bluuebunny/crossref_metadata_2025 huggingface dataset:
Unveiling the Binding between the Armadillo-Repeat Domain of Plakophilin 1 and the Intrinsically Disordered Transcriptional Repressor RYBP
Authors: Araujo-Abad Salome , Rizzuti Bruno , Vidal Miguel , Abian Olga , Fárez-Vidal María Esther , Velazquez-Campoy Adrian , de Juan Romero Camino , Neira José L.
Published: May 2024

Plakophilin 1 (PKP1), a member of the p120ctn subfamily of the armadillo (ARM)-repeat-containing proteins, is an important structural component of cell–cell adhesion scaffolds although it can also be ubiquitously found in the cytoplasm and the nucleus. RYBP (RING 1A and YY1 binding protein) is a multifunctional intrinsically disordered protein (IDP) best described as a transcriptional regulator. Both proteins are involved in the development and metastasis of several types of tumors. We studied the binding of the armadillo domain of PKP1 (ARM-PKP1) with RYBP by using in cellulo methods, namely immunofluorescence (IF) and proximity ligation assay (PLA), and in vitro biophysical techniques, namely fluorescence, far-ultraviolet (far-UV) circular dichroism (CD), and isothermal titration calorimetry (ITC). We also characterized the binding of the two proteins by using in silico experiments. Our results showed that there was binding in tumor and non-tumoral cell lines. Binding in vitro between the two proteins was also monitored and found to occur with a dissociation constant in the low micromolar range (~10 μM). Finally, in silico experiments provided additional information on the possible structure of the binding complex, especially on the binding ARM-PKP1 hot-spot. Our findings suggest that RYBP might be a rescuer of the high expression of PKP1 in tumors, where it could decrease the epithelial–mesenchymal transition in some cancer cells.
Plasma Applications in Biomedicine: A Groundbreaking Intersection between Physics and Life Sciences
Authors: Suschek Christoph V.
Published: May 2024

Plasma applications in biomedicine represent a groundbreaking intersection between physics and life sciences, unveiling novel approaches to disease treatment and tissue regeneration [...]
A Methodological Approach to Quantifying Silent Pauses, Speech Rate, and Articulation Rate across Distinct Narrative Tasks: Introducing the Connected Speech Analysis Protocol (CSAP)
Authors: Angelopoulou Georgia , Kasselimis Dimitrios , Goutsos Dionysios , Potagas Constantin
Published: May 2024

The examination of connected speech may serve as a valuable tool for exploring speech output in both healthy speakers and individuals with language disorders. Numerous studies incorporate various fluency and silence measures into their analyses to investigate speech output patterns in different populations, along with the underlying cognitive processes that occur while speaking. However, methodological inconsistencies across existing studies pose challenges in comparing their results. In the current study, we introduce CSAP (Connected Speech Analysis Protocol), which is a specific methodological approach to investigate fluency metrics, such as articulation rate and speech rate, as well as silence measures, including silent pauses’ frequency and duration. We emphasize the importance of employing a comprehensive set of measures within a specific methodological framework to better understand speech output patterns. Additionally, we advocate for the use of distinct narrative tasks for a thorough investigation of speech output in different conditions. We provide an example of data on which we implement CSAP to showcase the proposed pipeline. In conclusion, CSAP offers a comprehensive framework for investigating speech output patterns, incorporating fluency metrics and silence measures in distinct narrative tasks, thus allowing a detailed quantification of connected speech in both healthy and clinical populations. We emphasize the significance of adopting a unified methodological approach in connected speech studies, enabling the integration of results for more robust and generalizable conclusions.
Error Function Optimization to Compare Neural Activity and Train Blended Rhythmic Networks
Authors: Bourahmah Jassem , Sakurai Akira , Katz Paul , Shilnikov Andrey
Published: May 2024

We present a novel set of quantitative measures for “likeness” (error function) designed to alleviate the time-consuming and subjective nature of manually comparing biological recordings from electrophysiological experiments with the outcomes of their mathematical models. Our innovative “blended” system approach offers an objective, high-throughput, and computationally efficient method for comparing biological and mathematical models. This approach involves using voltage recordings of biological neurons to drive and train mathematical models, facilitating the derivation of the error function for further parameter optimization. Our calibration process incorporates measurements such as action potential (AP) frequency, voltage moving average, voltage envelopes, and the probability of post-synaptic channels. To assess the effectiveness of our method, we utilized the sea slug Melibe leonina swim central pattern generator (CPG) as our model circuit and conducted electrophysiological experiments with TTX to isolate CPG interneurons. During the comparison of biological recordings and mathematically simulated neurons, we performed a grid search of inhibitory and excitatory synapse conductance. Our findings indicate that a weighted sum of simple functions is essential for comprehensively capturing a neuron’s rhythmic activity. Overall, our study suggests that our blended system approach holds promise for enabling objective and high-throughput comparisons between biological and mathematical models, offering significant potential for advancing research in neural circuitry and related fields.
Innovation at the Intersection: Emerging Translational Research in Neurology and Psychiatry
Authors: Tanaka Masaru , Battaglia Simone , Giménez-Llort Lydia , Chen Chong , Hepsomali Piril , Avenanti Alessio , Vécsei László
Published: May 2024

Translational research in neurological and psychiatric diseases is a rapidly advancing field that promises to redefine our approach to these complex conditions [...]
Liquid-Phase Exfoliated Graphene and Polytetrafluoroethylene for Highly Durable and Reusable Chemical Leak Detection Sensors
Authors: Rubab Najaf , Sohn Eunbee , Kang Won-Seok , Kim TaeYoung
Published: May 2024

Graphene-based chemical sensors hold promise across diverse applications owing to their exceptional sensitivity and selectivity. However, achieving their long-term durability and reusability while preserving high sensitivity remains a significant challenge, particularly in harsh environments where exposure to strong chemicals is inevitable. This paper presents a novel approach to address this challenge by synergistically integrating liquid-phase exfoliated graphene (LPEG) with polytetrafluoroethylene (PTFE) within a single sensing strip. Through a comprehensive experimental investigation, we demonstrate the fabrication of highly durable and reusable chemical leak detection sensors by combining LPEG and PTFE. Furthermore, we explore the sensing mechanism, highlighting the roles of LPEG and PTFE in enhancing sensitivity and selectivity, along with durability and reusability. Performance evaluation reveals the sensors’ robustness against mechanical and chemical degradation, coupled with excellent recyclability. This innovative approach holds promise for applications in environmental monitoring, industrial safety, and healthcare, thus advancing the field of graphene-based chemical leak detection sensors.
Exploring TMA and H2O Flow Rate Effects on Al2O3 Thin Film Deposition by Thermal ALD: Insights from Zero-Dimensional Modeling
Authors: Karnopp Júlia , Neto Nilton Azevedo , Vieira Thaís , Fraga Mariana , Sobrinho Argemiro da Silva , Sagás Julio , Pessoa Rodrigo
Published: May 2024

This study investigates the impact of vapour-phase precursor flow rates—specifically those of trimethylaluminum (TMA) and deionized water (H2O)—on the deposition of aluminum oxide (Al2O3) thin films through atomic layer deposition (ALD). It explores how these flow rates influence film growth kinetics and surface reactions, which are critical components of the ALD process. The research combines experimental techniques with a zero-dimensional theoretical model, designed specifically to simulate the deposition dynamics. This model integrates factors such as surface reactions and gas partial pressures within the ALD chamber. Experimentally, Al2O3 films were deposited at varied TMA and H2O flow rates, with system conductance guiding these rates across different temperature settings. Film properties were rigorously assessed using optical reflectance methods and attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy. The experimental findings revealed a pronounced correlation between precursor flow rates and film growth. Specifically, at 150 °C, film thickness reached saturation at a TMA flow rate of 60 sccm, while at 200 °C, thickness peaked and then declined with increasing TMA flow above this rate. Notably, higher temperatures generally resulted in thinner films due to increased desorption rates, whereas higher water flow rates consistently produced thicker films, emphasizing the critical role of water vapour in facilitating surface reactions. This integrative approach not only deepens the understanding of deposition mechanics, particularly highlighting how variations in precursor flow rates distinctly affect the process, but also significantly advances operational parameters for ALD. These insights are invaluable for enhancing the application of ALD technologies across diverse sectors, including microelectronics, photovoltaics, and biomedical coatings, effectively bridging the gap between theoretical predictions and empirical results.
Influence of Selected Parameters of Zinc Electroplating on Surface Quality and Layer Thickness
Authors: Mascenik Jozef , Coranic Tomas , Kuchar Jiri , Hazdra Zdenek
Published: May 2024

Surface treatment technologies are pivotal across diverse industrial sectors such as mechanical engineering, electrical engineering, and the automotive industry. Continuous advancements in manufacturing processes are geared towards bolstering efficiency and attaining superior product quality. This study aimed to empirically compare practical outcomes with theoretical insights. Employing galvanic zinc plating under constant voltage with varying plating durations unveiled a correlation between coating thickness and electrolyte composition alongside plating duration. The graphical representation delineated the optimal electrolyte composition conducive to maximal coating thickness. Notably, an evident decrease in leveling ability was noted with prolonged plating durations. The experiment corroborated the notion that theoretical formulas for coating thickness estimation possess limited accuracy, often resulting in measured values surpassing theoretical predictions. These findings underscore the imperative for refined theoretical models to comprehensively grasp galvanic surface treatment processes.
A Hybrid Deep Learning Architecture for Apple Foliar Disease Detection
Authors: Ait Nasser Adnane , Akhloufi Moulay A.
Published: May 2024

Incorrectly diagnosing plant diseases can lead to various undesirable outcomes. This includes the potential for the misuse of unsuitable herbicides, resulting in harm to both plants and the environment. Examining plant diseases visually is a complex and challenging procedure that demands considerable time and resources. Moreover, it necessitates keen observational skills from agronomists and plant pathologists. Precise identification of plant diseases is crucial to enhance crop yields, ultimately guaranteeing the quality and quantity of production. The latest progress in deep learning (DL) models has demonstrated encouraging outcomes in the identification and classification of plant diseases. In the context of this study, we introduce a novel hybrid deep learning architecture named “CTPlantNet”. This architecture employs convolutional neural network (CNN) models and a vision transformer model to efficiently classify plant foliar diseases, contributing to the advancement of disease classification methods in the field of plant pathology research. This study utilizes two open-access datasets. The first one is the Plant Pathology 2020-FGVC-7 dataset, comprising a total of 3526 images depicting apple leaves and divided into four distinct classes: healthy, scab, rust, and multiple. The second dataset is Plant Pathology 2021-FGVC-8, containing 18,632 images classified into six categories: healthy, scab, rust, powdery mildew, frog eye spot, and complex. The proposed architecture demonstrated remarkable performance across both datasets, outperforming state-of-the-art models with an accuracy (ACC) of 98.28% for Plant Pathology 2020-FGVC-7 and 95.96% for Plant Pathology 2021-FGVC-8.
Elemental Uptake by Different Calcite Crystal Faces: An In Situ Study
Authors: Rezaei Mustafa , Gabitov Rinat , Sadekov Aleksey , Perez-Huerta Alberto , Borrelli Chiara , Stiles Andrea
Published: May 2024

This study aims to evaluate relationships between elemental signatures in calcite and the crystallographic orientation of its planes. The ability of calcite (a widespread calcium carbonate mineral) to entrap various trace and minor elements in its structure is the foundation of multiple methods (also called proxies) to reconstruct paleoenvironment conditions (e.g., temperature, pH, and marine chemistry). Although several element-to-calcium ratios (E/Ca) are routinely measured in marine carbonates and are widely used in paleoclimate studies, some of the controls on the incorporation of these elements are still unclear. Here, we examine the effect of crystallography on (E/Ca)calcite by growing thin layers of calcite on differently oriented Iceland Spar substrates immersed in modified seawater solution. Newly grown calcite layers were examined with Laser Ablation Inductivity–Coupled Plasma Mass Spectrometry (LA-ICP-MS), Backscattered Electron Imaging (BSE), and Energy Dispersive X-ray Spectroscopy (EDS). We propose that the crystallographic orientation might slightly influence the incorporation of lithium (Li), sodium (Na), magnesium (Mg), sulfur (S), and barium (Ba) into the studied calcite faces and have no impact on the incorporation of boron (B), potassium (K), and strontium (Sr) at least under the conditions of our experiment.
Structural Analysis of Xylose Isomerase from Streptomyces avermitilis
Authors: Nam Ki Hyun
Published: May 2024

Xylose isomerase (XI, also known as glucose isomerase) is an oxidoreductase that interconverts aldoses and ketoses. XI catalyzes the reversible isomerization of D-glucose and D-xylose into D-fructose and D-xylulose, respectively. The molecular function of XI is widely applied in producing high-fructose corn syrup (HFCS) in the food industry and bioethanol from hemicellulose in the biofuel industry. The structural information of XI from diverse strains is important for understanding molecular properties that can provide insights into protein engineering to improve enzyme efficiency. To extend the knowledge of the structural information on XI, the crystal structure of XI from Streptomyces avermitilis (SavXI) was determined at a 2.81 Å resolution. SavXI containing TIM barrel and extended α-helix domains formed the tetrameric assembly. The two metal-binding sites and their coordinating residues showed diverse conformations, providing the structural flexibility of the active site of SavXI. The structural comparison of SavXI and XI homologs exhibited unique metal-binding sites and conformations of the C-terminal α-helix domain. These structural results extend our knowledge of the molecular flexibility and mechanism of the XI family.
Treatment Outcomes in Patients with Muscular Temporomandibular Joint Disorders: A Prospective Case-Control Study
Authors: Izzetti Rossana , Carli Elisabetta , Gennai Stefano , Giuca Maria Rita , Graziani Filippo , Nisi Marco
Published: May 2024

Muscular temporomandibular joint disorders (M-TMDs) encompass a wide range of painful muscular conditions, which can provoke functional limitation and severely affect quality of life. The aim of the present study was to assess the treatment outcomes in patients affected by M-TMDs in terms of pain scores assessed with pressure pain threshold (PPT). The levels of depression, anxiety, and the Oral Health Impact Profile were also assessed and compared to healthy controls. Patients with a clinical diagnosis of M-TMDs and a control group of healthy subjects were enrolled. At baseline, OHIP-14, PHQ-9, and GAD-7 were administered. PPT was registered at the level of masseter and temporalis muscles. The patients affected by M-TMDs were then treated with oral splints and physio-kinesiotherapy following a standardized treatment protocol. At the 6-month follow-up of M-TMD-affected patients, PPT was registered, and the questionnaires were re-administered to evaluate treatment outcomes. In total, sixty patients and sixty controls were enrolled. The treatment of M-TMDs produced a significant improvement in PPT at the level of the masseter muscle. OHIP-14 at baseline in the M-TMD group was significantly higher compared to the control group (p < 0.05). At the 6-month follow-up, a significant reduction in OHIP-14 scores was registered, although with higher scores compared to the control group (p < 0.05). PHQ-9 was significantly higher at baseline in the M-TMD group (p < 0.05) and decreased to values comparable to the control group after treatment. GAD-7 presented statistically significant differences between the control group and M-TMD patients at baseline (p < 0.05) and between pre- and post-treatment in the M-TMD group. Following treatment, the GAD-7 scores were comparable to the control group. The symptom burden associated with M-TMDs negatively affects quality of life, with higher oral health impairment and a tendency towards depression and anxiety compared to healthy subjects. Following treatment, an improvement in both PPT and quality of life was observed.
Beyond CheatBots: Examining Tensions in Teachers’ and Students’ Perceptions of Cheating and Learning with ChatGPT
Authors: Mah Christopher , Walker Hillary , Phalen Lena , Levine Sarah , Beck Sarah W. , Pittman Jaylen
Published: May 2024

As artificial intelligence (AI) is increasingly integrated into educational technologies, teachers and students must acquire new forms of AI literacy, including an understanding of responsible use of AI. In this study, we explored tensions in teachers’ and students’ opinions about what constitutes learning and cheating with AI. Using qualitative methods, we asked Pre-K through postsecondary writing teachers (n = 16) and a linguistically diverse group of students (n = 12) to consider examples of how students might use ChatGPT, rank them in order of how much they thought each student learned and cheated, and explain their rankings. Our study yielded three findings. First, teachers and students used similar criteria to determine their rankings. Second, teachers and students arrived at similar conclusions about learning with ChatGPT but different conclusions about cheating. Finally, disagreements centered on four main tensions between (1) using ChatGPT as a shortcut versus as a scaffold; (2) using ChatGPT to generate ideas versus language; (3) getting support from ChatGPT versus analogous support from other sources; and (4) learning from ChatGPT versus learning without. These findings underscore the importance of student voice in co-constructing norms around responsible AI use.
Perceptions of UDL Teaching Practices among University Students with Disabilities
Authors: Li Yi-Fan , Zhang Dalun , Liu Chih-Tsen , Wang Ke , Yan Wei , Dong Xin
Published: May 2024

The purpose of this study was to investigate how students’ disabilities impacted their learning at the university and to measure their perceptions of universal design for learning (UDL). An online survey was administered at a large public research university in the south-central United States; 160 students with disabilities completed the survey. These students with disabilities described how their disabilities influenced their learning in the following aspects: attention issues, slow processing, absence, accessibility issues, reading and writing challenges, mental health challenges, and social interaction challenges. The results also revealed that some UDL teaching practices were not being fully utilized by instructors based on the perceptions of students with disabilities. We discussed those UDL practices which were not being used by instructors and highlighted the impact of using the practices on students’ learning. These practices include effective teaching methods such as helping students organize and summarize learning content, using technology to increase accessibility, providing flexibility in assessments and assignments, providing meaningful feedback, and recruiting students’ attention and engagement.
Strategies for Integrating Generative AI into Higher Education: Navigating Challenges and Leveraging Opportunities
Authors: Kurtz Gila , Amzalag Meital , Shaked Nava , Zaguri Yanay , Kohen-Vacs Dan , Gal Eran , Zailer Gideon , Barak-Medina Eran
Published: May 2024

The recent emergence of generative AI (GenAI) tools such as ChatGPT, Midjourney, and Gemini have introduced revolutionary capabilities that are predicted to transform numerous facets of society fundamentally. In higher education (HE), the advent of GenAI presents a pivotal moment that may profoundly alter learning and teaching practices in aspects such as inaccuracy, bias, overreliance on technology and algorithms, and limited access to educational AI resources that require in-depth investigation. To evaluate the implications of adopting GenAI in HE, a team of academics and field experts have co-authored this paper, which analyzes the potential for the responsible integration of GenAI into HE and provides recommendations about this integration. This paper recommends strategies for integrating GenAI into HE to create the following positive outcomes: raise awareness about disruptive change, train faculty, change teaching and assessment practices, partner with students, impart AI learning literacies, bridge the digital divide, and conduct applied research. Finally, we propose four preliminary scale levels of a GenAI adoption for faculty. At each level, we suggest courses of action to facilitate progress to the next stage in the adoption of GenAI. This study offers a valuable set of recommendations to decision-makers and faculty, enabling them to prepare for the responsible and judicious integration of GenAI into HE.
JEEMRC: Joint Event Detection and Extraction via an End-to-End Machine Reading Comprehension Model
Authors: Liu Shanshan , Zhang Sheng , Ding Kun , Liu Liu
Published: May 2024

Event extraction (EE) generally contains two subtasks: viz., event detection and argument extraction. Owing to the success of machine reading comprehension (MRC), some researchers formulate EE into MRC frameworks. However, existing MRC-based EE techniques are pipeline methods that suffer from error propagation. Moreover, the correlation between event types and argument roles is pre-defined by experts, which is time-consuming and inflexible. To avoid these issues, event detection and argument extraction are formalized as joint MRC. Different from previous methods, which just generate questions for argument roles for identified event types, questions are generated for all arguments that appear in the given sentence in our approach. Moreover, an end-to-end MRC model, JEEMRC, is proposed, which consists of an event classifier and a machine reader with a coarse-to-fine attention mechanism. Our proposed model can train two subtasks jointly to alleviate error propagation and utilizes interaction information between event types and argument roles to improve the performance of both tasks. Experiments on ACE 2005 verify that our JEEMRC achieves competitive results compared with previous work. In addition, it performs well when detecting events and extracting arguments in data-scarce scenarios.
Parasitic-Based Model for Characterizing False Turn-On and Switching-Based Voltage Oscillation in Hybrid T-Type Converter
Authors: Babaki Amir , Golsorkhi Mohammad Sadegh , Christensen Nicklas , Baharizadeh Mehdi , Behrendt Stefan , Beyer Jesco , Ebel Thomas
Published: May 2024

High frequency and high voltage switching converters utilizing wide bandgap semiconductors are gaining popularity thanks to their compactness and improved efficiency. However, the faster switching requirements gives rise to new challenges. A key issue is the increased oscillation of the drain–source voltage caused by the switching action of the complementary switch in the same phase or change of state of the other phase switches. The voltage stress caused by these oscillations can damage the switch. Furthermore, the high dv/dt during turning-on of one switch might result in false turn-on of the complementary switch due to the miller effect. In this paper, these issues are investigated in a T-type converter through analytical and experimental analysis. Based on the proposed analytical approach, simple and cost-wise solutions utilizing an optimum design of gate driver circuits and circuit layout modifications can be developed to cope with the aforementioned issues. A comprehensive analytical model of the converter with consideration of parasitic capacitances and inductances is developed. By performing sensitivity analysis on the model, the effect of the parasitic parameters on the drain–source voltage oscillation and gate–source voltage amplitude in case of false turn-on is studied. The validity of the model is then assessed through numerical simulations and experimental results.
Dependence of Body Stability on Optical Conditions during VR Viewing
Authors: Jeong Gi-Seong , Kang Hyun-Goo , Kim Sang-Yeob
Published: May 2024

The dependence of body stability on the distance between the optical centers of VR-device lenses and the refractive error status of users during VR viewing was investigated. Participants included 31 adults, and their postural-control ability was measured using a BTrackS device. The optical conditions were (1) COCD (comfortable optical center distance), (2) COCD+2D (comfortable optical center distance with 2D myopia), (3) COCD-2D (comfortable optical center distance with 2D hyperopia), (4) DOCD (uncomfortable optical center distance), (5) DOCD+2D (uncomfortable optical center distance with 2D myopia), and (6) DOCD-2D (uncomfortable optical center distance with 2D hyperopia). Posture was assessed under these six optical conditions while the participants were wearing a VR device and watching a 3D roller-coaster video. The sway-path length was significantly increased under the COCD-2D, DOCD, DOCD+2D, and DOCD-2D conditions compared to the COCD condition (p < 0.05). In the case of maximum sway velocity, the results showed significant increases under the DOCD, DOCD+2D, and DOCD-2D conditions compared to the COCD condition (p < 0.05). The analysis revealed that when users are viewing VR displays, optimization of the distance to the optical center of the VR-device lenses and correction of the refractive errors for individual users was a significant factor in minimizing body instability.
Optimal Planning of PV Sources and D-STATCOM Devices with Network Reconfiguration Employing Modified Ant Lion Optimizer
Authors: B. C. Sujatha , A. Usha , R. S. Geetha
Published: May 2024

This research emphasizes a meta-heuristic modified ant lion optimizer (MALO) optimization approach for the simultaneous utilization of DSTATCOM devices and distributed photovoltaic (PV) sources with network reconfiguration in a radial power distribution scheme. In a radial power distribution network with network reconfiguration, the majority of the research is based on constant power model analysis. However, it is noticed that load models have a substantial impact on the distributed PV sources and the DSTATCOM device’s optimal size and position. The effect of the constant power (CP) and polynomial (ZIP) with load growth load models for the simultaneous insertion of distributed PV sources and DSTATCOM devices with network reconfiguration is examined in this research work for power system planning. The penetration levels of distributed PV sources considered for the investigation are 25%, 50%, 75%, and 100%. The principal objective of this research is to reduce network total power losses, enhance the voltage magnitude profile at all buses, and reduce the overall operating cost while adhering to equality and inequality constraints. The proposed algorithm is verified on 118-node test systems. The investigation is carried out for planning network upgrading to a high-voltage distribution system (HVDS) on 317 nodes in the rural Bangalore Electricity Supply Company Limited (BESCOM) radial distribution scheme. The simulated results obtained with this method are validated with the BAT algorithm and techniques available in the literature. It is observed that in the IEEE 118-bus system, via the simultaneous placement and sizing of PV sources considering a 25% penetration level and DSTATCOM devices during network reconfiguration, the total power loss reduction is 41.47% and 42.98% for the constant power model and ZIP with the load growth model. For the 317-bus system, the total power loss reduction observed for 11 kV is 49.77% and 59.34% for the constant power model and ZIP model with load growth. Similarly, for the 22 kV system, the power loss reduction observed is 51.69% and 55.75% for the constant power model and ZIP with the load growth model.
Deriving Input Variables through Applied Machine Learning for Short-Term Electric Load Forecasting in Eskilstuna, Sweden
Authors: Netzell Pontus , Kazmi Hussain , Kyprianidis Konstantinos
Published: May 2024

As the demand for electricity, electrification, and renewable energy rises, accurate forecasting and flexible energy management become imperative. Distribution network operators face capacity limits set by regional grids, risking economic penalties if exceeded. This study examined data-driven approaches of load forecasting to address these challenges on a city scale through a use case study of Eskilstuna, Sweden. Multiple Linear Regression was used to model electric load data, identifying key calendar and meteorological variables through a rolling origin validation process, using three years of historical data. Despite its low cost, Multiple Linear Regression outperforms the more expensive non-linear Light Gradient Boosting Machine, and both outperform the “weekly Naïve” benchmark with a relative Root Mean Square Errors of 32–34% and 39–40%, respectively. Best-practice hyperparameter settings were derived, and they emphasize frequent re-training, maximizing the training data size, and setting a lag size larger than or equal to the forecast horizon for improved accuracy. Combining both models into an ensemble could the enhance accuracy. This paper demonstrates that robust load forecasts can be achieved by leveraging domain knowledge and statistical analysis, utilizing readily available machine learning libraries. The methodology for achieving this is presented within the paper. These models have the potential for economic optimization and load-shifting strategies, offering valuable insights into sustainable energy management.
Second Life of Used Lithium-Ion Batteries from Electric Vehicles in the USA
Authors: Meegoda Jay , Charbel Ghadi , Watts Daniel
Published: May 2024

This article focuses on the reuse and recycling of end-of-life (EOL) lithium-ion batteries (LIB) in the USA in the context of the rapidly growing electric vehicle (EV) market. Due to the recent increase in the enactment of both current and pending regulations concerning EV battery recycling, this work focuses on the recycling aspect for lithium-ion batteries rather than emphasizing the reuse of EOL batteries (although these practices have value and utility). A comparative analysis of various recycling methods is presented, including hydrometallurgy, pyrometallurgy, direct recycling, and froth flotation. The efficiency and commercial viability of these individual methods are highlighted. This article also emphasizes the practices and capabilities of leading companies, noting their current superior annual processing capacities. The transportation complexities of lithium-ion batteries are also discussed, noting that they are classified as hazardous materials and that stringent safety standards are needed for their handling. The study underscores the importance of recycling in mitigating environmental risks associated with EOL of LIBs and facilitates comparisons among the diverse recycling processes and capacities among key players in the industry.
Redbay (Persea borbonia L. Spreng) Seedling and Sapling Growth and Recruitment Near Trees with and without Putative Resistance to Laurel Wilt Disease
Authors: Eicholtz Matthew , Smith Jason , Vogel Jason
Published: May 2024

Laurel wilt, a fungal disease (Harringtonia lauricola T.C. Harr., Fraedrich and Aghayeva) spread by the Asian redbay ambrosia beetle [Xyleborus glabratus Eichhoff (Coleoptera: Curculionidae: Scolytinae)], presents an imminent threat to North American members of the Lauraceae family, having caused extensive mortality in several species, especially redbay (Persea borbonia L. Spreng). Varying levels of disease resistance have been recorded in redbay under controlled conditions. To investigate if previously monitored putatively resistant field redbays have influenced the regeneration and survival of conspecifics within the surrounding 0.08 ha, a survey was conducted in 2018-19 and compared to similar data collected years prior (2008-09, 2013) along the coasts of Florida, Georgia, and South Carolina, United States. Plots were originally established at six disease-infested study sites around large redbay (>7.5-cm diameter at breast height (1.37 m) (DBH)) that had survived the initial laurel wilt disease epidemic that began in approximately 2007. In 2018-19, a subset of 61 plots within 16 m of the original “survivor” redbay were recorded and compared to previous surveys. Among the original redbay selected for resistance, 22 of 61 (36%) survivors across all sites were alive in 2018 with survival rates varying from 0 to 70% between survey periods (average mortality 3.6%/year). Trees that died in years since 2008-09 had their plots reclassified as susceptible or “suscepts”. Changes in mean quadratic diameter at 1.37 m of redbay plots near survivors were significantly greater than those near suscepts, and in 2018-19, the average diameter of redbay near survivors was 7.62 cm vs. 4.90 cm for suscepts. The diameter distribution of dead and live redbay in the whole population showed a decrease in live individuals surviving past 8 cm DBH in 2018–2019, but 20 of 22 survivor candidate trees were larger than 8 cm DBH. Regeneration was occurring both clonally and sexually and tended to be greater near suscepts, but midstory resprouts per hectare and understory seedlings interacted significantly with the site and the latter differed between sites. These findings indicate that redbay is regenerating in these ecosystems, and disease resistance may allow for increased average tree size for some individuals, but an upper size threshold of around 8 cm DBH may still exist for much of the population. In addition, the importance of site variables in regeneration was apparent, making either local genetic or environmental effects an important topic for future research. Continuing to monitor these survivors while locating new candidates for disease screenings and breeding, preventing the introductions of new strains of H. lauricola, shedding light on the nature of resistance and its heritability, and initiating outplanting trials with resistant germplasm are instrumental steps in bringing redbay back to prominence in its historical range.
Assessing the Potential of Onboard LiDAR-Based Application to Detect the Quality of Tree Stems in Cut-to-Length (CTL) Harvesting Operations
Authors: Sagar Anwar , Kärhä Kalle , Einola Kalle , Koivusalo Anssi
Published: May 2024

This paper investigated the integration of LiDAR technology in cut-to-length (CTL) harvesting machines to enhance tree selection accuracy and efficiency. In the evolution of CTL forest machines towards improving operational efficiency and operator conditions, challenges persist in manual tree selection during thinning operations, especially under unmarked conditions and complex environments. These can be improved due to advances in technology. We studied the potential of LiDAR systems in assisting harvester operators, aiming to mitigate workload, reduce decision errors, and optimize the harvesting workflow. We used both synthetic and real-world 3D point cloud data sets for tree stem defect analysis. The former was crafted using a 3D modelling engine, while the latter originated from forest observations using 3D LiDAR on a CTL harvester. Both data sets contained instances of tree stem defects that should be detected. We demonstrated the potential of LiDAR technology: The analysis of synthetic data yielded a Root Mean Square Error (RMSE) of 0.00229 meters (m) and an RMSE percentage of 0.77%, demonstrating high detection accuracy. The real-world data also showed high accuracy, with an RMSE of 0.000767 m and an RMSE percentage of 1.39%. Given these results, we recommend using on-board LiDAR sensor technologies for collecting and analyzing data on tree/forest quality in real-time. This will help overcome existing barriers and drive forest operations toward enhanced efficiency and sustainability.
What Affects the Willingness of Farmers to Participate in Forest Ticket Trading? Empirical Analysis Based on Incomplete Information Theory
Authors: Song Boyao , Han Xiao , Lv Siyao , Fang Qiushuang , Wang Zhongping , Li Hongxun
Published: May 2024

Forest tickets refer to a type of forest resource usufruct certificate characterized by “cooperative operation, quantification of rights and interests, free circulation, and guaranteed dividends”. It is an important means to build a market-oriented mechanism for realizing the value of ecological resources. Incomplete information, based on field survey data from thirteen villages in eight townships (towns) in Sanming City, Fujian Province, China, and a binary logit model were used to explore the moderating effects of factors affecting farmers’ willingness to participate in forest ticket trading, the heterogeneity of farmers, and social capital. We found the following: In an environment with incomplete information, farmers’ willingness to participate in forest ticket trading is influenced by heterogeneity expectations, social capital, government propaganda, and individual family characteristics. There are certain differences in the influencing factors and degree of farmers’ willingness to participate in forest ticket trading among different groups of farmers with different levels of education and part-time employment. Social capital can strengthen the positive impact of income expectations and policy sustainability expectations, and alleviate the negative impact of risk expectations.
Effects of Mactra chinenesis Peptides on Alcohol-Induced Acute Liver Injury and Intestinal Flora in Mice
Authors: Wu Dong , Cheng Ming , Yi Xiangzhou , Xia Guanghua , Liu Zhongyuan , Shi Haohao , Shen Xuanri
Published: May 2024

Food-borne bioactive peptides have shown promise in preventing and mitigating alcohol-induced liver injury. This study was the first to assess the novel properties of Mactra chinenesis peptides (MCPs) in mitigating acute alcoholic liver injury in mice, and further elucidated the underlying mechanisms associated with this effect. The results showed that MCPs can improve lipid metabolism by modulating the AMPK signaling pathway, decreasing fatty acid synthase activity, and increasing carnitine palmitoyltransferase 1a activity. Meanwhile, MCPs ameliorate inflammation by inhibiting the NF-κB activation, leading to reduced levels of pro-inflammatory cytokines (tumor necrosis factor-α and interleukin-1β). Additionally, a 16S rDNA sequencing analysis revealed that MCPs can restore the balance of gut microbiota and increase the relative abundance of beneficial bacteria. These findings suggest that supplementation of MCPs could attenuate alcohol intake-induced acute liver injury, and, thus, may be utilized as a functional dietary supplement for the successful treatment and prevention of acute liver injury.
Biofortification and Valorization of Celery byproducts Using Selenium and PGPB under Reduced Nitrogen Regimes
Authors: Collado-González Jacinta , Piñero María Carmen , Otálora Alcón Ginés , López-Marín Josefa , del Amor Francisco M.
Published: May 2024

Due to climate change and exacerbated population growth, the search for new sustainable strategies that allow for greater food productivity and that provide greater nutritional quality has become imperative. One strategy for addressing this problem is the combined use of fertilization with a reduced dose of nitrogen and biostimulants. Celery processing produces a large amount of waste with its concomitant pollution. Therefore, it is necessary to address the valorization of its byproducts. Our results revealed reductions in the biomass, Na, P, Mn, B, sugars, and proteins in the byproducts and increased lipid peroxidation, Fe (all celery parts), and K (byproducts) when the N supplied was reduced. Plants inoculated with Azotobacter salinestris obtained a greater biomass, a higher accumulation of K (byproducts), a build-up of sugars and proteins, reduced concentrations of P, Cu, Mn, B, Fe (petioles), and Zn (byproducts), and reduced lipid peroxidation. The application of Se at 8 μM reinforced the beneficial effect obtained after inoculation with Azotobacter salinestris. In accordance with our results, edible celery parts are recommended as an essential ingredient in the daily diet. Furthermore, the valorization of celery byproducts with health-promoting purposes should be considered.
Near-Infrared Spectroscopy Combined with Fuzzy Improved Direct Linear Discriminant Analysis for Nondestructive Discrimination of Chrysanthemum Tea Varieties
Authors: Zhang Jiawei , Wu Xiaohong , He Chengyu , Wu Bin , Zhang Shuyu , Sun Jun
Published: May 2024

The quality of chrysanthemum tea has a great connection with its variety. Different types of chrysanthemum tea have very different efficacies and functions. Moreover, the discrimination of chrysanthemum tea varieties is a significant issue in the tea industry. Therefore, to correctly and non-destructively categorize chrysanthemum tea samples, this study attempted to design a novel feature extraction method based on the fuzzy set theory and improved direct linear discriminant analysis (IDLDA), called fuzzy IDLDA (FIDLDA), for extracting the discriminant features from the near-infrared (NIR) spectral data of chrysanthemum tea. To start with, a portable NIR spectrometer was used to collect NIR data for five varieties of chrysanthemum tea, totaling 400 samples. Secondly, the raw NIR spectra were processed by four different pretreatment methods to reduce noise and redundant data. Thirdly, NIR data dimensionality reduction was performed by principal component analysis (PCA). Fourthly, feature extraction from the NIR spectra was performed by linear discriminant analysis (LDA), IDLDA, and FIDLDA. Finally, the K-nearest neighbor (KNN) algorithm was applied to evaluate the classification accuracy of the discrimination system. The experimental results show that the discrimination accuracies of LDA, IDLDA, and FIDLDA could reach 87.2%, 94.4%, and 99.2%, respectively. Therefore, the combination of near-infrared spectroscopy and FIDLDA has great application potential and prospects in the field of nondestructive discrimination of chrysanthemum tea varieties.
The Impact of Political Orientation and Government Change on Public Satisfaction with Food Policy in South Korea
Authors: Min Sunhyung , Cho Sung Ju
Published: May 2024

This study investigates the relationship between political orientation, government change, and public satisfaction with food policies in the Republic of Korea. Utilizing data from the Consumer Behavior Survey for Foods (CBSF) conducted by the Korea Rural Economic Institute from 2020 to 2021, we employ fixed effects models to examine the impact of political orientation, the presence of a conservative government, and their interactions on satisfaction across various dimensions of food policy. We also analyze the change in satisfaction levels from 2020 to 2021 based on shifts in political orientation. The results reveal complex dynamics between political alignment, government performance, and public perceptions. While conservatives and liberals exhibit higher satisfaction with labeling policies, they show lower satisfaction with safety and redress policies. The presence of a conservative government is associated with higher satisfaction in specific policy areas but lower overall satisfaction. Changes in political orientation significantly influence policy satisfaction, with shifts away from conservatism and towards liberalism leading to decreased satisfaction. The findings highlight the importance of understanding the nuanced preferences of different political groups and the need for responsive and transparent food policy frameworks. This study advances the theoretical understanding of the political economy of policy satisfaction and provides novel policy implications for effective governance.
Endoscopic Ultrasonography-Guided Fine-Needle Biopsy for Patients with Resectable Pancreatic Malignancies
Authors: Chien Ming-Sheng , Lin Ching-Chung , Lai Jian-Han
Published: May 2024

Clinicians often use endoscopic ultrasonography to survey pancreatic tumors. When endoscopists conduct this examination and find the tumor to be unresectable, a fine-needle biopsy is subsequently performed for tissue confirmation. However, if the tumor is deemed resectable, the necessity of a pre-operative fine-needle biopsy remains debatable. Therefore, we performed a retrospective analysis of a single-center cohort of patients with pancreatic tumors who underwent an endoscopic ultrasound-guided fine-needle biopsy or aspiration (EUS-FNB or FNA) between 2020 and 2022. This study focused on patients diagnosed with resectable malignant pancreatic tumors. The exclusion criteria included individuals diagnosed with benign pancreatic lesions and those with unresectable tumors. A total of 68 patients were enrolled in this study. Histological examination revealed that pancreatic adenocarcinoma was the predominant type of tumor (n = 42, 61.8%), followed by neuroendocrine tumors (n = 22, 32.3%), and metastasis (n = 4, 5.9%). Notably, 17 patients had a history of other cancers, with 23.5% being diagnosed with a metastatic tumor rather than primary pancreatic cancer. Therefore, EUS-FNA/FNB is crucial in patients with a resectable pancreatic tumor and a history of cancer to differentiate between a primary and a metastatic tumor.