bluuebunny/crossref_metadata_2025_split huggingface dataset:
Discovering Novel Bioherbicides: The Impact of Hemp-derived Phytocannabinoid Applications on Zea mays L. and Relevant Weeds
Authors: Merkle Michael , Gerhards Roland 
Published: September 2024

AbstractIn addition to competition, phytotoxic plant metabolites contribute to the weed-suppressing properties of cover crops, which could be the basis for the development of novel bioherbicides. We investigated the impact of five Cannabis sativa L. -derived neutral phytocannabinoids and an aqueous C. sativa tissue extract (HE) at six concentrations on the germination rate (GR) and seedling root length (RL) of Zea mays L., two monocotyledonous and two dicotyledonous weed species in laboratory Petri dish bioassays. Additionally, the effect of pre-emergence applications of HE, cannabidiol (CBD), and cannabidivarin (CBDV) formulations on GR and shoot dry matter (SDM) were examined in greenhouse pot studies. The effects of phytocannabinoids and HE were analyzed in dose-response curves. For the highest rates, the effects on GR, RL and SDM were calculated by ANOVA and HSD test (p < 0.05). HE exhibited the greatest suppression on GR and RL for all plant species in the Petri dish bioassay, with RGR, RL exceeding −90%. Phytocannabinoids reduced mainly RL of all plants and decreased the GR of most weed species. Effects varied among plants and phytocannabinoids, with CBDV and CBD showing similar high inhibitory effects on RL as HE in the Petri dish bioassay. All pre-emergence applications resulted in a positive RGR across all studied plants and in a positive RSDM in Z. mays and Echinochloa crus-galli (L.) P. Beauv, whereas in the other weed species the RSDM was negative. In conclusion, phytocannabinoids play a major role in weed suppression of HEs. CBDV and CBD are the most promising candidates for bioherbicide development especially against annual dicotyledonous weed species.
Microstructure and topography of laterally confined porous anodic oxides produced with high growth rate in a maskless two-phase jet setup
Authors: Quitzke Susanne , Danilov Igor , Morgenstern Roy , Martin André , Lampke Thomas , Schubert Andreas 
Published: September 2024

Abstract
Maskless anodic oxidation with a continuous free jet of electrolyte can be used for the local surface functionalization and structuring of aluminum materials. In this study, a two-phase jet was applied with the aim of enhanced lateral confinement of the anodic oxide on the aluminum alloy EN AW-7075. The two-phase jet was realized by a coaxial arrangement. While the inner electrolyte nozzle with a diameter of 100 µm acted as the cathode and was used to provide the electrolyte with a flow rate of 10 ml min−1 resulting in an average jet velocity of approximately 21 m s−1, the outer nozzle with a diameter of 3000 µm was used to provide deionized water with a flow rate of 383 ml min−1 resulting in an average water jet velocity of 1 m s−1, which is sufficient to realize a continuous free two-phase jet. Process voltages from 10 to 60 V were investigated. The realized oxide layers were characterized by non-destructive as well as destructive methods to determine their microstructure and their thicknesses. Optically transparent anodic oxide layers were achieved in the voltage range between 10 and 25 V. Maximum total layer thicknesses between 1.1 µm and 16.9 µm were measured in these cases. Thicknesses of more than 50 µm were determined for higher voltages up to 60 V; however, burning effects and stronger discolorations were determined for voltages ranging from 30 to 60 V. Consequently, 25 V was derived as best suitable voltage for two-phase anodizing with high growth rate leading to oxide layers with low defects.

Graphical abstract
Advanced Methods for Analyzing in-Situ Observations of Magnetic Reconnection
Authors: Hasegawa H. , Argall M. R. , Aunai N. , Bandyopadhyay R. , Bessho N. , Cohen I. J. , Denton R. E. , Dorelli J. C. , Egedal J. , Fuselier S. A. , Garnier P. , Génot V. , Graham D. B. , Hwang K. J. , Khotyaintsev Y. V. , Korovinskiy D. B. , Lavraud B. , Lenouvel Q. , Li T. C. , Liu Y.-H. , Michotte de Welle B. , Nakamura T. K. M. , Payne D. S. , Petrinec S. M. , Qi Y. , Rager A. C. , Reiff P. H. , Schroeder J. M. , Shuster J. R. , Sitnov M. I. , Stephens G. K. , Swisdak M. , Tian A. M. , Torbert R. B. , Trattner K. J. , Zenitani S. 
Published: September 2024

AbstractThere is ample evidence for magnetic reconnection in the solar system, but it is a nontrivial task to visualize, to determine the proper approaches and frames to study, and in turn to elucidate the physical processes at work in reconnection regions from in-situ measurements of plasma particles and electromagnetic fields. Here an overview is given of a variety of single- and multi-spacecraft data analysis techniques that are key to revealing the context of in-situ observations of magnetic reconnection in space and for detecting and analyzing the diffusion regions where ions and/or electrons are demagnetized. We focus on recent advances in the era of the Magnetospheric Multiscale mission, which has made electron-scale, multi-point measurements of magnetic reconnection in and around Earth’s magnetosphere.
Cradle-to-gate life cycle assessment of cylindrical sulfide-based solid-state batteries
Authors: Rietdorf Chantal , De la Rúa Cristina , Kiemel Steffen , Miehe Robert 
Published: September 2024

Abstract
Purpose
Solid-state batteries (SSBs) are a current research hotspot, as they are safer and have a higher energy density than state-of-the-art lithium-ion batteries (LIBs). To date, their production only occurs on a laboratory scale, which provides a good opportunity to analyze the associated environmental impacts prior to industrialization. This paper investigates the environmental impacts of the production of cylindrical SSB, to identify environmental hotspots and optimization potentials.

Methods
Here, an attributional cradle-to-gate life cycle assessment (LCA) is performed, focusing on SSBs that use a NMC811/lithium germanium phosphorous sulfide (LiGPS) composite cathode, a sulfide-based solid separator electrolyte, and a lithium metal anode. The life cycle impact assessment (LCIA) is performed in Umberto 11 using the Environmental Footprint 3.1 method with primary and literature data and the Evoinvent 3.9 database for background data.

Results and discussion
The results show climate change impacts of 205.43 kg CO2 eq./kwh (for the base case), with hotspots primarily attributable to the electrolyte and cathode production, and more specifically to the LiPS and LiGPS synthesis as well as to the cathode active material. Additionally, the scenario analysis shows that an upscaling of the LiPS and LiGPS synthesis reduces environmental impacts across all assessed impact categories. In addition, it was shown that the use of an in situ anode further improves the overall environmental performance, while the use of alternative cathode active materials, such as NMC622 and LFP did not lead to any improvements, at least with reference to the storage capacity.

Conclusion
The article highlights the environmental hotspots of sulfide-based SSB production, namely electrolyte and catholyte synthesis. The results show that upscaling the synthesis reduces the environmental impact and that cells with higher energy density show a favorable environmental performance. However, SSBs are still in the development stage and no final recommendation can be made at this time.
Understanding the Role of Masculinity through the Perspectives of Countering Violent Extremism (CVE) Intervention Practitioners Engaged in Men’s (de)Radicalization
Authors: Stahl Garth , Adams Ben , Oberg Glenys 
Published: September 2024

AbstractWithin the field of Countering Violent Extremism (CVE) male clients are overwhelmingly represented, which reflects the key and complex significance ‘masculinity’ has for understanding susceptibility to violent extremism, processes of radicalisation and related issues. This article analyses the perspectives of 12 CVE Intervention Practitioners (n = 8 female, n = 4 male) in Australia and their views on the role gender plays in their work. We focus on the practitioners’ experiences with male clients and how they understood the relationship between gender, rapport building and (de)radicalization. The practitioners highlight how a common aspect of their clients’ ideology involved either explicit misogyny or quite rigid opinions regarding the roles of men and women in society. They often mentioned their male clients experienced difficulties in expressing and understanding their own masculinity. However, while it is important to identify commonalities within the gendered dynamics present in CVE work, the data suggest the interaction between practitioner and client continues to be quite varied. We argue that this highlights not only how masculinities are discursively produced, but how male clients may shift between identity positions during deradicalization depending on their interpersonal interactions.
Mathematical analysis of a norm-conservative numerical scheme for the Ostrovsky equation
Authors: Kawai Shuto , Sato Shun , Matsuo Takayasu 
Published: September 2024

AbstractThe target of this study is a norm-conservative scheme for the Ostrovsky equation, as its mathematical analysis has not been addressed. First, the existence and uniqueness of its numerical solutions are demonstrated. Subsequently, a convergence estimate in the two-norm is established. This, in turn, implies a convergence in the first-order Sobolev space using a supplementary sup-norm boundedness argument. Finally, this conservative scheme can be implemented in a differential form, which is considerably better than the integral form in terms of computational cost-effectiveness.
Virtual Waiting Room: The New Narrative of Waiting in Oncology Care
Authors: Světlák Miroslav 
Published: September 2024

AbstractThis conceptual study introduces the “virtual waiting room,” an innovative, interactive, web-based platform designed to enhance the waiting experience in oncology by providing personalized, educational, and supportive content. Central to our study is the implementation of the circular entry model, which allows for non-linear navigation of health information, empowering patients to access content based on their immediate needs and interests. This approach respects the individual journeys of patients, acknowledging the diverse pathways through which they seek understanding and manage their health. The virtual waiting room is designed not only to support patients but also to facilitate stronger communication and shared understanding between patients, caregivers, and families. By providing a shared digital space, the platform enables caregivers and family members to access the same information and resources, thereby promoting transparency and collective knowledge. This shared access is crucial in managing the emotional complexities of oncology care, where effective communication can significantly impact treatment outcomes and patient well-being. Furthermore, the study explores how the circular entry model within the virtual waiting room can enhance patient autonomy and engagement by offering customized interactions based on user feedback and preferences. This personalized approach aims to reduce anxiety, improve health literacy, and prepare patients more effectively for clinical interactions. By transforming passive waiting into active engagement, the virtual waiting room turns waiting time into a meaningful, informative period that supports both the psychological and informational needs of patients and their support networks.
On the glow of cremated remains: long-lived green photo-luminescence of heat-treated human bones
Authors: Schut Emma , Breedijk Ronald M. P. , Hilbers Michiel F. , Hink Mark A. , Krap Tristan , Aalders Maurice C. G. , Williams René M. 
Published: September 2024

AbstractThe long-lived green luminescence of human bone (that has been heated to 600 °C for a short duration) is attributed to a carbon quantum dot material (derived from collagen) encapsulated and protected by an inorganic matrix (derived from bone apatite) and is more intense in dense rigid and crystalline parts of (healthy) human bones. The strong collagen-apatite interaction results (upon decomposition) in a protective inorganic environment of the luminescent centers allowing long-lived triplet-based emission of a carbon (quantum) dot-like material at room temperature, as well as resilience against oxidation between 550 and 650 °C. The graphitic black phase (obtained upon heating around 400 °C) is a precursor to the luminescent carbon-based material, that is strongly interacting with the crystalline inorganic matrix. Human bone samples that have been heated to 600 °C were subjected to steady-state and time-resolved spectroscopy. Excitation-emission matrix (EEM) luminescence spectroscopy revealed a broad range of excitation and emission wavelengths, indicating a heterogeneous system with a broad density of emissive states. The effect of low temperature on the heat-treated bone was studied with Cryogenic Steady State Luminescence Spectroscopy. Cooling the bone to 80 K leads to a slight increase in total emission intensity as well as an intensity increase towards to red part of the spectrum, incompatible with a defect state model displaying luminescent charge recombination in the inorganic matrix. Time-resolved spectroscopy with an Optical Multichannel Analyzer (OMA) and Time Correlated Single Photon Counting (TCSPC) of these samples showed that the decay could be fitted with a multi-exponential decay model as well as with second-order decay kinetics. Confocal Microscopy revealed distinct (plywood type) structures in the bone and high intensity—fast decay areas as well as a spatially heterogeneous distribution of green and (fewer) red emissive species. The use of the ATTO 565 dye aided in bone-structure visualization by chemical adsorption. Conceptually our data interpretation corresponds to previous reports from the material science field on luminescent powders.
Graphical abstract
Unleashing the power of advanced technologies for revolutionary medical imaging: pioneering the healthcare frontier with artificial intelligence
Authors: Chauhan Ashish Singh , Singh Rajesh , Priyadarshi Neeraj , Twala Bhekisipho , Suthar Surindra , Swami Siddharth 
Published: September 2024

AbstractThis study explores the practical applications of artificial intelligence (AI) in medical imaging, focusing on machine learning classifiers and deep learning models. The aim is to improve detection processes and diagnose diseases effectively. The study emphasizes the importance of teamwork in harnessing AI’s full potential for image analysis. Collaboration between doctors and AI experts is crucial for developing AI tools that bridge the gap between concepts and practical applications. The study demonstrates the effectiveness of machine learning classifiers, such as forest algorithms and deep learning models, in image analysis. These techniques enhance accuracy and expedite image analysis, aiding in the development of accurate medications. The study evidenced that technologically assisted medical image analysis significantly improves efficiency and accuracy across various imaging modalities, including X-ray, ultrasound, CT scans, MRI, etc. The outcomes were supported by the reduced diagnosis time. The exploration also helps us to understand the ethical considerations related to the privacy and security of data, bias, and fairness in algorithms, as well as the role of medical consultation in ensuring responsible AI use in healthcare.
AraTSum: Arabic Twitter Trend Summarization Using Topic Analysis and Extractive Algorithms
Authors: Monir Enas , Salah Ahmad 
Published: September 2024

AbstractTwitter’s trending topics enable users to view the topics currently being discussed on the platform. Users can stay up to date with news, events, and conversations. However, the platform’s method of sorting tweets by time can make it hard to gather semantic information. To fully comprehend the various dimensions of trends and the diverse opinions surrounding them, users need to sift through a substantial number of results. Traditional techniques for content summarization, such as multi-document summarization, can facilitate information aggregation, categorization, and visualization of events, but there are two challenges. First, they fail to consider the topic’s polarity, which is essential to covering all aspects of the subject and incorporating less popular opinions. Second, some techniques only provide summaries at the topic level, potentially leaving out crucial dimensions that require representation in this summary. This research developed a novel summarization approach on Twitter which is known as ARAbic Trending SUMmarization (AraTSum). The proposed system generates the summary based on the extracted topics and aspects from the trend. The approach involves a topic sentiment-based technique that combines generative statistical Latent Dirichlet Allocation with a pre-trained model to automatically reflect the sentiments (negative or positive) of tweets in each topic; followed by extractive summarization algorithms in each cluster. The AraTSum was evaluated through several experiments on five different X datasets. The obtained results showed that AraTSum outperformed existing approaches on the ROUGE evaluation metric compared to state-of-the-art Twitter event summarizing algorithms. To ensure a comprehensive and accurate evaluation, three human experts were tasked with manually summarizing the utilized five datasets. The results demonstrated that the proposed AraTSum method is dependent on sentiment topical aspect analysis, and it enhances the summarization's performance.
Fuzzy Logic-Based Quantitative Development Model for Job Satisfaction in College Graduates
Authors: Cheng Yuqi 
Published: September 2024

AbstractCollege graduates’ job satisfaction is validated via their feedback, experiences and personal developments during their career progression. Validation is accomplished to ensure the students’ job satisfaction and retain them for a prolonged time. The traditional validation models have difficulties in analyzing the individual satisfaction level. The research issue is addressed by introducing the Quantitative Assessment Method (QAM) using Fuzzy Logic to validate the job satisfaction level of different newly placed students. The QAM approach assesses the student experience and personal development across various quarters. The fuzzy optimization uses two factors differentially using partial derivatives. The partial derivatives are extracted using the min–max functions of the fuzzification process such that the derivatives are halted after the maximum factors. The proposed method optimizes the validation using individual satisfaction levels and cumulative experience shared by the students. The available derivatives identify the best-afford job satisfaction level for different progression levels. This best-fit feature is handled using multiple min and max derivatives to extract optimal outputs. This proposed method is valid for improving satisfaction levels and experience analysis.
Against causal arguments in metaphysics
Authors: Vaassen Bram 
Published: September 2024

Abstract
Traditionally, causal arguments for physicalism have been taken to favour a ‘reductive’ brand of physicalism, according to which all the mental stuff is identical to some of the physical stuff. Many flaws have been found with these traditional causal arguments. Zhong (Asian Journal of Philosophy,
2(2), 1–9, 2023) develops a new causal argument that avoids these flaws and favours a milder, non-reductive brand of physicalism instead. The conclusion is that all mental stuff is metaphysically necessitated by some of the physical stuff. I argue that neither the traditional nor the new causal argument holds much sway over non-physicalism. The problem is that causation just does not run that deep. It is a fairly superficial relationship and a poor guide to the metaphysically weighty facts of our world, such as what is identical to what, and what metaphysically necessitates what.
Design and optimization of a high thrust density air-breathing pulsed plasma thruster array
Authors: Adkins Cameron , Belcher Dante , Akhter Mirza , Duggleby Andrew , Cardwell Nicholas D. , Staack David 
Published: September 2024

AbstractFor satellites in Very Low Earth Orbit (VLEO) or the upper atmosphere, Air-Breathing Electric Propulsion (ABEP) can be used to counteract the drag effects of the atmosphere or provide attitude control without the need for onboard propellant storage. The Dense Plasma Focus (DPF) inspired the design of an ABEP Pulsed Plasma Thruster (TAMU PPT) which takes advantage of the simplicity, scalability, and high energy density of the DPF. This work details the design and testing of a PPT for use on satellites in the upper atmosphere. Testing was conducted at a variety of pressures and pulsing energies to determine which conditions provide maximum thrust and thrust to power ratios (TPR). It was found through single pulse experiments that a pressure of 6.3 hPa (corresponding to an altitude of about 35 km) and an energy per pulse of 9 J yield a TPR of 21 mN/kW. High frequency operation at 20 Hz in similar conditions showed a TPR of up to 28 mN/kW with a corresponding thrust of 4.8 mN. High speed imaging of the plasma plume found plasma velocities in excess of 2 km/s and identified metal ejecta with velocities up to 350 m/s. The pinching phenomenon associated with a DPF was also observed and a plasma jet velocity up to 50 km/s was recorded. The TPR of the TAMU PPT demonstrated in this work is comparable to that of other ABEP thrusters, however the TAMU PPT has an advantage when comparing thrust density. The TAMU PPT has a thrust density between 4.8 and 19 mN/cm2 while other ABEP thrusters have thrust densities between 0.019 and 0.36 mN/cm2.
Enhancing Agricultural Productivity: A Machine Learning Approach to Crop Recommendations
Authors: Prity Farida Siddiqi , Hasan MD. Mehadi , Saif Shakhawat Hossain , Hossain Md. Maruf , Bhuiyan Sazzad Hossain , Islam Md. Ariful , Lavlu Md Tousif Hasan 
Published: September 2024

AbstractAgriculture constitutes the foundational pillar of the global economy, engaging a substantial segment of the workforce and making a considerable contribution to the Gross Domestic Product (GDP). However, agricultural productivity faces numerous challenges, including varying climatic conditions, soil types, and limited access to modern farming practices. Developing intelligent agricultural systems becomes imperative to address these challenges and enhance agricultural productivity. Therefore, this paper aims to present a Machine Learning (ML) based crop recommendation system tailored for the farming landscape. The proposed system utilizes historical data on climatic conditions, soil properties, crop yields, and farmer preferences to provide personalized crop recommendations. The goal of this study is to appraise the efficacy of nine distinct ML models—Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Bagging (BG), AdaBoost (AB), Gradient Boosting (GB), and Extra Trees (ET) to generate practical recommendations for crop selection. Numerous preprocessing methods are employed to cleanse and normalize the data, thereby ensuring its appropriateness for model training. The ML models are trained using historical data sets, including temperature, rainfall, humidity, soil pH, and nutrient levels, where crop yields are correlated with environmental and agronomic factors. The models undergo fine-tuning through methods such as cross-validation to enhance their performance and ensure robustness. Among those models, Radom Forest has achieved the highest accuracy (99.31%). The proposed Machine Learning-based crop recommendation system offers a promising approach to addressing the challenges faced by the farmers. By leveraging advanced data analytics and artificial intelligence techniques, the system empowers farmers with timely and personalized recommendations, ultimately leading to improved agricultural productivity, food security, and economic prosperity.