bluuebunny/crossref_metadata_2025_split huggingface dataset:
Applications of Rice Husk Cellulose Nano-crystals in Edible Wrappings
Authors: Nwanna E. C. , Nwabanne J. T. , Orakwe L. C. , Chukwuma E. C. , Oyeoka H. C. , Okafor C. E. , Okpala C. D. , Ibenta M. E. 
Published: September 2024

Abstract

Rice husk (RH) constitutes environmental challenge in rice producing areas, due to poor waste management. Research on application for high value use of RH is critical for rice mills in developing countries because rice husk is an abundant, available, and renewable waste material in the region. In this work, cellulose nano-crystals (CNC) from rice husk was re-inforced with gelatin/carboxyl methyl-cellulose (CMC) nano-composite film for use in food packaging and was made via the solution casting method. CMC-gelatin nano-composites re-inforced with CNC were successfully made using the solution casting process. Using gelatin, carboxyl methylcellulose and CNC film, the effects of the CNC content were examined using mechanical characteristics, total dissolved solid (TDS), fourier transform infrared spectroscopy (FTIR), transmission electron microscopy (TEM), scanning electron microscopy (SEM) and x-ray diffraction (XRD). Experimental results showed how the gelatin, carboxyl methyl-cellulose, and CNC films that were created had needle-like geometries. They had sizes of 8–21 nanometers in diameter, 81–286 nanometers in length, a 17 aspect ratio, and a 0.83 crystallinity index. The addition of five percent weight and ten percent weight CNC lead to homogenous nano-composite films, showing a nice like-mindedness betwixt CNC and gelatin/CMC. The CNC disseminate faultlessly throughout the matrix. The thickness (TKS) increased from 0.13 µm to 0.25 µm, the tensile strength (TSS) raised through 6.80 MPa to 7.70 MPa then elongation at break (EAB) reduced from 80.44–68.68% with the inclusion of CNC. The nano-composite films' TDS likewise dropped, from 72–65%. The outcomes show that using gelatin/CMC reinforced with CNC has several benefits because it is a naturally occurring, affordable, and plentiful material that can serve as a substitute for many non-degradable, oil-based materials.
A mechanical force sensor controls touch-triggered floral organ movement through manipulation of calcium signature in Torenia fournieri
Authors: SU SHIHAO , Zhou Xuan , Li Binghou , Li Jiahuizi , Sun Yufei , Xie Ruohan , Higashiyama Tetsuya , Xiao Shi , Xin Guo-Rong 
Published: September 2024

Abstract

Rapid movements of plant structures triggered by mechanical force has been documented for centuries. Previous studies showed that cytosolic calcium ([Ca2+]cyt) acts as a key signal mediating touch response and leaf movement in Mimosa pudica and several carnivorous plants, however, how plant cells sense mechanical stimuli and thereby produce the Ca2+ signals to generate a rapid response remains an outstanding question. Here we show the central role of a mechanosensitive channel gene in touch-triggered rapid stigma movement of Torenia fournieri. T. fournieri possesses a bilobed stigma that is sensitive to mechanical stimuli and close rapidly within seconds. Using calcium imaging, we detected that touch triggers a rapid wave of [Ca2+]cyt that is necessary for stigma movement. Transcriptomic sequencing unveiled distinct transcriptional atlas between stigma and style tissues, and we further identified two mechanosensitive ion channel genes, JUE1/2, which are preferentially expressed in the stigmas. Stigmas of jue1 CRISPR mutants showed complete loss of response to mechanical stimuli, while stigmas of jue2 mutants were still mechanosensitive, supporting JUE1’s function as a mechanical sensor. We further demonstrated that JUE1 is required for generation of the touch-induced [Ca2+]cyt wave, since [Ca2+]cyt wave was not detected in the jue1 mutant. To conclude, we report a plant mechanical sensor involved in touch-triggered organ movement via manipulation of calcium signature.
Audio-Textual Emotion Recognition using Pre-trained models: Investigating Various Representations and Fusion Techniques
Authors: Tafti Zahra Dehghani , BabaAli Bagher 
Published: September 2024

Abstract

Emotion recognition is vital in Human-Computer Interaction, enhancing artificial intelligence with emotional intelligence. Given the multimodal nature of human conversations, emotions can be detected through various modalities, leading to more accurate recognition. This makes multimodal emotion recognition a popular yet challenging research area. In this study, we focused on recognizing emotions from both audio and text modalities on the IEMOCAP dataset. We utilized transfer learning and fine-tuned transformer models for each modality, aiming to minimize the number of trainable parameters in the final system. One of the main challenges in multimodal emotion recognition lies in effectively fusing different modalities. To address this, we employed early fusion, cross-modal fusion, and late fusion techniques to integrate information from the audio and text models, using representations extracted from different layers of each model. Our results indicate that using the average of mean pooling across all layers for each modality, combined with an early fusion approach and a support vector machine (SVM) classifier, achieved the best performance. This approach resulted in an unweighted average recall (UAR) of 78.42%, a weighted average recall (WAR) of 77.75%, and a cross-entropy loss of 0.67, outperforming previous studies2.
Vehicle detection algorithm based on improved RT-DETR
Authors: wang yuhai , xu shuobo , wang peng , Liu Lele , Li yanshun , song ze 
Published: September 2024

Abstract

Vehicle detection algorithms are integral to intelligent traffic management and AI-assisted driving systems. However, the complexity and variability of traffic scenarios present significant challenges. Spatial pyramid pooling enhances a model’s ability to capture multi-scale contextual information by offering different levels of feature representation, which is particularly beneficial in complex traffic scenarios. In this study, we extend spatial pyramid pooling to Transformer-based models and introduce a linear spatial pyramid attention (LSPA) mechanism. This approach allows the model to focus on both global information and local details when processing vehicle image data. Additionally, we propose a spatial coordination filter (SCF) module to explicitly summarize spatial global dependencies and reduce spatial feature redundancy. To address computational costs, we employ partial convolution (PConv) in the model’s backbone network to minimize redundant computation. Experiments conducted on the BDD100K and KITTI datasets demonstrate that our model improves the mean average precision (mAP@50) by 2.7% and reduces model parameters by approximately 16% compared to the existing RT-DETR model. These findings confirm the efficacy and advantages of our proposed method.
Enhancing Crop Yield Estimation from Remote Sensing Data: A Comparative Study of the Quartile Clean Image Method and Vision Transformer
Authors: Thakkar Manan , Vanzara Rakeshkumar 
Published: September 2024

Abstract

The use of high-altitude remote sensing (RS) data from aerial and satellite platforms presents considerable challenges for agricultural monitoring and crop yield estimation due to the presence of noise caused by atmospheric interference, sensor anomalies, and outlier pixel values. This paper introduces a "Quartile Clean Image" pre-processing technique to address these data issues by analyzing quartile pixel values in local neighborhoods to identify and adjust outliers. Applying this technique to 20,946 Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2003 to 2015 improved the mean peak signal-to-noise ratio (PSNR) to 40.91 dB. Integrating Quartile Clean data with Convolutional Neural Networks (CNN) models with exponential decay learning rate scheduling achieved RMSE improvements up to 5.88% for soybeans and 21.85% for corn, while Long Short-Term Memory (LSTM) models demonstrated RMSE reductions up to 11.52% for soybeans and 29.92% for corn using exponential decay learning rates. To compare the proposed method with state-of-the-art techniques, we introduce the Vision Transformer (ViT) model for crop yield estimation. The ViT model, applied to the same dataset, achieves remarkable performance without explicit pre-processing, with R2 scores ranging from 0.9752 to 0.9875 for soybean and 0.9540 to 0.9888 for corn yield estimation. The RMSE values range from 7.75086 to 9.76838 for soybean and 26.25265 to 34.20382 for corn, demonstrating the ViT model's robustness. This research contributes by (1) introducing the Quartile Clean Image method for enhancing RS data quality and improving crop yield estimation accuracy, and (2) comparing it with the state-of-the-art ViT model. The results demonstrate the effectiveness of the proposed approach and highlight the potential of the ViT model for crop yield estimation, representing a valuable advancement in processing high-altitude imagery for precision agriculture applications.
Defect-mediated exciton localization and relaxation in CVD-grown monolayer MoS2
Authors: Gao Bo , Qu Jiafan , Wei Yadong , Zhao Liang , Tan Ruoxi , Li Weiqi , Shi Hongyan , Zhang Yueling , Yang Jianqun , Li Xingji 
Published: September 2024

Abstract

Defects in CVD-grown monolayer MoS2 are unavoidable and provide a powerful approach to create single-photon emitters and quantum information systems through localizing excitons. However, insight into A- trion and B/C exciton localization in monolayer MoS2 remains elusive. Here, we investigate defect-mediated A- trion and B/C exciton localization and relaxation in CVD-grown monolayer MoS2 samples via transient absorption spectra. The localization rate of A- trions exhibits five times faster than B excitons, which is attributed to the distinctions in Bohr radius, diffusion rate and multi-phonon emission. Furthermore, we obtain unambiguous experimental evidence for free C exciton localization and the direct excitation of localized C excitons. Varying gap energy at the band-nesting region revealed by first-principles calculations explains anomalous dependence of localized C exciton energy on delay time. We also find that the rapid dissociation of localized C excitons features a short characteristic time of ~0.14 ps, while the measured relaxation time is much longer. Our results provide a comprehensive picture of the defect-mediated excitonic relaxation and localization dynamics in monolayer MoS2.
A comparative analysis of residual stresses from friction stir processing of aluminum cast 380 and wrought 7075 alloy sheets: Experimental characterization and modeling
Authors: Balusu Kranthi , Samanta Avik , Shukla Shivakant , das hrishikesh , upadhyay piyush , Jana saumyadeep , soulami ayoub 
Published: September 2024

Abstract

Residual stresses are often overlooked in friction stir processing (FSP), but their significant impact on performance necessitates their consideration in optimizing processing parameters. The first step in this effort is to understand the effect of process conditions on residual stress distributions, especially in the context of different alloys. This study uses hole-drilling electronic speckle pattern interferometry (ESPI) and the thermal pseudo-mechanical (TPM) model within finite element analysis to study the 3D residual stress distributions in friction stir processed thin aluminum sheets of two alloys under various conditions. Using this approach, three new aspects of residual stresses from FSP were experimentally revealed, and simulations explained the underlying mechanisms.
First, notable stress variation through the thickness was observed. In the A7075 alloy, stress gradually reduced from the tool shoulder side to the bottom surface, while in the AA380 alloy, stresses sharply increased near the surfaces. This is because the cast A380 alloy's surface material is significantly harder than its interior, i.e., due to the “skin effect.”. This study also clarifies the opposite effects of process temperature on residual stress: increased temperature reduced stress in AA380 but increased it in AA7075. This is because AA7075, with its higher yield stress, undergoes plastic deformation only during heating, unlike AA380, which deforms during both the heating and cooling phases. Lastly, residual stresses in AA380 increased with an additional pass due to material hardening from the initial pass. All these findings highlight the crucial role of yield stress and its variation with alloy type, location within the material, and the number of passes in determining residual stresses.
Tlb‑yolo: a rapid and efcient real‑time algorithm for box-type classification and barcode recognition on the moving conveying and sorting systems
Authors: Shen Liang , Li Xin , Yang Wei , Wang Qiang 
Published: September 2024

Abstract

In recent years, camera-based vision sensors utilizing YOLO neural network algorithms have increasingly replaced traditional sensors such as photoelectric sensors. Due to their advantages in cost and efficiency, applications in object classification and barcode detection have become more widespread in industrial and logistics sectors. This study addresses the real-time items classification and barcode detection by proposing the Transmission Line Barcode YOLO (TLB-YOLO) model. We made improvements to the YOLOv8 model by introducing several new components. We integrated the Coordinate Attention (CA) mechanism into the backbone network to improve the model's sensitivity to object locations. The Wise-IoU loss function was employed to enhance localization accuracy, while the GSConv (Grouped Shuffle Convolution) and Slim Neck architecture were incorporated to boost detection accuracy and speed. The proposed model, with 3.8 million parameters and 8.5 GFLOPs, was trained on the COCO dataset, achieving an mAP0.5 of 68.1% and an mAP0.95 of 44.2%, which represent improvements of 7.9% and 5.2% over YOLOv8n, respectively. It attained a frame rate of 153.8 FPS. When retrained on a custom dataset incorporating synthetic data from Omniverse, the model demonstrated over 90% accuracy in detecting cardboard boxes, plastic containers, and barcodes. To resolve model export issues caused by the dynamic pooling layer, an equivalent code substitution was applied, enhancing inference speed. Testing on a Jetson Nano development board, with each experiment phase repeated 50 times, showed that the TLB-YOLO model achieved 100% detection accuracy for plastic containers side scanning, cardboard box top scanning, and barcode verification. The model’s detection accuracy and inference speed fully satisfy real-time detection requirements in practical scenarios.
Cloning, In Silico Characterization, and Physiochemical Analysis of the Zea mays PR-1 Gene: Implications for Developing Fungi-Resistant Transgenic Plants
Authors: Saleem Muhammad Zafar , Babar Zaher Uddin , Saif Sumaia , Saleem Ammara , Ahmad Nauman , Zulfiqar Asma , Shah Anis Ali , Iftikhar Muhammad , Shaffique Shifa , Gatasheh Mansour K. 
Published: September 2024

Abstract

Pathogenesis-related protein 1 (PR-1) is a critical component of plant defense mechanisms, exhibiting significant antifungal activity. This study focused on the PR-1 protein from Zea mays and five other plant species wheat, barley, rice, oats, and rye to explore their physicochemical, structural, and functional characteristics. The PR-1 gene was successfully isolated from Z. mays and amplified using Polymerase Chain Reaction (PCR). Following cloning into the pET15b vector, the gene was expressed in BL21-DE3 (E. coli) cells. Induction with 1mM Isopropyl β-D-1-thiogalactopyranoside (IPTG) for 4 hours at 37°C led to successful protein expression, as confirmed by 12% SDS-PAGE. The PR-1 protein exhibited resistance against a broad spectrum of oomycete pathogens, demonstrating its potential for developing fungi-resistant transgenic plants. The study revealed that PR-1 proteins from different plant species shared high sequence similarity, with identities ranging from 65–90%. However, their physicochemical properties varied based on plant origin. PR-1 proteins from cereals (maize, wheat, barley, and rice) were found to be more hydrophobic compared to those from grasses (oats and rye). Structurally, all PR-1 proteins adopted a similar three-dimensional conformation, featuring a central beta-barrel surrounded by alpha-helices. Functional assays confirmed that all six PR-1 proteins exhibited antifungal activity against a diverse array of fungal pathogens. The PR-1 protein from Z. mays, along with PR-1 proteins from other plant species, shows significant potential for agricultural biotechnology. By incorporating these genes into crop plants, it is possible to enhance their resistance to fungal pathogens, thereby advancing the development of more resilient and sustainable agricultural systems.
Unveiling the unseen: Exploring unmet needs among people living with HIV in Chittoor, Andhra Pradesh, India
Authors: Tarugu Jayanthi , Koni Kiranmayi , Chaudhuri Sirshendu , Udayasree K , Hema Kurugundla 
Published: September 2024

Abstract

Background and Objective: Discrimination, stigma, and social isolation pervade Indian society, significantly affecting HIV care outcomes. This study aims to investigate the perceived unmet needs of individuals living with HIV (PLWH).
Methods: This mixed-method study enrolled adult PLWH from ART centers in Chittoor district, Andhra Pradesh. Data collection involved semi-structured interviews using a pre-tested questionnaire. Analysis encompassed descriptive and thematic approaches.
Results: Among the 94 participants, the mean age was 39 years (SD 12 years), and the average duration of HIV diagnosis was 8 years (SD 5 years). The majority were female (n=65, 69%) and residents of rural areas (n=74, 78.7%). While financial needs were most prevalent, other pressing concerns included employment, child's education, housing, nutrition, healthcare, and travel. Participants voiced apprehensions about administrative hurdles and varying degrees of social challenges, such as stigma and discrimination, when accessing social protection services.
Conclusion: PLWH exhibit diverse socio-economic needs for themselves and their families, many of which remain unmet due to multifaceted bottlenecks, encompassing administrative complexities, social barriers, and perceived stigma.
Buckling upon Inflation in Soft Porous Nonlinear Geometric (SPoNGe) Metamaterials
Authors: Tubaldi Eleonora , Barvenik Kieran , Bonthron Michael , Jones Anthony 
Published: September 2024

Abstract

Cellular metamaterials represent unique platforms to manipulate structure property relationships and enhance mechanical responses. While their unconventional behaviors have traditionally been obtained via pattern-transformations under compressive loading or deflation, we theoretically investigate and experimentally realize a new class of soft porous metamaterials that undergo buckling
instability upon inflation, unlocking superior programming and sequencing capabilities for soft intelligent machines. Our soft porous nonlinear geometric (SPoNGe) metamaterial reimagines the traditional rubber slab with periodic holes by incorporating a single internal pressure cavity. This novel structure, actuated via a single internal positive pressure, can be engineered to exhibit global short-wavelength buckling modes with controllable circumferential lobe count of the cylindrical pores. First, we experimentally demonstrate the programmable post-buckling behavior by tuning the geometric parameters. Then, with a combination of analytical and numerical methods, we accurately predict the critical buckling pressure and pattern reconfiguration of the cellular metamaterial. By enabling different pattern rearrangements of the collapsing pores, we achieve a new actuation mechanism to suddenly reconfigure the global structure, selectively grasp slender objects, and operate multiple fluid channels with a single input.
Role of ZnO and TiO2 nanoparticles in improving seed germination, growth and biomass accumulation in Green gram (Cicer arietinum L.) cultivar
Authors:   Anuradha Navnath Karale1,,   Bhavna Nigam2,   Indra Jeet Chaudhary1*
Published: September 2024

Abstract

The world's food problem is partly caused by the enormous hurdles that the growing global population presents to agricultural output. Increased yield losses are caused by a number of stressors, including bacteria, fungus, salt, ozone, UV-B radiation, drought, and metal toxicity. Agricultural management uses a variety of techniques targeted at reducing yield loss and environmental impact to address these problems. In order to increase crop yields, nanoparticles are used in the creation of nano fertilizers, nano insecticides, and nano fungicides. Therefore, present study conducted to assess effectiveness of ZnO and TiO2 nanoparticle on Green gram seed germination, root length, and shoot length and fresh weight and dry weight of plants. Results demonstrated that TiO2 is more effective in seed germination than ZnO nanoparticles and showed 100% rate at 48-h observation. All nanomaterials stimulated root elongation and promoted shoot growth and biomass of plants at 50 ppm concentrations. The purpose of this study is to evaluate the mechanisms and efficacy of nanoparticles in promoting seed germination. The knowledge gathered from this research is essential for the creation of eco-friendly nanotechnologies, which will ultimately help to lessen the world's food crisis. According to the study, additional field research is necessary to have a more comprehensive understanding, especially in order to completely comprehend the impacts of NPs on the growth, improvement, and health of agricultural plants.
Practice Environment Scale of the Nursing Work Index (5 items version): Translation and validation in Greek
Authors: Katsiroumpa Aglaia , Moisoglou Ioannis , Konstantakopoulou Olympia , Kalogeropoulou Maria , Gallos Parisis , Tsiachri Maria , Galanis Petros 
Published: September 2024

Abstract

Background:Measuring nurse work environments is essential to address the nursing shortage.
Aim: To translate and validate the “Practice Environment Scale of the Nursing Work Index” (5 items version) in Greek.
Methods: Study population included 80 nurses in Greece. We performed our study during August 2024. We employed the forward-backward method to translate and adapt the “Practice Environment Scale of the Nursing Work Index” (PES-5) in Greek language. We examined the construct validity of the PES-5 by performing confirmatory factor analysis. We examined the concurrent validity of the PES-5 using the “Quiet Quitting Scale” (QQS), the single item burnout measure, and the single item turnover intention measure. We examined the reliability of the PES-5 by calculating Cronbach’s alpha.
Results: The PES-5 showed very good psychometric properties. Our confirmatory factor analysis confirmed the one-factor structure of the PES-5. Concurrent validity of the Greek version of the PES-5 was very good. We found a statistically significant negative correlation between PES-5 and QQS (r = -0.410, p-value < 0.001), and single item turnover intention measure (r = -0.227, p-value = 0.043). We found that the PES-5 had very good reliability since Cronbach’s coefficient alpha was 0.651.
Conclusions: The Greek version of the “Practice Environment Scale of the Nursing Work Index” is a reliable and valid tool to measure the nursing practice environment.