bluuebunny/crossref_metadata_2025 huggingface dataset:
Determination of PD-L1 Expression in Circulating Tumor Cells of Hypopharyngeal and Laryngeal Cancers and Correlation with Tissue Detection
Authors: Li Chen , Zhu Hongyu , Lin Qin , Chen Wei , Huang Xiaoting , Wang Desheng
Published: April 2024

PD-L1 plays a pivotal role as an immunoregulatory checkpoint within the immune system, exerting a critical influence on the internal functioning and survival mechanisms of cancer cells. Our study aimed to elucidate the clinical significance of PD-L1 expression in circulating tumor cells (CTCs) derived from individuals afflicted with Hypopharyngeal and Laryngeal Cancers (HLC), as well as its potential implications for clinical practice. CTC were identified using an immunomagnetic positive sorting methodology. Simultaneous detection was conducted on the CTC levels among PD-L1-positive patients, aiming to ascertain the dynamic relationship between real-time CTC fluctuations and the clinicopathological indices of the patients. The constructed immunolipid magnetic nano-beads demonstrated pronounced efficacy in capturing CTCs, and the lipid nanoparticles exhibited noteworthy capture efficiency coupled with minimal cytotoxic effects. This investigation encompassed a cohort exceeding 30 individuals, wherein PD-L1 expression analysis was executed to delineate CTC variations within PD-L1-positive patients, thereby yielding auxiliary insights for clinical diagnosis. Subsequent assessment of PD-L1 expression consistency between CTCs and tissue specimens revealed a substantial agreement surpassing 70%. Furthermore, inhibition of PD-L1 yielded a significant elevation in the cytokine TNF-α levels, accompanied by a concomitant reduction in IL-10 levels. The CTC sorting system devised in this investigation boasts attributes of remarkable specificity and sensitivity. By virtue of PD-L1 expression analysis, it holds the potential to offer instructive implications for tailoring individualized treatments in clinical scenarios.
Reading-selective areas in the cerebellum in adult bilingual readers
Authors: Chen Jiaqi , Huang Huiya , Ling Li , Hu Ke , Luo Yue-Jia , Li Hehui
Published: April 2024

Fluent word reading requires the processing and integration of orthography, Phonology, and semantics. Previous research has mainly focused on the relevant neural correlates in the cerebrum; however, it remains unclear whether the cerebellum also involved areas that are biased towards reading. Using functional magnetic resonance imaging studies, we conducted two experiments in adult bilingual readers. In experiment I, we compared cerebellar activation in different conditions, including the word reading, pseudoword reading, and picture viewing, as well as in different languages. The results revealed a significant enhancement in activation of the right cerebellar lobule VI and VIII in the word reading condition compared to the other two control conditions across languages. Additionally, these two regions also did not show preference for picture-viewing task, i.e., the emotional processing task, consolidating the specific contribution of these two regions to reading. Experiment II demonstrated that activation matrix of the reading-selective areas were significantly correlated with phonological matrix and showed high inter-subject correlation in the rhyming task, implying an association between this region with phonological processing. These findings offer a comprehensive investigation into the cerebellum's role in reading, enhancing our understanding of cognitive reading models and providing insights for improving reading abilities and interventions for reading disorders.
Tropical Isognomonids in the Mediterranean Sea: When the West Atlantic met the Indo-Pacific Region in the South Aegean Sea
Authors: Angelidis Akis , Gubili Chrysoula , Polyzoulis George
Published: April 2024

Sessile bivalves belonging to the genus Isognomon (Lightfoot, 1786) are native to tropical and subtropical intertidal and infralittoral zones. Recently, they were introduced to the Mediterranean Sea as nonindigenous species. Here, we report the presence of two Isognomon taxa, the West Atlantic Isognomon bicolor (CB Adams, 1845) and the Indo-Pacific Isognomon legumen (Gmelin, 1791), in the neighbouring Islands of Chalki and Alimia in the South Aegean Sea. DNA barcoding and phylogenetic analyses were used for their identification, which proved essential for the genus Isognomon because due to its highly irregular and habitat-dependent shell shapes, their sole morphological identification was problematic. Additional field observations during two successive years of hard substrata structure supplied information on the habitat preferences of these taxa, providing useful ecological insights into their establishment in the area.
PDE-CNNs: Axiomatic Derivations and Applications
Authors: Bellaard Gijs , Sakata Sei , Smets Bart M. N. , Duits Remco
Published: April 2024

PDE-based Group Convolutional Neural Networks (PDE-G-CNNs) utilize solvers of geometrically meaningful evolution PDEs as substitutes for the conventional components in G-CNNs. PDE-G-CNNs offer several key benefits all at once: fewer parameters, inherent equivariance, better performance, data efficiency, and geometric interpretability.
In this article we focus on Euclidean equivariant PDE-G-CNNs where the feature maps are two dimensional throughout. We call this variant of the framework a PDE-CNN. From a machine learning perspective, we list several practically desirable axioms and derive from these which PDEs should be used in a PDE-CNN.
Here our approach to geometric learning via PDEs is inspired by the axioms of classical linear and morphological scale-space theory, which we generalize by introducing semifield-valued signals.
Furthermore, we experimentally confirm for small networks that PDE-CNNs offer fewer parameters, increased performance, and better data efficiency when compared to CNNs. We also investigate what effect the use of different semifields has on the performance of the models.
Repair spinal cord injury with a versatile anti-oxidant and neural regenerative nanoplatform
Authors: Zhou Heng , Li Ziwei , Jing Shuili , Wang Ben , Ye Zhifei , Xiong Wei , Liu Yonghao , Liu Ye , Xu Chun , Kumeria Tushar , He Yan , Ye Qingsong
Published: April 2024

Spinal cord injury (SCI) often results in motor and sensory deficits, or even paralysis. Due to the role of the cascade reaction, the effect of excessive reactive oxygen species (ROS) in the early and middle stages of SCI severely damage neurons, and most antioxidants cannot consistently eliminate ROS at non-toxic doses, which leads to a huge compromise in antioxidant treatment of SCI. Selenium nanoparticles (SeNPs) have excellent ROS scavenging bioactivity, but the toxicity control problem limits the therapeutic window. Here, we propose a synergistic therapeutic strategy of SeNPs encapsulated by ZIF-8 (SeNPs@ZIF-8) to obtain synergistic ROS scavenging activity. Three different spatial structures of SeNPs@ZIF-8 were synthesized and coated with ferrostatin-1, a ferroptosis inhibitor (FSZ NPs), to achieve enhanced anti-oxidant and anti-ferroptosis activity without toxicity. FSZ NPs promoted the maintenance of mitochondrial homeostasis, thereby regulating the expression of inflammatory factors and promoting the polarization of macrophages into M2 phenotype. In addition, the FSZ NPs presented strong abilities to promote neuronal maturation and axon growth through activating the WNT4-dependent pathways, while prevented glial scar formation. The current study demonstrates the powerful and versatile bioactive functions of FSZ NPs for SCI treatment and offers inspiration for other neural injury diseases.
Causal association between mTOR-dependent circulating protein levels and autism spectrum disorder: a Mendelian randomization study
Authors: Fu Jingfen , Dong Meiyuan , Chen Lingling , Du Chunhui , Yu Ze , Wang Hongqiang
Published: April 2024

Background
Overactivation of the mTOR signaling pathway is a critical mechanism in the pathogenesis of autism spectrum disorder (ASD). However, the causal relationships between key molecules downstream of the mTOR signaling pathway and ASD remains unclear. This study aimed to explore the causal associations between seven mTOR-dependent circulating protein levels and ASD.
Methods
Instrumental variables (IVs) used as proxies for mTOR-dependent circulating protein levels were derived from the proteomics-GWAS INTERVAL study (3,301 participants) and a serum protein GWAS study in an Icelandic population (5,368 participants). Two ASD summary datasets were obtained from a meta-analysis study of ASD GWAS datasets (22,196 cases and 32,504 controls) and the FinnGen Consortium (646 cases and 301,879 controls), respectively. We applied two-sample Mendelian randomization (MR) to assess whether there is a causal association between seven mTOR-dependent circulating protein levels and ASD. Furthermore, validation analyses of positive exposures in the discovery cohort were performed to confirm the reliability of the findings.
Results
In this two-sample MR study, plasma PKCα level was found to be causally related to ASD in both the discovery cohort (IVW: OR = 1.15, 95% CI = 1.05–1.25, p = 0.002) and the validation cohort (IVW: OR = 1.14, 95% CI = 1.02–1.27, p = 0.017).
Conclusions
There was a causal association between the plasma level of PKCα and ASD, suggesting PKCα as a potential biomarker for the prevention, monitoring and treatment of ASD. However, whether PKCα can play a role in the clinical management of ASD remains to be demonstrated in future research.
A Feasibility-Preserved Quantum Approximate Solver for the Capacitated Vehicle Routing Problem
Authors: Xie Ningyi , Lee Xinwei , Cai Dongsheng , Saito Yoshiyuki , Asai Nobuyoshi , Lau Hoong Chuin
Published: April 2024

The Capacitated Vehicle Routing Problem (CVRP) is an NP-optimization problem (NPO) that arises in various fields including transportation and logistics. The CVRP extends from the Vehicle Routing Problem (VRP), aiming to determine the most efficient plan for a fleet of vehicles to deliver goods to a set of customers, subject to the limited carrying capacity of each vehicle. As the number of possible solutions skyrockets when the number of customers increases, finding the optimal solution remains a significant challenge. Recently, the Quantum Approximate Optimization Algorithm (QAOA), a quantum-classical hybrid algorithm, has exhibited enhanced performance in certain combinatorial optimization problems compared to classical heuristics. However, its ability diminishes notably in solving constrained optimization problems including the CVRP. This limitation primarily arises from the typical approach of encoding the given problems as penalty-inclusive binary optimization problems. In this case, the QAOA faces challenges in sampling solutions satisfying all constraints. Addressing this, our work presents a new binary encoding for the CVRP, with an alternative objective function of minimizing the shortest path that bypasses the vehicle capacity constraint of the CVRP. The search space is further restricted by the constraint-preserving mixing operation. We examine and discuss the effectiveness of the proposed encoding under the framework of the variant of the QAOA, Quantum Alternating Operator Ansatz (AOA), through its application to several illustrative examples. Compared to the typical QAOA approach, the proposed method not only preserves the feasibility but also achieves a significant enhancement in the probability of measuring optimal solutions.
Causal relationship between immune cells and posttraumatic stress disorder: a bulk two-sample Mendelian randomization study
Authors: Zhou Kai qi , Deng Zhou wei , Tan Yu lun
Published: April 2024

Background: There is increasing evidence that the immune system is associated with posttraumatic stress disorder (PTSD). However, the causal relationship between immune cells and PTSD and the possible related types of immune cells are still unclear.
Aim: To determine the causal relationship between immune cells and PTSD. Two-sample Mendelian randomization (MR) analysis was then performed.
Materials and Methods: The exposure and outcome data used in this study were obtained from a GWAS (https://gwas.mrcieu.ac.uk/). Two-sample MR analysis was performed to assess the causal relationships between 731 types of immune cell features and PTSD. We used 731 types of immune cells for exposure and PTSD as the outcome. Our MR analysis uses a variety of methods to ensure the robustness of the experiment. For each immune cell that may be related to PTSD, we adopted a sufficient number of methods to ensure the accuracy and effectiveness of the results.
Results: Our study identified potential causal relationships between various immune cells and PTSD. We identified 10 types of immune cells that are potentially causally linked to PTSD. CD33-related cells are the most prominently related immune cells.
Conclusions: Our study determined the causal relationship between a variety of immune cells and PTSD via MR, which can provide help for future clinical practice.
Effect of reductant on property and CO 2 sequestration for belite cement from phosphogypsum
Authors: Li Kaiwen , Liu Changrong , Ma Xiaoling , Tan Hongbin , Taha Yassine , Dong Faqin , Yang Feihua
Published: April 2024

Gypsum can be decomposed at low-temperature by adding reductant, which is beneficial to obtain cement. The effects of different reductant on the phases of belite cement raw meals were studied by calcining at 1300℃ for 2h and using phosphogypsum as raw materials. Diffraction peaks of calcium sulfate were not observed in the samples when 10 wt% activated carbon was added and the molar ratio of CaS to CaSO4 was 3, respectively. The Sulfur trioxide contents of in the clinkers were 0.80 and 0.64 wt%, respectively.
The effect of carbonation curing on the cement properties was also studied. Carbonation curing can promote cement hydration and increase strength at 20 ℃, 75 % relative humidity and 20% CO2 concentration. As the carbonation curing age increased, the compressive strengths of the samples gradually enhanced. When 10 wt% carbon was employed as the reducing agent, compressive strengths of the samples were 15.5, 15.6, and 15.6 MPa after carbonation curing at 3, 7 and 28 d, respectively. When CaS was employed as the reducing agent, the compressive strengths of the samples were 19.8, 27.2, and 34.1 MPa after carbonation curing for 3, 7 and 28 d, respectively. The carbon dioxide sequestration contents of the samples prepared with a 10 wt% carbon and CaS reductant, were 11.6% and 9.2% after carbonation curing for 28 d, respectively. These findings demonstrate the potential to use phosphogypsum with various reductants to enhance the quality of belite cement and at the same time consume more CO2 in the atmosphere during curing process.
Moisture content prediction of municipal sludge drying process based on genetic algorithm optimized BP neural network
Authors: Zhang Kaiqiang , Wang Ningfung
Published: April 2024

Municipal sludge drying process of internal moisture detection difficulties, and in order to predict the change rule of the internal moisture in the hot-air drying process of municipal sludge, the moisture change of municipal sludge under different drying conditions was monitored through experiments. The moisture prediction model of municipal sludge drying process was established by optimizing the BP neural network using mathematical model, back-propagation (BP) neural network and genetic algorithm (GA), and the performance analysis of the model was carried out. In addition, a sensitivity analysis of the GA-BP model was performed. The results showed that the temperature had the most significant effect on the drying process of municipal sludge, reducing the thickness of the mud layer could improve the drying rate of sludge, and the moisture reduction rate of municipal sludge showed a fast and then a slow trend during the whole drying process. Through the performance analysis, the coefficient of determination of the test set of GA-BP model is 0.9999, and the root mean square error is only 0.0039, which means that the GA-BP model has a better prediction effect and can predict the dynamic change of moisture in the drying process of municipal sludge more accurately. The sensitivity analysis shows that the drying time and drying temperature have the greatest influence on moisture ratio(MR), and the results of the study can provide a theoretical basis for the optimization of the sludge drying process and procedure, and provide a powerful reference for guiding the resource utilization of sludge.
Comparison of Displacement Estimation Techniques for Background Oriented Schlieren of High-speed Compressible Turbulent Flows
Authors: Zhou Tanbo , Gaskins Jonathan , Poggie Jonathan , Bane Sally
Published: April 2024

Background oriented schlieren (BOS) is a non-intrusive optical method for measuring density gradients in a fluid flow based on changes of local refractive index.The density gradients can be obtained by observing the displacement betweentwo images of a background pattern, with and without the presence of the flow.Existing methods to estimate displacements include block matching and optical flow. Image registration in computer vision seeks reasonable transformationsbetween two images such that one matches the other and has been under-utilizedin determining the displacement for BOS image processing. Deformable imageregistration (DIR) methods allow non-global transformations and are proposedas displacement estimation methods for processing BOS images. Numerical raytracing simulations are performed to generate synthetic BOS images of variousflows. The estimated density gradient results of block matching, optical flow,and DIR methods are compared to the ground truth (the path-averaged densitygradient of the schlieren object used in ray tracing) to assess and compare theirperformances. The performances of these methods are also validated on experimental BOS images to determine the displacement estimation method that ismost suitable for high-speed, turbulent flows.
Limits of outpatient palliative oncological care: A cost analysis of nutritional interventions.
Authors: Molnarova Veronika , Maresova Petra , Rezny Lukas , Bezrouk Ales , Milka Dalibor , Priester Iva , Ticha Alena , Novosadova Martina , Filip Stanislav
Published: April 2024

Background
The purpose of palliative medicine is to achieve and maintain the best possible quality of life for patients and support their families, relatives, and friends. Therefore, we conducted a case-control observational retrospective study evaluating the economic burden of nutritional intervention during ambulatory palliative oncology care, aiming to implement the obtained data to the quality of healthcare.
Methods
A comparison of total expenses per patient was made between the three groups of patients, i.e., Group A – 256 patients with systematic nutritional intervention during 2020-22, Group B − 1393 patients treated during 2020-22, and Group C – 940 patients without systematic nutritional intervention during in 2017-19. The expense analysis was performed using mixed methods monitoring nutritional intervention, i.e. medical records, laboratory tests, and insurance bills from the palliative oncology clinic. The collected data included four categories (direct and indirect cost): nutritional supplements, other medical expenses, requested care, and other care. The qualitative costs and benefits analyses were done within the CBA method framework, i.e. online interviews of relevant stakeholders who were involved in the process of nutritional measures.
Results
The Mann–Whitney U-test showed significant (P < 0.001) differences between the tested groups, with the lowest median of total expenses in Group B (2,921 CZK) and the highest in Group A (16,692 CZK), demonstrating a significant reduction in total expenses. The same was observed in the comparison between Group C (4,923 CZK) and Group B (2,921 CZK) during 2020-22.
Conclusion
The present study proves that nutritional intervention, and the related modifications in palliative treatment, benefit the patients and reduce the overall costs of healthcare.
Visual and Semantic Guided Scene Text Retrieval
Authors: Luo Hailong , Ibrayim Mayire , Hamdulla Askar , Deng Qilin
Published: April 2024

In this paper, we introduce a novel end-to-end trainable network for the task of scene text retrieval. Diverging from the state-of-the-art methods that match the visual features of individual character images for retrieval, our network transforms the entire query text into a single query image. By integrating visual and language modules, our network extracts rich visual and semantic features from the query image, facilitating efficient similarity modeling and query matching. This hybrid embedding approach using visual-semantic features of query images shows excellent robustness in dealing with complex text styles and layouts. Experimental results on multiple benchmark datasets validate the superiority of our framework, especially in multilingual retrieval tasks, where our framework achieves a 20.15% increase in mAP score compared to the current state-of-the-art. This significant performance boost showcases the potent potential of our network in multilingual scene text retrieval tasks.
Micro-mechanical properties of the Song Dynasty stone tiles based on nanoindentation tests and homogenization approach
Authors: Wang Zhi-Liu , Hu Meng-Xin , Wang Yu-Long , Li Xin-Ming , Yin Song
Published: April 2024

The Song Dynasty stone tiles are one of the representatives of ancient Chinese building materials and reflect the technical level and the aesthetic concept of architecture in the Song Dynasty. Studying the mechanical properties of the Song Dynasty stone tiles is a great significance to design and develop restoration materials. The stone cultural relics are different from modern buildings, because of their particularity, cultural relics protection should preserve their original historical features as much as possible. It is a challenge to sample and perform traditional tests, which require numerous large specimens. The nanoindentation technique and the homogenization calculation method based on the Mori-Tanaka model were proposed to calculate the mechanical parameters of the Song Dynasty stone tiles. With the help of X-ray diffraction (XRD) and scanning electron microscope (SEM), the indentation morphology and mineral composition in indentation area were quantitatively analyzed. The mechanical properties, such as elastic modulus, hardness and fracture toughness of the Song Dynasty stone tiles in Henan, China, were investigated by nanoindentation test. The homogenization approach based on the Mori-Tanaka model was developed to upscale mechanical parameters from micro-scale to meso-scale, which were further compared with uniaxial compression results of non-standard samples. The results show that there is a positive linear correlation between elastic modulus, hardness and the fracture toughness under micro-scale. The average elastic modulus of red stone tiles and green stone tiles are 29.47GPa and 30.21GPa respectively. Compared with the parameter results obtained by upscaling, the deviation rates are 10.3% and 9.6%, respectively. The mechanical parameters of the Song Dynasty stone tiles obtained by nanoindentation test and homogenization approach is reliable.The proposed methods in this work provide the basis and reference for obtaining the mechanical strength index of the Song Dynasty stone tiles.
Evaluation of Physical and Mechanical Properties of Graphene Oxide Reinforced Epoxy/Kevlar Hybrid Composites
Authors: Hassin Md Shadman , Faruk Mohammad Omar , Ali Mir Iffat , Begum Shahida
Published: April 2024

Graphene is a miracle material with low density, excellent mechanical strength, electrical conductivity, molecular barrier properties and many other remarkable properties. Hence, recent research has been more focused on delving into various application areas of graphene. In this study, the effects of graphene oxide (GO) on the mechanical properties of composites based on epoxy resin and Kevlar fibers were investigated. The composites were fabricated by incorporating varying concentrations (1%, 3%, 5%, 10%, and 16%) of graphene oxide (GO) into an epoxy matrix. The matrix was reinforced by two different wt.% (12.5% and 25%) of Kevlar fibers (KF). The hybrid composites were manufactured by hand lay-up process in a silicon mold at room temperature. Mechanical characterizations such as flexural, impact and hardness test exhibited an enhancement of the mechanical properties of the composites. Moreover, the measured density showed a significant reduction in density with increasing wt.% of GO. As a result, it was possible to fabricate a composite with a significant increase of specific flexural strength and specific flexural modulus. A microstructural analysis of the composites was performed by Scanning Electron Microscopy (SEM) and Energy Dispersive X-Ray Analysis (EDX). From the SEM images, it was revealed that the interphase bonding of the GO particles with Kevlar fibers and matrix was strong, thereby resulting in improved mechanical performance. It was observed that 5 wt.% GO incorporation along with 25wt.% KF showed improved flexural strength, hardness and impact strength than that of sample fabricated only with Kevlar.
Are Transformers more Suitable for Plant Disease Identification than Convolutional Neural Networks?
Authors: Zhou Changjian , Zhang Xin , Zhong Yujie
Published: April 2024

Recent studies suggest that the transformer-based architectures have made unprecedented achievements in various computer vision tasks, such as image classification, object detection, semantic segmentation, etc. However, as the de facto approach, convolutional neural networks (CNNs) have reigned for a decade in plant disease identification tasks. We cannot help but propose a scenario: are transformers more suitable for plant disease identification than CNNs? Conceivably, this work aims to further investigate and evaluate whether it is feasible to trivially switch to transformers in plant disease identification tasks. To address this issue, a series of experiments were curated into three training strategies and implemented on two representative plant disease image datasets. Experimental results indicated that the vanilla transformer methods performed unsatisfactorily when trained from scratch, and the two mainstream transfer learning-based approaches achieved better prediction performance but with little margin. It is encouraging that the self-supervision-based transformer methods outperform their CNN-based counterparts, which provides new insights for agricultural image processing. Importantly, this work revealed a model selection solution based on the experimental results and target datasets, and gained a better interpretation by visualizing the feature maps.
Stimulating the use of multigrain composite flours in instant multigrain semolina: A compositional and functional attributes
Authors: Vaduguru Viswanath , Puttananjaiah Mohankumari Honganoor
Published: April 2024

The study evaluated the proximate, functional properties, bioactive compound, and scavenging activity of multigrain semolina with and without acacia gum using composite flour (rice, ragi, cowpea, and black gram) formulations. The multigrain semolina is analyzed for its nutrient composition and biochemical properties. Bioactive components like total phenolic content, total flavonoid content, and antioxidant activity were also analyzed. In the case of semolina, S3-AG and S3 + AG reported highest protein content of 14.37 ± 0.44 and 14.32 ± 1.35%, respectively. S1-AG and S2 + AG show an increase in the protein. WAC of semolina was significantly higher than the values found for the control sample (wheat semolina, 103.86 ± 1.81). The main objective of this study is to replace wheat semolina with multigrain semolina as a rich source of protein to increase the nutritional quality and the functional properties of the product. Results also suggested that by blending cereals and pulse flour, it is possible to enhance the functional properties and bioactive components of the composite flours and semolina.
Exploring the Role of Governance Mechanisms in Promoting Sustainability Across Supply Chains
Authors: Reynolds Samantha
Published: April 2024

This qualitative research delves into the intricate relationship between governance mechanisms and sustainability practices within supply chains. Employing a qualitative approach, the study aims to elucidate the nuanced ways in which various governance mechanisms influence and promote sustainability initiatives across supply chains. Drawing upon interviews, case studies, and literature reviews, the research uncovers multifaceted insights into the role of governance mechanisms in fostering sustainable practices, thereby contributing to a deeper understanding of sustainable supply chain management. The findings reveal the significance of regulatory frameworks, industry standards, collaborative initiatives, contractual arrangements, buyer-supplier relationships, organizational culture, and leadership in shaping sustainability outcomes within supply chains. Regulatory frameworks and industry standards provide the foundation for ensuring compliance with environmental, social, and ethical requirements, while collaborative governance mechanisms foster transparency, accountability, and collective action across supply chain networks. Buyer-supplier relationships and organizational culture play critical roles in driving innovation, capacity-building, and continuous improvement in sustainability performance. Overall, the research underscores the interconnectedness of governance mechanisms across different levels of analysis and emphasizes the need for a holistic and integrated approach to sustainable supply chain management. By recognizing and leveraging these interdependencies, stakeholders can develop more effective strategies for advancing sustainability goals and creating shared value for stakeholders within and beyond the supply chain.
Dynamic Distribution Calibration Method for Few-Shot Learning
Authors: Khan Majid Habib , Liu Jinwei Zhao , Hei Xinhong , Liu Jiedong , Zhang Hongtao , Li Shuangjiang , Liu Yu , Imran Muhammad
Published: April 2024

Convolutional neural network (CNN) is frequently used in image classification. However, obtaining sufficient labelled data for training is difficult because labelling data is costly. Learning from a limited number of samples creates challenges since the learned model may become overfitted due to the biased distribution caused by a few training samples and the pow learning algorithm. This paper proposed a dynamic distribution calibration method for the few shot-learning. First, the base and new class samples were normalized using the normalization technique to eliminate the effect of different feature magnitudes. A pre-trained feature extraction model extracts the sample feature vectors of the base class and the new class. The distribution characteristics of the adjacent and remote base classes are dynamically selected for a new class sample in an embedding space by a threshold value method. A similar class usually has a similar feature distribution, such as mean and variance. So, the means and variance of the Gaussian distribution can be transferred between similar classes. In the paper, the feature distribution is assumed to follow the Gaussian distribution. Then, the distributional features of each sample in the new class are calibrated using the corrected hyperparameter based on the distribution features of the adjacent base class and far base class in the embedding space. Finally, the calibrated distribution features augment the sample set of the new class. In some experiments on the benchmark datasets miniImagenet and CUB, the new dynamic distribution calibration method achieves up to 4% accuracy gains in some few-shot classification challenges and achieves superior performance.
Integrated Text - Model Generation – Simulation – Machine Learning Framework on Developing Temperature Field Model of 3-side Protection Steel Beam
Authors: Li Yang
Published: April 2024

Automating the modelling and simulation process benefits all related areas such as research, design, manufacturing and especially the training of machine learning models. It significantly improves the modelling efficiency and speed, providing scalability in data generation, which motivates the discovery of new patterns. In addition, consistency and reproducibility through unified modelling methods improve accuracy by eliminating manual mistakes in repetitive processes. Machine learning provides an efficient approach to accessing large-scale simulation data and providing insights into unknown circumstances. The 3-side protection steel beam is a common yet rarely investigated section type in offshore oil platforms. With the top side of the upper flange exposed to fire, large convective and radiative heat flux is induced leading to a rapidly descending temperature diagram along the web. The increased lever arm caused by the elastic neutral axis descending under this temperature distribution and the elasticity reduction in the compressed flange cause earlier lateral torsional buckling failure compared to the 4-side protection beam. To provide a close temperature profile, an integrated framework automating modelling, simulation, data processing and machining learning sequence using ABAQUS kernel scripting method, Python and MATLAB is proposed. The modelling method automates the model generation process with inputs from a parameter text file and establishes restraints using the edge contact-detection algorithm for unusual shapes. Secondly, the model files (.inp) are submitted to ABAQUS, and MATLAB controls the simulation process. The output data are extracted and written into .csv files. Thirdly, the extracted data are divided by Python code into data batches and fed to machine learning models for training. All IPE, Universal Beam sections and 54 welding sections from a realistic oil processing structure with different protection limits are carried out with the protection thicknesses calibrated with an optimisation program of 5°C allowance. The gradient boosting method achieves a root of mean square error of 1.34°C compared to simulation results. The calculation time of developed software with graphical user interface is also tested with varied number of temperature points and output intervals.
Arima Modeling of International Visitor Arrivals in the Philippines: 2008 – 2023
Authors: Agosto Jhaina B. , Galos Thea C. , Obejas Lyka T.
Published: April 2024

This descriptive, quantitative, correlational, and predictive research aimed to analyze the monthly international visitor arrivals in the Philippines from January 2008 to December 2023 and forecast future values utilizing secondary data from the Department of Tourism, encompassing 192 months, with 24 months used for forecasting. The analysis employed various statistical methods such as Time Series Analysis, Exploratory Data Analysis utilizing the Winsorization process, Autocorrelation and Partial Autocorrelation Function, Augmented Dickey-Fuller Test, corrected Akaike Information Criterion, and Bayesian Information Criterion to assess the time series components.
The study identified an increasing trend in international visitor arrivals until January 2020, followed by a sudden drop in February 2020 due to the pandemic. The highest total number of international visitor arrivals in the Philippines on a monthly basis is January with 6,707,811 visitors and September obtained the lowest total number of international visitor arrivals in the Philippines with an increasing trend of 4,791,961 visitors from 2008 to 2023, irregularity was observed in the data due to pandemic. The best-fit model generated for forecasting the visitor arrivals was determined to be ARIMA(0, 1, 1)(0, 1, 3)[12] exhibiting an excellent predictive accuracy based on Mean Absolute Error (MAE) of over 17,000 and Mean Absolute Percentage Error (MAPE) of 5.35% serving as metrics of forecast accuracy. The forecasted arrivals appear to be higher in 2025 compared to 2024 for each corresponding month, specifically, in 2025 with 579,805 visitors compared with 516,739 visitors on average, which may indicate an expected growth in tourism over the year. Moreover, the findings concluded that the model projects that January 2025 had the highest total number of monthly international visitor arrivals, while September 2024 had the lowest in the two-year forecast. The researchers of the study recommended the provision of updated visitor arrival data for future studies, regional data to be used in analysis, alternative forecasting techniques, and an investigation into factors affecting visitor arrivals.
Harmful Brain Activity Classification of Spectrograms with Transfer Deep Learning
Authors: Ganesan Sambhu , Kiran Yajat Nagaraj , Ram Shanker
Published: April 2024

Electroencephalograms are medical tests used by doctors to determine any irregular brain activity in patients. The drawn-out process of manually classifying each of these signals can be time-consuming, costly, and even subjective among neurologists. We make use of the HMS dataset to classify spectrograms of brain activity into one of six abnormal brain activities. By applying basic data preprocessing techniques, we show the potential and power of using signal processing techniques like the Short-Time Fourier Transform and the Continuous Wavelet Transform in creating an accurate prediction model. By training on images of spectrograms and EEG signal plots, we create a three-model ensemble that can accurately predict brain abnormalities, easing the load off physicians. Our research showcases the potential of transforms in signal processing tasks.
Genomgrößen und Ploidie der deutschen Arten von Veronica (Plantaginaceae)
Authors: Albach Dirk C. , Daubert Mareike
Published: April 2024

Chromosomenzahlen und Genomgrößen liefern wichtige Informationen für die Unterscheidung von Pflanzenarten. Insbesondere eng verwandte Arten, die morphologisch schwer zu unterscheiden sind, lassen sich oft leichter anhand der Genomgröße unterscheiden. Die Durchflusszytometrie hat in den letzten Jahren den Nachweis einer solchen Differenzierung auf genomischer Ebene erleichtert. Sie hat außerdem dazu beigetragen, die Verbreitung des Ploidiegrades innerhalb der Arten zu verstehen. Die Gattung Veronica umfasst in Deutschland 37 Arten, darunter einige taxonomisch schwierige Artengruppen und einige Arten mit intraspezifischer Variation der Ploidie. Wir präsentieren hier 36 neue Genomgrößen und 44 Ploidie-Werte für diese 37 Arten, sechs bzw. sieben davon aus Deutschland. Die Messungen für V. aphylla, V. alpina, V. fruticans und V. fruticulosa sind die ersten Werte für diese Arten. Diese Werte bilden eine wichtige Grundlage für künftige Studien über die Gattung. Einige Beispiele werden ausführlicher besprochen, wie die Verteilung des Ploidiegrads bei V. longifolia und V. chamaedrys in Deutschland oder die Bedeutung der Untersuchung des Ploidiegrads bei V. satureiifolia und ihren Verwandten im Südwesten Deutschlands. Die englische Version dieses Manuskripts in der Fassung vor der Einreichung ist unter https://www.biorxiv.org/content/10.1101/2023.12.22.573074v1.full verfügbar.
Assessing the Impact of Integrated Dogwood Disease Management Practices on Labor Needs and Production Costs in Tennessee Nurseries
Authors: Liyanage Kumuditha Hikkaduwa Epa , Khanal Aditya , Witcher Anthony , Liyanapathiranage Prabha , Baysal-Gurel Fulya
Published: April 2024

Early and accurate detection of diseases, and implementation of efficient disease management practices are crucial to reducing the economic impact associated with plant disease outbreaks. Based on survey responses from dogwood nursery growers in Tennessee, USA, scouting was identified as an important disease management practice adopted by a majority of growers for disease management in field-grown, container-grown, and pot-in-pot production systems. Our results show a significant positive correlation between disease severity and scouting frequency for dogwood plants grown in container and pot-in-pot production systems. Our efficiency measure is a self-rated efficacy scale perceived by the nursery growers about their existing disease management system in nursery plants. A significant positive correlation was found between the efficacy of disease management and the number of workers involved in scouting and a negative association between the worker hours spent in scouting and the grower's experience/exposure to other disease detection methods. The majority of nursery growers followed a set spray schedule between May and October, with applications scheduled every other week. In addition, our results showed significant positive correlations between efficacy and spray-related factors, such as disease severity and worker hours spent in spraying; efficacy of disease management and spraying frequency in field-grown dogwoods; and foliar spray costs and efficacy of disease management. We estimated ≈$379/acre per year average costs for dogwood disease management, which the growers find to be one of the major components of the dogwood production budget. Moving to automated systems of disease scouting and management has the potential to reduce the cost of these labor-intensive disease management practices of dogwood production.
Effect of X-ray Irradiation and Carnauba Wax Coating on Quality of Lime (Citrus latifolia Tan.) Fruit
Authors: Sun Xiuxiu , Follett Peter , Shu Chang , Yusufali Zahra , Bai Jinhe , Wall Marisa
Published: April 2024

The quality of Persian (Tahiti) lime (Citrus latifolia Tan.) fruit was determined following coating with carnauba wax and X-ray irradiation at doses suitable for disinfestation of quarantine pests. Fruit with or without carnauba wax coating were treated with irradiation doses of 0, 150, 300, or 450 Gy, and stored for 14 days at 13 °C and 6 days at 20 °C to simulate commercial transportation and marketing conditions from Hawaii to the continental United States. The fruit color, weight loss, total soluble solids (TSS) content, and titratable acidity (TA) were analyzed at 7, 14, and 14 + 6 days post irradiation. Wax coating significantly delayed fruit peel discoloration, and reduced fruit weight loss by more than 7% compared with the unwaxed controls. Irradiation did not affect ΔE of the peel for coated fruit at day 14 + 6. Irradiation with or without coating did not affect ΔE of flesh color, weight loss, TSS content, or TA. Wax coating combined with irradiation treatment of limes at doses ≤450 Gy ensured marketable visual quality and chemical composition while providing quarantine security.
Study of the possibility of producing brine-ripened cheeses from frozen sheep’s milk
Authors: Mordvinova V. A. , Sviridenko G. M. , Ostroukhova I. L. , Ostroukhov D. V.
Published: April 2024

The article presents the results of a study of the quality indicators of brine-ripened cheeses produced from defrosted sheep’s milk. The objects of the study were natural and defrosted sheep’s milk, brine-ripened cheeses from this milk. The study of defrosted sheep’s milk (experiment) was carried out after 10 days of its storage at a temperature of –18 °C followed by defrostation at a temperature of 20 ± 2 °C. The composition of natural sheep’s milk (control) was examined after storage for 24 ± 12 hours at a temperature of 4 ± 2 °C. It has been found that the freezing of milk and its subsequent defrostation did not have a significant effect on the milk solids content, the mass fraction of protein and fat, titrated acidity; however, the mass fraction of calcium decreased, on average, by 20%. Freezing milk did not significantly affect the total number of micro- organisms, including spore aerobic microorganisms and molds. A decrease in the number of somatic cells (from 3.0 to 4.5 times) was found. There were no differences between the control and experimental milk samples in the renneting ability. The level of syneresis of clots in the control sample was 16 ± 1% higher compared to the experimental sample. The cheeses were produced according to the traditional technological scheme of brine-ripened cheese using a production mesophilic bacterial starter culture. The control and experimental cheeses did not differ significantly in physical and chemical parameters. According to the degree of proteolysis, there were no significant differences between the control and experimental variants. In the experimental sample, a decrease in the buffer capacity of the cheese mass was noted in comparison with the control cheeses. The freezing of milk did not have a significant effect on the taste characteristics of the cheeses: in both variants, the cheeses were characterized by a weakly expressed cheese, fermented milk taste with original taste of sheep’s milk. More noticeable differences were found in the consistency of the cheeses. In the control version, the consistency was characterized as tender, slightly brittle; in the experimental version, it was less tender, but brittle and slightly powdery. The greatest deterioration in consistency was observed in the samples of frozen cheese made from natural milk after defrosting; it was characterized as brittle, powdery, crumbly.
Biscuits enriched with the edible powder of Angoumois grain moth (Sitotroga cerealella): Optimization, characterization and consumer perception assessment
Authors: Mohsen S. M. , Ashraf A. , Ahmed S. S. , Abedelmaksoud T. G.
Published: April 2024

During this study, the dried powder of Angoumois grain moth (Sitotroga cerealella) (AGM) was used as an excellent source of protein to produce nutritional biscuits. Physical, chemical, rheological and microbiological characteristics of biscuits with the optimum AGM percentage (5%) were evaluated compared to the control. The response surface methodology (RSM) with optimal (custom) design was first employed for optimizing the percentage of the AGM powder in biscuits. The optimum selected percentage of the AGM powder was 5% and a quadratic model was found to yield the best fit. The evaluation of parameters of biscuits (ash, protein, fiber and fat content) showed that most of the values, except carbohydrate, significantly increased with AGM inclusion (5%) compared to the control. Also, an increase in diameter, spread ratio and weight values and a decrease in the thickness and break strength of the biscuits were observed with AGM powder inclusion. The presence of the 5% AGM powder slightly increased water absorption, and dough development time also increased compared to the control sample. However, stability of dough decreased. The sanitary conditions of the AGM biscuits were satisfactory as the analytical results were within the established values specified in the regulation. The study revealed that Angoumois grain moth is an excellent source of nutrients necessary for combating protein-energy-malnutrition rampant in our world today.