bluuebunny/crossref_metadata_2025 huggingface dataset:
Label-free analysis of bacterial growth and lysis at the single-cell level using droplet microfluidics and object detection-oriented deep learning
Authors: Tiwari Anuj , Nikolic Nela , Anagnostidis Vasileios , Gielen Fabrice
Published: December 2023

Bacteria identification and counting at the small population scale is important to many applications in the food safety industry, the diagnostics of infectious diseases and the study and discovery of novel antimicrobial compounds. There is still a lack of easy to implement, fast and accurate methods to count populations of motile cells at the single-cell level. Here, we report a label-free method to count and localize bacterial cells freely swimming in microfluidic anchored picolitre droplets. We used the object detection oriented YOLOv4 deep learning framework for cell detection from bright-field images obtained with an automated Z-stack setup. The neural network was trained to recognize Escherichia coli cell morphology with an average precision of approximately 84%. This allowed us to accurately identify individual cell division events, enabling the study of stochastic bacterial growth starting from initial populations as low as one cell. This work also demonstrates the ability to study single cell lysis in the presence of T7 lytic bacterial viruses (phages). The high precision in cell numbers facilitated the visualization of bacteria-phage interactions over timescale of hours, paving the way towards deciphering phage life cycles in confined environments.
RealTHASC—a cyber-physical XR testbed for AI-supported real-time human autonomous systems collaborations
Authors: Paradise Andre , Surve Sushrut , Menezes Jovan C. , Gupta Madhav , Bisht Vaibhav , Jang Kyung Rak , Liu Cong , Qiu Suming , Dong Junyi , Shin Jane , Ferrari Silvia
Published: December 2023

Today’s research on human-robot teaming requires the ability to test artificial intelligence (AI) algorithms for perception and decision-making in complex real-world environments. Field experiments, also referred to as experiments “in the wild,” do not provide the level of detailed ground truth necessary for thorough performance comparisons and validation. Experiments on pre-recorded real-world data sets are also significantly limited in their usefulness because they do not allow researchers to test the effectiveness of active robot perception and control or decision strategies in the loop. Additionally, research on large human-robot teams requires tests and experiments that are too costly even for the industry and may result in considerable time losses when experiments go awry. The novel Real-Time Human Autonomous Systems Collaborations (RealTHASC) facility at Cornell University interfaces real and virtual robots and humans with photorealistic simulated environments by implementing new concepts for the seamless integration of wearable sensors, motion capture, physics-based simulations, robot hardware and virtual reality (VR). The result is an extended reality (XR) testbed by which real robots and humans in the laboratory are able to experience virtual worlds, inclusive of virtual agents, through real-time visual feedback and interaction. VR body tracking by DeepMotion is employed in conjunction with the OptiTrack motion capture system to transfer every human subject and robot in the real physical laboratory space into a synthetic virtual environment, thereby constructing corresponding human/robot avatars that not only mimic the behaviors of the real agents but also experience the virtual world through virtual sensors and transmit the sensor data back to the real human/robot agent, all in real time. New cross-domain synthetic environments are created in RealTHASC using Unreal Engine™, bridging the simulation-to-reality gap and allowing for the inclusion of underwater/ground/aerial autonomous vehicles, each equipped with a multi-modal sensor suite. The experimental capabilities offered by RealTHASC are demonstrated through three case studies showcasing mixed real/virtual human/robot interactions in diverse domains, leveraging and complementing the benefits of experimentation in simulation and in the real world.
classLog: Logistic regression for the classification of genetic sequences
Authors: Zeller Michael A. , Arendsee Zebulun W. , Smith Gavin J.D. , Anderson Tavis K.
Published: December 2023

Introduction

Sequencing and phylogenetic classification have become a common task in human and animal diagnostic laboratories. It is routine to sequence pathogens to identify genetic variations of diagnostic significance and to use these data in realtime genomic contact tracing and surveillance. Under this paradigm, unprecedented volumes of data are generated that require rapid analysis to provide meaningful inference.

Methods

We present a machine learning logistic regression pipeline that can assign classifications to genetic sequence data. The pipeline implements an intuitive and customizable approach to developing a trained prediction model that runs in linear time complexity, generating accurate output rapidly, even with incomplete data. Our approach was benchmarked against porcine respiratory and reproductive syndrome virus (PRRSv) and swine H1 influenza A virus (IAV) datasets. Trained classifiers were tested against sequences and simulated datasets that artificially degraded sequence quality at 0, 10, 20, 30, and 40%.

Results

When applied to a poor-quality sequence data, the classifier achieved between >85% to 95% accuracy for the PRRSv and the swine H1 IAV HA dataset and this increased to near perfect accuracy when using the full dataset. The model also identifies amino acid positions used to determine genetic clade identity through a feature selection ranking within the model. These positions can be mapped onto a maximum-likelihood phylogenetic tree, allowing for the inference of clade defining mutations.

Discussion

Our approach is implemented as a python package with code available at https://github.com/flu-crew/classLog.
Numerical Simulation of Supersonic Turbulent Separated Flows Based on k–ω Turbulence Models with Different Compressibility Corrections
Authors: Luo Dahai
Published: December 2023

The accurate prediction of supersonic turbulent separated flows involved in aerospace vehicles is a great challenge for current numerical simulations. Based on the k–ω equations, several different compressibility corrections are incorporated in turbulence models to improve their prediction capabilities. Two benchmark test cases, namely the ramped cavity and the compression corner, are adopted for the numerical validation. Detailed comparisons between simulations and experiments are conducted to evaluate the effect of compressibility corrections on turbulence models. The computed results indicate that compressibility corrections have a significant impact on turbulence model performance. The compressibility correction, considering the effects of both dilatation dissipation and pressure dilatation, is suitable for the prediction of compressible free shear layers, but it may have a negative impact on the prediction of low-speed flows in the near-wall region due to the severe underprediction of the wall skin friction coefficient. In comparison, the compressibility correction only considering the effects of dilatation dissipation is conservative, with decreased predictability of free shear layers in supersonic flows, although it improves the predictions of the original models without corrections.
The Electrical Conductivity of Nutrient Solution Influenced the Growth, Centellosides Content and Gene Expression of Centella asiatica in a Hydroponic System
Authors: Shawon Md Rayhan Ahmed , Azad Md Obyedul Kalam , Ryu Byeong Ryeol , Na Jong Kuk , Choi Ki Young
Published: December 2023

Centella asiatica is a herbaceous plant containing medicinal and cosmetic properties: anti-bacterial, anti-aging, memory enhancing and wound healing. The lack of information impedes the development of suitable growth conditions for C. asiatica in the hydroponic system. Maintaining proper electrical conductivity (EC) of a nutrient solution is considered crucial for plant growth and the accumulation of bioactive compounds in a plant grown in hydroponics. This study aimed to investigate an optimal EC that enhances the growth of C. asiatica and its centellosides content. Seedlings were grown in commercial nutrient solution and treated with four different strengths of EC (0.6, 1.2, 1.8 and 2.4 indicated T1, T2, T3 and T4, respectively) under controlled environment conditions. Our results demonstrate that the number of leaves, leaf area, number of runners, shoot fresh weight and shoot dry weight were significantly increased in T2 among the treatments. However, these growth parameters were lowest in T4. Furthermore, the content of asiaticoside, madecassoside, asiatic acid and madecassic acid was the highest in plants that were treated with T2. The expression of centelloside biosynthesis-related genes is also affected by the strength of the nutrient solution. A positive correlation was observed between the number of leaves, leaf area and centellosides content. This study provides valuable background on optimal EC content in the nutrient solution in a hydroponic system with enhanced centellosides content to leverage the C. asiatica production.
Genome-Wide Association Study (GWAS) Identifies Key Candidate Genes Associated with Leaf Size in Alfalfa (Medicago sativa L.)
Authors: Xu Ming , Jiang Xueqian , He Fei , Sod Bilig , Yang Tianhui , Zhang Fan , Cong Lili , Long Ruicai , Li Mingna , Wang Xue , Yang Qingchuan , Zhang Tiejun , Kang Junmei
Published: December 2023

Leaf size significantly impacts photosynthetic capacity and forage yield in alfalfa, a major legume forage crop. Therefore, elucidating the genetic factors governing leaf development is critical for breeding improved alfalfa varieties. In this study, a genome-wide association analysis (GWAS) was performed to dissect the genetic architecture of leaf length (LL) and leaf width (LW) using 220 alfalfa accessions phenotyped over three years. Substantial variation for both traits was observed across environments, with coefficients of variation ranging from 10.09–16.53%. GWAS identified 26 significant SNPs associated with leaf morphology spread across seven chromosomes. Each SNP accounts for 9.7–15.6% of the phenotypic variance. Haplotype analyses confirmed positive correlations between the number of superior alleles and both LL and LW. BLAST searches revealed six candidate genes involved in leaf development within 20 kb flanking regions of significant SNPs. Our results provide novel marker-trait associations and candidate loci to facilitate molecular breeding efforts to optimize leaf size and improve productivity in alfalfa. This study establishes a foundation for integrating favorable alleles into future alfalfa varieties.
Regulation of Inorganic Zinc Supplementation on Intestinal Absorption, Metabolism, and Muscle Development in Broilers Fed Low-Protein Diets
Authors: Sun Ruihong , Zhou Changhai , Jia Yougang , Li Yumei , He Yuntong , Che Haoyu , Zhang Yonghong , Zhang Jing , Peng Dongqiao
Published: December 2023

The issue of nitrogen fertilizer contamination resulting from high-protein diets can be effectively solved by adopting low-protein diets. The objective of this study was to investigate the effects of inorganic zinc supplementation in low-protein diets on 42-day-old broilers across a multitude of parameters. To determine the optimal dose of inorganic zinc in broiler diets with a 1.5% reduction in crude protein, 1-day-old Arbor Acres broilers (n = 270) were randomly assigned to five groups, each containing 54 broilers. Our results revealed that inorganic zinc supplementation at levels of 130 mg/kg elevated growth performance and carcass traits (p < 0.05). It also significantly increased the ratio of intestinal villi heights to crypt depths (p < 0.001), changed intestinal morphology, and significantly increased albumin content in serum (p < 0.05). Furthermore, analysis of mRNA expression showed that 130 mg/kg and 150 mg/kg of inorganic zinc improved the myogenic differentiation involved in muscle development, as well as intestinal tight junction and liver metallothionein capacity (p < 0.001). Additionally, these groups exhibited lower zinc excretion compared with other treatments (p < 0.001). In summary, our findings suggest that inorganic zinc supplementation in low-protein diets holds the potential to support muscle and intestinal development in broilers, presenting a viable nutritional strategy.
The Response of Soil Bacterial Communities to Cropping Systems in Saline–Alkaline Soil in the Songnen Plain
Authors: Liu Xiaoqian , Ding Junnan , Li Jingyang , Zhu Dan , Li Bin , Yan Bohan , Mao Lina , Sun Guangyu , Sun Lei , Li Xin
Published: December 2023

The high salt content in saline–alkaline land leads to insufficient nutrients, thereby reducing agricultural productivity. This has sparked widespread interest in improving saline–alkaline soil. In this investigation, 16S rRNA gene high-throughput sequencing was employed to examine the impacts of three cropping systems (monoculture, rotation, and mixture) on soil bacterial communities. It was found that cropping rotations and mixtures significantly increased soil bacterial α-diversity. Random forest analysis showed a significant linear relationship between AK and EC and bacterial α-diversity. In addition, principal coordinates analysis (PCoA) further confirmed the significant differences in β-diversity between different soil layers. Through co-occurrence network analysis, it was found that cropping rotations and mixtures increased the stability and complexity of co-occurrence networks. By calculating NST to analyze the assembly process of soil bacterial communities in different cropping systems, it was found that the assembly process of soil bacterial communities was dominated by a stochastic process. Functional prediction results showed that a large number of C, N, and S cycling microbes appeared in soil bacterial communities. Our study aims to establish a fresh perspective on the improvement and recovery of saline–alkaline soil.
Modelling Soil Water Infiltration and Wetting Patterns in Variable Working-Head Moistube Irrigation
Authors: Zhai Yaming , Kurexi Wuerkaixi , Wang Ce , Zhu Chengli , Zhang Zhanyu , Li Yi
Published: December 2023

Moistube irrigation is an efficient method that accurately irrigates and fertilizes agricultural crops. Investigation into the mechanisms of infiltration behaviors under an adjusted working head (WKH) benefits a timely and artificially regulating moisture condition within root zones, as adapted to evapotranspiration. This study explores the laws of Moistube irrigated soil water movement under constant and adjusted working heads. Lysimeter experiments were conducted to measure Moistube irrigation cumulative infiltration, infiltration rate, and to observe wetting front area and water content distribution using digital image processing and time domain reflectometry, respectively. Treatments of constant heads (0, 1, and 2 m), increasing heads (0 to 1, 0 to 2 and 1 to 2 m) and deceasing heads (1 to 0, 2 to 0 and 2 to 1 m) were designed. The results show that (1) under constant heads, the cumulative infiltration increases linearly over time. The infiltration rate and cumulative infiltration are positively correlated with the pressure head. When WKH is increased or decreased, the infiltration rate and cumulative infiltration curves significantly change, followed by a gradual stabilization. The more the head is increased or decreased, the more evident this tendency will be. (2) When WKH is increased, the wetting front migration rate and the wetted soil moisture content marked increase; when WKH is decreased, the wetting front migration rate sharply decelerates, and the water content of the wetted soil slowly grows. They both tend to equilibrium with time. (3) By regarding the same cumulative infiltration of increased WKH and constant WKH treatments as a similar initial condition, we proposed a cumulative infiltration empirical model for Moistube irrigation under variable working head. Additionally, we treat the Moistube as a clayey porous medium and construct a HYDRUS-2D numerical model to predict the infiltration behaviors under variable WKH. The validity of the two models were well proven, with MRE and NRMSE close to 0 and NSE greater than 0.867, indicating good agreements with the experimental results. This model breaks through the limitation of constant boundary of traditional numerical model and applies variable head boundary to the boundary of the Moistube pipe, which can also effectively simulate the response mechanism of Moistube irrigation to variable WKH. The research results further confirmed the feasibility of manually adjusting the WKH to regulate the discharge of the Moistube pipe and soil moisture state. Based on the HYDRUS-2D numerical model simulation results and the root distribution and water demand of typical facility crops, the selection range of placement depth and the adjustable range of WKH of Moistube irrigation were proposed. The research results provide a theoretical reference for manual adjustment or automatic control of Moistube irrigation WKH to adapt to real-time crop water demand in agricultural production.
Hair Follicle Development of Rex Rabbits Is Regulated Seasonally by Wnt10b/β-Catenin, TGFβ-BMP, IGF1, and EGF Signaling Pathways
Authors: Liu Gongyan , Liu Ce , Zhang Yin , Sun Haitao , Yang Liping , Bai Liya , Gao Shuxia
Published: December 2023

This experiment was conducted to study the effects of different skinning seasons on the fur quality and hair follicle development of Rex rabbits. A total of 80,150-day-old Rex rabbits were slaughtered on 15 July 2022 (summer), 15 October 2022 (autumn), 15 January 2023 (winter), and 15 April 2023 (spring) in Shandong Province (10 males and 10 females in each season). The results show that the skin weight, skin area, skin thickness, and hair follicle density of the Rex rabbits (at 150 days of age) were lower in summer than in winter (p < 0.05). Moreover, the coat length was shorter in summer than in spring, autumn, and winter (p < 0.05). The shoulder fat weight, perirenal fat weight, and perigastric fat weight of the Rex rabbits in winter were higher than those in summer (p < 0.05). Furthermore, the leptin levels in serum were higher in winter than in summer in the Rex rabbits (p < 0.05). In terms of serum biochemistry, the glucose levels were higher in autumn and winter than in spring and summer (p < 0.05). The cholesterol, high-density lipoprotein cholesterol (HDL), and low-density lipoprotein cholesterol (LDL) in summer had higher values than in winter in the Rex rabbits (p < 0.05). In winter, the expression of the Wnt10b, catenin beta 1 (CTNNB1), glycogen synthase kinase 3 beta (GSK3β), insulin like growth factor I (IGF-I), Type I insulin-like growth factor receptor (IGF-IR), and epidermal growth factor (EGF) genes was higher (p < 0.05), and the expression of the dickkopf-1 (DDK1), transforming growth factor beta 1 (TGFβ-1), bone morphogenetic protein 2 (BMP2), and bone morphogenetic protein 4 (BMP4) genes was lower than in summer (p < 0.05). In summer, the heat shock 70 kDa protein (HSP70) expression and CTNNB1 protein phosphorylation levels in skin tissue were higher than in spring, autumn, and winter (p < 0.05). In winter, Wnt10b protein expression was higher (p < 0.05), and GSK-3β protein phosphorylation levels were lower than in spring, autumn, and winter (p < 0.05). These results show that the skinning season can affect the production performance and hair follicle development of Rex rabbits. Compared with other seasons, the quality of skin from rabbits slaughtered in winter is better. Seasons may regulate hair follicle development via the Wnt10b/β-catenin, TGFβ-BMP, IGF1, and EGF signaling pathways in Rex rabbits.
In Search of Suitable Breeding Sites: Habitat Heterogeneity and Environmental Filters Determine Anuran Diversity of Western Madagascar
Authors: Mausberg Nanäa , Dausmann Kathrin H. , Glos Julian
Published: December 2023

Environmental filtering shapes animal communities by preventing the colonization and persistence of certain species in a given habitat. More heterogenous environments are presumed to support a greater number of species and, consequently, increased species diversity, as environmental filters are also likely more heterogenous. Amphibians are especially sensitive to environmental influences due to distinct characteristics like permeable skin and low mobility. By analyzing the species richness and assemblage composition of tadpoles in 132 breeding ponds, we examined how the interplay of environmental variables shapes anuran species assemblages in breeding habitats of the dry forest of Western Madagascar. We found that environmental filtering is prevalent and habitat heterogeneity not only increases larval species richness but also alters species composition between these assemblages. Our study highlights the need for conserving heterogenous habitats to maintain local diversity. Furthermore, we recommend including multivariate modelling approaches to conservation efforts to acknowledge differences between specific habitats and beta diversity.
Innovative Approaches for Maintaining and Enhancing Skin Health and Managing Skin Diseases through Microbiome-Targeted Strategies
Authors: AL-Smadi Khadeejeh , Leite-Silva Vania Rodrigues , Filho Newton Andreo , Lopes Patricia Santos , Mohammed Yousuf
Published: December 2023

The skin microbiome is crucial in maintaining skin health, and its disruption is associated with various skin diseases. Prebiotics are non-digestible fibers and compounds found in certain foods that promote the activity and growth of beneficial bacteria in the gut or skin. On the other hand, live microorganisms, known as probiotics, benefit in sustaining healthy conditions when consumed in reasonable quantities. They differ from postbiotics, which are by-product compounds from bacteria that release the same effects as their parent bacteria. The human skin microbiome is vital when it comes to maintaining skin health and preventing a variety of dermatological conditions. This review explores novel strategies that use microbiome-targeted treatments to maintain and enhance overall skin health while managing various skin disorders. It is important to understand the dynamic relationship between these beneficial microorganisms and the diverse microbial communities present on the skin to create effective strategies for using probiotics on the skin. This understanding can help optimize formulations and treatment regimens for improved outcomes in skincare, particularly in developing solutions for various skin problems.
The Order of Limiting Amino Acids in a Wheat–Sorghum-Based Reduced-Protein Diet for Laying Hens
Authors: Jahan Afsana A. , Dao Thi Hiep , Akter Nasima , Sukirno  , Swick Robert A. , Morgan Natalie K. , Crowley Tamsyn M. , Moss Amy F.
Published: December 2023

Understanding the order of limiting amino acids (AA) in reduced-protein (RP) diets for laying hens will facilitate precise feed formulation and ensure that AA requirements are met cost-effectively. The order of the first three limiting AAs—lysine (Lys), methionine (Met), and threonine (Thr)—has been well established in RP laying hen diets. This study aimed to determine the priority order of eight additional limiting AAs (critically important AAs) when formulating wheat–sorghum-based RP diets for laying hens: tryptophan (Trp), valine (Val), isoleucine (Ile), arginine (Arg), leucine (Leu), histidine (His), phenylalanine (Phe), and glycineequivalent (Gly). A total of 330 Hy-Line Brown laying hens were randomly assigned to 11 dietary treatments (30 replicates of individual birds per treatment) from 20 to 39 weeks of age (WOA). Treatments were a standard-protein (17.24% CP) diet as the control (SP); a reduced-protein (15.00% CP) diet with sufficient levels of Lys, Met, and Thr but insufficient levels of the eight experimental essential AA (RP); a reduced-protein diet with sufficient levels of all essential AAs (RP-EAA); and eight subsequent dietary treatments of the RP-EAA diet with one of the experimental essential AAs removed: Trp (RP-EAA-Trp), Val (RP-EAA-Val), Ile (RP-EAA-Ile), Arg (RP-EAA-Arg), Leu (RP-EAA-Leu), His (RP-EAA-His), Phe (RP-EAA-Phe), and Gly (RP-EAA-Gly). Eggs were collected and weighed daily, and feed intake and feed conversion ratio (FCR) were calculated weekly. External and internal egg quality was measured at 29 and 39 WOA. Nutrient digestibility, serum uric acid concentration, caecal microbiota composition, and tibia parameters were measured at 40 WOA. Overall, hens fed the RP-EAA-Val, RP-EAA-Ile, and RP diets presented significantly lower egg mass compared to hens fed the SP, RP-EAA-His, and RP-EAA-Gly diets (p < 0.001). Hens fed the RP diet and RP-EAA-Val diet had a higher FCR compared to those offered the RP-EAA, RP-EAA-Leu, RP-EAA-Phe, and RP-EAA-Gly diets (p = 0.046). Lower protein intake and excretion were observed in hens offered the RP diets compared to hens fed the SP diet (p = 0.001 and 0.018, respectively). Based on the egg mass, Ile may be considered the fourth and Val the fifth limiting AA, after Lys, Met, and Thr, in laying hens fed wheat–sorghum-based RP diets during peak lay. However, if ranked based on FCR, Val may be considered the fourth limiting AA, followed by Trp, Ile, Arg, and His as the co-fifth limiting AAs. Leu, Phe, and Gly may be considered as non-essential AAs for laying hens fed RP diets.
Fault Detection and State Estimation in Automatic Control
Authors: Du Sheng , Wang Wei , Fu Hao , Wan Xiongbo
Published: December 2023

Fault detection and state estimation play pivotal roles in ensuring the reliability, safety, and performance of automatic control systems [...]
Radiomic Analysis for Human Papillomavirus Assessment in Oropharyngeal Carcinoma: Lessons and Pitfalls for the Next Future
Authors: Morelli Ilaria , Becherini Carlotta , Banini Marco , Valzano Marianna , Bertini Niccolò , Loi Mauro , Francolini Giulio , Meattini Icro , Salvestrini Viola , Bonomo Pierluigi , Livi Lorenzo , Desideri Isacco
Published: December 2023

Background: Oropharyngeal Squamous Cell Carcinoma (OPSCC) is rapidly increasing due to the spread of Human Papillomavirus (HPV) infection. HPV-positive disease has unique characteristics, with better response to treatment and consequent better prognosis. HPV status is routinely assessed via p16 immunohistochemistry or HPV DNA Polymerase Chain Reaction. Radiomics is a quantitative approach to medical imaging which can overcome limitations due to its subjective interpretation and correlation with clinical data. The aim of this narrative review is to evaluate the impact of radiomic features on assessing HPV status in OPSCC patients. Methods: A narrative review was performed by synthesizing literature results from PUBMED. In the search strategy, Medical Subject Headings (MeSH) terms were used. Retrospective mono- or multicentric works assessing the correlation between radiomic features and HPV status prediction in OPSCC were included. Selected papers were in English and included studies on humans. The range of publication date was July 2015–April 2023. Results: Our research returned 23 published papers; the accuracy of radiomic models was evaluated by ROC curves and AUC values. MRI- and CT-based radiomic models proved of comparable efficacy. Also, metabolic imaging showed crucial importance in the determination of HPV status, albeit with lower AUC values. Conclusions: Radiomic features from conventional imaging can play a complementary role in the assessment of HPV status in OPSCC. Both primary tumor- and nodal-related features and multisequencing-based models demonstrated higher accuracy.
A Java Application for Teaching Graphs in Undergraduate Courses
Authors: Migallón Violeta , Penadés José
Published: December 2023

Graph theory is a common topic that is introduced as part of the curricula of computing courses such as Computer Science, Computer Engineering, Data Science, Information Technology and Software Engineering. Understanding graphs is fundamental for solving many real-world problems, such as network routing, social network analysis, and circuit design; however, many students struggle to grasp the concepts of graph theory, as they often have difficulties in visualising and manipulating graphs. To overcome these difficulties, educational software can be used to aid in the teaching and learning of graph theory. This work focuses on the development of a Java system for graph visualisation and computation, called MaGraDa (Graphs for Discrete Mathematics), that can help both students and teachers of undergraduate or high school courses that include concepts and algorithms related to graphs. A survey on the use of this tool was conducted to explore the satisfaction level of students on a Discrete Mathematics course taken as part of a Computer Engineering degree at the University of Alicante (Spain). An analysis of the results showed that this educational software had the potential to enhance students’ understanding of graph theory and could enable them to apply these concepts to solve practical problems in the field of computer science. In addition, it was shown to facilitate self-learning and to have a significant impact on their academic performance.
A Multivariate Time Series Analysis of Electrical Load Forecasting Based on a Hybrid Feature Selection Approach and Explainable Deep Learning
Authors: Yaprakdal Fatma , Varol Arısoy Merve
Published: December 2023

In the smart grid paradigm, precise electrical load forecasting (ELF) offers significant advantages for enhancing grid reliability and informing energy planning decisions. Specifically, mid-term ELF is a key priority for power system planning and operation. Although statistical methods were primarily used because ELF is a time series problem, deep learning (DL)-based forecasting approaches are more commonly employed and successful in achieving precise predictions. However, these DL-based techniques, known as black box models, lack interpretability. When interpreting the DL model, employing explainable artificial intelligence (XAI) yields significant advantages by extracting meaningful information from the DL model outputs and the causal relationships among various factors. On the contrary, precise load forecasting necessitates employing feature engineering to identify pertinent input features and determine optimal time lags. This research study strives to accomplish a mid-term forecast of ELF study load utilizing aggregated electrical load consumption data, while considering the aforementioned critical aspects. A hybrid framework for feature selection and extraction is proposed for electric load forecasting. Technical term abbreviations are explained upon first use. The feature selection phase employs a combination of filter, Pearson correlation (PC), embedded random forest regressor (RFR) and decision tree regressor (DTR) methods to determine the correlation and significance of each feature. In the feature extraction phase, we utilized a wrapper-based technique called recursive feature elimination cross-validation (RFECV) to eliminate redundant features. Multi-step-ahead time series forecasting is conducted utilizing three distinct long-short term memory (LSTM) models: basic LSTM, bi-directional LSTM (Bi-LSTM) and attention-based LSTM models to accurately predict electrical load consumption thirty days in advance. Through numerous studies, a reduction in forecasting errors of nearly 50% has been attained. Additionally, the local interpretable model-agnostic explanations (LIME) methodology, which is an explainable artificial intelligence (XAI) technique, is utilized for explaining the mid-term ELF model. As far as the authors are aware, XAI has not yet been implemented in mid-term aggregated energy forecasting studies utilizing the ELF method. Quantitative and detailed evaluations have been conducted, with the experimental results indicating that this comprehensive approach is entirely successful in forecasting multivariate mid-term loads.
Computational Imaging at the Infrared Beamline of the Australian Synchrotron Using the Lucy–Richardson–Rosen Algorithm
Authors: Ng Soon Hock , Anand Vijayakumar , Han Molong , Smith Daniel , Maksimovic Jovan , Katkus Tomas , Klein Annaleise , Bambery Keith , Tobin Mark J. , Vongsvivut Jitraporn , Juodkazis Saulius
Published: December 2023

The Fourier transform infrared microspectroscopy (FTIRm) system of the Australian Synchrotron has a unique optical configuration with a peculiar beam profile consisting of two parallel lines. The beam is tightly focused using a 36× Schwarzschild objective to a point on the sample and the sample is scanned pixel by pixel to record an image of a single plane using a single pixel mercury cadmium telluride detector. A computational stitching procedure is used to obtain a 2D image of the sample. However, if the imaging condition is not satisfied, then the recorded object’s information is distorted. Unlike commonly observed blurring, the case with a Schwarzschild objective is unique, with a donut like intensity distribution with three distinct lobes. Consequently, commonly used deblurring methods are not efficient for image reconstruction. In this study, we have applied a recently developed computational reconstruction method called the Lucy–Richardson–Rosen algorithm (LRRA) in the online FTIRm system for the first time. The method involves two steps: training step and imaging step. In the training step, the point spread function (PSF) library is recorded by temporal summation of intensity patterns obtained by scanning the pinhole in the x-y directions across the path of the beam using the single pixel detector along the z direction. In the imaging step, the process is repeated for a complicated object along only a single plane. This new technique is named coded aperture scanning holography. Different types of samples, such as two pinholes; a number 3 USAF object; a cross shaped object on a barium fluoride substrate; and a silk sample are used for the demonstration of both image recovery and 3D imaging applications.
A Bilateral Craniectomy Technique for In Vivo Photoacoustic Brain Imaging
Authors: McGuire Laura S. , Zafar Mohsin , Manwar Rayyan , Charbel Fady T. , Avanaki Kamran
Published: December 2023

Due to the high possibility of mechanical damage to the underlying tissues attached to the rat skull during a craniectomy, previously described methods for visualization of the rat brain in vivo are limited to unilateral craniotomies and small cranial windows, often measuring 4–5 mm. Here, we introduce a novel method for producing bilateral craniectomies that encompass frontal, parietal, and temporal bones via sequential thinning of the skull while preserving the dura. This procedure requires the removal of a portion of the temporalis muscle bilaterally, which adds an additional 2–3 mm exposure within the cranial opening. Therefore, while this surgery can be performed in vivo, it is strictly non-survival. By creating large, bilateral craniectomies, this methodology carries several key advantages, such as the opportunity afforded to test innovate imaging modalities that require a larger field of view and also the use of the contralateral hemisphere as a control for neurophysiological studies.
Activity Concentrations of Cs-137, Sr-90, Am-241, Pu-238, and Pu-239+240 and an Assessment of Pollution Sources Based on Isotopic Ratio Calculations and the HYSPLIT Model in Tundra Landscapes (Subarctic Zone of Russia)
Authors: Puchkov Andrey , Yakovlev Evgeny
Published: December 2023

The paper is devoted to the assessment of the content of anthropogenic radionuclides in tundra landscapes of the subarctic zone of Russia. The authors of the article studied the features of accumulation and migration of anthropogenic radionuclides and identified probable sources of their entry into environmental objects. Peat samples were collected on the territory of the Kaninskaya Tundra of the Nenets Autonomous Okrug (Northwest Russia). A total of 46 samples were taken. The following parameters were determined in each peat sample: (1) activity and pollution density of anthropogenic radionuclides; (2) isotopic ratios of anthropogenic radionuclides; (3) activity ratios of each radionuclide for layers 10–20 cm and 0–10 cm. The results of the studies showed that the pollution density of the Nes River basin with the radionuclides Cs-137 and Sr-90 is up to 4.85 × 103 Bq×m−2 and 1.88 × 103 Bq×m−2, respectively, which is 2–5 times higher than the available data for the Kanin tundra, as well as for Russia and the world as a whole. The data obtained for Am-241, Pu-238, and Pu-239+240 showed insignificant activity of these radionuclides and generally correspond to the values for other tundra areas in Russia and the world. It was found that some tundra areas (“peat lowlands”) are characterized by increased radionuclide content due to the process of accumulation and migration along the vertical profile. Calculations of isotope ratios Sr-90/Cs-137, Pu-238/Pu-239+240, Pu-239+240/Cs-137, Am-241/Pu-239+240 and air mass trajectories based on the HYSPLIT model showed that the main sources of anthropogenic radionuclide contamination are global atmospheric fallout and the Chernobyl accident.
Enhancing Neonatal Incubator Energy Management and Monitoring through IoT-Enabled CNN-LSTM Combination Predictive Model
Authors: Aryanto I Komang Agus Ady , Maneetham Dechrit , Crisnapati Padma Nyoman
Published: December 2023

This research focuses on enhancing neonatal care by developing a comprehensive monitoring and control system and an efficient model for predicting electrical energy consumption in incubators, aiming to mitigate potential adverse effects caused by excessive energy usage. Employing a combination of 1-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) methods within the framework of the Internet of Things (IoT), the study encompasses multiple components, including hardware, network, database, data analysis, and software. The research outcomes encompass a real-time web application for monitoring and control, temperature distribution visualizations within the incubator, a prototype incubator, and a predictive energy consumption model. Testing the LSTM method resulted in an RMSE of 42.650 and an MAE of 33.575, while the CNN method exhibited an RMSE of 37.675 and an MAE of 30.082. Combining CNN and LSTM yielded an RMSE of 32.436 and an MAE of 25.382, demonstrating the potential for significantly improving neonatal care.
Improving the Maritime Traffic Evaluation with the Course and Speed Model
Authors: Lee Eui-Jong , Kim Hyun-Suk , Lee Eunkyu , Kim Kyungsup , Yu Yongung , Lee Yun-Sok
Published: December 2023

Recent projections from marine transportation experts highlight an uptick in maritime traffic, attributed to the fourth industrial revolution’s technological strides and global economic rebound. This trend underscores the need for enhanced systems for maritime accident prediction and traffic management. In this study, to analyze the flow of maritime traffic macroscopically, spatiality and continuity reflecting the output of ships are considered. The course–speed (CS) model used in this study involved analyzing COG, ROT, speed, and acceleration, which can be obtained from the ship’s AIS data, and calculating the deviation from the standard plan. In addition, spatiality and continuity were quantitatively analyzed to evaluate the smoothness of maritime traffic flow. A notable finding is that, in the target sea area, the outbound and inbound CS indices are measured at 0.7613 and 0.7501, suggesting that the outbound ship flows are more affected than inbound ship flows to the liquidity of maritime traffic flow. Using the CS model, a detailed quantitative evaluation of the spatiality and continuity of maritime traffic is presented. This approach facilitates robust comparisons over diverse scales and periods. Moreover, the research advances our understanding of factors dictating maritime traffic flow based on ship attributes. The study insights can catalyze the development of a novel index for maritime traffic management, enhancing safety and efficiency.
Rapid Induction of Astaxanthin in Haematococcus lacustris by Mild Electric Stimulation
Authors: Sathiyavahisan Laxmi Priya , Lakshmi Narasimhan Aditya , Mahadi Rendi , Kim Sangui , Christabel Catherine , Yu Hyoji , Kim Young-Eun , Oh You-Kwan
Published: December 2023

Efficient induction of astaxanthin (AXT) biosynthesis remains a considerable challenge for the industrialization of the biorefinement of the microalga Haematococcus lacustris. In this study, we evaluated the technical feasibility of photosynthetic electrotreatment to enhance AXT accumulation in H. lacustris. The AXT content of H. lacustris electrotreated at an optimal current intensity (10 mA for 4 h) was 21.8% to 34.9% higher than that of the untreated control group, depending on the physiological state of the initial palmella cells. The contents of other carotenoids (i.e., canthaxanthin, zeaxanthin, and β-carotene) were also increased by this electrotreatment. However, when H. lacustris cells were exposed to more intense electric treatments, particularly at 20 and 30 mA, cell viability significantly decreased to 84.2% and 65.6%, respectively, with a concurrent reduction in the contents of both AXT and the three other carotenoids compared to those of the control group. The cumulative effect of electric stimulation is likely related to two opposing functions of reactive oxygen species, which facilitate AXT biosynthesis as signaling molecules while also causing cellular damage as oxidizing radicals. Collectively, our findings indicate that when adequately controlled, electric stimulation can be an effective and eco-friendly strategy for inducing targeted carotenoid pigments in photosynthetic microalgae.
Biocompatible Fe-Based Metal-Organic Frameworks as Diclofenac Sodium Delivery Systems for Migraine Treatment
Authors: Galarda Aleksandra , Goscianska Joanna
Published: December 2023

Migraine is now the sixth most common disease in the world and affects approximately 15% of the population. Non-steroidal anti-inflammatory drugs, including ketoprofen, diclofenac sodium, and ibuprofen, are often used during migraine attacks. Unfortunately, their efficiency can be reduced due to poor water solubility and low cellular uptake. This requires the design of appropriate porous carriers, which enable drugs to reach the target site, increase their dissolution and stability, and contribute to a time-dependent specific release mode. In this research, the potential of the MIL-88A metal-organic frameworks with divergent morphologies as diclofenac sodium delivery platforms was demonstrated. Materials were synthesized under different conditions (temperature: 70 and 120 °C; solvent: distilled water or N,N-Dimethylformamide) and characterized using X-ray diffraction, low-temperature nitrogen adsorption/desorption, thermogravimetric analysis, infrared spectroscopy, and scanning electron microscopy. They showed spherical, rod- or diamond-like morphologies influenced by preparation factors. Depending on physicochemical properties, the MIL-88A samples exhibited various sorption capacities toward diclofenac sodium (833–2021 mg/g). Drug adsorption onto the surface of MIL-88A materials primarily relied on the formation of hydrogen bonds, metal coordination, and electrostatic interactions. An in vitro drug release experiment performed at pH 6.8 revealed that diclofenac sodium diffused to phosphate buffer in a controlled manner. The MIL-88A carriers provide a high percentage release of drug in the range of 58–97% after 24 h.
FedRDS: Federated Learning on Non-IID Data via Regularization and Data Sharing
Authors: Lv Yankai , Ding Haiyan , Wu Hao , Zhao Yiji , Zhang Lei
Published: December 2023

Federated learning (FL) is an emerging decentralized machine learning framework enabling private global model training by collaboratively leveraging local client data without transferring it centrally. Unlike traditional distributed optimization, FL trains the model at the local client and then aggregates it at the server. While this approach reduces communication costs, the local datasets of different clients are non-Independent and Identically Distributed (non-IID), which may make the local model inconsistent. The present study suggests a FL algorithm that leverages regularization and data sharing (FedRDS). The local loss function is adapted by introducing a regularization term in each round of training so that the local model will gradually move closer to the global model. However, when the client data distribution gap becomes large, adding regularization items will increase the degree of client drift. Based on this, we used a data-sharing method in which a portion of server data is taken out as a shared dataset during the initialization. We then evenly distributed these data to each client to mitigate the problem of client drift by reducing the difference in client data distribution. Analysis of experimental outcomes indicates that FedRDS surpasses some known FL methods in various image classification tasks, enhancing both communication efficacy and accuracy.
Technological Breakthroughs in Sport: Current Practice and Future Potential of Artificial Intelligence, Virtual Reality, Augmented Reality, and Modern Data Visualization in Performance Analysis
Authors: Cossich Victor R. A. , Carlgren Dave , Holash Robert John , Katz Larry
Published: December 2023

We are currently witnessing an unprecedented era of digital transformation in sports, driven by the revolutions in Artificial Intelligence (AI), Virtual Reality (VR), Augmented Reality (AR), and Data Visualization (DV). These technologies hold the promise of redefining sports performance analysis, automating data collection, creating immersive training environments, and enhancing decision-making processes. Traditionally, performance analysis in sports relied on manual data collection, subjective observations, and standard statistical models. These methods, while effective, had limitations in terms of time and subjectivity. However, recent advances in technology have ushered in a new era of objective and real-time performance analysis. AI has revolutionized sports analysis by streamlining data collection, processing vast datasets, and automating information synthesis. VR introduces highly realistic training environments, allowing athletes to train and refine their skills in controlled settings. AR overlays digital information onto the real sports environment, providing real-time feedback and facilitating tactical planning. DV techniques convert complex data into visual representations, improving the understanding of performance metrics. In this paper, we explore the potential of these emerging technologies to transform sports performance analysis, offering valuable resources to coaches and athletes. We aim to enhance athletes’ performance, optimize training strategies, and inform decision-making processes. Additionally, we identify challenges and propose solutions for integrating these technologies into current sports analysis practices. This narrative review provides a comprehensive analysis of the historical context and evolution of performance analysis in sports science, highlighting current methods’ merits and limitations. It delves into the transformative potential of AI, VR, AR, and DV, offering insights into how these tools can be integrated into a theoretical model.