bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Biochar Amendment on N2O Emissions from Soils with Different pH Levels
Authors: Lin Feng , Wang Hong , Shaghaleh Hiba , Ali Adam Hamad Amar , Zhang Yaojun , Yang Bairen , Alhaj Hamoud Yousef
Published: January 2024

Biochar application has the potential for mitigating N2O emissions from agricultural soils and has been suggested as a management practice to ameliorate soil fertility and increase crop productivity. Nevertheless, the influence of biochar addition on N2O emissions from soils with different pH levels is not yet clear, which results in a poor understanding of the mechanisms regarding biochar application to soil N2O mitigation. A 40-day incubation experiment was carried out in the present study to investigate the impact of biochar on N2O emissions from soils with different natural pH. Four treatments (control, nitrogen fertilizer application, biochar amendment, and N plus biochar amendment) were set up separately in soils with three different natural pH levels (acidic vegetable soil, neutral rice soil, and alkaline soil). Our results showed that adding biochar significantly decreased N2O emissions by 20.8% and 47.6% in acidic vegetable soil for both N and no N addition treatments, respectively. For neutral and alkaline soils, the reduction of N2O emissions by biochar amendment was only significant for N addition treatments in alkaline soil. Soil pH and NO3−-N concentration were significantly affected by biochar amendment (soil pH increased by 1.43–1.56, 0.57–0.70, and 0.29–0.37 units for acidic vegetable soil, neutral rice soil, and alkaline soil, respectively). Thus, biochar amendment could be used as an effective management practice for mitigating N2O emissions from acidic and alkaline soils.
Causes Investigation of PM2.5 and O3 Complex Pollution in a Typical Coastal City in the Bohai Bay Region of China in Autumn: Based on One-Month Continuous Intensive Observation and Model Simulation
Authors: Ji Yuanyuan , An Cong , Tang Jinghao , Li Jialin , Yan Xiaoyu , Gao Xiaoshuai , Chu Wanghui , Zhong Xuelian , Shang Fanyi , Li Jidong , Tan Luyao , Gao Rui , Bi Fang , Li Hong
Published: January 2024

In order to investigate the causes of complex air pollution in coastal cities in the Bohai Rim of China, a one-month intensive field observation combined with model simulation was carried out in a typical city named Dongying in September 2022. The results showed that two PM2.5 and O3 complex pollution episodes occurred in Dongying in the observation period, with the primary pollutant being O3. Atmospheric photochemical reactions occurring under unfavorable meteorological conditions led to the production of O3 while at the same time facilitating the generation of nitrate, sulfate, and other secondary components of PM2.5 by enhancing the atmospheric oxidizing capacity, which promoted the formation of complex air pollution. It was worth noting that in the context of high pollutants emission, the occurrence of complex air pollution was more sensitive to the variation in meteorological conditions than to the change in pollutants emission. To continuously improve air quality and protect human health in Dongying, it is recommended that an effective regional joint air pollution prevention and control mechanism with neighboring cities should be established in the premise of effective local pollutants reduction, and special attention should be paid to the adverse effect of the air mass transportation from Bohai Bay.
Cardio-Metabolic Health of Offspring Exposed in Utero to Human Immuno-Deficiency Virus and Anti-Retroviral Treatment: A Systematic Review
Authors: Matjuda Edna Ngoakoana , Engwa Godwill Azeh , Mungamba Muhulo Muhau , Sewani-Rusike Constance Rufaro , Goswami Nandu , Nkeh-Chungag Benedicta Ngwenchi
Published: January 2024

Background: Antiretroviral treatment (ART) use during pregnancy continues to rise as it is known to decrease the likelihood of HIV transmission from mother to child. However, it is still unknown whether foetal exposure to (ART) may affect the foetal environment, predisposing the offspring to cardiometabolic risk. Therefore, the aim of this study was to systematically review the cardio-metabolic effects of in utero exposure to HIV/ART on offspring. Methods: We carried out a systematic review and obtained literature from the Google scholar, PubMed, ProQuest, Web of Science, and Scopus databases. Two independent reviewers evaluated the titles, abstracts, and full-length English contents. Data from the eligible studies were included. Results: The search yielded 7596 records. After assessing all of these records, 35 of the full-length articles were included in this systematic review. Several studies showed that low birth weight, small head circumference, and altered mitochondrial content were more common among HIV-exposed uninfected (HEU) children compared to HIV-unexposed uninfected children (HUU). A few studies demonstrated elevated triglyceride levels, lower levels of insulin, and increased blood pressure, oxidative stress, vascular dysfunction, cardiac damage, and myocardial dysfunction among HEU children compared with HUU children. Conclusion: Most findings showed that there were cardio-metabolic health risk factors among HEU children, indicating that maternal exposure to HIV and ART may negatively affect foetal health, which may lead to cardio-metabolic morbidity later in life.
Lysosomal Dysfunction: Connecting the Dots in the Landscape of Human Diseases
Authors: Uribe-Carretero Elisabet , Rey Verónica , Fuentes Jose Manuel , Tamargo-Gómez Isaac
Published: January 2024

Lysosomes are the main organelles responsible for the degradation of macromolecules in eukaryotic cells. Beyond their fundamental role in degradation, lysosomes are involved in different physiological processes such as autophagy, nutrient sensing, and intracellular signaling. In some circumstances, lysosomal abnormalities underlie several human pathologies with different etiologies known as known as lysosomal storage disorders (LSDs). These disorders can result from deficiencies in primary lysosomal enzymes, dysfunction of lysosomal enzyme activators, alterations in modifiers that impact lysosomal function, or changes in membrane-associated proteins, among other factors. The clinical phenotype observed in affected patients hinges on the type and location of the accumulating substrate, influenced by genetic mutations and residual enzyme activity. In this context, the scientific community is dedicated to exploring potential therapeutic approaches, striving not only to extend lifespan but also to enhance the overall quality of life for individuals afflicted with LSDs. This review provides insights into lysosomal dysfunction from a molecular perspective, particularly in the context of human diseases, and highlights recent advancements and breakthroughs in this field.
Synthesis and Characterization of a Novel Concentration-Independent Fluorescent Chloride Indicator, ABP-Dextran, Optimized for Extracellular Chloride Measurement
Authors: Normoyle Kieran P. , Lillis Kyle P. , Staley Kevin J.
Published: January 2024

GABA, the primary inhibitory neurotransmitter, stimulates GABAA receptors (GABAARs) to increase the chloride conductance of the cytosolic membrane. The driving forces for membrane chloride currents are determined by the local differences between intracellular and extracellular chloride concentrations (Cli and Clo, respectively). While several strategies exist for the measurement of Cli, the field lacks tools for non-invasive measurement of Clo. We present the design and development of a fluorescent lifetime imaging (FLIM)-compatible small molecule, N(4-aminobutyl)phenanthridiunium (ABP) with the brightness, spectral features, sensitivity to chloride, and selectivity versus other anions to serve as a useful probe of Clo. ABP can be conjugated to dextran to ensure extracellular compartmentalization, and a second chloride-insensitive counter-label can be added for ratiometric imaging. We validate the utility of this novel sensor series in two sensor concentration-independent modes: FLIM or ratiometric intensity-based imaging.
The Quantitative Detection of Cystatin-C in Patient Samples Using a Colorimetric Lateral Flow Immunoassay
Authors: Bikkarolla Santosh Kumar , Venkatesan Kavipriya , Revathy Yeddula Rebecca , Parameswaran Sowmya , Krishnakumar Subramanian , Dendukuri Dhananjaya
Published: January 2024

A colloidal gold-based lateral flow immunoassay was developed for the rapid quantitative detection of Cystatin-C in serum and whole blood. This device has an assay time of 15 min, making it a convenient point-of-care diagnostic tool. The device has a quantification range spanning from 0.5 to 7.5 µg/mL, with a lower limit of detection at 0.18 µg/mL. To validate its accuracy, the test was compared to a standard nephelometric immunoassay, and the results exhibited a robust linear correlation with an adjusted r2 value of 0.95. Furthermore, the device demonstrates satisfactory levels of analytical performance in terms of precision, sensitivity, and interference, indicating its potential for precise Cystatin-C quantification, particularly in renal-failure patients. Notably, the Cystatin-C-LFA device also demonstrates satisfactory stability, as a 30-day accelerated stability study at 50 °C showed no change in the device performance, indicating a long shelf life for the product when stored at room temperature.
Performance Analysis of Self-Compacting Concrete with Use of Artificial Aggregate and Partial Replacement of Cement by Fly Ash
Authors: Patil Abhay , Jayale Vivek , Arunachalam Krishna Prakash , Ansari Khalid , Avudaiappan Siva , Agrawal Dhiraj , Kuthe Abhaykumar M. , Alharbi Yousef R. , Amir Khan Mohammad , Roco-Videla Ángel
Published: January 2024

Artificial aggregate (AF), i.e., silico manganese (SiMn) slag aggregate, is a byproduct of ferromanganese and silico manganese alloy production. The utilization of industrial waste and industrial byproducts in construction has increased the aim of conserving natural resources to nurture a pollution-free environment. The current study examines the performance of the use of artificial aggregate (AF) and partial replacement of cement with fly ash (FA). The properties of fresh concrete, as well as the compressive and flexural strength and split tensile strength of concrete were evaluated. Seven mix proportions were prepared for M30-grade concrete. The first was a control mix (with 0% AF and FA), three other mixes contained varying amounts of AF (20%, 40%, and 60%) as a partial replacement of CA with AF. The average compressive strength of the control SCC was found to be 32.87 MPa (megapascals) at the age of 28 days, and after replacing 20% natural aggregate with artificial aggregate, the compressive strength increased by 8.27%, whereas for 40% and 60% replacement, it decreased by 4.46% and 12.55%, respectively. Further investigation was performed on the optimum value obtained by replacing 20% of CA with AF. At this percentage, cement was replaced by FA at (15%, 25%, and 35%) where at 15%, the average compressive strength increased by 7.41%, whereas for 25% and 35% replacement, it decreased by 7.47% and 17.19%, respectively. For SCAF20 and SCF15, all strengths were at maximum due to the increase in its density. The findings show that the development of advanced construction materials is environmentally sustainable.
Nearly Zero-Energy Building Load Forecasts through the Competition of Four Machine Learning Techniques
Authors: Qin Haosen , Yu Zhen , Li Zhengwei , Li Huai , Zhang Yunyun
Published: January 2024

Heating, ventilation and air conditioning (HVAC) systems account for approximately 50% of the total energy consumption in buildings. Advanced control and optimal operation, seen as key technologies in reducing the energy consumption of HVAC systems, indispensably rely on an accurate prediction of the building’s heating/cooling load. Therefore, the goal of this research is to develop a model capable of making such accurate predictions. To streamline the process, this study employs sensitivity and correlation analysis for feature selection, thereby eliminating redundant parameters, and addressing distortion problems caused by multicollinearity among input parameters. Four model identification methods including multivariate polynomial regression (MPR), support vector regression (SVR), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost) are implemented in parallel to extract value from diverse building datasets. These models are trained and selected autonomously based on statistical performance criteria. The prediction models were deployed in a nearly zero-energy office building, and the impacts of feature selection, training set size, and real-world uncertainty factors were analyzed and compared. The results showed that feature selection considerably improved prediction accuracy while reducing model dimensionality. The research also recognized that prediction accuracy during model deployment can be influenced significantly by factors like personnel mobility during holidays and weather forecast uncertainties. Additionally, for nearly zero-energy buildings, the thermal inertia of the building itself can considerably impact prediction accuracy in certain scenarios.
Microclimate-Adaptive Morphological Parametric Design of Streets and Alleys in Traditional Villages
Authors: Lyu Yufei , Zhang Lei , Liu Xin , Ma Xuan
Published: January 2024

Microclimate is a reflection of the climatic conditions within the scope of human daily activities, so it is closely related to human activities. This paper uses Qingshui Village in Hancheng as the research object and the purpose of this paper was to study the influence mechanism of the traditional street spatial form on microclimate and thermal comfort, emphasizing the use of parameterized design platforms in technical methods to construct performance simulations and obtain relatively optimal solutions that are suitable for the spatial form of rural streets and alleys in the region. We select the Universal Thermal Climate Evaluation Index (UTCI) as the evaluation index for microclimate comfort and construct a performance-driven automatic optimization method for street and alley spaces. The results showed that: (1) When the street is in the northeast-southwest direction, the width is taken in the range of 6.5 m–7.3 m, the height of the building on the north side of the street is about 6.0 m, and the height of the building on the south side of the street is about 5.7 m, the comfort rate can reach up to 33.8%. (2) As for street and alley intersections, the focus remains on retaining their original forms while primarily controlling the scale changes. Within streets, the height of the building in the east-west direction is controlled at 5.6 m–6.1 m, the building in the north-south direction is controlled at 7 m–7.4 m, and street widths are controlled between 5.4 m and 6.3 m, resulting in a comfort level of 32.0%; (3) In alleys, east-west building heights are kept between 4.2 m and 5.5 m, and north-south building heights range from 4.5 m to 5.3 m, with widths at around 4.5 m, resulting in a similar comfort level of 32.0%. The research outcomes offer a scientific foundation for the design, creation, and enhancement of the physical environment of local village streets and alleys.
Pioneering the Future: A Trailblazing Review of the Fusion of Computational Fluid Dynamics and Machine Learning Revolutionizing Plasma Catalysis and Non-Thermal Plasma Reactor Design
Authors: Arshad Muhammad Yousaf , Ahmad Anam Suhail , Mularski Jakub , Modzelewska Aleksandra , Jackowski Mateusz , Pawlak-Kruczek Halina , Niedzwiecki Lukasz
Published: January 2024

The advancement of plasma technology is intricately linked with the utilization of computational fluid dynamics (CFD) models, which play a pivotal role in the design and optimization of industrial-scale plasma reactors. This comprehensive compilation encapsulates the evolving landscape of plasma reactor design, encompassing fluid dynamics, chemical kinetics, heat transfer, and radiation energy. By employing diverse tools such as FLUENT, Python, MATLAB, and Abaqus, CFD techniques unravel the complexities of turbulence, multiphase flow, and species transport. The spectrum of plasma behavior equations, including ion and electron densities, electric fields, and recombination reactions, is presented in a holistic manner. The modeling of non-thermal plasma reactors, underpinned by precise mathematical formulations and computational strategies, is further empowered by the integration of machine learning algorithms for predictive modeling and optimization. From biomass gasification to intricate chemical reactions, this work underscores the versatile potential of plasma hybrid modeling in reshaping various industrial processes. Within the sphere of plasma catalysis, modeling and simulation methodologies have paved the way for transformative progress. Encompassing reactor configurations, kinetic pathways, hydrogen production, waste valorization, and beyond, this compilation offers a panoramic view of the multifaceted dimensions of plasma catalysis. Microkinetic modeling and catalyst design emerge as focal points for optimizing CO2 conversion, while the intricate interplay between plasma and catalysts illuminates insights into ammonia synthesis, methane reforming, and hydrocarbon conversion. Leveraging neural networks and advanced modeling techniques enables predictive prowess in the optimization of plasma-catalytic processes. The integration of plasma and catalysts for diverse applications, from waste valorization to syngas production and direct CO2/CH4 conversion, exemplifies the wide-reaching potential of plasma catalysis in sustainable practices. Ultimately, this anthology underscores the transformative influence of modeling and simulation in shaping the forefront of plasma-catalytic processes, fostering innovation and sustainable applications.
The Role of Stem Cells as Therapeutics for Ischaemic Stroke
Authors: Ya Jingyuan , Pellumbaj Jessica , Hashmat Arshad , Bayraktutan Ulvi
Published: January 2024

Stroke remains one of the leading causes of death and disability worldwide. Current reperfusion treatments for ischaemic stroke are limited due to their narrow therapeutic window in rescuing ischaemic penumbra. Stem cell therapy offers a promising alternative. As a regenerative medicine, stem cells offer a wider range of treatment strategies, including long-term intervention for chronic patients, through the reparation and replacement of injured cells via mechanisms of differentiation and proliferation. The purpose of this review is to evaluate the therapeutic role of stem cells for ischaemic stroke. This paper discusses the pathology during acute, subacute, and chronic phases of cerebral ischaemic injury, highlights the mechanisms involved in mesenchymal, endothelial, haematopoietic, and neural stem cell-mediated cerebrovascular regeneration, and evaluates the pre-clinical and clinical data concerning the safety and efficacy of stem cell-based treatments. The treatment of stroke patients with different types of stem cells appears to be safe and efficacious even at relatively higher concentrations irrespective of the route and timing of administration. The priming or pre-conditioning of cells prior to administration appears to help augment their therapeutic impact. However, larger patient cohorts and later-phase trials are required to consolidate these findings.
An Examination of the Effect of Aspirin and Salicylic Acid on Soluble Fms-like Tyrosine Kinase-1 Release from Human Placental Trophoblasts
Authors: Zhao Jiawu , Duan Rui , Sun Jinghui , Chow Rebecca P. , Lyons Timothy J. , Yu Jeremy Y.
Published: January 2024

Low-dose aspirin (LDA) is efficacious in preventing preeclampsia, but its mechanism of action is unclear. Conflicting evidence suggests that it may inhibit placental trophoblast release of soluble fms-like tyrosine kinase-1 (sFlt1), a key mediator of preeclampsia. We examined whether, and at what concentrations, aspirin and its principal metabolite, salicylic acid, modulate sFlt1 release and/or expression in trophoblasts. Human trophoblast lines BeWo and HTR-8/SVneo were cultured; BeWo cells were also treated with 1% oxygen vs. normoxia to mimic hypoxia in preeclamptic placentas. Cells were treated with aspirin or salicylic acid vs. vehicle for 24 h at concentrations relevant to LDA and at higher concentrations. Protein concentrations (ELISA) and mRNA expression (RT-PCR) of sFlt1 were determined. Under normoxia, LDA-relevant concentrations of aspirin (10–50 µmol/L) or salicylic acid (20–100 µmol/L) had no significant effect on sFlt1 protein release or mRNA expression in BeWo cells. However, inhibition was observed at higher concentrations (1 mmol/L for aspirin and ≥200 μmol/L for salicylic acid). Hypoxia enhanced sFlt1 protein release and mRNA expression in BeWo cells, but these responses were not significantly affected by either aspirin or salicylic acid at LDA concentrations. Similarly, neither drug altered sFlt1 protein secretion or mRNA expression in normoxic HTR-8/SVneo cells at LDA concentrations. We suggest that direct modulation of trophoblast release or expression of sFlt1 is unlikely to be a mechanism underlying the clinical efficacy of LDA in preeclampsia.
The Influence of Microstructure Characteristics on Thickness Measurement of TBCs Using Terahertz Time-Domain Spectroscopy
Authors: Zhou Han , Xing Yifeng , Feng Yang , Geng Lilun , Shang Yong , Pei Yanling , Bi Xiaofang , Gong Shengkai
Published: January 2024

Thermal barrier coatings (TBCs) exhibit excellent thermal insulation capabilities, proving crucial in enhancing the performance of turbine blades. Accurate measurement of TBC thickness is pivotal for the quality control and health monitoring of turbine blades. However, the absence of suitable non-destructive testing (NDT) methods poses a challenge in ensuring precise quality control and health assessment of TBCs. This study investigates the efficacy of terahertz time-domain spectroscopy (THz-TDS) in measuring TBCs thickness, specifically focusing on the microstructure characteristics of the top coat (TC), including grain morphology, internal porosity, surface roughness, and agglomerates. The findings emphasize the significance of grain morphology in determining thickness measurement due to the varied terahertz wave propagation modes. Moreover, the study involved polishing EB-PVD and APS samples to mitigate surface roughness. This process revealed a discernible linear correlation between reduced surface roughness and decreased measurement errors. The slopes of the error reduction curves ranged from 0.59 to 1.7 for EB-PVD and 2.17 to 5.79 for APS samples. Furthermore, the research observed THz light scattering within internal pores, resulting in diminished outgoing energies and subsequent increments in measurement errors.
Monitoring of Carbonated Hydroxyapatite Growth on Modified Polycrystalline CVD-Diamond Coatings on Titanium Substrates
Authors: Carcione Rocco , Guglielmotti Valeria , Mura Francesco , Orlanducci Silvia , Tamburri Emanuela
Published: January 2024

Production of diamond coatings on titanium substrates has demonstrated as a promising strategy for applications ranging from biosensing to hard tissue engineering. The present study focuses on monitoring the nucleation and growth of bone-like carbonated-hydroxyapatite (C-HA) on polycrystalline diamond (PCD) synthetized on titanium substrate by means of a hot filament chemical vapor deposition (HF-CVD) method. The surface terminations of diamond coatings were selectively modified by oxidative treatments. The process of the C-HA deposition, accomplished by precipitation from simulated body fluid (SBF), was monitored from 3 to 20 days by Raman spectroscopy analysis. The coupling of morphological and structural investigations suggests that the modulation of the PCD surface chemistry enhances the bioactivity of the produced materials, allowing for the formation of continuous C-HA coatings with needle-like texture and chemical composition typical of those of the bone mineral. Specifically, after 20 days of immersion in SBF the calculated carbonate weight percent and the Ca/P ratio are 5.5% and 2.1, respectively. Based on these results, this study brings a novelty in tailoring the CVD-diamond properties for advanced biomedical and technological applications.
Thermal Tolerance and Vulnerability to Climate Change of a Threatened Freshwater Mussel
Authors: Wagner Annekatrin , Linke Daniel , Grunicke Felix , Berendonk Thomas U.
Published: January 2024

Freshwater pearl mussels (FPMs, Margaritifera margaritifera, Linnaeus, 1758) are endangered and particularly vulnerable to climate change. To create effective conservation strategies, we studied their thermal tolerance and the impact of elevated water temperatures on growth and survival. Our experiments included field mesocosm studies in five FPM-streams in the Vogtland region (Germany) (2016 to 2020), as well as laboratory experiments at temperatures ranging from 1 to 26 °C. Growth of juvenile FPMs increased significantly within a temperature gradient from 12 to 21 °C. In the streams, maximum growth was 8.9 µm/d in surface water and 6.5 µm/d in the interstitial. The upper thermal tolerance for the mussels ranged from 22.1 to 22.9 °C, resulting in low survival during hot summer periods in 2018 and 2019. Warming during winter (+5 °C) did not significantly affect growth and survival, but survival during winter increased with the pre-overwintering shell length. Exceeding a shell length of about 1100 µm in December indicating gill development corelated to 50% survival. Shell length in December is primarily controlled by growth depending on water temperatures during summer. These findings define the thermal niche of juvenile FPMs (average summer temperatures of 14.5–21 °C) and have implications for water management, conservation strategies, and site selection for releasing captive-breeding mussels.
Protocol for a Multi-Center Confirmatory Trial to Evaluate the Differential Diagnostic Performance of Contrast-Enhanced Ultrasonography Using Perflubutane in Patients with a Pancreatic Mass: A Multicenter Prospective Study
Authors: Yamashita Yasunobu , Shimokawa Toshio , Ashida Reiko , Hirooka Yoshiki , Iwashita Takuji , Kato Hironari , Kin Toshifumi , Masamune Atsushi , Miwa Haruo , Ohno Eizaburo , Shiomi Hideyuki , Sofuni Atsushi , Takenaka Mamoru , Kitano Masayuki
Published: January 2024

For pancreatic masses, an evaluation of their vascularity using contrast-enhanced ultrasonography can help improve their characterization. This study was designed to evaluate the utility and safety of contrast-enhanced transabdominal ultrasonography (CE-TUS) and endoscopic ultrasonography (CE-EUS) in the diagnosis of pancreatic masses including solid or cystic masses. This multi-center comparative open-label superiority study is designed to compare Plain (P)-TUS/EUS alone with P-TUS/P-EUS plus CE-TUS/CE-EUS. Three hundred and one patients with a total of 232 solid pancreatic masses and 69 cystic masses were prospectively enrolled. The primary endpoints are to compare the diagnostic accuracy between P-TUS/P-EUS alone and P-TUS/P-EUS plus CE-TUS/CE-EUS for both the TUS and EUS of solid pancreatic masses, and to compare the diagnostic accuracy between P-EUS alone and P-EUS plus CE-EUS in cystic pancreatic masses. The secondary endpoints are to compare the diagnostic sensitivity and specificity of P-TUS/P-EUS alone and P-TUS/P-EUS plus CE-TUS/CE-EUS for pancreatic solid/cystic masses, and the accuracy of P-TUS alone and P-TUS plus CE-TUS for pancreatic cystic masses. Other secondary endpoints included comparing the diagnostic sensitivity, specificity, and accuracy of CE-TUS, CE-EUS and CE-computed tomography (CT) for solid/cystic pancreatic masses. The safety, degree of effective enhancement, and diagnostic confidence obtained with CE-TUS/CE-EUS will also be assessed.
Coexistence of Colorectal Cancer and Immunoglobulin G4-Related Disease in the Same Lesion: A Rare Case with Molecular Classification
Authors: Na Kiyong , Kim So-Woon
Published: January 2024

Immunoglobulin G4-related disease (IgG4-RD) is a novel fibroinflammatory disorder characterized by enlargement of the involved organs, elevated IgG4 levels, and abundant infiltration of IgG4-positive plasma cells. Indeed, primary colon cancers arising from IgG4-RD are rare. This case report describes a rare occurrence of simultaneous colorectal cancer and IgG4-RD in the same lesion in a 62-year-old male patient. The patient underwent a right hemicolectomy under the suspicion of primary colon cancer. The mass was grossly well-defined and yellowish tan, and the background colon was fibrotic. Microscopically, the tumor cells showed glandular differentiation characteristic of adenocarcinoma in a background of dense lymphoplasmacytic infiltration with fibrosis and obliterative phlebitis in the pericolic fat tissue. IgG4 immunohistochemical staining showed diffuse positivity in infiltrating plasma cells. The patient was administered adjuvant chemotherapy and prednisolone therapy. The patient’s serum IgG4 levels gradually decreased, and a follow-up positron emission tomography-computed tomography scan 1 year after surgery showed no evidence of local or distant recurrence of colorectal cancer. IgG4-RD occurring concurrently with primary colon adenocarcinoma has not been reported. Increased awareness of this rare coexistence can guide clinicians in navigating diagnostic complexities and selecting optimal therapeutic strategies.
Improving Elementary Pre-Service Teachers’ Science Teaching Self-Efficacy through Garden-Based Technology Integration
Authors: Ingram Erin , Hill Trish Wonch , Harshbarger Dena , Keshwani Jenny
Published: January 2024

School gardens and outdoor learning spaces are increasingly available to support authentic, student-centered exploration in the areas of science, technology, engineering, and mathematics (STEM). Integrating technology tools into school garden spaces in alignment with modern agricultural practices can support inquiry-based learning in which students engage in science practices such as collecting and analyzing data. However, educators currently lack the necessary knowledge, skills, and instructional support to enact technology-rich, garden-based STEM learning experiences. The Garden TOOLS program was designed to support teachers in leveraging technology to support inquiry-based teaching (IBT) in outdoor learning spaces. In this paper, we examine the impact of combining Garden TOOLS professional development workshops with lesson plan implementation in a practicum setting on elementary pre-service teachers’ (PSTs) science teaching self-efficacy. We administered the STEBI-B pre- and post-intervention and assessed change in the STEBI-B subscales, personal science teaching efficacy (PSTE), and science teaching outcome expectancy (STOE). Participants included thirty-nine elementary PSTs enrolled in a 300-level science methods course in the rural Midwest. Garden TOOLS professional development workshop participants saw a statistically significant increase from pre- to post-workshop in their PSTE. Participants who also implemented the Garden TOOLS lesson showed both an increase in PSTE and STOE.
Social Media and Artificial Intelligence: Critical Conversations and Where Do We Go from Here?
Authors: Parra Julia Lynn , Chatterjee Suparna
Published: January 2024

Prior to and during the pandemic, social media platforms such as Twitter and Facebook emerged as dynamic online spaces for diverse communities facilitating engagement and learning. The authors of this article have explored the use of social media with a focus on Twitter for engagement and student-centered design of online courses in higher education. As with all technology, social media is also riddled with complex issues and unfortunately, is increasingly considered unsafe. Students have often been hesitant in their use of social media, especially for coursework and unfortunately, this hesitation has only worsened. Considering this and recent developments, social media has become a questionable tool for use in education, yet remains integral to the lives of many, both personally and professionally. The emergence and popularity of generative artificial intelligence (GenAI) tools such as ChatGPT, Lensa AI, and Canva Magic Write present new challenges and opportunities and cannot be avoided by the educational communities. Is there hope for social media and AI tools during these uncertain times? Through the combination of a current literature review and qualitative collaborative autoethnographic research, the authors take a step back and engage in critical conversations about what we have learned from our uses of social media for engagement and learning in our online courses, with a focus on (1) the intentional uses of social media, (2) the challenges and concerning issues of social media tools, and (3) exploring the implications of artificial intelligence. Centering on the theme of “hope,” the authors navigate these educational and technological landscapes and answer the question “where do we go from here?” The authors are faculty at a southwest border university teaching preservice and in-service teachers alongside those who want to learn more about education and design with learning technologies. Their voices represent faculty, teachers, and students who are engaging with and immediately impacted by the challenges and opportunities of rapidly advancing technologies.
Why We Can’t Wait: A Guide for Black Student Achievement Programs
Authors: Whiting Gilman W. , Nyberg Julia L.
Published: January 2024

Black student achievement is vital. There must be a focused national effort to establish and sustain Black Student Achievement Programs (BSAPs). The development of BSAPs centers on African American history, culture, language, knowledge, and values. This article describes Black Student Achievement Program standards and the components of service design, curriculum and instruction, scholar identity development, and social and emotional needs, connecting the home and community for Black students in K-12th grade settings. Educators can play a vital role in the efforts to build and sustain BSAPs at their school sites and school districts.
Introduction to Special Issue on Postsecondary and Tertiary Peer-Assisted Learning and a Future Research Agenda
Authors: Arendale David R.
Published: January 2024

The Journal of Education Sciences is pleased to welcome you to this Special Issue, “Postsecondary and Tertiary Peer Assisted Learning (PAL)” [...]
Transient Stability Assessment of Power Systems Based on the Transformer and Neighborhood Rough Set
Authors: Bei Tianyi , Xiao Jianmei , Wang Xihuai
Published: January 2024

Modern power systems are large in size and complex in features; the data collected by Phasor Measurement Units (PMUs) are often noisy and contaminated; and the machine learning models that have been applied to the transient stability assessment (TSA) of power systems are not sufficiently capable of capturing long-distance dependencies. All these issues make it difficult for data mining-based power system TSA methods to have sufficient accuracy, timeliness, and robustness. To solve this problem, this paper proposes a power system TSA model based on the transformer and neighborhood rough set. The model first uses the neighborhood rough set to deal with the redundant features of the power system trend data and then uses the transformer model to train the TSA model, in which various normalization methods such as Batch Normalization and Layer Normalization are introduced in the process to obtain better evaluation performance and speed up the convergence rate of the model. Finally, the model is evaluated by two evaluation indicators, F1−measure and accuracy, with values of 99.61% for accuracy and 0.9972 for F1−measure, as soon as the tests on noise contamination and missing data test results on the IEEE39 system show that the NRS-Transformer model proposed in this paper is superior in terms of prediction accuracy, training speed, and robustness.
Automatic Counting and Location Labeling of Rice Seedlings from Unmanned Aerial Vehicle Images
Authors: Yeh Jui-Feng , Lin Kuei-Mei , Yuan Li-Ching , Hsu Jenq-Muh
Published: January 2024

Traditional counting of rice seedlings in agriculture is often labor-intensive, time-consuming, and prone to errors. Therefore, agricultural automation has gradually become a prominent solution. In this paper, UVA detection, combining deep learning with unmanned aerial vehicle (UAV) sensors, contributes to precision agriculture. We propose a YOLOv4-based approach for the counting and location marking of rice seedlings from unmanned aerial vehicle (UAV) images. The detection of tiny objects is a crucial and challenging task in agricultural imagery. Therefore, we make modifications to the data augmentation and activation functions in the neural elements of the deep learning model to meet the requirements of rice seedling detection and counting. In the preprocessing stage, we segment the UAV images into different sizes for training. Mish activation is employed to enhance the accuracy of the YOLO one-stage detector. We utilize the dataset provided in the AIdea 2021 competition to evaluate the system, achieving an F1-score of 0.91. These results indicate the superiority of the proposed method over the baseline system. Furthermore, the outcomes affirm the potential for precise detection of rice seedlings in precision agriculture.
Development of the Engagement Playability and User eXperience (EPUX) Metric for 2D-Screen and VR Serious Games: A Case-Study Validation of Hellblade: Senua’s Sacrifice
Authors: Martinez Kim , Checa David , Bustillo Andres
Published: January 2024

Research into the design of serious games still lacks metrics to evaluate engagement with the experience so that users can achieve the learning aims. This study presents the new EPUX metric, based on playability and User eXperience (UX) elements, to measure the capability of any serious game to maintain the attention of players. The metric includes (1) playability aspects: game items that affect the emotions of users and that constitute the different layers of the game, i.e., mechanics, dynamics and aesthetics; and (2) UX features: motivation, meaningful choices, usability, aesthetics and balance both in the short and in the long term. The metric is also adapted to evaluate virtual reality serious games (VR-SGs), so that changes may be considered to features linked to playability and UX. The case study for the assessment of the EPUX metric is Hellblade, developed in two versions: one for 2D-screens and the other for VR devices. The comparison of the EPUX metric scores for both versions showed that (1) some VR dynamics augmented the impact of gameplay and, in consequence, engagement capacity; and (2) some game design flaws were linked to much lower scores. Among those flaws were low numbers of levels, missions, and items; no tutorial to enhance usability; and lack of strategies and rewards to increase motivation in the long term.
Training Data Augmentation with Data Distilled by Principal Component Analysis
Authors: Sirakov Nikolay Metodiev , Shahnewaz Tahsin , Nakhmani Arie
Published: January 2024

This work develops a new method for vector data augmentation. The proposed method applies principal component analysis (PCA), determines the eigenvectors of a set of training vectors for a machine learning (ML) method and uses them to generate the distilled vectors. The training and PCA-distilled vectors have the same dimension. The user chooses the number of vectors to be distilled and augmented to the set of training vectors. A statistical approach determines the lowest number of vectors to be distilled such that when augmented to the original vectors, the extended set trains an ML classifier to achieve a required accuracy. Hence, the novelty of this study is the distillation of vectors with the PCA method and their use to augment the original set of vectors. The advantage that comes from the novelty is that it increases the statistics of ML classifiers. To validate the advantage, we conducted experiments with four public databases and applied four classifiers: a neural network, logistic regression and support vector machine with linear and polynomial kernels. For the purpose of augmentation, we conducted several distillations, including nested distillation (double distillation). The latter notion means that new vectors were distilled from already distilled vectors. We trained the classifiers with three sets of vectors: the original vectors, original vectors augmented with vectors distilled by PCA and original vectors augmented with distilled PCA vectors and double distilled by PCA vectors. The experimental results are presented in the paper, and they confirm the advantage of the PCA-distilled vectors increasing the classification statistics of ML methods if the distilled vectors augment the original training vectors.
A Time Series-Based Approach to Elastic Kubernetes Scaling
Authors: Yuan Haibin , Liao Shengchen
Published: January 2024

With the increasing popularity of cloud-native architectures and containerized applications, Kubernetes has become a critical platform for managing these applications. However, Kubernetes still faces challenges when it comes to resource management. Specifically, the platform cannot achieve timely scaling of the resources of applications when their workloads fluctuate, leading to insufficient resource allocation and potential service disruptions. To address this challenge, this study proposes a predictive auto-scaling Kubernetes Operator based on time series forecasting algorithms, aiming to dynamically adjust the number of running instances in the cluster to optimize resource management. In this study, the Holt–Winter forecasting method and the Gated Recurrent Unit (GRU) neural network, two robust time series forecasting algorithms, are employed and dynamically managed. To evaluate the effectiveness, we collected workload metrics from a deployed RESTful HTTP application, implemented predictive auto-scaling, and assessed the differences in service quality before and after the implementation. The experimental results demonstrate that the predictive auto-scaling component can accurately predict the future trend of the metrics and intelligently scale resources based on the prediction results, with a Mean Squared Error (MSE) of 0.00166. Compared to the deployment using a single algorithm, the cold start time is reduced by 1 h and 41 min, and the fluctuation in service quality is reduced by 83.3%. This process effectively enhances the quality of service and offers a novel solution for resource management in Kubernetes clusters.
Exergy-Based Optimization of a CO2 Polygeneration System: A Multi-Case Study
Authors: Tashtoush Bourhan , Luo Jing , Morosuk Tatiana
Published: January 2024

A polygeneration system for power, heat, and refrigeration has been evaluated and optimized using exergy-based methods. CO2 is the working fluid. The study considered two environmental conditions for the potential implementation of the polygeneration system: cold (Casecold) and hot (Casehot). Aspen HYSYS® was used to perform steady-state simulations, Python was used for the automation of the process, and the connection of Aspen HYSYS® with Python was successfully applied for single-objective and multi-objective optimizations. A wide range of decision variables was implemented. The minimization of the average cost of a product per unit of exergy was the goal of single-objective optimization and was included in the multi-objective optimization in addition to the maximization of the overall exergy efficiency. Single-objective and multi-objective optimization were applied. Both optimization algorithms result in the necessity to increase the pinch temperature in the heat exchanger (ΔTpinch,HE), maintain the pinch temperature in the gas cooler (ΔTpinch,GC), and augment this value for the evaporator (ΔTpinch,EVAP). Notably, higher isentropic efficiency for turbomachinery correlates with improved optimization outcomes. These findings contribute to the applicability and performance of the polygeneration system, offering potential advancements in sustainable energy solutions.
Enhanced Coordination in the PV–HESS Microgrids Cluster: Introducing a New Distributed Event Consensus Algorithm
Authors: Al-Tameemi Zaid Hamid Abdulabbas , Lie Tek Tjing , Zamora Ramon , Blaabjerg Frede
Published: January 2024

To ensure reliable power delivery to customers under potential disturbances, the coordination of a microgrid cluster (MGC) is essential. Various control strategies—centralized, decentralized, distributed, and hierarchical—have been explored in the literature to achieve this goal. The hierarchical control method, with three distinct levels, has proven effective in fostering coordination among microgrids (MGs) within the cluster. The third control level, utilizing a time-triggering consensus protocol, relies on a continuous and reliable communication network for data exchange among MGs, leading to resource-intensive operations and potential data congestion. Moreover, uncertainties introduced by renewable energy sources (RESs) can adversely impact cluster performance. In response to these challenges, this paper introduces a new distributed event-triggered consensus algorithm (DETC) to enhance the efficiency in handling the aforementioned issues. The proposed algorithm significantly reduces communication burdens, addressing resource usage concerns. The performance of this approach is evaluated through simulations of a cluster comprising four DC MGs, in each of which were PV and a hybrid Battery-Super capacitor in the MATLAB environment. The key findings indicate that the proposed DETC algorithm achieves commendable results in terms of voltage regulation, precise power sharing among sources, and a reduction in triggering instants. Based on these results, this method can be deemed as a good development in MGC management, providing a more efficient and reliable means of coordination, particularly in scenarios with dynamic loads and renewable energy integration. It is also a viable option for current microgrid systems, due to its ability to decrease communication loads while retaining excellent performance.
Wildfire-Induced Risk Assessment to Enable Resilient and Sustainable Electric Power Grid
Authors: Kovvuri Srikar , Chatterjee Paroma , Basumallik Sagnik , Srivastava Anurag
Published: January 2024

To ensure the sustainability of the future power grid, the rate of expansion of distributed energy resources (DERs) has introduced operational challenges. These include managing transmission constraints with DER power injection, dispatching DERs efficiently, managing system frequency, and ensuring sufficient reactive power for voltage support. Coupled with the intensification of wildfires, power infrastructures across the United States face challenges to minimize the impact of these factors and maintain system reliability and resiliency. This research embarked on a comprehensive evaluation, beginning with an in-depth historical analysis to delineate regions most susceptible to wildfires. Utilizing a multidimensional approach, the study assessed wildfire-induced risks to power grids by integrating historical wildfire occurrences, real-time wildfire proximities, Moderate-Resolution Imaging Spectroradiometer (MODIS)-derived vegetation metrics, and system parameters. Principal component analysis (PCA)-based optimal weights were then used, leading to the formulation of a novel risk factor model. This risk factor model has the potential to be the key to ensuring the resilience of a renewable-rich smart grid when faced with a severe weather event. Our model’s applicability was further verified through an empirical assessment, selecting representative networks from diverse regions, providing insights into the geographical variability of risk factors. Ultimately, this study offers stakeholders and policymakers a comprehensive toolset, empowering decisions regarding infrastructure investments, grid reinforcements, and strategic power rerouting to ensure consistent energy delivery during wildfires.