bluuebunny/crossref_metadata_2025_split huggingface dataset:
Enhancing microbial fuel cell performance through microbial immobilization
Authors: Mersinkova Yana , Yemendzhiev Hyusein 
Published: June 2024

Abstract
Bio-electrochemical Systems (BES), particularly Microbial Fuel Cells (MFC), have emerged as promising technologies in environmental biotechnology. This study focused on optimizing the anode bacterial culture immobilization process to enhance BES performance. The investigation combines and modifies two key immobilization methods: covalent bonding with glutaraldehyde and inclusion in a chitosan gel in order to meet the criteria and requirements of the bio-anodes in MFC. The performance of MFCs with immobilized and suspended cultures was compared in parallel experiments. Both types showed similar substrate utilization dynamics with slight advantage of the immobilized bio-anode considering the lower concentration of biomass. The immobilized MFC exhibited higher power generation and metabolic activity, as well. Probably, this is due to improved anodic respiration and higher coulombic efficiency of the reactor. Analysis of organic acids content supported this conclusion showing significant inhibition of the fermentation products production in the MFC reactor with immobilized anode culture.
Chemical composition and the anti-inflammatory effect of volatile compounds from Anaxagorea luzonensis A. Gray
Authors: Tran Gia Buu , Pham Ty Viet , Tuan Le Anh , Nguyen Nguyen Hoai , Vo Nguyen Huy Hoang , Do Bich Hang 
Published: June 2024

Abstract

Anaxagorea luzonensis A. Gray, a member of the Annonaceae family, has been used to treat a variety of illnesses for a long time. For the first time, A. luzonensis volatile compounds (ALVCs) were extracted from the leaves, and the components were identified using gas chromatography-mass spectrometry (GC-MS). Further, the main compositions of ALVCs were also assessed for their ability to bind with anti-inflammatory proteins using a docking model. In addition, in vitro tests e.g. inhibition of protein degradation and the inhibition of nitric oxide release using RAW264.7 macrophage cells were utilized for evaluating the anti-inflammatory activity. The results showed that the principal compounds of ALVCs were bulnesol (34.1 %), cubitene (17.8 %), β-eudesmol (10.4 %), epi-longipinanol (5.9 %), and (Z)-nerolidyl acetate (5.5 %). Three compounds viz. bulnesol, cubitene, and β-eudesmol bound firmly to cyclooxygenase-1 (COX-1), cyclooxygenase-2 (COX-2) and 5-lipoxygenase (5-LOX), as shown by the in silico analysis, similar to the positive control diclofenac. ALVCs effectively inhibited protein degradation with the IC50 of 31 ± 2.3 μg/mL and inhibited nitric oxide production with the IC50 of 43.30 ± 3.37 μg/mL. These findings showed that ALVCs might have a promising anti-inflammatory effect by blocking several inflammatory proteins.
A Space to Be: Ability, Disability, and the Inevitability of Corporeal Decline in Hemingway's The Old Man and the Sea
Authors: Robin Dominic 
Published: June 2024

On July 2, 1961, nine years after he published The Old Man and the Sea, Ernest Hemingway died by suicide in his home in Ketchum, Idaho. With his death a persona was born. To many, Hemingway represents the quintessential anti-disability writer, the author who lived for ability, who lost ability, and who ended his life when no hope of regaining ability remained. Drawing from disability scholars such as Michael Bérubé, Timothy Jay Dolmage, David T. Mitchell and Sharon L. Snyder, I demonstrate the ways The Old Man and the Sea complicates this narrative. In particular, the novella works to legitimize the disabled body, actively subverting several common narrative tropes such narrative prosthesis, the overcoming narrative, and the kill-or-cure dichotomy. In doing so, Hemingway creates a space in which the inevitable decay of the human body must be seriously and honestly addressed. Through this research, a new more nuanced picture of Hemingway emerges, one that recognizes the complicated and dynamic nature his view of the able-bodied individual took.
Validity of Current Smartwatches for Triathlon Training: How Accurate Are Heart Rate, Distance, and Swimming Readings?
Authors: Jacko Tobias , Bartsch Julia , von Diecken Carlo , Ueberschär Olaf 
Published: June 2024

Smartwatches are one of the most relevant fitness trends of the past two decades, and they collect increasing amounts of health and movement data. The accuracy of these data may be questionable and requires further investigation. Therefore, the aim of the present study is to validate smartwatches for use in triathlon training. Ten different smartwatches were tested for accuracy in measuring heart rate, distance (via global navigation satellite systems, GNSS), swim stroke rates and the number of swim laps in a 50 m Olympic-size pool. The optical heart rate measurement function of each smartwatch was compared to that of a chest strap. Thirty participants (15 females, 15 males) ran five 3-minute intervals on a motorised treadmill to evaluate the accuracy of the heart rate measurements. Moreover, for each smartwatch, running und cycling distance tracking was tested over six runs of 4,000 m on a 400 m tartan stadium track, six hilly outdoor runs over 3.4 km, and four repetitions of a 36.8 km road bike course, respectively. Three swimming protocols ranging from 200 m to 400 m were performed in triplicate in a 50 m Olympic-size pool, evaluating tracked distance and detected number of strokes. The mean absolute percentage errors (MAPEs) for the average heart rate measurements varied between 3.1% and 8.3%, with the coefficient of determination ranging from 0.22 to 0.79. MAPE results ranged from 0.8% to 12.1% for the 4,000 m run on the 400 m track, from 0.2% to 7.5% for the 3.4 km outdoor run, and from 0.0% to 4.2% for the 36.8 km bike ride. For the swimming tests, in contrast, the deviations from the true distance varied greatly, starting at a 0.0% MAPE for the 400 m freestyle and reaching 91.7% for the 200 m medley with style changes every 25 m. In summary, for some of the smartwatches, the measurement results deviated substantially from the true values. Measurements taken while road cycling over longer distances with only a few curves were in relative terms more accurate than for outdoor runs and even more accurate than for the 400 m track. In the swimming exercises, the accuracy of the measured distances was severely deteriorated by the medley changes among the majority of the smartwatches. Altogether, the results of this study should help in assessing the accuracy and thus suitability of smartwatches for general triathlon training.
Conversion Birmingham Hip Resurfacing in Managing Post-traumatic Arthritis With Retained Femoral Hardware
Authors: Khan Shujaa T. , Pasqualini Ignacio , Mesko Nathan , McLaughlin John , Brooks Peter J. , Piuzzi Nicolas S. 
Published: June 2024

Case:
A 70-year-old man with a year-long history of arthritic pain in his left hip presented to our clinic. He had a left intertrochanteric hip fracture 6 years ago, fixed with an open reduction internal fixation with a cephalomedullary nail. He underwent a conversion Birmingham Hip Resurfacing (BHR) with removal of the proximal helicoidal blade and retention of the intramedullary nail. At 7-year follow-up, the patient reported satisfactory clinical outcomes and excellent radiographic fixation.

Conclusion:
This case highlights using conversion BHR in patients with post-traumatic arthritis with retained femoral hardware as an alternative to conventional total hip arthroplasty.
Innovative Service Quality Boosts Patient Satisfaction
Authors: Firdaus Muhammad Zaqqi Zamroni , Cahyana Atikha Sidhi 
Published: June 2024

This study examines factors affecting service quality, patient satisfaction, and loyalty at Puskesmas XYZ, using Structural Equation Modeling (SEM) on data from 108 respondents. The results pinpoint doctor-patient interaction as crucial for service quality (loading factor 0.732), satisfaction (0.692), and loyalty (0.758). Suggestions include improving queue management, proposing a web-based system for efficiency. Future research can expand service quality variables for broader impact assessment.

Highlight:

Key Findings: Doctor interaction highly impacts patient loyalty and satisfaction.
Recommendation: Implement web-based queue management for improved healthcare service.
Future Research: Expand service quality variables for a broader patient assessment.

Keyword: Service Quality, Patient Satisfaction, Loyalty, Structural Equation Modeling (SEM), Queue Management
BP neural network construction cost prediction model based on different optimization algorithms
Authors: Liu Qian , Yang Shujuan , Yu Dehu , Gai Tongtong 
Published: June 2024

Abstract

Aiming at the complexity of prefabricated construction project input cost estimation and the subjectivity and limitation of manual estimation, a method of cost prediction based on BP neural network is proposed. In this paper, 144 sets of assembled engineering data are selected and collected. By using the mapping relationship between construction cost and 11 cost impact indicators, the prediction results are compared and analyzed after training and prediction. The results show that BP neural network can effectively predict cost, but the convergence is slow and the fitting effect is not good. In this paper, three algorithms, Adam, SGD and Adadelta, are proposed to optimize the BP neural network respectively, and the prediction results are compared and analyzed. The maximum error of SGD-BP neural network prediction model is 6.56% and the minimum error is 0.91%, the maximum error of Adadelta-BP neural network prediction model is 17.10% and the minimum error is 0.82%, the Adam-BP neural network prediction model has a maximum error of 3.89% and a minimum error of 0.11%. From the error analysis, it can be seen that the prediction results of Adam-BP neural network prediction model are more accurate, with good generalization effect, providing more accurate assembly building construction cost reference data for decision makers to judge the feasibility of the project.
Prediction Model For Digital Image Tampering Using Customized Deep Neural Network Techniques
Authors: Saxena Sachin , Singh Archana , Tiwari Shailesh 
Published: June 2024

Abstract

Image tampering detection is a critical area of research, given the widespread use of manipulated images for deceptive purposes. Convolutional Neural Networks (CNNs) have shown significant potential in automating the identification of tampered images. This paper presents customized deep learning model to detect tampering class with comparative analysis of CNN architectures - ResNet50V2, InceptionNetV3, MobileNetV2, and the proposed CNN, for image tampering detection. The proposed approach encompasses a dataset comprising four distinct classes: copy-move, inpaint, splicing, and normal images. This study sheds light on the comparative strengths and weaknesses of these CNN architectures. The dataset encompasses the key tampered classes, offering a holistic assessment of each model's ability to identify various tampering techniques. The custom CNN architecture is specifically tailored for this task, aiming to evaluate its efficiency compared to the established CNNs. Metrics for training and evaluation are standardized to generate equitable comparisons, encompassing performance indicators such as accuracy, precision, recall, and F1-score.This research contributes the knowledge in the field of image tampering detection, offering a comprehensive evaluation of multiple CNN architectures. Additionally, the effectiveness of separable convolutional layers is explored in deep neural networks, showcasing their potential to enhance scalability and effectiveness across various tasks in machine learning and computer vision. The proposed model, designed with separable convolution layers, exhibits superior validation accuracy and training accuracy compared to the other models under evaluation. Notably, The proposed customized model achieved an impressive F1 score of 96%, highlighting its proficiency in accurately detecting tampered regions within images while minimizing false positives.
Harnessing Chemical Space Neural Networks to Systematically Annotate GPCR ligands
Authors: Jensen Emil , Hansson Frederik , Madsen Niklas Gesmar , Hansen Lea , Jakočiūnas Tadas , Lengger Bettina , Keasling Jay , Jensen Michael , Acevedo-Rocha Carlos 
Published: June 2024

Abstract

Drug-target interaction (DTI) databases comprise millions of manually curated data points, yet there are missed opportunities for repurposing established interaction networks to infer DTIs. To address this gap, we first collected DTIs on 128 unique G protein-coupled receptors across 187K molecules to establish an all-vs-all chemical space network. We next developed a chemical space neural network (CSNN), which operates on the graph structure of chemical space rather than on the graphs of compounds, to infer drug bioactivity classes with up to 98% accuracy. We combined this virtual library screen with a cost-efficient experimental platform to validate our predictions and discovered 14 novel DTIs in the process. Altogether, our platform integrates virtual library screening and experimental validation for fast and efficient coverage of missing DTIs.
Adaptive food price forecasts enhance public information during rapid economic changes
Authors: MacLachlan Matthew , Adjemian Michael , Etienne Xiaoli , Sweitzer Megan , Volpe Richard , Zeng Wendy 
Published: June 2024

Abstract

The advent of COVID-19 ended an era of stable US retail food prices that followed the world food price crisis of 2010-2012. Pandemic-related disruptions, avian influenza outbreaks, and the Russia-Ukraine war drove 2022 food-at-home inflation to its highest rate since 1974 (11.4%). In 2023, US Department of Agriculture (USDA) economists responded to these changes by updating food price forecasts with statistical learning protocols to select time-series models and prediction intervals to convey their uncertainty. We characterise the public good provided by these "adaptive" inflation forecasts and enhance them by continuously selecting exogenous variables, improving their precision and explanatory power. The all-items-less-food-and-energy ("core") index helps predict food prices until 2017; then, the money supply, wholesale-food prices, and food service wages help generate optimal forecasts. The strong relationships between food prices and other prices and the money supply indicate the sensitivity of food markets to macroeconomic forces and government policy choices.
Emerging Interest in Science during Early Childhood Period and Evaluating its Effectiveness
Authors: ASL Sowmya , R Neela Rani 
Published: June 2024

Abstract

The present study focused on fostering interest in science among underprivileged children belonging to government early childhood education centres in Telangana state, India. The research initiated with the development of seven science experiments, designing science kits for conducting these experiments, and training the teachers on how to conduct the experiments. When teachers conducted the science experiments with children aged 4 to 6 years, videos were carefully recorded. Video analysis was done through the Behavioural Observation Research Interactive Software (BORIS). For each experiment, ethogram was set with different behaviour codes, selected children as focal subjects, and extracted the data from the software. The results revealed that children showed keen interest in learning science experiments. They acquired basic science concepts and improved their vocabulary. Children not only gained hands-on experience but also nurtured intellectual curiosity and a scientific temper. This research also emphasized the significance of introducing science during the early years of childhood.
Cardiovascular health metrics and diabetic nephropathy: a nationally representative cross-sectional study
Authors: Mai Yanpei , Yan Si , Gong Liya 
Published: June 2024

Abstract

Background:
The pathogenesis of diabetic nephropathy is well-documented to be multifactorial. However, research available on the association between cardiovascular health and diabetic nephropathy is limited. Thus, this study aimed to investigate these potential associations and provide guidance for disease prevention.
Methods:
We applied Life's Essential 8 (LE8) identified by the American Heart Association, which integrates multiple health behaviors and health factors to measure cardiovascular health. This study covered 4,207 adults with diabetes from the National Health and Nutrition Examination Survey spanning 2007–2018. Weighted regression models assessed the estimated effect of LE8 score on the prevalence of diabetic nephropathy as well as their corresponding clinical indicators. Weighted restricted cubic spline models discussed the possible nonlinear dose-response relationships further. Subgroup analyses clarified the effects of other covariates on correlations.
Results:
After adjusting for all covariates, participants with moderate or high cardiovascular health showed a decreased prevalence of diabetic nephropathy (odds ratio [OR]:0.52; 95% confidence interval [CI]:0.42 ~ 0.63), and also a decrease in the urinary albumin-to-creatinine-ratio [UACR] (β:-0.83; 95% CI:-1.00~-0.65). The prevalence of diabetic nephropathy and the level of UACR tended to decrease linearly as the total LE8 score increased (P for nonlinear > 0.05). Subgroup analyses showed that the effects of increased overall LE8 score and the specific cardiovascular health construct varied across age and obesity strata.
Conclusions:
Elevated overall LE8 score was significantly associated with a lower prevalence of diabetic nephropathy in U.S. adults, and the effects of the specific cardiovascular health construct on diabetic nephropathy and their corresponding clinical indicators varied. In all, maintaining good cardiovascular health by refining LE8 metrics may help reduce the adverse effects.
Effect of In situ CO2 Mixing of Cement Paste on the Leachability of Hexavalent Chromium (Cr(VI))
Authors: Cho Kian , Kim Wonkyung , Moon Juhyuk , Cha Daniel , Park Junboum 
Published: June 2024

Abstract

In situ CO2 mixing technology is a potential technology for permanently sequestering CO2 during concrete manufacturing processes. Although it has been approved as a promising carbon capture and utilisation (CCU) method, its effect on the leachability of heavy metals from cementitious compounds has not yet been studied. This study focuses on the effect of in situ CO2 mixing of cement paste on the leaching of hexavalent chromium (Cr(VI)). The tank leaching test of the CO2 mixing cement specimen resulted in a Cr(VI) cumulative leaching of 0.614 mg/m2 in 28 d, which is ten times lower than that of the control mixing specimens. The results in thermogravimetric analysis indicated that a relatively significant amount of CrO42- is immobilised as CaCrO4 during the CO2-mixing, and a higher Cr-O extension is observed in the Fourier transform infrared spectra. Furthermore, a portion of the monocarboaluminate is inferred from microstructural analyses to incorporate CrO42- ions. These results demonstrate that in situ CO2 mixing is beneficial not only in reducing CO2 emissions, but also in controlling the leaching of toxic substances.
Influence of bacterial infection on human sperm
Authors: Sadeek Yousry Khalaf , Abdelghany Tarek , Gadel-Rab Aii Gamal , Mohamed Eman Hassan 
Published: June 2024

Abstract

Background
Invasion of bacteria in to the male reproductive system associated with decrease sperm function, leading to impair fertilizing ability, the human body harbors trillions of microbes, and their influence on human health has been explored in many parts of the human body, there is a general agreement on the negative impact of some pathogenic bacterial species on semen parameters, including sperm counts, motility, morphology, and sperm Deoxyribonucleic acid (DNA) integrity. The Aim of this study was to evaluate the impacts of microbial infection on human sperm parameters and sperm functions. This work included 186 semen specimen from sub fertile men unselected couples consulting infertility clinic center. All semen samples examined bacteriologically, also semen and sperm quality, evaluated according to world Health organization guideline (WHO,2010).
Results
from the 186 patients investigated for infertility, 65 (34.94%) of studied semen specimens were infected with bacteria of different species. The bacterial strains were identified as Enterococcus Fecails, staphylococcus haemolyticyus, Escherichia coli, Micrococcus lylae and Serratia Marcescens. Infected semen sub fertile men had negative impact on sperm quality (count, motility, sperm progressive, vitality and normality. Moreover, high Reactive oxygen species (ROS) with decrease sperm function which include Acrosin activity (A.A) and Hypo-osmotic swelling test (HOST) were noticed in infected sub fertile men in comparison to non-infected sub fertile men but non-significant.
Conclusion
Infected semen has negative impact on sperm parameters and sperm function this lead to impair fertilizing ability of human sperm.
Processing Feature Recognition Based on Improved YOLOv5 Algorithm
Authors: Miao Liqin , Li Jialong , Feng Xin , Tang Zhengwei , Liang Huaidan 
Published: June 2024

Abstract

Processing feature recognition technology can effectively integrate CAD and CAPP systems, thus realizing the effective interoperability of design information and manufacturing information. Classical processing feature recognition technology does not have the ability to learn, recognition efficiency is low and the accuracy of the recognition results can not be guaranteed, while the deep neural network technology not only has the ability to learn, and noise resistance can improve the accuracy of the recognition, therefore, the proposed deep learning-based processing feature classification and recognition technology, the use of multi-angle downscaling to capture the processing features of the processing features of the image method to create a processing feature dataset, and the deep neural network model through the YOLOv5 improved deep neural network model to train the dataset, and use the machining feature classifier to classify the processed part model and output the machining feature results. The machining feature recognition process is verified with typical parts as an example by combining with UG platform, which provides a new idea for machining feature recognition technology.
Methods to characterize Microplastics: Case study on freshwater fishes from a tropical lagoon in Colombia
Authors: Rojas-Luna René A. , Oquendo-Ruiz Luisa , GARCÍA-ALZATE CARLOS A , Arana Victoria A. , García-Alzate Roberto , Trilleras Jorge 
Published: June 2024

Abstract

We studied the prevalence, abundance and physical and chemical characteristics of microplastics (MPs) in fishes in the Luruaco lagoon, Colombia. Four samplings were carried out where fish were captured with trawl nets. Each individual was assessed for total weight, total and standard length, and then a uroventral cut was made to extract stomach and intestine (GIT). Each structure was weighed, measured and deposited in glass jars with filtered KOH solution. The abundance of MPs and frequency of occurrence were assessed. A principal component analysis (PCA) was used to describe the variation of the species dataset (%FO, proportion of MPs and their diet). Seven species were recorded and 271 individuals examined: Microplastics were identified in all species, and 1157 particles were found in their GIT, with a prevalence of 87.5% of MPs. Mugil liza and Andinoacara latifrons had the highest proportion of MPs. The highest %FO was found in cichlid species. Four typologies and 13 colors of MPs were analyzed; fibers and color blue were predominant. A direct relationship was observed between the type of diet and the proportion and %FO of MPs.