bluuebunny/crossref_metadata_2025_split huggingface dataset:
Implementation of Banded Smith-Waterman Sequence Alignment Algorithm on CPU and FPGA
Authors: Mukherji Prachi , Rajput Seema H. , Kendre Nandini , Mudaliar Vaishnavi 
Published: July 2024

Abstract

Sequence alignment(SA) is a fundamental aspect in the field of bioinformatics, crucial for various applications such as DNA sequencing and protein structure prediction. It involves the process of comparing a new genome sequence with the sequences previously stored in a database. However, the computational demands of Smith-Waterman alignment can be substantial, particularly when analyzing large genomic datasets. To address this challenge, we present a comprehensive comparative study that explores the acceleration of Smith-Waterman sequence alignment using different hardware platforms: Central Processing Units (CPUs) and Field-Programmable Gate Arrays (FPGAs. In this study, we evaluate and contrast the performance and scalability of Smith-Waterman alignment on these platforms, considering CPU and FPGA-based implementation. We assess their computational capabilities and memory requirements for various sequence lengths and scoring parameters. Through extensive benchmarking and sequence analysis, particularly on heterogeneous CPU + FPGA platforms profiling, we provide insights into the advantages and limitations of each platform, shedding light on the trade-offs between computation speed and hardware cost.
Thermal Frontal Polymerization in Polymer Composites: Numerical Simulation and the Role of Fe 3 O 4 Nanoparticle Fillers
Authors: Lang Margit , Schmidleitner Christoph , Kasinikota Venu Prakash , Rossegger Elisabeth 
Published: July 2024

Abstract

Recently Thermal Frontal Polymerization (TFP) has emerged as a low-energy alternative, that enables rapid and energy-efficient manufacturing of composites. Thus, compared to conventional processes, this innovative curing and polymerization process exhibits improved efficiency and reduced environmental impact and provides a promising strategy to address sustainability challenges. However, successful TFP requires a delicate balance of reaction rates, exothermicity, and efficient heat transport into unpolymerized media while minimizing heat losses to the surroundings. In this context, sustaining TFP of polymers reinforced with highly conductive fillers is challenging due to the increased energy dissipation and reduced availability of exothermic energy as the filler content increases at the cost of resin volume. In this work, a numerical study of the TFP based manufacturing of Bisphenol A Diglycidyl Ether (BADGE) filled with Fe3O4 nanoparticles is presented. The simulation provides insight into the thermo-chemical process and into the impact of different particle filling degrees on the key characteristics of TFP, i.e., maximum attainable degree of cure, maximum temperature, front shape, and front speed.
Efficacy of biochar on the phytoremediation potential of Tithonia diversifolia on spent oil-contaminated soil
Authors: Ogunremi Olamide Omolafe , Amubieya Omolara Faith , Ogunkunle Clement Oluseye , Fatoba Paul Ojo 
Published: July 2024

Abstract

The application of biochar has gained attention as a simple, affordable and sustainable strategy for the remediation of contaminated soils. However, the phytoremediating potential of certain plants and interactions with biochar are necessary to achieve effective environmental clean-up. This study evaluated the influence of biochar on the remediation potential of Tithonia diversifolia grown in spent oil-contaminated soil. T. diversifolia was grown in spent oil-contaminated soil amended with rice biochar and sorghum biochar at four different application rates (1, 2 and 3% w/w) for 6 weeks. The heavy metals studied included copper (Cu), zinc (Zn), lead (Pb), cadmium (Cd) and chromium (Cr). The results of this study showed that compared with the other treatments, rice biochar strongly enhanced the growth of T. diversifolia. RB3% influenced the phytostabilization potential of T. diversifolia for Cu. However, StB3% and SB3% enhanced T. diversifolia’s potential capacity for the phytoextraction of Cr and Pb, respectively. Generally, the response of T. diversifolia to phytoextraction and phytostabilization is dependent on the biochar and application rate used. Hence, there is a need for further research on the use of T. diversifolia for remediation purposes under the influence of specific biochar types and application rates.
FedCEA: Efficient Adaptive Personalized Federated Learning based on Critical Learning Periods
Authors: Yu Yichun , Yang Xiaoyi , Chen Zheping , Lan Yuqing , Xing Zhihuan , Yu Dan 
Published: July 2024

Abstract

Federated learning (FL) faces significant challenges due to statistical heterogeneity, which undermines the global model's generalization capability across diverse clients. Personalized FL (pFL) has been extensively studied to address this, but most existing approaches erroneously assume all stages of the FL training process are equally critical and require the participation of all clients, leading to substantial computational and communication overhead. Addressing this flaw, we propose an efficient adaptive pFL method, FedCEA, based on critical learning periods (CLP). FedCEA tailors efficient models for each client while ensuring data privacy and security by considering CLP during the training process to guide client selection. This approach reduces client-server communications, accelerating model convergence. To enhance the global model's generalization across clients with statistical heterogeneity, we introduce an Adaptive Initialization of Local Models (AILM) module with a personalized aggregation strategy. Additionally, training parameters are dynamically adjusted based on dataset quality to ensure efficiency. FedCEA also employs a compression method that assesses the importance of model parameters, reducing communication and computational costs. To evaluate the effectiveness of FedCEA, we conduct extensive experiments with four benchmark datasets in computer vision and natural language processing domains. The results consistently showed that FedCEA achieves improved accuracy and superior communication efficiency compared to state-of-the-art methods. This makes FedCEA a promising solution for handling heterogeneity in federated learning training. Code is available at https://github.com/buaaYYC/FedCEA/tree/main.
A Salutogenic Signature of the Placebo Effect in Brain Oscillations: A Systematic Review and Meta-Analysis
Authors: Fregni Felipe , Ortega-Márquez Jorge , Gonzalez-Gonzalez Luis Fernando , Sosa William , Pacheco-Barrios Kevin 
Published: July 2024

Abstract

Brain oscillatory activity and its role as a biomarker in mental health and human behavioral mechanisms stands as an unexplored tool to explain the placebo effect. This systematic review and meta-analysis characterize, for the first time, the neural mechanistic biomarkers of placebo effect in neurorehabilitation. A systematic literature search based on PRISMA guidelines was conducted in PubMed, Embase, and Cochrane Library including randomized controlled trials (RCTs), and cross-over trials up to August 2023. The review included studies reporting brain oscillations in resting state as outcome in placebo-controlled neurorehabilitation trials. A qualitative, semi-quantitative, quantitative analysis was performed using minimal statistical information from each study and calculating effect sizes of absolute power differences between placebo and active intervention groups using Hedges’ g statistic method. The analysis included 63 studies: 5 (180 healthy subjects) and 58 (1758 patients with neurological disorders). In healthy population, placebo interventions showed an increase in alpha power when comparing versus no intervention (g = 0.45, 95% CI [0.09; 0.8]). In subjects with neurological disorders sham intervention showed increase in alpha frontal (g = 0.08, 95% CI [0.07; 0.08] ), alpha central (g = 0.55, 95% CI [0.47; 0.65]), alpha parietal (g = 0.28, 95% CI [0.18; 0.44]), beta central (g = 1.31, 95% CI [1.06; 1.63]), and theta central (g = 0.58, 95% CI [0.46; 0.72]). Interestingly, these effects became non-significant when comparing with the active interventions. In both populations, the enhancement of alpha oscillations in fronto-central regions emerged as the primary neural biomarker of the placebo effect. Furthermore, increase in beta and theta bands within the same brain regions were also noted as potential biomarkers, particularly in non-healthy subjects. Intriguingly, these effects were reversed when compared to active rehabilitation interventions, suggesting that active rehabilitation techniques also encompass the placebo effect. Our novel results show that placebo neural effects depend on the brain oscillatory baseline (healthy vs. patient populations), and they seem to unmask a more homeostatic salutogenic rhythm, such as alpha oscillations in healthy subjects and theta and beta oscillations in patient populations.
Lactose-assimilating yeasts with high fatty acid accumulation uncovered by untargeted bioprospecting
Authors: Persson Karl , Onyema Vanessa , Nwafor Ijeoma , Peri Kameshwara V.R. , Otti Chika , Nnaemeka Priscilla , Onyishi Chioma , Okoye Sylvia , Moneke Anene , Amadi Onyetugo , Warringer Jonas , Geijer Cecilia 
Published: July 2024

Abstract

Bioprospecting can uncover new yeast strains and species with valuable biotechnological and industrial traits, such as the capacity to convert different carbon sources in industrial side- and waste streams into bioproducts. In this study, we conducted untargeted bioprospecting in tropical West Africa, collecting 1996 isolates and determining their growth in 70 different environments. While the collection contains numerous isolates with the potential to assimilate several cost-effective and sustainable carbon and nitrogen sources, we focused on characterizing strains capable of growing on lactose, the main carbon source in the abundant side stream cheese whey from dairy industries. Our screening identified 30 different species capable of growing on lactose in both synthetic complete yeast medium and whey, with 20 of these also capable of growth in lactose-containing minimal medium. The lactose-assimilating yeast collection contains both ascomycetes and basidiomycetes, and includes species known to be lactose-assimilating, species not previously known to grow on lactose, and candidates for new species. Observed differences in growth profiles and ratios of intra- and extracellular lactase activities suggest that the yeasts use a range of different strategies to metabolize lactose. Notably, several basidiomycetes, including Apiotrichum mycotoxinivorans, Papiliotrema laurentii and Moesziomyces antarcticus, which accumulated up to 40% of their cell dry weight, proving that they can convert lactose into a bioproduct of significant biotechnology interest. Our findings highlight the value of bioprospecting of underexplored regions to discover yeast strains with substantial biotechnological potential, which may aid the transition to a circular bioeconomy.
Which inflammatory markers should we assess following Knee replacement? : A comparative study of unilateral and bilateral robotic TKR.
Authors: Tiwari Anjali , Deshmukh Shobit , Yadav Amit Kumar , Vyas Parth , Bashyal Ravi , Bagaria Vaibhav 
Published: July 2024

Abstract

The presence of inflammatory markers may be a valuable indicator of postoperative healing and unforeseen complications following knee replacement surgery. They may also be a surrogate marker to compare and evaluate the impact of a particular surgical techniques and use of technology for performing a surgical procedure. The present study examined five common inflammatory markers [IL-6, CRP, LDH, AST and ESR] in patients who underwent robotic knee replacement surgery using surgeon controlled hand held CORI robotic system. Baseline venous blood samples were collected 12 hours before and after surgery and every 24 hours postop until discharge. A total of 48 participants were included in the study. Study participants were divided into two groups. In group I, 13 patients underwent bilateral total knee replacements (TKR); in group II, 35 underwent unilateral TKR. IL-6 increased significantly by 229.69 pg/ml at 12hrs in the bilateral group, and 115.51 post 24hr in the unilateral group (average IL-6 value <5 pg/ml). Peak CRP levels were observed 60 hours postop in the bilateral group at 34.87 and 16.71 in the unilateral group (standard CRP value < 0.6 mg/dL). It was found that ESR levels differed significantly both preoperatively and postoperatively (standard ESR value 15 to 20mm hour). Peak ESR levels were observed at 60hrs in both groups, 89mm/hr in the bilateral group and 46 mm/hr in the unilateral group. Peak LDH levels were observed 48 hours postop in both groups, 245.67 U/L in the bilateral and 244.15 U/L in the unilateral group (standard LDH value 140 to 280 U/L). AST level peak was observed at 48 hrs, 32.74 U/L in bilateral and 32 U/L in unilateral group (standard AST value 8 to 33 U/L). This is the first study to comprehensively evaluate and compare five expected inflammatory marker levels in patients who underwent robotic-assisted bilateral and unilateral TKR. This study found that IL-6 and CRP levels were statistically significantly higher in the bilateral knee replacement group than in the unilateral knee replacement group and may be the most relevant markers to monitor in the postoperative period.
The Identification of Guessing Patterns in Progress Testing as a Machine Learning Classification Problem
Authors: Atanet Iván Roselló , Sehy Victoria , Sieg Miriam , März Maren 
Published: July 2024

Abstract

Background
The detection of guessing patterns in low-stakes progress testing could naturally be understood as a statistical classification problem where test takers are assigned to groups according to probabilities given by a machine learning model. However, the relevant literature on this topic does not include many examples where this approach is discussed; to date, the strategies applied to tackle this problem have been mostly based either on rapid response counting or the detection of unusual answer patterns.
Methods
On the basis of 14,897 participations in the Progress Test Medizin test – which takes place twice a year since 1999 in selected medical schools of Germany, Austria and Switzerland - we formulate the identification of guessing patterns as a binary classification problem. Next, we compare the performance of a logistic regression algorithm in this setup to that of the nonparametric person-fit indices included in R´s PerFit package. Finally, we determine probability thresholds based on the values of the logistic regression functions obtained from the algorithm.
Results
Comparison of logistic regression algorithm with person-fit indices
The logistic regression algorithm included in Python´s Scikit-Learn reached ROC-AUC scores of 0.886 to 0.903 depending on the dataset, while the 11 person-fit indices analysed returned ROC-AUC scores of 0.548 to 0.761.
Best feature set
Datasets based on aggregate scores yielded better results than those were the sets of answers to every item were considered as individual features. The best results were reached with a feature set containing only two parameters (self-monitoring accuracy and number of answered questions); considering the amount of time spent on the test did not lead to any performance improvement.
Probability thresholds
Based on the values of the logistic regression function generated by the applied algorithm, it is possible to establish thresholds above which there is at least a 90% chance of having guessed most answers.
Conclusions
In our setting, logistic regression clearly outperformed nonparametric person-fit indices in the task of identifying guessing patterns. We attribute this result to the greater flexibility of machine learning methods, which makes them more adaptable to diverse test environments than person-fit indices.
Dejavu Forensic: Using Support Vector Machine to Improve Recovery Results for Formatted Data in JPEG and PNG Extensions
Authors: Bezerra Islan Amorim , de Lima Sidney Marlon Lopes , Silva Rubens Karman Paula da 
Published: July 2024

Abstract

Background: In line with technological advances, virtual crimes have a greater tendency to occur. The most routine situations are: cyberbullying and illicit sharing, but cases of invasion of privacy, dissemination of defamatory emails and child pornography also occur. When digital equipment is stolen, lost or disposed of, data is still stored on disks. This factor enables the recovery of this file.
Objective: The main focus of this article is the recovery of formatted files. The applicability of Foremost, Scalpel and Magic Rescue tools in the Linux environment is investigated. In addition, an authorial tool, equipped with machine learning, is used. The general objective of this research is to develop a recovery and validation tool for formatted files. The project aims to contribute to investigations of digital and cyber crimes. In addition to demonstrating knowledge, it brings new perceptions of analysis with regard to recovery methods for formatted files.
Methods: Using the pattern recognition methodology, the cluster is used as input, which, through the histogram, acts as an input neuron of the learning machine. This work applies machine learning aiming at recognizing the pattern of the blocks/clusters. In the first scenario, here named “simple”, the classification is binary. There is only class vs. counterclass. This methodology was developed by Pavel (2017) and replicated in the aforementioned simple scenario. In a second scenario, named “complex”, the one-against-all method was used, whose database contains 16,000 files.
Results: This research presents a cutting-edge approach that synergizes machine learning and data science to recover formatted data. Our innovative tool boasts a remarkable recovery rate of over 96% for formatted PNG and JPEG files, running in just a few seconds. This breakthrough holds significant promise for improving digital forensic investigations.
Conclusions: The experiences declared in the proposed work leave contributions for the advancement in studies about data recovery. Its relevance concerns the proposal to assist in the elucidation of digital crimes that surround the society of digital natives. The authorial system becomes a locus of how technology can serve human rights and improve the quality of life in contemporary society.
Low Titre of Agroinoculum with Prolonged Incubation Period and Low Auxin Concentration in the Regeneration Media are the Key to High Frequency of Transformation in Climate Resilient Aus type Rice Genotype Nagina 22
Authors: Kumar Amit , Priyanka  , K. Jeevanandhan , Kaushik Megha , Mulani Ekta , S. Meena , Roy Jeet , Phogat Sachin , Sareen Bhuvnesh , Madhavan Jayanthi , Sevanthi Amitha Mithra , Solanke Amolkumar U. , Kumar Prabhanshu , Mandal Pranab Kumar 
Published: July 2024

Abstract

Developing a highly efficient and reproducible plant regeneration protocol holds paramount significance in advancing technology for the genetic transformation of rice genotypes, facilitating their further utilization in crop improvement and breeding programs. Nagina 22 (N22), an aus type rice genotype, has tolerance against multiple stresses, requires very efficient transformation and regeneration protocol, for developing improved lines. However, a standardized regeneration protocol for this elite genotype has not been reported so far, limiting the use of this genotype as a background for genetic transformation. In the present study, for the first time, we are reporting a highly efficient transformation and regeneration protocol for the climate resilient N22. Mature seeds were used as explants for callus induction with varying concentrations (1-4 mg/L) of 2,4-dichlorophenoxyacetic acid (2.4-D), among which 3 mg/L of 2,4-D resulted in an impressive ~94% callus induction efficiency. Following this, the regeneration and resuspension media optimization is also carried out. Subsequent steps involved culturing embryogenic calli on media with different concentrations (2-6 mg/L) of 6-benzylaminopurine (BAP), in combination with 0.02 mg/L naphthalene acetic acid (NAA), to optimize the regeneration protocol. Media consisting of 5 mg/L BAP and 0.02 mg/L NAA exhibited a regeneration frequency of ~92% for the plantlets derived through non-transformed callus. Furthermore, our study revealed that reducing the salt concentration in the resuspension media resulted a notable enhancement in transformation efficiency of 44%. This in-vitro regeneration protocol provides valuable insights for future research and practical applications in genetic transformation of this elite rice genotype for various purposes.
Enhancing water efficiency programming in the City of Calgary
Authors: Jordan Rennie , Anderson Michelle , Saini Nisha , Pina Pablo 
Published: July 2024

ABSTRACT

This paper presents the steps undertaken in a two-phase study to enhance the City of Calgary's (the City) water efficiency programming for indoor and outdoor industrial, commercial, and institutional (ICI) customers and outdoor residential customers. Study objectives included evaluating programs in other urban jurisdictions for suitability to Calgary, and developing short-, medium-, and long-term recommendations for water efficiency programming for the City. Phase I of the study included a literature review of Calgary's water consumption trends and explored over 150 water efficiency programs implemented across jurisdictions in North America, identifying a subset of 33 programs for further evaluation. Phase II evaluated program options through an integrated assessment, including gap, Strengths, Weaknesses, Opportunities, and Threats, and cost-benefit analysis. An implementation strategy was developed for seven water efficiency programs, grouped into complementary bundles of indoor and outdoor ICI and residential landscape transformation programs. The study also identified areas for further research, and key supporting elements or success factors for water efficiency programming in the City. This paper adds value to the discussion on approaches to select suitable indoor and outdoor ICI and outdoor residential water efficiency programs, and aids in informing the City's current and future strategic water planning and programs.
Nano-silica modified with silane and fluorinated chemicals to prepare a superhydrophobic coating for enhancing self-cleaning performance
Authors: Zeng Guisheng , Gong Baichuan , Li Yingpeng , Wang Kun , Guan Qian 
Published: July 2024

ABSTRACT

Superhydrophobic coatings with excellent self-cleaning performance have attracted significant concerns from researchers. Although various superhydrophobic coatings with prominent superhydrophobic properties have been fabricated, most developed coatings are still inadequate in pipeline scale inhibition applications. In this work, nano-silica (nano-SiO2) was modified by silane coupling of vinyltriethoxysilane (VETS) and 1H, 1H, 2H, 2H-perfluorooctyltriethoxysilane (PFTS) to prepare a superhydrophobic coating. Organosilicon of PFTS and VETS was grafted onto the surface of SiO2 for preparing the superhydrophobic coating with low surface energy, and the superhydrophobic coating was cured via poly(vinylidene fluoride) (PVDF). The results showed that the contact angle of the prepared silica-based superhydrophobic coating, denoted as VETS-PFTS@SiO2/PVDF, is 159.2°, exhibiting outstanding superhydrophobicity performance. Furthermore, the superhydrophobicity coating also showed satisfactory durability performance in 200 g load wear test after 50 cycles. Importantly, the superhydrophobic coating displayed promising mechanical durability, chemical stability performance, as well as maintained excellent superhydrophobic properties after being placed in water for 3 weeks, indicating the potential for long-term utilization. In the simulated scale inhibition test, it was found that the synthesized coating can also significantly decrease the deposition rate of CaCO3 and successfully enhance its scale inhibition performance.