bluuebunny/crossref_metadata_2025 huggingface dataset:
High Speed and Accuracy of Animation 3D Pose Recognition Based on an Improved Deep Convolution Neural Network
Authors: Ding Wei , Li Wenfa
Published: June 2023

Pose recognition in character animations is an important avenue of research in computer graphics. However, the current use of traditional artificial intelligence algorithms to recognize animation gestures faces hurdles such as low accuracy and speed. Therefore, to overcome the above problems, this paper proposes a real-time 3D pose recognition system, which includes both facial and body poses, based on deep convolutional neural networks and further designs a single-purpose 3D pose estimation system. First, we transformed the human pose extracted from the input image to an abstract pose data structure. Subsequently, we generated the required character animation at runtime based on the transformed dataset. This challenges the conventional concept of monocular 3D pose estimation, which is extremely difficult to achieve. It can also achieve real-time running speed at a resolution of 384 fps. The proposed method was used to identify multiple-character animation using multiple datasets (Microsoft COCO 2014, CMU Panoptic, Human3.6M, and JTA). The results indicated that the improved algorithm improved the recognition accuracy and performance by approximately 3.5% and 8–10 times, respectively, which is significantly superior to other classic algorithms. Furthermore, we tested the proposed system on multiple pose-recognition datasets. The 3D attitude estimation system speed can reach 24 fps with an error of 100 mm, which is considerably less than that of the 2D attitude estimation system with a speed of 60 fps. The pose recognition based on deep learning proposed in this study yielded surprisingly superior performance, proving that the use of deep-learning technology for image recognition has great potential.
A Unique Specific Jumping Test for Measuring Explosive Power in Basketball Players: Validity and Reliability
Authors: Shalom Asaf , Gottlieb Roni , Alcaraz Pedro E. , Calleja-Gonzalez Julio
Published: June 2023

The aim of this study was to develop and assess the reliability and validity of an innovative field test that measures lower limb explosive power in basketball players (i.e., alactic anaerobic capacity) for the dominant and non-dominant legs. The test examines the performance of vertical, horizontal, and combined movements while holding the ball—similar to penetration to the basket or layup. Such capabilities are required throughout basketball practice and games, combined with upper and lower body coordination. The study included 22 male basketball players, ages 16–18, members of an elite youth league team in Israel. To assess validity, the participants performed the test for each leg, followed by nine standardized tests that were developed for a range of ball games, including basketball. To assess reliability, the participants performed a retest of the unique test 72-h later. Our findings indicate the validity and reliability of the proposed anaerobic alactic field test for basketball players, for the dominant and non-dominant legs. Moreover, strong correlations were seen between the novel test and the standardized tests, with a high correlation for horizontal explosive power (0.5 < r < 0.7), a very high correlation for vertical explosive power (0.7 < r < 0.9), and a nearly perfect correlation for the two combined (r > 0.9). In conclusion, this unique field test for basketball players could assist coaches in developing and applying optimal training programs and game plans, for players individually, and for the team as a whole. As the test measures each leg separately, it could also offer an assessment tool following players’ injuries.
Confirmation of Final Bolt Tightening via Deep Learning-Based Image Processing
Authors: Fukuoka Tomotaka , Minami Takahiro , Fujiu Makoto
Published: June 2023

In Japan, the final tightening of bolts in the bolt-tightening operations is guaranteed to have been performed correctly by visually determining the change in markings during the temporary tightening operation performed by the technician. However, the engineer must confirm many bolts; further, the amount of time needed for the confirmation work and the inability to keep an objective record of the confirmation results present problems. To solve these problems, we developed a system for automating the final tightening of bolts using deep learning-based image-processing technology. The proposed system takes as input videos of bolt fastening points, extracts individual bolts, extracts markings on the extracted bolts, and makes fastening decisions based on the markings. In the judgment stage, the system processes information on each bolt where a marking is detected; thus, it is possible to leave this information as objective data. In this paper, we evaluated the accuracy of each automated step using an actual bridge video. We also compared the confirmation time with human confirmation. As a result of the confirmation, our proposed method reduces the confirmation time by about 33% in comparison to human confirmation.
Special Issue: State-of-Art of Microbial Concerns in Food Safety
Authors: Iseppi Ramona , Messi Patrizia
Published: June 2023

The microbiological quality of food remains an essential factor for industrial production and for consumers, as foodborne illnesses are still a widespread health and economic problem [...]
An Easy Partition Approach for Joint Entity and Relation Extraction
Authors: Hou Jing , Deng Xiaomeng , Han Pengwu
Published: June 2023

The triplet extraction (TE) task aims to identify the entities and relations mentioned in a given text. TE consists of two tasks: named entity recognition (NER) and relation classification (RC). Previous work has either treated TE as two separate tasks with independent encoders, or as a single task with a unified encoder. However, both approaches have limitations in capturing the interaction and independence of the features for different subtasks. In this paper, we propose a simple and direct feature selection and interaction scheme. Specifically, we use a pretraining language model (e.g., BERT) to extract various features, including entity recognition, shared, and relation classification features. To capture the interaction, shared features consist of the common semantic information used by the two tasks simultaneously. We use a gate module to obtain the task-specific features. Experimental results on various public benchmarks show that our proposed method can achieve competitive performance, and the calculation speed of our model is seven times faster than CasRel, and two times faster than PFN.
Comparison of Differential Evolution and Nelder–Mead Algorithms for Identification of Line-Start Permanent Magnet Synchronous Motor Parameters
Authors: Paramonov Aleksey , Oshurbekov Safarbek , Kazakbaev Vadim , Prakht Vladimir , Dmitrievskii Vladimir , Goman Victor
Published: June 2023

Line-start permanent magnet synchronous motors (LSPMSMs) are of great interest to researchers because of their high energy efficiency, due to the growing interest of manufacturers in energy-efficient units. However, LSPMSMs face some difficulties in starting and synchronization processes. The LSPMSM lumped parameter model is applicable to estimating the successfulness of starting and further synchronization. The parameters of such a model can be determined using computer-aided identification algorithms applied to real motor transient processes’ curves. This problem demands significant computational time. A comparison between two algorithms, differential evolution and Nelder–Mead, is presented in this article. The algorithms were used for 0.55 kW, 1500 rpm LSPMSM parameter identification. Moreover, to increase computational speed, it is proposed to stop and restart the algorithms’ procedures, changing their parameters after a certain number of iterations. A significant advantage of the Nelder–Mead algorithm is shown for the solving of the considered problem.
Fine-Grained Image Recognition by Means of Integrating Transformer Encoder Blocks in a Robust Single-Stage Object Detector
Authors: Ali Usman , Oh Seungmin , Um Tai-Won , Hann Minsoo , Kim Jinsul
Published: June 2023

Fine-grained image classification remains an ongoing challenge in the computer vision field, which is particularly intended to identify objects within sub-categories. It is a difficult task since there is both minimal and substantial intra-class variance. Current methods address the issue through first locating selective regions with region proposal networks (RPNs), object localization, or part localization, followed by implementing a CNN network or SVM classifier to those selective regions. This approach, however, makes the process simple via implementing a single-stage end-to-end feature encoded with a localization method, which leads to improved feature representations of individual tokens/regions through integrating the transformer encoder blocks into the Yolov5 backbone structure. These transformer encoder blocks, with their self-attention mechanism, effectively capture global dependencies and enable the model to learn relationships between distant regions. This improves the model’s ability to understand context and capture long-range spatial relationships in an image. We also replaced the Yolov5 detection heads with three transformer heads at the output for object recognition using the discriminative and informative feature maps from transformer encoder blocks. We established the potential of the single-stage detector for the fine-grained image recognition task, achieving state-of-the-art 93.4% accuracy, as well as outperforming existing one-stage recognition models. The effectiveness of our approach is assessed using the Stanford car dataset, which includes 16,185 images of 196 different classes of vehicles with significantly identical visual appearances.
Strength Performance of Nonwoven Coir Geotextiles as an Alternative Material for Slope Stabilization
Authors: Adajar Mary Ann , Cutora Miller , Bolima Shayne Jostein , Chua Kyle Johnson , Isidro Irwyn Ainsley , Ramos John Vincent
Published: June 2023

Slope stability is one of the crucial factors to consider in every civil engineering project. One widely used method to stabilize slope is the use of polymeric products called geosynthetics. Natural fiber geosynthetics used for geotechnical applications have attracted attention because of their environmental and economic benefits. Coir fibers made into nonwoven geotextiles are utilized in this study as an alternative material for slope stabilization. One drawback of coir fiber geotextiles is their low tensile strength and limited life span due to their susceptibility to environmental factors. This study was conducted to evaluate the effect of mercerization and bleaching treatment on the strength performance of nonwoven coir geotextiles after exposure to conditions simulating biological and chemical degradation. Microscopic images of treated coir geotextiles show the removal of surface impurities that altered the physical components in the fiber. The grab tensile strength results prove that the mercerized coir geotextiles are suitable for field conditions and groundwater exposure. The untreated coir geotextiles showed superior puncture resistance relative to the chemically treated geotextiles. The chemical treatments improved the tensile strength; however, they weakened the puncture resistance of the coir geotextile due to the decrease in thickness. A slope stability simulation conducted using Rocscience Slide2 version 9.017 software proved that coir geotextiles can effectively reinforce slopes, with strength performance almost comparable to that of synthetic geotextiles.
Sentiment Analysis of Semantically Interoperable Social Media Platforms Using Computational Intelligence Techniques
Authors: Alqahtani Ali , Khan Surbhi Bhatia , Alqahtani Jarallah , AlYami Sultan , Alfayez Fayez
Published: June 2023

Competitive intelligence in social media analytics has significantly influenced behavioral finance worldwide in recent years; it is continuously emerging with a high growth rate of unpredicted variables per week. Several surveys in this large field have proved how social media involvement has made a trackless network using machine learning techniques through web applications and Android modes using interoperability. This article proposes an improved social media sentiment analytics technique to predict the individual state of mind of social media users and the ability of users to resist profound effects. The proposed estimation function tracks the counts of the aversion and satisfaction levels of each inter- and intra-linked expression. It tracks down more than one ontologically linked activity from different social media platforms with a high average success rate of 99.71%. The accuracy of the proposed solution is 97% satisfactory, which could be effectively considered in various industrial solutions such as emo-robot building, patient analysis and activity tracking, elderly care, and so on.
On Isospectral Composite Beams
Authors: Kawano Alexandre , Morassi Antonino
Published: June 2023

We consider a composite system consisting of two identical straight elastic beams under longitudinal vibration connected by an elastic interface capable of counteracting the relative vibration of the two beams with its shearing stiffness. We construct examples of isospectral composite beams, i.e., countable one-parameter families of beams having different shearing stiffness but exactly the same eigenvalues under a given set of boundary conditions. The construction is explicit and is based on the reduction to a one-dimensional Sturm–Liouville eigenvalue problem and the application of a Darboux’s lemma.
Optimization of Vehicle Powertrain Mounting System Based on Generalized Inverse Cascade Method under Uncertainty
Authors: Shui Yongbo , Wen Hansheng , Zhao Jian , Wu Yudong , Huang Haibo
Published: June 2023

This paper presents a summary of the optimization design process for a multi-objective, two-level engineering problem, utilizing the generalized inverse cascade method under uncertainty. The primary objective is to enhance the vibration isolation performance of a mounting system, considering the influence of uncertain factors on its stiffness. The focus is on determining the value range of the design variables at the bottom layer, ensuring that the design goal is met with a specified confidence level. To illustrate the application of this methodology, the optimization design of a powertrain mount is used as a case study. A data-driven approach is adopted, establishing a quantitative mapping relationship between mount stiffness, force transmission rate, modal decoupling rate, and other design indicators. This is achieved through the development of a CRBM-DBN approximate model, which combines Conditional Restricted Boltzmann Machines (CRBMs) and a Deep Belief Network (DBN). Additionally, an intelligent optimization algorithm and interval search technology are employed to determine the optimal design interval for the mount stiffness. Simulation and experimental verification are conducted using selected parameter combinations. The results demonstrate notable improvements in the vibration isolation performance, modal decoupling rate, and vehicle NVH performance when compared to the original state. These findings provide valuable insights for the interval optimization design of similar multi-objective, as well as two-level engineering problems, serving as useful references for future research and applications.
Sustainability in Maritime Transport: Advances, Solutions and Pending Tasks
Authors: Orosa José A.
Published: June 2023

This Special Issue “Sustainability in Maritime Transport: Advances, Solutions and Pending Tasks”, gives an up-to-date overview of the use new technologies to obtain more sustainable maritime transport based on new combustibles, working procedures and related industries, with all of them in agreement with the technical limitations indicated by safety on board [...]
A K-Value Dynamic Detection Method Based on Machine Learning for Lithium-Ion Battery Manufacturing
Authors: Zhang Hekun , Kong Xiangdong , Yuan Yuebo , Hua Jianfeng , Han Xuebing , Lu Languang , Li Yihui , Zhou Xiaoyi , Ouyang Minggao
Published: June 2023

During the manufacturing process of the lithium-ion battery, metal foreign matter is likely to be mixed into the battery, which seriously influences the safety performance of the battery. In order to reduce the outflow of such foreign matter defect cells, the production line universally adopted the K-value test process. In the traditional K-value test, the detection threshold is determined empirically, which has poor dynamic characteristics and probably leads to missing or false detection. Based on comparing the screening effect of different machine learning algorithms for the production data of lithium-ion cells, this paper proposes a K-value dynamic screening algorithm for the cell production line based on the local outlier factor algorithm. The analysis results indicate that the proposed method can adaptively adjust the detection threshold. Furthermore, we validated its effectiveness through the metal foreign matter implantation experiment conducted in the pilot manufacturing line. Experiment results show that the proposed method’s detection rate is improved significantly. The increase in the detection rate of foreign matter defects is beneficial to improving battery quality and safety.
Manufacturing High-Energy-Density Sulfidic Solid-State Batteries
Authors: Li Gang , Wang Shuo , Fu Jipeng , Liu Yuan , Chen Zehua
Published: June 2023

All-solid-state batteries (ASSBs) using sulfide solid electrolytes with high room-temperature ionic conductivity are expected as promising next-generation batteries, which might solve the safety issues and enable the utilization of lithium metal as the anode to further increase the energy density of cells. Most researchers in the academic community currently focus on developing novel sulfide solid electrolytes, clarifying the interface issues between sulfide electrolytes and solid electrodes and mechanism of lithium dendrite growth in ASSB. However, there is a lacking in the technical route analysis about the commercialization of ASSBs based on sulfide solid electrolytes. This review mainly introduces the specific preparation methods of various parts in sulfide-based ASSBs, including the preparation methods of sulfide solid electrolyte particles, sulfide-based composite electrolyte membranes, composite cathodes and anodes, and analyzes the advantages and disadvantages of these methods. In addition, several schemes of ASSB assembly are also introduced. Finally, a perspective of large-scale production of sulfide-based ASSBs is provided, which is expected to accelerate the commercialization of sulfide-based ASSBs.
Non-Trivial Dynamics in the FizHugh–Rinzel Model and Non-Homogeneous Oscillatory-Excitable Reaction-Diffusions Systems
Authors: Ambrosio Benjamin , Aziz-Alaoui M. A. , Mondal Argha , Mondal Arnab , Sharma Sanjeev K. , Upadhyay Ranjit Kumar
Published: June 2023

This article focuses on the qualitative analysis of complex dynamics arising in a few mathematical models in neuroscience context. We first discuss the dynamics arising in the three-dimensional FitzHugh–Rinzel (FHR) model and then illustrate those arising in a class of non-homogeneous FitzHugh–Nagumo (Nh-FHN) reaction-diffusion systems. FHR and Nh-FHN models can be used to generate relevant complex dynamics and wave-propagation phenomena in neuroscience context. Such complex dynamics include canards, mixed-mode oscillations (MMOs), Hopf-bifurcations and their spatially extended counterpart. Our article highlights original methods to characterize these complex dynamics and how they emerge in ordinary differential equations and spatially extended models.
In Vitro Characterization of chIFITMs of Aseel and Kadaknath Chicken Breeds against Newcastle Disease Virus Infection
Authors: Malarmathi Muthusamy , Murali Nagarajan , Selvaraju Mani , Sivakumar Karuppusamy , Gowthaman Vasudevan , Raghavendran Vadivel Balasubramanian , Raja Angamuthu , Peters Sunday O. , Thiruvenkadan Aranganoor Kannan
Published: June 2023

Newcastle disease (ND) is highly contagious and usually causes severe illness that affects Aves all over the world, including domestic poultry. Depending on the virus’s virulence, it can impact the nervous, respiratory, and digestive systems and cause up to 100% mortality. The chIFITM genes are activated in response to viral infection. The current study was conducted to quantify the mRNA of chIFITM genes in vitro in response to ND viral infection. It also examined its ability to inhibit ND virus replication in chicken embryo fibroblast (CEF) cells of the Aseel and Kadaknath breeds. Results from the study showed that the expression of all chIFITM genes was significantly upregulated throughout the period in the infected CEF cells of both breeds compared to uninfected CEF cells. In CEF cells of the Kadaknath breed, elevated levels of expression of the chIFITM3 gene dramatically reduced ND viral growth, and the viral load was 60% lower than in CEF cells of the Aseel breed. The expression level of the chIFITMs in Kadaknath ranged from 2.39 to 11.68 log2 folds higher than that of control CEFs and was consistently (p < 0.01) higher than Aseel CEFs. Similar to this, theIFN-γ gene expresses strongly quickly and peaks at 13.9 log2 fold at 48 hpi. Based on these cellular experiments, the Kadaknath breed exhibits the potential for greater disease tolerance than Aseel. However, to gain a comprehensive understanding of disease resistance mechanisms in chickens, further research involving in vivo investigations is crucial.
Molecular Basis of Transglutaminase-2 and Muscarinic Cholinergic Receptors in Experimental Myopia: A Target for Myopia Treatment
Authors: Barathi Veluchamy Amutha , Ho Candice E. H. , Tong Louis
Published: June 2023

Myopia, a prevalent refractive error disorder worldwide, is characterized by the elongation of the eye, leading to visual abnormalities. Understanding the genetic factors involved in myopia is crucial for developing therapeutic and preventive measures. Unfortunately, only a limited number of genes with well-defined functionality have been associated with myopia. In this study, we found that the homozygous TGM2-deleted gene in mice protected against the development of myopia by slowing down the elongation of the eye. The effectiveness of gene knockdown was confirmed by achieving a 60 percent reduction in TGM-2 transcript levels through the use of TGM-2-specific small interfering RNA (siRNA) in human scleral fibroblasts (SFs). Furthermore, treating normal mouse SFs with various transglutaminase inhibitors led to the down-regulation of TGM-2 expression, with the most significant reduction observed with specific TGM-2 inhibitors. Additionally, the study found that the pharmacological blockade of muscarinic receptors also slowed the progression of myopia in mice, and this effect was accompanied by a decrease in TGM-2 enzyme expression. Specifically, mice with homozygous mAChR5, mAChR1, and/or mAChR4 and knockout mice exhibited higher levels of TGM-2 mRNA compared to mice with homozygous mAChR2 and three knockout mice (fold changes of 5.8, 2.9, 2.4, −2.2, and −4.7, respectively; p < 0.05). These findings strongly suggest that both TGM-2 and muscarinic receptors play central roles in the development of myopia, and blocking these factors could potentially be useful in interfering with the progression of this condition. In conclusion, targeting TGM-2 may have a beneficial effect regarding myopia, and this may also be at least partially be the mechanism of anti-muscarinic drugs in myopia. Further studies should investigate the interaction between TGM-2 and muscarinic receptors, as well as the changes in other extracellular matrix genes associated with growth during the development of myopia.
Microbiome Alterations and Alzheimer’s Disease: Modeling Strategies with Transgenic Mice
Authors: López-Villodres Juan Antonio , Escamilla Alejandro , Mercado-Sáenz Silvia , Alba-Tercedor Carmen , Rodriguez-Perez Luis Manuel , Arranz-Salas Isabel , Sanchez-Varo Raquel , Bermúdez Diego
Published: June 2023

In the last decade, the role of the microbiota–gut–brain axis has been gaining momentum in the context of many neurodegenerative and metabolic disorders, including Alzheimer’s disease (AD) and diabetes, respectively. Notably, a balanced gut microbiota contributes to the epithelial intestinal barrier maintenance, modulates the host immune system, and releases neurotransmitters and/or neuroprotective short-chain fatty acids. However, dysbiosis may provoke immune dysregulation, impacting neuroinflammation through peripheral–central immune communication. Moreover, lipopolysaccharide or detrimental microbial end-products can cross the blood–brain barrier and induce or at least potentiate the neuropathological progression of AD. Thus, after repeated failure to find a cure for this dementia, a necessary paradigmatic shift towards considering AD as a systemic disorder has occurred. Here, we present an overview of the use of germ-free and/or transgenic animal models as valid tools to unravel the connection between dysbiosis, metabolic diseases, and AD, and to investigate novel therapeutical targets. Given the high impact of dietary habits, not only on the microbiota but also on other well-established AD risk factors such as diabetes or obesity, consistent changes of lifestyle along with microbiome-based therapies should be considered as complementary approaches.
The Correlation of Serum Calpain 1 Activity and Concentrations of Interleukin 33 in COVID-19 Acute Respiratory Distress Syndrome
Authors: Loinjak Domagoj , Mihić Damir , Smolić Robert , Maričić Lana , Šahinović Ines , Smolić Martina , Sikora Renata , Loinjak Sanja , Dinjar Kristijan , Včev Aleksandar
Published: June 2023

Acute respiratory distress syndrome (ARDS) is one of the most severe complications of the COVID-19 disease. The role of IL-33 and calpain 1 was previously described in lung infections and lung tissue damage. Our study examined the association between serum calpain 1 activity and IL-33 concentration in patients with COVID-19 ARDS. In the research, we included 80 subjects who had COVID-19 pneumonia and divided them into 2 groups: 40 subjects with ARDS and 40 subjects without ARDS. The basis of the research was the collection of subjects’ data and the sampling of peripheral venous blood. The concentration of IL-33 was determined by the ELISA method and the activity of calpain 1 by the fluorometry method. Our research showed elevated calpain 1 activity and IL-33 concentration in the serum of COVID-19 patients who developed ARDS compared to those who did not develop ARDS and a positive correlation between them was established. Further, a positive correlation was established between the examined parameters and the severity of the disease, proinflammatory markers, and the use of mechanical ventilation. These results indicate a possible association and role of calpain 1 and IL-33 with the development of ARDS in COVID-19 patients.
Screen-Printed Textile-Based Electrochemical Biosensor for Noninvasive Monitoring of Glucose in Sweat
Authors: Khosravi Safoora , Soltanian Saeid , Servati Amir , Khademhosseini Ali , Zhu Yangzhi , Servati Peyman
Published: June 2023

Wearable sweat biosensors for noninvasive monitoring of health parameters have attracted significant attention. Having these biosensors embedded in textile substrates can provide a convenient experience due to their soft and flexible nature that conforms to the skin, creating good contact for long-term use. These biosensors can be easily integrated with everyday clothing by using textile fabrication processes to enhance affordable and scalable manufacturing. Herein, a flexible electrochemical glucose sensor that can be screen-printed onto a textile substrate has been demonstrated. The screen-printed textile-based glucose biosensor achieved a linear response in the range of 20–1000 µM of glucose concentration and high sensitivity (18.41 µA mM−1 cm−2, R2 = 0.996). In addition, the biosensors show high selectivity toward glucose among other interfering analytes and excellent stability over 30 days of storage. The developed textile-based biosensor can serve as a platform for monitoring bio analytes in sweat, and it is expected to impact the next generation of wearable devices.
Determining the Essential Criteria for Choosing Appropriate Methods for Maintenance and Repair of Iraqi Healthcare Building Facilities
Authors: Chan Daniel W. M. , Sadeq Dher Abdulhadi , Sarvari Hadi , Edwards David J. , Parsaei Alireza , Javaherikhah Amirhossien
Published: June 2023

Today, building maintenance and repair (M&R) is a neglected aspect of the construction business throughout a building’s entire life cycle. Selecting appropriate M&R strategies is crucial, particularly for emerging economies like Iraq with severely constrained resources. This study seeks to identify the primary selection criteria for M&R methods of healthcare building facilities (HBFs) in Iraq. A comprehensive desktop literature analysis was undertaken to extract and determine the essential selection criteria for the most suited M&R approaches to buildings in general. Then, two rounds of the Delphi survey were conducted to consolidate the specific selection criteria to suit the circumstances of Iraq and HBFs. A total of 21 sub-criteria were identified and divided into six main groups. The main criteria and the associated sub-criteria were then analyzed and ranked using the fuzzy analytic hierarchy process (FAHP) technique. The ranking of the various main criteria revealed that the “cost” criterion was ranked first in terms of importance, followed by the “human resources” and “quality” criteria. The fourth, fifth, and sixth main criteria are “reliability/flexibility”, “safety/risk/environment”, and “facilities/technology”, respectively. The overall ranking of the sub-criteria placed “optimization and cost reduction” in the first position and “extending the life of the equipment and preserving their initial quality” in the bottom place. It is anticipated that the key findings and effective recommendations of this study will considerably contribute to the improvement of building maintenance and repair management practices in developing nations while enhancing different stakeholders’ understanding of the most important selection criteria for M&R methods, particularly with regard to healthcare building facilities in Iraq.
Could Metformin and Resveratrol Support Glioblastoma Treatment? A Mechanistic View at the Cellular Level
Authors: Ibrahim Raghad Sabaawi , Ibrahim Shahad Sabaawi , El-Naas Ahmed , Koklesová Lenka , Kubatka Peter , Büsselberg Dietrich
Published: June 2023

Glioblastoma, a malignant brain tumor, is a common primary brain tumor in adults, with diabetes mellitus being a crucial risk factor. This review examines how the antidiabetic drug metformin and dietary supplement resveratrol can benefit the treatment of glioblastoma. Metformin and resveratrol have demonstrated action against relevant pathways in cancer cells. Metformin and resveratrol inhibit cell proliferation by downregulating the PI3K/Akt pathway, activating mTOR, and increasing AMPK phosphorylation, resulting in lower proliferation and higher apoptosis levels. Metformin and resveratrol both upregulate and inhibit different cascades in the MAPK pathway. In vivo, the drugs reduced tumor growth and volume. These actions show how metformin and resveratrol can combat cancer with both glucose-dependent and glucose-independent effects. The pre-clinical results, alongside the lack of clinical studies and the rise in novel delivery mechanisms, warrant further clinical investigations into the applications of metformin and resveratrol as both separate and as a combination complement to current glioblastoma therapies.
Heterogeneous Expression of PD-L1, B7x, B7-H3, and HHLA2 in Pulmonary Sarcomatoid Carcinoma and the Related Regulatory Signaling Pathways
Authors: Wang Feng , Cali Daylan Ayse Ece , Deng Lei , Yang Jihua , Sharma Janaki , Su Christopher , Li Shenduo , Zang Xingxing , Halmos Balazs , Borczuk Alain , Cheng Haiying
Published: June 2023

Immunotherapy has transformed lung cancer management, but PSC remains an aggressive subtype with a poor prognosis. This study investigates the differential expression of PD-L1 and alternative immune checkpoints (ICs; B7x, B7-H3, and HHLA2), and genetic alterations in PSCs. Tumor specimens of 41 PSC patients were evaluated. PD-L1, B7x, B7-H3, and HHLA2 were positive in 75.0%, 67.6%, 73.0%, and 91.9% of tumors, respectively. PD-L1 expression was significantly higher in the epithelial compared to the sarcomatoid component (median TPS: 50% vs. 0%, p = 0.010). Expression of PD-L1 in both components was only seen in 32.1% of patients. However, at least one IC was expressed in 92.9% of epithelial and 100% of sarcomatoid components. Furthermore, METex14 was detected in 19.5% of patients and was associated with a higher sarcomatoid percentage. Our preclinical studies revealed that METex14 induced PD-L1 expression via MAPK or PI3K/Akt pathways, and MET inhibitors decreased PD-L1 expression. Our findings demonstrate distinct expressions of ICs in PSC subcomponents. Thus, combination IC inhibition as a therapeutic strategy in PSC warrants further exploration. A high percentage of METex14 in PSC and its role in regulating PD-L1 expression reveal different therapeutic targets in this aggressive NSCLC subtype.
Post-Traumatic Stress as a Psychological Effect of Mild Head Injuries in Children
Authors: Sinopidis Xenophon , Kallianezos Panagiotis , Petropoulos Constantinos , Gkentzi Despoina , Kostopoulou Eirini , Fouzas Sotirios , Dassios Theodore , Vervenioti Aggeliki , Karatza Ageliki , Roupakias Stylianos , Panagidis Antonios , Blevrakis Evangelos , Jelastopulu Eleni
Published: June 2023

Background: Head trauma is one of the most common pediatric emergencies. While the psychological effects of severe head injuries are well studied, the psychological consequences of mild head injuries often go overlooked. Head injuries with a Glasgow Coma Scale score of 13–15, with symptoms such as headache, vomiting, brief loss of consciousness, transient amnesia, and absence of focal neurological signs, are defined as mild. The aim of this study is to evaluate the stress of children with mild head injuries and their parents’ relevant perception during the early post-traumatic period. Methods: This is a prospective cross-sectional study on a cohort of children with mild head injuries and their parents. Two questionnaires were implemented, the Child Trauma Screening Questionnaire (CTSQ) which was compiled by the children, and the Children’s Revised Impact of Event Scale (CRIES-13), compiled by their parents. Both questionnaires are widely used and reliable. The first presents an excellent predictive ability in children with a risk of post-traumatic stress disorder, while the second is a weighted self-completed detecting instrument for the measurement of post-traumatic stress in children and adolescents, with a detailed evaluation of their reactions to the traumatic incident. The participants responded one week and one month after the traumatic event. Results: A total of 175 children aged 6–14 years and 174 parents participated in the study. Stress was diagnosed in 33.7% of children after one week, and in 9.9% after one month. Parental responses suggesting stress presence in their children were 19.0% and 3.9%, respectively. These outcomes showed that mild head injuries are not so innocent. They are often underestimated by their parents and may generate a psychological burden to the children during the early post-traumatic period. Conclusions: Mild head injuries may affect the emotional welfare of children. Healthcare providers should understand the importance of the psychological effect of this overlooked type of injury. They should be trained in the psychological effect of trauma and be aware of this probability, promptly notify the parents accordingly, and provide psychological assistance beyond medical treatment. Follow-up and support are needed to avoid the possibility of future post-traumatic stress disorder. More extensive research is needed as the outcomes of this study regarded a limited population in numbers, age, and survey period. Furthermore, many children with mild head injuries do not ever visit the emergency department and stay at home unrecorded. Community-based research on the topic should therefore be considered.
Innovative Family-Based Genetically Informed Series of Analyses of Whole-Exome Data Supports Likely Inheritance for Grammar in Children with Specific Language Impairment
Authors: Andres Erin M. , Earnest Kathleen Kelsey , Xuan Hao , Zhong Cuncong , Rice Mabel L. , Raza Muhammad Hashim
Published: June 2023

Individuals with specific language impairment (SLI) struggle with language acquisition despite average non-verbal intelligence and otherwise typical development. One SLI account focuses on grammar acquisition delay. The current study aimed to detect novel rare genetic variants associated with performance on a grammar assessment, the Test of Early Grammatical Impairment (TEGI), in English-speaking children. The TEGI was selected due to its sensitivity and specificity, consistently high heritability estimates, and its absence from all but one molecular genetic study. We performed whole exome sequencing (WES) in eight families with SLI (n = 74 total) and follow-up Sanger sequencing in additional unrelated probands (n = 146). We prioritized rare exonic variants shared by individuals with low TEGI performance (n = 34) from at least two families under two filtering workflows: (1) novel and (2) previously reported candidate genes. Candidate variants were observed on six new genes (PDHA2, PCDHB3, FURIN, NOL6, IQGAP3, and BAHCC1), and two genes previously reported for overall language ability (GLI3 and FLNB). We specifically suggest PCDHB3, a protocadherin gene, and NOL6 are critical for ribosome synthesis, as they are important targets of SLI investigation. The proposed SLI candidate genes associated with TEGI performance emphasize the utility of precise phenotyping and family-based genetic study.
Inhibition of GLI Transcriptional Activity and Prostate Cancer Cell Growth and Proliferation by DAX1
Authors: Hong Sung Pyo , Kim Kil Won , Ahn Soon Kil
Published: June 2023

The Hedgehog (Hh) signaling pathway plays an essential role in the initiation and progression of prostate cancer. This is mediated by transcriptional factors belonging to the GLI (glioma-associated oncogene) family, which regulate downstream targets to drive prostate cancer progression. The activity of GLI proteins is tightly controlled by a range of mechanisms, including molecular interactions and post-translational modifications. In particular, mitogenic and oncogenic signaling pathways have been shown to regulate GLI protein activity independently of upstream Hh pathway signaling. Identifying GLI protein regulators is critical for the development of targeted therapies that can improve patient outcomes. This study aimed to identify a novel protein that directly regulates the activity of GLI transcription factors in prostate cancer. We performed gene expression, cellular analyses, and reporter assays to demonstrate that DAX1 (dosage-sensitive sex reversal adrenal hypoplasia congenital critical region on X chromosome, gene 1) interacts with GLI1 and GLI2, the master regulators of Hh signaling. Interestingly, DAX1 overexpression significantly inhibited Hh signaling by reducing GLI1 and GLI2 activity, prostate cancer cell proliferation, and viability. Our results shed light on a novel regulatory mechanism of Hh signaling in prostate cancer cells. The interaction between DAX1 and GLI transcription factors provides insight into the complex regulation of Hh signaling in prostate cancer. Given the importance of Hh signaling in prostate cancer progression, targeting DAX1–GLI interactions may represent a promising therapeutic approach against prostate cancer. Overall, this study provides new insights into the regulation of the Hh pathway and its role in prostate cancer progression. The findings suggest that DAX1 could serve as a potential therapeutic target for the treatment of prostate cancer.
Metal-Supported TiO2/SiO2 Core-Shell Nanosphere Photocatalyst for Efficient Sunlight-Driven Methanol Degradation
Authors: Karimi Estahbanati M. R. , Vu Thuy-Dung , Do Trong-On , Nayernia Zahra , Iliuta Maria C.
Published: June 2023

The development of novel and active photocatalysts to industrialize photocatalysis technology is still a challenging task. In this work, a novel method is presented to prepare TiO2/SiO2 NSs by covering SiO2 nanospheres (NSs) with titanate-nanodiscs (TNDs) followed by calcination. In this regard, SiO2 NSs are first synthesized and then TNDs are deposited on the SiO2 NSs using a layer-by-layer deposition technique. The morphology of the prepared samples is analyzed via SEM and TEM analyses before and after the deposition. The analysis of metal (Cu, Pt, and Ni) loading on calcined TNDs/SiO2 NSs reveals the highest specific surface area (109 m2/g), absorption wavelength extension (up to 420 nm), and photocatalytic activity for the Cu-loaded sample. In addition, studying the effect of metal content shows that loading 3% Cu leads to the highest photocatalytic activity. Finally, it is demonstrated that H2S treatment can improve the photocatalytic activity by around 15%. These findings suggest the calcined TNDs/SiO2 NSs are a versatile photocatalyst with potential applications in other processes such as hydrogen production and CO2 valorization.
Investigation of the Effect Chloride Ions on Carbon Steel in Closed Environments at Different Temperatures
Authors: Ahmed Saleh , Hou Yang , Lepkova Katerina , Pojtanabuntoeng Thunyaluk
Published: June 2023

This paper investigated carbon steel corrosion in an enclosed environment, where one set of steel was immersed in 3.5 wt.% NaCl solution and another exposed to humid (condensation) conditions. The study employed electrochemical noise and electrochemical impedance spectroscopy techniques to monitor real time corrosion behaviour. The samples were evaluated with surface characterisation techniques including optical light microscope, scanning electron microscope with energy dispersive X-ray spectroscopy, and Raman spectroscopy. The results showed that carbon steel immersed in the liquid phase at a constant temperature exhibited the highest weight loss. However, the carbon steel exposed to water condensation (gas phase) at 80 °C manifested extensive localised corrosion. EN results agreed with the microscopy findings.
Exploring the Chemical Reactivity, Molecular Docking, Molecular Dynamic Simulation and ADMET Properties of a Tetrahydrothienopyridine Derivative Using Computational Methods
Authors: Bakheit Ahmed H. , Attwa Mohamed W. , Kadi Adnan A. , Ghabbour Hazem A. , Alkahtani Hamad M.
Published: June 2023

This study investigates the crystal structure, physicochemical properties, and pharmacokinetic profile of Ethyl 2-amino-6-methyl-4,5,6,7-tetrahydrothieno[2,3-c]pyridine-3-carboxylate (EAMT) as a potential therapeutic agent. The crystal structure was analyzed using Hirshfeld surface analysis in conjunction with the quantum theory of atoms in molecules (QT-AIM). Non-covalent interactions were evaluated through reduced-density gradient reduction, revealing that the EAMT crystal is stabilized by hydrogen bonds between EAMT molecules in the crystal and between EAMT molecules and water molecules. The molecular electrostatic nature of interactions was examined using MESP, while global and local descriptors were calculated to assess the compound’s reactivity. Molecular docking with the Adenosine A1 receptor was performed and validated through a 50 ns molecular dynamics simulation (MDS). Results suggest that EAMT influences protein structure, potentially stabilizing specific secondary structure elements. The compactness analysis showed a slightly more compact protein conformation and a marginally increased solvent exposure in the presence of the EAMT ligand, as indicated by Rg and SASA values. The total binding free energy (ΔG total) was determined to be −114.56 kcal/mol. ADMET predictions demonstrated EAMT’s compliance with Lipinski’s and Pfizer’s rule of five, indicating good oral availability. The compound may exhibit low-potency endocrine activity. In conclusion, EAMT presents potential as a therapeutic candidate, warranting further exploration of its molecular interactions, pharmacokinetics, and potential safety concerns.
Imaging of Ganglioneuroma: A Literature Review and a Rare Case of Cystic Presentation in an Adolescent Girl
Authors: Pacella Giulia , Brunese Maria , Donnarumma Federico , Barrassi Michele , Bellifemine Fabio , Sciaudone Guido , Vallone Gianfranco , Guerra Germano , Sallustio Giuseppina
Published: June 2023

Retroperitoneal ganglioneuroma is a rare neuroectodermal tumor with a benign nature. We performed a literature review among 338 studies. We included 9 studies, whose patients underwent CT and/or MRI to characterize a retroperitoneal mass, which was confirmed to be a ganglioneuroma by histologic exam. The most common features of ganglioneuroma are considered to be a solid nature, oval/lobulated shape, and regular margins. The ganglioneuroma shows a progressive late enhancement on CT. On MRI it appears as a hypointense mass in T1W images and with a heterogeneous high-intensity in T2W. The MRI-“whorled sign” is described in the reviewed studies in about 80% of patients. The MRI characterization of a primitive retroperitoneal cystic mass should not exclude a cystic evolution from solid masses, and in the case of paravertebral location, the differential diagnosis algorithm should include the hypothesis of ganglioneuroma. In our case, the MRI features could have oriented towards a neurogenic nature, however, the predominantly cystic-fluid aspect and the considerable longitudinal non-invasive extension between retroperitoneal structures, misled us to a lymphatic malformation. In the literature, it is reported that the cystic presentation can be due to a degeneration of a well-known solid form while maintaining a benign character: the distinguishing malignity character is the revelation of immature cells on histological examination.
Breast Cancer Diagnosis Based on IoT and Deep Transfer Learning Enabled by Fog Computing
Authors: Pati Abhilash , Parhi Manoranjan , Pattanayak Binod Kumar , Singh Debabrata , Singh Vijendra , Kadry Seifedine , Nam Yunyoung , Kang Byeong-Gwon
Published: June 2023

Across all countries, both developing and developed, women face the greatest risk of breast cancer. Patients who have their breast cancer diagnosed and staged early have a better chance of receiving treatment before the disease spreads. The automatic analysis and classification of medical images are made possible by today’s technology, allowing for quicker and more accurate data processing. The Internet of Things (IoT) is now crucial for the early and remote diagnosis of chronic diseases. In this study, mammography images from the publicly available online repository The Cancer Imaging Archive (TCIA) were used to train a deep transfer learning (DTL) model for an autonomous breast cancer diagnostic system. The data were pre-processed before being fed into the model. A popular deep learning (DL) technique, i.e., convolutional neural networks (CNNs), was combined with transfer learning (TL) techniques such as ResNet50, InceptionV3, AlexNet, VGG16, and VGG19 to boost prediction accuracy along with a support vector machine (SVM) classifier. Extensive simulations were analyzed by employing a variety of performances and network metrics to demonstrate the viability of the proposed paradigm. Outperforming some current works based on mammogram images, the experimental accuracy, precision, sensitivity, specificity, and f1-scores reached 97.99%, 99.51%, 98.43%, 80.08%, and 98.97%, respectively, on the huge dataset of mammography images categorized as benign and malignant, respectively. Incorporating Fog computing technologies, this model safeguards the privacy and security of patient data, reduces the load on centralized servers, and increases the output.