bluuebunny/crossref_metadata_2025 huggingface dataset:
Design and experimentation of a pressure testing platform for tea tree canopy
Authors: Wang Pengfei , Hu Yongguang , Liu Decheng , Pan Qingmin , Wu Wenqiang
Published: December 2024

In the mechanized operations of tea gardens, components like cutting blades and suspension systems frequently interact with the tea tree canopy, resulting in complex push, squeeze, and pressure interactions. This makes it challenging to accurately measure the distribution characteristics of the forces involved. The uncertainty in these mechanical properties complicates mechanical design, posture adjustment, and positioning control. To address this, this study designed and developed a testing platform for pressure measurement in the tea tree canopy, aiming to systematically investigate the force characteristics and structural changes of the canopy under different penetration depths. The platform enabling high-precision recording of canopy responses under varying pressure conditions. The experimental results indicated significant differences in pressure values at different depths and locations. At a penetration depth of 1.0 cm, the minimum pressure value was 3.0 N, while the maximum pressure at a depth of 4.0 cm reached 60.0 N. Moreover, the pressure exhibited a clear hierarchical distribution with changes in depth and position. The study observed a hierarchical structure within the tea tree canopy, comprise tender leaves, mature leaves, and branches. These findings provide important evidence and data support for the optimization of tea garden management and harvesting machinery design.
Enhancing innovation in elementary dance classrooms through the learning community model
Authors: Akkaamnuai Tiwaporn , Chenpong Sureerat , Poodej Chanut
Published: December 2024

This study aimed to: (1) investigate current conditions and practices in developing dance classrooms using the learning community model for elementary dance teachers; (2) create a dance classroom using the learning community model with elementary dance teachers; and (3) assess the effectiveness of this development approach. The study includes 20 experts studying existing circumstances and making recommendations using questionnaires and interviews. There were 150 elementary school kids and six volunteer instructors among the participants. The research techniques comprised a dance classroom setting, a competency evaluation form for dance teachers, an assessment of students' innovation ability, and an opinion questionnaire. The data was analyzed using frequency, percentage, standard deviation, and t-tests. It found that creating a dance classroom required four steps: (1) surveying, (2) planning, (3) managing, and (4) applying. The main results were: (1) dance teachers' abilities significantly improved at .05; (2) students' average abilities increased during the study, with the experimental group performing significantly better than the control group; and (3) the experimental group demonstrated a significantly higher ability to create dance innovations than the control group at the.05 level. The results revealed that employing a learning community model in dance classrooms effectively improved students into innovation creators.
Factors that influence the attitude of young people to participate in crowdfunding campaigns
Authors: Hejase Hussin Jose , Hamdar Bassam , Hejase Ale Jose , Raad Hadi , Kobeissi Karim
Published: December 2024

This paper highlights the factors that influence young people’s attitude to participate in crowdfunding campaigns, and to what extent such factors impact their investment intentions. A quantitative approach is employed using a survey to gather information from Al Maaref University students about the determinants influencing their intentions to invest in crowdfunding and to explain how such factors affect their attitudes towards crowdfunding decisions. This study found that emotions and attitudes as well as attitudes and PBC are statistically and positively related. Moreover, personality types ‘Agreeableness (A) and Neuroticism (N)’ have a positive impact on PBC (with p ˂ 10%), and subjective norms (SN) have a direct influence on engagement intention (EI) (p ˂ 5%). PBC has a positive impact on EI (p ˂ 5%), and risk preferences have a positive impact on EI (with p ˂ 10%). In addition, results show that personality types do not have a positive impact on SN, and personality types Extraversion (E), Conscientiousness (C), and Openness (O), do not have a positive impact on PBC. Subjective norms do not have a positive impact on attitudes (p ˃ 5%). Moreover, PBC does not have a direct influence on SN or behavior, risk preferences do not have a positive impact on EI (p ˃ 5%), and demographic factors do not have a positive impact on EI. The findings of the study shall promote the comprehension of the influences that motivate young entrepreneurs to participate in crowdfunding campaigns. This paper’s findings benefit investors, fintech decision-makers, and policymakers.
STRATEGY OF IMPROVEMENT OF THE LOGISTICAL PROCESSES IN AN ENTERPRISE
Authors: ŁUKASIK Zbigniew , KUŚMIŃSKA-FIJAŁKOWSKA Aldona , OLSZAŃSKA-WESOŁY Sylwia
Published: December 2024

The e-commerce industry is growing in strength year by year due to the growth in the number of consumers using this solution. The market of e-commerce industry allows reaching to a broader group of customers and reduction of costs. In order to gain a competitive advantage, any enterprise must work out an appropriate plan, action strategy within the scope of distribution. A basis for decision about strategy of distribution actions, or aiming at improvement of executed processes, is a proper identification and analysis of external and internal factors. Rationalization of conduct result in high quality of customer service in the aspect of promptness of delivery, and also affects planning of actions in the long term. The goal of the article was to analyze the logistics distribution of the enterprise acting within the scope of e-commerce and to propose a strategy to improve the execution of the process of completion. A model has been developed and simulation has been made.
ONGOING MONITORING OF LIQUID FUEL QUALITY AT STORAGE FACILITIES
Authors: SZCZUCKA-LASOTA Bożena
Published: December 2024

The newly developed method employs spectral analysis to evaluate fuel quality continuously. Unlike traditional approaches, it eliminates the need for sampling and laboratory analysis, provides real-time results, facilitates rapid decision-making regarding fuel quality, and enhances operational efficiency. A comparative analysis of the new method with laboratory tests carried out following ISO standards demonstrated its effectiveness in the assessment of liquid fuels containing biocomponents. The determined age in sample ageing index is highly correlated with the oxidative stability of the Diesel oil and resin content for Pb95 and Pb98. Statistically, significant transformation functions were developed. The results confirm the ability of the method to rapidly identify substandard fuels, thereby accelerating their withdrawal from the market. The implementation of this spectral analysis-based method represents a significant advance in fuel quality assessment. Its continuous monitoring capability and real-time reporting distinguish it from conventional approaches, thereby offering practical benefits for fuel management. Ensuring timely interventions to maintain quality standards are supported by enabling the prompt detection of degraded fuels. The applicability of this method to state fuel reserves and petrol stations underlines its usefulness in improving fuel quality control measures. Overall, its introduction offers both economic and environmental benefits to the transportation sector.
Assessment of the American College of Surgeons-Surgical Risk Calculator (ACS-SRC) for Prediction of Early Postoperative Complications in Patients Undergoing Cytoreductive Surgery for Ovarian Peritoneal Carcinomatosis
Authors: Kabeya Cedric , Khaled Charif , Polastro Laura , Moreau Michel , Bucella Dario , Fastrez Maxime , Liberale Gabriel
Published: December 2024

Ovarian cancer (OC) is diagnosed at a locally advanced stage in two-thirds of cases. The first line of treatment consists of cytoreductive surgery (CRS) combined with neoadjuvant and/or adju-vant chemotherapy. However, CRS can be associated with high rates of postoperative complica-tions (POCs) and detection of fragile patients at high risk of POCs is important. The American College of Surgeons Surgical Risk Calculator (ACS-SRC) provides a predictive model for early POCs (30 days) for any given surgical procedure. This study aimed to evaluate the performance of the ACS-SRC in predicting the occurrence of early POCs for patients undergoing CRS for OC. This was a retrospective study that included patients undergoing CRS for advanced OC between January 2010 and December 2022. Early POCs were reviewed and the rate of POCs was compared with those predicted by the ACS-SRC to evaluate its accuracy (i.e., discrimination and calibra-tion). A total of 218 patients were included, 112 of whom underwent extensive surgery/resection. A total of 94 complications were recorded. This cohort demonstrated correct calibration of the ACS-SRC for the prediction of surgical site infection, readmission, and the need for nursing care post-discharge (NCPD; transfer to revalidation center or need for nursing care at home). Using both the discrimination and calibration methods, the score only predicted NCPD. In this study, the ACS-SRC was shown to be of little value for patients undergoing cytoreductive surgery for ovarian peritoneal carcinomatosis, as it only accurately predicted NCPD.
Software Language Engineering - Text Processing Language Design, Implementation, Evaluation Methods
Authors: Willrich Lutalo Joseph
Published: December 2024

Programming languages drive most if not all of modern problem-solving using computational methods and power. Research into new programming languages and techniques is essential because it makes the design, implementation, and application of automation to general or particular problem-solving ever easier, more accessible, and more performant. GPLs typically are designed to be purely domain agnostic---meaning they can be applied in any field, for any problem kind. However, this normally also makes them hard and difficult to use in problems where non-programmers or even experts with little or no GPL programming skills are required to leverage programmatic problem-solving capabilities, which is why DSLs come into play; they are generally more fine-tuned toward improving human productivity and performance than that of the machine, while making solving particular, domain-oriented problems simpler. In this paper, we review the literature concerning how to design and then fully implement a new DSL, with a special focus on a DSL for generic problem-solving leveraging Text Processing methods---a Text Processing Language (TPL). We consider leveraging the Design Research paradigm as a systematic framework for guiding research into the development of new TPLs. We present for the first time, a new unifying theory concerning general, but also TPL-specific language engineering theory and guiding frameworks---UPLT, PLEF \& PLEf. With a re-introduction of the SOE framework, we consider quantitative and qualitative evaluation of software languages, with specific focus on programming languages. Finally, we highlight identified pending problems for future theoretical and pragmatic research into the field of language engineering, especially with a focus on TPLs.
Digitalizing Material Knowledge: A Practical Framework for Ontology-Driven Knowledge Graphs in Process Chains
Authors: García Trelles Elena , Schweizer Christoph , Thomas Akhil , von Hartrott Philipp , Janka-Ramm Marina
Published: December 2024

This paper proposes a robust methodology for integrating process-specific data and domain expert knowledge into linked knowledge graphs. These graphs utilize an ontology that provides a standardized vocabulary for material science and facilitates the creation of semantic models for various processes along the digital process chain. A generic template for structuring processes is proposed, simplifying subsequent data retrieval. The templates of specific processes are designed collaboratively by domain and ontology experts aided by a proposed interview template that bridges the knowledge gap. Following the digitalization of material data through semantic modeling, machine-readable data with contextual metadata is stored in a graph database, which can be efficiently queried using the SPARQL language, enabling seamless integration into data pipelines. To demonstrate this approach, a knowledge graph is developed to represent the process chain of AlSi10Mg objects manufactured via permanent mold casting, capturing their complete history from the initial manufacturing step to final non-destructive testing and mechanical characterization. This methodology enhances data interoperability and accessibility while providing context-rich data for training AI models, potentially accelerating new knowledge discovery in material science.
Energy Saving in University Buildings: The Potential Role of Smart Monitoring and IoT Technologies
Authors: Franco Alessandro , Crisostomi Emanuele , Leccese Francesco , Mugnani Antonio , Suin Stefano
Published: December 2024

Environmental monitoring systems integrated with IoT networks have rapidly evolved, enabling the collection of vast amounts of data accessible to facility managers and authorized users via smartphone apps. This paper presents a system developed to monitor environmental parameters across multiple buildings at the University of Pisa, with a focus on its potential for improving energy efficiency. Efficient energy management has become increasingly important, especially following the COVID-19 pandemic, which introduced legal requirements for mechanical ventilation. These measures have significantly increased energy consumption during both winter and summer seasons. Our system, built using low-cost components and a secure IoT network, demonstrates how CO2 monitoring and smart controls can reduce energy waste in buildings. In a case study conducted on selected buildings, the system achieved up to 40% energy savings. The paper highlights both the benefits and the limitations of current technology in this context, emphasizing the role of IoT in enhancing sustainability while ensuring safety and security within academic institutions.
Exploring Aquaporins in Human Studies: Mechanisms and Therapeutic Potential in Critical Illness
Authors: Vrettou Charikleia S. , Issaris Vasileios , Kokkoris Stelios , Poupouzas Georgios , Keskinidou Chrysi , Lotsios Nikolaos S. , Kotanidou Anstasia , Orfanos Stylianos E. , Dimopoulou Ioanna , Vassiliou Alice Georgia
Published: December 2024

Aquaporins (AQPs) are membrane proteins facilitating water and other small solutes to be transported across cell membranes. They are crucial in maintaining cellular homeostasis by regulating water permeability in various tissues. Moreover, they regulate cell migration, signaling pathways, inflammation, tumor growth, and metastasis. In critically ill patients, such as trauma, sepsis, and patients with acute respiratory distress syndrome (ARDS), which are frequently encountered in intensive care units (ICUs), water transport regulation is crucial for maintaining homeostasis, as dysregulation can lead to edema or dehydration, with the latter also implicating hemodynamic compromise. Indeed, AQPs are involved in fluid transport in various organs, including the lungs, kidneys, and brain, where their dysfunction can exacerbate conditions like ARDS, acute kidney injury (AKI), or cerebral edema. In this review, we discuss the implication of AQPs in the clinical entities frequently encountered in ICUs, such as systemic inflammation and sepsis, ARDS, acute kidney injury (AKI), and brain edema due to different types of primary brain injury from a clinical perspective. Current and possible future therapeutic implications are also considered.
Long-Term Greenness Effects of Urban Forests to Reduce PM10 Concentration: Does the Impact Benefit the Population Vulnerable to Asthma?
Authors: Jeong Jinsuk , Kim Chaewan , Choi Sumin , Sou Hong-Duck , Park Chan-Ryul
Published: December 2024

This study investigates the effect of urban forests in reducing particulate matter (PM) concentrations and its subsequent impact on the number of asthma care visits. Understanding the mechanisms behind the relationship between the greenness of blocking forests and the reduction of PM is crucial for assessing the associated human health benefits. This study analyzed the influencing factors for reducing long-term PM10 concentrations, utilizing the vegetation index and meteorological variables. Results showed that the reduction in PM10 began in 2011, five years after the establishment of the blocking forest. The annual mean PM10 concentrations decreased significantly, driven primarily by summer wind speed and summer Enhanced Vegetation Index (EVI), explaining about 62% of the variation. A decrease in the number of asthma care visits was observed, similar to the trend of PM10 reduction in the residential area and the increase in the greenness of the blocking forest. The influx of PM into the city, primarily driven by prevailing northwesterly winds, may have been mitigated by the growing blocking forest, contributing to a reduction in asthma-related medical visits among urban residents. In particular, since the onset of the COVID-19 pandemic in 2020, the increase in the PM2.5/PM10 ratio in residential area has become more closely linked to the increase in asthma-related medical visits. It suggests another PM2.5 emission source in the residential area. The number of asthma care visits among children (under 11) and the elderly (over 65) exhibited a strong positive correlation with PM10 levels and a negative correlation with the Normalized Difference Vegetation Index (NDVI). This suggests a link between air quality improvement from the greenness of blocking forests with their capacity to capture PM and respiratory health outcomes, especially for the vulnerable groups to asthma. These findings highlight the need to manage pollutant sources such as transportation and the heating system in residential areas beyond industrial emissions as the point pollution source. The management policies have to focus on protecting vulnerable populations, such as children and the elderly, by implementing small-sized urban forests to adsorb the PM2.5 within the city and establishing blocking forests to prevent PM10 near the industrial complex.
Youth Suicide in Japan: Exploring the Role of Subcultures, Internet Addiction, and Societal Pressures
Authors: Imataka George , Shiraishi Hideaki
Published: December 2024

Background: Youth suicide in Japan remains a significant societal issue, driven by a complex combination of academic pressures, social isolation, bullying, and family issues. In recent years, internet addiction and the influence of subcultures have increasingly played a role in shaping the psychological landscape of young people. The COVID-19 pandemic has exacerbated these challenges, intensifying feelings of loneliness and anxiety about the future. This study explores how internet addiction, gaming culture, and Japan’s unique subcultures contribute to the risk of youth suicide. Methods: This paper reviews existing literature and statistical data on youth suicide in Japan, examining the influence of internet addiction and subcultures on suicide risk. It also compares suicide rates in Japan with those in other developed countries and analyzes the impact of societal pressures on youth suicide. The study further investigates how these factors interact with the mental health crisis exacerbated by the pandemic. Results: In 2023, 513 school-aged youth died by suicide, maintaining a disturbingly high rate. Key risk factors identified include academic pressure, bullying (particularly cyberbullying), family issues, and internet addiction. The influence of subcultures, including anime, manga, and gaming, plays a significant role in the emotional struggles faced by youth, contributing to feelings of alienation and despair. The COVID-19 pandemic worsened social isolation, mental health issues, and suicide rates among youth, with significant long-term psychological effects. Conclusions: The study highlights the urgent need for comprehensive mental health support systems in Japan, including better access to counseling services in schools and communities. Addressing cultural attitudes toward mental health, regulating harmful online content, and understanding the impact of subcultures on youth are essential for preventing suicide. Family-based interventions and broader societal efforts are crucial for creating a supportive environment that can help reduce suicide rates among young people.
Continuous Learning Techniques for Robotics, Challenges & Solutions
Authors: Siddiqui Muhammad Yasir
Published: December 2024

All real-world applications require ongoing learning since static pre-trained models cannot handle non-stationary data distributions. This extensive study is particularly interested in learning algorithms that do continuous learning online from sizable, massive, or infinite sequential data while requiring sizable, little computational, and memory resources. Existing approaches in terms of computations, memory, and network/model complexity have been compared and critically analyzed the main issues/challenges with continuous learning for autonomous real-world systems. The goal of this study is to explore cutting-edge techniques that enable computational models to learn continuously over time. Numerous cutting-edge techniques have been investigated for continuous learning, existing problems discussed, and suggestions presented for continuous learning in robotics.
Integrated Analysis of Cell-Free DNA and Novel Protein Biomarkers for Stratification and Therapy Monitoring in Stage IV Pancreatic Cancer
Authors: Hussung Saskia , Hess Maria Elena , Haghighi Elham Bavafaye , Wittel Uwe A. , Boerries Melanie , Fritsch Ralph Michael
Published: December 2024

Background: Given the poor prognosis of metastatic pancreatic adenocarcinoma (mPDAC), closer disease monitoring through liquid biopsy, most frequently based on serial measurements of cell-free mutated KRAS (KRASmut cfDNA), has become a highly active research focus, aiming to improve patients´ long-term outcome. However, most of the available data show only a limited predictive and prognostic value of single-parameter based methods. We hypothesized that a combined longitudinal analysis of KRASmut cfDNA and novel protein biomarkers could improve risk stratification and molecular monitoring of patients with mPDAC. Methods: We prospectively collected 160 plasma samples from 47 patients with mPDAC at our institution. Highly sensitive single-target ddPCR assays were employed to detect and quantify KRASmut cfDNA. Additionally, analysis of ten protein biomarkers was performed through Enzyme-linked Immunosorbent Assay (ELISA), and CA 19-9 dynamics were registered. Results: KRASmut cfDNA was detectable in 37/47 (78.7%) patients through course of study, and CA 19-9 levels were elevated in 40 out of 47 (85.1%) patients. KRASmut cfDNA increase at time of first follow-up could predict inferior PFS (Hazard ratio (HR) = 3.40, P = 0.0003) and OS (HR = 4.91, P < 0.0001). In contrast to CA 19-9 kinetics, not predictive of outcome, integrated analysis of KRASmut cfDNA combined with six evaluated circulating protein biomarkers, allowed basal risk stratification at time of first follow-up (HR = 10.2, P = 0.0014). Conclusion: A combined longitudinal analysis of KRASmut cfDNA with selected protein biomarkers offers significantly improved prognostic value for patients with mPDAC compared to single-parameter methods. This innovative approach is a step forward in the molecular monitoring of mPDAC and should be validated in further prospective studies.
Corporate Governance and Obfuscation in Chairmen’s Letters: The Case of MENA Banks
Authors: Mahboub Rasha
Published: December 2024

The readability (RDB) of annual reports (AR) plays a crucial role in determining the effectiveness of disclosure of information to interested parties, particularly investors. Given that investors rely on the financial information provided in AR, the chairman's letter serves as a key communication tool and is the most extensively read section of the report. Consequently, companies are under pressure to provide understandable AR that can be easily interpreted by investors. Nevertheless, managers sometimes obscure such disclosures in an attempt to bury negative information and hide their own behavior. Drawing from the “managerial obfuscation hypothesis”, this study investigated how the corporate governance (CG) structures affect the RDB of AR of a sample of 95 banks across seven countries in the MENA region from 2018 to 2022. The findings revealed that board size, frequency of board meetings, and ownership concentration significantly affect the RDB of AR. Additionally, board independence and gender diversity have a significant negative effect on AR RDB. Conversely, the study found that the presence of role duality within the board had an insignificant effect on AR RDB. As a result, this study recommends enhancing the CG structures to enhance the clarity of banks' reports and boost investor trust.
Design and Development of an Advanced Sensor Prototype for the Detection of Microplastics
Authors: Campos-López Maximiliano , Aguilar-Garay Ricardo , Gonzalez-Rodriguez Dafne E. , Mejía-López Verónica Itzel , Gamboa-Lugo Margoth Marissa , Garibay-Febles Vicente , Reyes-Guzmán Marco Antonio , Gordillo-Sol Alvaro , Mendoza-Perez Jorge Alberto
Published: December 2024

Microplastics are ubiquitous in our environment and significantly threaten ecosystems and human health. This paper presents the design and fabrication of an innovative prototype sensor capable of detecting microplastics by employing multiple wavelengths. Our system combines mechanical, electronic, and optical components, providing an efficient and cost-effective solution for environmental monitoring.
Exploring the Role of ‘Outsourced Oxygen-to-the-Bedside’ in Expanding Oxygen Access for Patients
Authors: Schedwin Mattias , Graham Hamish , Losneanu Alex , Houdek Jason , Kitutu Freddy Eric , Mukisa Pius , Baker Tim , Mageni Msira , Raune Maire , Rai Amarpreet , Adeniji Aishat , Sofoluke Oladeji , Njuguna Michael , Achocho Francis , Mian Qaasim , Saleh Abdullah , Hawkes Michael , Dixon Miranda , Kasliwal Pritika , King Carina
Published: December 2024

Setting up sustainable oxygen systems within a health facility is complex. In this paper, we propose a conceptualisation of a particular delivery model that takes a broader approach than the traditional oxygen procurement and delivery – ‘Outsourced Oxygen-to-the-Bedside’ (O2B). We explore its potential strengths and limitations using early learnings from five pilot projects in India, Kenya, Tanzania, Nigeria and Uganda. O2B models involve oxygen supply services provided by external entities that provide a minimum set of oxygen supply equipment, alongside bundles of repair, maintenance and training, and are contracted to guarantee continuous medical oxygen supply to the bedside. This market-based approach may be particularly suitable for smaller rural facilities that have limited options and capacity. Envisioned benefits of this model are the reduced mental load on facilities to manage their oxygen and distribution arrangements, whilst building local skills and knowledge to increase equipment functionality, longevity, and oxygen availability. O2B providers currently offer different variations of this service, but in all cases their main value proposition is to make oxygen access easier for facilities. Potential risks include fragmentation of existing oxygen supply solutions, and difficulties in balancing business sustainability with affordable pricing - particularly to small and under-resourced facilities. Relying on financing from individual facilities is unlikely to succeed in most contexts – particularly for poorer hard-to-reach communities. Therefore, future evaluations should focus on long-term funding solutions and cost-effectiveness of different service bundles to determine best value for money in different contexts .
A Two-Decade Overview of the Ecological Carrying Capacity in Bahía Santa Maria-La Reforma Coastal Lagoon System
Authors: Calvario-Martinez Omar , Medina-Galvan Julio , Domínguez-Jiménez Virginia Patricia , Alonso-Rodríguez Rosalba , Sánchez-Rodríguez Miguel Angel , Reyes-Velarde Paulina Monserrat , Betancourt-Lozano Miguel , Serrano-Hernández David
Published: December 2024

Santa María Bay-La Reforma (SMBLR) is Mexico's most extensive estuarine lagoon system. The Secretariat of the Ramsar Convention on Wetlands declared it a wetland of international importance and it is made up of islands, estuaries, and mangrove areas, which provide a vital part of the habitat and refuge of a significant number of birds, fish, amphibians, reptiles, and mammals. The fishing of blue and brown shrimp, marine and estuarine fish, as well as the exploitation of crab and bivalve mollusks represent an important economic value for the established communities that live there and for the economic impact in the state of Sinaloa. This state ranked second in fisheries production and first in aquaculture production by 2023. Also, Mexico is the seventh largest producer of farmed crustaceans in the world. However, the biological richness of this ecosystem has historically been threatened by economic activities that, via watersheds, translate into continuous inputs of nutrients and other pollutants. This has led to modifications to the system such as changes in the structure of pelagic and benthic communities, mainly in response to the phenomenon of eutrophication. To understand the dynamics of nutrient inputs to the ecosystem, this work presents a comparative analysis of the trophic state of the system and the magnitude of the main economic activities which have been modified from dominance of livestock activities to aquaculture from 2007-2019. In the coastal component of SMBLR (agriculture, livestock and aquaculture) under hydrological criteria, the behavior of the system's trophic indices in each season of the year and its transitions were analyzed. We found, however, that in most of the seasons the system functions as a nitrogen and phosphorus sink, which is associated with autotrophic net ecosystem metabolism and nitrogen fixation processes. We suggest that while water residence times in SMBLR are short, these are strongly influenced by the high volumes of water and nutrient loads determined by the spatio-temporal variations of hydrological drainage from the drains of the basins of influence of the system. The influx of nutrients and other pollutants into an ecosystem affects the carrying capacity of the lagoon ecosystem. This is demonstrated in this study since SMBLR is mostly an autotrophic system; there are signs that the carrying capacity at some seasons of the year has been exceeded, and the adverse ecological and socioeconomic effects in the basin are evident.
Improvement of the TEB Algorithm for Local Path Planning of Car-like Mobile Robots Based on Fuzzy Logic Control
Authors: Chen Lei , Liu Rui , Jia Daiyang , Xian SiJing , Ma Guo
Published: December 2024

TEB (Timed Elastic Band) can efficiently generates optimal trajectories that match the motion characteristics of car-like robots. However, the quality of the generated trajectories is often unstable, and they sometimes violate boundary conditions. Therefore, this paper proposes a fuzzy logic control-TEB algorithm (FLC-TEB). This method adds smoothness and jerk objectives to make the trajectory generated by TEB smoother and the control more stable. Building on this, a fuzzy controller is proposed based on the kinematic constraints of car-like robots. It uses the narrowness and complexity of trajectory turns as inputs to dynamically adjust the weights of TEB's internal objectives. The results of real car-like robot tests show that compared to the classical TEB, FLC-TEB increased trajectory time by 16% but reduced trajectory length by 16%; The trajectory smoothness was significantly improved, the change in the turning angle on the trajectory was reduced by 39%, the smoothness of the linear velocity increased by 71%, and the smoothness of the angular velocity increased by 38%, with no reverse movement occurring. This indicates that when planning trajectories for car-like mobile robots, FLC-TEB provides more stable and smoother trajectories compared to the classical TEB.
Regarding the UN Sustainable Development Goals 3, 5, and 13, Reconsidering Reproductive Expectations of Women Using a Psychoanalytic Narrative Research Method
Authors: Nash Carol
Published: December 2024

Climate action represents the most comprehensive of the 2015 United Nations 17 Sustainable Development Goals (SDGs) in that climate change impacts all other goals. Urban overpopulation is a primary cause, as energy consumption is a significant source of carbon dioxide emissions directing climate change. The population increase origin is attributable to the agricultural/urban developments that became geographically widespread approximately 6,000 years ago. Simultaneously, religious belief stressed multiple children, with women obligated to produce them. This female duty created gender inequality and reduced the health and well-being of women, as pregnancy is a noted risk factor for decreased lifetime health. Regardless of the detrimental risk to their health and well-being, the gender inequality, and the adverse effects of birthing multiple children regarding climate action, women today continue to feel obliged to reproduce appropriately. This burden requires change to meet the three sustainable development goals of good health and well-being (SDG 3), gender equality (SDG 5), and climate action (SDG 13). A mindfulness intervention of an author-developed psychoanalytic narrative research method presents a means for promoting such change through a qualitative analysis of the responses of several participants regarding its success in clarifying the values of these women in overcoming career-related burnout.
The Role of Cf-HPV DNA as an Innovative Biomarker for Predicting Recurrence or Persistence of Cervical Cancer
Authors: Poinho Márcia , Dias Laura , Pinheiro Layane , Gomes Flávia Níniver , Rondon Heidy , Oliveira Mikele , Souza Jhonnatan , Figueiredo Higino , Lira Daniel , Levi José Eduardo , Martins Valquíria do Carmo , Torres Kátia
Published: December 2024

Background: Cervical cancer is highly prevalent among women in Amazonas, Brazil, mainly due to late-stage diagnosis, which compromises treatment efficacy and survival rates. This highlights the urgent need for less invasive biomarkers to monitor affected patients. Methods: This study employed real-time PCR targeting the E7 gene of HPV types 16 and 18 to analyze plasma samples from 39 cervical cancer patients treated at the Oncology Control Center Foundation in Amazonas, Brazil. Results: cf-HPV 16 DNA was detected in 54% of samples before treatment. Socioeconomic and behavioral data showed that 46.2% of patients had low educational levels, 77% reported low income, 79.5% experienced early sexual activity onset, and 15.4% had never undergone cytological screening. Recurrence or persistence occurred in 30.8% of cases over 4–33 months of follow-up, with cf-HPV DNA detectable (at any time, pre- or post-treatment) in 75% of these cases. Conclusions: cf-HPV DNA in plasma is a promising biomarker for post-treatment surveillance, facilitating earlier detection of recurrence and proactive interventions. Incorporating this biomarker into clinical protocols could enhance outcomes and survival, particularly in underserved regions like the Amazon, where access to healthcare is limited.
Long Read Sequencing of The CpG Island from The Promoter of O6-Methylguanine-DNA Methyltransferase Provides Insights into The Mechanism of De Novo Methylation Of G/C-Rich Regions
Authors: Sergeev Alexander V. , Malyshev Daniil P. , Genatullina Adelya I. , Pavlova Galina V. , Gromova Elizaveta S. , Zvereva Maria I.
Published: December 2024

Background: The methylation of cytosine residues at CpG sites within the O6-methylguanine-DNA methyltransferase (MGMT) promoter is a key biomarker in glioblastoma therapy. Single-molecule nanopore sequencing enables direct detection of methylation patterns across the genome. Our study investigates how potential G-quadruplex-forming sequences (PQS) in the MGMT promoter CpG island influence the activity of de novo DNA methyltransferase Dnmt3a and the accuracy of methylation pattern detection via nanopore sequencing. Methods: Nanopore sequencing was employed to analyze the methylation of 97 clinically significant CpG sites in the human MGMT promoter (MGMTp) using an in vitro de novo methylation system. Circular dichroism spectroscopy was used to identify G-quadruplex (G4) structures within the MGMTp CpG island. Interactions between the catalytic domain of Dnmt3a and the PQS from the MGMTp were examined by biolayer interferometry. Results: Guanine-rich DNA strands of the PQSs in the MGMTp were hypomethylated, while the complementary cytosine-rich strands were methylated by DNA methyltransferase Dnmt3a with higher efficiency. The accuracy of detecting modified bases in the PQS was significantly lower compared to surrounding sequences. Single-stranded guanine-rich DNA sequences from the MGMTp exhibited strong binding to Dnmt3a-CD, with an affinity approximately 10 times higher than their cytosine-rich complements (Kd = 3 × 10⁻⁸ M and 3 × 10⁻⁷ M, respectively). By binding to Dnmt3a, G4-forming oligonucleotides from MGMTp effectively inhibited the methylation reaction (IC50 6·10-7 M). Conclusions: The obtained data indicate the role of PQSs in establishing de novo the methylation of the MGMT promoter and highlight the challenges of sequencing guanine-rich regions using nanopore sequencing as well as impact of specific pattern of de novo methylation in the clinical data interpretation.
The Metabolism of Creatinine and its Usefulness to Evaluate Kidney Function and Body Composition in Clinical Practice
Authors: Avila Marcela , Mora Sánchez Mariana G. , Bernal Amador Alma Sofía , Paniagua Ramón
Published: December 2024

Abstract: Serum creatinine levels are the most used clinical marker to estimate renal function as Glomerular Function Rate, because it is simple, fast, and inexpensive. However, Creatinine has limitations, as its levels can be influenced by factors such as advanced age, physical activity, protein-rich diets, male gender, medications, and ethnicity. Serum Cystatin C and its combination with Serum creatinine may serve as an alternative since these factors do not affect it. Most creatinine synthesis occurs in the muscle, making it a valuable marker for assessing lean body mass within body composition. This measurement is crucial for evaluating and monitoring nutritional status in patients with chronic kidney disease. This review aimed to discuss the literature on creatinine metabolism, its advantages and disadvantages in assessing renal function, and its utility in measuring lean body mass. The variability in creatinine generation rate among individuals should be considered when assessing the glomerular function rate.
Applications and Future Perspectives of Artificial Intelligence in Cardiovascular Disease
Authors: Chowdhury Mohammed A. , Rizk Rodrigue , Chiu Conroy , Zhang Jing J. , Scholl Jamie L. , Bosch Taylor J. , Singh Arun , Baugh Lee A. , McGough Jeff S. , Santosh KC , Chen William C.W.
Published: December 2024

The application of artificial intelligence (AI) and machine learning (ML) in medicine and healthcare has been extensively explored across various areas. AI and ML can revolutionize cardiovascular disease management by significantly enhancing diagnostic accuracy, disease prediction, workflow optimization, and resource utilization. This review summarizes current advancements in AI and ML concerning cardiovascular disease, including their clinical investigation and use in primary cardiac imaging techniques, common cardiovascular disease categories, clinical research, patient care, and outcome prediction. We analyze and discuss commonly used AI and ML models, algorithms, and methodologies, highlighting their roles in improving clinical outcomes while addressing current limitations and future clinical applications. Furthermore, this review emphasizes the transformative potential of AI and ML in cardiovascular practice by improving clinical decision-making, reducing human error, enhancing patient monitoring and support, and creating more efficient healthcare workflows for complex cardiovascular conditions.
A Comprehensive Guideline for MPS-CAE Simulation of Jet Grouting Technology
Authors: Shakya Sudip , Chao Kuo Chieh , Inazumi Shinya
Published: December 2024

This paper presents a thorough guide to simulating jet grouting using the Moving Particle Semi-Implicit (MPS) method for numerical analysis and Computer-Aided Engineering (CAE) for model development. It addresses the shortcomings of previous jet grouting simulation studies, which often lacked clear and comprehensive guidelines, by providing a detailed step-by-step approach. The key aspects of the simulation that define and shape the output of real-world jet grouting technology, such as jet grouting spray settings and material parameter configurations, are validated against benchmark experimental data. The previously challenging task of accurately determining material parameters for soil when modeled as a Bingham fluid bi-viscosity model, is simplified into a universal guideline that can be easily applied to any soil type with known unconfined compressive strength. Finally, the reliability of the jet grouting simulation is confirmed by comparing the simulation results with benchmark experimental data under similar conditions, demonstrating the robustness and accuracy of the proposed method.
Polyamine Content of Enteral Nutrition Formulas. Effect of Daily Intake on the Feeding Tolerance of Patients During the First Week in the Intensive Care Unit
Authors: Sánchez Manuel , Rodríguez-Hernández Eva , Suárez Lorena , Cantabrana Begoña , González-García María
Published: December 2024

Enteral nutrition (EN) formulas are necessary for critically ill patients to meet their metabolic requirements. Polyamines (putrescine, spermidine and spermine) are crucial dietary components, with spermidine being particularly interesting due to its proposed multiple benefits. The requirements for and intake of polyamines have yet to be investigated in adult patients hospitalised in intensive care units (ICUs) exclusively fed via commercial EN formulas. The aim of this study was to determine the polyamine content of EN formulas and the total intake and gastric residual volume (GRV) in adult ICU patients during their first seven days of hospitalisation. Polyamines were analysed in 17 EN formulas using high-performance liquid chromatography (HPLC). The clinical data of 8 patients of both sexes aged 47 to 77 admitted to the ICU were analysed. Differences existed among the analysed EN formulas. The N-acetyl putrescine content was higher than the remaining amines. The daily intake in the ICU was less than 100 μmol (the dietary intake is above 400 μmol). An inverse correlation existed between total daily polyamine intake and daily GRV. Polyamine intake in critically ill patients receiving EN is low and could impact these patients’ feeding tolerance. These findings underscore the need for further research to explore the clinical implications of increasing the polyamine content of EN formulas.
The Saint Petersburg Paradox and Its Solution
Authors: Mattalia Claudio
Published: December 2024

In this paper we present the main historical facts concerning the Saint Petersburg paradox, the principal solutions that have been proposed, and the results of a new experimental evidence and of a simulation of the game that shed light on the solution of this paradox, that along 300 hundred years since it was first formulated has attracted the attention of many of the most important mathematicians and economists, and that has strongly influenced the development of new concepts adopted in economic and social sciences. The main conclusion is that the behavior of the individuals is not paradoxical at all, and the paradox is intrinsic to the game.
Microbial Ecology: Understanding the Dynamics of Microbial Communities and Their Impact on Ecosystems
Authors: Idowu Emmanuel
Published: December 2024

Microbial ecology is the study of microorganisms and their interactions within various environments, encompassing terrestrial, aquatic, and host-associated ecosystems. Microbial communities play a critical role in ecosystem functions, including nutrient cycling, carbon sequestration, and the decomposition of organic matter. Understanding the dynamics of microbial populations and the factors influencing their structure and function is essential to comprehending how these communities affect broader ecological processes and ecosystem stability. This paper explores the complex interactions among microbes and their environments, including mutualistic, commensal, and competitive relationships. It examines the impact of environmental variables, such as temperature, pH, and nutrient availability, on microbial community composition and diversity. Advances in molecular and genomic techniques, such as metagenomics, metatranscriptomics, and single-cell sequencing, have revolutionized microbial ecology, allowing for the identification and functional analysis of previously uncultivable microorganisms. The review highlights recent research on microbial community dynamics in critical ecosystems, such as soil, oceans, and the human gut. It also discusses the ecological implications of microbial activity, including their role in biogeochemical cycles, environmental resilience, and the response of microbial communities to climate change and anthropogenic disturbances. Additionally, the paper addresses the potential applications of microbial ecology, such as bioremediation, bioenergy production, and promoting sustainable agriculture through microbiome manipulation. By advancing our understanding of microbial ecology, scientists can better predict ecosystem responses to environmental change, harness microbial processes for environmental and industrial applications, and enhance ecosystem health. This comprehensive review provides insights into the vital roles of microbial communities and underscores the importance of protecting microbial diversity for ecosystem resilience and sustainability.
Adaptive Embedded Flexible Tensor Singular Spectrum Decomposition
Authors: Ma Huaicheng , Li Jingran , Huang Jinfeng , Wang Ruijian , Ge Rui , Zhang Feibin
Published: December 2024

To address the difficulty in extracting fault features from dual-channel signals, this work proposes a multichannel signal fusion processing method based on Flexible Tensor Singular Spectrum Decomposition (FTSSD) with adaptive embedding dimension selection. Firstly, the optimal embedding dimension of the trajectory tensor is adaptively determined using the proposed Trajectory Dimension Ratio (TDR) index. Once the optimal embedding dimension is obtained, the multichannel signals are represented as an optimal trajectory tensor. Then, FTSSD is employed to decompose the tensor and extract feature component signals. Moreover, by setting a residual threshold or maximum number of components to control the iterative process, the precision and rationality of the decomposition are ensured. Finally, all component signals are reconstructed, and their waveforms and spectra are comprehensively analyzed. Experimental results demonstrate that the proposed adaptive embedding FTSSD algorithm achieves high accuracy and robustness in multichannel signal decomposition and feature extraction, making it suitable for multicomponent analysis of complex dynamic signals such as mechanical fault diagnosis and vibration analysis.