bluuebunny/crossref_metadata_2025 huggingface dataset:
Design of a Data Acquisition, Correction and Retrieval of Na Doppler Lidar for Diurnal Measurement of Temperature and Wind in the Mesosphere and Lower Thermosphere Region
Authors: Xia Yuan , Cheng Xuewu , Wang Zelong , Liu Linmei , Yang Yong , Du Lifang , Jiao Jing , Wang Jihong , Zheng Haoran , Li Yajuan , Li Faquan , Yang Guotao
Published: October 2023

Based on the resonance fluorescence scattering mechanism, a narrowband sodium (Na) lidar can measure temperature and wind in the mesosphere and lower thermosphere (MLT) region. By using a narrowband spectral filter, background light noise during the day can be suppressed, allowing for continuous observations. To obtain full-diurnal-cycle temperature and wind measurement results, a complex and precise retrieval process is required, along with necessary corrections to minimize measurement errors. This paper introduces the design of a data acquisition unit for three frequencies in three directions of the Na lidar system in the Chinese Meridian Project (Phase II) and investigates the calibration and retrieval methods for obtaining diurnal temperature and horizontal wind in the MLT region, using a Na Doppler lidar with Faraday anomalous dispersion optical filter (FADOF). Furthermore, these methods are applied to observations conducted by a Na lidar in Beijing, China. The wind and temperature results over full diurnal cycles obtained from the all-solid-state Na Doppler lidar are reported for the first time and compared with temperature measurements from satellite, as well as wind observations from a meteor radar. The comparison demonstrates a reasonable agreement between the results, indicating the rationality of the lidar-retrieved results and the feasibility and effectiveness of the data correction and retrieval method.
A Practical Star Image Registration Algorithm Using Radial Module and Rotation Angle Features
Authors: Sun Quan , Liu Lei , Niu Zhaodong , Li Yabo , Zhang Jingyi , Wang Zhuang
Published: October 2023

Star image registration is the most important step in the application of astronomical image differencing, stacking, and mosaicking, which requires high robustness, accuracy, and real-time capability on the part of the algorithm. At present, there are no high-performance registration algorithms available in this field. In the present paper, we propose a star image registration algorithm that relies only on radial module features (RMF) and rotation angle features (RAF) while providing excellent robustness, high accuracy, and good real-time performance. The test results on a large amount of simulated and real data show that the comprehensive performance of the proposed algorithm is significantly better than the four classical baseline algorithms as judged by the presence of rotation, insufficient overlapping area, false stars, position deviation, magnitude deviation, and complex sky background, making it a more ideal star image registration algorithm than current alternatives.
Deep Learning Model Transfer in Forest Mapping Using Multi-Source Satellite SAR and Optical Images
Authors: Ge Shaojia , Antropov Oleg , Häme Tuomas , McRoberts Ronald E. , Miettinen Jukka
Published: October 2023

Deep learning (DL) models are gaining popularity in forest variable prediction using Earth observation (EO) images. However, in practical forest inventories, reference datasets are often represented by plot- or stand-level measurements, while high-quality representative wall-to-wall reference data for end-to-end training of DL models are rarely available. Transfer learning facilitates expansion of the use of deep learning models into areas with sub-optimal training data by allowing pretraining of the model in areas where high-quality teaching data are available. In this study, we perform a “model transfer” (or domain adaptation) of a pretrained DL model into a target area using plot-level measurements and compare performance versus other machine learning models. We use an earlier developed UNet based model (SeUNet) to demonstrate the approach on two distinct taiga sites with varying forest structure and composition. The examined SeUNet model uses multi-source EO data to predict forest height. Here, EO data are represented by a combination of Copernicus Sentinel-1 C-band SAR and Sentinel-2 multispectral images, ALOS-2 PALSAR-2 SAR mosaics and TanDEM-X bistatic interferometric radar data. The training study site is located in Finnish Lapland, while the target site is located in Southern Finland. By leveraging transfer learning, the SeUNet prediction achieved root mean squared error (RMSE) of 2.70 m and R2 of 0.882, considerably more accurate than traditional benchmark methods. We expect such forest-specific DL model transfer can be suitable also for other forest variables and other EO data sources that are sensitive to forest structure.
CHP Engine Anomaly Detection Based on Parallel CNN-LSTM with Residual Blocks and Attention
Authors: Chung Won Hee , Gu Yeong Hyeon , Yoo Seong Joon
Published: October 2023

The extreme operating environment of the combined heat and power (CHP) engine is likely to cause anomalies and defects, which can lead to engine failure; thus, detecting engine anomalies is essential. In this study, we propose a parallel convolutional neural network–long short-term memory (CNN-LSTM) residual blocks attention (PCLRA) anomaly detection model with engine sensor data. To our knowledge, this is the first time that parallel CNN-LSTM-based networks have been used in the field of CHP engine anomaly detection. In PCLRA, spatiotemporal features are extracted via CNN-LSTM in parallel and the information loss is compensated using the residual blocks and attention mechanism. The performance of PCLRA is compared with various hybrid models for 15 cases. First, the performances of serial and parallel models are compared. In addition, we evaluated the contributions of the residual blocks and attention mechanism to the performance of the CNN–LSTM hybrid model. The results indicate that PCLRA achieves the best performance, with a macro f1 score (mean ± standard deviation) of 0.951 ± 0.033, an anomaly f1 score of 0.903 ± 0.064, and an accuracy of 0.999 ± 0.002. We expect that the energy efficiency and safety of CHP engines can be improved by applying the PCLRA anomaly detection model.
Revolutionizing Urban Mobility: IoT-Enhanced Autonomous Parking Solutions with Transfer Learning for Smart Cities
Authors: Abbas Qaiser , Ahmad Gulzar , Alyas Tahir , Alghamdi Turki , Alsaawy Yazed , Alzahrani Ali
Published: October 2023

Smart cities have emerged as a specialized domain encompassing various technologies, transitioning from civil engineering to technology-driven solutions. The accelerated development of technologies, such as the Internet of Things (IoT), software-defined networks (SDN), 5G, artificial intelligence, cognitive science, and analytics, has played a crucial role in providing solutions for smart cities. Smart cities heavily rely on devices, ad hoc networks, and cloud computing to integrate and streamline various activities towards common goals. However, the complexity arising from multiple cloud service providers offering myriad services necessitates a stable and coherent platform for sustainable operations. The Smart City Operational Platform Ecology (SCOPE) model has been developed to address the growing demands, and incorporates machine learning, cognitive correlates, ecosystem management, and security. SCOPE provides an ecosystem that establishes a balance for achieving sustainability and progress. In the context of smart cities, Internet of Things (IoT) devices play a significant role in enabling automation and data capture. This research paper focuses on a specific module of SCOPE, which deals with data processing and learning mechanisms for object identification in smart cities. Specifically, it presents a car parking system that utilizes smart identification techniques to identify vacant slots. The learning controller in SCOPE employs a two-tier approach, and utilizes two different models, namely Alex Net and YOLO, to ensure procedural stability and improvement.
Pion Detection Using Single Photon Avalanche Diodes
Authors: Bulling Anthony Frederick , Underwood Ian
Published: October 2023

We present the first reported use of a CMOS-compatible single photon avalanche diode (SPAD) array for the detection of high-energy charged particles, specifically pions, using the Super Proton Synchrotron at CERN, the European Organization for Nuclear Research. The results confirm the detection of incident high-energy pions at 120 GeV, minimally ionizing, which complements the variety of ionizing radiation that can be detected with CMOS SPADs.
Driving Intention Recognition of Surrounding Vehicles Based on a Time-Sequenced Weights Hidden Markov Model for Autonomous Driving
Authors: Liu Pujun , Qu Ting , Gao Huihua , Gong Xun
Published: October 2023

Accurate perception, especially situational awareness, is central to the evolution of autonomous driving. This necessitates understanding both the traffic conditions and driving intentions of surrounding vehicles. Given the unobservable nature of driving intentions, the hidden Markov model (HMM) has emerged as a popular tool for intention recognition, owing to its ability to relate observable and hidden variables. However, HMM does not account for the inconsistencies present in time series data, which are crucial for intention recognition. Specifically, HMM overlooks the fact that recent observations offer more reliable insights into a vehicle’s driving intention. To address the aforementioned limitations, we introduce a time-sequenced weights hidden Markov model (TSWHMM). This model amplifies the significance of recent observations in recognition by integrating a discount factor during the observation sequence probability computation, making it more aligned with practical requirements. Regarding the model’s input, in addition to easily accessible states of a target vehicle, such as lateral speed and heading angle, we also introduced lane hazard factors that reflect collision risks to capture the traffic environment information surrounding the vehicle. Experiments on the HighD dataset show that TSWHMM achieves recognition accuracies of 94.9% and 93.4% for left and right lane changes, surpassing both HMM and recurrent neural networks (RNN). Moreover, TSWHMM recognizes lane-changing intentions earlier than its counterparts. In tests involving more complex roundabout scenarios, TSWHMM achieves an accuracy of 87.3% and can recognize vehicles’ intentions to exit the roundabout 2.09 s in advance.
Application of Deep Reinforcement Learning to UAV Swarming for Ground Surveillance
Authors: Arranz Raúl , Carramiñana David , Miguel Gonzalo de , Besada Juan A. , Bernardos Ana M.
Published: October 2023

This paper summarizes in depth the state of the art of aerial swarms, covering both classical and new reinforcement-learning-based approaches for their management. Then, it proposes a hybrid AI system, integrating deep reinforcement learning in a multi-agent centralized swarm architecture. The proposed system is tailored to perform surveillance of a specific area, searching and tracking ground targets, for security and law enforcement applications. The swarm is governed by a central swarm controller responsible for distributing different search and tracking tasks among the cooperating UAVs. Each UAV agent is then controlled by a collection of cooperative sub-agents, whose behaviors have been trained using different deep reinforcement learning models, tailored for the different task types proposed by the swarm controller. More specifically, proximal policy optimization (PPO) algorithms were used to train the agents’ behavior. In addition, several metrics to assess the performance of the swarm in this application were defined. The results obtained through simulation show that our system searches the operation area effectively, acquires the targets in a reasonable time, and is capable of tracking them continuously and consistently.
A Facial Feature and Lip Movement Enhanced Audio-Visual Speech Separation Model
Authors: Li Guizhu , Fu Min , Sun Mengnan , Liu Xuefeng , Zheng Bing
Published: October 2023

The cocktail party problem can be more effectively addressed by leveraging the speaker’s visual and audio information. This paper proposes a method to improve the audio’s separation using two visual cues: facial features and lip movement. Firstly, residual connections are introduced in the audio separation module to extract detailed features. Secondly, considering the video stream contains information other than the face, which has a minimal correlation with the audio, an attention mechanism is employed in the face module to focus on crucial information. Then, the loss function considers the audio-visual similarity to take advantage of the relationship between audio and visual completely. Experimental results on the public VoxCeleb2 dataset show that the proposed model significantly enhanced SDR, PSEQ, and STOI, especially 4 dB improvements in SDR.
Vibration Event Recognition Using SST-Based Φ-OTDR System
Authors: Yao Ruixu , Li Jun , Zhang Jiarui , Wei Yinshang
Published: October 2023

We propose a method based on Synchrosqueezing Transform (SST) for vibration event analysis and identification in Phase Sensitive Optical Time-Domain Reflectometry (Φ-OTDR) systems. SST has high time-frequency resolution and phase information, which can distinguish and enhance different vibration events. We use six tap events with different intensities and six other events as experimental data and test the effect of attenuation. We use Visual Geometry Group (VGG), Vision Transformer (ViT), and Residual Network (ResNet) as deep classifiers for the SST transformed data. The results show that our method outperforms the methods based on Continuous Wavelet Transform (CWT) and Short-Time Fourier Transform (STFT), while ResNet is the best classifier. Our method can achieve high recognition rate under different signal strengths, event types, and attenuation levels, which shows its value for Φ-OTDR system.
Spatial and Temporal Analysis of Water Quality in High Andean Lakes with Sentinel-2 Satellite Automatic Water Products
Authors: Ayala Izurieta Johanna Elizabeth , Beltrán Dávalos Andrés Agustín , Jara Santillán Carlos Arturo , Godoy Ponce Sofía Carolina , Van Wittenberghe Shari , Verrelst Jochem , Delegido Jesús
Published: October 2023

The water of high Andean lakes is strongly affected by anthropic activities. However, due to its complexity this ecosystem is poorly researched. This study analyzes water quality using Sentinel-2 (S2) images in high Andean lakes with apparent different eutrophication states. Spatial and temporal patterns are assessed for biophysical water variables from automatic products as obtained from versions of C2RCC (Case 2 Regional Coast Color) processor (i.e., C2RCC, C2X, and C2X-COMPLEX) to observe water characteristics and eutrophication states in detail. These results were validated using in situ water sampling. C2X-COMPLEX appeared to be an appropriate option to study bodies of water with a complex dynamic of water composition. C2RCC was adequate for lakes with high transparency, typical for lakes of highlands with excellent water quality. The Yambo lake, with chlorophyll-a concentration (CHL) values of 79.6 ± 5 mg/m3, was in the eutrophic to hyper-eutrophic state. The Colta lake, with variable values of CHL, was between the oligotrophic to mesotrophic state, and the Atillo lakes, with values of 0.16 ± 0.1 mg/m3, were oligotrophic and even ultra-oligotrophic, which remained stable in the last few years. Automatic S2 water products give information about water quality, which in turn makes it possible to analyze its causes.
Refresh Rate-Based Caching and Prefetching Strategies for Internet of Things Middleware
Authors: Medvedev Alexey , Hassani Alireza , Belov Gleb , Weerasinghe Shakthi , Huang Guang-Li , Zaslavsky Arkady , Loke Seng W. , Jayaraman Prem Prakash
Published: October 2023

One of the research directions in Internet of Things (IoT) is the field of Context Management Platforms (CMPs) which is a specific type of IoT middleware. CMPs provide horizontal connectivity between vertically oriented IoT silos resulting in a noticeable difference in how IoT data streams are processed. As these context data exchanges can be monetised, there is a need to model and predict the context metrics and operational costs of this exchange to provide relevant and timely context in a large-scale IoT ecosystem. In this paper, we argue that caching all transient context information to satisfy this necessity requires large amounts of computational and network resources, resulting in tremendous operational costs. Using Service Level Agreements (SLAs) between the context providers, CMP, and context consumers, where the level of service imperfection is quantified and linked to the associated costs, we show that it is possible to find efficient caching and prefetching strategies to minimize the context management cost. So, this paper proposes a novel method to find the optimal rate of IoT data prefetching and caching. We show the main context caching strategies and the proposed mathematical models, then discuss how a correctly chosen proactive caching strategy and configurations can help to maximise the profit of CMP operation when multiple SLAs are defined. Our model is accurate up to 0.0016 in Root Mean Square Percentage Error against our simulation results when estimating the profits to the system. We also show our model is valid using the t-test value tending to 0 for all the experimental scenarios.
Progress in Active Infrared Imaging for Defect Detection in the Renewable and Electronic Industries
Authors: Zhao Xinfeng , Zhao Yangjing , Hu Shunchang , Wang Hongyan , Zhang Yuyan , Ming Wuyi
Published: October 2023

In recent years, infrared thermographic (IRT) technology has experienced notable advancements and found widespread applications in various fields, such as renewable industry, electronic industry, construction, aviation, and healthcare. IRT technology is used for defect detection due to its non-contact, efficient, and high-resolution methods, which enhance product quality and reliability. This review offers an overview of active IRT principles. It comprehensively examines four categories based on the type of heat sources employed: pulsed thermography (PT), lock-in thermography (LT), ultrasonically stimulated vibration thermography (UVT), and eddy current thermography (ECT). Furthermore, the review explores the application of IRT imaging in the renewable energy sector, with a specific focus on the photovoltaic (PV) industry. The integration of IRT imaging and deep learning techniques presents an efficient and highly accurate solution for detecting defects in PV panels, playing a critical role in monitoring and maintaining PV energy systems. In addition, the application of infrared thermal imaging technology in electronic industry is reviewed. In the development and manufacturing of electronic products, IRT imaging is used to assess the performance and thermal characteristics of circuit boards. It aids in detecting potential material and manufacturing defects, ensuring product quality. Furthermore, the research discusses algorithmic detection for PV panels, the excitation sources used in electronic industry inspections, and infrared wavelengths. Finally, the review analyzes the advantages and challenges of IRT imaging concerning excitation sources, the PV industry, the electronics industry, and artificial intelligence (AI). It provides insights into critical issues requiring attention in future research endeavors.
Relationship between MRI Findings and Urodynamic Parameters in Patients with Multiple Sclerosis: Prediction of Upper Urinary Tract Damage?
Authors: Stritt Kevin , Bohner Perrine , Ortlieb Niklas , Ochs Vincent , Grilo Nuno
Published: October 2023

Lower urinary tract dysfunction is frequently observed in individuals with multiple sclerosis (MS), significantly impacting their quality of life and increasing the risk of upper urinary tract (UUT) damage. Magnetic resonance imaging (MRI) serves as the gold standard imaging technique for identifying demyelinating lesions and aiding in the clinical diagnosis of MS. However, despite its diagnostic utility, the precise relationship between MRI lesions and bladder dysfunction remains poorly established. We aimed to examine the correlation between MRI lesion localizations and both urodynamic parameters and risk factors for UUT damage. In this retrospective study, we conducted a comprehensive review of 201 patients diagnosed with MS who were referred for primary neurourological evaluation, including a videourodynamic study (VUDS). To explore potential significant relationships between the independent variable of MRI lesion localization and the dependent outcome variables, we conducted a multivariate analysis of variance (MANOVA) regression. A significant correlation was observed between the presence of a brainstem lesion and specific urodynamic parameters, including lower maximum cystometric bladder capacity and higher bladder compliance. Similarly, an increased number of diverse MRI lesion localizations demonstrated a significant correlation with these urodynamic parameters. In conclusion, MRI findings did not exhibit a significant association with urodynamic risk factors for UUT damage, thereby limiting their utility in stratifying MS patients for subsequent neurourological assessment and treatment.
Sediment Mercury, Geomorphology and Land Use in the Middle Araguaia River Floodplain (Savanna Biome, Brazil)
Authors: Moraes Lilian , Bernardi José Vicente Elias , de Souza João Pedro Rudrigues , Portela Joelma Ferreira , Vieira Ludgero Cardoso Galli , Sousa Passos Carlos José , de Souza Jurandir Rodrigues , Bastos Wanderley Rodrigues , Monteiro Lucas Cabrera , Rodrigues Ygor Oliveira Sarmento , Dorea José Garrofe
Published: October 2023

In order to assess the influencing factors of the presence of mercury in a river within the Savanna biome (Cerrado), we surveyed total mercury (THg) in bottom sediment from 50 lakes along 750 km of the Middle Araguaia floodplain. The sampling sites included non-urban and urban surroundings over three distinct geomorphologies. We measured water physicochemical parameters at each site and tested statistically if land use nested within the geological formation influenced the THg concentration in bottom sediments and related water parameters. Multivariate results indicate that the interaction between geological groups and land use is statistically significant (p < 0.05). Nested ANOVA and Tukey HSD tests confirmed that the geological formation with its nested land use influences the THg, pH, DO, conductivity, and TDS (p < 0.05). THg was significantly lower in Quaternary terrains (p < 0.05) and differed significantly between non-urban and urban areas in Neoproterozoic terrains (p = 0.02). The spatial projections of the THg eigenvector on the main axes with the scoring factors of the Neoproterozoic/Paleoproterozoic terrains, and urban/non-urban, confirmed the spatial correlations. These results indicate that the association of land use and geology could be the main driver of THg in the bottom sediments of lakes from the Middle Araguaia floodplain.
Correlating Groundwater Storage Change and Precipitation in Alabama, United States from 2000–2021 by Combining the Water Table Fluctuation Method and Statistical Analyses
Authors: Oluwaniyi Olaoluwa , Zhang Yong , Gholizadeh Hossein , Li Bailing , Gu Xiufen , Sun HongGuang , Lu Chengpeng
Published: October 2023

The complexity of aquifers poses a challenge for fully comprehending the impact of climate change on groundwater. In this study, we employed a suite of hydrological and statistical methods, including the water table fluctuation (WTF) method, wavelet analysis, the Hurst exponent, and temporal trend analysis, to assess groundwater storage (GWS) changes and their correlation with precipitation in Alabama, located in the southeastern United States. These approaches were used to evaluate the temporal variability of GWS as derived from well data and large-scale model estimates that incorporated satellite observations. The results unveiled a nuanced and regionally variable relationship between GWS changes and precipitation over the past two decades. While the Mann–Kendall test did not reveal any statistically significant overarching trends in GWS changes, Sen’s slope analysis indicated subtle regional variations, including a minor decline of −0.2 mm/year for GWS in southern Alabama and modest increases of 0.5 mm/year and 0.38 mm/year in the western and northern regions, respectively, from 2000–2021. Wavelet coherence analysis showed significant co-variation between GWS and precipitation in cycles ranging from 8 to 32 months, suggesting potential cyclic or intermittent influences. Furthermore, we detected strong persistence within the groundwater system using the Hurst exponent, indicating the substantial temporal memory impact. These findings are useful for developing effective groundwater management strategies in a changing climate.
Machine-Learning-Based Comprehensive Properties Prediction and Mixture Design Optimization of Ultra-High-Performance Concrete
Authors: Sun Chang , Wang Kai , Liu Qiong , Wang Pujin , Pan Feng
Published: October 2023

Ultra-high-performance concrete (UHPC) is widely used in the field of large-span and ultra-high-rise buildings due to its advantages such as ultra-high strength and durability. However, the large amount of cementitious materials used results in the cost and carbon emission of UHPC being much higher than that of ordinary concrete, limiting the wide application of UHPC. Therefore, optimizing the design of the UHPC mix proportion to meet the basic properties of UHPC with low carbon and low cost at the same time will help to realize the wide application of UHPC in various application scenarios. In this study, the basic properties of UHPC, including the compressive strength, flexural strength, fluidity, and shrinkage properties, were predicted by machine-learning algorithms. It is found that the XGBoost algorithm outperforms others in predicting basic properties, with MAPE lower than 5% and R2 higher than 0.9 in four output properties. To evaluate the comprehensive performance of UHPC, a further analysis was conducted to calculate the cost- and carbon-emissions-per-unit volume for 50,000 UHPC random mixes. Combined with the analytical hierarchy process (AHP) model, the comprehensive performance of UHPC, including basic properties, cost-per-unit volume, and carbon-emissions-per-unit volume, was evaluated. This study proposes an optimized UHPC mix proportion, based on low-cost or low-carbon emission, oriented to comply with the excellent overall performance and obtain its corresponding various properties.
Valuation of a Heatwave Early Warning System for Mitigating Risks Associated with Heat-Related Illness in Central Vietnam
Authors: Kien Nguyen Duc , My Nguyen H. D. , Thu Dang Thi Anh , Tri Ton That Canh , Son Nghiem Hong , Phong Thai Khanh , Tin Hoang Cong , Lan Nguyen Hoang , Thang Tran Binh , The Bui Dung , Dung Phung Tri
Published: October 2023

Heatwaves are increasingly recognized as a severe natural hazard that pose substantial threats to human wellbeing and sustainability with physical, mental, and economic impacts. Heatwave Early Warning Systems (HEWSs) have emerged as a promising solution to mitigate these adverse impacts. This study aims to valuate a Short Message Service (SMS)-based early warning system specifically targeting heat-vulnerable populations and their determinants in central Vietnam, an area prone to heat-related illnesses. By employing the contingent valuation method, individuals’ willingness to pay (WTP) for the warning system and the determinants of the WTP were assessed. The findings showed an average WTP of VND 283,110 (~USD 11) per person per year, according to the parametric estimate. The total WTP for the affected population in central Vietnam reached VND 857,283,678,000 or USD 37,273,203 per year, assuming that 50% of the heat-vulnerable population contributes to HEWSs financially. Significant determinants of WTP were identified, including bid levels, cost of heat-related illnesses, background diseases, and exposure to heat. Policymakers and health authorities can utilize these findings to develop effective adaptation strategies for preventing and mitigating heat-related illnesses, while also promoting sustainable development in central Vietnam.
Delignification of Wheat Straw Using DES-like Mixtures
Authors: Jančíková Veronika , Jablonský Michal , Voleková Katarína
Published: October 2023

Wheat straw, an agricultural residue, is emerging as a viable alternative to wood biomass alongside other annual plants. The exploration of novel methods, particularly those involving ecofriendly solvents, is at the forefront of harnessing the potential of these annual crops. This study focuses on the delignification of wheat straw using DES-like mixtures composed of choline chloride and lactic acid in a molar ratio of 1:5. We closely monitored three critical factors influencing delignification: temperature, time, and the ratio of biomass to the DES-like mixtures. Temperature ranged from 80 to 160 °C, time varied from 60 to 240 min, and the ratio spanned from 1:10 to 1:60. The percentage of lignin remaining in the samples after delignification varied between 16.13% and 35.49%, while the cellulose content fell within the range of 43.77% to 69.53%. To pinpoint optimal conditions for delignification, we performed calculations. The ideal lignin content of 16.44% was achieved at a temperature of 111 °C, a duration of 60 min, and a biomass-to-solvent ratio of 1:27. For maximum delignification efficiency, reaching 37.32%, the optimal conditions were determined as a temperature of 120 °C, a duration of 150 min, and a ratio of 1:34.
A Study of Solar Power Usage in the Southern Region of Pakistan
Authors: Hashmi Syed Tauqeer Ahmed , Raees Fahim , Baig Mirza Mahmood
Published: October 2023

Over the past two decades, significant progress has been observed in the energy sector. Solar power systems have emerged as a popular choice for both individuals and companies. Despite the country’s energy crisis and reliance on fossil fuels, the adoption rate of solar power systems in Pakistan remains slow. This research aims to develop a cost-effective model that can help consumers in Pakistan adopt solar power systems. The unified theory of acceptance and use of technology 2 (UTAUT2) served as the foundation for this research, which is used to understand and predict individuals’ acceptance of new technology. This study included an internet-based survey conducted among 200 respondents in the southern region of Pakistan. Deductive reasoning was used to analyse the data, and the structural equation model was utilised through SmartPLS 3 software to test the research hypothesis. The survey results revealed that effort expectancy and performance expectancy were not significantly associated with attitude towards use. However, all other factors, such as environmental belief, social influence, hedonic motivation, price value, and facilitating conditions used in the model, have positive and significant influences on the use of solar technology. The findings of this project also enable government and private sector organisations to optimise their planning and resources to promote environment-friendly energy, and they should work on significant factors such as cost, which is one of the most dominant indicators that can attract consumers to shift towards alternate energy resources. This study presents the first attempt to model the intention and behaviour of Pakistani consumers towards using solar technology with the help of UTAUT2.
Comprehensive Analysis of Kinetic Energy Recovery Systems for Efficient Energy Harnessing from Unnaturally Generated Wind Sources
Authors: Zishan Shaikh , Molla Altaf Hossain , Rashid Haroon , Wong Kok Hoe , Fazlizan Ahmad , Lipu Molla Shahadat Hossain , Tariq Mohd , Alsalami Omar Mutab , Sarker Mahidur R.
Published: October 2023

Alternative energy is a rapidly expanding research area primarily driven by concerns over pollution caused by inefficient conventional energy sources. However, many developing nations rely heavily on these conventional sources. In response, numerous researchers have focused on developing kinetic energy recovery systems (KERS) to capture and utilize the energy lost due to inefficiency. These KERS can be implemented in various scenarios, such as near railroad tracks, industrial flue stacks, cooling towers, and air conditioning outlets. The primary objective of this paper is to critically and comprehensively evaluate the research conducted on the development of these systems. The review reveals that the wind speed in the studied cases ranged between 15 and 22 m/s, providing a consistent and theoretically maximum potential higher than any location worldwide. Furthermore, the impact of these systems on the Betz limit, as well as their drawbacks and crucial advancements necessary for practical implementation, have been thoroughly assessed. This paper contributes to the existing body of knowledge by presenting a comprehensive analysis of the research conducted on KERS development. It highlights the potential of these systems in harnessing untapped energy sources and identifies key areas that require further attention for successful practical application.
Intelligent Personalized Lighting Control System for Residents
Authors: Zhang Jialing , Chen Zhanxu , Wang An , Li Zhenzhang , Wan Wei
Published: October 2023

The demand for personalized lighting environments based on households is steadily increasing among users. This article proposes a novel intelligent control system for personalized lighting in home environments, aiming to automatically capture user information, such as homecoming time and light switching behavior, in order to train a model that intelligently regulates the lights for users. Facial recognition technology is employed by this system to identify users and record their lighting data. Subsequently, nine commonly used machine learning models were evaluated, revealing that the error back-propagation neural network algorithm exhibits excellent performance in time-series analysis. The BPNN weights were optimized using genetic algorithms, resulting in an improved coefficient of determination (R2) of 0.99 for turn-on time and 0.82 for turn-off time test sets. Furthermore, testing was conducted on data collection duration which demonstrated that even with only 20 time-series data collected from new users, the model still displayed exceptional performance in training prediction tasks. Overall, this system effectively identifies users and automatically adjusts the lighting environment according to their preferences, providing comfortable and convenient lighting conditions tailored to individual needs. Consequently, a broader goal of energy conservation and environmental sustainability can be achieved.
The Impact of a Sustainable Progressive STEAM Program on Primary School Students’ Critical Thinking Dispositions and Mathematics Achievements
Authors: Küçük Hasan , Perkan Zeki Canan , İskifoğlu Gökhan , Caner Hamit
Published: October 2023

This study, which was supported by a quantitative research paradigm with two experiments and two control groups of 4 × 4 quasi-experimental design, aimed to find out the impact of a sustainable progressive STEAM (SP-STEAM model) application model on 5th-grade primary school students’ critical thinking dispositions and mathematics achievements in North Cyprus. The treatment model was applied to two independent experimental groups for 14 weeks. The split-plot multiple group analysis of variance (split-plot ANOVA) statistical techniques was used to calculate between- and within-group significances regarding exogenous variables. The SPSS-24 software package was used for the analysis. The Pre-test and post-test results deriving from the experiment and control groups revealed a significant effect of the SP-STEAM model upon the 5th-grade primary school students’ critical thinking dispositions, as measured using the CCTDI, mathematic achievements, as well as measured using a dedicated exam. The results were discussed in detail in light of the related literature, with suggestions for further studies proposed.
Spatial and Temporal Distribution and Source Analysis of Heavy Metals in Agricultural Soils of Ningxia, Northwest of China
Authors: Chen Lin , Ma Kun
Published: October 2023

The spatial and temporal dynamic monitoring of the heavy metal concentration in agricultural soils can help us to understand the extent and changes of regional soil heavy metal pollution, allowing us safeguard food safety and human health and provide basic data for the prevention and control of heavy metal pollution in agricultural soils. The heavy metals’ concentrations, including Cd, Cr, Cu, Hg, Ni, Pb, Zn, and As, were measured in surface (0–20 cm) soil samples collected in 2017 and 2021 from agriculture land to evaluate their pollution levels in the Ningxia Hui Autonomous Region (Hereinafter referred to as Ningxia), northwest of China. A correlation analysis, principal component analysis, and positive matrix factorization were used jointly to identify possible sources. The results showed that, for most soils, the concentrations of As, Cr, Cu, Ni, Pb, and Zn were lower than the contamination thresholds stipulated by the national guideline. The average concentrations of As, Cd, Zn, and Ni in 2021 were significantly lower than the levels in 2017 (p < 0.05), but the average concentration of Cu was significantly higher in 2021 than in 2017 (p < 0.05), and the average concentrations of Cr, Pb, and Hg did not change significantly during the last five years. According to the pollution assessment index (mean Igeo values), Cd and Hg were found to accumulate in the farmland environment and were the major pollutants in the region, and the hotspots with high concentrations of heavy metals were mainly located in the Yellow River irrigation area in Northern Ningxia. Four main sources of heavy metals in the soils were identified: As mainly originated from natural sources; Pb, Cd, and Ni from atmospheric deposition and industrial activities; Cr, Zn, and Cu from agricultural activities; and Hg from the coal-related industrial activities. The concentration of heavy metals from anthropogenic sources accounted for 77.10% in agricultural soils, indicating the strong influence of this source on soil heavy metal accumulation. These findings provide scientific evidence and valuable information to prevent heavy metal contamination and control of farmland.
Advancing Sustainability in the Future of Work through the Design of Post-Pandemic Work-from-Home Systems
Authors: Sigahi Tiago F. A. C. , Yeow Paul H. P. , Thatcher Andrew
Published: October 2023

The COVID-19 pandemic accelerated and solidified the adoption of work-from-home systems (WFHSs) across all economic sectors, and finding ways to incorporate sustainability into these systems is a fundamental aspect of a sustainable future of work. Designing sustainable WFHSs requires an innovative approach capable of understanding complex interrelationships between multiple systems. This paper applies the sustainable system-of-systems (SSoSs) framework to find innovative design solutions for post-pandemic/endemic WFHSs. First, the SSoSs framework is depicted using examples of applications in systems to clarify its main ideas and concepts. Then, the literature on WFHSs is considered to discuss the impacts of COVID-19 on work from a systemic perspective. This provides inputs for the factors to be considered at each level of the SSoSs analysis. Finally, the SSoSs framework is applied to analyze the WFHSs’ requirements in the context of COVID-19. The nature of the application of this framework is theoretical, facilitating the provision of a multi-layered perspective on sustainability in work systems that are increasingly prevalent across various economic sectors. This study contributes to research and practice by characterizing the various relevant systems and their impact on the design of sustainable WFHSs and identifies multiple intervention points to make predictions about how they might influence other systems.
Enhancing Sustainability through Analysis and Prevention: A Study of Fatal Accidents on Trap Boats within the Commercial Fishing Industry
Authors: Kim Su-Hyung , Ryu Kyung-Jin , Lee Seung-Hyun , Lee Kyoung-Hoon , Kim Seong-Hun , Lee Yoo-Won
Published: October 2023

The global commercial fishing industry, which employs approximately 159,800 seafarers worldwide (as reported by the Food and Agriculture Organization of the United Nations), faces a significant challenge in terms of safety. According to estimates by the International Labour Organization, approximately 24,000 seafarers lose their lives each year in fishing-related accidents. However, most existing guidelines for preventing maritime accidents primarily target vessels involved in international navigation, often inadequately addressing the unique risks faced by small-scale boats operating in coastal areas. This study focuses on trap fishery, a widely practiced fishing method globally, analyzing quantitative data from 1790 maritime accidents and conducting a survey involving 101 seafarers in South Korea. Utilizing Bayesian network analysis, aligned with Formal Safety Assessment protocols, the authors developed preventive guidelines aiming to reduce the rate of fatal accidents. The guidelines, derived from the data analysis, are anticipated to provide invaluable assistance to seafarers engaged in trap fishery not only in South Korea but also across various countries worldwide. By enhancing safety measures in this critical sector, this research will contribute to the overarching goal of sustainability within the global commercial fishing industry.
Analysis of Chinese National Standards on Packaging and the Environment (CNSPE) Based on Standard Literature Bibliometrics
Authors: Ding Shuang , Gao Yanxin , Lin Haoxin , Zhu Yi , Zhang Rui , Lin Ling , Xu Bingsheng , Gao Dongfeng
Published: October 2023

This paper aims to understand the status of the Chinese National Standards on Packaging and the Environment (CNSPE), analyze the existing problems, and discover their possible solutions. Based on the standard bibliometric literature, the metadata information from the CNSPE have been collected and analyzed. The results indicated that the number of CNSPE released showed a wave-like growth with two peaks during 2006–2010 and 2016–2020 driven by both external and internal factors. The basic standard was the main standard sort. The average valid period of a CNSPE was 5.90 years, while the update rate was 23.33%. Public institutions lead the drafting committee of the CNSPE. The percentage of adopting the international or foreign standards was 28.86%, and the average conversion time was 7.61 years. There were four problems deduced from the results, including the unbalanced supply of different sorts of CNSPE, long valid period of standards with low update rates, low engagement of the enterprises, and loose links between national standards and international standards. The corresponding solutions are (1) strengthening market research and combining with policy development to optimize the supply of different sorts of CNSPE; (2) improving the process management ability of standardization organizations to enhance the supervision and feedback of standards development and implementation; (3) providing trainings and broadening dissemination while fulfilling the reward mechanism; and (4) enhancing the compatibility between the national standards system and the international standards system to promote the standard internationalization level.
Regarding the Ideal Convergence of Triple Sequences in Random 2-Normed Spaces
Authors: Bani-Ahmad Feras , Rashid Mohammad H. M.
Published: October 2023

In our ongoing study, we explore the concepts of I3-Cauchy and I3-Cauchy for triple sequences in the context of random 2-normed spaces (RTNS). Moreover, we introduce and analyze the notions of I3-convergence, I3-convergence, I3-limit points, and I3-cluster points for random 2-normed triple sequences. Significantly, we establish a notable finding that elucidates the connection between I3-convergence and I3-convergence within the framework of random 2-normed spaces, highlighting their interrelation. Additionally, we provide an illuminating example that demonstrates how I3-convergence in a random 2-normed space might not necessarily imply I3-convergence. Our observations underscore the importance of condition (AP3) when examining summability using ideals. Furthermore, we thoroughly investigate the relationship between the properties (AP) and (AP3), illustrating through an example how the latter represents a less strict condition compared to the former.
Lipophilic Shellfish Poisoning Toxins in Marine Invertebrates from the Galician Coast
Authors: Rossignoli Araceli E. , Ben-Gigirey Begoña , Cid Mónica , Mariño Carmen , Martín Helena , Garrido Soledad , Rodríguez Francisco , Blanco Juan
Published: October 2023

For the purpose of assessing human health exposure, it is necessary to characterize the toxins present in a given area and their potential impact on commercial species. The goal of this research study was: (1) to screen the prevalence and concentrations of lipophilic toxins in nine groups of marine invertebrates in the northwest Iberian Peninsula; (2) to evaluate the validity of wild mussels (Mytilus galloprovincialis) as sentinel organisms for the toxicity in non-bivalve invertebrates from the same area. The screening of multiple lipophilic toxins in 1150 samples has allowed reporting for the first time the presence of 13-desmethyl spirolide C, pinnatoxin G, okadaic acid, and dinophysistoxins 2 in a variety of non-traditional vectors. In general, these two emerging toxins showed the highest prevalence (12.5–75%) in most of the groups studied. Maximum levels for 13-desmethyl spirolide C and pinnatoxin G were found in the bivalves Magallana gigas (21 µg kg−1) and Tellina donacina (63 µg kg−1), respectively. However, mean concentrations for the bivalve group were shallow (2–6 µg kg−1). Okadaic acid and dinophysistoxin 2 with lower prevalence (1.6–44.4%) showed, on the contrary, very high concentration values in specific species of crustaceans and polychaetes (334 and 235 µg kg−−1, respectively), to which special attention should be paid. Statistical data analyses showed that mussels could be considered good biological indicators for the toxicities of certain groups in a particular area, with correlations between 0.710 (for echinoderms) and 0.838 (for crustaceans). Polychaetes could be an exception, but further extensive surveys would be needed to draw definitive conclusions.