bluuebunny/crossref_metadata_2025 huggingface dataset:
Spatially and temporally precise microbiome profiling in the small intestine using the SIMBA capsule with X-ray tracking
Authors: Wang Gang , Menon Sharanya , Wilsack Lynn , Rehak Renata , Lou Lawrence , Turbide Christian , Auger Jeremie , Tremblay Annie , Mathieu Olivier , Binda Sylvie , Tompkins Thomas A. , Bruehlmann Sabina , Andrews Christopher N.
Published: July 2024

Introduction

Few minimally invasive options for sampling the small intestinal (SI) luminal fluid exist to study the SI microbiota in health and disease. To address the lack of tools and methods to study GI regions that are difficult to access, Nimble Science developed a fully autonomous and passive sampling method, the Small Intestine MicroBiome Aspiration (SIMBA™) capsule, for convenient, high-quality, and reliable sampling to study the diet-microbiota interactions in the SI.

Methods

The sealing efficacy and microbial DNA preservation capacity of the SIMBA capsules was first validated through in vitro simulation assays. Then, a clinical study was conducted with 20 healthy participants to validate the in vivo use of SIMBA capsules to reliably capture samples for SI microbiome analysis before and after an intervention (NCT04489329). Briefly, participants ingested the capsules at baseline and 7 days later, with a probiotic capsule containing a blend of L. rhamnosus R0011 and B. longum R0175. Following baseline SIMBA capsule ingestion, multiple low-dosage x-ray scans were performed to track the sampling location. Fecal samples corresponding with the baseline and intervention capsule were analyzed for comparison.

Results

The SIMBA capsules’ performance in vitro demonstrated the potential for contamination-free sampling with preservation of the microbial communities. Within the clinical study, the capsules performed safely and reliably for collection of SI content. X-ray tracking confirmed that 97.2% of the capsules completed sample collection in the SI regions before reaching the colon. Importantly, our data showed that the capsules sampled in the right area of the intestines and that baseline SIMBA microbiome profile is significantly different from fecal microbiome profile. SIMBA successfully detected a concurrent probiotic intervention in the small intestine, which was not detectable using stool samples.

Discussions

The high accuracy of sampling location and sealing efficacy of the SIMBA capsules makes them potentially useful research tools in clinical trials for studying diet-microbiota interactions in health and disease, and perhaps eventually for the clinical diagnosis of GI tract conditions affecting the SI such as SIBO.
Oblique instability of quasi-parallel whistler waves in the presence of cold and warm electron populations
Authors: Roytershteyn Vadim , Delzanno Gian Luca , Holmes Justin C.
Published: July 2024

Whistler waves propagating nearly parallel to the ambient magnetic field experience a nonlinear instability due to transverse currents when the background plasma has a population of sufficiently low energy electrons. Intriguingly, this nonlinear process may generate oblique electrostatic waves, including whistlers near the resonance cone with properties resembling oblique chorus waves in the Earth’s magnetosphere. Focusing on the generation of oblique whistlers, earlier analysis of the instability is extended here to the case where low-energy background plasma consists of both a “cold” population with energy of a few eV and a “warm” electron component with energy of the order of 100 eV. This is motivated by spacecraft observations in the Earth’s magnetosphere where oblique chorus waves were shown to interact resonantly with the warm electrons. The main new results are: 1) the instability producing oblique electrostatic waves is sensitive to the shape of the electron distribution at low energies. In the whistler range of frequencies, two distinct peaks in the growth rate are typically present for the model considered: a peak associated with the warm electron population at relatively low wavenumbers and a peak associated with the cold electron population at relatively high wavenumbers; 2) overall, the instability producing oblique whistler waves near the resonance cone persists (with a reduced growth rate) even in the cases where the temperature of the cold population is relatively high, including cases where cold population is absent and only the warm population is included; 3) particle-in-cell simulations show that the instability leads to heating of the background plasma and formation of characteristic plateau and beam features in the parallel electron distribution function in the range of energies resonant with the instability. The plateau/beam features have been previously detected in spacecraft observations of oblique chorus waves. However, they have been attributed to external sources and have been proposed to be the mechanism generating oblique chorus. In the present scenario, the causality link is reversed and the instability generating oblique whistler waves is shown to be a possible mechanism for formation of the plateau and beam features.
Stealth Unmanned Aerial Vehicle Penetration Efficiency Optimization Based on Radar Detection Probability Model
Authors: Yuan Chengen , Ma Dongli , Jia Yuhong , Zhang Liang
Published: July 2024

Aerodynamic/stealth optimization is a key issue during the design of a stealth UAV. Balancing the weight of different incident angles of the RCS and combining stealth characteristics with aerodynamic characteristics are hotspots of aerodynamic/stealth optimization. To address this issue, this paper introduces a radar detection probability model to solve the weight balance problem of incident angles of the RCS and a penetration efficiency model to transfer the multi-object optimization into single-objective optimization. In this paper, a parameterized model of a flying-wing UAV is selected as the research object. A gradient-free optimization algorithm based on the genetic algorithm is used for maximizing efficiency. The optimization model balances the influence of the RCS mean value and RCS peak value on stealth performance. Moreover, the model achieves an optimal entire life cycle penetration efficiency coefficient by balancing aerodynamic and stealth optimization. The results show that the optimized model improves the penetration efficiency coefficient by 13.84% and increases maximum flight sorties by 1.8%. These results prove that the model has a reasonable combination of aerodynamic and stealth optimization for UAVs undertaking penetration missions.
ChickenSense: A Low-Cost Deep Learning-Based Solution for Poultry Feed Consumption Monitoring Using Sound Technology
Authors: Amirivojdan Ahmad , Nasiri Amin , Zhou Shengyu , Zhao Yang , Gan Hao
Published: July 2024

This research proposes a low-cost system consisting of a hardware setup and a deep learning-based model to estimate broiler chickens’ feed intake, utilizing audio signals captured by piezoelectric sensors. The signals were recorded 24/7 for 19 consecutive days. A subset of the raw data was chosen, and events were labeled in two classes, feed-pecking and non-pecking (including singing, anomaly, and silence samples). Next, the labeled data were preprocessed through a noise removal algorithm and a band-pass filter. Then, the spectrogram and the signal envelope were extracted from each signal and fed as inputs to a VGG-16-based convolutional neural network (CNN) with two branches for 1D and 2D feature extraction followed by a binary classification head to classify feed-pecking and non-pecking events. The model achieved 92% accuracy in feed-pecking vs. non-pecking events classification with an f1-score of 91%. Finally, the entire raw dataset was processed utilizing the developed model, and the resulting feed intake estimation was compared with the ground truth data from scale measures. The estimated feed consumption showed an 8 ± 7% mean percent error on daily feed intake estimation with a 71% R2 score and 85% Pearson product moment correlation coefficient (PPMCC) on hourly intake estimation. The results demonstrate that the proposed system estimates broiler feed intake at each feeder and has the potential to be implemented in commercial farms.
Spatio-Temporal Evolution of Fogwater Chemistry in Alsace
Authors: Khoury Dani , Millet Maurice , Jabali Yasmine , Weissenberger Thomas , Delhomme Olivier
Published: July 2024

For the current article, forty-two fogwater samples are collected at four sites in Alsace (Strasbourg, Geispolsheim, Erstein, and Cronenbourg) between 2015 and 2021, except 2019 and 2020. Spatio-temporal evolution is studied for their inorganic fraction (ions and heavy metals), and physico-chemical properties (pH, conductivity (K), liquid water content (LWC), and dissolved organic carbon (DOC)). The analyses show a remarkable shifting in pH from acidic to basic mainly due to the significant decrease in sulfate and nitrate levels. The calculated median LWC is somehow low (37.8–69.5 g m3) in fog samples, preventing the collection of large fog volumes. The median DOC varies between 14.3 and 24.4 ppm, whereas the median conductivity varies from 97.8 to 169.8 µS cm−1. Total ionic concentration (TIC) varies from 1338.3 to 1952.4 µEq L−1, whereas the total concentration of metals varies in the range of 1547.2 and 2860.3 µg L−1. The marine contribution is found to be negligible at all sites for the investigated elements. NH4+, in most samples, is capable alone to neutralize the acidity. On one hand, NH4+, Ca2+, NO3−, and SO42− are the dominant ions found in all samples, accounting for more than 80% of the TIC. On the other hand, Zn and Ni are the dominant metals accounting for more than 78% of the total elemental concentration. Heavy metals are found to primarily originate from crust as well as human-made activities. The median concentrations of individual elements either decrease or increase over the sampling period due to the wet deposition phenomenon or weather conditions. A Pearson analysis proves some of the suggested pollutant sources due to the presence of strong and significant correlations between elements.
Production of Poly(3-Hydroxybutyrate-Co-3-Hydroxyvalerate) by Bacillus megaterium LVN01 Using Biogas Digestate
Authors: Martínez Amanda Lucía Mora , Yepes-Pérez María , Contreras Karent Alexandra Carrero , Moreno Paola Eliana Zapata
Published: July 2024

The Bacillus megaterium LVN01 species native to Colombia has demonstrated the ability to metabolize different coproducts or industrial waste (such as fique juice, cane molasses, and residual glycerol) and accumulate polyhydroxybutyrate (PHB), giving it potential in the bioplastics industry. In this research, the potential of liquid digestate as a carbon source for the production of PHA polymers in fermentation processes with this bacterial strain was evaluated. Favorably, it was found that B. megaterium utilizes the nutrients from this residual substrate to multiply appropriately and efficiently synthesize poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV). Bench-scale aerobic batch fermentation, under the operational conditions of this research [volume: 3 L; temperature: 30.8 °C; agitation: 400 rpm; pH: 7.0 ± 0.2; dissolved oxygen: 100% saturation; antifoam: 10% (v/v)], generated maximum values of dry cell weight (DCW) (0.56 g cell L−1) at 60 h, while the maximum PHBV yield (360 mg PHBV L−1) occurred at 16 h, which is very favorable for sustainable degradable bioplastics production. Additionally, GC–MS and NMR analyses confirmed that the PHBV copolymer synthesized by B. megaterium is made up of the monomers 3-hydroxybutyrate (3HB) and 3-hydroxyvalerate (3HV). Furthermore, the thermal properties determined by TGA (Tonset = 283.1 °C; Tendset = 296.98 °C; Td = 290.114 °C) and DSC (Tm = °C 155.7 °C; ΔHf = 19.80 J g−1; Xcr = 18.17%) indicate that it is a thermally stable biopolymer with low percentages of crystallinity, providing flexibility that facilitates molding, adaptation, and application in various industrial sectors.
World Modeling for Autonomous Wheel Loaders
Authors: Aoshima Koji , Fälldin Arvid , Wadbro Eddie , Servin Martin
Published: July 2024

This paper presents a method for learning world models for wheel loaders performing automatic loading actions on a pile of soil. Data-driven models were learned to output the resulting pile state, loaded mass, time, and work for a single loading cycle given inputs that include a heightmap of the initial pile shape and action parameters for an automatic bucket-filling controller. Long-horizon planning of sequential loading in a dynamically changing environment is thus enabled as repeated model inference. The models, consisting of deep neural networks, were trained on data from a 3D multibody dynamics simulation of over 10,000 random loading actions in gravel piles of different shapes. The accuracy and inference time for predicting the loading performance and the resulting pile state were, on average, 95% in 1.2 ms and 97% in 4.5 ms, respectively. Long-horizon predictions were found feasible over 40 sequential loading actions.
A Survey of Bug Bounty Programs in Strengthening Cybersecurity and Privacy in the Blockchain Industry
Authors: Arshad Junaid , Talha Muhammad , Saleem Bilal , Shah Zoha , Zaman Huzaifa , Muhammad Zia
Published: July 2024

The increasing reliance on computer networks and blockchain technology has led to a growing concern for cybersecurity and privacy. The emergence of zero-day vulnerabilities and unexpected exploits has highlighted the need for innovative solutions to combat these threats. Bug bounty programs have gained popularity as a cost-effective way to crowdsource the task of identifying vulnerabilities, providing a secure and efficient means of enhancing cybersecurity. This paper provides a comprehensive survey of various free and paid bug bounty programs in the computer networks and blockchain industry, evaluating their effectiveness, impact, and credibility. The study explores the structure, incentives, and nature of vulnerabilities uncovered by these programs, as well as their unique value proposition. A comparative analysis is conducted to identify advantages and disadvantages, highlighting the strengths and weaknesses of each program. The paper also examines the role of ethical hackers in bug bounty programs and their contributions to strengthening cybersecurity and privacy. Finally, the study concludes with recommendations for addressing the challenges faced by bug bounty programs and suggests potential future directions to enhance their impact on computer networks and blockchain security.
Age-Based Developmental Biomarkers in Eye Movements: A Retrospective Analysis Using Machine Learning
Authors: Hunfalvay Melissa , Bolte Takumi , Singh Abhishek , Greenstein Ethan , Murray Nicholas P. , Carrick Frederick Robert
Published: July 2024

This study aimed to identify when and how eye movements change across the human lifespan to benchmark developmental biomarkers. The sample size comprised 45,696 participants, ranging in age from 6 to 80 years old (M = 30.39; SD = 17.46). Participants completed six eye movement tests: Circular Smooth Pursuit, Horizontal Smooth Pursuit, Vertical Smooth Pursuit, Horizontal Saccades, Vertical Saccades, and Fixation Stability. These tests examined all four major eye movements (fixations, saccades, pursuits, and vergence) using 89 eye-tracking algorithms. A semi-supervised, self-training, machine learning classifier was used to group the data into age ranges. This classifier resulted in 12 age groups: 6–7, 8–11, 12–14, 15–25, 26–31, 32–38, 39–45, 46–53, 54–60, 61–68, 69–76, and 77–80 years. To provide a descriptive indication of the strength of the self-training classifier, a series of multiple analyses of variance (MANOVA) were conducted on the multivariate effect of the age groups by test set. Each MANOVA revealed a significant multivariate effect on age groups (p < 0.001). Developmental changes in eye movements across age categories were identified. Specifically, similarities were observed between very young and elderly individuals. Middle-aged individuals (30s) generally showed the best eye movement metrics. Clinicians and researchers may use the findings from this study to inform decision-making on patients’ health and wellness and guide effective research methodologies.
Management of Coastline Variability in an Endangered Island Environment: The Case of Noirmoutier Island (France)
Authors: Meziane Imane , Robin Marc , Fattal Paul , Rahmani Oualid
Published: July 2024

This article presents a diachronic study of evolution along the coastline of Noirmoutier Island in France, a sandy shore particularly susceptible to erosion and submersion risks, which are exacerbated by climate change due to two-thirds of its territory being below sea level. The study is based on an analysis of aerial images covering a period of 72 years, divided into five distinct periods: 1950–1974, 1974–1992, 1992–2000, 2000–2010, and 2010–2022. The methodology used combines two complementary approaches: the Digital Shoreline Analysis System (DSAS) for taking linear measurements of the erosion and accretion that have taken place along various shorelines, and the surface method to evaluate the amount of surface lost or gained between different shorelines while calculating the uncertainties associated with the obtained results. The overall trend observed between 1950 and 2022 indicates that the Noirmoutier coastline studied has gained surface area (81 hectares) at an average rate of +0.57 ± 0.06 m per year. The article then presents an application of the method developed by Durand and Heurtefeux in 2006 to estimate the future position of the shoreline. A map of the local area is also provided, identifying the areas susceptible to coastal erosion by 2052 and by 2122, in accordance with the provisions of the Climate and Resilience Law adopted in France on 22 August 2021. The results reveal that there are many sources of uncertainty in predicting the future evolution of the shoreline using this methodology. Therefore, it is crucial to consider these uncertainties when planning future coastal management actions and adopting appropriate adaptation methods to counteract unforeseen developments.
Conservation of Threatened Grassland Birds in the Mediterranean Region: Going Up or Giving Up?
Authors: Santos Mário , Lourenço José
Published: July 2024

Grassland bird populations in the Mediterranean lowlands have declined dramatically over the past few decades. This decline is due to a combination of factors, including changes in land use and farming practices as well as the impacts of climate change. In particular, more intensive agricultural methods have played a significant role in this reduction. However, in the higher-altitude uplands of the region, traditional practices like pastoralism and rotational low-intensity farming are still common, and these areas continue to support substantial populations of several threatened grassland bird species. In this viewpoint, we discuss the challenges that the uplands are facing and suggest rethinking regional development to better balance the needs of people and nature.
“Being There for Each Other”: Hospital Nurses’ Struggle during the COVID-19 Pandemic
Authors: Admi Hanna , Inchi Liron , Bord Shiran , Shahrabani Shosh
Published: July 2024

This study aims to explore the experiences of frontline hospital nurses over 18 months of struggle with the COVID-19 pandemic. The qualitative thematic analysis method was applied. Twenty-three nurses from nine tertiary hospitals in Israel were interviewed using semi-structured interviews via the ZOOM platform between August and September 2021. Interviews were video recorded and transcribed verbatim. Trustworthiness was assured by using qualitative criteria and the COREQ checklist. Results: Both negative and positive experiences were reported: threat and uncertainty along with awareness of their important mission; anxiety and helplessness alongside courage and heroism. Personal management strategies emerged: regulating overwhelming emotions and managing work–life balance. Team support emerged as the most meaningful source of nurses’ struggle with the pandemic. A sense of intimacy and solidarity enabled the processing of the shared traumatic experiences. Conclusions: A deeper understanding of nurses’ experiences through the pandemic was gained. Informal peer support has proven effective in struggling with the events. Formal interventions, such as affective–cognitive processing of traumatic events, need to be integrated into practice. Healthcare policymakers should promote better support for caregivers, which will contribute to their well-being and impact the quality of care they provide.
Cancer Prevention and Cultural Continuity for Métis Peoples in Canada: A Scoping Review
Authors: Diaz Vega Maria , Santos José Diego Marques , Witham Stephanie , Friesen Marg , Brock Tegan , Laroque Sheila , Sedgewick Jennifer , Carr Tracey , Groot Gary
Published: July 2024

The purpose of this scoping review was to map the literature on the relationship between cultural continuity and health among Métis people as well as how this knowledge could be translated into cancer prevention initiatives. We included any Métis-specific literature evaluating connections between culture, health, and well-being. We conducted electronic searches of Medline, PubMed, Embase, PsychInfo, I-Portal, and hand-searched journals, and reviewed the grey literature. Based on our inclusion criteria, articles were screened and assessed for eligibility, resulting in a sample of 22 publications. Qualitative, quantitative, and mixed methods designs were considered. The 22 publications included in this review were diverse, ranging from population-based studies to reports and news articles. There were no limitations to publication year, and most of the data presented in this review were published more than five years ago. Nevertheless, the results demonstrate the potential role of Métis cultural continuity in cancer prevention. The scoping review revealed the current lack of Métis-specific data regarding health and its intersectionality with culture. However, the existing literature indicates that cultural continuity for Métis appears to influence health and well-being positively. As such, there may be benefits to incorporating cultural continuity in cancer prevention efforts.
Radiosurgically Treated Recurrent Cerebellar Hemangioblastoma: A Case Report and Literature Review
Authors: Fabi François , Chamberland Ève , D’Astous Myreille , Michaud Karine , Côté Martin , Thibault Isabelle
Published: July 2024

Background: Cystic, sporadic hemangioblastomas (HBLs) represent a unique, therapeutically challenging subset of central nervous system tumors, mainly due to their unpredictable growth patterns and potential for symptomatic progression. This study aims to explore the complexities surrounding the diagnosis, treatment, and long-term management of these lesions. Methods: A comprehensive literature review was performed, and a detailed case study of a 56-year-old patient with a cystic, sporadic cerebellar HBL was produced. Results: The case highlights the multiphasic growth pattern typical of cystic, sporadic HBLs, characterized by periods of dormancy and subsequent rapid expansion. An initial surgical intervention offered temporary control. Tumor recurrence, mainly through cystic enlargement, was treated by SRS. A subsequent recurrence, again caused by cystic growth, eventually led to the patient’s death. The intricacies of treatment modalities, focusing on the transition from surgical resection to stereotactic radiosurgery (SRS) upon recurrence, are discussed. Parameters indicating impending tumor growth, coupled with symptomatic advances, are also explored. Conclusions: The management of cystic, sporadic cerebellar HBLs requires a strategic approach that can be informed by radiological characteristics and tumoral behavior. This study underscores the importance of a proactive, individualized management plan and suggests guidelines that could inform clinical decision making.
Total Iliocaval Reconstruction in a Complex Palliative Patient with Malignant Inferior Vena Cava Syndrome
Authors: Lofroth Jack , Pourghadiri Amir , Hadjivassiliou Anastasia , Heran Manraj , Legiehn Gerald , Ho Stephen , Dhatt Ravjot
Published: July 2024

Inferior vena cava (IVC) compression secondary to mass effect is accompanied by edema, ascites, back and abdominal pain, and central nervous system symptoms. Most IVC syndrome cases described in the literature focus on the focal treatment of IVC lesions, and reports of complete iliocaval reconstructions secondary to malignant IVC syndrome in the palliative context are limited. In this case report, we describe the clinical presentation, technical approach, and symptomatic outcomes of a patient with extensive malignant compression and invasion of the iliofemoral venous system. An 82-year-old male with metastatic lung cancer invading the right upper quadrant of the abdomen presented with scrotal and bilateral lower extremity edema, as well as anasarca. Computed tomography (CT) demonstrated an 11 cm right adrenal metastasis and extensive retroperitoneal lymphadenopathy resulting in the compression of the IVC and iliac veins. Femoral venography demonstrated extensive collateral venous pathway formation with the opacification of the para-lumbar and vertebral veins, in addition to the vertebral/sacral venous plexus. Iliocaval reconstruction was performed using venous-dedicated stents. This case report highlights a technically successful total iliocaval reconstruction in a complex palliative patient with diffuse metastatic disease resulting in IVC compression and syndrome.
Optimization and Evaluation of Tourism Mascot Design Based on Analytic Hierarchy Process–Entropy Weight Method
Authors: Wang Jing , Cheng Fangmin , Chen Chen
Published: July 2024

With the tourism industry continuing to boom, the importance of tourism mascots in promoting and publicizing tourism destinations is becoming increasingly prominent. Three core dimensions, market trend, appearance design, and audience feedback, are numerically investigated for deeply iterating tourism mascot design. Further, a subjective and objective evaluation weighting model based on the hierarchical analysis method (AHP) and entropy weighting method is proposed, aiming to utilize the advantages of these methods and ensure the entireness and correctness of results. Taking the mascots of six famous tourist attractions in Xi’an as an example, the feasibility and effectiveness of the evaluation model are verified. Data analysis and modeling results confirm that the three core evaluation indexes of scalability, innovation, and recommendation should be focused on in the design of tourism mascots in the three dimensions of market trends, appearance design, and audience feedback. The evaluation index scores are 0.1235, 0.1170, and 0.1123, respectively, which further illustrates the priority of mascot design. The evaluation model constructed by the research provides decision-makers with a comprehensive evaluation tool from the perspective of tourist experience, and also effectively assists the optimization process of mascot design. In addition, the model has good versatility and adaptability in structural design and evaluation logic and can be widely used in the optimization and evaluation research of brand mascots.
Integration of UH SUH, HEC-RAS, and GIS in Flood Mitigation with Flood Forecasting and Early Warning System for Gilireng Watershed, Indonesia
Authors: Mustamin Muhammad Rifaldi , Maricar Farouk , Lopa Rita Tahir , Karamma Riswal
Published: July 2024

A flood forecasting and early warning system is critical for rivers that have a large flood potential, one of which is the Gilireng watershed, which floods every year and causes many losses in Wajo Regency, Indonesia. This research also introduces an integration model between UH SUH and HEC-RAS in flood impact analysis, as a reference for flood forecasting and early warning systems in anticipating the timing and occurrence of floods, as well as GIS in the spatial modeling of flood-prone areas. Broadly speaking, this research is divided into four stages, namely, a flood hydrological analysis using UH SUH, flood hydraulic tracing using a 2D HEC-RAS numerical model, the spatial modeling of flood-prone areas using GIS, and the preparation of flood forecasting and early warning systems. The results of the analysis of the flood forecasting and early warning systems obtained the flood travel time and critical time at the observation point, the total time required from the upstream observation point to level 3 at Gilireng Dam for 1 h 35 min, Mamminasae Bridge for 4 h 35 min, and Akkotengeng Bridge for 8 h 40 min. This is enough time for people living in flood-prone areas to evacuate to the 15 recommended evacuation centers.
Comparing Energetics and Physiological Trait Patterns of North American Birds to Support Ecological Risk Assessment
Authors: Muller Erik B. , Romoli Carlo , Goussen Benoit , Maul Jonathan D. , Brain Richard , Galic Nika
Published: July 2024

The environmental risk assessment (ERA) of plant protection products (PPPs) is commonly conducted for a number of standard laboratory species, though its scope includes all species, especially those that are vulnerable or endangered. This begs the question of how the eco-physiological and life history traits of those data-rich species compare to those of less documented species. This paper investigates the utility of a database of such traits, the Add-my-Pet collection of Dynamic Energy Budget theory, for the ERA of North American birds. We identified 72 bird species in the collection that have a federal listing status or are otherwise of interest for the ERA of PPPs and compared their eco-physiological and life history traits in relation to body size, feeding guild, and taxonomic identity to those of 446 terrestrial Holarctic birds, as well as those of standard lab species, notably the northern bobwhite, mallard, and zebra finch. The properties of standard lab species generally align with those of a similar taxonomy and size, with the notable exception of the relatively high reproductive capacity of the northern bobwhite and mallard due to the unique properties of laboratory study data. In addition, taxonomy appears to be a better predictor of eco-physiological traits than feeding guild. We conclude that, to identify representative species for the ERA of PPPs, more traits need to be included, such as those directly linked to the likelihood of exposure and those related to reproductive behavior.
Hardware-in-the-Loop Emulation of a SEPIC Multiplier Converter in a Photovoltaic System
Authors: Posada Contreras Johnny , Rosas-Caro Julio C.
Published: July 2024

This article presents the development and execution of a Single-Ended Primary-Inductor Converter (SEPIC) multiplier within a Hardware-in-the-Loop (HIL) emulation environment tailored for photovoltaic (PV) applications. Utilizing the advanced capabilities of the dSPACE 1104 platform, this work establishes a dynamic data exchange mechanism between a variable voltage power supply and the SEPIC multiplier converter, enhancing the efficiency of solar energy harnessing. The proposed emulation model was crafted to simulate real-world solar energy capture, facilitating the evaluation of control strategies under laboratory conditions. By emulating realistic operational scenarios, this approach significantly accelerates the innovation cycle for PV system technologies, enabling faster validation and refinement of emerging solutions. The SEPIC multiplier converter is a new topology based on the traditional SEPIC with the capability of producing a larger output voltage in a scalable manner. This initiative sets a new benchmark for conducting PV system research, offering a blend of precision and flexibility in testing supervisory strategies, thereby streamlining the path toward technological advancements in solar energy utilization.
Dynamic Consensus-Based ADMM Strategy for Economic Dispatch with Demand Response in Power Grids
Authors: Dhamala Bhuban , Pokharel Kabindra , Karki Nava Raj
Published: July 2024

This paper introduces a dynamic consensus-based economic dispatch (ED) algorithm utilizing the Alternating Direction Method of Multipliers (ADMM) to optimize real-time pricing and generation/demand decisions within a decentralized energy management framework. The increasing complexity of modern energy markets, driven by the proliferation of Distributed Energy Resources (DER) and variable demands from hybrid electric vehicles, necessitates a departure from traditional centralized dispatch methods. This research proposes a novel ADMM-based solution tailored for non-responsive and responsive demand units that integrates demand response mechanisms to adaptively manage real-time fluctuations while enhancing security and privacy through distributed data management. The testing of the algorithm on the IEEE 39 bus system under various load conditions over 24 h demonstrated the algorithm’s effectiveness in handling traditional and renewable energy sources, particularly highlighting the economic benefits of shifting controllable loads to periods of low-cost renewable availability. The findings underscore the algorithm’s potential to reduce energy costs, enhance energy efficiency, and offer a scalable solution across diverse grid systems, contributing significantly to advancing global energy policy and sustainable management practices.
Conditions for the Development of Wind Energy for Individual Consumers: A Case Study in Poland
Authors: Augustowski Łukasz , Kułyk Piotr
Published: July 2024

This article presents the economic viability of using 10 m wind turbines in households with financial support from the government. The aim of this study was to indicate whether and how state subsidies affect the efficiency and estimated payback periods of wind energy investments for individual households. The research conducted thus far has focused on the analysis of effectiveness, feasibility, and economic profitability, but it has not taken into account government support for the investment readiness of households, which constitutes a research gap in the literature and economic practice. In addition, this study analyzed a new program that is scheduled to come into force this year. The analysis used the Homer Pro software (ver. x64) module, and simulations were performed for three locations in Poland. Due to differences in the location of wind zones, as well as the location of two locations on the Baltic Sea, an additional factor characterizing the studied voivodeships was introduced. Government support may therefore constitute a mechanism for correcting the geographical location and local wind potential. It has been shown that financial support significantly accelerates the payback period, even in locations with weaker wind potential. Complementary and substitutive possibilities for renewable energy sources, such as sun and wind, were indicated. The conclusions from this research can be used by decision makers and individual households to take advantage of government support to shorten the return on investment in wind energy and the validity of this support.
Risk Management and Assessment Hybrid Framework for Business Process Reengineering Projects: Application in Automotive Sector
Authors: Hicham Raffak , Abdallah Lakhouili , Mohamed Mansouri
Published: July 2024

This study introduces an integrated method for managing process risks in a business process reengineering (BPR) project using robust data envelopment analysis (RDEA) and machine learning (ML). The goal is to prioritize risks based on three standard factors of PFMEA (severity, occurrence and detection (S-O-D)) and incorporating two additional factors (breakdown cost and breakdown duration) seen as undesirable outputs. The model also accounts for the effect of uncertainty on expert-estimated values by applying disturbance percentages in the linear PFMEA-RDEA model. A machine-learning model is proposed to predict new values if partial or total modifications have been made to the processes. The approach was implemented in an automotive sector company, and the results showed the impact of uncertainty on values by comparing different approaches, such as RPN, PFMEA-DEA and PFMEA-RDEA. A new reduced risk categorization was achieved, which allowed for decision makers to focus on the necessary actions for reengineering.
Chemical Composition as the Indicator of Thermally Treated Pine (Pinus sylvestris L.) Wood Colour
Authors: Kučerová Viera , Hrčka Richard , Hýrošová Tatiana
Published: July 2024

This study aimed to determine the influence of increased temperature on the mass loss, chemical composition, and colour of pine wood because of the lack of such information. The colour was measured on samples of wood, extracted sawdust, holocellulose, and lignin isolated from the extracted sawdust of pine heartwood and sapwood. A wood sample labelled 20 °C was considered as wood with the original composition. Subsequently, we verified the measured values with the proposed mixing colour model. Pine heartwood and sapwood samples were thermally treated at temperatures of 100, 150, 200, 220, 240, and 260 °C for 1, 3, and 5 h. It was found that sapwood degraded faster than heartwood. The thermal treatment of wood increases lignin content and decreases holocellulose content, especially at 260 °C. The maximum extractive content of 3.60% was at 1 h and a temperature of 260 °C for both parts of the wood. Lightness values decreased with increasing temperature and time of treatment. The coordinate a* of heartwood showed a positive slope until one hour of treatment duration and a temperature of 240 °C. Then, it decreased for the subsequent duration of treatment. The same course was shown for the coordinate b* of sapwood at a temperature of 200 °C. The proposed model of mixing colours proved that changes in both parts of a wood-extracted substance, holocellulose, and lignin content, were responsible for the changing colour of extracted wood.
Advances and Challenges in Biomanufacturing of Glycosylation of Natural Products
Authors: Hu Shunyang , Wang Bangxu , Pei Liang , Wang Jisheng , Gan Ya , Jiang Liangzhen , Liu Bingliang , Cheng Jie , Li Wei
Published: July 2024

Glycosylation is one of the most common and important modifications in natural products (NPs), which can alter the biological activities and properties of NPs, effectively increase structural diversity, and improve pharmacological activities. The biosynthesis of glycosylation in natural products involves multiple complex biological processes, which are coordinated by many enzymes. UDP-glycosyltransferases (UGTs) play a crucial role in glycosylation modification, and have attracted long-term and widespread research attention. UGTs can catalyze the O-, C-, S-, and N-glycosylation of different substrates, producing a variety of glycosides with broad biological activity, while improving the solubility, stability, bioavailability, pharmacological activity, and other functions of NPs. In recent years, the rapid development of synthetic biology and advanced manufacturing technologies, especially the widespread application of artificial intelligence in the field of synthetic biology, has led to a series of new discoveries in the biosynthesis of NP glycosides by UGT. This work summarizes the latest progress and challenges in the field of NP glycosylation, covering the research results and potential applications of glycosylated derivatives of terpenes, flavonoids, polyphenols, aromatic compounds, and other compounds in terms of biogenesis. Looking to the future, research may leverage artificial intelligence-driven synthetic biology techniques to decipher genes related to the synthetic pathway, which is expected to further promote the large-scale synthesis and application of glycosylated NPs, and increase the diversity of NPs in the pharmaceutical, functional food, and cosmetic industries.
Comparative Analysis between Remote Sensing Burned Area Products in Brazil: A Case Study in an Environmentally Unstable Watershed
Authors: da Silva Junior Juarez Antonio , Pacheco Admilson da Penha , Ruiz-Armenteros Antonio Miguel , Henriques Renato Filipe Faria
Published: July 2024

Forest fires can profoundly impact the hydrological response of river basins, modifying vegetation characteristics and soil infiltration. This results in a significant increase in surface flow and channel runoff. In response to these effects, many researchers from different areas of earth sciences are committed to determining emergency measures to rehabilitate river basins, intending to restore their functions and minimize damage to soil resources. This study aims to analyze the mapping detection capacity of burned areas in a river basin in Brazil based on images acquired by AMAZÔNIA-1/WFI and the AQ1KM product. The effectiveness of the AMAZÔNIA-1 satellite in this regard is evaluated, given the importance of the subject and the relatively recent introduction of the satellite. The AQ1KM data were used to analyze statistical trends and spatial patterns in the area burned from 2003 to 2023. The U-Net architecture was used for training and classification of the burned area in AMAZÔNIA-1 images. An increasing trend in burned area was observed through the Mann–Kendall test map and Sen’s slope, with the months of the second semester showing a greater occurrence of burned areas. The NIR band was found to be the most sensitive spectral resource for detecting burned areas. The AMAZÔNIA-1 satellite demonstrated superior performance in estimating thematic accuracy, with a correlation of above 0.7 achieved in regression analyses using a 10 km grid cell resolution. The findings of this study have significant implications for the application of Brazilian remote sensing products in ecology, water resources, and river basin management and monitoring applications.
On the Algebraic Geometry of Multiview
Authors: Ballico Edoardo
Published: July 2024

We study the multiviews of algebraic space curves X from n pin-hole cameras of a real or complex projective space. We assume the pin-hole centers to be known, i.e., we do not reconstruct them. Our tools are algebro-geometric. We give some general theorems, e.g., we prove that a projective curve (over complex or real numbers) may be reconstructed using four general cameras. Several examples show that no number of badly placed cameras can make a reconstruction possible. The tools are powerful, but we warn the reader (with examples) that over real numbers, just using them correctly, but in a bad way, may give ghosts: real curves which are images of the emptyset. We prove that ghosts do not occur if the cameras are general. Most of this paper is devoted to three important cases of space curves: unions of a prescribed number of lines (using the Grassmannian of all lines in a 3-dimensional projective space), plane curves, and curves of low degree. In these cases, we also see when two cameras may reconstruct the curve, but different curves need different pairs of cameras.
Myelodysplastic/Myeloproliferative Neoplasms with Features Intermediate between Primary Myelofibrosis and Chronic Myelomonocytic Leukemia: Case Series and Review of the Entity
Authors: Bonometti Arturo , Zanella Simone , Rahal Daoud , Milanesi Chiara , Caselli Rossella , Della Porta Matteo Giovanni , Uccella Silvia , Fraticelli Sara
Published: July 2024

Diagnosis of myeloid neoplasm is currently performed according to the presence of a predetermined set of clinical, morphological, and molecular diagnostic criteria agreed upon by a consensus of experts. Even strictly adhering to these criteria, it is possible to encounter patients who present features that are not easily ascribable to a single disease category. This is the case, e.g., of patients with de novo myeloid neoplasms with features intermediate between primary myelofibrosis (PMF) and chronic myelomonocytic leukemia (CMML). In this study, we retrospectively searched the pathological database of IRCCS Humanitas Research Hospital to identify cases of chronic myeloid neoplasm with monocytosis with a driver mutation of classic myeloproliferative neoplasms (MPN) and showing morphological MPN features. For each case, we assessed all epidemiological, clinical, histopathological, and molecular data. Then, we carried out a literature review, searching for cases with features similar to those of our patients. We retrieved a total of 13 cases presenting such criteria (9 from the literature review and 4 from our institution); in all of them, there was a coexistence of clinical, histopathological, and molecular myelodysplastic and myeloproliferative features. To date, according to current classifications (World Health Organization and International Consensus Classification), given the presence/absence of essential features for PMF or CMML, these patients should be formally diagnosed as myelodysplastic/myeloproliferative neoplasm unclassified/not otherwise specified (U/NOS). This review aims to summarize the features of these difficult cases and discuss their differential diagnosis and their classification according to the novel classifications and the existing literature on overlapping myeloid neoplasms.
Optimizing the In Vitro Propagation of Tea Plants: A Comparative Analysis of Machine Learning Models
Authors: Bozkurt Taner , İnan Sezen , Dündar İjlal , Isak Musab A. , Şimşek Özhan
Published: July 2024

In this study, we refine in vitro propagation techniques for Camellia sinensis using a machine learning approach to ascertain the influence of different shooting and rooting conditions on key growth metrics. This was achieved by applying random forest (RF), XGBoost, and multilayer perceptron (MLP) models to dissect the complexities of micropropagation and rooting processes. The research unveiled significant disparities in growth metrics under varying media conditions, underscoring the profound impact of media composition on plant development. The meticulous statistical analysis, employing ANOVA, highlighted statistically significant differences in growth metrics, indicating the critical role of media composition in optimizing growth conditions. Methodologically, the study utilized explants from 2–3-year-old tea plants, which underwent sterilization before being introduced to two distinct culture media for their micropropagation and rooting phases. Statistical analyses were conducted to evaluate the differences in growth outcomes between media, while machine learning models were employed to predict the efficacy of micropropagation and rooting based on various growth regulators. This approach allowed for a comprehensive evaluation of the model’s performance in simulating plant growth under different conditions, leveraging metrics like R2, RMSE, and MAE. The findings from this study significantly advance the understanding of tea plant micropropagation, highlighting the utility of machine learning models in agricultural optimization. This research contributes to enhancing micropropagation strategies for the tea plant and exemplifies the transformative potential of integrating machine learning into plant science, paving the way for improved agricultural and horticultural practices. This interdisciplinary approach offers a novel perspective on optimizing in vitro propagation processes, contributing substantially to plant tissue culture and biotechnology.