bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Deep Learning Framework for Face Sketch Synthesis Using Generative Adversarial Network
Authors: LATHA BUDDANNAGARI , VELMURUGAN A. K. 
Published: March 2024

Abstract
Face sketch synthesis phenomenon, a kind of image-image translation, generates synthesized face/sketch with wide range of applications pertaining law enforcement and entertainment to mention few. Despite the compelling results produced by many existing methods of late, there are still challenges due to deformation and blurred effects on facial components resulting in unrealistic face/sketch. To overcome this problem, in this paper, we proposed a novel framework known as Deep Face-Sketch Synthesis Framework (DFSSF). The framework is realized with different building blocks including an algorithm known as Deep Face-Sketch Synthesis for High Perceptual Quality (DFSS-HPQ). The framework is based on the architecture of Generative Adversarial Network (GAN) which exploits facial structures and a novel labelling mechanism. It takes paired inputs compromising of face images and sketches. The framework also considers extraction of GANs with heterogeneity from inputs. Afterwards, they are stacked to obtain additional features that can be effectively used to rectify defects if any. Two algorithms known as Hybrid GAN for Face Sketch Synthesis (HGAN-FSS) and Stacked Hybrid GAN for Face Sketch Synthesis (SHGAN-FSS) are proposed. We used two frequently used datasets namely CUFS and CUFSF having samples collected from 606 and 1194 persons respectively. The proposed framework is built using Python data science platform. Empirical results of the framework are evaluated and compared with traditional face sketch methods, deep learning models and deep learning models based on GANs. The proposed framework showed better performance over the state of the art in presence of different styles, lighting conditions and head poses.
Evaluating the Efficiency of 16S-ITS-23S operon Sequencing: A Comparison of Primer Pairs, Sequencing Platforms, and Taxonomic Classifiers
Authors: Srini Meghana , Walsh Calum J. , Crispie Fiona , O'Sullivan Orla , Cotter Paul D. , Sinderen Douwe , Kenny John G. 
Published: March 2024

Abstract
Background
The field of 16S rRNA-targeted metagenetics has been enhanced through the improved accuracy of long-read sequencing. More specifically, recent advances have facilitated the transition from short-read sequencing of 16S rRNA gene regions to full-length sequencing of the entire 16S gene (~1500 bp) and, in turn, sequencing of the 16S, Internal Transcribed Spacer (ITS), and 23S regions covering a DNA region known as the ribosomal RNA operon (RRN) (~4500 bp). These technological advances offer the potential to achieve at least species-level resolution when analysing microbiomes, increasing interest in RRN sequencing. However, before widespread adoption of this approach can occur successfully, a thorough assessment of its strengths and limitations is necessary.
Results
This study assesses the effects of RRN primer pairs and sequencing platforms on RRN sequencing, while also aiming to benchmark taxonomic classification methods. In this context, we study the effect four RRN primer combinations; four mock communities, three sequencing platforms (PacBio, Oxford Nanopore Technologies, and Illumina), two classification approaches (Minimap2 alignment and OTU clustering), and four RRN reference databases (MIrROR, rrnDB, and two iterations of FANGORN) alongside two 16S databases (Greengenes2 and SILVA). Our study reveals that choice of primer pair and sequencing platform do not substantially bias the taxonomic profiles provided by RRN sequencing for a majority of the mock communities. However, community composition was identified as a confounding factor. The classification method significantly impacts the accuracy of species-level taxonomic assignment. Applying Minimap2 in combination with the FANGORN database was found to provide the most accurate profile for most microbial communities, irrespective of sequencing platform.
Conclusions
Long-read sequencing of the RRN operon provides species-level resolution surpassing that of Illumina-based 16S rRNA gene sequencing. Our findings advocate for the use of RRN sequencing in species-level microbial profiling. We extensively benchmark the factors involved to provide a valuable resource, aiding the advancement and adoption of RRN sequencing, while highlighting some ongoing challenges.
PiTeQA-Net: Picto-Textual Question Answering Network for Multimodal Document Image Analysis
Authors: Jiaming Li 
Published: March 2024

Abstract

By integrating visual and natural language understanding, Visual Question Answering (VQA) holds promise for enhancing the intelligence of computer systems, thereby improving user work efficiency. However, current research in the VQA field faces two major challenges: low precision in feature pre-extraction and low efficiency in feature fusion. This study proposes a pre-trained framework based on RSFE-TBT for question-answering tasks aimed at improving feature pre-extraction accuracy and feature fusion efficiency across different modalities. Addressing the issue of low pre-extraction accuracy in existing methods, ResNet50-SF is proposed for pre-extracting image features. Considering ResNet50's limitations in recognizing small objects and specific spatial positions, a bidirectional feature pyramid network(BiFPN) with spatial attention is introduced. Text block features bridging image and text are introduced to address challenges in aligning multi-modal features and fusion efficiency. These encompass block position, shape, sequence, and relative arrangements. Image features are segmented into semantic and spatial aspects, while text blocks are divided into positional and index attributes. Efficient multimodal fusion is achieved with multi-layer Transformer Encoders. Experimental results on the MultiDoc-InfoExtract Dataset demonstrate superior performance of this method in Semantic Entity Recognition (SER) and Relation Extraction (RE) tasks, achieving F1, Precision, and Recall scores of 0.975, 0.975, and 0.975, respectively, in SER tasks, and 0.969, 0.953, and 0.986, respectively, in RE tasks, with a single image inference time of only 0.082s. Ablation studies validate the significance of the improved image feature extraction model, the inclusion of text block features, feature refinement strategies, and the Transformer Encoder architecture in enhancing the performance of the question-answering system. Additionally, comparative studies have shown that RSFE-TBT outperforms competing models regarding accuracy, speed, and size.
Serial Crime Detection Through Selective Resemblance Measure Calculation (SRMC) and Threshold Value Accuracy Measure (TVAM): A novel approach
Authors: Goala Soumendra 
Published: March 2024

Abstract

Serial crime detection using automation is becoming one of the most important study due to its immense applicability and ability to reduce time consumption and energy during serial crime investigation. For this reason, it is always to have efficient methodologies for serial crime detection under different types of uncertain environment. In this paper, two new methodologies has been developed named as Serial crime detection through Selective Resemblance Measure Calculation (SRMC) and Serial crime detection through Threshold Value Accuracy Measure (TVAM) by modifying an existing methodology for Serial crime detection through Resemblance measure under Intuitionistic fuzzy environment. For each methodologies, case studies has been carried out for and justification has been done about the applicability and reliability of these methodologies. In addition, a comparative analysis on the basis of the efficiency of the newly developed methodologies has been given.
Experimental behaviour of ductile diagonal connections for rack supported warehouses
Authors: Natali Agnese , Morelli Francesco , Vulcu Cristian , Tsarpalis Dimitrios , Vamvatsikos Dimitrios , Salvatore Walter , Hoffmeister Benno , Vayas Ioannis 
Published: March 2024

Abstract

Steel racking systems are widely adopted for storage purposes: they are thin-walled structures composed of consecutive trusses, connected with beams on which the palletized goods are stored. Their geometry and structural configuration strongly depend on market and operator necessities, and, in modern applications, the racks can also function as the supporting structure of the warehouse itself in the form of Rack Supported or High-Bay Warehouses. With the increase of the overall geometric dimensions and the global weight of the stored material, the seismic action becomes more relevant for the design. Along these lines, we present the development and experimental testing of a dedicated seismic design approach for ductile steel racks, with particular attention to Rack Supported Warehouses. This approach exploits the ductility of trusses introduced via the plastic ovalization mechanism of the diagonal-to-upright connections while a tailored capacity design is used to assure the elastic behaviour of the rest of the structure and to keep the brittle failure mechanisms at bay.
Transcriptome analyses of salicylic acid- and jasmonic acid-treated cell suspension cultures of Andrographis paniculata
Authors: Ahmed Nihal , Nagella Praveen 
Published: March 2024

Abstract

Transcriptomic studies provide valuable insights into the molecular mechanisms underlying plant responses to various stimuli, including phytohormones such as salicylic acid (SA) and jasmonic acid (JA), which are produced in response to attack by biotrophic and necrotrophic pathogens, respectively. Interactions between SA and JA signaling pathways are complex and context dependent; sometimes they are antagonistic, and sometimes they are synergistic. In this study, RNA sequencing was employed to investigate the transcriptomic changes induced by SA treatment, JA treatment and SA + JA treatment in cell suspension cultures of Andrographis paniculata, a medicinal plant with pharmacological properties; untreated cultures served as controls. Differential gene expression analysis revealed significant alterations in the transcriptomes in each treatment group. Gene Ontology enrichment analysis highlighted the modulation of biological processes related to defense response, secondary metabolism, and signal transduction. Notably, unique sets of genes were found to be differentially expressed in response to SA, JA and SA + JA, suggesting the activation of distinct signaling cascades. Furthermore, in the combination treatment, SA + JA, SA was shown to suppress the effect of JA on the overall transcriptome. A correlation of 0.932 was detected between the transcriptomes of SA-treated cells and SA + JA-treated cells. Several key transcripts associated with the enzymes involved in andrographolide biosynthesis, were both significantly overexpressed and significantly underexpressed in all the treatment groups, suggesting the need for proteomics studies for definitive validation. The findings lay the groundwork for further elucidation of mechanisms underlying defense responses against both biotrophic and necrotrophic pathogens, and hold promise for their exploitation in improving the medicinal value of A. paniculata.
Understanding the role of soil microbes and enzymes in regulating nitrogen dynamics: Promoting sustainable crop rotation systems
Authors: Zhang Liqiang , Ji Wenxiu , Jiang Xinbo , Wang Yunlong , An Xiaoya , Rao Demin , Meng Fangang , Cui Jinhu , Zhang Wei , Zhao Hongyan 
Published: March 2024

Abstract
Aims
Soil nitrogen is recognized as a vital nutrient influencing soybean growth and yield. Hence, a comprehensive understanding of the intricate connections between shifts in nitrogen patterns and the behaviors of soil microbial communities and crucial enzymes in the nitrogen cycle is highly desirable.
Methods
This study involved a rotation positioning experiment spanning 9 to 11 years. Measurement of soil microenvironment changes during the mature period for three consecutive years, focusing on the corn-soybean rotation with varying fertilizer application rates. Six distinct treatment groups were established for investigation. Based on these groups, the study delved into the alterations in nitrogen patterns within the soybean rotation, examining both soil enzyme activity and microbial community dynamics.
Results
Long-term crop rotation and nitrogen application led to an increase ranging from 2.16% to 108.34% in the nine components of soil nitrogen. The variations in total nitrogen, heavy fraction organic nitrogen, and light fraction organic nitrogen were primarily influenced by the enrichment of the Actinobacteriota phylum. The environmental factors affecting the changes in inorganic nitrogen, alkaline hydrolyzable nitrogen, exchangeable ammonium and acid hydrolyzable nitrogen were linked to the Ascomycota phylum. The Proteobacteria phylum and urease were key factors in the variations of organic nitrogen and nitrate-nitrogencomponents, respectively.
Conclusions
Changes in inorganic nitrogen and total organic nitrogen resulting from crop rotation enhanced the richness of soil microbial communities, reducing their diversity. This alteration influenced the bacterial and fungal communities composition, ultimately augmenting their functional capacities.
Las Bolitas Syndrome in Penaeus vannamei hatcheries in Latin America
Authors: Intriago Pablo , Montiel Bolivar , Valarezo Mauricio , Romero Xavier , Arteaga Kelly , Cercado Nicole , Burgos Milena , Shinn Andrew P. , Montenegro Alejandra , Medina Andrés , Gallardo Jennifer 
Published: March 2024

Abstract

Several hatcheries in Latin America reported mortality of zoea stage 2 Penaeus vannamei. In fresh mounts, round structures resembling lipid droplets were observed, reminiscent of a disease called "Las Bolitas Syndrome" first identified in 1987. Closer examination under routine histopathology revealed the presence of detached cells and tissue in the digestive tract, whereas unaffected tanks displayed a typical intestinal content containing algae cells. Polymerase Chain Reaction of diseased and healthy batches of larvae for 22 shrimp pathogens revealed similar test results. The larvae were negative for nineteen pathogens, including AHPND. The detection of Vibrio spp. in both samples of affected zoea 3 (Z3) was the principal difference. Histology of affected zoeas were characterized by tissue degeneration in the hepatopancreas forming spheres that eventually moved into the upper gut, midgut and midgut caeca - a pathology known as Bolitas syndrome (BS). Microbiological analysis showed Vibrio spp. at ≤ 105 CFU zoea/g, ≈ 2 orders of magnitude higher than healthy zoea. Isolation of bacteria from healthy and BS affected zoea onto TCBS and CHROMagar™ and consequentially identified by API 20 E revelated six strains of V. alginolyticus. Though fresh mounts resembled the general description for “Las Bolitas Syndrome”, the histopathology differed from the original description. The intestine contained sloughed cells; the lateral lobes constituting the developing hepatopancreas in Z3 could be differentiated by their colour, with sloughed cells inside the peritrophic membrane. PCR and microbiological analyses verified that the origin of Las Bolitas Syndrome is bacterial in nature, with Vibrio playing a significant role.
Mendelian randomization study shows no causal effects of polycystic ovarian syndrome on the risk of preeclampsia
Authors: Yin Fufen , Yin Xiuju , Xie Junshu , Zhu Ye , Zhang Xiaohong 
Published: March 2024

Abstract

Background
Previous studies have shown an association between polycystic ovary syndrome (PCOS) and the increased risk of developing preeclampsia (PE). However, other studies have not found an independent association between the two. A causal association between PCOS and PE remains unclear. The objective of this study was to determine whether PCOS has a causal relationship with PE.
Methods
A two-sample Mendelian randomization (MR) analysis was performed by using the inverse‐variance weighted (IVW), weighted median, MR‐Egger regression, simple mode, and weighted mode methods. We used the publicly available summary statistics data sets of genome‐wide association studies (GWAS) meta‐analyses for PCOS (N = 113238) as the exposure and a GWAS for PE (N = 267242) as the outcome. In addition, the heterogeneity, horizontal pleiotropy, and stability were assessed through several sensitivity analyses.
Results
13 single nucleotide polymorphisms (SNPs) at genome-wide significance from GWASs on PCOS were selected as the instrumental variables (IVs). The results of all the methods including IVW, weighted median, MR‐Egger regression, simple mode, and weighted mode were consistent and showed no causal association between PCOS and PE (P > 0.05)
Conclusion
The results of MR analysis support that PCOS may not be causally associated with an increased risk of PE. We suggest PCOS should not be presently included as a risk factor in obstetrical guidelines and prediction models for PE. To determine whether PCOS and PE are associated, further research is needed.
Enhanced efficiency of silicon solar cell by new salophen complexes embedded PMMA as light concentrators
Authors: Ahmed Radwa K. , Mohamed Naglaa M. , Reda S. M. 
Published: March 2024

Abstract

The Schiff base N,N-bis(salicylidene)-o-phenylenediamine (salophen) was prepared by the condensation of salicylaldehyde with o-phenylenediamine in ethanol solution. Two new Zn(II) and Ni (II) salophen complexes, were synthesized, fully characterized by infrared (IR), 1H NMR spectroscopic measurements, UV-Vis spectra, photoluminescence (PL), and X-ray diffraction. The prepared complexes were used as phosphors to fabricate complexes/PMMA slab-based luminescent solar concentrators (LSC). The thermal stability of pure and doped PMMA polymer was examined by differential scanning calorimetry. Various parameters such as the optical energy gap, refractive index, AC and DC conductivity, dielectric constant, dielectric loss, Urbach energy, fluorescence quantum yield, and Stokes shift have been calculated and discussed. Optical absorption is carried out in wavelength region 200-900 nm at room temperature before and after the samples have been exposed to sunlight for up to 8 hrs. Photodegradation studies showed that the Zn (II) complex/PMMA LSC have the lowest rate of degradation compared with Ni (II) complex/PMMA LSC with the same concentration (0.06% weight). I–V characteristics of the photovoltaic devices with and without collectors were examined. The PV cell coupled with LSC shows an increase in maximum efficiency by about 50% compared to the normal one. This indicates that the proposed technique is very useful for improving the efficiency of solar cells.
Network Type Recognition Using Machine Learning Techniques
Authors: Ray Debmalya 
Published: March 2024

Abstract
The telecom industry is going through a massive digital transformation with the adoption of ML, AI, feedback-based automation and advanced analytics to handle the next generation applications and services. AI concepts are not new; the algorithms used by Machine Learning and Deep Learning are being currently implemented in various industries and technology verticals. With growing data and immense volume of information over 5G, the ability to predict data proactively, swiftly and with accuracy, is critically important. Data-driven decision making will be vital in future communication networks due to the traffic explosion and Artificial Intelligence (AI) will accelerate the 5G network performance. Mobile operators are looking for a programmable solution that will allow them to accommodate multiple independent tenants on the same physical infrastructure and 5G networks allow for end-to-end network resource allocation using the concept of Network Slicing (NS). Network Slicing will play a vital role in enabling a multitude of 5G applications, use cases, and services. Network slicing functions will provide an end-to-end isolation between slices with an ability to customize each slice based on the service demands (bandwidth, coverage, security, latency, reliability, etc).
Impact of Nutrient-Stimulated Hormone (NUSH) Dynamics on Body Weight
Authors: Andrade Luís Jesuino de Oliveira , de Oliveira Gabriela Correia Matos , de Oliveira Luisa Correia Matos , Bittencourt Alcina Maria Vinhaes , de Oliveira Luis Matos 
Published: March 2024

Abstract
Introduction: Nutrient-stimulated hormones (NUSH) play a critical role in regulating energy metabolism. Dysregulation of NUSH signaling is associated with obesity, there is a lack of quantitative models to investigate the complex dynamics of NUSH signaling and its impact on obesity development.
Objective: To explore the relationship between NUSH and body weight using mathematical modeling.
Methods: Data on elevated body weight were collected from meta-analysis studies available on Pubmed, utilizing incretin-based therapies. A mathematical model was developed using software to integrate interactions between NUSH levels and changes in body weight. The model accurately captured the complex dynamics and feedback loops involved in obesity-related hormonal regulation, employing differential equations and statistical techniques. Parameter estimation was performed using meta-analysis results to minimize the discrepancy between model predictions and observed data.
Results: This study included 15 meta-analysis studies on liraglutide, semaglutide, and tirzepatide for the treatment of obesity. A mathematical model was developed to understand NUSH dynamics in relation to obesity. The model deduced the formula: NUSH(t) = N0 * (1 - e^(-kt)) + I * [1 - e^(-βt)] / β, which considers NUSH levels over time, initial levels, decay rate, impact of nutrient intake on hormone secretion, and the rate at which the effect of nutrient intake reaches its maximum.
Conclusion: Evaluating the association between NUSH and increased body weight through mathematical modeling can provide insights into the complex interactions between nutrient stimuli, hormonal responses, and obesity development.
Learning to Accelerate Tightening of Convex Relaxations of the AC Optimal Power Flow Problem
Authors: Cengil Fatih , Nagarajan Harsha , Bent Russell , Eksioglu Sandra , Eksioglu Burak 
Published: March 2024

Abstract
We propose a machine learning (ML)-based method aimed at accelerating convergence to global solutions for the Alternating Current Optimal Power Flow (AC-OPF) problem. Our method enhances the efficiency of the optimality-based bound tightening (OBBT) algorithm, known in the literature for effectively tightening variable bounds in the non-convex AC-OPF problem. While the OBBT algorithm provides nearly global optimum solutions through tight convex relaxations, its inherent computational burden persists, even for medium-scaled power networks. The proposed novel ML-based policy, integrated into the OBBT algorithm, replaces the exhaustive OBBT without compromising optimality guarantees. This policy dynamically selects a subset of variables whose sequential bound tightening contributes to tighten the convex relaxation of the AC-OPF problem. To this end, we leverage historical data to learn a correlation between the load profiles and variable subsets via their rankings. Our policy, coupled with the parallel implementation of the OBBT algorithm, facilitates the discovery of near-global optimal solutions in significantly reduced computation times. We demonstrate this through detailed numerical experiments on medium to large-scale instances with up to 3,375 buses. Across the held-out set of instances, we observe up to 20x speed-up in run times of the ML-accelerated OBBT algorithm. To our knowledge, this is the first ML-based approach to demonstrate such large speed-ups for tightening convex relaxations on realistic large-scale power grid instances.