bluuebunny/crossref_metadata_2025 huggingface dataset:
Novel Aptamer Strategies in Combating Bacterial Infections: From Diagnostics to Therapeutics
Authors: Ye Zijian , Chen Huaizhi , Weinans Harrie , van der Wal Bart , Rios Jaqueline Lourdes
Published: August 2024

Bacterial infections and antimicrobial resistance are posing substantial difficulties to the worldwide healthcare system. The constraints of conventional diagnostic and therapeutic approaches in dealing with continuously changing infections highlight the necessity for innovative solutions. Aptamers, which are synthetic oligonucleotide ligands with a high degree of specificity and affinity, have demonstrated significant promise in the field of bacterial infection management. This review examines the use of aptamers in the diagnosis and therapy of bacterial infections. The scope of this study includes the utilization of aptasensors and imaging technologies, with a particular focus on their ability to detect conditions at an early stage. Aptamers have shown exceptional effectiveness in suppressing bacterial proliferation and halting the development of biofilms in therapeutic settings. In addition, they possess the capacity to regulate immune responses and serve as carriers in nanomaterial-based techniques, including radiation and photodynamic therapy. We also explore potential solutions to the challenges faced by aptamers, such as nuclease degradation and in vivo instability, to broaden the range of applications for aptamers to combat bacterial infections.
Impact of Plant Community Diversity on Greenhouse Gas Emissions in Riparian Zones
Authors: Li Guanlin , Xu Jiacong , Tang Yi , Wang Yanjiao , Lou Jiabao , Xu Sixuan , Iqbal Babar , Li Yingnan , Du Daolin
Published: August 2024

Plant community succession can impact greenhouse gas (GHG) emissions from the soil by altering the soil carbon and nitrogen cycles. However, the effects of community landscape diversity on soil GHG emissions have rarely been fully understood. Therefore, this study investigated how plant landscape diversity, structure type, and species composition, affect soil GHG emissions in a riparian zone. Soil GHG emissions were assessed by measuring the air samples collected from four study sites, which have different plant community structure types and species compositions (natural sites with complex plants, landscaped sites with fruit trees and grasses, untended sites with ruderals, and farmland sites), using the static chamber method. Significant differences were observed in soil carbon dioxide (CO2; p < 0.001), nitrous oxide (N2O; p < 0.001), and methane (CH4; p = 0.005) emissions. The untended site with ruderals exhibited the highest CO2 emissions, while N2O emissions increased as plant community diversity decreased. All sites acted as sinks for CH4 emissions, with decreased CH4 uptake efficiency in more diverse plant communities. The Mantel test and variance partitioning analysis revealed soil microbial biomass as an indirect influencer of GHG emissions. This study could help predict soil GHG emissions and their global warming potential under future changes in the island riparian zones.
The Impact of Changing Climate on an Endangered Epiphytic Orchid (Pleione formosana) in a Montane Cloud Forest and the Conservation Challenge Ahead
Authors: Hsu Rebecca C.-C. , Chen Yi-Chiann , Lin Chienyu
Published: August 2024

Pleione formosana Hayata is an endemic orchid that was once widely distributed across the mid-elevations of Taiwan. However, populations of this orchid have steadily shrunk due to orchid poaching in most of its habitats. By correlating data from micrometeorological stations that we installed in the cloud forest canopy at the study site, Yuanyang Lake (YYL) from 2017, we discovered the critical role of spring rainfall in triggering flowering and summer rainfall in promoting the growth of new bulbs. We found that rising temperatures and frequent drought events threaten orchid growth, potentially leading to pathogen infections. We climbed old-growth yellow cypresses to collect seed capsules of P. formosana for in vitro germination at YYL beginning in the autumn of 2018. Orchid plantlets were reintroduced to the study site in mid-August of 2022. However, the seedlings failed to survive the summer of 2023. This study is the first persistent monitoring of this rare orchid in the forest canopy of this old-growth cloud forest. Based on the result, we propose conservation strategies and directions for protecting this orchid on a regional scale. Our study highlights the mounting challenge to conservation efforts posed by global climate change.
A Comparative Study on the Melt Crystallization of Biodegradable Poly(butylene succinate-co-terephthalate) and Poly(butylene adipate-co-terephthalate) Copolyesters
Authors: Qin Pengkai , Wu Linbo
Published: August 2024

As an important biodegradable and partially biobased copolyester, poly(butylene succinate-co-terephthalate) (PBST) possesses comparable thermal and mechanical properties and superior gas barrier performance when compared with poly(butylene adipate-co-terephthalate) (PBAT), but it was found to display poorer melt processability during pelletizing and injection molding. To make clear its melt crystallization behavior under rapid cooling, PBST48 and PBST44 were synthesized, and their melt crystallization was investigated comparatively with PBAT48. PBST48 showed a PBAT48-comparable melt crystallization performance at a cooling rate of 10 °C/min or at isothermal conditions, but it showed a melt crystallization ability at a cooling rate of 40 °C/min which was clearly poorer. PBST44, which has the same mass composition as PBAT48, completely lost its melt crystallization ability under the rapid cooling. The weaker chain mobility of PBST, resulting from its shorter succinate moiety, is responsible for its inferior melt crystallization ability and processability. In comparison with PBAT48, PBST48 displayed higher tensile modulus, and both PBST48 and PBST44 showed higher light transmittance. The findings in this study deepen the understanding of PBST’s properties and will be of guiding significance for improving PBST’s processability and application development.
Comparative Study of the Dehydrothermal Crosslinking of Electrospun Collagen Nanofibers: The Effects of Vacuum Conditions and Subsequent Chemical Crosslinking
Authors: Kužma Ján , Suchý Tomáš , Horný Lukáš , Šupová Monika , Sucharda Zbyněk
Published: August 2024

Collagen nanofibrous materials have become integral to tissue engineering due to their exceptional properties and biocompatibility. Dehydrothermal crosslinking (DHT) enhances stability and maintains structural integrity without the formation of toxic residues. The study involved the crosslinking of electrospun collagen, applying DHT with access to air and under vacuum conditions. Various DHT exposure times of up to 72 h were applied to examine the time dependance of the DHT process. The DHT crosslinked collagen was subsequently chemically crosslinked using carbodiimides. The material crosslinked in this way evinced elevated Young’s modulus values and ultimate tensile strength values, a lower swelling rate and lower shrinkage ratio during crosslinking, and a higher degree of resistance to degradation than the material crosslinked solely with DHT or carbodiimides. It was shown that the crosslinking mechanism using DHT occupies different binding sites than those using chemical crosslinking. Access to air for 12 h or less did not exert a significant impact on the material properties compared to DHT under vacuum conditions. However, concerning longer exposure times, it was determined that access to air results in the deterioration of the properties of the material and that reactions take place that occupy the free bonding sites, which subsequently reduces the effectiveness of chemical crosslinking using carbodiimides.
MXene Based Flame Retardant and Electrically Conductive Polymer Coatings
Authors: Lin Bo , Li Ao , De Cachinho Cordeiro Ivan Miguel , Jia Ming , Lee Yuan Xien , Yuen Anthony Chun Yin , Wang Cheng , Wang Wei , Yeoh Guan Heng
Published: August 2024

Modern polymer coatings possess tremendous multifunctionalities and have attracted immense research interest in recent decades. However, with the expeditious development of technologies and industries, there is a vast demand for the flame retardancy and electrical conductivity of engineered polymer coatings. Traditional functional materials that render the polymer coatings with these properties require a sophisticated fabrication process, and their high mass gains can be a critical issue for weight-sensitive applications. In recent years, massive research has been conducted on a newly emerged two-dimensional (2D) nanosize material family, MXene. Due to the excellent electrical conductivity, flame retardancy, and lightweightness, investigations have been launched to synthesise MXene-based polymer coatings. Consequently, we performed a step-by-step review of MXene-involved polymer coatings, from solely attaching MXene to the substrate surface to the multilayered coating of modified MXene with other components. This review examines the performances of the fire safety enhancement and electrical conductivity as well as the feasibility of the manufacturing procedures of the as-prepared polymer composites. Additionally, the fabricated polymer coatings’ dual property mechanisms are well-demonstrated. Finally, the prospect of MXene participating in polymer coatings to render flame retardancy and electrical conductivity is forecasted.
Mechanical, Thermal and Morphological Study of Bio-Based PLA Composites Reinforced with Lignin-Rich Agri-Food Wastes for Their Valorization in Industry
Authors: Soriano-Cuadrado Belén , Fontecha-Cámara Mᵃ Ángeles , Mañas-Villar María , Delgado-Blanca Irene , Ramírez-Rodríguez María Dolores
Published: August 2024

This study investigates the performance of different poly(lactic acid) (PLA) composites incorporating agri-food waste additives and commercial lignin, comparing their properties with those of virgin PLA. The following composites were prepared using a single-screw extruder: PLA with 20% rice husk, PLA with 20% wheat straw and PLA with 20% olive pit. Additionally, PLA was blended with commercial lignin at the maximum feasible proportion using the same methodology. The resulting composites were injection-molded into specimens for analysis of their mechanical, thermal and morphological behavior. The primary objectives were to assess the dispersion of the additives within the PLA matrix and to evaluate the mechanical properties of the composites. The results indicate that the addition of high percentages of agricultural residues does not significantly compromise the mechanical properties of the composites. Notably, in the case of the PLA with 20% rice husk composite, the elastic modulus surpassed that of virgin PLA, despite the evident heterogeneity in filler particle sizes. It was feasible to incorporate a higher percentage of agricultural residues compared to commercial lignin, attributed to the larger volume occupied by the latter.
Prediction of Glass Transition Temperature of Polymers Using Simple Machine Learning
Authors: Fatriansyah Jaka Fajar , Linuwih Baiq Diffa Pakarti , Andreano Yossi , Sari Intan Septia , Federico Andreas , Anis Muhammad , Surip Siti Norasmah , Jaafar Mariatti
Published: August 2024

Polymer materials have garnered significant attention due to their exceptional mechanical properties and diverse industrial applications. Understanding the glass transition temperature (Tg) of polymers is critical to prevent operational failures at specific temperatures. Traditional methods for measuring Tg, such as differential scanning calorimetry (DSC) and dynamic mechanical analysis, while accurate, are often time-consuming, costly, and susceptible to inaccuracies due to random and uncertain factors. To address these limitations, the aim of the present study is to investigate the potential of Simplified Molecular Input Line Entry System (SMILES) as descriptors in simple machine learning models to predict Tg efficiently and reliably. Five models were utilized: k-nearest neighbors (KNNs), support vector regression (SVR), extreme gradient boosting (XGBoost), artificial neural network (ANN), and recurrent neural network (RNN). SMILES descriptors were converted into numerical data using either One Hot Encoding (OHE) or Natural Language Processing (NLP). The study found that SMILES inputs with fewer than 200 characters were inadequate for accurately describing compound structures, while inputs exceeding 200 characters diminished model performance due to the curse of dimensionality. The ANN model achieved the highest R2 value of 0.79; however, the XGB model, with an R2 value of 0.774, exhibited the highest stability and shorter training times compared to other models, making it the preferred choice for Tg prediction. The efficiency of the OHE method over NLP was demonstrated by faster training times across the KNN, SVR, XGB, and ANN models. Validation of new polymer data showed the XGB model’s robustness, with an average prediction deviation of 9.76 from actual Tg values. These findings underscore the importance of optimizing SMILES conversion methods and model parameters to enhance prediction reliability. Future research should focus on improving model accuracy and generalizability by incorporating additional features and advanced techniques. This study contributes to the development of efficient and reliable predictive models for polymer properties, facilitating the design and application of new polymer materials.
Nickel-Stage Addition in Si-MCM-41 Synthesis for Renewable Hydrogen Production
Authors: Oliveira Lígia Gomes , do Nascimento Cleuciane Tillvitz , Cazula Bárbara Bulhões , Tait Anabelle , de Oliveira Carlos de Jesus , Souza Guilherme Emanuel Queiros , Gasparrini Lázaro José , Alencar Áquila de Oliveira , Ritter Gabriela , Jorge Natália Neumann , Alves Helton José
Published: August 2024

Among the countless routes for renewable hydrogen (H2) production, Biogas Dry Reforming (DR) has been highlighted as one of the most promising for the circular bio-economy sector. However, DR requires high operating temperatures (700 °C–900 °C), and, for greater efficiency, a thermally stable catalyst is necessary, being, above all, resistant to coke formation, sintering, and sulfur poisoning. Mesoporous metallic catalysts, such as nickel (Ni) supported on silica, stand out due to their high catalytic activity concerning such characteristics. In this regard, the presented work evaluated the influences of the nickel addition stage during the synthesis of mesoporous catalyst type Si-MCM-41. Two different catalysts were prepared: catalyst A (Ni/Si-MCM-41_A), synthesized through the in situ addition of the precursor salt of nickel (Ni(Ni(NO3)2·6H2O) before the addition of TEOS (Tetraethyl orthosilicate) and after the addition of the directing agent; and catalyst B (Ni/Si-MCM-41_B), resulting from the addition of the precursor salt after the TEOS, following the conventional methodology, by wet impregnation in situ. The results evidenced that the metal addition stage has a direct influence on the mesoporous structure. However, no significant influence was observed on the efficiency concerning BDR, and the conversions into H2 were 97% and 96% for the Ni/SiMCM-41_A and Ni/Si-MCM-41_B catalysts, respectively.
Thermodynamic Study of Production of Vanadium–Nitrogen Alloy and Carbon Monoxide by Reduction and Nitriding of Vanadium Oxide
Authors: Cui Xiaojie , Yu Jianing , Wang Baohua , Hu Changqing , Zhao Dingguo , Wu Jianwei , Yao Xin , Xue Yuekai
Published: August 2024

In the quest to produce high-strength steel, the preparation technology for vanadium–nitrogen alloy (VN) was refined through thermodynamic analysis, employing it as an additive to enhance the strength and hardness of microalloyed steel. Changes in the Gibbs free energies associated with the reactions between vanadium oxides and carbon in a nitrogen atmosphere were meticulously calculated and examined. This study explored the effects of the carbon to V2O5 ratio, the nitrogen to V2O5 ratio, and the pressure on the production of VN and CO at various temperatures. The results indicate that the productivity of VN is highest under conditions of approximately 1000 °C, a C:N2:V2O5 ratio of 10:8:1, and a pressure of 1 bar. Under these conditions, VN constitutes approximately 70% of solid products, with a conversion rate of around 67.92%. CO accounts for approximately 38.17% of the exhaust gas, resulting in a yield of approximately 45.28%. The CO generated can be utilized as fuel in the production of iron in blast furnaces, providing an opportunity for secondary use of resources.
The Evaluation and Optimization of a Decentralized Incineration Facility for Animal By-Products: Performance, Cost Analysis and Resource Recovery
Authors: Charitidis Panagiotis J. , Eftaxias Alexandros , Voudrias Evangelos A. , Diamantis Vasileios
Published: August 2024

In this study, a decentralized incineration facility was evaluated over a three-year period, focusing on performance aspects, such as the burning rate, ash production, fuel and electricity consumption, and ash composition, while processing animal by-products (ABPs). The total cost for ABP incineration was determined to be EUR 159 t−1 ABP, with the major components being capital depreciation (42%), maintenance expenditures (26%), labor (18%), and transportation costs (9%). Liquified petroleum gas consumption ranged from 3 to 7 kg t−1, while electricity use was between 15 and 20 kWh t−1 incinerated ABP. The incineration process generated 7–10% (by weight) ABP ash, which was characterized by high calcium and phosphorus contents and low levels of hazardous trace elements. Leaching tests demonstrated the potential for recovering an alkaline supernatant for flue gas treatment and a phosphorus-rich slurry for fertilizer production. The findings suggest that energy recovery and valorization are crucial for minimizing the operational costs and the environmental impact, emphasizing the benefits of integrating advanced resource recovery techniques in ABP incineration facilities.
Cultural Expression and Liturgical Theology in the Worship Songs Sung by British-Born Chinese
Authors: Tang James Yat-Man , Perigo Jeremy
Published: August 2024

Multilingual and multicultural worship can take on many models and expressions. Initially, Chinese immigration to the UK in the 1950s and 1960s led to an increased population of churches. Many Chinese church services were conducted in Cantonese, catering to the needs of first-generation immigrants, mainly from Hong Kong. Yet, the children of these older generations grew up with a bicultural hybridized identity expressed first in small English-speaking youth groups that led to English-speaking worship services within Chinese churches. Contributing to the field of worship studies through music repertoire studies over four weeks in June and July 2017 at Birmingham Chinese Evangelical Church, we raised the following questions: (1) what do the chosen worship songs represent with regards to the liturgical theology and cultural expressions of this community and (2) how is self-perception and perception of the divine expressed in the lyrical themes of these songs? Our study revealed that singing English songs from the West dominated the corporate liturgical identity of these services. Yet, through a British-born Chinese evangelical cultural reading, some lyrical themes were particularly resonant within Chinese culture, such as honor, shame, reverence, and bowing.
FFA: Foreground Feature Approximation Digitally against Remote Sensing Object Detection
Authors: Zhu Rui , Ma Shiping , He Linyuan , Ge Wei
Published: August 2024

In recent years, research on adversarial attack techniques for remote sensing object detection (RSOD) has made great progress. Still, most of the research nowadays is on end-to-end attacks, which mainly design adversarial perturbations based on the prediction information of the object detectors (ODs) to achieve the attack. These methods do not discover the common vulnerabilities of the ODs and, thus, the transferability is weak. Based on this, this paper proposes a foreground feature approximation (FFA) method to generate adversarial examples (AEs) that discover the common vulnerabilities of the ODs by changing the feature information carried by the image itself to implement the attack. Specifically, firstly, the high-quality predictions are filtered as attacked objects using the detector, after which a hybrid image without any target is made, and the hybrid foreground is created based on the attacked targets. The images’ shallow features are extracted using the backbone network, and the features of the input foreground are approximated towards the hybrid foreground to implement the attack. In contrast, the model predictions are used to assist in realizing the attack. In addition, we have found the effectiveness of FFA for targeted attacks, and replacing the hybrid foreground with the targeted foreground can realize targeted attacks. Extensive experiments are conducted on the remote sensing target detection datasets DOTA and UCAS-AOD with seven rotating target detectors. The results show that the mAP of FFA under the IoU threshold of 0.5 untargeted attack is 3.4% lower than that of the advanced method, and the mAP of FFA under targeted attack is 1.9% lower than that of the advanced process.
Early-Stage Mapping of Winter Canola by Combining Sentinel-1 and Sentinel-2 Data in Jianghan Plain China
Authors: Liu Tingting , Li Peipei , Zhao Feng , Liu Jie , Meng Ran
Published: August 2024

The early and accurate mapping of winter canola is essential in predicting crop yield, assessing agricultural disasters, and responding to food price fluctuations. Although some methods have been proposed to map the winter canola at the flowering or later stages, mapping winter canola planting areas at the early stage is still challenging, due to the insufficient understanding of the multi-source remote sensing features sensitive for winter canola mapping. The objective of this study was to evaluate the potential of using the combination of optical and synthetic aperture radar (SAR) data for mapping winter canola at the early stage. We assessed the contributions of spectral features, backscatter coefficients, and textural features, derived from Sentinel-2 and Sentinel-1 SAR images, for mapping winter canola at early stages. Random forest (RF) and support vector machine (SVM) classification models were built to map winter canola based on early-stage images and field samples in 2017 and then the best model was applied to corresponding satellite data in 2018–2022. The following results were obtained: (1) The red edge and near-infrared-related spectral features were most important for the mapping of early-stage winter canola, followed by VV (vertical transmission, vertical reception), DVI (Difference vegetation index), and GOSAVI (Green Optimized Soil Adjusted Vegetation Index); (2) based on Sentinel-1 and Sentinel-2 data, winter canola could be mapped as early as 130 days prior to ripening (i.e., early overwinter stage), with the F-score over 0.85 and the OA (Overall Accuracy) over 81%; (3) adding Sentinel-1 could improve the OA by about 2–4% and the F-score by about 1–2%; and (4) based on the classifier transfer approach, the F-scores of winter canola mapping in 2018–2022 varied between 0.75 and 0.97, and the OAs ranged from 79% to 86%. This study demonstrates the potential of early-stage winter canola mapping using the combination of Sentinel-2 and Sentinel-1 images, which could enable the large-scale early mapping of canola and provide valuable information for stakeholders and decision makers.
Identification of Beef Odors under Different Storage Day and Processing Temperature Conditions Using an Odor Sensing System
Authors: Liu Yuanchang , Peng Nan , Kang Jinlong , Onodera Takeshi , Yatabe Rui
Published: August 2024

This study used an odor sensing system with a 16-channel electrochemical sensor array to measure beef odors, aiming to distinguish odors under different storage days and processing temperatures for quality monitoring. Six storage days ranged from purchase (D0) to eight days (D8), with three temperature conditions: no heat (RT), boiling (100 °C), and frying (180 °C). Gas chromatography–mass spectrometry (GC-MS) analysis showed that odorants in the beef varied under different conditions. Compounds like acetoin and 1-hexanol changed significantly with the storage days, while pyrazines and furans were more detectable at higher temperatures. The odor sensing system data were visualized using principal component analysis (PCA) and uniform manifold approximation and projection (UMAP). PCA and unsupervised UMAP clustered beef odors by storage days but struggled with the processing temperatures. Supervised UMAP accurately clustered different temperatures and dates. Machine learning analysis using six classifiers, including support vector machine, achieved 57% accuracy for PCA-reduced data, while unsupervised UMAP reached 49.1% accuracy. Supervised UMAP significantly enhanced the classification accuracy, achieving over 99.5% with the dimensionality reduced to three or above. Results suggest that the odor sensing system can sufficiently enhance non-destructive beef quality and safety monitoring. This research advances electronic nose applications and explores data downscaling techniques, providing valuable insights for future studies.
Lightweight Single-Stage Ship Object Detection Algorithm for Unmanned Surface Vessels Based on Improved YOLOv5
Authors: Sun Hui , Zhang Weizhe , Yang Shu , Wang Hongbo
Published: August 2024

Object detection is applied extensively in various domains, including industrial manufacturing, road traffic management, warehousing and logistics, and healthcare. In ship object detection tasks, detection networks are frequently deployed on devices with limited computational resources, e.g., unmanned surface vessels. This creates a need to balance accuracy with a low parameter count and low computational load. This paper proposes an improved object detection network based on YOLOv5. To reduce the model parameter count and computational load, we utilize an enhanced ShuffleNetV2 network as the backbone. In addition, a split-DLKA module is devised and implemented in the small object detection layer to improve detection accuracy. Finally, we introduce the WIOUv3 loss function to minimize the impact of low-quality samples on the model. Experiments conducted on the SeaShips dataset demonstrate that the proposed method reduces parameters by 71% and computational load by 58% compared to YOLOv5s. In addition, the proposed method increases the mAP@0.5 and mAP@0.5:0.95 values by 3.9% and 3.3%, respectively. Thus, the proposed method exhibits excellent performance in both real-time processing and accuracy.
Improved Cell Allocation Strategies Using K-Means Clustering in Congested 6TiSCH Environments
Authors: Koesnadi Fransiskus Xaverius Kevin , Chung Sang-Hwa
Published: August 2024

The 6TiSCH protocol (IEEE 802.15.4e) is crucial for the Industrial Internet of Things (IIoT), utilizing a time-slotted channel hopping (TSCH) mode based on node distribution. In this study, we propose an innovative cell allocation strategy based on node position clustering using the K-means algorithm, specifically designed to address congestion and optimize resource distribution in the 6TiSCH network. Our mechanism effectively groups nodes into clusters, allowing for dynamic adjustment of cell capacities in congested areas by analyzing traffic patterns and the spatial distribution of nodes. This clustering approach enhances the efficiency of slot frame utilization and minimizes communication delays by reducing interference and improving routing stability. The proposed strategy leverages the clustering results to improve cell usage efficiency and reduce communication latency between nodes. By tailoring cell allocation to the specific traffic needs of each cluster, we significantly reduce packet loss, manage congestion more effectively, and enhance data transmission reliability. We evaluated the clustering method using the K-means algorithm through experiments with the 6TiSCH simulator. Additionally, we considered using objective functions in Routing Protocol for Low-Power and Lossy Networks (RPL), such as OF0 and MRHOF, to assess clustering results and their impact on throughput and packet delivery. Our method resulted in significantly improved average performance metrics. Under the OF0 routing protocol, we achieved a 30.01% latency reduction, a 15.95% faster joining time, an 8% higher packet delivery ratio, and a 13.82% throughput increase. Similarly, we observed a 12.34% improvement in packet delivery ratio, 21.06% latency reduction, 12.68% faster joining time, and 25.97% higher throughput speed with the MRHOF routing protocol. These findings highlight the effectiveness of the improved cell allocation strategy in congested 6TiSCH environments, offering a better solution for enhancing network performance in IIoT applications.
Quantifying the Impact of Light on Ascorbic Acid Content in Lettuce: A Model Proposal
Authors: Fasciolo Benedetta , van Brenk Jordan , Verdonk Julian C. , Bakker Evert-Jan , van Mourik Simon
Published: August 2024

Vitamin C, also known as ascorbic acid (AsA), is an essential organic compound that is crucial for both plants and animals. Due to the inability of humans and some other animals to synthesize AsA, it is essential for them to consume sufficient plant products, especially leaves and fruits, which are good sources of AsA. Numerous studies have attempted to understand how different environmental factors influence crop AsA development. However, a comprehensive understanding of how environmental conditions affect ascorbic acid development remains elusive. This challenge may be due, in part, to the inherent difficulty of accurately and consistently measuring plant AsA. Measurements vary significantly depending on the tools and techniques used to capture them, and consequently, comparing results from different studies is complex. To address this challenge, our study develops a regression model to predict the AsA content in lettuce based on different light conditions. By analyzing how the varying daily light integral (DLI) and the blue light spectrum affect AsA levels, the model provides actionable insights for optimizing light treatments. This model not only aids in enhancing the development of AsA in lettuce but also assists farmers in achieving more sustainable agricultural practices by identifying optimal light spectra and DLI, thus promoting efficient resource utilization.
Creating a Transnational Green Knowledge Commons for a Socially Just Sustainability Transition
Authors: Farley Joshua , Walker Dakota , Geffert Bryn , Chandler Nina , Eisel Lauren , Friedberg Murray , Portelli Dominic
Published: August 2024

Humanity faces numerous deeply interconnected systemic risks to sustainability—a global polycrisis. We need economic institutions that produce the knowledge required to address this polycrisis at the lowest cost, maximize the benefits that knowledge generates, and distribute those benefits fairly. Knowledge improves through use; its value is maximized when it is freely available. Intellectual property rights (IPRs), a form of monopoly, direct knowledge production towards market goods, raise the cost of doing research, and reduce the benefits by price-rationing access. Building on theories of the commons, the anticommons, and market failures, we propose the creation of a transnational green knowledge commons (TGKC) in which all knowledge that contributes to solving the polycrisis be made open access on the condition that any subsequent improvements also be open access. We argue that a TGKC is more sustainable, just, and efficient than restrictive IPRs and well suited to the motivations and governance institutions of public universities. We show how a single university could initiate the process and estimate that the cost would be more than offset by reduced IPR expenses. A TGKC would reduce the costs of generating and disseminating knowledge directed towards a sustainable future and help stimulate the transnational cooperation, reciprocity, and trust required for sustainable management of the global biophysical commons.
Assessing Psychosocial Work Conditions: Preliminary Validation of the Portuguese Short Version of the Copenhagen Psychosocial Questionnaire III
Authors: Pinto Ana , Carvalho Carla , Mónico Lisete S. , Moio Isabel , Alves Joel , Lima Tânia M.
Published: August 2024

The working environment is a crucial aspect to considerfor guaranteeing a sustainable life. However, workers are exposed to various health risks daily, namely, psychological risks. These risks can be due to imbalances on the part of the workers themselves or to organisational and inter-functional risk factors arising from interactions within companies and the challenges of professional responsibilities. Over the past 20 years, the Copenhagen Psychosocial Questionnaire (COPSOQ) has become one of the most prominent tools for assessing psychological and social factors at work. This study aimed to present, discuss, and evaluate aspects of the cultural adaptation and preliminary psychometric validation of the short version of COPSOQ III for a Portuguese sample. For this purpose, we used data from 361 participants employed in the industrial (30.7%) and services (69.3%) sectors across various regions of Portugal. A third-order confirmatory factor analysis (CFA) was performed using AMOS, revealing an acceptable fit. The results also demonstrate the robustness of the measurement model, confirming its reliability and validity. In light of some limitations of this preliminary study, directions for future research are proposed. Thus, a tool for assessing psychosocial risks is disseminated, making it possible to achieve more sustainable working environments where the operator’s health and well-being are prioritised.
Urban Resilience of Large Public Health Events Based on NPP-VIIRS Nighttime Light Images: A Case Study of 35 Large Cities in China
Authors: Liu Rui , Li Xin , Zhang Zizhe
Published: August 2024

The COVID-19 outbreak directly and severely threatens global public health. Non-drug interventions in response to the COVID-19 pandemic have significantly altered urban socioeconomic activity. Understanding the different levels of city resilience to the impact of COVID-19 on urban human activities is essential. In this paper, 35 large cities in China were selected as research areas, and based on NPP-VIIRS night light images, the spatial pattern changes in human activities during the epidemic period from the end of December 2019 to December 2022 were explored. The results are as follows: (1) In the first two months of the epidemic, the luminous value of large cities showed an extensive range of decline, and the decline in different urban functional places was different. (2) There is a significant positive correlation between the urban population and the luminous change value. The closer the relationship between urban places and human activities, the stronger the correlation between the population and the luminous change value of urban places. (3) In the middle and later stages of the epidemic, the night light value of all cities showed an upward trend, but there was a difference. (4) The increase in the number of confirmed cases in the middle and later stages of the epidemic could hardly lead to a significant decrease in the value of night light on a monthly scale unless the city had a relatively large area and a relatively strict lockdown policy in that month. This study will help inform future strategies and decisions to effectively combat epidemics and the construction of resilient cities.
A Neural Network Approach to Physical Information Embedding for Optimal Power Flow
Authors: Liu Chenyuchuan , Li Yan , Xu Tianqi
Published: August 2024

With the increasing share of renewable energy in the power system, traditional power flow calculation methods are facing challenges of complexity and efficiency. To address these issues, this paper proposes a new framework for AC optimal power flow analysis based on a physics-informed convolutional neural network (PICNN) approach, which enables the neural network to learn solutions that follow physical laws by embedding the power flow equations and other physical constraints into the loss function of the network. Compared with the traditional power flow calculation method, the calculation speed of this method is improved by 10–30 times. Compared to traditional neural network models, the method provides higher accuracy, with an average increase in accuracy of up to 2.5–10 times. In addition, this paper introduces a methodology to extract worst-case guarantees for violations of the neural network’s predicted power generation constraints, determining the worst possible violation that could result from any neural network output across the entire input domain, and taking appropriate measures to reduce the violation. The method is experimentally shown to be highly accurate and reliable for the AC optimal power flow (AC-OPF) analysis problem, while reducing the dependence on a large amount of labelled data.
Developing a Typology Based on Energy Practices and Environmental Attitudes
Authors: Karasmanaki Evangelia , Galatsidas Spyros , Tsantopoulos Georgios
Published: August 2024

The European Union is increasingly directing efforts and resources toward promoting the adoption of energy saving measures among citizens, but the effectiveness of these efforts cannot be ensured if the heterogeneity of citizens is neglected. This paper assesses whether Greek citizens present heterogeneity in terms of their energy behavior and investigates whether they can be classified based on the practices they apply to reduce energy costs and their environmental attitudes. To that end, the study collected a representative citizen sample, and k-means cluster analysis was performed. Results indicated significant heterogeneity and four clusters were identified: ‘Extreme heat savers and pro-environmental food consumers’ saved on heating but neglected practices regarding lighting, hot water use, and transport; ‘Heat savers and environmentally unaware’ applied energy saving measures only to lower heating costs; ‘Environmentally aware energy savers’ were the most engaged segment in the application of energy saving measures driven by pro-environmental attitudes; and ‘Mindful resources and transport users’ did not apply any measures for heating despite having positive attitudes in terms of electricity, water and transport use. Therefore, it is necessary to train citizens on pro-environmental practices, and in doing so, the recorded heterogeneity can guide the design of differentiated and effective approaches.
A Comprehensive Security Framework for Asymmetrical IoT Network Environments to Monitor and Classify Cyberattack via Machine Learning
Authors: Alqahtani Ali , Alsulami Abdulaziz A. , Alqahtani Nayef , Alturki Badraddin , Alghamdi Bandar M.
Published: August 2024

The Internet of Things (IoT) is an important component of the smart environment, which produces a large volume of data that is considered challenging to handle. In addition, the IoT architecture is vulnerable to many cyberattacks that can target operational devices. Therefore, there is a need for monitoring IoT traffic to analyze, detect malicious activity, and classify cyberattack types. This research proposes a security framework to monitor asymmetrical network traffic in an IoT environment. The framework offers a network intrusion detection system (NIDS) to detect and classify cyberattacks, implemented using a machine learning (ML) model residing in the middleware layer of the IoT architecture. A dimensionality reduction technique known as principal component analysis (PCA) is utilized to facilitate data transmission, which is intended to be sent from the middleware layer to the cloud layer with reduced complexity and fewer unnecessary inputs without compromising the information content. Therefore, the reduced IoT traffic data are sent to the cloud and the PCA data are retransformed to approximate the original data for visualizing the IoT traffic. The NIDS is responsible for reporting the attack type to the cloud in the event of an attack. Our findings indicate that the proposed framework has promising results in classifying the attack type, which achieved a classification accuracy of 98%. In addition, the dimension of the IoT traffic data is reduced by around 50% and it has a similarity of around 90% compared to the original data.
Unlocking Potential: Key Factors Shaping Undergraduate Self-Directed Learning in AI-Enhanced Educational Environments
Authors: Wu Di , Zhang Shuling , Ma Zhiyuan , Yue Xiao-Guang , Dong Rebecca Kechen
Published: August 2024

This study investigates the factors influencing undergraduate students’ self-directed learning (SDL) abilities in generative Artificial Intelligence (AI)-driven interactive learning environments. The advent of generative AI has revolutionized interactive learning environments, offering unprecedented opportunities for personalized and adaptive education. Generative AI supports teachers in delivering smart education, enhancing students’ acceptance of technology, and providing personalized, adaptive learning experiences. Nevertheless, the application of generative AI in higher education is underexplored. This study explores how these AI-driven platforms impact undergraduate students’ self-directed learning (SDL) abilities, focusing on the key factors of teacher support, learning strategies, and technology acceptance. Through a quantitative approach involving surveys of 306 undergraduates, we identified the key factors of motivation, technological familiarity, and the quality of AI interaction. The findings reveal the mediating roles of self-efficacy and learning motivation. Also, the findings confirmed that improvements in teacher support and learning strategies within generative AI-enhanced learning environments contribute to increasing students’ self-efficacy, technology acceptance, and learning motivation. This study contributes to uncovering the influencing factors that can inform the design of more effective educational technologies and strategies to enhance student autonomy and learning outcomes. Our theoretical model and research findings deepen the understanding of applying generative AI in higher education while offering important research contributions and managerial implications.
Governmental Revenue Compensation during COVID-19: Did Firm Resources and Institutional Factors Explain Who Received It?
Authors: Aarstad Jarle , Jakobsen Stig-Erik , Fløysand Arnt , Kvitastein Olav Andreas
Published: August 2024

During COVID-19, the Norwegian Government provided compensation to amend firms’ decreased revenues, yet it should be neutral and tailored to losses only. However, firm resources or institutional factors may have influenced the compensation, which we study here. Survey data showed a high probability of compensation among large firms, although they were not hit particularly hard by COVID-19. Also, compensation was prevalent for firms in the hospitality, tourism, and culture industry, and a likely explanation is that they were hit hard. However, their probability of compensation was prevalent compared to firms in other industries, regardless of revenue losses. We assume that large firms’ compensation was due to their resources to apply for funding successfully, while institutional factors explained the compensation for firms in the hospitality, tourism, and culture industry.
Understanding Mumps Dynamics: Epidemiological Traits and Breakthrough Infections in the Population under 15 Years of Age in Jiangsu Province, China, 2023
Authors: Li Mingma , Wang Zhiguo , Liu Zhihao , Deng Xiuying , Wang Li , Zhu Yuanyuan , Xu Yan , Zhang Lei , Liu Yuanbao , Wang Bei
Published: August 2024

Despite coverage of two doses of mumps-containing vaccines, mumps epidemics persist among children and young adults in China. This study aimed to analyze the epidemiological characteristics of mumps in Jiangsu Province, with a particular focus on breakthrough cases among high-incidence groups. Mumps cases reported in 2023 were systematically collected from the Infectious Disease Surveillance and Reporting System. A comprehensive descriptive epidemiological analysis was performed to elucidate the characteristics of the reported cases. A joinpoint regression (JPR) model was utilized to identify the temporal trends across various periods. Subsequently, immunization information for cases under 15 years of age was obtained through the Jiangsu Province Vaccination Integrated Service Management Information System to identify breakthrough cases and conduct exploratory analyses. A total of 4142 mumps cases were reported in Jiangsu Province in 2023, yielding an annual incidence rate of 4.86/100,000. A total of 81.75% of the cases were students and childcare children, and the gender ratio was 1.5:1 (male/female). The JPR model analysis of weekly reported cases identified five distinct trend segments (1st: 1–8, weekly percent change (WPC) = 26.67 *; 2nd: 9–28, WPC = 3.11 *; 3rd: 29–34, WPC = −5.31; 4th: 35–37, WPC = 15.48; 5th: 38–52, WPC = −4.06 *), and the gender subgroups demonstrated similar trends to the overall pattern. Notably, 89.14% (3692/4142) of the total cases were among individuals under 15 years, with 96.02% (3545/3692) having been vaccinated against mumps. The number of single-dose breakthrough cases (SdBCs) was approximately fourfold (2847/698) that of two-dose breakthrough cases (TdBCs). The main population composition of TdBCs was children aged 0–5 years old, and the classification was dominated by childcare children and scattered children. The median time interval between initial immunization and onset were shorter in TdBCs than in the SdBCs group, and the median time interval between the last immunization and onset was interestingly similarly shorter. However, these situations were interestingly reversed in 105 laboratory-confirmed breakthrough cases. Therefore, the current vaccination strategies have demonstrated tangible effectiveness in preventing and controlling mumps. However, the high incidence of breakthrough cases among high-risk pediatric populations indicates that mumps immunization strategies still deserve more attention and research for better herd protection.