bluuebunny/crossref_metadata_2025 huggingface dataset:
CUS3D: A New Comprehensive Urban-Scale Semantic-Segmentation 3D Benchmark Dataset
Authors: Gao Lin , Liu Yu , Chen Xi , Liu Yuxiang , Yan Shen , Zhang Maojun
Published: March 2024

With the continuous advancement of the construction of smart cities, the availability of large-scale and semantically enriched datasets is essential for enhancing the machine’s ability to understand urban scenes. Mesh data have a distinct advantage over point cloud data for large-scale scenes, as they can provide inherent geometric topology information and consume less memory space. However, existing publicly available large-scale scene mesh datasets are limited in scale and semantic richness and do not cover a wide range of urban semantic information. The development of 3D semantic segmentation algorithms depends on the availability of datasets. Moreover, existing large-scale 3D datasets lack various types of official annotation data, which hinders the widespread applicability of benchmark applications and may cause label errors during data conversion. To address these issues, we present a comprehensive urban-scale semantic segmentation benchmark dataset. It is suitable for various research pursuits on semantic segmentation methodologies. This dataset contains finely annotated point cloud and mesh data types for 3D, as well as high-resolution original 2D images with detailed 2D semantic annotations. It is constructed from a 3D reconstruction of 10,840 UVA aerial images and spans a vast area of approximately 2.85 square kilometers that covers both urban and rural scenes. The dataset is composed of 152,298,756 3D points and 289,404,088 triangles. Each 3D point, triangular mesh, and the original 2D image in the dataset are carefully labeled with one of the ten semantic categories. Six typical 3D semantic segmentation methods were compared on the CUS3D dataset, with KPConv demonstrating the highest overall performance. The mIoU is 59.72%, OA is 89.42%, and mAcc is 97.88%. Furthermore, the experimental results on the impact of color information on semantic segmentation suggest that incorporating both coordinate and color features can enhance the performance of semantic segmentation. The current limitations of the CUS3D dataset, particularly in class imbalance, will be the primary target for future dataset enhancements.
Snow Water Equivalent Monitoring—A Review of Large-Scale Remote Sensing Applications
Authors: Schilling Samuel , Dietz Andreas , Kuenzer Claudia
Published: March 2024

Snow plays a crucial role in the global water cycle, providing water to over 20% of the world’s population and serving as a vital component for flora, fauna, and climate regulation. Changes in snow patterns due to global warming have far-reaching impacts on water management, agriculture, and other economic sectors such as winter tourism. Additionally, they have implications for environmental stability, prompting migration and cultural shifts in snow-dependent communities. Accurate information on snow and its variables is, thus, essential for both scientific understanding and societal planning. This review explores the potential of remote sensing in monitoring snow water equivalent (SWE) on a large scale, analyzing 164 selected publications from 2000 to 2023. Categorized by methodology and content, the analysis reveals a growing interest in the topic, with a concentration of research in North America and China. Methodologically, there is a shift from passive microwave (PMW) inversion algorithms to artificial intelligence (AI), particularly the Random Forest (RF) and neural network (NN) approaches. A majority of studies integrate PMW data with auxiliary information, focusing thematically on remote sensing and snow research, with limited incorporation into broader environmental contexts. Long-term studies (>30 years) suggest a general decrease in SWE in the Northern Hemisphere, though regional and seasonal variations exist. Finally, the review suggests potential future SWE research directions such as addressing PMW data issues, downsampling for detailed analyses, conducting interdisciplinary studies, and incorporating forecasting to enable more widespread applications.
Developing a Multi-Scale Convolutional Neural Network for Spatiotemporal Fusion to Generate MODIS-like Data Using AVHRR and Landsat Images
Authors: Zhang Zhicheng , Ao Zurui , Wu Wei , Wang Yidan , Xin Qinchuan
Published: March 2024

Remote sensing data are becoming increasingly important for quantifying long-term changes in land surfaces. Optical sensors onboard satellite platforms face a tradeoff between temporal and spatial resolutions. Spatiotemporal fusion models can produce high spatiotemporal data, while existing models are not designed to produce moderate-spatial-resolution data, like Moderate-Resolution Imaging Spectroradiometer (MODIS), which has moderate spatial detail and frequent temporal coverage. This limitation arises from the challenge of combining coarse- and fine-spatial-resolution data, due to their large spatial resolution gap. This study presents a novel model, named multi-scale convolutional neural network for spatiotemporal fusion (MSCSTF), to generate MODIS-like data by addressing the large spatial-scale gap in blending the Advanced Very-High-Resolution Radiometer (AVHRR) and Landsat images. To mitigate the considerable biases between AVHRR and Landsat with MODIS images, an image correction module is included into the model using deep supervision. The outcomes show that the modeled MODIS-like images are consistent with the observed ones in five tested areas, as evidenced by the root mean square errors (RMSE) of 0.030, 0.022, 0.075, 0.036, and 0.045, respectively. The model makes reasonable predictions on reconstructing retrospective MODIS-like data when evaluating against Landsat data. The proposed MSCSTF model outperforms six other comparative models in accuracy, with regional average RMSE values being lower by 0.005, 0.007, 0.073, 0.062, 0.070, and 0.060, respectively, compared to the counterparts in the other models. The developed method does not rely on MODIS images as input, and it has the potential to reconstruct MODIS-like data prior to 2000 for retrospective studies and applications.
Joint Retrieval of Multiple Species of Ice Hydrometeor Parameters from Millimeter and Submillimeter Wave Brightness Temperature Based on Convolutional Neural Networks
Authors: Chen Ke , Wu Jiasheng , Chen Yingying
Published: March 2024

Submillimeter wave radiometers are promising remote sensing tools for sounding ice cloud parameters. The Ice Cloud Imager (ICI) aboard the second generation of the EUMETSAT Polar System (EPS−SG) is the first operational submillimeter wave radiometer used for ice cloud remote sensing. Ice clouds simultaneously contain three species of ice hydrometeors—ice, snow, and graupel—the physical distributions and submillimeter wave radiation characteristics of which differ. Therefore, jointly retrieving the mass parameters of the three ice hydrometeors from submillimeter brightness temperatures is very challenging. In this paper, we propose a multiple species of ice hydrometeor parameters retrieval algorithm based on convolutional neural networks (CNNs) that can jointly retrieve the total content and vertical profiles of ice, snow, and graupel particles from submillimeter brightness temperatures. The training dataset is generated by a numerical weather prediction (NWP) model and a submillimeter wave radiative transfer (RT) model. In this study, an end to end ICI simulation experiment involving forward modeling of the brightness temperature and retrieval of ice cloud parameters was conducted to verify the effectiveness of the proposed CNN retrieval algorithm. Compared with the classical Unet, the average relative errors of the improved RCNN–ResUnet are reduced by 11%, 25%, and 18% in GWP, IWP, and SWP retrieval, respectively. Compared with Bayesian Monte Carlo integration algorithm, the average relative error of the total content retrieved by RCNN–ResUnet is reduced by 71%. Compared with BP neural network algorithm, the average relative error of the vertical profiles retrieved by RCNN–ResUnet is reduced by 69%. In addition, this algorithm was applied to actual Advanced Technology Microwave Sounder (ATMS) 183 GHz observed brightness temperatures to retrieve graupel particle parameters with a relative error in the total content of less than 25% and a relative error in the profile of less than 35%. The results show that the proposed CNN algorithm can be applied to future space borne submillimeter wave radiometers to jointly retrieve mass parameters of ice, snow, and graupel.
Multidimensional Evaluation Methods for Deep Learning Models in Target Detection for SAR Images
Authors: Wang Pengcheng , Liu Huanyu , Zhou Xinrui , Xue Zhijun , Ni Liang , Han Qi , Li Junbao
Published: March 2024

As artificial intelligence technology advances, the application of object detection technology in the field of SAR (synthetic aperture radar) imagery is becoming increasingly widespread. However, it also faces challenges such as resource limitations in spaceborne environments and significant uncertainty in the intensity of interference in application scenarios. These factors make the performance evaluation of object detection key to ensuring the smooth execution of tasks. In the face of such complex and harsh application scenarios, methods that rely on single-dimensional evaluation to assess models have had their limitations highlighted. Therefore, this paper proposes a multi-dimensional evaluation method for deep learning models used in SAR image object detection. This method evaluates models in a multi-dimensional manner, covering the training, testing, and application stages of the model, and constructs a multi-dimensional evaluation index system. The training stage includes assessing training efficiency and the impact of training samples; the testing stage includes model performance evaluation, application-based evaluation, and task-based evaluation; and the application stage includes model operation evaluation and model deployment evaluation. The evaluations of these three stages constitute the key links in the performance evaluation of deep learning models. Furthermore, this paper proposes a multi-indicator comprehensive evaluation method based on entropy weight correlation scaling, which calculates the weights of each evaluation indicator through test data, thereby providing a balanced and comprehensive evaluation mechanism for model performance. In the experiments, we designed specific interferences for SAR images in the testing stage and tested three models from the YOLO series. Finally, we constructed a multi-dimensional performance profile diagram for deep learning object detection models, providing a new visualization method to comprehensively characterize model performance in complex application scenarios. This can provide more accurate and comprehensive model performance evaluation for remote sensing data processing, thereby guiding model selection and optimization. The evaluation method proposed in this study adopts a multi-dimensional perspective, comprehensively assessing the three core stages of a model’s lifecycle: training, testing, and application. This framework demonstrates significant versatility and adaptability, enabling it to transcend the boundaries of remote sensing technology and provide support for a wide range of model evaluation and optimization tasks.
Multi-Channel Hyperspectral Imaging Spectrometer Design for Ultraviolet Detection in the Atmosphere of Venus
Authors: Zhang Xv , Fang Xin , Li Tao , Gu Guochao , Li Hanshuang , Shao Yingqiu , Jiang Xue , Li Bo
Published: March 2024

The spectroscopic detection of SO2 and unknown UV absorber substance in the H2SO4 cloud layer of Venus’ atmosphere is currently a focal point in the study of the habitability of Venusian atmospheric clouds. This paper addresses the simultaneous detection requirements of multiple substances in the ultraviolet range of Venus’ atmosphere and proposes a multi-channel hyperspectral imaging system design using pupil separation prisms and grating multilevel spectra. The system achieves a multi-channel design by splitting the entrance pupil of the telescope using prisms. Spectra from different channels are diffracted to the same detector through different orders of the grating. The system features a single spectrometer and detector, enabling simultaneous detection of spectra from different channels. It also boasts advantages such as compact size, ultra-high spectral resolution, and simultaneous multi-channel detection. The system design results indicate that within the working spectral range of three channels, the spectral resolution is better than 0.15 nm, surpassing previous in-orbit or current in-orbit planetary atmospheric detection spectrometers. With a Nyquist frequency of 56 lp/mm, the full-field MTF exceeds 0.7. The system’s smile is less than 0.05 μm, and the keystone is less than 0.04 μm, meeting the requirements for imaging quality.
Design Optimization of Piezocomposites Using a Homogenization Model: From Analytical Model to Experimentation
Authors: Camus Corentin , Cottinet Pierre-Jean , Richard Claude
Published: March 2024

In the process of activating non-conductive smart-structures using piezoelectric patches, one possible method is to add a conductive layer to ensure electrical contact of both electrodes of the ceramic. Therefore, depending on the stiffness and the thickness of this layer, changes in the overall piezoelectric properties lead to a loss in the electromechanical coupling that can be implemented. The purpose of this work is to study the impact of this added electrode layer depending on its thickness. A model of the effect of this layer on the piezoelectrical coefficients has been derived from the previous approach of Hashimoto and Yamagushi and successfully compared to experimental data. This global model computes the variation of all the piezoelectric coefficients, and more precisely of k31 or d31 for various brass electrode volumes relative to the ceramic volume. A decrease in the lateral electromechanical coupling factor k31 was observed and quantified. NAVY II PZT piezoelectric transducers were characterized using IEEE standard methods, with brass electrode thicknesses ranging from 50 to 400 microns. The model fits very well as shown by the results, leading to good expectations for the use of this design approach for actuators or sensors embedded in smart-structures.
Design Aspects for Portable LED-Based Colorimetric Characterisation Systems Targeting Liquid Analytes
Authors: Dupont François , Stoukatch Serguei , Laurent Philippe , Eersels Kasper , van Grinsven Bart , Redouté Jean-Michel
Published: March 2024

Colorimetric characterisation systems based on LEDs and RBG sensors are straightforward to implement, are highly integrable allowing for portable measurement systems and can be constructed using widespread and affordable components. They have already proved to be a satisfactory solution in several applications related to chemical analysis. In this paper, we present an RGB sensor-based prototype for colorimetric characterisation, which can accommodate cuvettes with optical paths of 10 mm and 40 mm. We assessed the impact of experimental condition parameters such as the variability of the analyte volume in the cuvette, as well as the presence of floating particles or deposits at the bottom of the cuvette. While these would not impact the result given by a spectrophotometer that generally has a directional light source, they must be considered in LED/RGB sensor analysers in which the light path is not tightly controlled. We demonstrated that there is a minimal sensor height above the bottom of the cuvette and a minimal analyte level (both depending on the prototype optical path length) above which the analyte volume and the presence of floating particles and deposits have no impact on the prototype output signal. Finally, based on these results, we proposed a test method for a quick dye-displacement assay, in which the reagent is a dye-loaded molecularly imprinted polymer that is poured directly into a cuvette.
The Effects of Speed and Delays on Test-Time Performance of End-to-End Self-Driving
Authors: Tampuu Ardi , Roosild Kristjan , Uduste Ilmar
Published: March 2024

This study investigates the effects of speed variations and computational delays on the performance of end-to-end autonomous driving systems (ADS). Utilizing 1:10 scale mini-cars with limited computational resources, we demonstrate that different driving speeds significantly alter the task of the driving model, challenging the generalization capabilities of systems trained at a singular speed profile. Our findings reveal that models trained to drive at high speeds struggle with slower speeds and vice versa. Consequently, testing an ADS at an inappropriate speed can lead to misjudgments about its competence. Additionally, we explore the impact of computational delays, common in real-world deployments, on driving performance. We present a novel approach to counteract the effects of delays by adjusting the target labels in the training data, demonstrating improved resilience in models to handle computational delays effectively. This method, crucially, addresses the effects of delays rather than their causes and complements traditional delay minimization strategies. These insights are valuable for developing robust autonomous driving systems capable of adapting to varying speeds and delays in real-world scenarios.
A Joint State and Fault Estimation Scheme for State-Saturated System with Energy Harvesting Sensors
Authors: Zhu Li , Huang Cong , Shi Quan , Gao Ruifeng , Ping Peng
Published: March 2024

In this article, the issue of joint state and fault estimation is ironed out for delayed state-saturated systems subject to energy harvesting sensors. Under the effect of energy harvesting, the sensors can harvest energy from the external environment and consume an amount of energy when transmitting measurements to the estimator. The occurrence probability of measurement loss is computed at each instant according to the probability distribution of the energy harvesting mechanism. The main objective of the addressed problem is to construct a joint state and fault estimator where the estimation error covariance is ensured in some certain sense and the estimator gain is determined to accommodate energy harvesting sensors, state saturation, as well as time delays. By virtue of a set of matrix difference equations, the derived upper bound is minimized by parameterizing the estimator gain. In addition, the performance evaluation of the designed joint estimator is conducted by analyzing the boundedness of the estimation error in the mean-squared sense. Finally, two experimental examples are employed to illustrate the feasibility of the proposed estimation scheme.
A Global Seawater Density Distribution Model Using a Convolutional Neural Network
Authors: Liu Qin , Li Liyan , Zhou Yan , Zhang Shiwen , Liu Yuliang , Wang Xinwei
Published: March 2024

Seawater density is an important physical property in oceanography that affects the accuracy of calculations such as gravity fields and tidal potentials and the calibration of acoustic and optical oceanographic sensors. In related studies, constant density values are frequently used, which can introduce significant errors. Therefore, this study employs a basic convolutional neural network model to construct a comprehensive model showing the seawater density distribution across the globe. The model takes into account depth, latitude, longitude, and month as inputs. Numerous real seawater datasets were used to train the model, and it has been shown that the model has an absolute mean error and root mean square error of less than 1 kg/m3 in 99% of the test set samples. The model effectively demonstrates the influence of input parameters on the distribution of seawater density. In this paper, we present a newly developed global model for distributing seawater density which is both comprehensive and accurate, surpassing previous models. The utilization of the model presented in this paper for estimating seawater density can minimize errors in theoretical ocean models and serve as a foundation for designing and analyzing ocean exploration systems.
An Adjustable Wireless Backhaul Link Selection Algorithm for LEO-UAV-Sensor-Based Internet of Remote Things Network
Authors: Chen Rui , Wang Wennai , Wu Wei
Published: March 2024

Internet of Remote Things (IoRT) networks utilize the backhaul links between unmanned aerial vehicles (UAVs) and low-earth-orbit (LEO) satellites to transfer the massive data collected by sensors. However, the backhaul links change rapidly due to the fast movement of both the UAVs and the satellites, which is different from conventional wireless networks. Additionally, due to the various requirements of IoRT multiservices, the system performance should be comprehensively considered. Thus, an adjustable wireless backhaul link selection algorithm for a LEO-UAV-sensor-based IoRT network is proposed. Firstly, an optimization model for backhaul link selection is proposed. This model uses Q, which integrates the remaining service time and capacity as the objective function. Then, based on the snapshot method, the dynamic topology is converted into the static topology and a heuristic optimization algorithm is proposed to solve the backhaul link selection problem. Finally, the proposed algorithm is compared with two traditional algorithms, i.e., maximum service time and maximum capacity algorithms. Numerical simulation results show that the proposed model can achieve better system performance, and the overload of the satellites is more balanced. The algorithm can obtain a trade-off between remaining service time and capacity by dynamically adjusting model parameters. Thus, the adjustable backhaul link selection algorithm can apply to multiservice IoRT scenarios.
Mid-Infrared Photothermal Spectroscopy for the Detection of Caffeine in Beverages
Authors: Ricchiuti Giovanna , Riedlsperger Lisa , Dabrowska Alicja , Rosenberg Erwin , O’Faolain Liam , Lendl Bernhard
Published: March 2024

Caffeine is the most widely consumed stimulant and is the subject of significant ongoing research and discussions due to its impact on human health. The industry’s need to comply with country-specific food and beverage regulations underscores the importance of monitoring caffeine levels in commercial products. In this study, we propose an alternative technique for caffeine analysis that relies on mid-infrared laser-based photothermal spectroscopy (PTS). PTS exploits the high-power output of the quantum cascade laser (QCL) sources to enhance the sensitivity of the mid-IR measurement. The laser-induced thermal gradient in the sample scales with the analytes’ absorption coefficient and concentration, thus allowing for both qualitative and quantitative assessment. We evaluated the performance of our experimental PTS spectrometer, incorporating a tunable QCL and a Mach–Zehnder interferometer, for detecting caffeine in coffee, black tea, and an energy drink. We calibrated the setup with caffeine standards (0.1–2.5 mg mL−1) and we benchmarked the setup’s capabilities against gas chromatography (GC) and Fourier-transform infrared (FTIR) spectroscopy. Quantitative results aligned with GC analysis, and limits of detection matched the research-grade FTIR spectrometer, indicating an excellent performance of our custom-made instrument. This method offers an alternative to established techniques, providing a platform for fast, sensitive, and non-destructive analysis without consumables as well as with high potential for miniaturization.
Realistic 3D Phantoms for Validation of Microwave Sensing in Health Monitoring Applications
Authors: Särestöniemi Mariella , Singh Daljeet , Dessai Rakshita , Heredia Charline , Myllymäki Sami , Myllylä Teemu
Published: March 2024

The development of new medical-monitoring applications requires precise modeling of effects on the human body as well as the simulation and the emulation of realistic scenarios and conditions. The first aim of this paper is to develop realistic and adjustable 3D human-body emulation platforms that could be used for evaluating emerging microwave-based medical monitoring/sensing applications such as the detection of brain tumors, strokes, and breast cancers, as well as for capsule endoscopy studies. New phantom recipes are developed for microwave ranges for phantom molds with realistic shapes. The second aim is to validate the feasibility and reliability of using the phantoms for practical scenarios with electromagnetic simulations using tissue-layer models and biomedical antennas. The third aim is to investigate the impact of the water temperature in the phantom-cooking phase on the dielectric properties of the stabilized phantom. The evaluations show that the dielectric properties of the developed phantoms correspond closely to those of real human tissue. The error in dielectric properties varies between 0.5–8%. In the practical-scenario simulations, the differences obtained with phantoms-based simulations in S21 parameters are 0.1–13 dB. However, the differences are smaller in the frequency ranges used for medical applications.
Persona-PhysioSync AV: Personalized Interaction through Personality and Physiology Monitoring in Autonomous Vehicles
Authors: Giron Jonathan , Sela Yaron , Barenboim Leonid , Gilboa-Freedman Gail , Amichai-Hamburger Yair
Published: March 2024

The emergence of autonomous vehicles (AVs) marks a transformative leap in transportation technology. Central to the success of AVs is ensuring user safety, but this endeavor is accompanied by the challenge of establishing trust and acceptance of this novel technology. The traditional “one size fits all” approach to AVs may limit their broader societal, economic, and cultural impact. Here, we introduce the Persona-PhysioSync AV (PPS-AV). It adopts a comprehensive approach by combining personality traits with physiological and emotional indicators to personalize the AV experience to enhance trust and comfort. A significant aspect of the PPS-AV framework is its real-time monitoring of passenger engagement and comfort levels within AVs. It considers a passenger’s personality traits and their interaction with physiological and emotional responses. The framework can alert passengers when their engagement drops to critical levels or when they exhibit low situational awareness, ensuring they regain attentiveness promptly, especially during Take-Over Request (TOR) events. This approach fosters a heightened sense of Human–Vehicle Interaction (HVI), thereby building trust in AV technology. While the PPS-AV framework currently provides a foundational level of state diagnosis, future developments are expected to include interaction protocols that utilize interfaces like haptic alerts, visual cues, and auditory signals. In summary, the PPS-AV framework is a pivotal tool for the future of autonomous transportation. By prioritizing safety, comfort, and trust, it aims to make AVs not just a mode of transport but a personalized and trusted experience for passengers, accelerating the adoption and societal integration of autonomous vehicles.
High-Frequency Workpiece Image Recognition Model Integrating Multi-Level Network Structure
Authors: Ou Yang , Sun Chenglong , Yuan Rong , Luo Jianqiao
Published: March 2024

High-frequency workpieces have the characteristics of complex intra-class textures and small differences between classes, leading to the problem of low recognition rates when existing models are applied to the recognition of high-frequency workpiece images. We propose in this paper a novel high-frequency workpiece image recognition model that uses EfficientNet-B1 as the basic network and integrates multi-level network structures, designated as ML-EfficientNet-B1. Specifically, a lightweight mixed attention module is first introduced to extract global workpiece image features with strong illumination robustness, and the global recognition results are obtained through the backbone network. Then, the weakly supervised area detection module is used to locate the locally important areas of the workpiece and is introduced into the branch network to obtain local recognition results. Finally, the global and local recognition results are combined in the branch fusion module to achieve the final recognition of high-frequency workpiece images. Experimental results show that compared with various image recognition models, the proposed ML-EfficientNet-B1 model has stronger adaptability to illumination changes, significantly improves the performance of high-frequency workpiece recognition, and the recognition accuracy reaches 98.3%.
Seasonal Variation and the Effect of the Transition Period on Physical Fitness Parameters in Youth Female Soccer Players
Authors: Parpa Koulla , Katanic Borko , Michaelides Marcos
Published: March 2024

This study examined seasonal variation and the effect of the transition period on physical fitness parameters in female youth soccer players. Twenty-four players (13–17 years old) were assessed through field and laboratory testing. They completed laboratory testing three times: (1) at the beginning of the season (post-preseason), (2) at the end of the regular season (postseason), and (3) following the transition period (preseason). Field testing was conducted post-preseason and postseason. Results indicated that weight and body fat significantly increased from post-preseason to postseason and following the transition period. A paired samples T-test revealed that the players performed significantly better in the vertical jump and 30 m sprint test (all p < 0.01) postseason compared to post-preseason. Also, it was indicated that torque production for the right quadriceps, left quadriceps, and right hamstring (all p < 0.01) was significantly reduced after the transition period by 10%, 11.8%, and 10.5%, respectively. Cardiorespiratory measurements demonstrated that performance on an incremental cardiopulmonary treadmill test, maximal oxygen consumption, velocity at the first ventilatory threshold, velocity at the second ventilatory threshold, and velocity at maximal oxygen consumption (all p < 0.01) were significantly reduced postseason and following the transition period. Based on the results, coaches and trainers should primarily focus on maintaining the aerobic capacity of the players during the season, as it has been indicated to be reduced from the beginning to the end of the competitive period (VO2max reduced by 3.3%). In addition, they should focus on maintaining lower body strength and aerobic capacity during the transition period.
A Note on Simultaneous Confidence Intervals for Direct, Indirect and Synthetic Estimators
Authors: Valvason Christophe Quentin , Sperlich Stefan
Published: March 2024

Direct, indirect and synthetic estimators have a long history in official statistics. While model-based or model-assisted approaches have become very popular, direct and indirect estimators remain the predominant standard and are therefore important tools in practice. This is mainly due to their simplicity, including low data requirements, assumptions and straightforward inference. With the increasing use of domain estimates in policy, the demands on these tools have also increased. Today, they are frequently used for comparative statistics. This requires appropriate tools for simultaneous inference. We study devices for constructing simultaneous confidence intervals and show that simple tools like the Bonferroni correction can easily fail. In contrast, uniform inference based on max-type statistics in combination with bootstrap methods, appropriate for finite populations, work reasonably well. We illustrate our methods with frequently applied estimators of totals and means.
The Distribution and Evolution of Groundwater Level Depths and Groundwater Sustainability in the Hexi Corridor over the Last Five Years
Authors: Cao Le , Liu Xuequan , Zhu Pucheng , Wang Lifang
Published: March 2024

Groundwater overexploitation for agricultural irrigation is prone to lead to numerous ecological concerns. This study delved into the present distribution and recent trend of groundwater levels in the plain areas of the Hexi Corridor in Northwest China according to the groundwater level depth (GWD) data from 264 monitoring wells in the Shiyang River Basin (SYB) and 107 in the Shule River Basin (SLB), recorded annually in April from 2019 to 2023. The key findings include the following: (1) Over the five-year span, the SYB’s GWD experienced change rates (CRs) ranging from −12.17 to 9.11 m/a (average: −0.13 m/a), with the number of monitoring wells showing increased and decreased GWDs accounting for 50% and 50%, respectively. By contrast, the SLB’s GWD exhibited CRs ranging from −1.87 to 2.06 m/a (average: 0.01 m/a), with the number of monitoring wells showing increased and decreased GWDs accounting for 52% and 48%, respectively; (2) the Wuwei (CR = 0.09 m/a) and Changning (0.58 m/a) basins in the SYB and the Yumen (0.06 m/a), Guazhou (0.05 m/a), and Huahai (0.03 m/a) basins in the SLB, witnessed rising groundwater levels. In contrast, the Minqin Basin (0.09 m/a) in the SYB and the southern Dunhuang Basin (0.04 m/a) in the SLB witnessed declines in the groundwater levels; (3) The groundwater sustainability assessment showed that the groundwater is still extremely unsustainable. This study’s insights are instrumental in targeted treatment, as well as the preparation and adjustment of sustainable groundwater protection strategies.
Market Diversification and Competitiveness of Fresh Grape Exports in Peru
Authors: Montes Ninaquispe Jose Carlos , Vasquez Huatay Kelly Cristina , Ludeña Jugo Diego Alejandro , Pantaleón Santa María Alberto Luis , Farías Rodríguez Juan César , Suárez Santa Cruz Fernando , Escalona Aguilar Erik Omar , Arbulú-Ballesteros Marco Agustín
Published: March 2024

Global trade and globalization have driven growth and diversification in the horticultural industry. Fresh grapes, a key product, face challenges of market volatility, trade barriers, and logistics. Market diversification is essential for stability and competitiveness in international trade. This research focused on analyzing market diversification in Peru’s fresh grape exports during the 2013–2022 period, exploring its implications for international trade. A quantitative methodology was used, along with the analysis of publicly recorded data on the website of the National Superintendence of Customs and Tax Administration, specifically exports from companies shipping under the national subheading for Grapes (0806.10.00.00). Diversification was analyzed using the Herfindahl–Hirschman concentration index (HHI), and competitiveness through the Balassa index. The main findings were that between 2013 and 2022, Peruvian grape exports grew in value (CAGR of 12.02%) and volume (CAGR of 12.13%). The sector expanded with an average of 151.2 companies and 59.1 destination markets. Diversification varied: the USA (12.4%), the Netherlands (1.6%), Hong Kong (4.2%), and Mexico (63.2%). The Herfindahl–Hirschman index showed concentration in the USA (4533 in 2020, 4519 in 2022) and stability in companies (2318 in 2014, 2450 in 2022). Finally, it is recommended to strengthen the Peruvian viticultural sector by maintaining geographic diversification through policies seeking new markets, monitoring global trends, constantly analyzing market concentration, and promoting fair competition among participating companies, allowing new ones to enter this sector.
Algae Extracts in Horticulture: Characterization of Algae-Based Extracts and Impact on Turnip Germination and Radish Culture
Authors: Santos Daniel , Cotas João , Pereira Leonel , Bahcevandziev Kiril
Published: March 2024

Algae are rich in nutrients and bioactive compounds, playing a crucial role as biostimulants for plants, enhancing growth and resilience. Four algae-based extracts were tested: the raw extract of red macroalgae Calliblepharis jubata (CJ), Ulasco (UA), Grasco (GR) and “AgriAlgae Foliar” (AA), the latter with microalgae. The extracts were evaluated for their physicochemical parameters (pH, electrical conductivity and solids), macro and microelements, phenolic compounds and antioxidants. Afterwards, seed germination trials were carried out with turnip seeds (Brassica rapa var. cymosa L.), and pot trials were carried out with Cherry Belle (Flora Lusitana, Cantanhede, Portugal) radish plants (Raphanus sativus L.), to verify the biostimulant potential of the extracts in horticulture. In the pot trials, all the treatments led to better yields and nutritional quality. The UA 0.12 extract influenced the heaviest roots (40.32 ± 11.89 g), on average, and the GR 0.10 extract in roots with the highest percentage of proteins (1.866 ± 0.004% dm), phenolic compounds (0.12121 mg eq. gallic acid/g fm) and antioxidants (0.0754 ± 0.0000 mg eq. ascorbic acid/g fm). The radishes treated with the AA 0.003 extract showed the greatest uniformity, the healthiest leaves, with the highest flavonoid content and the heaviest aerial part (19.52 ± 5.99 g). All the extract treatments resulted in a visible mitigation of abiotic stress and consequently better results, showing that these can be crucial for sustainable agriculture.
Consumer Preference for Fisheries Improvement Project: Case of Bigeye Tuna in Japan
Authors: Wakamatsu Hiroki , Maruyama Yuki
Published: March 2024

In recent years, demand for sustainable fisheries certification, also known as seafood ecolabeling, has grown worldwide, with retailers actively promoting ecolabeled seafood, mainly in Europe and the United States. However, the costs associated with assessment and maintenance are typically incurred before certification, and the potential benefits are uncertain, which deters many fisheries from entering the certification process. The Fishery Improvement Project (FIP) is a market-driven mechanism that allows a fishery to gain recognition for its sustainable management efforts aimed at achieving sustainable certification. Market differentiation of FIP-participating fisheries from conventional fisheries has the potential to generate additional benefits that may offset some of the certification costs. However, successful differentiation efforts require consumer awareness, willingness to pay a premium, and effective communication strategies. This study investigates consumer preferences for bigeye tuna sashimi in Japan using a discrete choice experiment to determine if Japanese consumers are willing to pay a price premium for FIP-participating fisheries. The analysis resulted in a significant price premium for FIP and domestic certification valued more than international brands. These findings suggest that FIP-participating fisheries have the potential for cost recovery, even during the certification process.
(Micro-)Plastics in Saturated and Unsaturated Groundwater Bodies: First Evidence of Presence in Groundwater Fauna and Habitats
Authors: Sforzi Laura , Tabilio Di Camillo Agostina , Di Lorenzo Tiziana , Galassi Diana Maria Paola , Balestra Valentina , Piccini Leonardo , Cabigliera Serena Benedetta , Ciattini Samuele , Laurati Marco , Chelazzi David , Martellini Tania , Cincinelli Alessandra
Published: March 2024

Microplastic (MP) pollution is a growing concern in every known ecosystem. However, MP presence in groundwaters and the ecological impact they can have on groundwater fauna is still poorly investigated. Here, we assess the presence of MPs in three Italian groundwater bodies, comprising two karst caves and two monitoring bores of a saturated alluvial aquifer. In addition to water samples, groundwater invertebrates were collected to assess their potential ingestion of MPs. For water samples, chemical characterization of polymers was done by Fourier Transform Infrared Spectroscopy (FTIR) 2D imaging, while fauna samples were analyzed with a tandem microscopy approach (fluorescence microscopy and FTIR). The abundance of MPs in water samples varied from 18 to 911 items/L. The majority of MPs were fibers (91%), with a mean size in the range of 100 µm and 1 mm. Black, red, and blue were the most abundant colors (30%, 25%, and 19%, respectively). The most abundant polymer was artificial/textile cellulose (65%), followed by PET (21%). MPs were found in every groundwater taxon. Pellets were the most abundant in each specimen (87% on average), while the largest were fragments, with a mean dimension of 26 µm. Cellulose was found to be the most abundant polymer (51%). This study is the first to highlight the presence of MPs ingested by groundwater fauna. Further investigations are urgently required to assess the potential ecological impact MPs can have on the resident fauna in these sensitive ecosystems.
The Impact of Financial Development and Economic Growth on Renewable Energy Supply in South Africa
Authors: Ngcobo Reitumetse , De Wet Milan Christian
Published: March 2024

Eskom’s power plants in South Africa face frequent breakdowns due to a lack of maintenance and increasing energy demand. The high dependence of South Africa on coal for power generation, which is a resource that significantly contributes to carbon dioxide (CO2) emissions that impact the environment negatively, could be reduced by considering renewable energy sources. Renewable energy supply, dependent on private sector funding and economic growth, is seen as a solution to energy and environmental problems. The study aimed to examine if financial development and economic growth impact renewable energy supply in South Africa and to discover if co-integration exists between these variables, including the variables defined as the determinants of renewable energy supply, namely: CO2 emission by coal power generation; secondly, coal electricity supply; thirdly, coal price changes; and lastly, load shedding levels. The research gap identified for the study is twofold. Firstly, there is a lack of research on the relationship between renewable energy supply, financial development, and economic growth, specifically in South Africa. Furthermore, the existing research on these variables in other countries has produced inconclusive results. Secondly, minimal research has been conducted on how economic growth impacts renewable energy supply in emerging markets. Thus, the present study sought to bridge the gap and contribute to the scientific body of knowledge related to the drivers of renewable energy supply. The autoregression distributed lag (ARDL) model was employed to test if economic growth and financial development have a statistically significant impact on renewable energy supply, as well as to test the direction of the relationship, for an observation period from 1990 to 2021. The results proved that financial development and economic growth were reported to have a statistically significant positive impact on renewable energy supply in the long run and the short run. A study on the relationship between financial development, economic growth, and renewable energy supply in South Africa can influence policy reforms and assist the National Energy Regulator of South Africa (NERSA) and the government in developing and implementing renewable energy policies that encourage the deployment of renewable energy infrastructure to increase renewable energy supply, particularly regarding factors associated with addressing challenges in financial development and economic growth.
Soil Quality Evaluation in Mono and Mixed Eucalypt Plantation
Authors: Pereira Arthur P. A. , Cherubin Maurício R. , de Araujo Ademir S. F. , Santana Maiele C. , de Medeiros Erika V. , da Costa Diogo P. , de Souza Adijailton J. , Lima Antonio Y. V. , da Silva Danilo F. , Estrada Paula A. C. , Mendes Lucas W. , Verma Jay P. , Koutika Lydie S. , Cardoso Elke J. B. N.
Published: March 2024

Soil quality (SQ) pertains to the intricate and ongoing capacity of soil to function as a thriving ecosystem that supports the growth of plants and animals. However, there is a limited understanding of SQ assessment in mixed forest plantations. Therefore, we formulated and tested the hypothesis that the inclusion of a nitrogen-fixing tree species (such as Acacia mangium) improves SQ indicators in mixed treatments involving Eucalyptus trees. To evaluate the changes in SQ, we conducted a field experiment that employed the Soil Management Assessment Framework (SMAF) tool to analyze pure and mixed plantations of Eucalyptus grandis and A. mangium. Soil samples were collected at a depth of 0–20 cm from different treatments, including pure E. grandis without nitrogen fertilization (E), pure A. mangium (A), pure E. grandis with nitrogen fertilization (E + N), and mixed E. grandis and A. mangium (E + A). Sampling took place at 27 and 39 months after planting. We selected seven indicators of SQ: two biological indicators (soil microbial biomass carbon and β-glucosidase enzyme activity), four chemical indicators (soil organic carbon, pH, available phosphorus, and potassium), and one physical indicator (bulk density). By applying the SMAF tool, we determined the SQ scores for each indicator. The results revealed that E + A stands exhibited higher SMAF scores than pure stands, particularly in terms of pH (0.49 and 0.52 at 27 and 39 months, respectively) and phosphorus levels (0.84 and 0.82, at 27 and 39 months), respectively. Forest management practices and the sampling period had the most pronounced impact on biological and chemical indicators. Notably, significant positive correlations were observed between SMAF scores and pH, available phosphorus content, enzymes, soil organic carbon, and microbial biomass in both sampling periods. This study effectively provided novel information that introducing a nitrogen-fixing tree species in combination with eucalyptus trees enhances SQ, as indicated by the SMAF tool, which could reduce the need for external inputs (e.g., mineral fertilizers) by the farmers. Future studies should analyze the effects of A. mangium not only with other E. grandis varieties but also with other forestry essences.
Modeling of the Acceptable Waiting Time for EV Charging in Japan
Authors: Hanni Umm e , Yamamoto Toshiyuki , Nakamura Toshiyuki
Published: March 2024

The limited number of charging stations for electric vehicles (EVs) necessitates periodic charging, resulting in extended queues at charging stations as drivers await their availability. This study contributes to the existing body of literature by providing estimates of consumer preferences for allowable waiting times at charging stations, as well as furthering the understanding of the roles of the explanatory variables influencing these preferences. The study also compares the average and maximum waiting times experienced by EV drivers, with the acceptable waiting time. Responses from the stated preference survey in Japan in 2021 were analyzed using a generalized ordered logit model. The results show that (a) the sex, age, household income, employment status, and vehicle usage frequency significantly influenced the preferences for allowable waiting times, and (b) the allowable waiting time preferences were significantly associated with the charging locations. Our estimation model indicated a positive association of convenience stores, large commercial facilities, and highway locations with short and medium allowable waiting times. The results provide useful insights into the policy implications of the charging infrastructure.