bluuebunny/crossref_metadata_2025 huggingface dataset:
Topical Micro-Emulsion of 5-Fluorouracil by a Twin Screw Processor-Based Novel Continuous Manufacturing Process for the Treatment of Skin Cancer: Preparation and In Vitro and In Vivo Evaluations
Authors: Nikam Ajinkya Nitin , Jacob Angela , Raychaudhuri Ruchira , Fernandes Gasper , Pandey Abhijeet , Rao Vinay , Ahmad Sheikh F. , Pannala Ananth S. , Mutalik Srinivas
Published: August 2023

5-Fluorouracil (5-FU), a BCS class III drug, has low oral bioavailability and is cytotoxic in nature causing severe systemic side effects when administered through the intravenous route. Topical drug delivery could potentially mitigate the systemic side-effects. Microemulsions (MEs) would be an apt solution due to enhanced partitioning of the drug to the skin. However, conventional methods for preparing MEs are inefficient since they are not continuous and are very tedious and time-consuming processes hence revealing the need for the development of continuous manufacturing technology. In our study, 5-FU MEs were prepared using a continuous manufacturing Twin Screw Process (TSP) and its efficiency in the treatment of skin cancer was evaluated. Water-in-oil MEs were prepared using isopropyl myristate as the oil phase and Aerosol OT and Tween 80 as the surfactants. The average particle size was observed to be 178 nm. Transmission electron microscopy was employed to confirm the size and shape of the MEs. FTIR study proved no physical or chemical interaction between the excipients and the drug. In vitro drug release using vertical diffusion cells and ex vivo skin permeation studies showed that the drug was released sustainably and permeated across the skin, respectively. In in vitro cytotoxicity studies, 5-FU MEs were accessed in HaCat and A431 cell lines to determine percentage cell viability and IC50. Skin irritation and histopathological examination implied that the 5-FU MEs did not cause any significant irritation to the skin. In vivo pharmacodynamics studies in rats suggested that the optimised formulation was effective in treating squamous cell carcinoma (SCC). Therefore, 5-FU MEs efficiently overcame the various drawbacks faced during oral and intravenous drug delivery. Also, TSP proved to be a technique that overcomes the various problems associated with the conventional methods of preparing MEs.
Modified mRNA Formulation and Stability for Cardiac and Skeletal Muscle Delivery
Authors: Żak Magdalena M. , Kaur Keerat , Yoo Jimeen , Kurian Ann Anu , Adjmi Matthew , Mainkar Gayatri , Yoon Seonghun , Zangi Lior
Published: August 2023

Directly injecting naked or lipid nanoparticle (LNP)-encapsulated modified mRNA (modRNA) allows rapid and efficient protein expression. This non-viral technology has been used successfully in modRNA vaccines against SARS-CoV-2. The main challenges in using modRNA vaccines were the initial requirement for an ultra-cold storage to preserve their integrity and concerns regarding unwanted side effects from this new technology. Here, we showed that naked modRNA maintains its integrity when stored up to 7 days at 4 °C, and LNP-encapsulated modRNA for up to 7 days at room temperature. Naked modRNA is predominantly expressed at the site of injection when delivered into cardiac or skeletal muscle. In comparison, LNP-encapsulated modRNA granted superior protein expression but also additional protein expression beyond the cardiac or skeletal muscle injection site. To overcome this challenge, we developed a skeletal-muscle-specific modRNA translation system (skeletal muscle SMRTs) for LNP-encapsulated modRNA. This system allows controlled protein translation predominantly at the site of injection to prevent potentially detrimental leakage and expression in major organs. Our study revealed the potential of the SMRTs platform for controlled expression of mRNA payload delivered intramuscularly. To conclude, our SMRTs platform for LNP-encapsulated modRNA can provide safe, stable, efficient and targeted gene expression at the site of injection.
Methadone Treatment Gap in Tennessee and How Medication Units Could Bridge the Gap: A Review
Authors: Risby Joanna , Schlesinger Erica , Geminn Wesley , Cernasev Alina
Published: August 2023

The opioid epidemic has been an ongoing public health concern in the United States (US) for the last few decades. The number of overdose deaths involving opioids, hereafter referred to as overdose deaths, has increased yearly since the mid-1990s. One treatment modality for opioid use disorder (OUD) is medication-assisted treatment (MAT). As of 2022, only three pharmacotherapy options have been approved by the Food and Drug Administration (FDA) for treating OUD: buprenorphine, methadone, and naltrexone. Unlike buprenorphine and naltrexone, methadone dispensing and administrating are restricted to opioid treatment programs (OTPs). To date, Tennessee has no medication units, and administration and dispensing of methadone is limited to licensed OTPs. This review details the research process used to develop a policy draft for medication units in Tennessee. This review is comprised of three parts: (1) a rapid review aimed at identifying obstacles and facilitators to OTP access in the US, (2) a descriptive analysis of Tennessee’s geographic availability of OTPs, pharmacies, and federally qualified health centers (FQHCs), and (3) policy mapping of 21 US states’ OTP regulations. In the rapid review, a total of 486 articles were imported into EndNote from PubMed and Embase. After removing 152 duplicates, 357 articles were screened based on their title and abstract. Thus, 34 articles underwent a full-text review to identify articles that addressed the accessibility of methadone treatment for OUD. A total of 18 articles were identified and analyzed. A descriptive analysis of Tennessee’s availability of OTP showed that the state has 22 OTPs. All 22 OTPs were matched to a county and a region based on their address resulting in 15 counties (16%) and all three regions having at least one OTP. A total of 260 FQHCs and 2294 pharmacies are in Tennessee. Each facility was matched to a county based on its address resulting in 70 counties (74%) having at least one FQHC and 94 counties (99%) having at least one pharmacy. As of 31 December 2022, 17 states mentioned medication units in their state-level OTP regulations. Utilizing the regulations for the eleven states with medication units and federal guidelines, a policy draft was created for Tennessee’s medication units.
Recent Advances in Applications of Oxidases and Peroxidases Polymer-Based Enzyme Biocatalysts in Sensing and Wastewater Treatment: A Review
Authors: Kyomuhimbo Hilda Dinah , Feleni Usisipho , Haneklaus Nils H. , Brink Hendrik
Published: August 2023

Oxidase and peroxidase enzymes have attracted attention in various biotechnological industries due to their ease of synthesis, wide range of applications, and operation under mild conditions. Their applicability, however, is limited by their poor stability in harsher conditions and their non-reusability. As a result, several approaches such as enzyme engineering, medium engineering, and enzyme immobilization have been used to improve the enzyme properties. Several materials have been used as supports for these enzymes to increase their stability and reusability. This review focusses on the immobilization of oxidase and peroxidase enzymes on metal and metal oxide nanoparticle-polymer composite supports and the different methods used to achieve the immobilization. The application of the enzyme-metal/metal oxide-polymer biocatalysts in biosensing of hydrogen peroxide, glucose, pesticides, and herbicides as well as blood components such as cholesterol, urea, dopamine, and xanthine have been extensively reviewed. The application of the biocatalysts in wastewater treatment through degradation of dyes, pesticides, and other organic compounds has also been discussed.
3D Embedded Printing of Complex Biological Structures with Supporting Bath of Pluronic F-127
Authors: Hu Tianzhou , Cai Zhengwei , Yin Ruixue , Zhang Wenjun , Bao Chunyan , Zhu Linyong , Zhang Honbo
Published: August 2023

Biofabrication is crucial in contemporary tissue engineering. The primary challenge in biofabrication lies in achieving simultaneous replication of both external organ geometries and internal structures. Particularly for organs with high oxygen demand, the incorporation of a vascular network, which is usually intricate, is crucial to enhance tissue viability, which is still a difficulty in current biofabrication technology. In this study, we address this problem by introducing an innovative three-dimensional (3D) printing strategy using a thermo-reversible supporting bath which can be easily removed by decreasing the temperature. This technology is capable of printing hydrated materials with diverse crosslinked mechanisms, encompassing gelatin, hyaluronate, Pluronic F-127, and alginate. Furthermore, the technology can replicate the external geometry of native tissues and organs from computed tomography data. The work also demonstrates the capability to print lines around 10 μm with a nozzle with a diameter of 60 μm due to the extra force exerted by the supporting bath, by which the line size was largely reduced, and this technique can be used to fabricate intricate capillary networks.
Different Polymers for the Base of Removable Dentures? Part I: A Narrative Review of Mechanical and Physical Properties
Authors: Le Bars Pierre , Bandiaky Octave Nadile , Le Guéhennec Laurent , Clouet Roselyne , Kouadio Alain Ayepa
Published: August 2023

Even before considering their introduction into the mouth, the choice of materials for the optimization of the prosthesis depends on specific parameters such as their biocompatibility, solidity, resistance, and longevity. In the first part of this two-part review, we approach the various mechanical characteristics that affect this choice, which are closely related to the manufacturing process. Among the materials currently available, it is mainly polymers that are suitable for this use in this field. Historically, the most widely used polymer has been polymethyl methacrylate (PMMA), but more recently, polyamides (nylon) and polyether ether ketone (PEEK) have provided interesting advantages. The incorporation of certain molecules into these polymers will lead to modifications aimed at improving the mechanical properties of the prosthetic bases. In the second part of the review, the safety aspects of prostheses in the oral ecosystem (fragility of the undercuts of soft/hard tissues, neutral pH of saliva, and stability of the microbiota) are addressed. The microbial colonization of the prosthesis, in relation to the composition of the material used and its surface conditions (roughness, hydrophilicity), is of primary importance. Whatever the material and manufacturing process chosen, the coating or finishes dependent on the surface condition remain essential (polishing, non-stick coating) for limiting microbial colonization. The objective of this narrative review is to compile an inventory of the mechanical and physical properties as well as the clinical conditions likely to guide the choice between polymers for the base of removable prostheses.
A Printing Strategy for Embedding Conductor Paths into FFF Printed Parts
Authors: Banko Timo , Grünwald Stefan , Kronberger Rainer , Seitz Hermann
Published: August 2023

A novel approach to manufacture components with integrated conductor paths involves embedding and sintering an isotropic conductive adhesive (ICA) during fused filament fabrication (FFF). However, the molten plastic is deposited directly onto the adhesive path which causes an inhomogeneous displacement of the uncured ICA. This paper presents a 3D printing strategy to achieve a homogeneous cross-section of the conductor path. The approach involves embedding the ICA into a printed groove and sealing it with a wide extruded plastic strand. Three parameter studies are conducted to obtain a consistent cavity for uniform formation of the ICA path. Specimens made of polylactic acid (PLA) with embedded ICA paths are printed and evaluated. The optimal parameters include a groove printed with a layer height of 0.1 mm, depth of 0.4 mm, and sealed with a PLA strand of 700 µm diameter. This resulted in a conductor path with a homogeneous cross-section, measuring 660 µm ± 22 µm in width (relative standard deviation: 3.3%) and a cross-sectional area of 0.108 mm2 ± 0.008 mm2 (relative standard deviation 7.2%). This is the first study to demonstrate the successful implementation of a printing strategy for embedding conductive traces with a homogeneous cross-sectional area in FFF 3D printing.
Engineering Toughness in a Brittle Vinyl Ester Resin Using Urethane Acrylate for Additive Manufacturing
Authors: Idrees Mohanad , Yoon Heedong , Palmese Giuseppe R. , Alvarez Nicolas J.
Published: August 2023

Thermosetting polymers tend to have a stiffness–toughness trade-off due to the opposing relationship of stiffness and toughness on crosslink density. We hypothesize that engineering the polymer network, e.g., by incorporating urethane oligomers, we can improve the toughness by introducing variations in crosslink density. In this work, we show that a brittle methacrylated Bis-GMA resin (known as DA2) is toughened by adding a commercial urethane acrylate resin (known as Tenacious) in different proportions. The formulations are 3D printed using a vat photopolymerization technique, and their mechanical, thermal, and fracture properties are investigated. Our results show that a significant amount of Tenacious 60% w/w is required to produce parts with improved toughness. However, mechanical properties drop when the Tenacious amount is higher than 60% w/w. Overall, our results show that optimizing the amount of urethane acrylate can improve toughness without significantly sacrificing mechanical properties. In fact, the results show that synergistic effects in modulus and strength exist at specific blend concentrations.
Islam and the Emancipatory Ethic: Islamic Law, Liberation Theology and Prison Abolition
Authors: Bashir Haroon
Published: August 2023

This paper provides a genealogical overview of discourses pertaining to emancipation within Islamic thought. I demonstrate how classical Islamic scholarship developed a tradition in which a clear emancipatory ethic can be located. Further, I explore how emancipation came to be read as anticipating the abolition of slavery in the contemporary period through focusing on the work of Muhammad Abduh. Finally, I discuss the potential engagements between Islamic notions of emancipation and contemporary discourses pertaining to prison abolition. I argue that the strong emancipatory ethic found within the classical legal tradition would not abide by the exploitative prison systems found across various nations. Engaging Islamic law through a Liberation Theology framework, I claim that a serious engagement with prison abolition discourses is a natural continuation for a tradition with such a strong precedent of emancipatory impetus.
Locating ‘Praxis’ in Islamic Liberation Theology: God, Scripture, and the Problem of Suffering in Egyptian Prisons
Authors: Quisay Walaa
Published: August 2023

The paper examines the tenability of a project for Islamic liberation theology by exploring the religious lives of Egyptian prisoners—with an emphasis on their encounters with the Qur’an, devotional and contentious contemplation, and theodicy. It employs an ethnographic approach to the study of Islam in Egyptian prisons by interviewing former political prisoners incarcerated after the 2013 military coup. By examining the work of key liberation theologians Farid Esack (b. 1959), Hamid Dabashi (b. 1951), and Asghar Ali Engineer (b. 1939), I ask: can a justice-oriented hermeneutics, concerned with pluralism and breaking down binaries, be a meaningful starting point to those struggling under oppression? I posit that the concern with developing hermeneutics can potentially limit the praxis whereby the faithful struggle with the text in the very moment of suffering. It shows how Egyptian prisoners’ devotional (and contentious) contemplation (taddabur) of the Qur’an—rather than reading liberation into the Qur’an—allowed for emancipatory embodiments of scripture. Furthermore, I show how prisoners stripped of their agentic power come to understand human action and divine action in history and how the metaphysical responses to human suffering inevitably shaped how they view both structures of inequality and domination as well as their potential liberation from it.
Towards a Theology of Class Struggle: A Critical Analysis of British Muslims’ Praxis against Class Inequality
Authors: Chaudhry Sharaiz
Published: August 2023

The primary goal of Liberation Theology is to change the material conditions of marginalised and oppressed groups in society. Within Islamic Liberation Theology, however, issues related to class and economic inequality are notably missing. This paper seeks to begin this conversation and highlight the necessity of addressing economic exploitation, which affects most of the world’s population and Muslims disproportionately. Using a praxis-based methodology, it centres the interpretation of activists from Nijjor Manush, a British Bengali activist group, and seeks to understand how Islam is used as a liberative tool to combat class oppression. Through interviews and focus groups, an alternative and revolutionary Islam emerges. Echoing a Marxist understanding of class, it sees exploitation as an inherent part of the current capitalist system and recognises the necessity of people seizing economic power. This overarching objective is the lens through which activism in the here and now is interpreted and tactics decided. Establishing economic justice therefore means trying to secure “non-reformist reforms” in the short term, which resist the logic of capital and secure the interests of the marginalised, while working towards the ultimate goal of ending economic exploitation and, by extension, abolishing class.
Tawhid Paradigm and an Inclusive Concept of Liberative Struggle
Authors: Saffari Siavash
Published: August 2023

Building on previous studies on a mid- and late-twentieth-century recasting of Islam’s doctrine of monotheism, or tawhid, as a distinctly Islamic framework for liberative praxis, this article considers the interplay between the particular and the universal in the tawhidic paradigms of Iranian lay theologian Ali Shariati (1933–1977) and African-American pro-faith and pro-feminist theologian amina wadud (b. 1952). The article proposes that although it was developed in a distinctly Islamic register by means of Quranic exegesis and intrareligious conversations, the tawhidic paradigm has always been conversant with a range of non-Islamic liberative paradigms, and these conversations have been integral to the negotiation of a more inclusive concept of tawhid. To continue to recast tawhid in a more inclusive register, the article further argues, requires taking account of the non-Muslim ‘other’ as an equal moral agent in liberative struggles and embracing Islam’s theological and ideological ‘others’ as equally significant repositories of liberative potential.
Potato Leaf Area Index Estimation Using Multi-Sensor Unmanned Aerial Vehicle (UAV) Imagery and Machine Learning
Authors: Yu Tong , Zhou Jing , Fan Jiahao , Wang Yi , Zhang Zhou
Published: August 2023

Potato holds significant importance as a staple food crop worldwide, particularly in addressing the needs of a growing population. Accurate estimation of the potato Leaf Area Index (LAI) plays a crucial role in predicting crop yield and facilitating precise management practices. Leveraging the capabilities of UAV platforms, we harnessed their efficiency in capturing multi-source, high-resolution remote sensing data. Our study focused on estimating potato LAI utilizing UAV-based digital red–green–blue (RGB) images, Light Detection and Ranging (LiDAR) points, and hyperspectral images (HSI). From these data sources, we computed four sets of indices and employed them as inputs for four different machine-learning regression models: Support Vector Regression (SVR), Random Forest Regression (RFR), Histogram-based Gradient Boosting Regression Tree (HGBR), and Partial Least-Squares Regression (PLSR). We assessed the accuracy of individual features as well as various combinations of feature levels. Among the three sensors, HSI exhibited the most promising results due to its rich spectral information, surpassing the performance of LiDAR and RGB. Notably, the fusion of multiple features outperformed any single component, with the combination of all features of all sensors achieving the highest R2 value of 0.782. HSI, especially when utilized in calculating vegetation indices, emerged as the most critical feature in the combination experiments. LiDAR played a relatively smaller role in potato LAI estimation compared to HSI and RGB. Additionally, we discovered that the RFR excelled at effectively integrating features.
Tempo-Spatial Landslide Susceptibility Assessment from the Perspective of Human Engineering Activity
Authors: Zeng Taorui , Guo Zizheng , Wang Linfeng , Jin Bijing , Wu Fayou , Guo Rujun
Published: August 2023

The expansion of mountainous urban areas and road networks can influence the terrain, vegetation, and material characteristics, thereby altering the susceptibility of landslides. Understanding the relationship between human engineering activities and landslide occurrence is of great significance for both landslide prevention and land resource management. In this study, an analysis was conducted on the landslide caused by Typhoon Megi in 2016. A representative mountainous area along the eastern coast of China—characterized by urban development, deforestation, and severe road expansion—was used to analyze the spatial distribution of landslides. For this purpose, high-precision Planet optical remote sensing images were used to obtain the landslide inventory related to the Typhoon Megi event. The main innovative features are as follows: (i) the newly developed patch generating land-use simulation (PLUS) model simulated and analyzed the driving factors of land-use land-cover (LULC) from 2010 to 2060; (ii) the innovative stacking strategy combined three strong ensemble models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM)—to calculate the distribution of landslide susceptibility; and (iii) distance from road and LULC maps were used as short-term and long-term dynamic factors to examine the impact of human engineering activities on landslide susceptibility. The results show that the maximum expansion area of built-up land from 2010 to 2020 was 13.433 km2, mainly expanding forest land and cropland land, with areas of 8.28 km2 and 5.99 km2, respectively. The predicted LULC map for 2060 shows a growth of 45.88 km2 in the built-up land, mainly distributed around government residences in areas with relatively flat terrain and frequent socio-economic activities. The factor contribution shows that distance from road has a higher impact than LULC. The Stacking RF-XGB-LGBM model obtained the optimal AUC value of 0.915 in the landslide susceptibility analysis in 2016. Furthermore, future road network and urban expansion have intensified the probability of landslides occurring in urban areas in 2015. To our knowledge, this is the first application of the PLUS and Stacking RF-XGB-LGBM models in landslide susceptibility analysis in international literature. The research results can serve as a foundation for developing land management guidelines to reduce the risk of landslide failures.
The Suitability of Machine-Learning Algorithms for the Automatic Acoustic Seafloor Classification of Hard Substrate Habitats in the German Bight
Authors: Breyer Gavin , Bartholomä Alexander , Pesch Roland
Published: August 2023

The automatic calculation of sediment maps from hydroacoustic data is of great importance for habitat and sediment mapping as well as monitoring tasks. For this reason, numerous papers have been published that are based on a variety of algorithms and different kinds of input data. However, the current literature lacks comparative studies that investigate the performance of different approaches in depth. Therefore, this study aims to provide recommendations for suitable approaches for the automatic classification of side-scan sonar data that can be applied by agencies and researchers. With random forests, support vector machines, and convolutional neural networks, both traditional machine-learning methods and novel deep learning techniques have been implemented to evaluate their performance regarding the classification of backscatter data from two study sites located in the Sylt Outer Reef in the German Bight. Simple statistical values, textural features, and Weyl coefficients were calculated for different patch sizes as well as levels of quantization and then utilized in the machine-learning algorithms. It is found that large image patches of 32 px size and the combined use of different feature groups lead to the best classification performances. Further, the neural network and support vector machines generated visually more appealing sediment maps than random forests, despite scoring lower overall accuracy. Based on these findings, we recommend classifying side-scan sonar data with image patches of 32 px size and 6-bit quantization either directly in neural networks or with the combined use of multiple feature groups in support vector machines.
Case Study on the Evolution and Precipitation Characteristics of Southwest Vortex in China: Insights from FY-4A and GPM Observations
Authors: Xiang Jie , Wang Hao , Li Zhi , Bu Zhichao , Yang Rong , Liu Zhihao
Published: August 2023

This research investigates Southwest Vortex (SWV) events in China’s Sichuan Basin using Fengyun-4A (FY-4A) and Global Precipitation Mission (GPM) observations. We selected representative cloud systems and precipitation cases, divided into developing, mature, and dissipating stages. Detailed analysis revealed critical characteristics of precipitation cloud systems at each stage. Our findings reveal that (1) during the SWV’s developing and mature stages, a high concentration of water particles and ice crystals stimulates precipitation. In contrast, the dissipating stage is marked by fewer mixed-phase and ice particles, reducing precipitation area and intensity. (2) Near-surface precipitation in all stages is predominantly liquid, with a bright band of around 5.5 km. At the same time, stratiform precipitation is dominant in each life stage. Stratiform precipitation remains dominant throughout the life stages of the SWV, with localized convective activity evident in the developing and mature stages. (3) Mature stage particles, characterized by a configuration of 1.0–1.2 mm Dm and 31–35 dBNW (dBNW = 10log10NW), contribute significantly to near-surface precipitation. The Cloud Top Height (CTH) serves as an indicator of convective intensity and assists in characterizing raindrop concentration. These findings considerably enhance routine observations, advance our understanding of SWV events, and propose a novel approach for conducting refined observational experiments.
A Remote Sensing Image Quality Interpretation Scale Characterization Method Based on the TTP Criterion
Authors: Li Yue , Wang Xiaorui , Zhang Chao
Published: August 2023

Accurate grading of remote sensing image interpretation is crucial for improving image classification and screening efficiency. Through extensive research, the General Image Quality Equation (GIQE) based on the National Imagery Interpretability Rating Scale (NIIRS) has been developed. However, poor reliability and low accuracy issues remain due to the failure to consider human visual characteristics. This paper introduces the Target Task Performance (TTP) criterion as a key parameter to reflect the cascading degradation factors of human visual perception characteristics and system imaging links, which improves the reliability of the model. A New Optimized Remote Sensing Image Quality Equation (NORSIQE), which effectively predicted the interpretability of image information, is constructed. Using 200 sets of test data, the quantitative relationship between key parameters (GSD, TTP, and SNR) of the NORSIQE and the subjective NIIRS level is obtained by a least squares regression fit, and the determination coefficient of the model is as high as 0.916. The model is evaluated for accuracy using 120 sets of validation data, showing an 87% improvement compared to the GIQE4. This method provides theoretical support for the development of new methods for remote sensing image quality evaluation and the design of payloads for remote sensing imaging systems.
SSA Net: Small Scale-Aware Enhancement Network for Human Pose Estimation
Authors: Li Shaohua , Zhang Haixiang , Ma Hanjie , Feng Jie , Jiang Mingfeng
Published: August 2023

In the field of human pose estimation, heatmap-based methods have emerged as the dominant approach, and numerous studies have achieved remarkable performance based on this technique. However, the inherent drawbacks of heatmaps lead to serious performance degradation in methods based on heatmaps for smaller-scale persons. While some researchers have attempted to tackle this issue by improving the performance of small-scale persons, their efforts have been hampered by the continued reliance on heatmap-based methods. To address this issue, this paper proposes the SSA Net, which aims to enhance the detection accuracy of small-scale persons as much as possible while maintaining a balanced perception of persons at other scales. SSA Net utilizes HRNetW48 as a feature extractor and leverages the TDAA module to enhance small-scale perception. Furthermore, it abandons heatmap-based methods and instead adopts coordinate vector regression to represent keypoints. Notably, SSA Net achieved an AP of 77.4% on the COCO Validation dataset, which is superior to other heatmap-based methods. Additionally, it achieved highly competitive results on the Tiny Validation and MPII datasets as well.
DUA: A Domain-Unified Approach for Cross-Dataset 3D Human Pose Estimation
Authors: Manesco João Renato Ribeiro , Berretti Stefano , Marana Aparecido Nilceu
Published: August 2023

Human pose estimation is an important Computer Vision problem, whose goal is to estimate the human body through joints. Currently, methods that employ deep learning techniques excel in the task of 2D human pose estimation. However, the use of 3D poses can bring more accurate and robust results. Since 3D pose labels can only be acquired in restricted scenarios, fully convolutional methods tend to perform poorly on the task. One strategy to solve this problem is to use 2D pose estimators, to estimate 3D poses in two steps using 2D pose inputs. Due to database acquisition constraints, the performance improvement of this strategy can only be observed in controlled environments, therefore domain adaptation techniques can be used to increase the generalization capability of the system by inserting information from synthetic domains. In this work, we propose a novel method called Domain Unified approach, aimed at solving pose misalignment problems on a cross-dataset scenario, through a combination of three modules on top of the pose estimator: pose converter, uncertainty estimator, and domain classifier. Our method led to a 44.1mm (29.24%) error reduction, when training with the SURREAL synthetic dataset and evaluating with Human3.6M over a no-adaption scenario, achieving state-of-the-art performance.
Triple Reporter Assay: A Non-Overlapping Luciferase Assay for the Measurement of Complex Macromolecular Regulation in Cancer Cells Using a New Mushroom Luciferase–Luciferin Pair
Authors: Mujawar Aaiyas , Phadte Pratham , Palkina Ksenia , Markina Nadezhda , Mohammad Ameena , Thakur Bhushan , Sarkisyan Karen , Balakireva Anastasia , Ray Pritha , Yampolsky Ilia , De Abhijit
Published: August 2023

This study demonstrates the development of a humanized luciferase imaging reporter based on a recently discovered mushroom luciferase (Luz) from Neonothopanus nambi. In vitro and in vivo assessments showed that human-codon-optimized Luz (hLuz) has significantly higher activity than native Luz in various cancer cell types. The potential of hLuz in non-invasive bioluminescence imaging was demonstrated by human tumor xenografts subcutaneously and by the orthotopic lungs xenograft in immunocompromised mice. Luz enzyme or its unique 3OH-hispidin substrate was found to be non-cross-reacting with commonly used luciferase reporters such as Firefly (FLuc2), Renilla (RLuc), or nano-luciferase (NLuc). Based on this feature, a non-overlapping, multiplex luciferase assay using hLuz was envisioned to surpass the limitation of dual reporter assay. Multiplex reporter functionality was demonstrated by designing a new sensor construct to measure the NF-κB transcriptional activity using hLuz and utilized in conjunction with two available constructs, p53-NLuc and PIK3CA promoter-FLuc2. By expressing these constructs in the A2780 cell line, we unveiled a complex macromolecular regulation of high relevance in ovarian cancer. The assays performed elucidated the direct regulatory action of p53 or NF-κB on the PIK3CA promoter. However, only the multiplexed assessment revealed further complexities as stabilized p53 expression attenuates NF-κB transcriptional activity and thereby indirectly influences its regulation on the PIK3CA gene. Thus, this study suggests the importance of live cell multiplexed measurement of gene regulatory function using more than two luciferases to address more realistic situations in disease biology.
Automated Age-Related Macular Degeneration Detector on Optical Coherence Tomography Images Using Slice-Sum Local Binary Patterns and Support Vector Machine
Authors: Yu Yao-Wen , Lin Cheng-Hung , Lu Cheng-Kai , Wang Jia-Kang , Huang Tzu-Lun
Published: August 2023

Artificial intelligence has revolutionised smart medicine, resulting in enhanced medical care. This study presents an automated detector chip for age-related macular degeneration (AMD) using a support vector machine (SVM) and three-dimensional (3D) optical coherence tomography (OCT) volume. The aim is to assist ophthalmologists by reducing the time-consuming AMD medical examination. Using the property of 3D OCT volume, a modified feature vector connected method called slice-sum is proposed, reducing computational complexity while maintaining high detection accuracy. Compared to previous methods, this method significantly reduces computational complexity by at least a hundredfold. Image adjustment and noise removal steps are excluded for classification accuracy, and the feature extraction algorithm of local binary patterns is determined based on hardware consumption considerations. Through optimisation of the feature vector connection method after feature extraction, the computational complexity of SVM detection is significantly reduced, making it applicable to similar 3D datasets. Additionally, the design supports model replacement, allowing users to train and update classification models as needed. Using TSMC 40 nm CMOS technology, the proposed detector achieves a core area of 0.12 mm2 while demonstrating a classification throughput of 8.87 decisions/s at a maximum operating frequency of 454.54 MHz. The detector achieves a final testing classification accuracy of 92.31%.
On-Site Calibration of an Electric Drive: A Case Study Using a Multiphase System
Authors: Soto-Marchena David , Barrero Federico , Colodro Francisco , Arahal Manuel R. , Mora Jose L.
Published: August 2023

Modern electric machines are attracting great interest from the research community as a result of the increasing number of current applications, including electric vehicles and wind power generators, among others. Different machines, power converters, and control technologies are used, and the number of sensors is usually minimized to reduce the total cost of the system. Particularly interesting are the current and speed sensors, which are essential to the normal functioning of the entire system. This work analyzes different calibration techniques of these sensors, using as a case example a five-phase induction motor drive. Experimental results are included to show the influence of calibration techniques on the system analyzed. The obtained results can be extrapolated to any other similar system.
Acoustic Waves in Piezoelectric Layered Structure for Selective Detection of Liquid Viscosity
Authors: Smirnov Andrey , Anisimkin Vladimir , Shamsutdinova Elizaveta , Signore Maria-Assunta , Francioso Luca , Zykov Kirill , Baklaushev Vladimir , Kuznetsova Iren
Published: August 2023

The acoustic waves of higher orders propagating in a layered structure consisting of a silicon plate coated with piezoelectric ZnO and/or AlN films were used for the development of a sensor with selective sensitivity to liquid viscosity η in the range of 1–1500 cP. In that range, this sensor possessed low sensitivity to liquid conductivity σ and temperature T in the ranges of 0–2 S/m and 0–55 °C, respectively. The amplitude responses insensitive to the temperature instead of the phase were used to provide the necessary selectivity. The sensor was based on a weak piezoactive acoustic wave of higher order. The volume of the probes sufficient for the measurements was about 100 μL. The characteristics of the sensors were optimized by varying the thicknesses of the structure layers, number of layers, wavelength, wave propagation direction, and the order of the acoustic waves. It was shown that in the case of the layered structure, it is possible to obtain practically the same selective sensitivity toward viscosity as for acoustic waves in pure ST, X quartz. The most appropriate waves for this purpose are quasi-longitudinal and Lamb waves of higher order with in-plane polarization. It was found that for various ranges of viscosity η = 1–20 cP, 20–100 cP, and 100–1500 cP, the maximum sensitivity of the appropriate wave is equal to 0.26 dB/cP, 0.087 dB/cP, and 0.013 dB/cP, respectively. The sensitivity of the waves under study toward the electric conductivity of the liquid is much less than the sensitivity to liquid viscosity. These two responses become comparable only for very small η < 2 cP. The waves investigated have shown no temperature responses in contact with air, but in the presence of liquid, they increase depending on liquid properties. The temperature dependence of liquid viscosity is measurable by the same sensors. The results obtained have shown the possibility of designing acoustic liquid viscosity sensors based on multilayered structures. The set of possible acoustic waves in layered structures possesses modified propagation characteristics (various polarization, phase velocities, electromechanical coupling coefficients, and attenuations). It allows choosing an optimal acoustic wave to detect liquid viscosity only.
Hierarchical Fusion Network with Enhanced Knowledge and Contrastive Learning for Multimodal Aspect-Based Sentiment Analysis on Social Media
Authors: Hu Xiaoran , Yamamura Masayuki
Published: August 2023

Aspect-based sentiment analysis (ABSA) is a task of fine-grained sentiment analysis that aims to determine the sentiment of a given target. With the increased prevalence of smart devices and social media, diverse data modalities have become more abundant. This fuels interest in multimodal ABSA (MABSA). However, most existing methods for MABSA prioritize analyzing the relationship between aspect–text and aspect–image, overlooking the semantic gap between text and image representations. Moreover, they neglect the rich information in external knowledge, e.g., image captions. To address these limitations, in this paper, we propose a novel hierarchical framework for MABSA, known as HF-EKCL, which also offers perspectives on sensor development within the context of sentiment analysis. Specifically, we generate captions for images to supplement the textual and visual features. The multi-head cross-attention mechanism and graph attention neural network are utilized to capture the interactions between modalities. This enables the construction of multi-level aspect fusion features that incorporate element-level and structure-level information. Furthermore, for this paper, we integrated modality-based and label-based contrastive learning methods into our framework, making the model learn shared features that are relevant to the sentiment of corresponding words in multimodal data. The results, based on two Twitter datasets, demonstrate the effectiveness of our proposed model.
Federated Learning for Predictive Maintenance and Anomaly Detection Using Time Series Data Distribution Shifts in Manufacturing Processes
Authors: Ahn Jisu , Lee Younjeong , Kim Namji , Park Chanho , Jeong Jongpil
Published: August 2023

In the manufacturing process, equipment failure is directly related to productivity, so predictive maintenance plays a very important role. Industrial parks are distributed, and data heterogeneity exists among heterogeneous equipment, which makes predictive maintenance of equipment challenging. In this paper, we propose two main techniques to enable effective predictive maintenance in this environment. We propose a 1DCNN-Bilstm model for time series anomaly detection and predictive maintenance of manufacturing processes. The model combines a 1D convolutional neural network (1DCNN) and a bidirectional LSTM (Bilstm), which is effective in extracting features from time series data and detecting anomalies. In this paper, we combine a federated learning framework with these models to consider the distributional shifts of time series data and perform anomaly detection and predictive maintenance based on them. In this paper, we utilize the pump dataset to evaluate the performance of the combination of several federated learning frameworks and time series anomaly detection models. Experimental results show that the proposed framework achieves a test accuracy of 97.2%, which shows its potential to be utilized for real-world predictive maintenance in the future.
Development of Fiber-Bragg-Grating-Integrated Artificial Embedded Tendon for Multifunctional Assessment of Temperature, Strain, and Curvature
Authors: Pires-Junior Robertson , Frizera Anselmo , Marques Carlos , Leal-Junior Arnaldo
Published: August 2023

This paper presents the development and application of an optical fiber-embedded tendon based on biomimetic multifunctional structures. The tendon was fabricated using a thermocure resin (polyurethane) and the three optical fibers with one fiber Bragg grating (FBG) inscribed in each fiber. The first step in the FBG-integrated artificial tendon analysis is the mechanical properties assessment through stress–strain curves, which indicated the customization of the proposed device, since it is possible to tailor the Young’s modulus and strain limit of the tendon as a function of the integrated optical fibers, where the coated and uncoated fibers lead to differences in both parameters, i.e., strain limits and Young’s modulus. Then, the artificial tendon integrated with FBG sensors undergoes three types of characterization, which assesses the influence of temperature, single-axis strain, and curvature. Results show similarities in the temperature responses in all analyzed FBGs, where the variations are related to the heterogeneity on the polyurethane matrix distribution. In contrast, the FBGs embedded in the tendon presented a reduction in the strain sensitivity when compared with the bare FBGs (i.e., without the integration in the artificial tendon). Such results demonstrated a reduction in the sensitivity as high as 77% when compared with the bare FBGs, which is related to strain field distributions in the FBGs when embedded in the tendon. In addition, the curvature tests indicated variations in both optical power and wavelength shift, where both parameters are used on the angle estimation using the proposed multifunctional artificial tendon. To that extent, root mean squared error of around 3.25° is obtained when both spectral features are considered. Therefore, the proposed approach indicates a suitable method for the development of smart structures in which the multifunctional capability of the device leads to the possibility of using not only as a structural element in tendon-driven actuators and devices, but also as a sensor element for the different structures.
Preliminary Screening of South African Plants for Binding Affinity to the Serotonin Reuptake Transporter and Adenosine A1/A2A Receptors
Authors: Mnqika Andisiwe , Aremu Adeyemi O. , Janse van Rensburg H. D. , Lekhooa Makhotso
Published: August 2023

In South African traditional medicine, Gomphocarpus fruticosus (L.) W.T. Aiton, Hypoxis hemerocallidea Fisch. & C.A. Mey., and Leonotis leonurus. (L.) R.Br. have been recorded among different ethnic groups to be a valuable herbal remedy for the management of depression-related conditions. The current study investigated the affinity of these three plants toward the serotonin reuptake transporter (SERT) and adenosine A1/A2 receptors. Six solvents (water, methanol, acetone, dichloromethane, petroleum ether, and hexane) were used to extract the selected plants. We established that eight extracts exerted potential affinity based on the applied in vitro binding experiment. The methanol and acetone extracts of Hypoxis hemerocallidea had 60% specific binding of [3H]citalopram, an indication that almost 40% of the plant extracts were bound to the SERT. For the adenosine receptor binding assays, methanol and hexane extracts of Leonotis leonurus were the most active, with rA1Ki values of 0.038 and 0.176 mg/mL, respectively. In addition, the dichloromethane extract of Gomphocarpus fruticosus had an rA1Ki value of 6.46 mg/mL. Extracts from the more polar solvents methanol and dichloromethane had higher binding affinity. Additionally, these plant extracts acted as antagonists at the adenosine A1 receptor. Overall, the current findings provide an indication of the potential antidepressant effects of some of the tested extracts based on their binding to the receptors evaluated. However, a combination of other in vitro assays is needed to establish possible mechanisms of action. In addition, computational analysis and profiling of plant extracts is crucial to identify the bioactive compounds with a higher affinity to the receptors. Ultimately, in vivo studies remain essential to allow for an in-depth elucidation of the mechanisms of action.
Exploring the Biodiversity and Conservation Value of Alpine Grasslands in the Bucegi Massif, Romanian Carpathians
Authors: Bita-Nicolae Claudia , Yildiz Faruk , Kaya Ozkan
Published: August 2023

The Carpathian region harbors a wide range of threatened species, making it an area of exceptional conservation value. In the Alpine belt, grasslands cover the entire region and this study aims to describe the communities in the Bucegi Massif of the Romanian Carpathians and highlight their importance for conservation. The Braun–Blanquet approach was used to record floristic data from 47 phytosociological surveys, identifying a total of 235 plant species from 40 different families, including 30 threatened species. Canonical correspondence analysis was used to analyze the data, revealing that the distribution of vegetation is mainly influenced by elevation, slope and vegetation cover. Dominant grass species in these communities include Nardus stricta, Festuca violacea, Kobresia myosuroides, Festuca amethystina, Festuca airoides, Sesleria rigida, Festuca versicolor and Festuca carpatica. The alpine and boreal siliceous grasslands of the Carpathian Mountains, identified by Natura 2000 codes 6150, 6130 and 6170, host a wide range of plant species of significant conservation value. The higher altitude grasslands, especially, have outstanding plant species richness. We argue that although the habitats have been grazed, significant parts of the area are still in good ecological condition, having many typical natural features.