bluuebunny/crossref_metadata_2025 huggingface dataset:
Spatiotemporal Distribution, Sources, and Impact on Atmospheric Oxidation of Reactive Nitrogen Oxides in the North China Plain Agricultural Regions in Summer
Authors: Wei Shaocong , Hong Qianqian , Tan Wei , Chen Jian , Li Tianhao , Wang Xiaohan , Xue Jingkai , Fang Jiale , Liu Chao , Tanvir Aimon , Xing Chengzhi , Liu Cheng
Published: August 2024

The lack of vertical observation of reactive nitrogen oxides in agricultural areas has posed a significant challenge in fully understanding their sources and impacts on atmospheric oxidation. Ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations were conducted in the agricultural regions of the North China Plain (NCP) during the summer of 2019 to measure the vertical distributions of aerosols, nitrogen dioxide (NO2), and nitrous acid (HONO). This study aimed at revealing the spatiotemporal distribution, sources, and environmental effects of reactive nitrogen oxides in the NCP agricultural areas. Our findings indicated that the vertical profiles of aerosols and NO2 exhibited a near-Gaussian distribution, with distinct peak times occurring between 8:00–10:00 and 16:00–18:00. HONO reached its maximum concentration near the surface around 8:00 in the morning and decreased exponentially with altitude. After sunrise, the concentration of HONO rapidly decreased due to photolysis. Additionally, the potential source contribution function (PSCF) was used to evaluate the potential sources of air pollutants. The results indicated that the main potential pollution sources of aerosols were located in the southern part of the Hebei, Shanxi, Shandong, and Jiangsu provinces, while the potential pollution sources of NO2 were concentrated in the Beijing–Tianjin–Hebei region. At altitudes exceeding 500 m, the heterogeneous reactions of NO2 on aerosol surfaces were identified as one of the important contributors to the formation of HONO. Furthermore, we discussed the production rate of hydroxyl radicals (OH) from HONO photolysis. It was found that the production rate of OH from HONO photolysis decreased with altitude, with peaks occurring in the morning and late afternoon. This pattern was consistent with the variations in HONO concentration, indicating that HONO was the main contributor to OH production in the agricultural regions of the NCP. This study provides a new perspective on the sources of active nitrogen in agricultural regions and their contribution to atmospheric oxidation capacity from a vertical perspective.
Illumination Model of Lunar Terrain around Lunar Laser Retroreflectors Based on LOLA Data
Authors: Li Yuzuo , Han Xida , Wu Xianlin , Lin Xudong , Zhao Qianji , Zhou Chengkai , Yan Yong
Published: August 2024

Lunar laser retroreflectors are a crucial target for lunar laser ranging (LLR). During LLR operations at all lunar laser ranging stations, the considerable distance between the Earth and the Moon makes it impossible to visually identify the position of the lunar laser retroreflectors within the camera’s field of view (FOV). Typically, we use the camera to observe various lunar surface features illuminated by sunlight, and then we align the telescope with the corresponding area for accurate positioning. Based on lunar digital elevation model (DEM) data and INPOP19a planetary ephemeris, our research presents an illumination model for the lunar terrain around the lunar laser retroreflectors, suitable for the 1’ camera FOV constraint at the TianQin laser ranging station. The illumination model is linked to universal time coordinated (UTC) time and DEM data. By inputting the UTC time, the illumination conditions of the terrain surrounding the five lunar laser retroreflectors can be determined for that specific time. The terrain surrounding the Apollo 15 and Lunokhod 2 retroreflectors exhibits distinctive terrain features under illumination, making them easier to locate compared to other retroreflectors. This illumination model provides a better solution to the problem of aligning laser ranging stations to lunar laser retroreflectors.
Cascade Clutter Suppression Method for Airborne Frequency Diversity Array Radar Based on Elevation Oblique Subspace Projection and Azimuth-Doppler Space-Time Adaptive Processing
Authors: Lu Rongwei , Wu Yifeng , Zhang Lei , Chen Ziyi
Published: August 2024

Airborne Frequency Diversity Array (FDA) radar operating at a high pulse repetition frequency encounters severe range-ambiguous clutter. The slight frequency increments introduced by the FDA result in angle and range coupling. Under these conditions, conventional space-time adaptive processing (STAP) often exhibits diminished performance or fails, complicating target detection. This paper proposes a method combining elevation oblique subspace projection with azimuth-Doppler STAP to suppress range-ambiguous clutter. The method compensates for the quadratic range dependence by analyzing the relationship between elevation frequency and range. It uses an elevation oblique subspace projection technique to construct an elevation adaptive filter, which separates clutter from ambiguous regions. Finally, residual clutter suppression is achieved through azimuth-Doppler STAP, enhancing target detection performance. Simulation results demonstrate that the proposed method effectively addresses range dependence and ambiguity issues, improving target detection performance in complex airborne FDA radar environments.
Spatio-Temporal Pruning for Training Ultra-Low-Latency Spiking Neural Networks in Remote Sensing Scene Classification
Authors: Li Jiahao , Xu Ming , Chen He , Liu Wenchao , Chen Liang , Xie Yizhuang
Published: August 2024

In remote sensing scene classification (RSSC), restrictions on real-time processing on power consumption, performance, and resources necessitate the compression of neural networks. Unlike artificial neural networks (ANNs), spiking neural networks (SNNs) convey information through spikes, offering superior energy efficiency and biological plausibility. However, the high latency of SNNs restricts their practical application in RSSC. Therefore, there is an urgent need to research ultra-low-latency SNNs. As latency decreases, the performance of the SNN significantly deteriorates. To address this challenge, we propose a novel spatio-temporal pruning method that enhances the feature capture capability of ultra-low-latency SNNs. Our approach integrates spatial fundamental structures during the training process, which are subsequently pruned. We conduct a comprehensive evaluation of the impacts of these structures across classic network architectures, such as VGG and ResNet, demonstrating the generalizability of our method. Furthermore, we develop an ultra-low-latency training framework for SNNs to validate the effectiveness of our approach. In this paper, we successfully achieve high-performance ultra-low-latency SNNs with a single time step for the first time in RSSC. Remarkably, our SNN with one time step achieves at least 200 times faster inference time while maintaining a performance comparable to those of other state-of-the-art methods.
Moving Real-Target Imaging of a Beam-Broaden ISAL Based on Orthogonal Polarization Receiver and Along-Track Interferometry
Authors: Gao Jinghan , Li Daojing , Wu Jiang , Cui Anjing , Wu Shumei
Published: August 2024

In response to the application requirement of wide-range high-resolution imaging of non-cooperative moving real targets by inverse synthetic-aperture ladar (ISAL), experiments were conducted on the depolarization effect of target materials, and the polarization selection of ISAL receiving and transmitting channels was discussed. Considering the impact of target depolarization and the demand for along-track interferometry, combined with beam-broaden and high-gain amplifiers, an ISAL system design method that can stably image multiple non-cooperative real targets has been proposed. Under the condition of broadening the transmitting and receiving beams to 3° in the elevation direction for non-cooperative moving vehicles, echo data with a duration of 1 s is obtained. The spatial correlation algorithm combined with along-track interferometry is used to estimate the vibration phase error. The sub-aperture Range-Doppler algorithm is used for imaging. The ISAL imaging results of the moving vehicle validated the high-resolution imaging ability of ISAL and its potential for stable imaging of non-cooperative moving real targets.
Quantification of the Uncertainty in Ultrasonic Wave Speed in Concrete: Application to Temperature Monitoring with Embedded Transducers
Authors: Hariri Rouba , Chaix Jean-Francois , Shokouhi Parisa , Garnier Vincent , Saïdi-Muret Cécile , Durand Olivier , Abraham Odile
Published: August 2024

This article presents an overall examination of how small temperature fluctuations affect P-wave velocity (Vp) measurements and their uncertainties in concrete using embedded piezoelectric transducers. This study highlights the fabrication of custom transducers tailored for long-term concrete monitoring. Accurate and reliable estimation of ultrasonic wave velocities is challenging, since they can be impacted by multiple experimental and environmental factors. In this work, a reliable methodology incorporating correction models is introduced for the quantification of uncertainties in ultrasonic absolute and relative velocity measurements. The study identifies significant influence quantities and suggests uncertainty estimation laws, enhancing measurement accuracy. Determining the onset time of the signal is very time-consuming if the onset is picked manually. After testing various methods to pinpoint the onset time, we selected the Akaike Information Criterion (AIC) due to its ability to produce sufficiently reliable results. Then, signal correlation was used to determine the influence of temperature (20 °C to 40 °C) on Vp in different concrete samples. This technique proved effective in evaluating velocity changes, revealing a persistent velocity decrease with temperature increases for various concrete compositions. The study demonstrated the capability of ultrasonic measurements to detect small variations in the state of concrete under the influence of environmental variables like temperature, underlining the importance of incorporating all influencing factors.
A Linear Regression Approach for Best Scanline Determination in the Object to Image Space Transformation Using Pushbroom Images
Authors: Ahooei Nezhad Seyede Shahrzad , Valadan Zoej Mohammad Javad , Youssefi Fahimeh , Ghaderpour Ebrahim
Published: August 2024

The use of linear array pushbroom images presents a new challenge in photogrammetric applications when it comes to transforming object coordinates to image coordinates. To address this issue, the Best Scanline Search/Determination (BSS/BSD) field focuses on obtaining the Exterior Orientation Parameters (EOPs) of each individual scanline. Current solutions are often impractical for real-time tasks due to their high time requirements and complexities. This is because they are based on the Collinearity Equation (CE) in an iterative procedure for each ground point. This study aims to develop a novel BSD framework that does not need repetitive usage of the CE with a lower computational complexity. The Linear Regression Model (LRM) forms the basis of the proposed BSD approach and uses Simulated Control Points (SCOPs) and Simulated Check Points (SCPs). The proposed method is comprised of two main steps: the training phase and the test phase. The SCOPs are used to calculate the unknown parameters of the LR model during the training phase. Then, the SCPs are used to evaluate the accuracy and execution time of the method through the test phase. The evaluation of the proposed method was conducted using ten various pushbroom images, 5 million SCPs, and a limited number of SCOPs. The Root Mean Square Error (RMSE) was found to be in the order of ten to the power of negative nine (pixel), indicating very high accuracy. Furthermore, the proposed approach is more robust than the previous well-known BSS/BSD methods when handling various pushbroom images, making it suitable for practical and real-time applications due to its high speed, which only requires 2–3 s of time.
The Estimation of Knee Medial Force with Substitution Parameters during Walking and Turning
Authors: Liu Shizhong , Wang Ziyao , Chen Jingwen , Xu Rui , Ming Dong
Published: August 2024

Purpose: Knee adduction, flexion moment, and adduction angle are often used as surrogate parameters of knee medial force. To verify whether these parameters are suitable as surrogates under different walking states, we investigated the correlation between knee medial loading with the surrogates during walking and turning. Methods: Sixteen healthy subjects were recruited to complete straight walk (SW), step turn (ST), and crossover turn (CT). Knee joint moments were obtained using inverse dynamics, and knee medial force was computed using a previously validated musculoskeletal model, Freebody. Linear regression was used to predict the peak of knee medial force with the peaks of the surrogate parameters and walking speed. Results: There was no significant difference in walking speed among these three tasks. The peak knee adduction moment (pKAM) was a significant predictor of the peak knee medial force (pKMF) for SW, ST, and CT (p < 0.001), while the peak knee flexion moment (pKFM) was only a significant predictor of the pKMF for SW (p = 0.034). The statistical analysis showed that the pKMF increased, while the pKFM and the peak knee adduction angle (pKAA) decreased significantly during CT compared to those of SW and ST (p < 0.001). The correlation analysis indicated that the knee parameters during SW and ST were quite similar. Conclusions: This study investigated the relationship between knee medial force and some surrogate parameters during walking and turning. KAM was still the best surrogate parameter for SW, ST, and CT. It is necessary to consider the type of movement when comparing the surrogate predictors of knee medial force, as the prediction equations differ significantly among movement types.
An Ionization-Based Aerosol Sensor and Its Performance Study
Authors: Zhang Yong , Wang Chunqi , Xie Liang , Peng Yuqi , Wang Ruizhe
Published: August 2024

In recent years, with the rapid development of new energy vehicles, the safety issues of lithium-ion batteries have attracted attentions from all sectors of society. Research has found that during the thermal runaway process of lithium-ion batteries, aerosol emissions usually occur earlier than other gases. Accurate and timely measurement of these aerosol concentrations can help to warn the power battery pack fires. However, existing aerosol sensors are unable to meet the requirements of real-time monitoring and high precision. This article proposes an ionization mechanism based aerosol sensor that works at principles of field emission, field charging and gas discharge, and investigates its static and dynamic response characteristics. The sensor is manufactured and assembled using Microelectro Mechanical Systems processing technology. The sensor exhibits superior performances in terms of range, sensitivity, nonlinearity, repeatability, response time, and other aspects. The study provides a new solution for current aerosol detection with great potential for application.
Development of Highly Efficient Lamb Wave Transducers toward Dual-Surface Simultaneous Atomization
Authors: Gai Chenhui , Ma Qinghe , Ning Jia , Ding Yizhan , Lei Yulin , Li Honggeng , Guo Chunhua , Hu Hong
Published: August 2024

Highly efficient surface acoustic wave (SAW) transducers offer significant advantages for microfluidic atomization. Aiming at highly efficient atomization, we innovatively accomplish dual-surface simultaneous atomization by strategically positioning the liquid supply outside the IDT aperture edge. Initially, we optimize Lamb wave transducers and specifically investigate their performance based on the ratio of substrate thickness to acoustic wavelength. When this ratio h/λ is approximately 1.25, the electromechanical coupling coefficient of A0-mode Lamb waves can reach around 5.5% for 128° Y-X LiNbO3. We then study the mechanism of droplet atomization with the liquid supply positioned outside the IDT aperture edge. Experimental results demonstrate that optimized Lamb wave transducers exhibit clear dual-surface simultaneous atomization. These transducers provide equivalent amplitude acoustic wave vibrations on both surfaces, causing the liquid thin film to accumulate at the edges of the dual-surface and form a continuous mist.
Improved MobileNet V3-Based Identification Method for Road Adhesion Coefficient
Authors: Li Binglin , Xu Jianqiang , Lian Yufeng , Sun Fengyu , Zhou Jincheng , Luo Jun
Published: August 2024

To enable the timely adjustment of the control strategy of automobile active safety systems, enhance their capacity to adapt to complex working conditions, and improve driving safety, this paper introduces a new method for predicting road surface state information and recognizing road adhesion coefficients using an enhanced version of the MobileNet V3 model. On one hand, the Squeeze-and-Excitation (SE) is replaced by the Convolutional Block Attention Module (CBAM). It can enhance the extraction of features effectively by considering both spatial and channel dimensions. On the other hand, the cross-entropy loss function is replaced by the Bias Loss function. It can reduce the random prediction problem occurring in the optimization process to improve identification accuracy. Finally, the proposed method is evaluated in an experiment with a four-wheel-drive ROS robot platform. Results indicate that a classification precision of 95.53% is achieved, which is higher than existing road adhesion coefficient identification methods.
Adjacent Image Augmentation and Its Framework for Self-Supervised Learning in Anomaly Detection
Authors: Kwon Gi Seung , Choi Yong Suk
Published: August 2024

Anomaly detection has gained significant attention with the advancements in deep neural networks. Effective training requires both normal and anomalous data, but this often leads to a class imbalance, as anomalous data is scarce. Traditional augmentation methods struggle to maintain the correlation between anomalous patterns and their surroundings. To address this, we propose an adjacent augmentation technique that generates synthetic anomaly images, preserving object shapes while distorting contours to enhance correlation. Experimental results show that adjacent augmentation captures high-quality anomaly features, achieving superior AU-ROC and AU-PR scores compared to existing methods. Additionally, our technique produces synthetic normal images, aiding in learning detailed normal data features and reducing sensitivity to minor variations. Our framework considers all training images within a batch as positive pairs, pairing them with synthetic normal images as positive pairs and with synthetic anomaly images as negative pairs. This compensates for the lack of anomalous features and effectively distinguishes between normal and anomalous features, mitigating class imbalance. Using the ResNet50 network, our model achieved perfect AU-ROC and AU-PR scores of 100% in the bottle category of the MVTec-AD dataset. We are also investigating the relationship between anomalous pattern size and detection performance.
Datafication of Care: Security and Privacy Issues with Health Technology for People with Diabetes
Authors: Bertolazzi Alessia , Marzęda-Młynarska Katarzyna , Kięczkowska Justyna , Zanier Maria Letizia
Published: August 2024

Through the conceptual framework of datafication, dataism and dataveillance, this study investigates beliefs and attitudes regarding datafication and the related privacy and security concerns among individuals with Type 1 diabetes. Qualitative research was conducted through interviews among fifty-two individuals with Type 1 diabetes in Poland and Italy. The findings reveal a dynamic interplay between self-discipline and empowerment. The majority of interviewees emphasized the benefits of technologies for gaining a better understanding of their health condition and for more effective disease management. However, a minority of interviewees perceived the negative effects of datafication, including dataveillance, which leads to hyper-control of the disease, and dataism, characterized by excessive reliance on and dependency on technology. Critical beliefs about technologies fuelled rejection attitudes, leading some interviewees to suspend or abandon their use. Lastly, reflexivity on privacy and security issues appears to be low, particularly among older individuals with lower levels of education and socioeconomic status. This results in a poor understanding and underestimation of the potential risks associated with security and privacy. The findings increase the understanding of the factors that can facilitate or hinder the adoption of technology among people with diabetes.
Applying and Improving Pyranometric Methods to Estimate Sunshine Duration for Tropical Site
Authors: Fanjirindratovo Tovondahiniriko , Calogine Didier , Chau Oanh , Ramiarinjanahary Olga
Published: August 2024

The aim of this paper is to apply all the existing pyranometric methods to estimate the sunshine duration from global solar irradiation in order to find the most suitable method for a tropical site in its original form. Then, in a second step, one of these methods will be optimized to effectively fit tropical sites. Five methods in the literature (Step algorithm, Carpentras Algorithm, Slob and Monna Algorithm, Slob and Monna 2 Algorithm, and linear algorithm) were applied with eleven years of global and diffuse solar radiation data. As a result, with regard to its original form, the step algorithm is in the first rank. But in the second step, after improving its main coefficients, the Carpentras Algorithm was found to be the best algorithm for tropical sites in the southern hemisphere.
The User Perception at the Urban Scale, Local Characteristics and Recommendations: The Example of Morphou in Northern Cyprus
Authors: Özal Atiye Bıçak , Turkan Zihni
Published: August 2024

The recent changes and developments in the physical, economic, and population structure in Northern Cyprus have led to the emergence of ideas about the scale of cities and protection of urban life. The pursuit of improving the quality of life in settlements while preserving the local features channel the solution proposals towards the practice of cittaslow. This study aims to examine the district of Morphou and develop recommendations based on the aforementioned approaches. Hence, the current physical status of the city was analysed, and several arguments were developed regarding the intervention zones towards the principles of cittaslow in accordance with the public perception. In our study, physical analyses and qualitative and quantitative methods, including surveys, were used within the framework of a literature review and field study. In consideration with analyses and surveys, the existing status and future perspective of Morphou were identified, and some recommendations were presented for the city regarding the concept of cittaslow. As a result of the research and studies carried out, the location, physical structure, population, transportation facilities, and agriculturally suitable topography of Güzelyurt city reveal that it is an important factor for this place to be a slow city. In addition, all the analyses we conducted regarding the city and the results of the participants’ evaluations show that the city complies with the cittaslow criteria.
Exploring Sustainable Development Goal Research Trajectories in Small Island Developing States
Authors: Raman Raghu , Leal Filho Walter , Martin Hector , Ray Suparna , Das Dayana , Nedungadi Prema
Published: August 2024

This study investigates the alignment of research on small island developing states (SIDSs) with sustainable development goals (SDGs), identifying key focus areas, interdisciplinary connections, research gaps, thematic clusters, and insights from case studies. By employing a mixed methods research design that combines quantitative scientometric analysis with qualitative case studies, this unique study examines the nexus between SIDS research and its contribution to SDGs. The findings reveal that SDG 13 (climate action) and SDG 14 (life below water) dominate SIDS research, emphasizing the need to address climate challenges and marine biodiversity. Economic aspects are underscored by SDG 8 (decent work and economic growth) and SDG 12 (responsible consumption and production), while urban development and inequality are addressed through SDG 11 (sustainable cities and communities) and SDG 10 (reduced inequalities). Renewable energy and food security are focal points in SDG 7 (affordable and clean energy) and SDG 2 (zero hunger). Moderate research outputs in SDG 3 (good health and well-being) and SDG 15 (life on land) suggest a holistic approach, while lower activity in SDGs 16 (peace), 6 (clean water), 4 (education), and 5 (gender) presents opportunities for further exploration. SIDS research is highly interdisciplinary, as seen through three distinct SDG networks: foundational social goals, economic and environmental objectives, and environmental preservation. The thematic clusters in SIDS research include environmental challenges and adaptation, public health, and socioeconomic development, and energy policy and environmental economics. Case studies from various SIDSs illustrate the complex interplay between environmental sustainability, economic development, and social well-being, highlighting the importance of tailored approaches, international cooperation, and local community engagement. These findings underscore the need for integrated research strategies to address the complex challenges of SIDSs, emphasizing climate action, sustainable economic practices, and comprehensive development approaches to achieve the SDGs effectively.
AI Capability and Sustainable Performance: Unveiling the Mediating Effects of Organizational Creativity and Green Innovation with Knowledge Sharing Culture as a Moderator
Authors: Gazi Md. Abu Issa , Rahman Md. Kazi Hafizur , Masud Abdullah Al , Amin Mohammad Bin , Chaity Naznin Sultana , Senathirajah Abdul Rahman bin S. , Abdullah Masuk
Published: August 2024

The purpose of this study is to investigate the role of AI capability (AIC) on organizational creativity (OC), green innovation (GI), and sustainable performance (SP). It also aims to investigate the mediating roles of OC and GI, as well as the moderating role of knowledge sharing culture (KNC). This study used quantitative methodology and utilized a survey to collect data from 421 employees in different organizations in Bangladesh. We used the structural equation modeling (SEM) technique to analyze the data. This study finds that AI capability significantly influences OC, GI, and SP. OC and GI work as mediators, and KNC serves as a moderator among the suggested relationships. This study is notable for its novelty in examining multiple unexplored aspects in the current body of research. This research also provides valuable insights for policymakers and practitioners regarding the effective integration of AI to enhance organizational competitiveness.
Effective Fuel Cell Electrocatalyst with Ultralow Pd Loading on Ni-N-Doped Graphene from Upcycled Water Bottle Waste
Authors: Balčiūnaitė Aldona , Elessawy Noha A. , Šljukić Biljana , Toghan Arafat , Al-Hussain Sami A. , Gouda Marwa H. , Youssef M. Elsayed , Santos Diogo M. F.
Published: August 2024

Environmental pollution due to the excessive consumption of fossil fuels for energy production is a critical global issue. Fuel cells convert chemical energy directly into electricity in a clean and silent electrochemical process, but face challenges related to hydrogen storage, handling, and transportation. The direct borohydride fuel cell (DBFC), utilizing sodium borohydride as a liquid fuel, is a promising alternative to overcome such issues but requires the design of cost-effective nanostructured electrocatalysts. In this study, we synthesized nitrogen-doped graphene anchoring Ni nanoparticles (Ni@NG) by thermal degradation of polyethylene terephthalate bottle waste with urea and metallic Ni, and evaluated it as a sustainable carbon support. Electrocatalysts were prepared by incorporating ultralow amounts (0.09 to 0.27 wt.%) of Pd into the Ni@NG support. The resulting PdNi@NG electrocatalysts were characterized using ICP-OES, XPS, TEM, N2-sorption analysis, XRD, and Raman and FTIR spectroscopy. Voltammetry assessed the materials’ electrocatalytic activity for oxygen reduction and borohydride oxidation reactions in alkaline media, corresponding to the anodic and cathodic reactions in DBFCs. The electrocatalyst with 0.27 wt.% Pd loading (PdNi_15@NG) exhibited the best performance for both reactions. Consequently, it was employed as an anodic and cathodic material in a lab-scale DBFC, achieving a specific power of 3.46 kW gPd−1.
Adaptation of the Workplace for Disabled People—Sustainable Participation in the Labor Market
Authors: Tylutki Krzysztof , Konopacka Żaklina , Woźniak Justyna , Solatycka Dagmara
Published: August 2024

The article concerns the issue of equal opportunities for disabled employees as sustainable participation in the labor market in selected workplaces in Poland. Disabled people are part of society; therefore, they should not be discriminated against by exclusion and allowed to participate fully in public life. This work analyzes scientific publications and fragments of reports, which have made it possible to draw conclusions on what areas of life disabled people have problems with regarding functioning and what should be changed to improve their quality of life. The aim of the article was to analyze examples of facilities used, among others, at universities that allow disabled people to move safety and freely. The substantive scope includes reference to Polish legal regulations regarding the process of employing disabled people in a balanced labor market against the background of statistical data. The article indicates that workplace facilities for disabled people (mobile and visually impaired) have a positive impact on the functioning of the rest of society due to the elimination of architectural barriers.
Evaluation of Machine Learning Models in Air Pollution Prediction for a Case Study of Macau as an Effort to Comply with UN Sustainable Development Goals
Authors: Lei Thomas M. T. , Cai Jianxiu , Molla Altaf Hossain , Kurniawan Tonni Agustiono , Kong Steven Soon-Kai
Published: August 2024

To comply with the United Nations Sustainable Development Goals (UN SDGs), in particular with SDG 3, SDG 11, and SDG 13, a reliable air pollution prediction model must be developed to construct a sustainable, safe, and resilient city and mitigate climate change for a double win. Machine learning (ML) and deep learning (DL) models have been applied to datasets in Macau to predict the daily levels of roadside air pollution in the Macau peninsula, situated near the historical sites of Macau. Macau welcomed over 28 million tourists in 2023 as a popular tourism destination. Still, an accurate air quality forecast has not been in place for many years due to the lack of a reliable emission inventory. This work will develop a dependable air pollution prediction model for Macau, which is also the novelty of this study. The methods, including random forest (RF), support vector regression (SVR), artificial neural network (ANN), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), were applied and successful in the prediction of daily air pollution levels in Macau. The prediction model was trained using the air quality and meteorological data from 2013 to 2019 and validated using the data from 2020 to 2021. The model performance was evaluated based on the root mean square error (RMSE), mean absolute error (MAE), Pearson’s correlation coefficient (PCC), and Kendall’s tau coefficient (KTC). The RF model best predicted PM10, PM2.5, NO2, and CO concentrations with the highest PCC and KTC in a daily air pollution prediction. In addition, the SVR model had the best stability and repeatability compared to other models, with the lowest SD in RMSE, MAE, PCC, and KTC after five model runs. Therefore, the results of this study show that the RF model is more efficient and performs better than other models in the prediction of air pollution for the dataset of Macau.
Fish Farming Activities in Mbanza-Ngungu: Farmer Socio-Professional Profiles, Production Practices, and Improvement Opportunities for Sustainable Aquaculture
Authors: Bambi Langa Lydie , Wamuini Lunkayilakio Soleil , Mafwila Kinkela Patrick , Gennotte Vincent , Rougeot Carole
Published: August 2024

The study describes the distribution of fish farming activities, identifies and characterizes the stakeholders involved, and assesses their level of knowledge and the techniques used for fish farming in the Territory of Mbanza-Ngungu. Furthermore, it proposes avenues to improve the sustainability and profitability of the sector. The methodology relied on a snowball approach to recruit a representative sample of 350 fish farmers. Field visits allowed for detailed data collection through a specially designed questionnaire. The data were analyzed using statistical methods (chi-square test of independence) to identify trends and challenges across the sectors of the territory of Mbanza-Ngungu. The findings revealed that fish farming activities are distributed across the territory of Mbanza-Ngungu, with a male predominance (96%) among fish farmers and a growing trend of this activity among older individuals over 40 years old (48%). Additionally, the results demonstrate that aquaculture is mainly a secondary activity for 97% of respondents, yet it could play a pivotal role in household economic diversification. The study identifies predominant practices, such as extensive farming (89%) and monoculture (92%), while highlighting gaps in pond management and fish farmers’ general knowledge. Challenges are mainly related to feed access (28%), financing (27%), training (23%), and water quality monitoring (100%). The commercial orientation of production towards sales (94%) rather than self-consumption offers an opportunity to improve this sector in this territory. Finally, recommendations are made to enhance the sustainability and profitability of aquaculture in the region, including promoting integrated practices, more efficient pond management practices, and aquaculture product processing.
Sustainable Recovery of the Health of Soil with Old Petroleum Hydrocarbon Contamination through Individual and Microorganism-Assisted Phytoremediation with Lotus corniculatus
Authors: Meištininkas Rimas , Vaškevičienė Irena , Piotrowicz-Cieślak Agnieszka I. , Krupka Magdalena , Žaltauskaitė Jūratė
Published: August 2024

Due to the large number of areas contaminated with TPH, there is significant interest in biological remediation technology research, offering a comprehensive and sustainable approach to soil decontamination and health recovery at the same time. This study aimed to investigate the effectiveness of remediating TPH-contaminated soil (6120 mg kg−1) using Lotus corniculatus along with a microorganism consortium (GTC-GVT/2021) isolated from historic TPH-contaminated sites. This study evaluated the removal of TPH and soil health recovery through changes in soil nutrient content, soil enzymatic activity, and the microbiological community. The growth of L. corniculatus was reduced in TPH-contaminated soil, particularly affecting root biomass by 52.17%. Applying inoculum positively affected total plant biomass in uncontaminated (51.44%) and contaminated (33.30%) soil. The GTC-GVT/2021 inoculum significantly enhanced the degradation of TPH in contaminated soil after 90 days by 20.8% and in conjunction with L. corniculatus by 26.33% compared to the control. The soil enzymatic activity was more pronounced in TPH-contaminated soil treatments, and in most cases, the presence of L. corniculatus and inoculum led to a significantly higher soil enzymatic activity. The cultivation of L. corniculatus and the inoculum resulted in an increased concentration of inorganic P, NH4+, and water-soluble phenols in the soil, while no rise in NO3− was observed.
Influence of Forecrop on Soil Quality Estimated on the Basis of the Growth and Development of Faba Bean and Biochemical Properties of the Soil
Authors: Baćmaga Małgorzata , Wyszkowska Jadwiga , Kucharski Jan
Published: August 2024

Proper crop rotation determines soil fecundity, which is pertinent for successor crops. With this problem in mind, a study was performed to evaluate the interference of spring wheat (Triticum aestivum L. subsp. aestivum), winter wheat (T. aestivum L. subsp. aestivum), maize (Zea mays L.), and winter rape (Brassica napus L.) as forecrops on the increase in faba bean (Vicia faba L.) and the biochemical and physicochemical properties of the soil. Tests with faba bean were performed in pots in the vegetation hall. The pots were filled with soil from under the abovementioned four plant species, and faba bean was grown. Soil unsown with faba bean was also tested to measure the rhizosphere effect. At the beginning of the experiment, and after its completion, enzymatic and physicochemical analyses of the soil were performed. On the 120th day of the study, faba bean was harvested and biometry was performed. The forecrop substantially influenced the biometric features of the faba bean and the biochemical activity of the soil. Faba beans grown in soil under spring wheat and winter wheat had the highest seed yield, while those grown in soil under the winter rape had the lowest yield. The geometric mean of the enzyme activity index was only significantly positively correlated with the number of faba bean seeds and the soil pH, as well as with the seed dry matter yield and the faba bean plant height. Faba bean cultivation increased the soil biochemical activity. The values of the biochemical and physicochemical parameters of the unsown soil were lower compared to the soil sown with faba bean. The conducted research can help to estimate the changes occurring in arable soils and maintain their stability thanks to the use of appropriate bioindicators, which are the soil enzymes. Moreover, the use of a diversified crop rotation in soil cultivation can provide a lot of information about its function, which can ultimately be used for planning the plant rotation, leading to the improvement of the soil structure and fertility, as well as its protection.
Lighting Design for Lanna Buddhist Architecture: A Case Study of Suan Dok Temple, Chiang Mai, Thailand
Authors: Tayhuadong Lattapon , Inkarojrit Vorapat
Published: August 2024

In Chiang Mai, Thailand, there has been an increasing demand for lighting design strategies that enhance tangible and intangible values of cultural heritage destinations. This research explores the role of lighting design in enhancing the cultural experiences and promoting the cultural tourism of Lanna Buddhist architecture. Suan Dok Temple was selected as a case study due to its historical, social, and cultural significance. This research uses qualitative approaches such as field surveys, photographic documentation, and interviews with temple authorities, cultural heritage experts and tourism professionals for the formulation of initial users’ requirements and lighting design criteria and concept. Data analysis suggested that cultural heritage and conservation, cultural tourism management, and operation and maintenance are important factors in heritage lighting design for cultural tourism. Realistic rendering images of lighting design scenes for the temple’s chedis and vihara are generated based on the concept of luminance layers representing the Buddhist cosmology idea. In addition, lighting operation and installation details are developed and presented to stakeholders for discussion on contextual and operational appropriateness. Finally, the study discusses critical lighting design factors for the development of lighting design solutions for heritage sites, particularly Lanna Buddhist temples, contributing to sustainable cultural tourism.
Microbial Production of Biopesticides for Sustainable Agriculture
Authors: Verma Madan Lal , Kumar Ashutosh , Chintagunta Anjani Devi , Samudrala Prashant Jeevan Kumar , Bardin Marc , Lichtfouse Eric
Published: August 2024

Food security is threatened by biotic stress on crops, e.g., from invasive pests, in the context of climate change. Pest control has traditionally been achieved using synthetic pesticides, yet pollution and the persistence and toxicity of some pesticides are inducing food contamination and, in turn, generating public concern. As a consequence, biopesticides are increasingly used, notably for organic crops. For instance, some microorganisms produce biopesticidal compounds such as secondary metabolites or growth-inhibitory enzymes. Most reviews on this topic mainly focus on describing microbial species and their active compounds. However, there are very few studies and reviews describing various process parameters. Here, we review both microbial biopesticides and factors controlling physicochemical conditions for the scaling up of biopesticide production. We present biopesticides from bacteria (e.g., Bacillus thuringiensis), algae, fungi, and viruses and review the biocontrol mechanisms and applications of commercial biopesticides. The benefits of genetic engineering for enhancing activity and drawbacks such as commercialization are also discussed.
Selective Phenolics Recovery from Aqueous Residues of Pyrolysis Oil through Computationally Designed Green Solvent
Authors: Qaisar Amna , Bartolucci Lorenzo , Cancelliere Rocco , Chemmangattuvalappil Nishanth G. , Mele Pietro , Micheli Laura , Paialunga Elisa
Published: August 2024

Leveraging advanced computational techniques, this study introduces an innovative hybrid computational-experimental approach for the recovery of hydroquinone and p-benzoquinone from the aqueous residue of pyrolysis oil derived from spent coffee grounds, offering a sustainable pathway for value-added chemicals recovery. A screw-type reactor operating within the temperature range of 450–550 °C was utilized for the conversion of spent coffee grounds into pyrolysis oil. A comprehensive characterization of the bio-oil was conducted using gas chromatography–mass spectroscopy (GC–MS) and high-performance liquid chromatography (HPLC), revealing hydroquinone and benzoquinone as the predominant phenolic compounds. Employing computer-aided molecular design (CAMD), we identified 1-propanol as an optimal green solvent for the selective extraction of quinones, offering superior process efficiency and economic viability. Notably, the extraction efficiency achieved for hydroquinone and p-benzoquinone reached up to 23.38 g/L and 14.39 g/L, respectively, from the aqueous fraction of pyrolysis oil at 550 °C, with an extraction time of 1 h. Techno-economic analysis indicated a robust rate of return of 20% and a payback period of 1.1 years for the separation process. This study underscores the critical role of a hybrid experimental-modelling approach in developing sustainable processes for the valorization of biowaste into valuable materials.
Business Continuity and Sustainability in Government Organisations
Authors: Boudi Abdullah Abdullatif , Alshaikhmubarak Abdulrahman
Published: August 2024

This study aimed to investigate the effects of business continuity management (BCM) practices on the organisational performance of government organizations in the Kingdom of Saudi Arabia. The study considered a set of variables, with BCM practices as the dependent variable and planned resilience and adaptive resilience as independent variables of organisational performance. The sample comprised 200 individuals from Saudi Arabia. Data collection was facilitated through an electronic questionnaire, using Google Docs as the platform. Questionnaires were distributed to 211 individuals, with 200 responses obtained, resulting in a 94% response rate. Distribution was carried out in Arabic and English through various channels, including social media platforms, email, WhatsApp, and LinkedIn. The study revealed a significant link between organisational performance, using BCM practices, and planned and adaptive resilience. These findings contribute to the understanding of the impact of BCM practices on organisational performance within the context of Saudi Arabia.
Dynamics of a Stochastic Vector-Borne Model with Plant Virus Disease Resistance and Nonlinear Incidence
Authors: Zhang Liang , Wang Xinghao , Zhang Xiaobing
Published: August 2024

Symmetry in mathematical models often refers to invariance under certain transformations. In stochastic models, symmetry considerations must also account for the probabilistic nature of inter- actions and events. In this paper, a stochastic vector-borne model with plant virus disease resistance and nonlinear incidence is investigated. By constructing suitable stochastic Lyapunov functions, we show that if the related threshold R0s<1, then the disease will be extinct. By using the reproduction number R0, we establish sufficient conditions for the existence of ergodic stationary distribution to the stochastic model. Furthermore, we explore the results graphically in numerical section and find that random fluctuations introduced in the stochastic model can suppress the spread of the disease, except for increasing plant virus disease resistance and decreasing the contact rate between infected plants and susceptible vectors. The results reveal the correlation between symmetry and stochastic vector-borne models and can provide deeper insights into the dynamics of disease spread and control, potentially leading to more effective and efficient management strategies.
Oxytocin: A Shield against Radiation-Induced Lung Injury in Rats
Authors: Kayalı Ahmet , Arda Duygu Burcu , Bora Ejder Saylav , Uyanikgil Yiğit , Atasoy Özüm , Erbaş Oytun
Published: August 2024

Background: Radiation-induced lung injury (RILI), a serious side effect of thoracic radiotherapy, can lead to acute radiation pneumonitis (RP) and chronic pulmonary fibrosis (PF). Despite various interventions, no effective protocol exists to prevent pneumonitis. Oxytocin (OT), known for its anti-inflammatory, antiapoptotic, and antioxidant properties, has not been explored for its potential in mitigating RILI. Materials and Methods: This study involved 24 female Wistar albino rats, divided into three groups: control group, radiation (RAD) + saline, and RAD + OT. The RAD groups received 18 Gy of whole-thorax irradiation. The RAD + OT group was treated with OT (0.1 mg/kg/day) intraperitoneally for 16 weeks. Computerizing tomography (CT) imaging and histopathological, biochemical, and blood gas analyses were performed to assess lung tissue damage and inflammation. Results: Histopathological examination showed significant reduction in alveolar wall thickening, inflammation, and vascular changes in the RAD + OT group compared to the RAD + saline group. Biochemical analysis revealed decreased levels of TGF-beta, VEGF, and PDGF, and increased BMP-7 and prostacyclin in the RAD + oxytocin group (p < 0.05). Morphometric analysis indicated significant reductions in fibrosis, edema, and immune cell infiltration. CT imaging demonstrated near-normal lung parenchyma density in the RAD + oxytocin group (p < 0.001). Conclusion: Oxytocin administration significantly mitigates radiation-induced pneumonitis in rats, implying that is has potential as a therapeutic agent for preventing and treating RILI.