bluuebunny/crossref_metadata_2025 huggingface dataset:
Peripheral Eosinophil Count May Be the Prognostic Factor for Overall Survival in PDAC Patients Undergoing Surgical Resection
Authors: Ciesielski Wojciech , Kupryś-Lipińska Izabela , Kumor-Kisielewska Anna , Grząsiak Oliwia , Borodacz Julia , Niedźwiecki Sebastian , Hogendorf Piotr , Durczyński Adam , Strzelczyk Janusz , Majos Alicja
Published: October 2024

(1) Background: Eosinophils importance as T2 inflammation markers is known to be fundamental in asthma, chronic sinusitis, vasculitis. In cancer, despite their potential antiproliferative effect, their role remains unclear. Our purpose was to describe the relation between baseline blood eosinophil count (EOS) and overall survival in pancreatic ductal adenocarcinoma (PDAC) patients. (2) Methods: We have analyzed retrospectively the data of 137 adult patients, who underwent surgical treatment of PDAC between years 2012-2019 with no history of systemic steroid use directly before surgical treatment and no metastases found intraoperatively. (3) Results: EOS<0,1 G/l (vs. ≥0,1 G/l) was an independent prognostic factor for OS both the uni- and multivariate Cox regression (respectively: HR=1,48, p=0,035 and HR=1,57, p=0,021). According to EOS and PDAC stage, median OS were: in stage I-III, EOS ≥0,1 G/l group: 14,5 months, in stage I-III, EOS <0,1 G/l group: 8,0 months, in stage IV, EOS ≥0,1 G/l group: 7,0 months, in stage IV, EOS <0,1 G/l group: 5,0 months. (4) Conclusions: Peripheral eosinophilia seems to be a potential independent prognostic factor. Further studies are necessary to confirm this hypothesis, since our findings suggest that T2 inflammation may be the factor directly or indirectly lengthening survival of PDAC patients.
Thurstonian Scaling for Sensory Discrimination Methods
Authors: Bi Jian , Kuesten Carla
Published: October 2024

Thurstonian scaling, i.e., Thurstonian discriminal distance δ or d' can be used as a sensory measurement index to measure and monitor food sensory difference/similarity between test and control samples due to potential food contamination. It can be obtained from any one of the sensory discrimination methods. Thurstonian scaling is theoretically independent of methods or scales used for its estimation. This paper discusses statistical inference including estimations and tests of hypothesis for d'. Ten basic sensory discrimination methods including 6 forced-choice methods and 4 methods with response bias are used in this paper to estimate d' values and their variances. Statistical tests are conducted based on the estimated d' values and their variances. The statistical tests include difference testing and equivalence/similarity testing for individual d' values for test and control samples and for two or multiple d' values for test samples. Application and significance of Thurstonian scaling for sensory discrimination methods is discussed generally. R codes for estimations and tests for d' values are provided in the paper.
Comparison of Three Different IBD Vaccination Protocols in Broiler Chickens – Efficacy, Serological Baselines and Histopathological Lesions in the Bursa of Fabricii
Authors: Śmiałek Marcin , Kowalczyk Joanna , Gesek Michal
Published: October 2024

Background: IBD is a highly infectious disease of chickens found in all latitudes, due to the very high IBDV resistance to environmental conditions and commonly used disinfectants. Methods: The study was conducted to evaluate 3 IBD vaccination protocols in broiler chickens, in terms of their effectiveness (clinical observations and production performance of the flock), estimation of serological baseline values (with the use of two different commercial ELISA kits), and the degree of progression of BF lesions (histopathological lesion score - HLS) after vaccination with: (I) single vaccination using intermediate plus vaccine; (II) double vaccination with intermediate vaccine; (III) double vaccination with intermediate plus vaccine. Results: Birds on farms vaccinated with protocol II were characterized by the lowest antibody titers in both ELISA tests, and the lowest average HLS. Highest IDEXX titers were obtained in birds vaccinated with protocol III, while in the BIOCHEK test the highest titers were obtained for birds vaccinated with protocol I. Protocols I and III were characterized by similar HLS. Birds vaccinated with protocols I and III had higher immune uniformity. Conclusions: The estimated serological baseline values and the degree of HLS presents a clear picture of differences between different vaccination protocols and allows their adaptation for different farms depending on the current IBD epidemiological situation.
Optimization of Production Parameters for Impact Strength of 3D-Printed Carbon/Glass Fiber Reinforced Nylon Composite in Critical ZX Printing Orientation
Authors: Hartomacioğlu Selim
Published: October 2024

Additive Manufacturing (AM) methods are increasingly beign abopted in the industry as an alternative for mass production. In particular, Fused Deposition Modeling (FDM) technology is leading the way in this field. However, the adhesion of layers in products produced using FDM technology is an important issue. These products are particularly vulnerable to forces acting parallel to the layers and specially to impact strength. This study aims to optimize the impact strength against loads acting parallel to the layers (ZX orientation) of PA6, one of the most widely used materials in the industry. This orientation is critical in term of mechanical properties, and the mechanical characteristics are significantly lower compared to other orientation. In this study, filaments containing pure PA6 with 15% short carbon fiber and 30% glass fiber were utilized. Additionally, printing temperature, layer thickness and heat treatment duration were used as independent variables. An L9 orthogonal array was employed in this study, and each experiment was repeated three times to conduct impact strength tests. Characterization, Taguchi optimization, a factor analyses were performed, followed by fracture surface characterization by SEM. As a result, the highest impact strength was achieved with pure PA6 at 8.9 kJ/m2, followed by PA6 GF30 at 8.1 kJ/m2 and the lowest impact strength was obtained with PA6 CF15 at 6.258 kJ/m2. When compared to the literature and manufacturer datasheets, it was concluded that the impact strength values have significantly increased, and the chosen experimental factors and their levels, particularly nozzle temperature, were effective.
Biomass Refined: 99% of Organic Carbon in Soils
Authors: Blakemore Robert J.
Published: October 2024

Soil carbon is newly refined as ~32,000 Gt C (gigatonnes) over ten times previous totals. Soil organic carbon (SOC) of ~22,000 Gt C plus plants at ~2,400 Gt C in both above- and below-ground stocks hold 99% of Earth’s biomass. Occupying a topographic surface area of 25 Gha with mean depth 21 m, soil has more organic carbon than all plants, oceans, fossil fuels, or the atmosphere combined. Soils are both the greatest biotic carbon store and the most active CO2 source. Basic inventory is required for proper understanding and utilization of Earth’s natural resources, especially with increasing soil degradation and species loss. Values are raised considerably, the disparity due to lack of full soil depth survey, neglect of terrain and of other omissions. Herein new totals are determined for mineral soils, permafrost, and peats (of all forms and ages), to full depth (easily doubling shallow values) and allowing for terrain that is ignored in all terrestrial models (doubling most values again), plus SOC in recalcitrant glomalins (~30%) and friable saprock (+26%). Additionally, soil inorganic carbon (SIC), aquatic sediments and dissolved fractions are factored in. Soil biota (e.g. fungi, bacteria and earthworms) are similarly upgraded. Primary productivity, also upped for terrain, is confirmed at 220 Gt C/yr on land supported by C/O isotope fluxes. Priority issues of species extinction, humic topsoil loss, and excess atmospheric CO2 are all remedied by SOC restoration and biomass recycling via (vermi-)compost for use in 100% organic husbandry and adoption of wider practices of Permaculture based upon scientific observation of Nature.
A Review of Potential on Possible Greener Production of Slow-Release Nitrogen Fertilizer: From Fundamentals of Plasma and Nanotechnology Application
Authors: Kyebogola Stewart , Kabiri Stella , Onwonga Richard Ndemo , Semalulu Onesimus , Yost Russell , Sseruwu Godfrey
Published: October 2024

The potential of both plasma and nanotechnology in the production of slow-release fertilizers is immense. These technologies, when combined, may offer green and cheap nitrogen fertilizers, from rich renewable resources available in local areas. Together these technologies may overcome some limitations of conventional synthetic fertilizers which currently are expensive and associated with low nitrogen use efficiency and significant environmental concerns. This review explores how recent advances in plasma and nanotechnology together might be leveraged to formulate new slow-release nitrogen fertilizers. While no attempt has been made so far to produce slow-release nano fertilizers from low-cost liquid nitrate generated by emission-free non-thermal plasma, plasma nitrate has already been reported as effective as a conventional fertilizer for crop production. We propose a more efficient electrocatalytic conversion of plasma nitrate to ammonium salt, followed by coating using plant-based cellulose nanoparticles into a slow-release form. This would synchronize nutrient release with the dynamic N requirements of plants. Formulations using agro-based low-cost cellulose nanomaterials might then replace high-cost carrier hydrogels associated with low mechanical strength. The review also highlights the isolation of nanocellulose from various plant materials and its characterization for use in different formulations of slow-release nano plasma N fertilizer. Additionally, we discuss mechanisms of N loss, slow-release, and retention in the soil. The knowledge from this review has potential to contribute to the production and use of efficient, sustainable fertilizers, to improving food security and, consequently the health of our planet.
Microbiological Aspects and Enzymatic Characterization of Curvularia kusanoi L7: Ascomycete with Great Biomass Degradation Potentialities
Authors: Alberto Vazquez Maryen , Saa Luis Rodrigo , Valiño Elaine , Torta Livio , Laudicina Vito Armando
Published: October 2024

The complex structure of the plant cell wall makes it difficult to use the biomass produced by biosynthesis. For this reason, the search for new strains of microorganisms capable of efficiently degrading fiber is a topic of interest. For these reasons, the present study aimed to evaluate both the microbiological and enzymatic characteristics of the fungus Curvularia kusanoi L7strain. For this, its growth in different culture media was evaluated. Wheat straw mineralization was evaluated by gas chromatography assisted by infrared spectroscopy. The production of endo- and exoglucanase, laccase and peroxidase enzymes in submerged solid fermentation of wheat and sugarcane bagasse were characterized. The strain efficiently mineralized raw wheat straw, showing a significant decrease in signals associated with cellulose, hemicellulose and lignin in the infrared spectra. High enzyme productions were achieved in submerged solid fermentation of both substrates, highlighting the high production of endoglucanases in sugarcane bagasse (2.87 IU/mL) and laccases in wheat (1.64 IU/mL). It is concluded that C. kusanoi L7 is an ascomycete with a versatile enzyme production that allows it to exhaustively degrade complex fibers such as raw wheat straw and sugar cane bagasse, making it a microorganism with great potential in the bioconversion of plant biomass.
Decoherence, Quantum Measurement Problem, and Spontaneous Symmetry Breaking
Authors: Li Yuanxin
Published: October 2024

In this paper, we propose a mechanism analogous to the spontaneous symmetry breaking to explain quantum measurement. It is actually a supplement to the decoherence theory. We introduce the effective potential between eigenstates and show that a wave function in a superposition of several eigenstates reduces to a single eigenstate due to the spontaneous-symmetry-breaking-like kinetic effect.
Navigating the COVID-19 Therapeutic Landscape: Unveiling Novel Perspectives on FDA-Approved Medications, Vaccination Targets, and Emerging Novel Strategies
Authors: Barghash Reham , Gemmati Donato , Awad Ahmed M. , Elbakry Mustafa Mahmoud Mohamed , Tisato Veronica , Awad Kareem , Singh Ajay Vikram
Published: October 2024

Amidst the ongoing global challenge of the SARS-CoV-2 pandemic, the quest for effective antiviral medications remains paramount. This comprehensive review delves into the dynamic landscape of FDA-approved medications repurposed for COVID-19, categorized as antiviral and non-antiviral agents. Our focus extends beyond conventional narratives, encompassing vaccination targets, repurposing efficacy, clinical studies, innovative treatment modalities, and future outlooks. Unveiling the genomic intricacies of SARS-CoV-2 variants, including the WHO-designated Omicron variant, we explore diverse antiviral categories such as Fusion inhibitors, Protease inhibitors, Transcription inhibitors, Neuraminidase inhibitors, Nucleoside reverse transcriptase, and non-antiviral interventions like Importin α/β1-mediated nuclear import inhibitors, Neutralizing antibodies and convalescent plasma. Notably, Molnupiravir emerges as a pivotal player, now licensed in the UK. This review offers a fresh perspective on the historical evolution of COVID-19 therapeutics, from repurposing endeavors to the latest developments in oral anti-SARS-CoV-2 treatments, ushering in a new era of hope in the battle against the pandemic.
Advancing Additive Manufacturing Through Machine Learning Techniques: A State-of-the-Art Review
Authors: Xiao Shaoping , Li Junchao , Wang Zhaoan , Chen Yingbin , Tofighi Soheyla
Published: October 2024

In the fourth industrial revolution, artificial intelligence (AI) and machine learning (ML) have increasingly been applied to manufacturing, particularly additive manufacturing (AM), to enhance processes and production. This study provides a comprehensive review of the state-of-the-art achievements in this domain, highlighting not only the widely discussed supervised learning but also the emerging applications of semi-supervised learning and reinforcement learning (RL). These advanced ML techniques have recently garnered significant attention due to their potential to further optimize and automate AM processes. The review aims to offer insights into various ML technologies employed in current research projects and to promote the diverse applications of ML in AM. By exploring the latest advancements and trends, this study seeks to foster a deeper understanding of ML’s transformative role in AM, paving the way for future innovations and improvements in manufacturing practices.
Accelerating and Compressing Transformer-based PLMs for Enhanced Comprehension of Computer Terminology
Authors: Peng Jian , Zhong Kai
Published: October 2024

Pre-trained language models (PLMs) have significantly advanced natural language processing (NLP), establishing the "pre-training + fine-tuning" paradigm as a cornerstone approach in the field. However, the vast size and computational demands of Transformer-based PLMs present challenges, particularly regarding storage efficiency and processing speed. This paper addresses these limitations by proposing a novel lightweight PLM optimized for accurately understanding domain-specific computer terminology. Our method involves a pipeline parallelism algorithm designed to accelerate training. It is paired with an innovative mixed compression strategy that combines pruning and knowledge distillation to effectively reduce the model size while preserving its performance. The model is further fine-tuned using a dataset that mixes source and target languages to enhance its versatility. Comprehensive experimental evaluations demonstrate that the proposed approach successfully achieves a balance between model efficiency and performance, offering a scalable solution for NLP tasks involving specialized terminology.
Photoacoustic Resonator for Noninvasive Blood Glucose Detection through Photoacoustic Spectroscopy: A Systematic Review
Authors: Kaysir MD Rejvi , Zaman Thasin Mohammad , Rassel Shazzad , Wang Jishen , Ban Dayan
Published: October 2024

Diabetes mellitus is a prevalent disease with a rapidly increasing incidence projected worldwide, affecting both industrialized and developing regions. Effective diabetes management requires precise therapeutic strategies, primarily through self-monitoring of blood glucose levels to achieve tight glycemic control, thereby mitigating the risk of severe complications. In recent years, there have been significant advancements in non-invasive techniques for measuring blood glucose using photoacoustic spectroscopy (PAS) as it shows great promise for the detection of glucose using the Infrared region (e.g. MIR & NIR) of light. A critical aspect of this method is the detection of the photoacoustic signal generated from blood glucose, which needs to be amplified through a photoacoustic resonator (PAR). In this work, an overview of various types of PARs used for noninvasive glucose sensing is reviewed, highlighting their operating principle, design requirements, limitations, and potential improvements needed to enhance the analysis of photoacoustic signals. This analysis will be helpful for the basic understanding and achieving the highly sensitive PAR required for noninvasive glucose monitoring.
On a Convergence of Quasi-Periodic Interpolations Exact for the Polyharmonic-Neumann Eigenfunctions
Authors: Poghosyan Arnak , Poghosyan Lusine , Barkhudaryan Rafayel
Published: October 2024

Fourier expansions by the polyharmonic-Neumann eigenfunctions showed improved convergence compared to the Fourier expansions by the classical trigonometric system due to the rapid decay of the corresponding Fourier coefficients. Based on this evidence, we investigate interpolations on a finite interval that are exact for the polyharmonic-Neumann eigenfunctions and show the same benefits. Further, we improve the convergence of the interpolations by applying the idea of quasi-periodicity, where the basis functions are periodic on a slightly extended interval. We show that those interpolations have much better convergence rates away from the endpoints of the approximation interval. Moreover, the interpolations are more accurate on the entire interval. We prove those properties for a specific case of the polyharmonic-Neumann eigenfunctions known as the modified Fourier system. For other basis functions, we provide evidence based on the results of numerical experiments.
Security Proof of SSSD Protocols Built on Top of SMC Protocol
Authors: Anagreh Mohammad , Laud Peeter
Published: October 2024

Secure Secret Sharing Single-Source Shortest Distance (SSSD) protocols, built on the Secure Multi-Party Computation (SMC) foundation, have emerged as a promising solution to address the secure distribution and management of sensitive secrets among multiple parties. This paper focuses on the crucial aspect of security proof within SSSD protocols based on SMC, aiming to provide a scientifically rigorous understanding of the formal verification process and the robust guarantees they offer. Through a comprehensive examination of the fundamental principles underpinning SMC and the specific design considerations unique to SSSD protocols, this paper meticulously explores the key components and intricate steps involved in constructing a rigorous security proof. Furthermore, it discusses the adversarial model, precise security definitions, cryptographic assumptions utilized within the proof, and sophisticated techniques and reductions to establish its validity. By means of a meticulous analysis of the security proof for SSSD protocols, this paper critically evaluates both the strengths and limitations of the proposed approach, offering valuable insights to inform future research and development within this significant domain of study.
Forest Fire Discrimination Based on Angle Slope Index and Himawari-8
Authors: Liu Pingbo , Zhang Gui
Published: October 2024

In the context of high frequency and intensity forest fires driven by future warming and drying climate, early detection and effective control of fires are extremely important to reduce losses. Satellite remote sensing, characterized by its broad observational scope and strong repeat observation capability, has become an important method for rapid monitoring of forest fires, replacing traditional means such as manual measurement and aerial photography. This paper addresses the misjudgments caused by solely relying on changes in infrared band brightness values and single-band separation in forest fire discrimination. It constructs the angle slope index ANIR based on the red band, near-infrared band and short-wave infrared band, Angle slope index AMIR based on the short-wave infrared band, mid-infrared band and far-infrared band, and angle slope index AMNIR based on the difference between the AMIR and ANIR, integrating the strong inter-band correlations and the reflectance characteristics of visible and short-wave infrared bands to simultaneously monitor smoke and fuel biomass changes in forest fires. To address the omissions caused by single-image fire discrimination algorithms, the paper constructs time-series Angle slope difference indices ∆ANIR and ∆AMIR. To address the different fire point discrimination thresholds caused by background difference in different time and space, the paper uses the decomposed three-dimensional OTSU(Maximum inter-class variance method) algorithm to calculate the segmentation threshold of the sub-regions constructed from the AMNIR data. The methods were validated using high temporal resolution meteorological satellite Himawari-8 and ground truth fire data from Hunan Province, China, from 2018-2019. The results show that the discrimination method based on the ASITR(Angle slope indices thresholds) method exhibits an accuracy of 0.88, a missed detection rate of 0.17, and an overall evaluation value of 0.85, which is 0.09 higher in accuracy and 0.03 higher in overall evaluation than the discrimination method based on infrared bands brightness values proposed by Feng et al. [1] (accuracy 0.79, missed detection rate 0.14, overall evaluation value 0.82); the method based on the FAMN_OTSU_ASITR (Fusion of Angle slope difference index (false) data, Decomposed three-dimensional OTSU adaptive threshold segmentation Algorithm and ASITR) exhibits an accuracy of 0.85, a missed detection rate of 0.08, and an overall evaluation value of 0.88, which is 0.09 lower in missed detection rate and 0.03 higher in overall evaluation as compared to the ASITR. The results demonstrate that the forest fire discrimination method based on Angle slope indices, Himawari-8 satellite data, and the decomposed OTSU adaptive threshold segmentation algorithm can improve the accuracy of forest fire discrimination and reduce errors in missed detections.
Advances in Yeast Probiotic Production and Formulation for Preventative Health
Authors: Moonsamy Ghaneshree , Roets-Dlamini Yrielle , Langa Cebeni Nkosihawukile , Ramchuran Santosh Omrajah
Published: October 2024

The use of probiotics has been gaining popularity in terms of inclusion into human diets over recent years. Based on properties exerted by these living organisms, several benefits have been elucidated and conferred to the host. Bacteria has been more commonly used in probiotic preparations, in comparison to yeast candidates, however, yeast exhibit several beneficial properties such as the prevention and treatment of diarrhoea, production of antimicrobial agents, prevention of pathogen adherence to intestinal sites, maintain microbial balance, modulation of the immune system, resistant to some antibiotics, amongst others. This review details the use of yeast organisms as biotherapeutics and has a special focus on production considerations and their formulation into different delivery formats.
Development of High-Aspect Ratio Soft Magnetic Microarrays for Magneto-Mechanical Actuation via Field-Induced Injection Molding
Authors: Shin Da Seul , Park Jin Wook , Gal Chang Woo , Kim Jina , Yang Woo Seok , Yang Seon Yeong , Kim Min Jik , Kwak Ho Jae , Park Sang Min , Kim Jong Hyun
Published: October 2024

Magnetorheological elastomers (MREs) are in demand in the field of high-tech micro- and nano-industries such as biomedical applications and soft robotics due to their exquisite magneto-sensitive response. Among various MRE applications, programmable actuators are emerging as promising soft robots because of their combined advantages of excellent flexibility and precise controllability in a magnetic system. Here, we present the development of a magnetically programmable soft magnetic microarray actuators through field-induced injection molding using MREs, which consist of styrene-ethylene/butylene styrene (SEBS) elastomer and carbonyl iron powder (CIP). The ratio of the CIP/SEBS matrix is designed to maximize the CIP fraction based on a critical solids loading. Further, as part of the design of the magnetization distribution in micropillar arrays, the magnetorheological response of the molten composites is analyzed using the static and dynamic viscosity results for both the on- and off- magnetic states, which reflect the particle dipole interaction and subsequent particle alignment during field-induced injection molding process. To develop high aspect ratio soft magnetic microarray, X-ray lithography is applied to prepare the sacrificial molds with a height-to-width ratio of 10. The alignment of CIP is designed to achieve a parallel magnetic direction along the micropillar columns, and consequently, the micropillar arrays successfully achieve the uniform and large bending actuation of up to approximately 81˚ with an applied magnetic field. This study suggests that the injection molding process offers a promising manufacturing approach to build a programmable soft magnetic microarray actuator.
Incretin Agonism: Sustainable Efficacy or Surreptitious Hazard?
Authors: Janket Sok-Ja , Chatanaka Miyo K. , Sohaei Dorsa , Tamimi Faleh , Meurman Jukka H. , Diamandis Eleftherios P.
Published: October 2024

Recent clinical trials using synthetic incretin hormones, glucagon-like peptide 1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) receptor agonists have demonstrated that these treatments ameliorated many complications related to obesity, emphasizing the significant impact of body weight on overall health.
Incretins are enteroendocrine hormones secreted by gut endothelial cells triggered by nutrient ingestion. The phenomenon that oral ingestion of glucose elicited much higher insulin secretion than intra-venous injection of equimolar glucose is known as incretin effect. This also alludes to the thesis that food intake is the root cause of insulin resistance.
Amylin is co-expressed with insulin from the pancreas β-cells but does not have insulinotropic function. Amylin suppresses glucagon secretion, slowing gastric emptying, and suppressing the central nervous system (CNS) reward center leading to weight loss. However, amylin can self-aggregate and cause serious cytotoxicity and may cause β-cell apoptosis.
Glucagon is secreted by the pancreatic α-cells and participates in glucose homeostasis in glucose-dependent manner. In hypoglycemia, glucagon increases blood glucose level by glycogenolysis and gluconeogenesis and inhibits glycogenesis in the liver. Some triple agonists in combination with glucagon and dual incretins are already being developed.
These advances bring to the question “Are the benefits of these anti-obesity treatments sustainable?” Chronic agonism may decrease the number of receptors. Also, long-term stimulation may cause β-cell exhaustion and failure. Additionally, instead of endogenous control of the appetite, exogenous control of satiety and food intake may hinder the long-term sustainability of these treatments. We will discuss the incretins’ mechanism of action, challenges, and future directions.
Applications of Machine Learning and Remote Sensing in Soil and Water Conservation
Authors: Kim Ye Inn , Park Woo Hyeon , Shin Yongchul , Park Jin-Woo , Engel Bernie , Yun Young Jo , Jang Won Seok
Published: October 2024

The application of machine learning (ML) and remote sensing (RS) in soil and water conserva-tion has become a powerful tool. As analytical tools continue to advance, the variety of ML al-gorithms and RS sources has expanded, providing opportunities for more sophisticated analyses. At the same time, researchers are required to select appropriate technologies based on research objectives, topic, and scope of the study area. In this paper, we present a comprehensive review on the application of ML algorithms and RS that has been implemented to advance research in soil and water conservation. The key contribution of this review paper is that it provides an overview of current research areas within soil and water conservation and their effectiveness in improving prediction accuracy and resource management in categorized subfields, including biomass-vegetation, soil properties, hydrology and water resources, and wildfire management. We also highlight challenges and future directions based on limitations of ML and RS applica-tions in soil and water conservation. This review aims to serve as a reference for researchers and decision-makers by offering insights into the effectiveness of ML and RS applications in the field of soil and water conservation.
Upper Limb Orthoses: Integrating Topology Optimization and 3D Printing for Custom Fit and Function
Authors: Voulgaris Stefanos , Kazakis George , Ypsilantis Konstantinos Iason , Galanis Dimitrios , Kousiatza Charoula , Mitropoulou Chara Ch. , Gkara Maria , Georgantzinos Stelios K. , Soultanis Konstantinos , Lagaros Nikos D.
Published: October 2024

Customized wrist splints, particularly for upper extremity orthoses like wrist support braces, are commonly used across numerous clinical scenarios. However, the traditional process for producing personalized wrist splints is largely manual and highly dependent on the expertise of orthopedic specialists. This experience-based approach often leads to suboptimal outcomes, necessitating further refinement of the designs. Recent advancements in Additive Manufacturing (AM) have brought significant innovation to various industries, including orthopedics. This study aims to present a comprehensive methodology that integrates advanced design tools ,like 3D Scanning, with digital manufacturing techniques to produce tailored wrist splints. The produced hand brace aims to offer enhanced mechanical performance and comfort by precisely fitting an individual’s anatomy while minimizing material usage and weight. To achieve optimal design efficiency, the study explores the application of a Topology Optimization (TO) approach for design, while the manufacturing process utilizes Fused Deposition Modeling (FDM), an evolving technology within the Additive Manufacturing (AM) sector.
Research Progress on Using Inorganic Nanomaterials to Enhance the Efficacy of Drug Therapy for Chronic Mountain Sickness
Authors: Liang Boshen , Zhou Yang , Qin Yuliang , Li Xinyao , Zhou Sitong , Yuan Kai , Zhao Rong , Lv Xiaoman , Qin Dongdong
Published: October 2024

Chronic mountain sickness (CMS) poses a significant health risk to individuals who rapidly ascend to high altitudes, potentially endangering their lives. Inorganic nanoparticles offer an effective means of transporting and delivering drugs, protecting nucleic acids from nuclease degradation, and mediating the expression of target genes in specific cells. These nanoparticles are almost non-toxic, easy to prepare and store, possess a large surface area, exhibit good biocompatibility and degradability, and maintain good stability. They can be utilized in the treatment of CMS to enhance the therapeutic efficacy of drugs. This paper provides an overview of the impact of inorganic nanomaterials on CMS, discussing their roles as nanocarriers and their potential in CMS treatment. It aims to present novel therapeutic strategies for the clinical management of CMS and summarizes the relevant pathways through which nanomaterials contribute to plateau disease treatment, providing a theoretical foundation for future clinical research.
Prognostic Impact of Pulmonary Diseases in 952 Patients with Thoracic and/or Abdominal Aortic Aneurysm
Authors: Kobayashi Yoichi , Ishiguro Takashi , Kagiyama Naho , Sumi Makoto , Takayanagi Noboru
Published: October 2024

Background/Objectives: Pulmonary diseases are common in patients with thoracic aortic aneurysm (TAA) and abdominal aortic aneurysm (AAA). Although high prevalences of chronic obstructive pulmonary disease and lung cancer (LC) are known, the prevalence of these and other pulmonary diseases regarding their relation to outcome of TAA and/or AAA are not determined. Methods: Pulmonary diseases present at aortic aneurysm (AA) diagnosis and follow-up periods and cause of death of 952 patients with TAA, AAA, or TAA+AAA (including thoracoabdominal AA) treated at our institution in Japan, were retrospectively analyzed. Cox regression analysis was used to investigate potential risk factors of mortality. Results: Mean patient age was 72.4 years, and median follow-up was 4.92 years. At diagnosis, 528 (55.5%) patients had pulmonary diseases, including emphysema without interstitial lung disease (ILD) or LC, LC, idiopathic pulmonary fibrosis (IPF) without LC, non-IPF ILD without LC, and interstitial lung abnormalities (ILAs) without LC in 250, 85, 65, 15, and 58 patients, respectively. During follow-up, LC and acute exacerbation (AE) of IPF developed in 50 and 12 patients, respectively. In 213 patients who died, there were 45 (21.1%) aortic disease-related deaths. Other causes of death included LC (27.7%), cardiovascular event (9.4%), pneumonia (5.6%), and interstitial lung disease (4.7%). In a multivariate Cox regression hazard model, age, larger maximum aneurysm diameter, and coexisting LC, IPF, or concomitant cancer were associated with poor prognosis. Conclusions: In patients with AA, not only age and aneurysm diameter but also coexisting LC and IPF were prognostic factors for mortality.
Linguistic Secret Sharing via Ambiguous Token Selection for IoT Security
Authors: Gao Kai , Horng Ji-Hwei , Chang Ching-Chun , Chang Chin-Chen
Published: October 2024

The proliferation of Internet of Things (IoT) devices has introduced significant security challenges, including weak authentication, insufficient data protection, and firmware vulnerabilities. To address these issues, we propose a linguistic secret sharing scheme tailored for IoT applications. This scheme leverages neural networks to embed private data within texts transmitted by IoT devices, using an ambiguous token selection algorithm that maintains the textual integrity of the cover messages. Our approach eliminates the need to share additional information for accurate data extraction, while also enhancing security through a secret sharing mechanism. Experimental results indicate that the generated steganographic text effectively preserves the sentiment and semantic information of the cover text.
Multi-Agent Reinforcement Learning for Smart Community Energy Management
Authors: Wilk Patrick , Wang Ning , Li Jie
Published: October 2024

This paper investigates a Local Strategy-Driven Multi-Agent Deep Deterministic Policy Gradient (LSD-MADDPG) method for demand-side energy management systems (EMS) in smart communities. Addressing critical challenges in EMS solutions such as data overhead, single-point failures, nonstationary environments, and scalability, the proposed LSD-MADDPG effectively harmonizes individual building needs with entire community energy management goals. By leveraging and sharing only strategic information among agents, the proposed approach demonstrates to optimize the EMS decision-making processes, while enhancing training efficiency and safeguarding data privacy - a critical concern in the community setting. The proposed LSD-MADDPG has proven to be capable of reducing energy costs and flattening community demand curve by coordinating indoor temperature control and electric vehicle charging schedules across multiple buildings. Comparative case studies reveal that LSD-MADDPG excels in both cooperative and competitive settings, aligning individual buildings’ energy management actions with overall community goals in a fair manner, highlighting its potential for future smart community energy management advancements.
Biomass Productivity and Photosynthetic Activity in Ulva compressa (Chlorophyta) in Raceway Photobioreactors Under Stress Conditions
Authors: Robles-Carnero Victor , Sesmero Rafael , L.Figueroa Felix
Published: October 2024

Research in seaweed cultivation technologies aims to increase production and reduce costs, leading to more efficient and sustainable processes. In this study, we analyzed the outdoor production of Ulva compressa cultured in summertime at different stocking densities of 0.6, 0.8 and 1.0 kg Fresh weight (FW) m-2 in a raceway photobioreactor with 30 m2 surface (3000L) and its relation to pho-tosynthetic activity. Under the experimental conditions of high temperature (> 28-30 oC) and pH > 9 in culture water, higher seaweed density resulted in lower specific growth rate. The biomass pro-duction has been related to photosynthetic activity by using in vivo chlorophyll a fluorescence. Dynamic photoinhibition was observed at noon, which was less severe in cultures with higher algal densities. However, photosynthesis recovered in the afternoon. Seaweeds that were acclimatized for a week to the conditions of 1.0 kg FW m-2 stocking density showed an increase in biomass growth and absence of photoinhibition compared to non-acclimatized thalli. In conclusion, by using high densities (1.0 kg FW m-2), U. compressa presented the highest growth rate in a mid-scale raceway photobioreactor under stressed conditions related to the increase of photosynthetic capacity.
The Impact of Bioactive Molecules from Probiotics on Child Health: A Comprehensive Review
Authors: Guamán Linda P. , Carrera-Pacheco Saskya E. , Zúñiga-Miranda Johana , Teran Enrique , Erazo Cesar , Barba-Ostria Carlos
Published: October 2024

This review investigates the impact of bioactive molecules produced by probiotics on child health, focusing on their roles in modulating gut microbiota, enhancing immune function, and supporting overall development. Key metabolites, including short-chain fatty acids (SCFAs), bacteriocins, exopolysaccharides (EPS), vitamins, and gamma-aminobutyric acid (GABA), are highlighted for their ability to maintain gut health, regulate inflammation, and support neurodevelopment. Clinical studies demonstrate that specific probiotics and their metabolites can reduce gastrointestinal disorders, enhance immune responses, and decrease the incidence of allergies and respiratory infections in pediatric populations. Additionally, postbiotics—bioactive compounds from probiotic fermentation—offer promising benefits, such as improved gut barrier function, reduced inflammation, and enhanced nutrient absorption, while presenting fewer safety concerns compared to live probiotics. By examining the mechanisms of action and clinical evidence, this review underscores the potential of integrating probiotics and postbiotics into pediatric healthcare strategies to promote optimal growth, development, and overall health in children.
Analysis of Energy Flow to the Interface Microstructure and its Effect on Weld Strength in Ultrasonic Additive Manufacturing
Authors: Venkatraman Gowtham , Headings Leon M , Dapino Marcelo
Published: October 2024

Ultrasonic additive manufacturing (UAM) is a process for three-dimensional printing of metal foil stock that can produce near-net shape metallic parts. This work details the development of an energy-based tool to identify relationships between input energy, stored energy in the interface microstructure, and the strength of the weld interface in UAM. The stored energy in the grain boundaries of the crystallized grains in the interface microstructure are estimated using the Read-Shockley relationship. The energy stored in the interface is found to be positively correlated with the resulting weld strength. An energy flow diagram is developed to map the flow of energy from the welder to the workpiece and quantify the key participating energies such as the energy of plastic deformation, energy stored in the interface microstructure, energy required for asperity collapse, and heat generation. A better understanding of the flow of energy in UAM can assist in optimizing the process to maximize the portion of input energy from the welder that is used for bond formation.
Enhanced Forecasting of Groundwater Level Incorporating Exogenous Variable: Evaluating Conventional Multivariate Time Series and Artificial Neural Network Models
Authors: Hoque Md Abrarul , Apon Asib Ahmmed , Hassan Md Arafat , Adhikary Sajal Kumar , Islam Md Ariful
Published: October 2024

Continuous and uncontrolled extraction of groundwater often creates tremendous pressure on groundwater levels. As a part of the sustainable planning and effective management of water resources, it is crucial to assess the existing as well as future groundwater level (GWL) condition. In the current study, an attempt was made to model and forecast GWL using artificial neural networks (ANN) and multivariate time series models. Autoregressive integrated moving average (ARIMA) and ARIMA incorporating exogenous variables (ARIMAX) were adopted as the time series models. Kushtia district in Bangladesh was selected as the case study area, and GWL data of five monitoring wells in the study are used to demonstrate the modeling exercise. Rainfall was taken as the exogeneous variable to explore whether its inclusion enhanced the performance of GWL forecasting using the developed models. The performance of each time series and ANN model was assessed based on various model evaluation criteria. It was evident from the results that the multivariate ARIMAX model (SSE of 15.361) performed better than the univariate ARIMA model with an SSE of 17.217 for GWL forecasting. This demonstrates the fact that the multivariate time series models generated enhanced forecasting of GWL compared to the univariate time series models. When comparing the time series and ANN models, it was found that the ANN-based model outperformed the time series models with the enhanced forecasting accuracy (SSE of 9.894). Results also exhibit a significant correlation coefficient value R of 0.995 (ANN 6-8-1) for the existing and predicted data. The current study conclusively proves the superiority of ANN over the time series models for the enhanced forecasting of GWL in the study area. Thus, the ANN approach was not only carried out for model building and simulation but also to provide a valuable tool for managing water resources amidst changing environmental conditions.
Unveiling the Antifungal Potential of Trans-Cinnamaldehyde: A Comparative Study Against Biofilm and Planktonic Forms of Candida spp.
Authors: Tuan Dang Anh , Uyen Pham Vu Nhat , Masak Jan
Published: October 2024

Biofilm-associated infections caused by Candida species represent a significant clinical challenge due to their inherent resistance to antifungal therapies. This study evaluates the antifungal efficacy of trans-Cinnamaldehyde, a natural compound derived from Cinnamomum cassia, in comparison to the widely used antifungal agent Nystatin, against both planktonic and biofilm forms of Candida albicans, Candida glabrata, and Candida tropicalis. Biofilms were established in 96-well polystyrene plates and treated with various concentrations of trans-Cinnamaldehyde and Nystatin. The Minimum Biofilm Inhibitory Concentration (MBIC) and planktonic minimum inhibitory concentration (PMIC) were determined using the MTT assay and Scanning Electron Microscopy (SEM) was employed to assess structural changes in biofilms. The results demonstrated that Nystatin exhibited superior antifungal efficacy across all tested species, particularly in biofilm inhibition, with MBIC50 values as low as 0.0015 mg/mL for C. albicans and C. glabrata. In contrast, trans-Cinnamaldehyde showed moderate antifungal activity with MBIC50 values of 0.16 mg/mL for C. albicans and C. glabrata and higher resistance observed in C. tropicalis (MBIC50 of 0.32 mg/mL). SEM analysis revealed significant biofilm disruption in C. albicans following exposure to trans-Cinnamaldehyde, but C. tropicalis exhibited more resilient biofilm structures. Overall, while Nystatin remains the gold standard for antifungal treatment, trans-Cinnamaldehyde presents potential as an adjunctive treatment, particularly for biofilm-related infections in C. albicans and C. glabrata. Further research is required to explore synergistic effects between trans-Cinnamaldehyde and conventional antifungal agents and to optimize its therapeutic application in clinical settings. This study reused measurement results data from our previous publication to include in tables and charts.
Loss of Surfactant Protein a Alters Perinatal Lung Morphology and Susceptibility to Hyperoxia-Induced Bronchopulmonary Dysplasia
Authors: Amatya Shaili , Lanza Matthew , Umstead Todd M , Chroneos Zissis
Published: October 2024

Background-Bronchopulmonary dysplasia (BPD) is a condition of poor alveolar formation that causes chronic breathing impairment in infants born prematurely. Preterm lungs lack surfactant and are vulnerable to oxidative injuries driving the development of BPD. Our recent studies reported that surfactant protein A (SP-A) genetic variants influence susceptibility to neonatal lung disease. SP-A modulates activation of alveolar macrophages and parturition onset in late gestation. We asked whether a lack of SP-A alters alveolarization in a mouse model of hyperoxia-induced BPD. Methods- SP-A-deficient and control newborn mice were exposed to either clinically relevant 60% O2 hyperoxia or normoxia for 5-7 days. Alveolar formation was then assessed by mean linear intercept (MLI) and radial alveolar count (RAC) measurements in lung tissue sections. Results- We report that the combination of SP-A deficiency and hyperoxia reduces alveolar growth compared to WT mice. The morphometric analysis of normoxic SP-A-deficient lungs showed lower RAC compared to controls, indicating reduced alveolar number. In the presence of hyperoxia, MLI was higher in SP-A-deficient lungs compared to controls. Differences were statistically significant for female pups. Spatial proteomic profiling of lung tissue sections showed that hyperoxia caused a 4-fold increase in the DNA damage marker gammaH2Ax in macrophages of SP-A-deficient lungs compared to normoxia. Conclusions- Our short report suggests an important role for SP-A in perinatal lung development and in protecting lung macrophages from oxidant injury. These studies warrant future investigation to discern the temporal interaction of SP-A, gender, oxidant injury, and lung macrophages in perinatal alveolar formation and development of BPD.