Overall, the dairy cattle slurry N was utilized efficiently as mineral N for alfalfa biomass, improved the photosynthetic characteristics of alfalfa leaves, and increased the N and P use efficiency. Our results clarify the optimal amount of dairy cattle slurry to be applied and provide a scientific basis for the use of dairy cattle slurry in agricultural systems.
Financial Literacy and Financial Well-Being in Rural Households in Ghana: The Role of Financial Information Consumption
Authors: Kuutol Peter Kwame , Mbonigaba Josue , Garidzirai Rufaro
Published: September 2024

While financial literacy is crucial in improving the population’s financial well-being, its effectiveness can be enhanced by exposure to financial information. This paper investigates the nexus between financial literacy, financial information consumption, and financial well-being in rural Ghana, framed within the perspectives of prospect theory and resource dependency theory. The study employed cross-sectional data from a survey of 663 rural households using simple random and cluster sampling with reflective-reflective constructs. The data were analysed using partial least squares structural equation modelling. The findings reveal that financial literacy and financial information consumption significantly enhance financial well-being among rural households in Ghana. Financial literacy also promotes financial information consumption. Notably, financial literacy’s impact on financial well-being is stronger when mediated by the consumption of financial information. These findings underscore the importance of improving financial literacy and information access to uplift financial well-being in rural areas. Moreover, the study highlights that financial literacy education is crucial as it plays a mediating role; recipients of financial education experience a more substantial impact. Such findings emphasise the importance of acquiring financial knowledge and effectively processing financial information to achieve financial prosperity, particularly in rural areas. These findings should motivate individuals, especially those in rural areas, to process financial information successfully rather than merely acquiring financial knowledge to attain financial prosperity.