bluuebunny/crossref_metadata_2025 huggingface dataset:
Structural, Mechanical, and Barrier Properties of the Polyvinylidene Fluoride-Bacterial Nanocellulose-Based Hybrid Composite
Authors: Janićijević Aleksandra , Filipović Suzana , Sknepnek Aleksandra , Salević-Jelić Ana , Jančić-Heinemann Radmila , Petrović Miloš , Petronijević Ivan , Stamenović Marina , Živković Predrag , Potkonjak Nebojša , Pavlović Vladimir B.
Published: April 2024

This study presents an analysis of films which consist of two layers; one layer is PVDF as the matrix, along with fillers BaTiO3 (BT), and the second is one bacterial nanocellulose (BNC) filled with Fe3O4. The mass fraction of BT in PVDF was 5%, and the samples were differentiated based on the duration of the mechanical activation of BT. This innovative PVDF laminate polymer with environmentally friendly fillers aligns with the concept of circular usage, resulting in a reduction in plastic content and potential improvement of the piezoelectric properties of the entire composite. This work presents new, multifunctional “green” packaging materials that potentially could be a good alternative to specific popular materials used for this purpose. The synthesis of the films was carried out using the hot press method. Tensile tests, water vapor permeability examination, and structural analyses using SEM-EDS and FTIR have been conducted. The sample PVDF/BT20/BNC/Fe3O4 exhibited the best barrier properties (impermeability to water vapor), while the highest tensile strength and toughness were exhibited by the PVDF/BT5/BNC/Fe3O4 sample.
Enhancing Oil Recovery in Low-Permeability Reservoirs Using a Low-Molecular Weight Amphiphilic Polymer
Authors: Yang Yang , Wang Youqi , Liu Yiheng , Liu Ping
Published: April 2024

Polymer flooding has achieved considerable success in medium–high permeability reservoirs. However, when it comes to low-permeability reservoirs, polymer flooding suffers from poor injectivity due to the large molecular size of the commonly used high-molecular-weight (high-MW) partially hydrolyzed polyacrylamides (HPAM). Herein, an amphiphilic polymer (LMWAP) with a low MW (3.9 × 106 g/mol) was synthesized by introducing an amphiphilic monomer (Allyl-OP-10) and a chain transfer agent into the polymerization reaction. Despite the low MW, LMWAP exhibited better thickening capability in brine than its counterparts HPAM-1800 (MW = 1.8 × 107 g/mol) and HPAM-800 (MW = 8 × 106 g/mol) due to the intermolecular hydrophobic association. LMWAP also exhibited more significant shear-thinning behavior and stronger elasticity than the two counterparts. Furthermore, LMWAP possesses favorable oil–water interfacial activity due to its amphiphilicity. The oil–water interfacial tension (IFT) could be reduced to 0.88 mN/m and oil-in-water (O/W) emulsions could be formed under the effect of LMWAP. In addition, the reversible hydrophobic association endows the molecular chains of LMWAP with dynamic association–disassociation transition ability. Therefore, despite the similar hydrodynamic sizes in brine, LMWAP exhibited favorable injectivity under low-permeability conditions, while the counterpart HPAM-1800 led to fatal plugging. Furthermore, LMWAP could enhance oil recovery up to 21.5%, while the counterpart HPAM-800 could only enhance oil recovery by up to 11.5%, which could be attributed to the favorable interfacial activity of LMWAP.
The Use of an Advanced Intelligent–Responsive Polymer for the Study of Dynamic Water–Carbon Dioxide Alternating Displacement
Authors: Zhang Feng , Zhang Jingong , Yuan Yidong , Yong Zishu , Yan Zhuoyue , Zhang Jiayuan , Lu Guochao
Published: April 2024

Addressing the issue of inadequate temperature tolerance in traditional polymers, in this study, we successfully executed a one-step synthesis of intelligent–responsive polymers which have excellent adaptability in water–gas alternating displacement scenarios. Utilizing the fatty acid method, we produced OANND from oleic acid (OA) and N,N-dimethyl-1,3-propanediamine (NND). Upon testing the average particle size in the aqueous solution both prior and subsequent to CO2 passage, it became evident that OANND assumes the form of a small-molecule particle in the aqueous phase, minimizing damage during formation. Notably, upon CO2 exposure, it promptly organizes into stable micelles with an average size of 88 nm and a relatively uniform particle distribution. This unique characteristic endows it with a rapid CO2 response mechanism and the ability to form a highly resilient gel. In the exploration of viscoelastic fluids, we observed the remarkable behavior of the AONND aqueous solution when CO2/N2 was introduced. This system displayed repeatable transitions between aqueous and gel states, with the highest viscosity peaking at approximately 3895 mPa·s, highlighting its viscosity reversibility and reusability properties. The rheological property results that we obtained indicate that an elongated micellar structure is present in the solution system, with the optimal concentration ratio for its formation determined as 0.8, which is the molar ratio of the OANND-NaOA system. In the sealing performance tests, a 1.0 wt% concentration of the gel system exhibited excellent injectability properties. At 80 °C, this gel effectively reduced the permeability of a sand-filled model to 94.5% of its initial value, effectively sealing potential leakage paths or gas fluxes. This remarkable ability to block leakage paths and reduce seepage capacity highlights the material’s superior blocking effect and erosion resistance properties. Furthermore, even at a temperature of 90 °C and an injection pore volume (PV) of 3, this plugging system could reduce the permeability of a high-permeability sand-filled model to over 90% of its initial value.
A PCF Sensor Design Using Biocompatible PDMS for Biosensing
Authors: Yang Yanxin , Li Jinze , Sun Hao , Xi Jiawei , Deng Li , Liu Xin , Li Xiang
Published: April 2024

A novel photonic crystal fiber (PCF) sensor for refractive index detection based on polydimethylsiloxane (PDMS) is presented in this research, as well as designs for single-channel and dual-channel structures for this PDMS-PCF sensor. The proposed structures can be used to develop sensors with biocompatible polymers. The performance of the single-channel PDMS-PCF sensor was studied, and it was found that adjusting parameters such as pore diameter, lattice constant, distance between the D-shaped structure and the fiber core, and the radius of gold nanoparticles can optimize the sensor’s performance. The findings indicate that the detection range of the single-channel photonic crystal is 1.21–1.27. The maximum wavelength sensitivity is 10,000 nm/RIU with a resolution of 1×10−5 RIU, which is gained when the refractive index is set to 1.27. Based on the results of the single-channel PCF, a dual-channel PDMS-PCF sensor is designed. The refractive index detection range of the proposed sensor is 1.2–1.28. The proposed sensor has a maximum wavelength sensitivity of 13,000 nm/RIU and a maximum resolution of 7.69×10−6 RIU at a refractive index of 1.28. The designed PDMS-PCF holds tremendous potential for applications in the analysis and detection of substances in the human body in the future.
Compound Jamming Recognition Based on a Dual-Channel Neural Network and Feature Fusion
Authors: Chen Hao , Chen Hui , Lei Zhenshuo , Zhang Liang , Li Binbin , Zhang Jiajia , Wang Yongliang
Published: April 2024

Jamming recognition is a significant prior step to achieving effective jamming suppression, and the precise results of the jamming recognition will be beneficial to anti-jamming decisions. However, as the electromagnetic environment becomes more complex, the received signals may contain both suppression jamming and deception jamming, which is more challenging for existing methods focused on a single kind of jamming. In this paper, a recognition method for compound jamming based on a dual-channel neural network and feature fusion is proposed. First, feature images of compound jamming are extracted by the short-time Fourier transform and the wavelet transform. Feature images are then employed as inputs for the proposed network. During parallel processing in dual-channel, the proposed network can adaptively extract and learn task-relevant features via the attention modules. Finally, the output features in dual-channel are fused in the fusion subnetwork. Compared with existing methods, the proposed method can yield better recognition performance with less inference time. Additionally, compared with existing fusion strategies, the fusion subnetwork can further improve the recognition performance under low jamming-to-noise ratio conditions. Results with the semi-measured datasets also verify the feasibility and generalization performance of the proposed method.
Concept of a Satellite Cross-Calibration Radiometer for In-Orbit Calibration of Commercial Optical Satellites
Authors: Thankappan Medhavy , Christopherson Jon , Cantrell Simon , Ryan Robert , Pagnutti Mary , Bright Courtney , Naughton Denis , Ruslander Kathryn , Wang Lan-Wei , Hudson David , Shaw Jerad , Ramaseri Chandra Shankar Nag , Anderson Cody
Published: April 2024

The satellite Earth observation (EO) sector is burgeoning with hundreds of commercial satellites being launched each year, delivering a rich source of data that could be exploited for societal benefit. Data streams from the growing number of commercial satellites are of variable quality, limiting the potential for their combined use in science applications that need long time-series data from multiple sources. The quality of calibration performed on optical sensors onboard many satellite systems is highly variable due to calibration methods, sensor design, mission objective, budget, or other operational constraints. A small number of currently operating well-characterised satellite systems with onboard calibration, such as Landsat-8/9 and Sentinel-2, and planned future missions, like the NASA Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder, the European Space Agency (ESA)’s Traceable Radiometry Underpinning Terrestrial and Helio Studies (TRUTHS), and LIBRA from China, are considered benchmarks for optical data quality due to their traceability to international measurement standards. This paper describes the concept of a space-based transfer calibration radiometer called the Satellite Cross-Calibration Radiometer (SCR) that would enable the calibration parameters from satellites such as Landsat-8/9, Sentinel-2, or other benchmark systems to be transferred to a range of commercial optical EO satellite systems while in orbit. A description of the key characteristics of the SCR to successfully operate in orbit and transfer calibration from reference systems to client systems is presented. A system like the SCR in orbit could complement SI-Traceable satellites (SITSats) to improve data quality and consistency and facilitate the interoperable use of data from multiple optical sensor systems for delivering higher returns on the global investment in EO.
Physical Activity Detection for Diabetes Mellitus Patients Using Recurrent Neural Networks
Authors: Dénes-Fazakas Lehel , Simon Barbara , Hartvég Ádám , Kovács Levente , Dulf Éva-Henrietta , Szilágyi László , Eigner György
Published: April 2024

Diabetes mellitus (DM) is a persistent metabolic disorder associated with the hormone insulin. The two main types of DM are type 1 (T1DM) and type 2 (T2DM). Physical activity plays a crucial role in the therapy of diabetes, benefiting both types of patients. The detection, recognition, and subsequent classification of physical activity based on type and intensity are integral components of DM treatment. The continuous glucose monitoring system (CGMS) signal provides the blood glucose (BG) level, and the combination of CGMS and heart rate (HR) signals are potential targets for detecting relevant physical activity from the BG variation point of view. The main objective of the present research is the developing of an artificial intelligence (AI) algorithm capable of detecting physical activity using these signals. Using multiple recurrent models, the best-achieved performance of the different classifiers is a 0.99 area under the receiver operating characteristic curve. The application of recurrent neural networks (RNNs) is shown to be a powerful and efficient solution for accurate detection and analysis of physical activity in patients with DM. This approach has great potential to improve our understanding of individual activity patterns, thus contributing to a more personalized and effective management of DM.
3MSF: A Multi-Modal Adaptation of the 6TiSCH Minimal Scheduling Function for the Industrial IoT
Authors: Elsas Robbe , Van Leemput Dries , Hoebeke Jeroen , De Poorter Eli
Published: April 2024

Although wireless devices continuously gain communication capabilities, even state-of-the-art Industrial Internet of Things (IIoT) architectures, such as Internet Protocol version 6 over the Time-Slotted Channel Hopping (TSCH) mode of IEEE 802.15.4 (6TiSCH), continue to use network-wide, fixed link configurations. This presents a missed opportunity to (1) forego the need for rigorous manual setup of new deployments; and (2) provide full coverage of particularly heterogeneous and/or dynamic industrial sites. As such, we devised the Multi-Modal Minimal Scheduling Function (3MSF) for the TSCH link layer, which, combined with previous work on the routing layer, results in a 6TiSCH architecture able to dynamically exploit modern multi-modal hardware on a per-link basis through variable-duration timeslots, simultaneous transmission, and routing metric normalization. This paper describes, in great detail, its design and discusses the rationale behind every choice made. Finally, we evaluate three basic scenarios through simulations, showcasing both the functionality and flexibility of our 6TiSCH implementation.
A Time-Differential BOCDA Sensor Measurement System Applied to a 1 km Long SMF Using a Semiconductor Optical Amplifier as a Pump Chopper
Authors: Choi Bo-Hun
Published: April 2024

A time-differential (TD) Brillouin optical correlation domain analysis (BOCDA) sensor system was applied to measure the Brillouin gain spectrum of a 1 km long sensing optical fiber. The optical delay line used in all BOCDA measurement systems was eliminated in the TD-BOCDA system by using a bit-delayed modulation relationship between the probe and pump lightwaves. These lightwaves were phase modulated using 216-1 pseudo-random binary sequence codes at 5 Gbps. A 2 cm dispersion-shifted fiber placed at the end of the 1 km optical fiber was distinctly identified by the Brillouin frequency extracted from the Brillouin gain spectrum measurement. To investigate the measurement stability of the TD-BOCDA system, experiments were conducted under two different pumping conditions. A semiconductor optical amplifier (SOA) and an intensity modulator (MOD) were compared for the pump chopper used in the TD-BOCDA system to detect the extinction ratio of the pump and the resulting noise in the Brillouin gain measurement. The stability of the Brillouin frequency measurement from the Brillouin gain spectrum in the TD-BOCDA system was investigated by increasing the average value of the measurement using either the SOA or MOD. The repeated-measurement deviation of the system with the SOA was only half of the deviation observed in the system with the MOD. The performance of TD-BOCDA is equivalent to or better than that of conventional BOCDAs in terms of measurement reliability. Moreover, TD-BOCDA is free from the drawbacks of traditional BOCDA, which uses time-delayed fibers and varies the bit rates.
Advantages of Bistable Microwires in Digital Signal Processing
Authors: Jacko Patrik , Duranka Peter , Varga Rastislav
Published: April 2024

The advantageous applications of magnetic bistable microwires have emerged during long-lasting research. They have a wide range of applications in the scientific sphere or technical practice. They can be used for various applications, including magnetic memories, biomedicine, and sensors. This manuscript is focused on the last-mentioned application of microwires—sensors—discussing various digital signal processing techniques used in practical applications. Thanks to the highly sensitive properties of microwires and their two stable states of magnetization, it is possible to perform precise measurements with less demanding digital processing. The manuscript presents four practical signal-processing methods of microwire response using three different experiments. These experiments are focused on detecting the signal in a simple environment without an external magnetic background, measuring with the external background of a ferromagnetic core, and measuring in harsh conditions with a strong magnetic background. The experiments aim to propose the best method under various conditions, emphasizing the quality and signal processing speed of the microwire signal.
In-Season Cotton Yield Prediction with Scale-Aware Convolutional Neural Network Models and Unmanned Aerial Vehicle RGB Imagery
Authors: Niu Haoyu , Peddagudreddygari Janvita Reddy , Bhandari Mahendra , Landivar Juan A. , Bednarz Craig W. , Duffield Nick
Published: April 2024

In the pursuit of sustainable agriculture, efficient water management remains crucial, with growers relying on advanced techniques for informed decision-making. Cotton yield prediction, a critical aspect of agricultural planning, benefits from cutting-edge technologies. However, traditional methods often struggle to capture the nuanced complexities of crop health and growth. This study introduces a novel approach to cotton yield prediction, leveraging the synergy between Unmanned Aerial Vehicles (UAVs) and scale-aware convolutional neural networks (CNNs). The proposed model seeks to harness the spatiotemporal dynamics inherent in high-resolution UAV imagery to improve the accuracy of the cotton yield prediction. The CNN component adeptly extracts spatial features from UAV-derived imagery, capturing intricate details related to crop health and growth, modeling temporal dependencies, and facilitating the recognition of trends and patterns over time. Research experiments were carried out in a cotton field at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, with three replications evaluating four irrigation treatments (rainfed, full irrigation, percent deficit of full irrigation, and time delay of full irrigation) on cotton yield. The prediction revealed that the proposed CNN regression models outperformed conventional CNN models, such as AlexNet, CNN-3D, CNN-LSTM, ResNet. The proposed CNN model showed state-of-art performance at different image scales, with the R2 exceeding 0.9. At the cotton row level, the mean absolute error (MAE) and mean absolute percentage error (MAPE) were 3.08 pounds per row and 7.76%, respectively. At the cotton grid level, the MAE and MAPE were 0.05 pounds and 10%, respectively. This shows the proposed model’s adaptability to the dynamic interplay between spatial and temporal factors that affect cotton yield. The authors conclude that integrating UAV-derived imagery and CNN regression models is a potent strategy for advancing precision agriculture, providing growers with a powerful tool to optimize cultivation practices and enhance overall cotton productivity.
Enhancing Productivity at Home: The Role of Smart Work and Organizational Support in the Public Sector
Authors: Barbieri Barbara , Mondo Marina , De Simone Silvia , Pinna Roberta , Galletta Maura , Pileri Jessica , Bellini Diego
Published: April 2024

In recent years, Italian Public Administrations (PAs) have swiftly adapted to flexible work arrangements due to the impact of COVID-19. The flexible work has led to addressing new challenges including the need to balance work demands with family commitments, a lack of social support, struggles with inadequate technology, and managing home interruptions that negatively affect home performance. Based on the job demands-resources (JD-R) model, the present study aims at examining the negative impact of home interruptions on smart working performance and the moderating role of two potential job resources: organizational support and perceived quality of the smart working. A convenience sample of 301 Italian public employees engaged in smart working completed an online questionnaire. Multivariate regression analysis results showed that organizational support plays a moderating role in reducing the negative effect of interruptions on home performance. However, the findings did not support a moderating role for the perceived quality of smart working. Overall, these findings highlight the critical role of social factors in buffering the negative effects of smart working, as compared to the quality of the technological arrangements. This information could be valuable for organizations looking to enhance the effectiveness of smart working by focusing on organizational support.
Developing a Data-Driven AI Model to Enhance Energy Efficiency in UK Residential Buildings
Authors: Seraj Hamidreza , Bahadori-Jahromi Ali , Amirkhani Shiva
Published: April 2024

Residential buildings contribute 30% of the UK’s total final energy consumption. However, with less than one percent of its housing stock being replaced annually, retrofitting existing homes has significant importance in meeting energy-efficiency targets. Consequently, many physics-based and data-driven models and tools have been developed to analyse the effects of retrofit strategies from various points of view. This paper aims to develop a data-driven AI model that predicts buildings’ energy performance based on their features under various retrofit scenarios. In this context, four different machine learning models were developed based on the EPC (Energy Performance Certificate) dataset for residential buildings and standard assessment procedure (SAP) guidelines in the UK. Additionally, an interface was designed that enables users to analyse the effect of different retrofit strategies on a building’s energy performance using the developed AI models. The results of this study revealed the artificial neural network as the most accurate predictive model, with a coefficient of determination (R2) of 0.82 and a mean percentage error of 11.9 percent. However, some conceptual irregularities were observed across all the models when dealing with different retrofit scenarios. All summary, such tools can be further improved to offer a potential alternative or support to physics-based models, enhancing the efficiency of retrofitting processes in buildings.
The Impact of the Integration of the Culture Industry and Tourism on Regional Green Development: Empirical Evidence from China
Authors: Qi Zhenyu , You Yuezhou
Published: April 2024

The concept of “green development” has garnered increasing recognition and prominence in recent years, attracting the attention of various countries and regions. How to achieve green development has become a pressing issue for the government. The integration of the culture industry and tourism has had a profound impact on regional green development. This paper employs the entropy method to assess the level of regional green development, and it employs the coupling coordination model to evaluate the degree of integration of the culture industry and tourism across 30 provincial administrative regions in China from 2011 to 2021. Additionally, an econometric model is constructed to empirically examine the impact of the integration of the culture industry and tourism on regional green development, as well as its underlying mechanism. The findings of this study indicate the following: (1) The integration of the culture industry and tourism significantly enhances regional green development. (2) With the improvement in the regional green development level, the integration of the culture industry and tourism is playing a gradually stronger role in promoting regional green development. (3) Further analysis of the mechanism reveals that the integration of the culture industry and tourism enhances regional green development by facilitating the upgrading of the tourism industrial structure. (4) Environmental regulation policies reinforce the role of the integration of the culture industry and tourism in promoting regional green development. These research findings contribute to enhancing our understanding of the impact of the integration of the culture industry and tourism on regional green development, providing empirical evidence and policy recommendations for the government to facilitate the integration of the culture industry and tourism and further promote green development.
COVID-19 Impacts on the Spatial Network of City Tourism in Fujian Province, China
Authors: Song Huimin , Zeng Wei , Chen Jamie M. , Ma Emily
Published: April 2024

This study examined the tourism spatial distribution of nine cities in the Fujian province and assessed the impacts of COVID-19. The modified gravity model found that it was widely dispersed, with uneven and relatively independent tourism development in different cities. The social network analysis showed that tourism connections across cities were significantly reduced after the pandemic. The impacts of brand awareness and transport accessibility on spatial networks were positive in the pre-pandemic period but became negative during the pandemic. In contrast, tourist volume had negative impacts on spatial networks pre-pandemic but had positive ones during the pandemic. Tourism resources and market performance had significantly positive impacts in the post-pandemic era. These findings provide advice on tourism recovery and destination management in coping with future critical events. In the spatial distribution network of the tourism economy in Fujian Province, cities have different positions and roles, so development strategies should be differentiated according to the characteristics of each city. For example, more supportive policies should be introduced to help the tourism development of cities with disadvantages. It also contributes to the theoretical gravity framework in tourism and the research scope of the social networks analysis at the city level.
A Logging Data Based Method for Evaluating the Fracability of a Gas Storage in Eastern China
Authors: Huang Famu , Huang Lei , Zhu Ziheng , Zhang Min , Zhang Wenpeng , Jiang Xingwen
Published: April 2024

Underground storage of natural gas has the characteristics of clean and low-carbon, and has the ability to provide a sustainable and stable supply. It is a very high-quality green energy that can increase the storage efficiency of gas storage through fracturing, achieving the sustainable development goal of “Carbon Peaking and Carbon Neutrality”. To improve the storage efficiency of natural gas, it is necessary to carry out refracturing. Moreover, it is of great significance to estimate the fracability of the potential refracturing formation. At present, research on fracability is mainly based on qualitative characterization or quantitative evaluation based on rock mechanics and fracturing construction parameters, which cannot fully reflect the rock composition and structure of each stage. Firstly, based on logging data, this paper analyzes the evolution laws of strain energy such as elastic properties, pre-peak dissipation energy, and post-peak fracture energy during the transition of rock materials from plastic deformation to brittle fracture from an energy perspective, and determines the key energy that affects the brittle characteristics of rocks. Secondly, a brittleness index evaluation approach has been established that can comprehensively reflect the mechanical properties of rocks during pre-peak deformation and post-peak damage stages. In addition, this article focuses on the impact of a reservoir stratigraphic environment by combining the influence of geo-stresses with the rock brittleness index, and proposes a new method for evaluating reservoir fracability. Finally, this paper conducts a study on the fracability evaluation of three wells in a gas storage facility in Eastern China. The results indicate that low modulus and fracability index are beneficial for fracturing, thereby improving the gas production and peak shaving ability of gas storage.
The Influence of In-Store Recycling Signage on Consumer Behavior: A Study of Visual Attention and Usage of Store Drop-Off Bins
Authors: Zhao Mengmeng , Anderson Shannon , Hurley Rupert Andrew , Anzures Sheila , Nowak Paul , Burt Kelly
Published: April 2024

This study examined the impact of using three types of in-store signage to educate consumers about packaging recyclability as well as to measure its effectiveness in increasing awareness of a store drop-off recycling method for packaged granola. A total of 67 shoppers were recruited and divided into two groups to shop for granola in a retail store. Group A was not exposed to signage near the granola, while Group B was exposed to the in-store signage comprising shelf strips and aisle invaders with How2Recycle educational information containing messaging about the store drop-off recycling. A store drop-off bin was positioned at the entrance of the testing environment for both groups. The results showed that Group B had a significantly higher total fixation duration (p = 0.02) and fixation count (p = 0.03) than Group A in response to the recycling instructions on the drop-off bin. When comparing the three methods of in-store signage—shelf strips, aisle invaders, and the printed store drop-off bin panels—the shelf strips performed the best with regard to total fixation duration (1.72 s) and fixation count (5.91 counts) and were noticed by the highest percentage of shoppers. The store drop-off bin panels had the best performance with regard to time to first fixation (0.12 s). The aisle invaders had the lowest performance in all three eye tracking key metrics and were noticed by the lowest percentage of participants. These responses illustrate shopper challenges related to plastic recycling. This study demonstrates that recycling information can be effectively provided through strategically positioned in-store signage, especially messaging on shelf strips. While consumers are open to the usage of Store Drop-off Bins, affirming customer knowledge of these processes is essential to develop new consumer recycling habits.
Current Strategies in Controlling Aspergillus flavus and Aflatoxins in Grains during Storage: A Review
Authors: Gong Andong , Song Mengge , Zhang Jingbo
Published: April 2024

Aspergillus flavus is a ubiquitous pathogen that can infect many foods and grains, and it produces large amounts of aflatoxins during their storage. Aflatoxins are considered highly toxic and carcinogenic to humans, and they cause great damage to crop production, food security, and human health. Thus, controlling A. flavus and aflatoxins in grains presents a great challenge to humans worldwide. Over the past decade, many strategies have been demonstrated to be useful in controlling A. flavus and aflatoxins during food storage. These methods involve physical agents, chemical agents, biological agents, etc. Some of these methods are currently used in actual production. In this review, we summarize the reported methods for controlling A. flavus and aflatoxins during food storage in the past ten years and elucidate their advantages and disadvantages. The methods discussed include irradiation technology; low oxygen atmospheres; chemical fungicides (benzalkonium chloride, iodine, ammonium bicarbonate, and phenolic and azole compounds); biological agents from plants, animals, and micro-organisms; and aflatoxin elimination methods. We expect that this review will promote the applications of current strategies and be useful for the development of novel technologies to prevent or eliminate A. flavus and aflatoxins in food and feed during storage.
The Impact of Vertical Fiscal Imbalances and Local Government Tax Efforts on the Quality of Economic Development—A Study Based on Threshold Regression and Simultaneous Equation Models
Authors: Wang Ke , Liew Venus Khim-Sen
Published: April 2024

China’s economy has been growing at a rapid pace in recent years, but the quality of economic development has not been consistent with the quantity, which is not conducive to sustainable economic development. How to improve the quality of economic development and achieve sustainable economic development has become an urgent economic issue for the Chinese government to address. Utilizing panel data encompassing 30 provinces in China from 2008 to 2022, we explore the mechanisms and impacts of vertical fiscal imbalances and tax efforts on the quality of local economic development. For this purpose, this investigation employs threshold regression and three-stage least squares methodologies. The results of the study show that an increase in the level of vertical fiscal imbalance does not contribute significantly to the improvement of the quality of local economic development. However, a vertical fiscal imbalance can suppress the level of tax effort of local government. Moreover, a reduction in local government tax efforts has a favorable effect on the quality of local economic development. As such, a vertical fiscal imbalance promotes economic development indirectly. Therefore, it is necessary to reasonably control the level of vertical fiscal imbalance, build a fiscal relationship between the central and local governments with clear powers and responsibilities, coordinate financial resources and regional balance, and prevent excessive tax efforts from suppressing the production enthusiasm of microeconomic subjects to improve the quality of China’s economic development and realize the sustainable development of the local and national economies.
Towards Sustainability: A Framework for Evaluating Portuguese Small-Scale Fisheries
Authors: Apresentação Sara , Rangel Mafalda , Cristas Assunção
Published: April 2024

Portugal has a historical tradition in fisheries, and its small-scale fishery (SSF) communities are the backbone of the national industry. There have been increased concerns about the state of fisheries and fish resources, particularly the sustainable performance of fishing sectors. In light of this aspect, understanding economic, social, environmental, and governmental aspects is a key element in assessing sustainability. Given the relevance of the fishing industry in Portugal, it is paramount to understand if the Portuguese SSFs are sustainable. This study aims to develop a contextualized indicator framework for SSFs’ sustainability with economic, social, environmental, and governance dimensions. Further, we developed a systematic literature review to evaluate Portuguese SSFs using such a framework. Of the list of suggested 31 indicators (8 economy, 9 social, 12 environmental, and 8 governance), 1394 published references were recovered from the systematic review for Portuguese SSFs. Results suggests that Portugal must improve socioeconomic and environmental data and strengthen governmental support for a sustainable fishing industry. In particular, understanding fishers’ financial stability and ecological knowledge and promoting further studies on fisheries’ impact on the marine environment is essential. Overall, the qualitative list of indicators provided represents an innovative and unique framework for evaluating fisheries’ sustainability in different worldwide settings.
A Multi-Objective Scenario Study of County Land Use in Loess Hilly Areas: Taking Lintao County as an Example
Authors: Luo Zhanfu , Zheng Wei , Liu Juanqin , Wang Jin , Bai Xue
Published: April 2024

Land use serves as a connecting link between human activities and the natural ecology of the surface; under the multi-objective background of national policies and dual-carbon tasks, land use transformation is studied and simulated in multiple scenarios, and carbon stock changes are analyzed based on future land use to explore the path for a region to achieve multi-objective coordination. Drawing upon land use data from 2000 to 2020 in Lintao County, Gansu Province, we conducted an in-depth analysis of the dynamics governing land use transformation. Subsequently, employing the FLUS (Future Land Use Simulation) model, we simulated the projected land use for Lintao County in 2035 under various scenarios. Furthermore, we utilized the InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) model to assess the change in carbon stock within the study area under each scenario. These analyses aim to furnish a robust scientific foundation for future land use planning endeavors in Lintao County. The conclusions are as follows: (1) The land use transition in Lintao County from 2000 to 2020 showed the strongest motivation for construction land growth, with continued rapid growth in the scale of urban land and other construction land and relatively slow growth in the land for rural settlement areas, while cropland and water areas continued to decrease, forest land grew slowly, the magnitude of land use change exhibited a higher intensity in river townships compared with mountainous townships. (2) The simulation results of cropland protection scenario (CPS), ecological protection scenario (EPS), economic development scenario (EDS), and comprehensive development scenario (CDS) in 2035 are better. Among them, the CDS, which considers various types of higher-level strategic requirements and can compensate for the single-goal nature of the single-demand scenario, demonstrates a higher level of rationality in the land use pattern. (3) The total carbon stock in descending order is the EPS, CDS, EDS, and CPS. Among these, the CDS is at a higher level of total carbon stock, and the changes in carbon stock in each land use site are more balanced, which is an ideal carbon stock state and a scenario more in line with multi-objective coordination.
Editorial for Special Issue “Symmetry in Physics of Plasma Technologies II”
Authors: Kukushkin Alexander B.
Published: April 2024

The success of the Special Issue (SI) “Symmetry in Physics of Plasma Technologies” https://www [...]
Edodin: A New Type of Toxin from Shiitake Mushroom (Lentinula edodes) That Inactivates Mammalian Ribosomes
Authors: Citores Lucía , Ragucci Sara , Gay Claudia C. , Russo Rosita , Chambery Angela , Di Maro Antimo , Iglesias Rosario , Ferreras José M.
Published: April 2024

Ribosome-inactivating proteins (RIPs) are a group of proteins with rRNA N-glycosylase activity that irreversibly inhibit protein synthesis and consequently cause cell death. Recently, an RIP called ledodin has been found in shiitake; it is cytotoxic, strongly inhibits protein synthesis, and shows rRNA N-glycosylase activity. In this work, we isolated and characterized a 50 kDa cytotoxic protein from shiitake that we named edodin. Edodin inhibits protein synthesis in a mammalian cell-free system, but not in insect-, yeast-, and bacteria-derived systems. It exhibits rRNA N-glycosylase and DNA-nicking activities, which relate it to plant RIPs. It was also shown to be toxic to HeLa and COLO 320 cells. Its structure is not related to other RIPs found in plants, bacteria, or fungi, but, instead, it presents the characteristic structure of the fold type I of pyridoxal phosphate-dependent enzymes. Homologous sequences have been found in other fungi of the class Agaricomycetes; thus, edodin could be a new type of toxin present in many fungi, some of them edible, which makes them of great interest in health, both for their involvement in food safety and for their potential biomedical and biotechnological applications.
Roles of Human Endogenous Retrovirus-K-Encoded Np9 in Human Diseases: A Small Protein with Big Functions
Authors: Fan Jiaojiao , Qin Zhiqiang
Published: April 2024

Human Endogenous Retrovirus Sequences (HERVs) constitute up to 8% of the human genome, yet not all HERVs remain silent passengers within our genomes. Some HERVs, especially HERV type K (HERV-K), have been found to be frequently transactivated in a variety of inflammatory diseases and human cancers. Np9, a small protein translated from the HERV-K env reading frame, has been reported as an oncogenic protein and is present in a variety of tumors and transformed cells. The Np9 protein can crosstalk with many cellular factors and is involved in the pathogenicity of various diseases, including some oncogenic virus infections. In the current review, we summarize recent findings about Np9 clinical relevance/implications, its mediated cellular functions/mechanisms, and potential targeted therapies in development.
Inclusion of Anti-Tick Vaccines into an Integrated Tick Management Program in Mexico: A Public Policy Challenge
Authors: Rosario-Cruz Rodrigo , Domínguez-García Delia Inés , Almazán Consuelo
Published: April 2024

Acaricides are the most widely used method to control the cattle tick Rhipicephalus microplus. However, its use increases production costs, contaminates food and the environment, and directly affects animal and human health. The intensive use of chemical control has resulted in the selection of genes associated with resistance to acaricides, and consumers are increasingly less tolerant of food contamination. This scenario has increased the interest of different research groups around the world for anti-tick vaccine development, in order to reduce the environmental impact, the presence of residues in food, and the harmful effects on animal and human health. There is enough evidence that vaccination with tick antigens induces protection against tick infestations, reducing tick populations and acaricide treatments. Despite the need for an anti-tick vaccine in Mexico, vaccination against ticks has been limited to one vaccine that is used in some regions. The aim of this review is to contribute to the discussion on tick control issues and provide a reference for readers interested in the importance of using anti-tick vaccines encouraging concerted action on the part of Mexican animal health authorities, livestock organizations, cattle producers, and academics. Therefore, it is suggested that an anti-tick vaccine should be included as a part of an integrated tick management program in Mexico.
Influence of Technological Factors on the Formation and Transformation of Iron-Containing Phases in the Process of Ferritization of Exhausted Etching Solutions
Authors: Samchenko Dmitry , Kochetov Gennadii , Trach Yuliia , Chernyshev Denys , Kravchuk Andriy
Published: April 2024

Every year, metallurgical enterprises generate a massive amount of toxic exhausted high-concentration etching solutions. Application of the ferritization process to recycle exhausted etching solutions can help to prevent environmental pollution. It enables a cost-efficient use of water at an industrial plant and allows the plant to obtain products from toxic industrial waste and utilize it. The aim of the study was to analyze the qualitative and quantitative composition of the formed sediment and its grain size composition. Variable study parameters were the initial pH values of the solutions, the initial concentrations of total iron, and the duration of the aeration process of the reaction mixture. Thermal activation and alternating magnetic fields were used to activate the ferritization. The XRD showed that the formed sediments contained phases of γ-FeOOH, δ-FeOOH, Fe3O4, and γ-Fe2O3. Granulometry analysis showed that these sediments were highly dispersed and heterogeneous. Chemically stable phases of magnetite were obtained in the composition of sediments, with an initial concentration of iron in the reaction mixture of 16.6 g/dm3, a pH of 11.5, and a process duration of 15 min. The study results demonstrated the feasibility of further study and possible use of such sediments with a high magnetite content for the production of materials with ferromagnetic and sorption properties.
En-WBF: A Novel Ensemble Learning Approach to Wastewater Quality Prediction Based on Weighted BoostForest
Authors: Su Bojun , Zhang Wen , Li Rui , Bai Yongsheng , Chang Jiang
Published: April 2024

With the development of urbanization, the accurate prediction of effluent quality has become increasingly critical for the real-time control of wastewater treatment processes. The conventional method for measuring effluent biochemical oxygen demand (BOD) suffers from significant time delays and high equipment costs, making it less feasible for timely effluent quality assessment. To tackle this problem, we propose a novel approach called En-WBF (ensemble learning based on weighted BoostForest) to predict effluent BOD in a soft-sensing manner. Specifically, we sampled several independent subsets from the original training set by weighted bootstrap aggregation to train a series of gradient BoostTrees as the base models. Then, the predicted effluent BOD was derived by weighting the base models to produce the final prediction. Experiments on real datasets demonstrated that on the UCI dataset, the proposed En-WBF approach achieved a series of improvements, including by 28.4% in the MAE, 40.9% in the MAPE, 29.8% in the MSE, 18.2% in the RMSE, and 2.3% in the R2. On the Fangzhuang dataset, the proposed En-WBF approach achieved a series of improvements, including by 8.8% in the MAE, 9.0% in the MAPE, 12.8% in the MSE, 6.6% in the RMSE, and 1.5% in the R2. This paper contributes a cost-effective and timely solution for wastewater treatment management in real practice with a more accurate effluent BOD prediction, validating the research in the application of ensemble learning methods for environmental monitoring and management.
Big Data management in the university system
Authors: Панова Е.С.
Published: April 2024

В статье представлен обзор преимуществ и ограничений использования технологии больших данных в системе высшего образования. Показано, что управление ВУЗами, которое основывается на аналитике больших данных, позволяет повышать качество научной и образовательной деятельности. Повышение качества научной и образовательной деятельности российских ВУЗов — это одна из приоритетных национальных задач, в том числе для решения которой создан и реализуется национальный проект «Образование». Кроме этого, использование технологии больших данных решает многие организационные, функциональные, интеллектуальные и кооперационные задачи, стоящие перед системой российского высшего образования, которая в настоящее время находится в процессе цифровой трансформации.

The article provides an overview of the advantages and limitations of using big data technology in the higher education system. It is shown that the management of universities, which is based on big data analytics, makes it possible to improve the quality of scientific and educational activities. Improving the quality of scientific and educational activities of Russian universities is one of the priority national tasks, including the creation and implementation of the national project “Education”. In addition, the use of big data technology solves many organizational, functional, intellectual and cooperative tasks facing the Russian higher education system, which is currently in the process of digital transformation. Keywords: university, analytics, big data, technology, quality of education.