bluuebunny/crossref_metadata_2025 huggingface dataset:
Outdoor Vision-and-Language Navigation Needs Object-Level Alignment
Authors: Sun Yanjun , Qiu Yue , Aoki Yoshimitsu , Kataoka Hirokatsu
Published: June 2023

In the field of embodied AI, vision-and-language navigation (VLN) is a crucial and challenging multi-modal task. Specifically, outdoor VLN involves an agent navigating within a graph-based environment, while simultaneously interpreting information from real-world urban environments and natural language instructions. Existing outdoor VLN models predict actions using a combination of panorama and instruction features. However, these methods may cause the agent to struggle to understand complicated outdoor environments and ignore the details in the environments to fail to navigate. Human navigation often involves the use of specific objects as reference landmarks when navigating to unfamiliar places, providing a more rational and efficient approach to navigation. Inspired by this natural human behavior, we propose an object-level alignment module (OAlM), which guides the agent to focus more on object tokens mentioned in the instructions and recognize these landmarks during navigation. By treating these landmarks as sub-goals, our method effectively decomposes a long-range path into a series of shorter paths, ultimately improving the agent’s overall performance. In addition to enabling better object recognition and alignment, our proposed OAlM also fosters a more robust and adaptable agent capable of navigating complex environments. This adaptability is particularly crucial for real-world applications where environmental conditions can be unpredictable and varied. Experimental results show our OAlM is a more object-focused model, and our approach outperforms all metrics on a challenging outdoor VLN Touchdown dataset, exceeding the baseline by 3.19% on task completion (TC). These results highlight the potential of leveraging object-level information in the form of sub-goals to improve navigation performance in embodied AI systems, paving the way for more advanced and efficient outdoor navigation.
Retention Time Trajectory Matching for Peak Identification in Chromatographic Analysis
Authors: Zang Wenzhe , Sharma Ruchi , Li Maxwell Wei-Hao , Fan Xudong
Published: June 2023

Retention time drift caused by fluctuations in physical factors such as temperature ramping rate and carrier gas flow rate is ubiquitous in chromatographic measurements. Proper peak matching and identification across different chromatograms is critical prior to any subsequent analysis but is challenging without using mass spectrometry. The purpose of this work was to describe and validate a peak matching and identification method called retention time trajectory (RTT) matching that can be used in targeted analyses free of mass spectrometry. This method uses chromatographic retention times as the only input and identifies peaks associated with any subset of a predefined set of target compounds. An RTT is a two-dimensional (2D) curve formed uniquely by the retention times of the chromatographic peaks. The RTTs obtained from the chromatogram of a sample under test and those pre-installed in a library are matched and statistically compared. The best matched pair implies identification. Unlike most existing peak-alignment methods, no mathematical warping or transformation is involved. Based on the experimentally characterized RTT, an RTT hybridization method was also developed to rapidly generate more RTTs and expand the library without performing actual time-consuming chromatographic measurements, which enables successful peak matching even for chromatograms with severe retention time drifts. Additionally, 3.15 × 105 tests using experimentally obtained gas chromatograms and 2 × 1012 tests using two publicly available fruit metabolomics datasets validated the proposed method, demonstrating real-time peak/interferent identification.
Assessing Quadriceps Muscle Contraction Using a Novel Surface Mechanomyography Sensor during Two Neuromuscular Control Screening Tasks
Authors: Linderman Shannon E. , Scarborough Donna Moxley , Aspenleiter Ryan , Stein Hannah S. , Berkson Eric M.
Published: June 2023

Electromyography (EMG) is the clinical standard for capturing muscle activation data to gain insight into neuromuscular control, yet challenges surrounding data analysis limit its use during dynamic tasks. Surface mechanomyography (sMMG) sensors are novel wearable devices that measure the physical output of muscle excursion during contraction, which may offer potential easy application to assess neuromuscular control. This study aimed to investigate sMMG detection of the timing patterns of muscle contraction compared to EMG. Fifteen healthy participants (mean age = 31.7 ± 9.1 y; eight males and seven females) were donned with EMG and sMMG sensors on their right quadriceps for simultaneous data capture during bilateral deep squats, and a subset performed three sets of repeated unilateral partial squats. No significant difference in the total duration of contraction was detected by EMG and sMMG during bilateral (p = 0.822) and partial (p = 0.246) squats. sMMG and EMG timing did not differ significantly for eccentric (p = 0.414) and concentric (p = 0.462) phases of muscle contraction during bilateral squats. The sMMG magnitude of quadriceps excursion demonstrated excellent intra-session retest reliability for bilateral (ICC3,1 = 0.962 mm) and partial (ICC3,1 = 0.936 mm, n = 10) squats. The sMMG sensors accurately and consistently provided key quadriceps muscle performance metrics during two physical activities commonly used to assess neuromuscular control for injury prevention, rehabilitation, and exercise training.
An Innovative Low-Power, Low-Cost, Multi-Constellation Geodetic-Grade Global Navigation Satellite System Reference Station for the Densification of Permanent Networks: The GREAT Project
Authors: Curone Davide , Savarese Giovanni , Antonini Mirko , Baucry Raphaël , Amani Elie , Boulandet Antonin , Cataldo Marco , Chambon Paul , Chersich Massimiliano , Hussein Ahmed B. , Menuel Bruno , Tsaturyan Avag
Published: June 2023

Geodetic-grade Global Navigation Satellite System (GNSS) receivers designed to implement permanent stations represent the most complex and costly technology in the field of GNSS instrumentation. On the other hand, a large number of innovative applications, highly demanding in terms of positioning precision and accuracy, is pushing the implementation of networks of permanent stations with a higher and higher spatial density. In this scenario, the development of brand new GNSS reference stations, which combine the most advanced technologies in the field of data availability and integrity together with reduced costs (of instrumentation, installation and management) is becoming of paramount importance. For this reason, in 2019 the EU Agency for the Space Programme (EUSPA) has financed a research project, called “next Generation gnss REference stATion—GREAT”, aimed at developing and demonstrating the potentiality of a brand new GNSS receiver suitable to implement permanent stations. This paper describes the solution developed by the project consortium, composed of four Small or Medium Enterprises (SMEs) based in Italy, France and Belgium, and the preliminary results achieved in the field tests.
A Trusted Reputation Management Scheme for Cross-Chain Transactions
Authors: Chen Kuongho , Lee Lin-Fa , Chiu Wayne , Su Chunhua , Yeh Kuo-Hui , Chao Han-Chieh
Published: June 2023

Blockchain has become a well-known, secured, decentralized datastore in many domains, including medical, industrial, and especially the financial field. However, to meet the requirements of different fields, platforms that are built on blockchain technology must provide functions and characteristics with a wide variety of options. Although they may share similar technology at the fundamental level, the differences among them make data or transaction exchange challenging. Cross-chain transactions have become a commonly utilized function, while at the same time, some have pointed out its security loopholes. It is evident that a secure transaction scheme is desperately needed. However, what about those nodes that do not behave? It is clear that not only a secure transaction scheme is necessary, but also a system that can gradually eliminate malicious players is of dire need. At the same time, integrating different blockchain systems can be difficult due to their independent architectures, and cross-chain transactions can be at risk if malicious attackers try to control the nodes in the cross-chain system. In this paper, we propose a dynamic reputation management scheme based on the past transaction behaviors of nodes. These behaviors serve as the basis for evaluating a node’s reputation to support the decision on malicious behavior and enable the system to intercept it in a timely manner. Furthermore, to establish a reputation index with high precision and flexibility, we integrate Particle Swarm Optimization (PSO) into our proposed scheme. This allows our system to meet the needs of a wide variety of blockchain platforms. Overall, the article highlights the importance of securing cross-chain transactions and proposes a method to prevent misbehavior by evaluating and managing node reputation.
A Speech Recognition Method Based on Domain-Specific Datasets and Confidence Decision Networks
Authors: Dong Zhe , Ding Qianqian , Zhai Weifeng , Zhou Meng
Published: June 2023

This paper proposes a speech recognition method based on a domain-specific language speech network (DSL-Net) and a confidence decision network (CD-Net). The method involves automatically training a domain-specific dataset, using pre-trained model parameters for migration learning, and obtaining a domain-specific speech model. Importance sampling weights were set for the trained domain-specific speech model, which was then integrated with the trained speech model from the benchmark dataset. This integration automatically expands the lexical content of the model to accommodate the input speech based on the lexicon and language model. The adaptation attempts to address the issue of out-of-vocabulary words that are likely to arise in most realistic scenarios and utilizes external knowledge sources to extend the existing language model. By doing so, the approach enhances the adaptability of the language model in new domains or scenarios and improves the prediction accuracy of the model. For domain-specific vocabulary recognition, a deep fully convolutional neural network (DFCNN) and a candidate temporal classification (CTC)-based approach were employed to achieve effective recognition of domain-specific vocabulary. Furthermore, a confidence-based classifier was added to enhance the accuracy and robustness of the overall approach. In the experiments, the method was tested on a proprietary domain audio dataset and compared with an automatic speech recognition (ASR) system trained on a large-scale dataset. Based on experimental verification, the model achieved an accuracy improvement from 82% to 91% in the medical domain. The inclusion of domain-specific datasets resulted in a 5% to 7% enhancement over the baseline, while the introduction of model confidence further improved the baseline by 3% to 5%. These findings demonstrate the significance of incorporating domain-specific datasets and model confidence in advancing speech recognition technology.
Enhancing UAV Detection in Surveillance Camera Videos through Spatiotemporal Information and Optical Flow
Authors: Sun Yu , Zhi Xiyang , Han Haowen , Jiang Shikai , Shi Tianjun , Gong Jinnan , Zhang Wei
Published: June 2023

The growing intelligence and prevalence of drones have led to an increase in their disorderly and illicit usage, posing substantial risks to aviation and public safety. This paper focuses on addressing the issue of drone detection through surveillance cameras. Drone targets in images possess distinctive characteristics, including small size, weak energy, low contrast, and limited and varying features, rendering precise detection a challenging task. To overcome these challenges, we propose a novel detection method that extends the input of YOLOv5s to a continuous sequence of images and inter-frame optical flow, emulating the visual mechanisms employed by humans. By incorporating the image sequence as input, our model can leverage both temporal and spatial information, extracting more features of small and weak targets through the integration of spatiotemporal data. This integration augments the accuracy and robustness of drone detection. Furthermore, the inclusion of optical flow enables the model to directly perceive the motion information of drone targets across consecutive frames, enhancing its ability to extract and utilize features from dynamic objects. Comparative experiments demonstrate that our proposed method of extended input significantly enhances the network’s capability to detect small moving targets, showcasing competitive performance in terms of accuracy and speed. Specifically, our method achieves a final average precision of 86.87%, representing a noteworthy 11.49% improvement over the baseline, and the speed remains above 30 frames per second. Additionally, our approach is adaptable to other detection models with different backbones, providing valuable insights for domains such as Urban Air Mobility and autonomous driving.
Measurement of Pneumatic Valve Flow Parameters on the Test Bench with Interchangeable Venturi Tubes and Their Practical Use
Authors: Dindorf Ryszard
Published: June 2023

A test bench with interchangeable venturi tubes was built to automatically measure the flow parameters of pneumatic valves of a wide range of sizes. This measuring stand contained components recommended by the ISO 6358 standard, an individually configured flow meter circuit, and HMI measurement and control panels. The flow meter circuit, individually configured with interchangeable venturi tubes, bypass loops, and Setaram thermal microflow meter, was calibrated using Molbloc/Molbox equipment. The tuning curve and theoretical flow rate characteristics of the tested valve were fitted to the flow rate measurement data. The best fit value of the critical pressure ratio was obtained using the numerical method of least squares minimization. The pneumatic valve with measured flow parameters was compared with data from the catalogue on the discharge characteristics of the compressed air tank. A practical solution for high-pressure tank discharge time using two valves connected in series to the hybrid tricycle bike (HTB) pneumatic propulsion system is presented. This article presents a solution to the practical problem of measuring the flow parameters of industrial pneumatic valves with a wide range of nominal diameters on a test bench with replaceable venturi tubes.
Study on Multi-Heterogeneous Sensor Data Fusion Method Based on Millimeter-Wave Radar and Camera
Authors: Duan Jianyu
Published: June 2023

This study presents a novel multimodal heterogeneous perception cross-fusion framework for intelligent vehicles that combines data from millimeter-wave radar and camera to enhance target tracking accuracy and handle system uncertainties. The framework employs a multimodal interaction strategy to predict target motion more accurately and an improved joint probability data association method to match measurement data with targets. An adaptive root-mean-square cubature Kalman filter is used to estimate the statistical characteristics of noise under complex traffic scenarios with varying process and measurement noise. Experiments conducted on a real vehicle platform demonstrate that the proposed framework improves reliability and robustness in challenging environments. It overcomes the challenges of insufficient data fusion utilization, frequent leakage, and misjudgment of dangerous obstructions around vehicles, and inaccurate prediction of collision risks. The proposed framework has the potential to advance the state of the art in target tracking and perception for intelligent vehicles.
Embedded Ultrasonic Inspection on the Mechanical Properties of Cold Region Ice under Varying Temperatures
Authors: Han Huimin , Wei Li , Alkayem Nizar Faisal , Cao Maosen
Published: June 2023

The mechanical properties of ice in cold regions are significantly affected by the variation in temperature. The existing methods to determine ice properties commonly rely on one-off and destructive compression and strength experiments, which are unable to acquire the varying properties of ice due to temperature variations. To this end, an embedded ultrasonic system is proposed to inspect the mechanical properties of ice in an online and real-time mode. With this system, ultrasonic experiments are conducted to testify to the validity of the system in continuously inspecting the mechanical properties of ice and, in particular, to verify its capabilities to obtain ice properties for various temperature conditions. As an extension of the experiment, an associated refined numerical model is elaborated by mimicking the number, size, and agglomeration of bubbles using a stochastic distribution. This system can continuously record the wave propagation velocity in the ice, giving rise to ice properties through the intrinsic mechanics relationship. In addition, this model facilitates having insights into the effect of properties, e.g., porosity, on ice properties. The proposed embedded ultrasonic system largely outperforms the existing methods to obtain ice properties, holding promise for developing online and real-time monitoring techniques to assess the ice condition closely related to structures in cold regions.
An Efficient Improved Harris Hawks Optimizer and Its Application to Form Deviation-Zone Evaluation
Authors: Liu Guangshuai , Li Zuoxin , Sun Si , Yang Yuzou , Li Xurui , Yi Wenyu
Published: June 2023

Evaluation of the deviation zone based on discrete measured points is crucial for quality control in manufacturing and metrology. However, deviation-zone evaluation is a highly nonlinear problem that is difficult to solve using traditional numerical optimization methods. Swarm intelligence has many advantages in solving this problem: it produces gradient-free, high-quality solutions and is characterized by its ease of implementation. Therefore, this study applies an improved Harris hawks algorithm (HHO) to tackle the problem. The average fitness is applied to replace the random operator in the exploration phase to solve the problem of conflicting exploration strategies due to randomness. In addition, the salp swarm algorithm (SSA) with a nonlinear inertia weight is embedded into the HHO, such that the superior explorative ability of SSA can fill the gap in the exploration of HHO. Finally, the optimal solution is greedily selected between SSA-based individuals and HHO-based individuals. The effectiveness of the proposed improved HHO optimizer is checked through a comparison with other swarm intelligence methods in typical benchmark problems. Moreover, the experimental results of form deviation-zone evaluation on primitive geometries show that the improved method can accurately solve various form deviations, providing an effective general solution for primitive geometries in the manufacturing and metrology fields.
Transcriptomics Analysis of Tomato Ripening Regulated by Carbon Dioxide
Authors: Bobokalonov Jamshed , Liu Yanhong , Mahalak Karley K. , Firrman Jenni A. , Sheen Shiowshuh , Zhou Siyuan , Liu LinShu
Published: June 2023

Tomatoes are a perishable and seasonal fruit with a high economic impact. Carbon dioxide (CO2), among several other reagents, is used to extend the shelf-life and preserve the quality of tomatoes during refrigeration or packaging. To obtain insight into CO2 stress during tomato ripening, tomatoes at the late green mature stage were conditioned with one of two CO2 delivery methods: 5% CO2 for 14 days (T1) or 100% CO2 for 3 h (T2). Conventional physical and chemical characterization found that CO2 induced by either T1 or T2 delayed tomato ripening in terms of color change, firmness, and carbohydrate dissolution. However, T1 had longer-lasting effects. Furthermore, ethylene production was suppressed by CO2 in T1, and promoted in T2. These physical observations were further evaluated via RNA-Seq analysis at the whole-genome level, including genes involved in ethylene synthesis, signal transduction, and carotenoid biosynthesis. Transcriptomics analysis revealed that the introduction of CO2 via the T1 method downregulated genes related to fruit ripening; in contrast, T2 upregulated the gene encoding for ACS6, the enzyme responsible for S1 ethylene synthesis, even though there was a large amount of ethylene present, indicating that T1 and T2 regulate tomato ripening via different mechanisms. Quantitative real-time PCR assays (qRT-PCR) were used for validation, which substantiated the RNA-Seq data. The results of the present research provide insight into gene regulation by CO2 during tomato ripening at the whole-genome level.
Treatment of Water and Wastewater: Challenges and Solutions
Authors: Mojiri Amin
Published: June 2023

Water is widely recognized as a fundamental factor in ecosystems and environments, playing a crucial role in human life and health [...]
Optimization and Kinetic Study of Treating Dye-Contaminated Wastewater Using Bio-Composite Synthesized from Natural Waste
Authors: Iqbal Mana , Hanif Muhammad Asif , Rashid Umer , Jilani Muhammad Idrees , Alharthi Fahad A. , Kazerooni Elham Ahmed
Published: June 2023

The main objective of the present research project was to investigate the possibility of using low cost, eco-friendly, and easily available adsorbents, such as mint biomass and marble stone waste, for the removal of dyes, DRIM blue HS-RL and DRIM black ep-B, from wastewater using an efficient procedure, which is adsorption. Nine different combinations of these adsorbents were prepared with and without modification using sodium metasilicate and potassium ferricyanide. Spectroscopic analysis was carried out to investigate the λmax of the dyes. Adsorbent nanocomposites were characterized using Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM) and zeta (ζ) potential. Adsorption equilibrium studies were investigated by determining the adsorption at the following conditions: initial dye concentrations (5–50 ppm), adsorbent doses (0.005–0.5 g), contact times (15–240 min), temperatures (30–70 °C) and pH (5–10). Of all the nine adsorbents, MTPF showed the maximum adsorption capacity at 50 ppm initial dye concentration, 0.005 g dosage of adsorbent and 240 min contact time for both dyes. DRIM Blue HS-RL was adsorbed efficiently at 6 pH and temperature 60 °C and DRIM black ep-B was adsorbed at pH 5 and temperature 50 °C by MTPF (mint–tawera composite treated with potassium ferricyanide). Among the various adsorption isotherms (Langmuir, Dubinin–Radushkevich, Freundlich, Herkin–Jura, and Temkin isotherms), some adsorbent followed the Freundlich isotherm while the others followed the Langmuir isotherm. The best-fit model was decided based on their high R2 value and agreement between qe calculated from isotherms and those obtained experimentally. At equilibrium concentration, application of kinetic models (pseudo-first-order, and pseudo-second-order) revealed that the best-fit model was pseudo-second-order kinetic model for both dyes, as their R2 > 0.9, and qe calculated was close to qe obtained experimentally.
The Perilous Mix of Populism and Pandemics: Lessons from COVID-19
Authors: Touchton Michael , Knaul Felicia Marie , McDonald Timothy , Frenk Julio
Published: June 2023

Populist leaders have consistently rejected evidence-based policies in responding to the ongoing COVID-19 pandemic. They acted later and with less intensity than non-populists in implementing public health measures such as physical distancing, lockdowns, and developing public health data sets. We describe the responses of ten large countries with populist leadership at the onset of the pandemic (Brazil, Hungary, India, Mexico, the Philippines, Poland, Russia, Turkey, the United Kingdom, and the United States). Together, these countries account for a disproportionately large number of cases and deaths associated with COVID-19 relative to their population. We categorize the policy responses into two types: (1) slow and ineffective, and (2) strict and illiberal. We conclude that while not all countries that responded poorly to the pandemic were led by populists, no countries with populist leadership performed well in either applying public health measures or achieving desirable health outcomes.
Determinants of Cross-Border Food Purchases on the European Union Market: Research Results from the Lithuanian–Polish Border
Authors: Batyk Iwona M. , Žukovskis Jan , Pilelienė Lina
Published: June 2023

Cross-border trade is one of the possible ways to promote sustainable development by facilitating the exchange of knowledge, technology, and resources and fostering transnational cooperation and innovation towards more environmentally and socially responsible practices. Considering this, the aim of this study was to identify the determinants of food purchases in the EU market in the complex context of factors influencing cross-border purchasing behavior. The novelty of this research lies in its focus on analyzing the determinants of food purchases by inhabitants of border regions in the EU market, specifically within the context of cross-border shopping behavior, using face-to-face self-report interviews conducted in 2022 among Lithuanian consumers purchasing food from the Polish border market. Employing multivariate analysis and scoring methods, a model of the market behavior of inhabitants of the border regions inside the European Union was built and statistically verified. The identification of key determinants, i.e., economic factors (p = 0.013), marketing factors (p = 0.003), risk related to economic factors (p = 0.036), material status (p = 0.009), professional activity (p = 0.044), and age of respondents (p = 0.020), offers valuable insights to scholars investigating consumer cross-border shopping behavior and empowers trade organizers and managers in making informed corporate strategy decisions.
Quantitative Analysis of Water Injection Mass and Timing Effects on Oxy-Fuel Combustion Characteristics in a GDI Engine Fuelled with E10
Authors: Chen Hao , Wang Chenxi , Li Xiang , Li Yongzhi , Zhang Miao , Peng Zhijun , Pei Yiqiang , Ma Zhihao , Zhang Xuewen , Ni Peiyong , Weerasinghe Rohitha , Mobasheri Raouf
Published: June 2023

The climate change issue has become a growing concern due to the increasing greenhouse gas emissions. To achieve carbon neutrality for mitigating the climate problem, the oxy-fuel combustion (OFC) technique on internal combustion engines (ICEs) has attracted much attention. Furthermore, the water injection (WI) strategy was proven effective in improving the combustion process and thermal efficiency in engines under OFC mode. However, WI strategy effects on gasoline direct injection (GDI) engines fuelled with gasoline–alcohol blends have not been reported. This study quantitatively analysed WI mass and timing effects on oxy-fuel combustion performance from a GDI engine fuelled with E10 (10% ethanol and 90% gasoline in mass) by simulation. The results show that equivalent brake-specific fuel consumption (BSFCE) shows a monotonically decreasing trend with the increase in the water–fuel mass ratio (Rwf) from 0 to 0.2. However, further increasing Rwf would cause a deterioration in BSFCE due to the enhanced cooling effects of water vaporisation. Moreover, an appropriate water injection timing (tWI) could be explored for improving OFC performance, especially for large Rwf conditions. The difference in BSFCE between tWI = −100°CA and tWI = −60°CA can be up to around 6.3 g/kWh by increasing Rwf to 0.6.
Proposal for an Eco-Innovation Concept for Small- and Medium-Sized Enterprises (SMEs)
Authors: Bucheli-Calvache Juan Manuel , Zuñiga-Collazos Alexander , Osorio-Tinoco Fabian , Cervantes-Rosas María de los Ángeles
Published: June 2023

Eco-innovation is an important factor not only for the competitiveness of companies in the face of the greening of markets but also as one of the primary means on the road to sustainable development. However, there is a remarkable conceptual diversity in the subject given the theoretical perspective from which eco-innovation is analyzed. Therefore, this research aims to establish an integrating concept of eco-innovation, based on elements of acceptance in the scientific literature, for its application in SMEs. To this end, 40 articles from recognized scientific databases such as ScienceDirect, Scopus and Web of Science (WoS) were reviewed, resulting in a concept that covers the economic approach, definition, dimensions and drivers of eco-innovation. It was found that the implementation of eco-innovation is dependent on the degree of formalization in each element according to the institutional context in question.
Multispectral Remote Sensing for Estimating Water Quality Parameters: A Comparative Study of Inversion Methods Using Unmanned Aerial Vehicles (UAVs)
Authors: Yan Yong , Wang Ying , Yu Cheng , Zhang Zhimin
Published: June 2023

Multispectral remote sensing technology using unmanned aerial vehicles (UAVs) is able to provide fast, large-scale, and dynamic monitoring and management of water environments. We here select multiple water-body indices based on their spectral reflection characteristics, analyze correlations between the reflectance values of water body indices and the water quality parameters of synchronous measured sampling points, and obtain an optimal water body index. A representative selection, such as statistical analysis methods, neural networks, random forest, XGBoost and other schemes are then used to build water-quality parameter inversion models. Results show that the XGBoost model has the highest accuracy for dissolved oxygen parameters (R2 = 0.812, RMSE = 0.414 mg L−1, MRE = 0.057) and the random forest model has the highest accuracy for turbidity parameters (R2 = 0.753, RMSE = 0.732 NTU, MRE = 0.065). Finally, spatial distribution maps of dissolved oxygen and turbidity of water bodies in the experimental domain are drawn to visualize water-quality parameters. This study provides a detailed comparative analysis of multiple inversion methods, including parameter quantity, processing speed, algorithm rigor, solution accuracy, robustness, and generalization, and further evaluates the technical characteristics and applicability of several inversion methods. Our results can provide guidance for improved small- and medium-sized surface-water quality monitoring, and provide an intuitive data analysis basis for urban water environment management.
Sustainable Development Goals and Climate Change in Spanish Technology Disciplines’ Curricula: From LOMCE to LOMLOE
Authors: Núñez-Sánchez Sara , Valente Maria João
Published: June 2023

Understanding the prevalence of climate change and sustainable development in the new curriculum of compulsory secondary education (ESO) and the baccalaureate is crucial for educational communities in Spain. However, there was a lack of studies that examined the integration of climate change and Sustainable Development Goals (SDGs) as cross-cutting themes in the new education framework, particularly in the technology disciplines. This study aimed to address this gap by conducting a comparative analysis of the new legislative content (LOMLOE) and the previous legislation (LOMCE). The analysis quantified the presence of keywords related to climate change and sustainable development in both legal texts, focusing on the definition of objectives, level skills, evaluation criteria, and blocks of knowledge. Additionally, the study assessed the inclusion of SDGs and the ethical implications associated with the use and production of technologies at both education levels. The analysis of the curriculum content revealed a significant presence of references to climate change, sustainable development, and SDGs throughout the LOMLOE curriculum, particularly in the baccalaureate. Notably, education’s role in addressing climate change and promoting sustainable development was explicitly recognized as an objective at this level. Regarding technology disciplines, LOMLOE placed considerable emphasis on fostering awareness of the environmental impact of technological development by introducing a new cross-level knowledge block named “Sustainable Technology”, spanning from ESO to the final courses of baccalaureate. This integration was further reinforced by evaluation criteria and specific skills that strongly aligned with sustainability principles, encouraging assessments centred around environmental awareness, ethical responsibilities, and sustainable entrepreneurship. Further studies are required to evaluate the effectiveness of incorporating SDGs and climate change into technology disciplines following the implementation of LOMLOE, with the aim of identifying best practices for effectively combatting climate change and promoting sustainability in technology education.
Life Cycle Analysis of an On-the-Road Modular Vehicle Concept
Authors: Ulrich Christian , Feinauer Mario , Bieber Katharina , Schmid Stephan A. , Friedrich Horst E.
Published: June 2023

In order to reduce the environmental impacts caused by the transport sector, autonomous and electrified on-the-road modular vehicles (otrm) could be a solution. By separating the drive unit from the transport unit, they enable use cases for various transport tasks and reduce individual and motorized transport and its generated emissions. Therefore, the goal of this study is to assess the environmental impacts from cradle to grave by applying the LCA methodology for a defined otrm—the U-Shift—vehicle fleet considering a specific use case relative to a reference vehicle fleet. The results indicate that the U-Shift fleet reduces the life cycle environmental impacts in a range of 3–28% for all of the seven impact categories, which are analyzed in detail. While emissions from the use phase are similar, U-Shift has an environmental benefit in the production phase due to a low amount of resource-intensive driveboards. Considering the early development stage of U-Shift, several measures are discussed, addressing the material and configuration aspects of the vehicles as well as optimized use case applications, which promise further impact-reduction potential.
Assessment of Land Cover Changes and Climate Variability Effects on Catchment Hydrology Using a Physically Distributed Model
Authors: Kumar Sanjeet , Mishra Ashok , Singh Umesh Kumar
Published: June 2023

Land use/land cover, along with climate variability, play vital roles in hydrological functionality of catchments and are leading threats to inter-related hydrological processes. In the current study, a physically distributed Soil and Water Assessment Tool model is used to investigate the impact of historical changes on the hydrologic response of the Damodar catchment (Jharkhand, India) in terms of inflow to the Panchet reservoir. The model was validated for the monthly runoff and inflow at the outlets of four watersheds and three reservoirs in the Damodar catchment before the assessment of changes in inflow at the Panchet reservoir was performed. The analysis of land cover thematic maps prepared using satellite images of Landsat 4, 5 and 7 showed that from 1972 to 2001, the land cover in the Damodar catchment changed considerably. The interpretation of land cover results indicates that significant increases in settlements (140%), waterbodies (98.42%) and agricultural land (26.71%), along with decreases in wasteland (32.63%) and forest (15.28%), occurred due to development. The Mann–Kendall test was used for measuring the rainfall and temperature for the Damodar catchment, which showed that this region became drier during 1970–2005, with decreases in the annual rainfall and increases in the mean temperature. A simulated hydrological impact under land cover dynamics and climate variability in the historical time frame of 1970–2000 using the model revealed a gradual increase of 26.16% in the Panchet reservoir inflow. The study revealed that the increased inflow is relatively greater under the influence of climate variability due to changes in rainfall and temperature, rather than land cover, that were observed over the region.
Research on the Spatial-Temporal Patterns of Carbon Effects and Carbon-Emission Reduction Strategies for Farmland in China
Authors: Wang Ying , Yang Juan , Duan Caiquan
Published: June 2023

Agriculture has the dual effects of serving as a carbon source and uptaking carbon. Studying the carbon effects of agriculture has great theoretical and practical importance. Based on China’s provincial panel data from 2007 to 2020, using the life cycle method, this paper studied the carbon effects of farmland in China from the perspective of carbon sources and uptake. The spatiotemporal distribution characteristics of carbon effects were analysed, and the carbon-emission reduction potential was calculated. The results displayed that the carbon emissions from farmland in China had a fluctuating downwards trend from 2007 to 2020, with the highest carbon emissions in 2013. The carbon-emission intensity generally displayed a downwards trend, exhibiting a “cold north and hot south” spatial pattern. Furthermore, carbon uptake displayed an overall upwards trend during the study period, increasing by 27.73% compared to that in 2007. Rice, maize, and wheat were the main sources of carbon uptake, and high-carbon-uptake areas were mainly distributed in eastern China; conversely, low-carbon-uptake areas were mainly distributed in southwestern China. Chinese farmland mainly served as net carbon-uptake areas, increasing from 522.81 × 106 t in 2007 to 734.50 × 106 t in 2020. Notably, there were significant differences in net carbon uptake among 31 provinces in China, with a prominent polarization phenomenon. China has great potential for reducing carbon emissions from farmland. Finally, based on the results of clustering carbon-emissions reduction potential, differentiated agricultural management strategies were developed to provide a reference and solutions for decision making related to agricultural “dual-carbon” strategies.
Assessment and Ranking of the Behavioural Leadership Model in the Process of Implementing Reforms in Public Sector of the Republic of Serbia Using the PIPRECIA Method
Authors: Janovac Tatjana , Djokovic Goran , Pusara Aleksandra , Misic Viktorija , Milankovic Ksenija , Pavicevic Aleksandra , Vukovic Adrijana , Jovanovic Sasa Virijevic
Published: June 2023

The establishment of sustainable leadership is essential for the process of implementing reforms in public sector. Sustainable leadership implies effective leadership capable of responding to changes that come from both the internal and external environment. To achieve this, it is necessary to apply multi-criteria decision-making methods. In this paper, the new multi-criteria decision-making method PIPRECIA was used to assess the criteria and five models of leader behaviour that are significant for the process of implementing reforms in the public sector of the Republic of Serbia. In essence, the paper aims to emphasise the simplicity of the practical application of the PIPRECIA method, which was discussed through a numerical illustration on the basis of which the conclusions were drawn. The research results indicate that the criterion Individual competence of the leader (C4-0.24) has the greatest weight. By ranking the alternatives, it was estimated that the Charismatic model of behaviour (A5-0.219) is the leader’s behaviour model that can have the greatest importance in the process of implementing public sector reforms. The Directive model of behaviour (A3-0.197) was derived as the second most important, which is understandable if we consider the fact that this model of leader’s behaviour traditionally dominates the work of the public sector. Furthermore, Participatory behaviour model (A1-0.193), Supportive behaviour model (A2-0.193) and Transformational behaviour model (A4-0.193) have equal rank, which means that they have the same importance for the decision-maker. The findings provide significant information to leaders of public organisations on how to implement reforms and manage human resources more effectively.
Spatial Distribution of Soil Organic Carbon in Relation to Land Use, Based on the Weighted Overlay Technique in the High Andean Ecosystem of Puno—Peru
Authors: Canaza Daniel , Calizaya Elmer , Chambi Walter , Calizaya Fredy , Mindani Carmen , Cuentas Osmar , Caira Cirilo , Huacani Walquer
Published: June 2023

Soil organic carbon (SOC) is a crucial component of the planet and is essential for agriculture development. Our region is known for its livestock and agricultural activities. Hence, understanding the spatial distribution of SOC is crucial for sustainable land management of soils in the mountain ecosystems in the Andes. The methodology consisted of obtaining 53 soil samples from depths of 0 to 105 cm, which were analyzed to obtain SOC (Kg/m2) and organic matter (OM) (%). Ordinary kriging, a geostatistical method, was used to determine SOC. Pearson’s statistical method was applied to determine the association between SOC and precipitation, temperature, altitude, and organic matter and gave acceptable correlations of 0.38, −0.32, 0.40, and 0.59. These results were used to apply weighting criteria for climatological and environmental variables. The weighted overlay tool was used for modeling and mapping the spatial variability of SOC. The estimated spatial distribution of SOC in the micro-watershed reveals an increasing trend from south to north, specifically within the 0–20 cm depth profile. The study confirmed through the spatial analysis that regions with intensive agriculture have low reserves (<3 Kg/m2) of SOC, and areas without agricultural activity but with grazing have average resources of 3 Kg/m2 to 5 Kg/m2 of SOC. Finally, in the upper micro-watersheds where there is no agricultural activity, the reserves are high (5 Kg/m2 to 6.8 Kg/m2). Accordingly, we can promote sustainable and responsible land use practices that support long-term productivity, environmental protection, and societal well-being by prioritizing efficient land utilization, soil conservation, biodiversity conservation, land restoration, and informed land use planning in the high Andean ecosystem of Puno–Peru.
Administrative Aspects Regarding the Valorisation of Geothermal Waters for Balneological Purposes in Bihor County, Romania
Authors: Ciurba (Pastor) Anca-Paula , Haidu Ionel , Ianc Dorina
Published: June 2023

This study aims to analyse the development of the balneological phenomenon in Bihor County generated by its administration, consequent of which, a classification of rural settlements and new tourist resorts has been made. The objective is to identify the current trend in the exploitation of the balneological potential of the localities in Bihor County. A comparative analysis has also been carried out between the influence of the political regimes on the exploitation of geothermal water sources at the level of rural settlements and new tourist resorts during the last five decades. The comparative analysis has revealed that the development of the balneological phenomenon has been carried out sequentially. This sequence has been influenced by the political environment and the administrative factor from the period before and after the Revolution. In the period before the Revolution, all balneological sources were managed by the public administrative factor; they were maintained and kept functional, until the Revolution of 1989, against the background of a stable political environment. In the post-Revolution period, when the decision and involvement was sometimes undertaken on a small scale (private administration), and where the interest in development was focused exclusively on the valorisation of the balneological resources, it can be said that capitalist policy favourably influenced the evolution of the balneological phenomenon at the level of rural settlements and new tourist resorts. We have concluded that the capitalist period has been favourable because, when the thermal baths were found to be unanimously managed by the public administration, they were functional in greater numbers at the rural level as compared to the post-revolutionary period. We have not analysed any “policy” documents in order to make this statement.
Urban Smartness and City Performance: Identifying Brazilian Smart Cities through a Novel Approach
Authors: Fachinelli Ana Cristina , Yigitcanlar Tan , Sabatini-Marques Jamile , Cortese Tatiana Tucunduva Philippi , Sotto Debora , Libardi Bianca
Published: June 2023

While smart city transformation is a remarkably popular topic among urban policymakers across the globe, there is little evidence on how to evaluate a city’s smartness level accurately. This study aims to bridge this knowledge gap by applying a novel assessment framework to a case study context and generating useful insights. To achieve this aim, the study evaluates the smartness levels of 27 Brazilian state capital cities through the indicators of productivity and innovation, livability and well-being, sustainability and accessibility, governance and planning, and connectivity and innovation. This urban smartness analysis is conducted through a smart city assessment framework that brings up three categories of smart city performance types—i.e., leading, following, and developing. The findings of the analysis revealed that the common characteristics of cities with leading smartness performance are having: (a) a strong innovation ecosystem; (b) Specific legislation for developing entrepreneurship; (c) Training opportunities for skilled labor; and (d) Conditions for knowledge-based development and digital transformation offerings and readiness. The analysis identified the smartest cities in Brazil as follows: Florianópolis, São Paulo, Vitória, Curitiba, Porto Alegre, Brasília, Belo Horizonte, Rio de Janeiro, and Cuiabá. This study offers insights from the application of a novel method in the Brazilian context for the local authorities to consider adopting for smart city performance and progress analyses and subsequently making necessary interventions to transform their smart city policy and practice to realize their desired goals.
Modeling of Agricultural Nonpoint-Source Pollution Quantitative Assessment: A Case Study in the Mun River Basin, Thailand
Authors: Zhao Zhonghe , Liu Kun , Yu Bowei , Liu Gaohuan , Wang Youxiao , Wu Chunsheng
Published: June 2023

Agricultural nonpoint-source pollution (ANPSP) is a key cause of global environmental problems. However, the estimation of ANPSP, based on agricultural land use type, crop management, and attenuation of pollutants with distance, is lacking. Using the Mun River Basin as an example, this study established quantitative response relationships between subbasin flows and hydrological and water quality parameters. A good matching of the monitored sections and the control area based on flow relationships was achieved. By determining flow paths and flow distances, the overland and in-river transport attenuations of ANPSP were clarified. The overland and in-river transport and attenuation parameters were also quantified. The land use distribution and structure were further refined through crop management, which included crop types and crop rotation (monocropping or double cropping). Based on the above procedures, quantitative relationships among land use pattern, crop management, attenuation of pollutants with distance, and river water quality were established and used to construct six kinds of regression models. Among these models, the best modeling results were obtained when the parameters of water quality, land use structure, crop management, and soil nutrient attenuation were included. The modeling accuracy in the dry season increased from 0.398 to 0.881 when information about attenuation with distance and crop management was included. Similarly, the modeling accuracy in the wet season increased from 0.365 to 0.727. This study’s findings indicate that the constructed water quality model is effective and has significance for the quantitative determination of ANPSP.