bluuebunny/crossref_metadata_2025 huggingface dataset:
Towards Monitoring and Identification of Red Palm Weevil Gender Using Microwave CSRR-Loaded TL Sensors
Authors: Bait-Suwailam Mohammed M.
Published: July 2023

This paper presents for the first time the design of a microwave sensing setup for the potential monitoring and identification of red palm weevil (RPW) gender type. The microwave sensor consists of a planar two-port transmission line (TL) with a single complementary split-ring resonant (CSRR) inclusion etched from the bottom metallic layer. The CSRR sensor is placed on top of a customized non-conductive container. The microwave sensing setup was designed, numerically demonstrated, fabricated and tested experimentally. Simulated results correlate quite well with the experimental data. Moreover, the sensitivity of the CSRR sensor when in close proximity to different RPW genders was evaluated both numerically and experimentally. Based on the measured results from 15 RPW samples with different body sizes, different RPW gender types showed unique microwave signatures. A notable shift in the sensor’s resonance frequency was achieved, where on average a resonant frequency shift of 10% for adult RPWs was achieved, while a 2.4% frequency change was obtained for larvae (young) RPWs. Hence, the proposed microwave sensing setup can be adopted in field trials to examine and differentiate between various RPW genders at various developmental stages.
Facial Expression Recognition Based on Fine-Tuned Channel–Spatial Attention Transformer
Authors: Yao Huang , Yang Xiaomeng , Chen Di , Wang Zhao , Tian Yuan
Published: July 2023

Facial expressions help individuals convey their emotions. In recent years, thanks to the development of computer vision technology, facial expression recognition (FER) has become a research hotspot and made remarkable progress. However, human faces in real-world environments are affected by various unfavorable factors, such as facial occlusion and head pose changes, which are seldom encountered in controlled laboratory settings. These factors often lead to a reduction in expression recognition accuracy. Inspired by the recent success of transformers in many computer vision tasks, we propose a model called the fine-tuned channel–spatial attention transformer (FT-CSAT) to improve the accuracy of recognition of FER in the wild. FT-CSAT consists of two crucial components: channel–spatial attention module and fine-tuning module. In the channel–spatial attention module, the feature map is input into the channel attention module and the spatial attention module sequentially. The final output feature map will effectively incorporate both channel information and spatial information. Consequently, the network becomes adept at focusing on relevant and meaningful features associated with facial expressions. To further improve the model’s performance while controlling the number of excessive parameters, we employ a fine-tuning method. Extensive experimental results demonstrate that our FT-CSAT outperforms the state-of-the-art methods on two benchmark datasets: RAF-DB and FERPlus. The achieved recognition accuracy is 88.61% and 89.26%, respectively. Furthermore, to evaluate the robustness of FT-CSAT in the case of facial occlusion and head pose changes, we take tests on Occlusion-RAF-DB and Pose-RAF-DB data sets, and the results also show that the superior recognition performance of the proposed method under such conditions.
Fusion of Appearance and Motion Features for Daily Activity Recognition from Egocentric Perspective
Authors: Lye Mohd Haris , AlDahoul Nouar , Abdul Karim Hezerul
Published: July 2023

Vidos from a first-person or egocentric perspective offer a promising tool for recognizing various activities related to daily living. In the egocentric perspective, the video is obtained from a wearable camera, and this enables the capture of the person’s activities in a consistent viewpoint. Recognition of activity using a wearable sensor is challenging due to various reasons, such as motion blur and large variations. The existing methods are based on extracting handcrafted features from video frames to represent the contents. These features are domain-dependent, where features that are suitable for a specific dataset may not be suitable for others. In this paper, we propose a novel solution to recognize daily living activities from a pre-segmented video clip. The pre-trained convolutional neural network (CNN) model VGG16 is used to extract visual features from sampled video frames and then aggregated by the proposed pooling scheme. The proposed solution combines appearance and motion features extracted from video frames and optical flow images, respectively. The methods of mean and max spatial pooling (MMSP) and max mean temporal pyramid (TPMM) pooling are proposed to compose the final video descriptor. The feature is applied to a linear support vector machine (SVM) to recognize the type of activities observed in the video clip. The evaluation of the proposed solution was performed on three public benchmark datasets. We performed studies to show the advantage of aggregating appearance and motion features for daily activity recognition. The results show that the proposed solution is promising for recognizing activities of daily living. Compared to several methods on three public datasets, the proposed MMSP–TPMM method produces higher classification performance in terms of accuracy (90.38% with LENA dataset, 75.37% with ADL dataset, 96.08% with FPPA dataset) and average per-class precision (AP) (58.42% with ADL dataset and 96.11% with FPPA dataset).
Measurement Technologies of Light Field Camera: An Overview
Authors: Hu Xiaoming , Li Zhuotong , Miao Li , Fang Fengzhou , Jiang Zhongjie , Zhang Xiaodong
Published: July 2023

Visual measurement methods are extensively used in various fields, such as aerospace, biomedicine, agricultural production, and social life, owing to their advantages of high speed, high accuracy, and non-contact. However, traditional camera-based measurement systems, relying on the pinhole imaging model, face challenges in achieving three-dimensional measurements using a single camera by one shot. Moreover, traditional visual systems struggle to meet the requirements of high precision, efficiency, and compact size simultaneously. With the development of light field theory, the light field camera has garnered significant attention as a novel measurement method. Due to its special structure, the light field camera enables high-precision three-dimensional measurements with a single camera through only one shot. This paper presents a comprehensive overview of light field camera measurement technologies, including the imaging principles, calibration methods, reconstruction algorithms, and measurement applications. Additionally, we explored future research directions and the potential application prospects of the light field camera.
3D Printed Fused Deposition Modeling (FDM) Capillaries for Chemiresistive Gas Sensors
Authors: Adamek Martin , Mlcek Jiri , Skowronkova Nela , Zvonkova Magdalena , Jasso Miroslav , Adamkova Anna , Skacel Josef , Buresova Iva , Sebestikova Romana , Cernekova Martina , Buckova Martina
Published: July 2023

This paper discusses the possible use of 3D fused deposition modeling (FDM) to fabricate capillaries for low-cost chemiresistive gas sensors that are often used in various applications. The disadvantage of these sensors is low selectivity, but 3D printed FDM capillaries have the potential to increase their selectivity. Capillaries with 1, 2 and 3 tiers with a length of 1.5 m, 3.1 m and 4.7 m were designed and manufactured. Food and goods available in the general trade network were used as samples (alcohol, seafood, chicken thigh meat, acetone-free nail polish remover and gas from a gas lighter) were also tested. The “Vodka” sample was used as a standard for determining the effect of capillary parameters on the output signal of the MiCS6814 sensor. The results show the shift of individual parts of the signal in time depending on the parameters of the capillary and the carrier air flow. A three-tier capillary was chosen for the comparison of gas samples with each other. The graphs show the differences between individual samples, not only in the height of the output signal but also in its time characteristic. The tested 3D printed FDM capillaries thus made it possible to characterize the output response by also using an inexpensive chemiresistive gas sensor in the time domain.
Positioning System of Infrared Sensors Based on ZnO Thin Film
Authors: Tsai Chia-Yu , Lin Yan-Wen , Ku Hong-Ming , Lee Chia-Yen
Published: July 2023

Infrared sensors incorporating suspended zinc oxide (ZnO) pyroelectric films and thermally insulated silicon substrates are fabricated using conventional MEMS-based thin-film deposition, photolithography, and etching techniques. The responsivity of the pyroelectric film is improved via annealing at 500 °C for 4 h. The voltage response of the fabricated sensors is evaluated experimentally for a substrate thickness of 1 µm over a sensing range of 30 cm. The results show that the voltage signal varies as an inverse exponential function of the distance. A positioning system based on three infrared sensors is implemented in LabVIEW. It is shown that the position estimates obtained using the proposed system are in excellent agreement with the actual locations. In general, the results presented in this study provide a useful source of reference for the further development of MEMS-based pyroelectric infrared sensors.
Application of Social Robots in Healthcare: Review on Characteristics, Requirements, Technical Solutions
Authors: Ragno Luca , Borboni Alberto , Vannetti Federica , Amici Cinzia , Cusano Nicoletta
Published: July 2023

Cyber-physical or virtual systems or devices that are capable of autonomously interacting with human or non-human agents in real environments are referred to as social robots. The primary areas of application for biomedical technology are nursing homes, hospitals, and private homes for the purpose of providing assistance to the elderly, people with disabilities, children, and medical personnel. This review examines the current state-of-the-art of social robots used in healthcare applications, with a particular emphasis on the technical characteristics and requirements of these different types of systems. Humanoids robots, companion robots, and telepresence robots are the three primary categories of devices that are identified and discussed in this article. The research looks at commercial applications, as well as scientific literature (according to the Scopus Elsevier database), patent analysis (using the Espacenet search engine), and more (searched with Google search engine). A variety of devices are enumerated and categorized, and then our discussion and organization of their respective specifications takes place.
A Survey of Image-Based Fault Monitoring in Additive Manufacturing: Recent Developments and Future Directions
Authors: Kim Ryanne Gail , Abisado Mideth , Villaverde Jocelyn , Sampedro Gabriel Avelino
Published: July 2023

Additive manufacturing (AM) has emerged as a transformative technology for various industries, enabling the production of complex and customized parts. However, ensuring the quality and reliability of AM parts remains a critical challenge. Thus, image-based fault monitoring has gained significant attention as an efficient approach for detecting and classifying faults in AM processes. This paper presents a comprehensive survey of image-based fault monitoring in AM, focusing on recent developments and future directions. Specifically, the proponents garnered relevant papers from 2019 to 2023, gathering a total of 53 papers. This paper discusses the essential techniques, methodologies, and algorithms employed in image-based fault monitoring. Furthermore, recent developments are explored such as the use of novel image acquisition techniques, algorithms, and methods. In this paper, insights into future directions are provided, such as the need for more robust image processing algorithms, efficient data acquisition and analysis methods, standardized benchmarks and datasets, and more research in fault monitoring. By addressing these challenges and pursuing future directions, image-based fault monitoring in AM can be enhanced, improving quality control, process optimization, and overall manufacturing reliability.
Joint Optimization of Bandwidth and Power Allocation in Uplink Systems with Deep Reinforcement Learning
Authors: Zhang Chongli , Lv Tiejun , Huang Pingmu , Lin Zhipeng , Zeng Jie , Ren Yuan
Published: July 2023

Wireless resource utilizations are the focus of future communication, which are used constantly to alleviate the communication quality problem caused by the explosive interference with increasing users, especially the inter-cell interference in the multi-cell multi-user systems. To tackle this interference and improve the resource utilization rate, we proposed a joint-priority-based reinforcement learning (JPRL) approach to jointly optimize the bandwidth and transmit power allocation. This method aims to maximize the average throughput of the system while suppressing the co-channel interference and guaranteeing the quality of service (QoS) constraint. Specifically, we de-coupled the joint problem into two sub-problems, i.e., the bandwidth assignment and power allocation sub-problems. The multi-agent double deep Q network (MADDQN) was developed to solve the bandwidth allocation sub-problem for each user and the prioritized multi-agent deep deterministic policy gradient (P-MADDPG) algorithm by deploying a prioritized replay buffer that is designed to handle the transmit power allocation sub-problem. Numerical results show that the proposed JPRL method could accelerate model training and outperform the alternative methods in terms of throughput. For example, the average throughput was approximately 10.4–15.5% better than the homogeneous-learning-based benchmarks, and about 17.3% higher than the genetic algorithm.
A Novel Method for Quantifying Plant Morphological Characteristics Using Normal Vectors and Local Curvature Data via 3D Modelling—A Case Study in Leaf Lettuce
Authors: Wada Kaede C. , Hayashi Atsushi , Lee Unseok , Tanabata Takanari , Isobe Sachiko , Itoh Hironori , Maeda Hideki , Fujisako Satoshi , Kochi Nobuo
Published: July 2023

Three-dimensional measurement is a high-throughput method that can record a large amount of information. Three-dimensional modelling of plants has the possibility to not only automate dimensional measurement, but to also enable visual assessment to be quantified, eliminating ambiguity in human judgment. In this study, we have developed new methods that could be used for the morphological analysis of plants from the information contained in 3D data. Specifically, we investigated characteristics that can be measured by scale (dimension) and/or visual assessment by humans. The latter is particularly novel in this paper. The characteristics that can be measured on a scale-related dimension were tested based on the bounding box, convex hull, column solid, and voxel. Furthermore, for characteristics that can be evaluated by visual assessment, we propose a new method using normal vectors and local curvature (LC) data. For these examinations, we used our highly accurate all-around 3D plant modelling system. The coefficient of determination between manual measurements and the scale-related methods were all above 0.9. Furthermore, the differences in LC calculated from the normal vector data allowed us to visualise and quantify the concavity and convexity of leaves. This technique revealed that there were differences in the time point at which leaf blistering began to develop among the varieties. The precise 3D model made it possible to perform quantitative measurements of lettuce size and morphological characteristics. In addition, the newly proposed LC-based analysis method made it possible to quantify the characteristics that rely on visual assessment. This research paper was able to demonstrate the following possibilities as outcomes: (1) the automation of conventional manual measurements, and (2) the elimination of variability caused by human subjectivity, thereby rendering evaluations by skilled experts unnecessary.
Welding Spark Detection on Construction Sites Using Contour Detection with Automatic Parameter Tuning and Deep-Learning-Based Filters
Authors: Jin Xi , Ahn Changbum Ryan , Kim Jinwoo , Park Moonseo
Published: July 2023

One of the primary causes of fires at construction sites is welding sparks. Fire detection systems utilizing computer vision technology offer a unique opportunity to monitor fires in construction sites. However, little effort has been made to date in regard to real-time tracking of small sparks that can lead to major fires at construction sites. In this study, a novel method is proposed to detect welding sparks in real-time contour detection with deep learning parameter tuning. An automatic parameter tuning algorithm employing a convolutional neural network was developed to identify the optimum hue saturation value. Additional filtering methods regarding the non-welding zone and a contour area-based filter were also newly developed to enhance the accuracy of welding spark prediction. The method was evaluated using 230 welding spark images and 104 videos. The results obtained from the welding images indicate that the suggested model for detecting welding sparks achieves a precision of 74.45% and a recall of 63.50% when noise images, such as flashing and reflection light, were removed from the dataset. Furthermore, our findings demonstrate that the proposed model is effective in capturing the number of welding sparks in the video dataset, with a 95.2% accuracy in detecting the moment when the number of welding sparks reaches its peak. These results highlight the potential of automated welding spark detection to enhance fire surveillance at construction sites.
A Wideband True Time Delay Circuit Using 0.25 µm GaN HEMT Technology
Authors: Kim Jeong-Geun , Baek Donghyun
Published: July 2023

This paper presents a wideband 4-bit true time delay IC using a 0.25 μm GaN HEMT (High-Electron-Mobility Transistor) process for the beam-squint-free phased array antennas. The true time delay IC is implemented with a switched path circuit topology using DPDT (Double Pole Double Throw) with no shunt transistor in the inter-stages to improve the bandwidth and SPDT (Single Pole Single Throw) switches at the input and the output ports. The delay lines are implemented with CLC π-networks with the lumped element to ensure a compact chip size. A negative voltage generator and an SPI controller are implemented in the PCB (Printed Circuit Board) due to the lack of digital control logic in GaN technology. A maximum time delay of ~182 ps with a time delay resolution of 10.5 ps is achieved at DC–6 GHz. The RMS (Root Mean Square) time delay and amplitude error are <5 ps and <0.6 dB, respectively. The measured insertion loss is <6.8 dB and the input and output return losses are >10 dB at DC–6 GHz. The current consumption is nearly zero with a 3.3 V supply. The chip size including pads is 2.45 × 1.75 mm2. To the authors’ knowledge, this is the first demonstration of a true time delay IC using GaN HEMT technology.
A Physics-Inspired Deep Learning Framework for an Efficient Fourier Ptychographic Microscopy Reconstruction under Low Overlap Conditions
Authors: Bouchama Lyes , Dorizzi Bernadette , Klossa Jacques , Gottesman Yaneck
Published: July 2023

Two-dimensional observation of biological samples at hundreds of nanometers resolution or even below is of high interest for many sensitive medical applications. Recent advances have been obtained over the last ten years with computational imaging. Among them, Fourier Ptychographic Microscopy is of particular interest because of its important super-resolution factor. In complement to traditional intensity images, phase images are also produced. A large set of N raw images (with typically N = 225) is, however, required because of the reconstruction process that is involved. In this paper, we address the problem of FPM image reconstruction using a few raw images only (here, N = 37) as is highly desirable to increase microscope throughput. In contrast to previous approaches, we develop an algorithmic approach based on a physics-informed optimization deep neural network and statistical reconstruction learning. We demonstrate its efficiency with the help of simulations. The forward microscope image formation model is explicitly introduced in the deep neural network model to optimize its weights starting from an initialization that is based on statistical learning. The simulation results that are presented demonstrate the conceptual benefits of the approach. We show that high-quality images are effectively reconstructed without any appreciable resolution degradation. The learning step is also shown to be mandatory.
Paediatric Homecare Risk Management: An Application of Functional Resonance Analysis Method (FRAM)
Authors: Hoy Kevin M. , Fallon Enda , Kelly Martina
Published: July 2023

Paediatric homecare is an advancing field of healthcare, bringing care direct to patients in their own homes. Risk management is an integral component of homecare services, including incident and risk assessment management. The objective of the study was to investigate risk management in homecare focusing on two aspects: incident reporting and risk assessments. A Grounded Theory approach was used to gather key functions of these aspects; these were then mapped using the Functional Resonance Analysis method (FRAM). Nineteen nurses working in paediatric homecare services were interviewed for the study. The interviews were semi-structured and focused on risk, quality, complaints, audit, care, and management. The interview data were transcribed and coded using Nvivo; the data were then converted into functions for utilization in the FRAM tool. The FRAM detailed the process of incident reporting and risk assessment management of the actual work carried out as viewed by the participants of the study. The information was then analysed and contrasted with the organizational policy to gain an understanding of the systems of incident reporting and risk assessments, which then led to the development of a refined process that could have less variability in function. Consequently, changes to policy and training in risk management were recommended to enhance the systems.
Superior Removal of Toxic Cr(VI) from Wastewaters by Natural Pine Bark
Authors: Yang Hanui , Kim Namgyu , Park Donghee
Published: July 2023

Hexavalent chromium (Cr(VI)) is one of the most toxic heavy metals found in industrial wastewater, so many researchers are working to develop efficient and environmentally friendly removal methods. It has been reported that natural biomass and its derivatives can be used to treat wastewaters containing Cr(VI). However, biomass with sufficient Cr(VI) removal performance to replace the existing chemical method, which is cheap and simple, has not been reported yet. This study reports that inexpensive, abundant, and commercially available pine bark has the highest Cr(VI) removal capacity (i.e., 376.3 mg/g) compared to biomass reported elsewhere. This value is six times higher than the theoretical value of an inorganic reducing agent (iron(II) sulfate heptahydrate). The main mechanism of Cr(VI) removal by pine bark was clearly identified through kinetic experiments, Fourier-transform infrared spectrometer, and X-ray photoelectron spectroscopy analyses, which were used to study the compositions, functional groups, and bonding states of pine bark. It was found that pine bark consists of various acidic functional groups that can act as electron donors to promote the removal of Cr(VI) through redox reactions. In conclusion, pine bark may be a promising candidate for the removal of Cr(VI) from wastewater, owing to its excellent removal capacity.
Study of Estimated Ultimate Recovery Prediction and Multi-Stage Supercharging Technology for Shale Gas Wells
Authors: Luo Yanli , Yang Jianying , Chen Man , Yang Liu , Peng Hao , Liang Jinyuan , Zhang Liming
Published: July 2023

The development of shale gas reservoirs often involves the utilization of horizontal well segmental multi-stage fracturing techniques. However, these reservoirs face challenges, such as rapid initial wellhead pressure and production decline, leading to extended periods of low-pressure production. To address these issues and enhance the production during the low-pressure stage, pressurized mining is considered as an effective measure. Determining the appropriate pressurization target and method for the shale gas wells is of great practical significance for ensuring stable production in shale gas fields. This study takes into account the current development status of shale gas fields and proposes a three-stage pressurization process. The process involves primary supercharging at the center station of the block, secondary supercharging at the gas collecting station, and the introduction of a small booster device located behind the platform separator and in front of the outbound valve group. By incorporating a compressor, the wellhead pressure can be reduced to 0.4 MPa, resulting in a daily output of 12,000 to 14,000 cubic meters from the platform. Using a critical liquid-carrying model for shale gas horizontal wells, this study demonstrates that reducing the wellhead pressure decreases the critical flow of liquid, thereby facilitating the discharge of the accumulated fluid from the gas well. Additionally, the formation pressure of shale gas wells is estimated using the mass balance method. This study calculates the cumulative production of different IPR curves based on the formation pressure. It develops a dynamic production decline model for gas outlet wells and establishes a relationship between the pressure depletion of gas reservoirs and the cumulative gas production before and after pressurization of H10 −2 and H10 −3 wells. The final estimated ultimate recovery of two wells is calculated. In conclusion, the implementation of multi-stage pressurization, as proposed in this study, effectively enhances the production of, and holds practical significance for, stable development of shale gas fields.
Water-Based Microwave-Assisted Extraction of Pigments from Madder Optimized by a Box–Behnken Design
Authors: Chambaud Marine , Colas Cyril , Destandau Emilie
Published: July 2023

Water is one of the greenest and most accessible solvents. To harness its potential, the water-based microwave-assisted extraction (MAE) of pigments from madder was optimized by the response surface methodology (RSM) using a Box–Behnken experimental design. The extract that presented both the highest extraction yield and the most intense color was obtained after a 30-s cycle at 1000 W using 100 mg of madder for 20 mL of water. This water-based MAE was more efficient than Soxhlet extraction and proved comparable to hydroalcoholic MAE. The optimized extract was further characterized using UHPLC-HRMS/MS to identify its main compounds. Glycosylated flavonoids and anthraquinones were putatively identified, as well as free anthraquinones, generally found in madder. The microwave-assisted extraction extended the range of polarity of the extracted compounds, making the water more versatile.
Are You Really Your Own Boss? Flexi-Vulnerability and False Consciousness of Autonomy in the Digital Labor Culture of Riders
Authors: López-Martínez Gabriel , Haz-Gómez Francisco Eduardo , Real Deus José Eulogio
Published: July 2023

In the European Union, over 28 million people work through more than 500 available digital platforms, and it is estimated that by 2025, this number will reach 43 million. However, we lack up-to-date and sufficient data on employed individuals, as platforms practice a policy of non-disclosure of data. This paper focuses on the so-called location-based platforms and specifically the figure of the rider, understood as the individual who, through a commercial or labor relationship with a company, performs tasks such as the delivery of goods to end customers. By conducting 143 surveys and 15 in-depth interviews with riders, we identified a series of characteristics that allow us to analyze this archetype of contemporary work–digital relations and delve deeper into relevant questions related to this figure, which have to do with the modality linked to the performance of their activity (self-employed or salaried), the levels of job satisfaction with respect to their activity, or the strategies for work or personal conciliation. Specifically, we focus on those discourses that refer to the characteristics of flexibility and autonomy inherent to this type of work, analyzing a heterogeneity of discourses that explain, on the one hand, a situation of precariousness and, in other cases, a job opportunity and a self-employment strategy, introducing the idea of flexi-vulnerability understood as a concept that captures the dual nature of flexibility and vulnerability experienced by individuals who work as self-employed in the so-called “gig” economy.
Who Wants School Vouchers in America? A Comprehensive Study Using Multilevel Regression and Poststratification
Authors: Su Yu-Sung , Gelman Andrew
Published: July 2023

The debate surrounding school vouchers in educational policy remains contentious, with conflicting survey data presenting contradictory levels of public endorsement. To gain a more nuanced comprehension of viewpoints towards vouchers, we propose deconstructing the American populace into distinct demographic and geographical sectors. However, this approach encounters challenges due to data insufficiency arising from small sample sizes in individual segments. To address this quandary, we advocate employing a model-based simulation methodology called multilevel regression and poststratification to derive more accurate estimates of voucher attitudes. Our exploration reveals that vouchers enjoy the most favor among affluent White Catholics and Evangelicals, with popularity escalating alongside income within the White demographic. In contrast, among non-White individuals, voucher support exhibits a different pattern, with less favor observed within the middle-income bracket and greater acceptance among those in lower-income categories. Our findings shed light on the complex interplay of demographic and geographic factors influencing public opinion on school vouchers, offering valuable insights for policymakers and education reform efforts.
Phytomanagement of Zn- and Cd-Contaminated Soil: Helianthus annuus Biomass Production and Metal Remediation Abilities with Plant-Growth-Promoting Microbiota Assistance
Authors: Paulo Ana M. S. , Caetano Nidia S. , Castro Paula M. L. , Marques Ana P. G. C.
Published: July 2023

Mining and industrial activity are contributing to the increase in heavy metal (HM) pollution in soils. Phytoremediation coupled to selected rhizosphere microbiota is an environmentally friendly technology designed to promote HM bioremediation in soils. In this study, sunflower (Helianthus annuus L.) was used together with Rhizophagus irregularis, an arbuscular mycorrhizal fungi (AMF), and Cupriavidus sp. strain 1C2, a plant growth promoting rhizobacteria (PGPR), as a phytoremediation strategy to remove Zn and Cd from an industrial soil (599 mg Zn kg−1 and 1.2 mg Cd kg−1). The work aimed to understand if it is possible to gradually remediate the tested soil while simultaneously obtaining significant yields of biomass with further energetic values by comparison to the conventional growth of the plant in agricultural (non-contaminated) soil. The H. annuus biomass harvested in the contaminated industrial soil was 17% lower than that grown in the agricultural soil—corresponding to yields of 19, 620, 199 and 52 g m−2 of roots, stems, flowers and seeds. It was possible to remove ca. 0.04 and 0.91% of the Zn and Cd of the industrial soil, respectively, via the HM accumulation on the biomass produced. The survival of applied microbiota was indicated by a high root colonization rate of AMF (about 50% more than in non-inoculated agricultural soil) and identification of strain 1C2 in the rhizosphere at the end of the phytoremediation assay. In this study, a phytoremediation strategy encompassing the application of an energetic crop inoculated with known beneficial microbiota applied to a real contaminated soil was successfully tested, with the production of plant biomass with the potential for upstream energetic valorisation purposes.
In-Season Longitudinal Hydration/Body Cell Mass Ratio Changes in Elite Rugby Players
Authors: Cebrián-Ponce Álex , Petri Cristian , Izzicupo Pascal , Micheli Matteo Levi , Cortis Cristina , Fusco Andrea , Carrasco-Marginet Marta , Mascherini Gabriele
Published: July 2023

Background: Hydration status has a direct role in sports performance. Bioelectrical Impedance Vector Analysis (BIVA) and Urine Specific Gravity (USG) are commonly used to assess hydration. The study aims to identify the sensitivity and relationship between BIVA and USG in a field sports setting. Methods: BIVA and USG measurements were conducted five times throughout one rugby season. 34 elite male rugby players (25.1 ± 4.4 years; 184.0 ± 7.8 cm; 99.9 ± 13.4 kg) were enrolled. Differences over time were tested using one-way repeated measures ANOVA, and Bonferroni’s post-hoc test was applied in pairwise comparisons. Resistance-reactance graphs and Hotelling’s T2 test were used to characterize the sample and to identify bioelectrical changes. A repeated measures correlation test was conducted for BIVA-USG associations. Results: Two clear trends were seen: (1) from July to September, there was a vector shortening and an increase of the phase angle (p < 0.001); and (2) from December to April, there was a vector lengthening and a decrease of the phase angle (p < 0.001). USG reported neither changes nor correlation with BIVA longitudinally (p > 0.05). Vector variations indicated a body fluid gain (especially in the intracellular compartment) and a body cell mass increase during the preseason, suggesting a physical condition and performance improvement. During the last months of the season, the kinetic was the opposite (fluid loss and decreased body cell mass). Conclusions: Results suggested that BIVA is sensitive to physiological changes and a better option than USG for assessing hydration changes during a rugby sports season.
Drought Prediction: A Comprehensive Review of Different Drought Prediction Models and Adopted Technologies
Authors: Nandgude Neeta , Singh T. P. , Nandgude Sachin , Tiwari Mukesh
Published: July 2023

Precipitation deficit conditions and temperature anomalies are responsible for the occurrence of various types of natural disasters that cause tremendous loss of human life and economy of the country. Out of all natural disasters, drought is one of the most recurring and complex phenomenons. Prediction of the onset of drought poses significant challenges to societies worldwide. Drought occurrences occur across the world due to a variety of hydro-meteorological causes and anomalies in sea surface temperature. This article aims to provide a comprehensive overview of the fundamental concepts and characteristics of drought, its complex nature, and the various factors that influence drought, drought indicators, and advanced drought prediction models. An extensive survey is presented in the different drought prediction models employed in the literature, ranging from statistical approaches to machine learning and deep learning models. It has been found that advanced techniques like machine learning and deep learning models outperform traditional models by improving drought prediction accuracy. This review article critically examines the advancements in technology that have facilitated improved drought prediction, identifies the key challenges and opportunities in the field of drought prediction, and identifies the key trends and topics that are likely to give new directions to the future of drought prediction research. It explores the integration of remote sensing data, meteorological observations, hydrological modeling, and climate indices for enhanced accuracy. Under the frequently changing climate conditions, this comprehensive review provides a valuable resource for researchers, practitioners, and policymakers engaged in drought prediction and management and fosters a deeper understanding of their capabilities and limitations. This article paves the way for more accurate and effective drought prediction strategies, contributing to improved resilience and sustainable development in drought-prone regions.
Actual and Expected Competencies in the Meetings Industry Sector during the Post-COVID-19 Era: The Case Studies Poland, Ireland, and Hungary
Authors: Nocoń Marek , Jurzak Lucyna , Kruszelnicki Paweł , Ziobro Marcin , Fabin-Jadczak Aleksandra
Published: July 2023

The main purpose of the article is to identify the key competencies of the management staff and employees of meetings industry enterprises during the COVID-19 pandemic, which made it possible to secure the functioning of these entities during the crisis. The results of this survey, conducted in 2020 and 2021 among employees of this industry in three countries—Poland, Ireland, and Hungary, are the basis for a discussion on the competency profiles and competency gaps of employees in the meetings industry. Further research in this area of MICE competencies and the preparation of recommendation packages are necessary in order to prepare the industries for future possible crises based on the knowledge gained during the COVID-19 pandemic.
The Influence of Rule of Law on Government’s Sustainable Economic Management: Evidence from China
Authors: Xiao Wensheng , Tang Yu , Obuobi Bright , Qu Shaojian , Yuan Minglan , Tang Decai
Published: July 2023

The Chinese government has continuously stressed the Chinese model and its characteristics for national economic governance. As a result, it is worth studying the effect of China’s rule of law on the government’s sustainable economic management, and the rule of law regarding this model can also provide a reference for governing other countries. This paper adopts the multiple linear regression analysis method using time-series data from 2007 to 2017 and from 2002 to 2019. This reflects the rule of law in the government’s sustainable economic management via the impact on government institutions, people’s courts, people’s procuratorates, and social organizations on the economy. The results show that the economic effect of the rule of law on government sustainable economic management is not significant. The rule of law in fiscal revenue and social organizations, rather than being arbitrarily dictated by powerful people, positively affects the economy. This effect was seen only in the 2002–2019 time series, but not in the 2007–2017 time series. Based on these results, it is suggested that China should strengthen the rule of law in managing its courts and procuratorates to enhance their roles in ensuring and accelerating sustainable economic development and regulating government activities. That is, the economic management effect of the public sectors, which indirectly participate in economic governance, is insufficient. This and other insightful policy recommendations are suggested to assist the government and policymakers in more effective and efficient sustainable economic management. Therefore, the system and regulations regarding their governance should not only focus on economic effects but also pay attention to the sustainable effects of economic development.
A Study of Student and Teacher Challenges in Smart Synchronous Hybrid Learning Environments
Authors: Carruana Martín Adrián , Alario-Hoyos Carlos , Delgado Kloos Carlos
Published: July 2023

The COVID-19 pandemic has led to the growth of hybrid and online learning environments and the trend of introducing more technology into the classroom. One such change could be the use of smart synchronous hybrid learning environments (SSHLEs), which are settings with both onsite and online students concurrently, where technology plays a key role in sensing, analyzing, and reacting throughout the teaching and learning process. These changing environments and the incorporation of new technologies can place a greater workload on participants and reduce teacher agency. In light of this, this paper aimed to analyze the workload and teacher agency across various SSHLEs. The NASA-TLX model was used to measure the workload in several scenarios. Questionnaires and interviews were used to measure teacher agency. The results obtained indicated that the workload of the teacher tended to be high (between 60 and 70 points out of 100 for the NASA-TLX workload), especially when they lacked experience in synchronous hybrid learning environments, and the workload of the students tended to have average values (between 50 and 60) in the SSHLEs analyzed. Meanwhile, the teacher agency did not appear to be altered but showed potential for improvement.
Does Gender and Cultural Diversity Matter for Sustainability in Healthcare? Evidence from Global Organizations
Authors: de Klerk Kylie , Singh Favil
Published: July 2023

Global healthcare organizations are fundamental in addressing the healthcare needs of local and global communities. This highly regulated sector means it is under constant scrutiny for health, safety, and ethical compliance risks by federal regulatory bodies. Despite higher monitoring, an increasing number of healthcare companies receive fines for their irresponsible practices, manifesting significant questions about their corporate governance and sustainability practices. Against this backdrop, this study examines the relationship between boardroom diversity on the sustainability performance of companies operating in healthcare. Utilizing a global sample of publicly listed healthcare companies, using panel regression data and the system-GMM estimator accounting for endogeneity, we find evidence of a positive association between board diversity (gender and culture) and sustainability performance. These findings support critical mass theoretical expectations for board diversity and sustainability performance, suggesting that a meaningful representation (three or more) of women and ethnic directors on the board of healthcare organizations significantly improves sustainability performance. The findings remain robust in a series of robustness tests and continue to hold after accounting for potential endogeneity concerns. This paper has important implications for global healthcare organizational policy concerning diversity management practices and their implications for sustainability performance.
Protected Areas and Nature-Based Tourism: A 30-Year Bibliometric Review
Authors: Silva Sara , Silva Luís Filipe , Vieira António
Published: July 2023

Nature-based tourism constitutes a major segment of world tourism. Protected areas are considered a popular theme in the literature, which is characterised by a great heterogeneity in concepts, contents, and purposes. This study analyses many of the indicators of interest related to the research on the themes of protected areas and nature-based tourism. Consequently, we aim to offer a global and updated vision of the research produced regarding these themes over the last 30 years. With bibliometric tools (Bibliometrix and VOSviewer), 1033 scientific articles were analysed. The results indicate that the research in this area exponentially increased in the last decade. Using a co-occurrence network of keywords, five major themes in this study were identified: (1) ecotourism; (2) nature conservation, biodiversity, and sustainability; (3) national parks, recreation, and climate change; (4) sustainable management and development; and (5) with a lesser degree of representation, themes related to China and ecosystem services. A timespan analysis on this network enabled the identification of six trends in the research over the last years: (i) sustainable tourism; (ii) climate change; (iii) geotourism and rural tourism; (iv) ecosystem services and cultural ecosystem services; (v); visitor studies; and (vi) wildlife tourism. Nature-based tourism plays a significant and crucial role in sustainable development. Analysing research in protected areas and nature-based tourism provides insights into key themes and emerging trends, serving as a valuable resource for knowledge advancement.
Supposed Effects of Wetland Restoration on Hydrological Conditions and the Provisioning Ecosystem Services—A Model-Based Case Study at a Hungarian Lowland Catchment
Authors: Kozma Zsolt , Decsi Bence , Ács Tamás , Kardos Máté Krisztián , Hidy Dóra , Árvai Mátyás , Kalicz Péter , Kern Zoltán , Pinke Zsolt
Published: July 2023

Climate change and water scarcity increase the vulnerability of crop production and other ecosystem services (ES) in flood-protected lowlands under a continental climate. Restoration of wetlands leads to a higher water-buffering capacity of the landscape, strengthening various ecosystem services, and fostering adaptation to climatic, ecological, and agricultural challenges. Such restoration efforts require extensive land-use change, leading to trade-offs in provisioning and regulating ES. However, knowledge is limited about these situations, especially in the case of lowland areas. Here, we introduce a hydrological analysis in a 243 km2 flood-protected catchment in the Great Hungarian Plain, mapping the potential hydrological effects of water-retention scenarios on groundwater levels. We point out how the simulated groundwater levels will be used for estimating the changes in crop yields and tree growth (provisioning services). The introduced hydrological analysis and preliminary results for crop-yield estimates suggest a significant and scalable capacity for a nature-based hydrological adaptation: the extent of inundated areas could be increased stepwise and water retention could locally compensate dry periods due to the buffering effect of inundated meanders.
Identifying the Drivers of Circular Food Packaging: A Comprehensive Review for the Current State of the Food Supply Chain to Be Sustainable and Circular
Authors: Ada Erhan , Kazancoglu Yigit , Lafcı Çisem , Ekren Banu Y. , Çimitay Çelik Cansu
Published: July 2023

The resilience of food systems is jeopardized by using food packaging materials that have adverse impacts on the environment, food quality, food safety, shelf-life, food loss, and waste. Therefore, a transition into a more sustainable system can only be possible by adopting circular economy principles and practices that can facilitate the elimination of unsustainable packaging, irresponsible disposal behaviors, and waste management. This paper mainly focuses on circular packaging practices in the existing literature to reveal the drivers of circular food packaging applications. The study also displays the triple combinations of material-sector, material-CE, and sector-CE principles. As a methodology, a systematic literature review (SLR) has been used for this study. Furthermore, this study investigates the literature findings, such as the most frequently mentioned food sector and sub-sector, CE principles, materials adopted for food packaging, and so on. The primary contribution of this study to the body of literature is the synthesis and mapping of the literature as a whole from the perspectives of CE principles, both sector-based and national, and the materials used through circular food packaging, and the attempt to facilitate this transition into a more circular system by outlining the drivers of circular food packaging.