bluuebunny/crossref_metadata_2025 huggingface dataset:
A Terahertz Point Source Meta-Sensor in Reflection Mode for Trace-Amount Bio-Sensing Applications
Authors: Zheng Luwei , Tonouchi Masayoshi , Serita Kazunori
Published: August 2024

Biosensors in the Terahertz (THz) region are attracting significant attention in the biomedical and chemical analysis fields owing to their potential for ultra-trace sensing of various solutions with high sensitivity. However, the development of compact, highly sensitive chips and methods for easy, rapid, and trace-amount measurements have been significantly hindered by the limited spatial resolution of THz waves and their strong absorption by water. In this study, we developed a nonlinear optical crystal (NLOC)-based compact THz sensor chip, and a near-field point THz source with a diameter of ~ϕ20 μm was locally generated via optical rectification. Here, only the single central meta-atom was excited. The reflective resonance responses highly depend on the array number and period of the meta-atom structures. The sensing performance was examined with several liquid biological samples, such as mineral water, DNA, and human blood. 1 μL of samples was directly dropped onto the meta-surface with an effective sensing area of 0.32 mm2 (564 μm × 564 μm). Obvious resonance frequency shifts were clearly observed. This research holds significance in advancing liquid bio-sample sensing methodologies by facilitating easy, rapid, and trace-amount measurements and promoting the development of compact and highly sensitive THz sensors tailored for liquid biological samples.
A Multi-Spectral Temperature Field Reconstruction Technology under a Sparse Projection
Authors: Zhang Xuan , Han Yan
Published: August 2024

In optical sparse projection reconstruction, the reconstruction of the tested field often requires the utilization of a priori knowledge to compensate for the lack of information due to the sparse projection angle. For situations where the radiation field of unknown materials is reconstructed or prior knowledge cannot be obtained, this paper proposes a multi-spectral temperature field reconstruction technology under a sparse projection. This technology utilizes the principles of multi-spectral temperature measurement technology, takes the correlation of radiation information between sub-regions of the temperature field as the optimization objective, and establishes statistical rules between the missing information by combining the equation constraint optimization algorithm and multi-spectral temperature measurement technology. Finally, the temperature field to be measured is reconstructed. The simulation and experimental tests show that, without any prior knowledge, the proposed method can reconstruct the temperature field under two projection angles, with an accuracy of 1.64~12.25%. Moreover, the projection angle is lower, and the robustness is stronger than that of the other methods.
Investigation of the Propagation Characteristics of Double-Ring Perfect Vortex Beams in Atmospheric Turbulence
Authors: Xu Xiang , Luo Chuankai , Qian Xianmei , Zhu Wenyue
Published: August 2024

Double-ring perfect vortex beams (DR–PVBs) have attracted increasing attention due to their unique characteristics of carrying independent information channels and exhibiting higher security and stability during propagation. In this study, we theoretically simulated and experimentally generated DR–PVBs with various topological charges. We investigated the propagation characteristics of these beams under von Karman spectrum turbulence conditions through numerical simulations based on multiple-phase screen methods. The effects of different inner and outer ring topological charges and varying turbulence intensities on the intensity distribution, beam spreading, and beam wander of DR–PVBs over different propagation distances were examined and compared with double-ring Gaussian vortex beams (DR–GVBs). The simulation results indicate that within a propagation range of 0–500 m, the effective radius of DR–PVBs with different topological charges remains essentially unchanged and stable. For propagation distances exceeding 1000 m, DR–PVBs exhibit superior beam wander characteristics compared to DR–GVBs. Additionally, two occurrences of self-focusing effects were observed during propagation, each enhancing beam stability and reducing the beam spreading and beam wander of the DR–PVBs. This study provides valuable insights for applications of DR–PVBs in optical communication, optical manipulation, and optical measurement.
Mitigation of Drought Stress for Quinoa (Chenopodium quinoa Willd.) Varieties Using Woodchip Biochar-Amended Soil
Authors: Akram Muhammad Zubair , Rivelli Anna Rita , Libutti Angela , Liu Fulai , Andreasen Christian
Published: August 2024

Drought stress deteriorates agro-ecosystems and poses a significant threat to crop productivity and food security. Soil amended with biochar has been suggested to mitigate water stress, but there is limited knowledge about how biochar affects the physiology and vegetative growth of quinoa plants under soil water deficits. We grew three quinoa (Chenopodium quinoa Willd.) varieties, Titicaca (V1), Quipu (V2), and UAFQ7 (V3) in sandy loam soil without (B0) and with 2% woodchip biochar (B2) under drought conditions. The drought resulted in significant growth differences between the varieties. V3 performed vegetatively better, producing 46% more leaves, 28% more branches, and 25% more leaf area than the other two varieties. Conversely, V2 displayed significantly higher yield-contributing traits, with 16% increment in panicle length and 50% more subpanicles compared to the other varieties. Woodchip biochar application significantly enhanced the root development (i.e., root biomass, length, surface, and projected area) and plant growth (i.e., plant height, leaf area, and absolute growth rate). Biochar significantly enhanced root growth, especially fresh and dry weights, by 122% and 127%, respectively. However, biochar application may lead to a trade-off between vegetative growth and panicle development under drought stress as shown for V3 grown in soil with woodchip biochar. However, V3B2 produced longer roots and more biomass. Collectively, we suggest exploring the effects of woodchip biochar addition to the soil on the varietal physiological responses such as stomatal regulations and mechanisms behind the increased quinoa yield under water stress conditions.
The Development of an Online Decision Support System to Select Optimal Nature-Based Solutions to Protect Streams and the Sea
Authors: Koutalakis Paschalis , Zaimes George
Published: August 2024

Nonpoint source pollutants primarily originate from agricultural areas, settlements, and contaminated lands. Soil erosion and deposition are the means of transportation of pollutants since soil particles not only absorb but also transport contaminants through the stream network. Nature-based solutions (NbSs) are quite popular around the world to mitigate soil erosion and deposition, which has accelerated due to climate change and other anthropogenic activities. To promote their adoption, we developed an online decision support system (DSS) to provide land and water managers and particularly stakeholders with the optimal NbSs and ecosystem-based approaches (EbAs) that could help protect watersheds, streams, and consequently seas from pollutants. This DSS incorporates a descriptive data management system to handle datasets (questions, answers/criteria, outputs/solutions) from various stakeholders (e.g., policymakers, urban planners, environmentalists) and other non-experts. The questions of the DSS are related to different characteristics (criteria) of the areas of interest for the NbS or EbA. The questions provide various answers (which serve as descriptive data) in order to weigh the criteria/characteristics and, ultimately, the proposed NbS. The NbSs of the DSS were recorded based on a bibliographic review and from stakeholders’ responses via forums, meetings, workshops, etc. The primary testing results by stakeholders showed that the online DSS has the potential to be used as a complementary service in the near future. Specifically, it can provide the optimal NbS based on the participants’ answers about the study area. This communication paper may act as an invitation to reach a greater audience of stakeholders for the improvement of the online DSS.
Study of Injection Molding Process to Improve Geometrical Quality of Thick-Walled Polycarbonate Optical Lenses by Reducing Sink Marks
Authors: Vanek Jiri , Ovsik Martin , Stanek Michal , Hanzlik Jan , Pata Vladimir
Published: August 2024

This study investigates the challenges and potential of conventional injection molding for producing thick-walled optical components. The research primarily focuses on optimizing process parameters and mold design to enhance product quality. The methods include software simulations and experimental validation using polycarbonate test samples (optical lenses). Significant parameters such as melt temperature, mold temperature, injection pressure, and packing pressure were varied to assess their impact on geometric accuracy and visual properties. The results show that lower melt temperatures and higher mold temperatures significantly reduce the occurrence of dimensional defects. Additionally, the design of the gate system was found to be crucial in minimizing defects and ensuring uniform material flow. Effective packing pressure was essential in reducing volumetric shrinkage and sink marks. Furthermore, we monitored the deviation between the predicted and actual defects relative to the thickness of the sample wall. After optimization, the occurrence of obvious defects was eliminated across all sample thicknesses (lenses), and the impact of the critical defect, the sink mark on the planar side of the lens, was minimized. These findings demonstrate the substantial potential of conventional injection molding to produce high-quality thick-walled parts when these parameters are precisely controlled. This study provides valuable insights for the efficient design and manufacturing of optical components, addressing the growing demand for high-performance thick-walled plastic products.
Enhancing the Quality of Polypropylene Recyclates: Predictive Modelling of the Melt Flow Rate and Shear Viscosity
Authors: Seifert Lukas , Leuchtenberger-Engel Lisa , Hopmann Christian
Published: August 2024

The extensive use of polypropylene (PP) in various industries has heightened interest in developing efficient methods for recycling and optimising its mixtures. This study focuses on formulating predictive models for the Melt Flow Rate (MFR) and shear viscosity of PP blends. The investigation involved characterising various grades, including virgin homopolymers, copolymers, and post-consumer recyclates, in accordance with ISO 1133 standards. The research examined both binary and ternary blends, utilising traditional mixing rules and symbolic regression to predict rheological properties. High accuracy was achieved with the Arrhenius and Cragoe models, attaining R2 values over 0.99. Symbolic regression further enhanced these models, offering significant improvements. To mitigate overfitting, empirical noise and variable swapping were introduced, increasing the models’ robustness and generalisability. The results demonstrated that the developed models could reliably predict MFR and shear viscosity, providing a valuable tool for improving the quality and consistency of PP mixtures. These advancements support the development of recycling technologies and sustainable practices in the polymer industry by optimising processing and enhancing the use of recycled materials.
Effects of Reclaimed Water Supplementation on the Occurrence and Distribution Characteristics of Antibiotic Resistance Genes in a Recipient River
Authors: Zhao Xiaohui , Wang Xiaofei , Lang Hang , Zhang Panwei , Ni Jie , Wu Wenqiang
Published: August 2024

Due to the serious scarcity of water resources and the aggravation of water pollution in northern China, replenishing reclaimed water can alleviate the water shortage problem in northern rivers to a certain extent, but has also become an important way for antibiotic resistance genes (ARGs) to spread into rivers. In order to study the characteristics of ARGs in reclaimed water recharge rivers (Wenyu River), the abundance and distribution of ARGs in a typical reclaimed water replenishment river were analyzed by metagenomic sequencing technology combined with river water quality. The results showed that: due to the input of reclaimed water, the water quality characteristics of downstream sampling points of the river were significantly different from those upstream. Water quality factors such as total organic carbon, chemical oxygen demand, chlorophyll, and total nitrogen tended to increase gradually. Quinolones and macrolides were the main types of antibiotics. A total of 1217 ARGs were detected in the reclaimed water river system, including multidrug resistance, macrolide-lincosamide-streptogramin (MLS), tetracycline, glycopeptide, peptide, aminocoumarin, etc. The average abundance of ARGs in reclaimed water was higher than that in rivers. Among them, multidrug ARGs existed most widely, which may gradually become the main trend of ARGs’ evolutionary variation. RDA results revealed that the environmental factors EC and DO, as well as tetracycline antibiotics (TCs), may be important environmental factors affecting the distribution of ARGs.
Current Status and Challenges in the Commercial Production of Polyhydroxyalkanoate-Based Bioplastic: A Review
Authors: Gautam Shina , Gautam Alok , Pawaday Juily , Kanzariya Rekha Karshanbhai , Yao Zhitong
Published: August 2024

The escalating worldwide concerns over the difficult degradation and pollution of plastic and its associated environmental and health risks have amplified the urgent need to develop biodegradable alternatives to traditional plastics. Polyhydroxyalkanoates (PHAs) have emerged as a promising class of biopolymers that offer a sustainable solution. Their commercial success in various applications has highlighted PHAs’ potential to mitigate environmental impact. Critical to the economic feasibility of PHA production is the optimization of downstream processing methods, crucial for scaling operations from pilot to industrial scales. This paper reviews two decades of pilot-scale studies on PHA extraction, emphasizing the advancements and challenges encountered. It also discusses chemical extraction methods applied across different feedstock and microbial strains, highlighting their role in enhancing efficiency and sustainability. This comprehensive review underscores the imperative for advancing PHA technologies, particularly in refining extraction techniques, to facilitate broader adoption in industries seeking environmentally friendly alternatives to conventional plastics.
Scenario-Driven Optimization Strategy for Energy Storage Configuration in High-Proportion Renewable Energy Power Systems
Authors: Yang Hui , Liu Qine , Xiao Kang , Guo Long , Yang Lucheng , Zou Hongbo
Published: August 2024

The output of renewable energy sources is characterized by random fluctuations, and considering scenarios with a stochastic renewable energy output is of great significance for energy storage planning. Existing scenario generation methods based on random sampling fail to account for the volatility and temporal characteristics of renewable energy output. To enhance photovoltaic (PV) absorption capacity and reduce the cost of planning distributed PV and energy storage systems, a scenario-driven optimization configuration strategy for energy storage in high-proportion renewable energy power systems is proposed, incorporating demand-side response and bidirectional dynamic reconfiguration strategies into the planning model. Firstly, this paper designs a time series scenario generation method for renewable energy output based on a Deep Belief Network (DBN) to fully explore the characteristics of renewable energy output. Then, considering various cost factors of PV and energy storage, a capacity determination model is established by analyzing the relationship between annual planning costs, PV connection capacity, energy storage installation capacity, and power. Case studies are conducted on the IEEE-33 node system to compare and analyze the impact of active distribution network strategies on the planning results of PV and energy storage equipment under different scenarios. The results show that by incorporating demand-side response and bidirectional dynamic reconfiguration strategies into the active distribution network, the selection and sizing of PV energy storage can significantly improve the PV absorption capacity, achieve the lowest planning cost, and address the issue of low voltage levels during periods of excess PV output due to bidirectional reconfiguration. This improves the economic efficiency and reliability of the operation of power distribution networks with a high proportion of PV, providing a solution for energy storage planning that considers the randomness of renewable energy output.
Neotropical Biodiversity as Microbial Frontline for Obtaining Bioactive Compounds with Potential Insecticidal Action
Authors: Santos Maicon S. N. , Ody Lissara P. , Kerber Bruno D. , Castro Isac A. , de Villa Bruna , Ugalde Gustavo A. , Guedes Jerson V. C. , Mazutti Marcio A. , Zabot Giovani L. , Tres Marcus V.
Published: August 2024

The occurrence of insect pests in crops directly affects the yield of plants and grains. This scenario led to the mass investigation of chemical products that overcome these adversities and provide control potential. Nonetheless, over the years, this strategy resulted in high production costs, generation of waste harmful to the environment, and resistance of target insects. The adoption of alternative practices, such as the formulation and production of products of microbial origin, emerges as an encouraging tool compared to control alternatives, indicating a sustainability bias, and allowing a reduction in the risks of human and animal contamination. The purpose of this study was to perform bioprospecting for microbial agents with potential insecticidal effects. The isolated microorganisms were submitted to submerged fermentation, at 28 °C and 120 rpm, for seven days. The fermented broth was filtered using a vacuum pump and centrifuged at 3200× g and 10 °C for 10 min. Initially, 163 microbial agents were collected. Subsequently, a pre-selection of the 50 most promising bioagents was conducted, based on the mortality rates (%) of the applied isolates to target pests. Furthermore, a global mathematical modeling design was created, indicating the best potential microorganisms. Moreover, to stipulate the difference between treatments, dilutions of the fermented broths of each microorganism were conducted (n × 10−5–n × 10−8). Mortality was maximum (100%) for Helicoverpa zea and Euschistus heros. Other encouraging results were indicated in the control of Anticarsia gemmatalis and Chrysodeixis includens (up to 87.5%) and Elasmopalpus lignosellus (up to approximately 83.5%). Fungal isolates were identified as Talaromyces piceae. Among the bacteria, based on sequencing of the 16S ribosomal gene, the isolates were identified as Lysinibacillus fusiformis, Paenibacillus ottowii, and Clostridium sphenoides. The results obtained are relevant to the scientific community and, especially, are interesting for companies that are operating in this field in the agricultural sector.
An Integrated Approach of Fuzzy AHP-TOPSIS for Multi-Criteria Decision-Making in Industrial Robot Selection
Authors: Tran Ngoc-Tien , Trinh Van-Long , Chung Chen-Kuei
Published: August 2024

In recent times, industrial robots have gained immense significance and popularity in various industries. They not only enhance labor safety and reduce costs but also greatly improve productivity and efficiency in the production process. However, selecting the most suitable robot for a specific production process is a complex task. There are numerous criteria to consider, often conflicting with each other, making decision-making challenging. In order to tackle this problem, the multi-criteria decision-making (MCDM) method is employed, which aids in ranking decisions based on criteria weights. However, traditional MCDM methods are now considered outdated, and researchers are concentrating on hybrid models that include multiple MCDM techniques to tackle decision-making problems effectively. This study presents an effective MCDM model that integrates Fuzzy-AHP-TOPSIS to evaluate and choose the best robot. The Fuzzy-AHP is utilized to establish a set of weights for the evaluation criteria. Subsequently, the proposed technique analyzes, prioritizes, and chooses the best robot option from the ranking list for the factory. The experimental results demonstrate that by employing the integrated fuzzy analytical hierarchy process, taking into account parameter weights and expert judgment, the robots are identified in order of best to worst alternatives to factories. The outcomes of this research possess significant implications for robot selection and can be applied in various fields to cater to production requirements.
Deep Learning Prediction of Moisture and Color Kinetics of Apple Slices by Long Short-Term Memory as Affected by Blanching and Hot-Air Drying Conditions
Authors: Jia Zehui , Liu Yanhong , Xiao Hongwei
Published: August 2024

This study aimed to improve apple slices’ color and drying kinetics by optimizing the hot-air drying process, utilizing machine and deep learning models. Different steam blanching times (30, 60, 90, and 120 s), drying temperatures (50, 55, 60, 65, and 70 °C), and humidity control methods (full humidity removal or temperature–humidity control) were examined. These factors significantly affected the quality of apple slices. 60 s blanching, 60 °C temperature, and full dehumidification represented the optimal drying conditions for apple slices’ dehydration, achieving better drying kinetics and the best color quality. However, the fastest drying process (40 min) was obtained at a 60 °C drying temperature combined with complete dehumidification after 90 s blanching. Furthermore, machine and deep learning models, including backpropagation (BP), convolutional neural network–long short-term memory (CNN-LSTM), temporal convolutional network (TCN), and long short-term memory (LSTM) networks, effectively predicted the moisture content and color variation in apple slices. Among these, LSTM networks demonstrated exceptional predictive performance with an R2 value exceeding 0.98, indicating superior accuracy. This study provides a scientific foundation for optimizing the drying process of apple slices and illustrates the potential application of deep learning in the agricultural processing and engineering fields.
Effects of Pectinase on Bacterial Succession during Hemp Retting
Authors: Fu Yu , Zhang Yan , Allen Michael , Shi Sheldon
Published: August 2024

Pectinase accelerates hemp retting. An elevated temperature during the enzymatic action is favorable for bacterial colonization. Industrial hemp (Cannabis sativa L.) bast fiber was retted in a 40 °C water bath under four different conditions: water retting, pectinase retting, bacterial retting, and bacterial retting with the presence of pectinase. Bacterial communities were sampled from the retting liquid of each condition at the beginning of retting and on days 1, 3, and 5. The bacterial successions were identified by 16S rRNA gene metagenomic sequencing. The results showed that Bacillaceae dominated the hemp retting conditions containing 1% (m/v) pectinase, suggesting that pectinase can manipulate the bacterial community succession by changing the nutrients available to bacteria through the breakdown of pectin. Micromorphological analysis also observed the degradation of a gum-like substance and the aggregation of bacteria with the addition of pectinase.
Evaluation of Caprock Sealing Performance for CO2 Saline Aquifer Storage: A Numerical Study
Authors: Shu Xiaohan , Zhang Lijun , Zhang Lei , Wang Xiabin , Tian Xiaofeng , Meng Lingdong
Published: August 2024

The integrity of caprock sealing is a crucial factor in guaranteeing the safety and long-term feasibility of CO2 saline aquifer storage. In this study, we identified three principal mechanisms that give rise to topseal failure: (1) gradual CO2 seepage through the upper cap, (2) capillary seal failure resulting from the pressure increment due to CO2 injection, and (3) localized overpressure causing cap rupture. Through the integration of numerical simulation and geomechanics, this study offers a sealing assessment for the caprock. The thorough analysis of the sealing performance of the Guantao formation reveals that after 2000 years of CO2 injection, the caprock would undergo intrusion by 35 m without any leakage risk. Moreover, investigations into CO2–water–rock interactions suggest that precipitation reactions outweigh dissolution reactions, leading to a decreased permeability and an enhanced sealing performance. The most likely fracture mode identified is shear fracture with a critical caprock fracture pressure of 36.48 MPa. In addition to these discoveries, it is significant to consider ongoing research aimed at enhancing our ability to predict and manage potential risks associated with carbon capture and storage technologies.
Recent Progress of Self-Powered Optoelectronic Devices Based on 2D Materials
Authors: Xu Jiyuan , Zhang Zailan , Zhang Wei , Chen Zhesheng
Published: August 2024

Harvesting energy through light radiation is an attractive solution for powering wireless devices in the future. In particular, self-driven optoelectronic devices are especially attractive for low-energy devices on the Internet of Things. Two-dimensional (2D) materials at atomic scale thicknesses are very attractive for future self-powered optoelectronic devices due to not only their unique electronic and optical properties, but also the feasibility to fabricate desirable heterostructures, which differ from their bulk counterparts and conventional optoelectronic materials. In this review, we mainly summarized the mechanism and performance of self-powered optoelectronic devices based on 2D materials. The figure of merit and mechanism of self-driving optoelectronic devices including the interface-junction effect, bulk photovoltaic effect, and photothermoelectric effect are discussed in detail. In addition, the recent progress on the performance of self-powered optoelectronic devices based on 2D materials is compared, followed by the perspective of this field. This review is aimed to give a generalized knowledge of 2D self-powered optoelectronic devices, and thus prompt the exploration and development of novel functional devices based on 2D materials.
Assessment of Yield, Flavonoid and Phytosterol Contents, and Fatty Acid Composition of Baru Almond Oil (Dipteryx alata Vogel) by Supercritical CO2 Extraction
Authors: Ferreira Ana Carolina , Lopes Guilherme , Filho Antonio Carlos , Postaue Najla , Belisário Celso , Paim Leonardo Lataro , Cardozo-Filho Lúcio , da Silva Camila , Ferreira-Pinto Leandro , Favareto Rogério
Published: August 2024

The objective of this study was to investigate the influence of temperature and pressure on the extraction of Baru almond oil using pressurized CO2. From the obtained data, it was found that variations in pressure and temperature influenced the oil yield and total amount of phytosterols. The maximum yield of Baru oil achieved was approximately 30%, obtained at a pressure of 28 MPa and 60 °C. The phytosterols identified were campesterol, stigmasterol, and β-sitosterol, with a maximum content of 169.5 ± 4.2 mg/100 g of oil obtained at 20 MPa and 60 °C. Among these, β-sitosterol was the most abundant, accounting for 60% of the total phytosterol content under all the experimental conditions. The flavonoid content in the extracts was also quantified, with the total flavonoid levels ranging between 255 and 275 mg/100 g of oil depending on the experimental conditions. The fatty acid profile of the extracted oil predominantly consisted of oleic (51%) and linoleic (28%) acids.
The Current Molecular and Cellular Landscape of Chronic Obstructive Pulmonary Disease (COPD): A Review of Therapies and Efforts towards Personalized Treatment
Authors: Farrell Luke A. , O’Rourke Matthew B. , Padula Matthew P. , Souza-Fonseca-Guimaraes Fernando , Caramori Gaetano , Wark Peter A. B. , Dharmage Shymali C. , Hansbro Phillip M.
Published: August 2024

Chronic obstructive pulmonary disease (COPD) ranks as the third leading cause of global illness and mortality. It is commonly triggered by exposure to respiratory irritants like cigarette smoke or biofuel pollutants. This multifaceted condition manifests through an array of symptoms and lung irregularities, characterized by chronic inflammation and reduced lung function. Present therapies primarily rely on maintenance medications to alleviate symptoms, but fall short in impeding disease advancement. COPD’s diverse nature, influenced by various phenotypes, complicates diagnosis, necessitating precise molecular characterization. Omics-driven methodologies, including biomarker identification and therapeutic target exploration, offer a promising avenue for addressing COPD’s complexity. This analysis underscores the critical necessity of improving molecular profiling to deepen our comprehension of COPD and identify potential therapeutic targets. Moreover, it advocates for tailoring treatment strategies to individual phenotypes. Through comprehensive exploration-based molecular characterization and the adoption of personalized methodologies, innovative treatments may emerge that are capable of altering the trajectory of COPD, instilling optimism for efficacious disease-modifying interventions.
Breathe! The Experience of the Body in Passive Contemplation
Authors: Barber Michael D.
Published: August 2024

Phenomenological research has focused on embodiment. This paper examines how the body is experienced in passive contemplation, understood as a finite province of meaning in Alfred Schutz’s sense. The six features characteristic of any province of meaning (epoché, form of spontaneity, tension of consciousness, sense of self, temporality, and sociality) function distinctively in each province of meaning and alter one’s experience of one’s body. In the foundational province of pragmatic everyday life, one transforms deliberately and bodily the surrounding world, but this experience undergoes modifications in the religious/spiritual contemplative province. To clarify passive contemplation, the paper develops, as a contrast, active contemplation, the active, restless pursuit of chains of appresentational symbols and images. In passive contemplation, one separates from everyday life, orients toward unity rather than dialogue with God, refrains from following appresentative chains, and relaxes one’s tension of the consciousness/body (e.g., through breathing), single-mindedly attending to God’s presence. One can trace passive contemplation genetically to a primordial entwinement with one’s mother’s body, in which subjective/objective boundaries are blurred (as in Merleau-Ponty’s “flesh”). In passive contemplation, one assumes an often-wordless social role (child, lover) toward God and abides tranquilly in the present rather than moving distractedly toward any future.
Setting a Pedagogical Course: Four Modes Clarifying the Dynamics of Shared Religious Education
Authors: Meyer Karlo
Published: August 2024

On the level of fundamental didactic decisions and hermeneutic clarifications, this article examines the possible orientations of Shared Religious Education. The prerequisite for this is the assumption that in such lessons, the opportunity should be used to empower children and young people to become personally and creatively involved in teaching and learning when different denominations, religions, and worldviews come together in education. Against this background, four modes of possible activation are proposed as a structuring aid for didactic decisions: Pupils can (a) plan appropriate forms of encounter themselves and develop ways of dealing with mutually experienced foreignness and with bridges and gaps between traditions; (b) they can be activated to engage in existential discussions about ultimate questions, (c) they can carry out small-scale “research” projects into each other’s religious practices and concepts; and (d) they can get involved in joint (ethical, ecological, neighbourly) projects that have an impact on the region around the school that may also have global applications. The model of these four modes can be represented graphically and this helps to analyse and locate existing concepts and approaches to RE. The article concludes with a closer look at the underlying concept of religion and current research.
Trading Option Portfolios Using Expected Profit and Expected Loss Metrics
Authors: Venter Johannes Hendrik , de Jongh Pieter Juriaan
Published: August 2024

When trading in the call and put contracts of option chains, the portfolios of strikes must be selected. The trader must also decide whether to take long or short positions at the selected strikes. Dynamic strategies for making these decisions are discussed in this paper. On any day, the strategies estimate the drift and volatility parameters of the future probability distribution of the price of the underlying asset. From this distribution, the trader can further estimate the future expected profit and expected loss that may be experienced for any portfolio of strikes of the call and put contracts. Expected profit and expected loss are the reward and risk metrics of such portfolios. An optimal portfolio can then be selected by making the reward as high as possible under the risk tolerance set by the trader. Extensive back-testing applications to historical data of SPY option chains illustrate the effectiveness of these strategies, particularly when dealing with short-term expiry options and when acting as a seller of put and call options.
DCFF-Net: Deep Context Feature Fusion Network for High-Precision Classification of Hyperspectral Image
Authors: Chen Zhijie , Chen Yu , Wang Yuan , Wang Xiaoyan , Wang Xinsheng , Xiang Zhouru
Published: August 2024

Hyperspectral images (HSI) contain abundant spectral information. Efficient extraction and utilization of this information for image classification remain prominent research topics. Previously, hyperspectral classification techniques primarily relied on statistical attributes and mathematical models of spectral data. Deep learning classification techniques have recently been extensively utilized for hyperspectral data classification, yielding promising outcomes. This study proposes a deep learning approach that uses polarization feature maps for classification. Initially, the polar co-ordinate transformation method was employed to convert the spectral information of all pixels in the image into spectral feature maps. Subsequently, the proposed Deep Context Feature Fusion Network (DCFF-NET) was utilized to classify these feature maps. The model was validated using three open-source hyperspectral datasets: Indian Pines, Pavia University, and Salinas. The experimental results indicated that DCFF-NET achieved excellent classification performance. Experimental results on three public HSI datasets demonstrated that the proposed method accurately recognized different objects with an overall accuracy (OA) of 86.68%, 94.73%, and 95.14% based on the pixel method, and 98.15%, 99.86%, and 99.98% based on the pixel-patch method.
2D DOA and Polarization Estimation Using Parallel Synthetic Coprime Array of Non-Collocated EMVSs
Authors: Yang Yunlong , Shan Mengru , Jiang Guojun
Published: August 2024

For target detection and recognition in a complicated electromagnetic environment, the two-dimensional direction-of-arrival and polarization estimation using a polarization-sensitive array has been receiving increased attention. To efficiently improve the performance of such multi-parameter estimation in practice, this paper proposes a parallel synthetic coprime array with reduced mutual coupling and hardware cost saving and then presents a dimension-reduction compressive sensing-based estimation method. For the proposed array, the polarization types, numbers, and positions of antennas in each subarray are jointly considered to effectively mitigate mutual coupling in the physical array domain and to both enhance degrees of freedom and extend the aperture in the difference coarray domain with the limited physical antennas. By exploring the array configuration, the parameter estimation can be formulated as a block-sparse signal reconstruction problem, and then the one-dimensional sparse reconstruction algorithm is only used once to achieve multi-parameter estimation with automatic pair-matching. The theoretical analysis and simulation results are provided to demonstrate the superior performance of the proposed array and method over the existing techniques.
Accurate Ultrasonic Thickness Measurement for Arbitrary Time-Variant Thermal Profile
Authors: Palanisamy Rajendra P. , Pyun Do-Kyung , Findikoglu Alp T.
Published: August 2024

Ultrasonic thickness measurement of mechanical structures is one of the most popular and commonly used nondestructive methods for various kinds of process control and corrosion monitoring. With ultrasonic propagation speed being temperature-dependent, the thickness measurement can be performed reliably only when the thermal profile is completely known. Most conventional techniques assume the temperature of the test structure is uniform and at room temperature across its thickness. Such assumptions may lead to large errors in the thickness measurement, especially when there are significant temperature variations across the thickness. State-of-the-art techniques use external temperature measurements or implement iterative methods to compensate for the unknown thermal profiles. However, such techniques produce unsatisfactory results when the heat distribution is complex or varies rapidly with time. In this work, we propose a two-sensors technique, using both compressive and shear excitations, with a non-iterative rapid data processing method for accurate thickness measurement under arbitrary time-variant thermal profile. The independent behavior of shear and compressive waves is used to formulate a real-time thickness estimation technique. The developed technique is experimentally validated on a steel plate with fixed acoustic sensors. Test results show that the error in thickness estimation can be reduced by up to 98% compared to conventional thickness gauging methods.
Development of an Enantioselective Method by Liquid Chromatography to Monitor 3,4-Methylenedioxypyrovalerone in Culture Media from Ecotoxicity Assays
Authors: Pérez-Pereira Ariana , Gonçalves Virgínia M. F. , Ribeiro Ana R. L. , Fernandes Carla , Carrola João S. , Ribeiro Cláudia , Tiritan Maria E.
Published: August 2024

3,4-Methylenedioxypyrovalerone (MDPV) is an emerging, chiral, new psychoactive substance belonging to the synthetic cathinones group that has been frequently detected in wastewater effluents and aquatic environments. However, the knowledge of its enantioselective occurrence and toxicity toward aquatic organisms is scarce. The aim of this work was to develop an enantioselective liquid chromatography (LC) method to monitor the enantiomers of MDPV in environmental and ecotoxicological assays. For that, different chiral columns and mobile phases in both normal and reversed elution modes were attempted. The optimized conditions were achieved using a Daicel® 3 μm—CHIRALPAK® IF-3 column with 5 mM of ammonium bicarbonate (NH4HCO3, pH 8.8) in ultra-pure water (UPW) and acetonitrile (ACN) (10:90, v/v) as a mobile phase, at a flow rate of 0.3 mL min−1. This condition was applied to monitor the racemate and the single enantiomers of MDPV in culture medium collected from ecotoxicity experiments. Racemization was observed for MDPV enantiomers (in individual exposure). The enantiomeric ratio (e.r.) of (S)-MDPV changed from an initial e.r. of 96.4/3.6 to 78.0/22.0 and for the (R)-enantiomer, the e.r changed from 15.6/84.4 to 28.3/71.7). These data highlight the importance of enantioselective monitoring of culture media in toxicity assays that involve chiral substances, since racemization can occur and lead to inaccuracy in the toxicity evaluation. Nevertheless, it is also important to stress that racemization may occur during storage conditions or sample procedures. Therefore, the enantioselective methodology is of utmost importance to warrant the quality of the results in enantioselective ecotoxicological studies.
An Extraction Process Based on the Collaborative Extraction of Coptis chinensis Franch. Phytoconstituents Using a Deep Eutectic Solvent and an Organic Solvent
Authors: Liu Cheng , Gong Fangyuan , Xiong Zhengwei , Wang Cun , Ran Xinhe , Ran Jiahua , Li Runzi , Ou Yangjin , Xia Qingqing , Wei Pei , Guo Jin
Published: August 2024

A low-cost method for the simultaneous extraction of alkaloids and water-insoluble flavonoids and esters from Coptis chinensis Franch. (Abbrev. C. chinensis) was explored to provide a reference for the production of green plant-based preparations and traditional Chinese medicine formula granules. A combined extraction method with the deep eutectic solvents (DESs) of choline chloride and urea (molar mass ratio of 1:2) and organic solvent ethanol was used, supplemented by ultrasonic-assisted extraction (ultrasonic power: 150 W; ultrasonic temperature: 60 °C; treatment time: 15 min). The extraction efficiency of the 50% DES (choline chloride and urea) aqueous solution for berberine, palmatine, jatrorrhizine, and magnoflorine was found to be the highest and was superior to traditional ultrasonic extraction and water bath reflux extraction methods. Furthermore, the flavonoids and esters from C. chinensis residue were extracted using ethanol. The results from high-performance liquid chromatography and gas chromatography–mass spectrometry indicated a high extraction efficiency overall.
Children’s Wellbeing and Children’s Rights—A Nordic Perspective
Authors: Alminde Sarah , Warming Hanne
Published: August 2024

Children’s rights and wellbeing have increasingly attracted attention both politically and in the scholarly field [...]
Presenting a Model to Predict Changing Snow Albedo for Improving Photovoltaic Performance Simulation
Authors: Pike Christopher , Riley Daniel , Toal Henry , Burnham Laurie
Published: August 2024

As photovoltaic (PV) deployment increases worldwide, PV systems are being installed more frequently in locations that experience snow cover. The higher albedo of snow, relative to the ground, increases the performance of PV systems in northern and high-altitude locations by reflecting more light onto the PV modules. Accurate modeling of the snow’s albedo can improve estimates of PV system production. Typical modeling of snow albedo uses a simple two-value model that sets the albedo high when snow is present, and low when snow is not present. However, snow albedo changes over time as snow settles and melts and a binary model does not account for transitional changes, which can be significant. Here, we present and validate a model for estimating snow albedo as it changes over time. The model is simple enough to only require daily snow depth and hourly average temperature data, but can be improved through the addition of site-specific factors, when available. We validate this model to quantify its ability to more accurately predict snow albedo and compare the model’s performance against satellite imagery-based methods for obtaining historical albedo data. In addition, we perform modeling using the System Advisor Model (SAM) to show the impact of changes in albedo on energy modeling for PV systems. Overall, our albedo model has a significantly improved ability to predict the solar insolation on PV modules in real time, especially on bifacial PV modules where reflected irradiance plays a larger role in energy production.
Sensorimotor Processing in Elite and Sub-Elite Adolescent Sprinters during Sprint Starts: An Electrophysiological Study
Authors: Hsieh Yueh-Ling , Yen Shiuk-Wen , Chang Chia-Ming , Li Wei-Chun , Yang Nian-Pu , Chen Han-Yu
Published: August 2024

Most studies on sprint performance have focused on kinematics and kinetics of the musculoskeletal system for adults, with little research on the central sensorimotor transmission and processes, especially for adolescent sprinters. This study aimed to determine whether differences in the integrity of the central auditory system and audiomotor transmissions between the elite and sub-elite adolescent sprinters may affect their performance in the 100 m time. Twenty-nine adolescent junior high school students, including elite national-class and sub-elite regional-class athletes, were assessed. Visual and auditory evoked potentials (VEP and AEP) as well as electroencephalography (EEG) and electromyography (EMG) were recorded and analyzed during a sprint start. The electrophysiological results clearly reveal differences in central auditory transmission between elite and sub-elite groups, and between sexes. There were significant differences between elite and sub-elite groups, and during a sprint start, the EEG activities for elite female and male athletes showed significant time-dependent differences in peak amplitudes following the three auditory cues (ready, set, and gunshot). These findings can provide coaches with a more comprehensive consideration for sports-specific selection based on the athletes’ individual conditions, e.g., sensorimotor neuroplastic training for providing precise and efficient training methods to improve young sprinters’ performance.
Changes in Physical Fitness, Muscle Damage and Cognitive Function in Elite Rugby Players over a Season
Authors: Karamti Mohamed Houssem , Zouhal Hassane , Bousselmi Mariem , Darragi Manel , Khannous Hamdi , Ben Hmid Ahlem , Zamali Imen , Ben Ahmed Mélika , Laher Ismail , Granacher Urs , Moussa Amira Zouita Ben
Published: August 2024

This study proposes to monitor the physical, immune and cognitive responses and adaptations of elite rugby players throughout the season based on the loads performed. Anthropometric measurements, physical fitness tests (e.g., muscle strength and power, linear and change-of-direction speed, cardiorespiratory fitness) and analyses of serum concentrations of markers of muscle damage (creatine kinase [CK] and lactate dehydrogenase [LDH]) and brain-derived neurotrophic factor (BDNF) were carried out over a sporting season (24 weeks) for 17 elite rugby players (10 forwards and 7 backs) aged 18.91 ± 0.76 years. The physical fitness test results show improvements in the performance of both forwards and backs over the season (p < 0.05), with an advantage for backs compared with forwards in most tests (p < 0.05). Muscle damage markers decreased at the end of the season compared with the baseline levels for forwards (p < 0.05). CK levels were unchanged for the backs, but there were increased LDH concentrations at the end of the season compared with baseline (p < 0.05). Serum BDNF levels decreased for the total group between the second and third sampling (p < 0.05). The muscular and physical capacities of rugby players differ according to their playing position. Immune responses and adaptations, as well as BDNF levels, vary throughout the season and depend on the physical load performed.