bluuebunny/crossref_metadata_2025 huggingface dataset:
Cholesterol Metabolism Pathways Disturbances in Atherosclerosis—Analyses Using Stochastic Petri Net-Based Model
Authors: Rybarczyk Agnieszka , Formanowicz Dorota , Radom Marcin , Formanowicz Piotr
Published: May 2023

Atherosclerosis is a multifactorial disease that affects large arteries and causes much morbidity and mortality worldwide. Despite ongoing research for several decades, it is still a global health problem that cannot be stopped and cured completely. Furthermore, the development of this disease is contributed to by various processes, primarily disturbances in cholesterol metabolism, local low-grade inflammation, and oxidative stress, resulting in the formation of atherosclerotic plaques. In this work, a stochastic Petri net model was constructed and subsequently analyzed to examine the impact of these factors on the development and progression of atherosclerosis. The use of knockout- and simulation-based analysis allowed for a comprehensive investigation of the studied phenomena. Our research has demonstrated that while cholesterol is a contributing factor in atherosclerosis, blocking its impact alone is insufficient in halting the progression of this disorder. Inhibition of oxidative stress is also important when blocking the impact of phosphoprotein phosphatase inhibitor-1 (PPI-1), microsomal triglyceride transfer protein (MTTP), and 3-hydroxy-3-methyl-glutaryl coenzyme A reductase (HMGCR), as our model shows that this action reduces the number of foam cells underlying atherosclerosis. The results obtained further support the previous observations that the combined treatment is significantly effective in enhancing therapeutic efficacy against atherosclerosis.
Recent Advancements in the Valorization of Agro-Industrial Food Waste for the Production of Nanocellulose
Authors: Nargotra Parushi , Sharma Vishal , Tsai Mei-Ling , Hsieh Shu-Ling , Dong Cheng-Di , Wang Hui-Min David , Kuo Chia-Hung
Published: May 2023

The rising climate change concerns over the excessive exploitation of non-renewable sources have necessitated the need for alternative renewable and eco-friendly resources for the production of innovative materials, achieving the targets of bioeconomy. Lignocellulosic biomass (LB) constituted by polymeric sugars and lignin is an abundantly available plant-based renewable material in the form of agro-industrial food waste and crop residues that can be exploited for the production of an array of value-added chemicals and bioproducts. Cellulose is the most abundant natural and biodegradable organic polymer on earth derived from LB, with wide scale applications in the lignocellulosic biorefineries and food industries. The negative effects of food waste from agro-industrial activities could be reduced through the recovery of cellulose from these wastes and converting it into valuable forms. However, the extraction of cellulose from LB is a difficult task owing to the recalcitrant nature of biomass that restricts the easy accessibility of cellulose for value addition. Therefore, a suitable cellulose extraction method through the fractionation of LB is necessary for a better cellulose yield. Furthermore, owing to the enormous potential of nanocellulose (NC), researchers are keenly interested in developing ecologically acceptable cellulose extraction methods. Cellulose nanofibrils and nanocrystals confer excellent mechanical properties, non-toxic characteristics and biodegradability, due to which they possess wide-scale applications in diverse industrial sectors. The current review emphasizes the potential role of cellulose extraction and NC production from agro-food waste. The different pretreatment methods for their extraction from LB are outlined. The applications of nanocellulose in different areas are also discussed. The review also highlights the recent trends, challenges and future directions in the development of cellulose and NC-based commercial products.
The Effects of 6-Month Multi-Component Exercise Intervention on Body Composition in Aged Women: A Single-Arm Experimental with Follow-Up Study
Authors: Rodrigues Filipe , Teixeira José Eduardo , Monteiro António Miguel , Forte Pedro
Published: May 2023

Multicomponent exercise programs, which combine multiple modalities such as aerobic exercises, strength training exercises, flexibility exercises, and balance exercises, can help to promote healthy aging and prevent chronic diseases in aged women. Thus, the goal of this study is to examine if a multicomponent exercise program could improve body composition in community-dwelling aged women. A 6-month single-arm quasi-experimental research was conducted using a multicomponent exercise program for older adults. The sample included 38 women with a mean age of 63.50 years (SD = 6.47 years). Body composition and anthropometric measurement was conducted from baseline (T1), after intervention (T2), and follow-Up (T3). In addition, after exercise intervention, a significant difference with moderate to large effects was reported for fat mass [η2p = 0.374, p < 0.001)], bone density [η2p = 0.374, p < 0.05)], percentage of water [η2p = 0.374, p < 0.001)], and a metabolic equivalent task [η2p = 0.374, p < 0.05]. Additionally, a significant large effect size between T1 and T2 was verified. However, body composition indicators seem to decrease below baseline levels after concluding exercise intervention (T3). Muscle mass decreased significantly after exercise intervention and mean scores were lower compared to baseline data (T1). Thus, a positive effect of the multicomponent exercise program on body composition was established in this group of community-dwelling aged women. However, the relative improvement in body fat and muscle mass were lost after the exercise program’s conclusion for values below the baselines. Avoiding detraining periods is, therefore, fundamental to maintaining the normal relative body composition.
Evaluation of Virtual Reality Application in Construction Teaching: A Comparative Study of Undergraduates
Authors: An Dongyang , Deng Hui , Shen Cheng , Xu Yiwen , Zhong Lina , Deng Yichuan
Published: May 2023

Construction courses are characterized by a combination of theoretical and practical knowledge; however, the teaching of practical knowledge is often absent due to safety and cost considerations. VR can improve the teaching of practical knowledge by facilitating interactions between teachers and students through virtual means, regardless of location, which is a weakness of current lecture-based teaching, especially in the COVID-19 era. Therefore, this paper aims to evaluate the effect and discuss the prospect of VR in construction teaching, with a comparative study of 50 students who were evenly divided into two groups and taught using traditional teaching and VR teaching, respectively. This experiment shows that VR teaching improves the students’ learning enthusiasm and satisfaction, especially in terms of practical knowledge. Additionally, students believe the combination of traditional and VR teachings can be more helpful in construction teaching. The findings of this research strengthened the advantages of VR in delivering practical knowledge in construction teaching.
Android-Based Audio Video Navigation System Forensics: A Case Study
Authors: Kang Haein , Seong Hojun , Kim Ilkyu , Jeong Wookjae , Cho Seong-Je , Park Minkyu , Han Sangchul
Published: May 2023

Vehicle digital forensics includes the process of collecting and analysing digital data stored in vehicles to find evidence related to traffic accidents or crime scenes. Through this process, we can reconstruct digital events by recognizing various information such as driver behaviour, driving patterns, vehicle destinations, and smartphones connected to a vehicle. Recently, many vehicle digital forensic studies have been conducted, but few of them have dealt with Android-based infotainment (or audio video navigation, AVN) systems. While many AVN systems adopt Android as the operating system, digital forensics of Android-based AVN systems is not easy. This is because Android-based AVN systems support various storage devices and data formats and have various data sources, making consistent data collection or analysis difficult. In this article, we perform digital forensics on four AVN systems: two Android 4.2.2 Jellybean-based and two Android 4.4.2 KitKat-based, aiming to develop a data acquisition and analysis method appropriate for each Android version. As a data collection method for the AVN system forensics, logical data acquisition is performed on the Jellybean-based systems and physical data acquisition on the KitKat-based systems. For the collected data, we identify and analyse Bluetooth data, navigation data, and system logs individually. Next, we investigate the differences in the storage structure and file location of major digital artefacts depending on the Android versions and show the limitations of the individual data analysis. Finally, we construct a timeline for the driver’s activities by integrating and analysing the diverse artefacts which consist of Bluetooth data, navigation data, and system logs.
Visual Description Augmented Integration Network for Multimodal Entity and Relation Extraction
Authors: Zuo Min , Wang Yingjun , Dong Wei , Zhang Qingchuan , Cai Yuanyuan , Kong Jianlei
Published: May 2023

Multimodal Named Entity Recognition (MNER) and multimodal Relationship Extraction (MRE) play an important role in processing multimodal data and understanding entity relationships across textual and visual domains. However, irrelevant image information may introduce noise that misleads the recognition of information. Additionally, visual and semantic features originate from different modalities, and modal disparity hinders semantic alignment. Therefore, this paper proposes the Visual Description Augmentation Integration Network (VDAIN), which introduces an image description generation technique that allows semantic features generated from image descriptions to be presented in the same modality as the semantic features of textual information. This not only reduces the modal gap but also captures more accurately the high-level semantic information and underlying visual structure in the images. To filter out the modal noise, we use VDAIN to adaptively fuse visual features, semantic features of image descriptions, and textual information, thus eliminating irrelevant modal noise. The F1 score of the proposed model in this paper reaches 75.8% and 87.78% for the MNER task and 82.54% for the MRE task on the three public data sets, respectively, which are significantly better than the baseline model. The experimental results demonstrate the effectiveness of the proposed method in solving the modal noise and modal gap problems.
Numerical Evaluation of Residual Stress Influence on SIF in CT Specimen
Authors: Janulionis Remigijus , Dundulis Gintautas
Published: May 2023

Residual stresses are usually associated with stresses induced by heterogeneous deformations as a cause of phase transition and thermal stress. The residual stresses can appear during the manufacturing process, repair process, or in some cases due to operational loads. These stresses should be taken into account in the structural integrity evaluation of low-toughness materials or in the case of fatigue and/or stress corrosion cracking (SCC) situations. Indeed, it is known that residual stresses affect crack growth rates. For a better understanding of how these stresses can interact with crack propagation in pre-strained stainless-steel specimens, numerical modeling has been performed. The tension of the compact tension (CT) specimen was simulated and as a result, the stress intensity factor (SIF) was calculated. The main goal of this paper is to numerically calculate the stress intensity factors along the crack front of the CT specimen with residual stresses and compare them with the results of tension of the same specimen just without residual stresses. For this task finite element analysis (FEA), code CAST3M was used. Simulation results showed that the higher SIF values were calculated at the sides and the lower in the middle part of the CT specimen machined from a highly pre-strained plate which is opposite to what could be expected in a specimen without residual stresses.
Investigating the Acid- and Enzyme-Induced Coagulation of Raw Milk Using Dielectric and Rheological Measurements
Authors: Dobozi Réka , Jákói Zoltán Péter , Csanádi József , Beszédes Sándor
Published: May 2023

In this study, the enzyme-induced and acid coagulation of raw milk samples were investigated, and these two processes were monitored by determining the changes in dielectric and rheological behavior to see if any correlations could be found between these two properties. For the enzyme-induced experiments, 250 cm3 of raw milk sample and industrial chymosin were used, and during the acid coagulation, industrial thermophile yogurt culture was added to the same amount of milk. The apparent viscosity was monitored with a vibro viscometer, and the dielectric constant was measured in a frequency range of 200–2400 MHz with an open-ended dielectric assessment kit (DAK) connected to a vector network analyzer (VNA). Our experimental findings revealed that during both enzyme-induced and acid coagulation, two distinct phases could be found in terms of dielectric and rheological properties, and regardless of the molecular mechanisms behind the aggregation of casein micelles, a strong correlation (with R2-values ranging between 0.967 and 0.992) can be found between the extent of the apparent viscosity and dielectric constant, suggesting that dielectric measurements might be adequately used in these dairy processing technologies. These results verified the applicability of the non-destructive dielectric measurements in dairy technologies.
Rediscovering Automatic Detection of Stuttering and Its Subclasses through Machine Learning—The Impact of Changing Deep Model Architecture and Amount of Data in the Training Set
Authors: Filipowicz Piotr , Kostek Bozena
Published: May 2023

This work deals with automatically detecting stuttering and its subclasses. An effective classification of stuttering along with its subclasses could find wide application in determining the severity of stuttering by speech therapists, preliminary patient diagnosis, and enabling communication with the previously mentioned voice assistants. The first part of this work provides an overview of examples of classical and deep learning methods used in automated stuttering classifications as well as databases and features used. Then, two classical algorithms (k-NN (k-nearest neighbor) and SVM (support vector machine) and several deep models (ConvLSTM; ResNetBiLstm; ResNet18; Wav2Vec2) are examined on the available stuttering dataset. The experiments investigate the influence of individual signal features such as Mel-Frequency Cepstral Coefficients (MFCCs), pitch-determining features in the signal, and various 2D speech representations on the classification results. The most successful algorithm, i.e., ResNet18, can classify speech disorders at the F1 measure of 0.93 for the general class. Additionally, deep learning shows superiority over a classical approach to stuttering disorder detection. However, due to insufficient data and the quality of the annotations, the results differ between stuttering subcategories. Observation of the impact of the number of dense layers, the amount of data in the training set, and the amount of data divided into the training and test sets on the effectiveness of stuttering event detection is provided for further use of this methodology.
Model of the Selective Laser Melting Process-Powder Deposition Models in Multistage Multi-Material Simulations
Authors: Svyetlichnyy Dmytro
Published: May 2023

This paper presents one of the final stages in the development of a holistic model of the selective laser melting (SLM) process. The holistic model developed previously allows for modeling of only one stage of SLM, which limits simulations to one cycle with one material. The lattice Boltzmann method is applied for simulation of laser treatment, melting, fluid flow, and solidification. Several models of powder bed generation were developed within the framework of the holistic model and are described in this paper. They were developed on the basis of static and dynamic physical principles with the use of cellular automata, their own code, and the Unity® platform. They employed continuous and discrete particle representation and incorporated a model of powder deposition with particles of atomized or arbitrary shape. The closing of the external simulation circuit, which contains the powder bed generation model, cycle initialization, its realization by the model based on the Lattice Boltzmann Method (LBM), and the powder removal model, allows us to finish one simulation cycle of laser treatment and initialize the next, thereby enabling multistage multi-material simulations. The simulation results of the multistage SLM process with the Ti-6Al-V alloy and bioactive glass are presented in this paper. These simulation results confirm the possibility of modeling several SLM stages with two different materials. The holistic model can be used for simulation, design, and optimization of multistage, multi-material SLM processes.
Future Ship Emission Scenarios with a Focus on Ammonia Fuel
Authors: Schwarzkopf Daniel A. , Petrik Ronny , Hahn Josefine , Ntziachristos Leonidas , Matthias Volker , Quante Markus
Published: May 2023

Current efforts by the International Maritime Organization (IMO) to decarbonize the shipping sector have gained momentum, although the exact path to achieve this goal is currently unclear. However, it can be safely assumed that alternative cleaner and zero-carbon fuels will be key components in the strategy. In this work, three ship emission scenarios for 2025, 2040, and 2050 were developed that cover the area of the North and Baltic Seas. They aim at a fundamental transition in the usage of marine fuels towards ammonia as the mainly used fuel in 2050, via an intermediate step in 2040 with liquefied natural gas as the main fuel. Additionally, expected trends and developments for the shipping sector were implemented, i.e., a fleet growth by vessel size and number. Efficiency improvements were included that are in accordance with the Energy Efficiency Design Index of the IMO. The scenarios were created using a novel method based on modifications to a virtual shipping fleet. The vessels in this fleet were subject to decommission and renewal cycles that adapt them to the scenario’s target year. Emissions for this renewed shipping fleet were calculated with the Modular Ship Emission Modeling System (MoSES). With respect to ammonia engine technology, two cases were considered. The first case deals with compression ignition engines and marine gas oil as pilot fuel, while the second case treats spark ignition engines and hydrogen as the pilot fuel. The first case is considered more feasible until 2050. Reductions with the first case in 2050 compared to 2015 were 40% for CO2 emissions. However, CO2 equivalents were only reduced by 22%, with the difference mainly resulting from increased N2O emissions. NOX emissions were reduced by 39%, and different PM components and SO2 were between 73% and 84% for the same target year. The estimated NH3 slip from ammonia-fueled ships in the North and Baltic Seas was calculated to be 930 Gg in 2050. For the second ammonia engine technology that is considered more advanced, emission reductions were generally stronger and ammonia emissions smaller.
An Energy-Efficient Optimal Operation Control Strategy for High-Speed Trains via a Symmetric Alternating Direction Method of Multipliers
Authors: Ma Shan , Ma Feng , Tang Chaoyu
Published: May 2023

Train operation control is of great importance in reducing train operation energy consumption and improving railway operation efficiency. This paper investigates the design of optimal control inputs for multiple trains on a single railway line with several stations. Firstly, a distributed optimal control problem for multiple train operation is formulated to reduce the energy consumption and improve the punctuality of trains. Then, we propose an efficient algorithm based on the framework of the symmetric alternating direction method of multipliers to solve this optimization problem. Finally, numerical simulations show that the method can obtain the optimal train control sequence in fewer iterative steps compared to the alternating direction multiplier method, thus illustrating the effectiveness of the algorithm.
Investigating the Accuracy of Autoregressive Recurrent Networks Using Hierarchical Aggregation Structure-Based Data Partitioning
Authors: Oliveira José Manuel , Ramos Patrícia
Published: May 2023

Global models have been developed to tackle the challenge of forecasting sets of series that are related or share similarities, but they have not been developed for heterogeneous datasets. Various methods of partitioning by relatedness have been introduced to enhance the similarities of sets, resulting in improved forecasting accuracy but often at the cost of a reduced sample size, which could be harmful. To shed light on how the relatedness between series impacts the effectiveness of global models in real-world demand-forecasting problems, we perform an extensive empirical study using the M5 competition dataset. We examine cross-learning scenarios driven by the product hierarchy commonly employed in retail planning to allow global models to capture interdependencies across products and regions more effectively. Our findings show that global models outperform state-of-the-art local benchmarks by a considerable margin, indicating that they are not inherently more limited than local models and can handle unrelated time-series data effectively. The accuracy of data-partitioning approaches increases as the sizes of the data pools and the models’ complexity decrease. However, there is a trade-off between data availability and data relatedness. Smaller data pools lead to increased similarity among time series, making it easier to capture cross-product and cross-region dependencies, but this comes at the cost of a reduced sample, which may not be beneficial. Finally, it is worth noting that the successful implementation of global models for heterogeneous datasets can significantly impact forecasting practice.
Scalable and High-Throughput In Vitro Vibratory Platform for Vocal Fold Tissue Engineering Applications
Authors: Biehl Andreea , Colmon Ramair , Timofeeva Anastasia , Gracioso Martins Ana Maria , Dion Gregory R. , Peters Kara , Freytes Donald O.
Published: May 2023

The vocal folds (VFs) are constantly exposed to mechanical stimulation leading to changes in biomechanical properties, structure, and composition. The development of long-term strategies for VF treatment depends on the characterization of related cells, biomaterials, or engineered tissues in a controlled mechanical environment. Our aim was to design, develop, and characterize a scalable and high-throughput platform that mimics the mechanical microenvironment of the VFs in vitro. The platform consists of a 24-well plate fitted with a flexible membrane atop a waveguide equipped with piezoelectric speakers which allows for cells to be exposed to various phonatory stimuli. The displacements of the flexible membrane were characterized via Laser Doppler Vibrometry (LDV). Human VF fibroblasts and mesenchymal stem cells were seeded, exposed to various vibratory regimes, and the expression of pro-fibrotic and pro-inflammatory genes was analyzed. Compared to current bioreactor designs, the platform developed in this study can incorporate commercial assay formats ranging from 6- to 96-well plates which represents a significant improvement in scalability. This platform is modular and allows for tunable frequency regimes.
Women in Biosensors Science
Authors: Madrid Rossana E.
Published: May 2023

From the first glucose biosensor from Updike and Hicks (1968), there was an explosion of research in biosensors for detecting a wide range of analytes [...]
Comparison of Artificial Intelligence-Based Applications for Mandible Segmentation: From Established Platforms to In-House-Developed Software
Authors: Ileșan Robert R. , Beyer Michel , Kunz Christoph , Thieringer Florian M.
Published: May 2023

Medical image segmentation, whether semi-automatically or manually, is labor-intensive, subjective, and needs specialized personnel. The fully automated segmentation process recently gained importance due to its better design and understanding of CNNs. Considering this, we decided to develop our in-house segmentation software and compare it to the systems of established companies, an inexperienced user, and an expert as ground truth. The companies included in the study have a cloud-based option that performs accurately in clinical routine (dice similarity coefficient of 0.912 to 0.949) with an average segmentation time ranging from 3′54″ to 85′54″. Our in-house model achieved an accuracy of 94.24% compared to the best-performing software and had the shortest mean segmentation time of 2′03″. During the study, developing in-house segmentation software gave us a glimpse into the strenuous work that companies face when offering clinically relevant solutions. All the problems encountered were discussed with the companies and solved, so both parties benefited from this experience. In doing so, we demonstrated that fully automated segmentation needs further research and collaboration between academics and the private sector to achieve full acceptance in clinical routines.
3D Bioprinting for Vascularization
Authors: Mir Amatullah , Lee Eugenia , Shih Wesley , Koljaka Sarah , Wang Anya , Jorgensen Caitlin , Hurr Riley , Dave Amartya , Sudheendra Krupa , Hibino Narutoshi
Published: May 2023

In the world of clinic treatments, 3D-printed tissue constructs have emerged as a less invasive treatment method for various ailments. Printing processes, scaffold and scaffold free materials, cells used, and imaging for analysis are all factors that must be observed in order to develop successful 3D tissue constructs for clinical applications. However, current research in 3D bioprinting model development lacks diverse methods of successful vascularization as a result of issues with scaling, size, and variations in printing method. This study analyzes the methods of printing, bioinks used, and analysis techniques in 3D bioprinting for vascularization. These methods are discussed and evaluated to determine the most optimal strategies of 3D bioprinting for successful vascularization. Integrating stem and endothelial cells in prints, selecting the type of bioink according to its physical properties, and choosing a printing method according to physical properties of the desired printed tissue are steps that will aid in the successful development of a bioprinted tissue and its vascularization.
Altered Purinergic Signaling in Neurodevelopmental Disorders: Focus on P2 Receptors
Authors: Boccazzi Marta , Raffaele Stefano , Zanettin Thomas , Abbracchio Maria P. , Fumagalli Marta
Published: May 2023

With the umbrella term ‘neurodevelopmental disorders’ (NDDs) we refer to a plethora of congenital pathological conditions generally connected with cognitive, social behavior, and sensory/motor alterations. Among the possible causes, gestational and perinatal insults have been demonstrated to interfere with the physiological processes necessary for the proper development of fetal brain cytoarchitecture and functionality. In recent years, several genetic disorders caused by mutations in key enzymes involved in purine metabolism have been associated with autism-like behavioral outcomes. Further analysis revealed dysregulated purine and pyrimidine levels in the biofluids of subjects with other NDDs. Moreover, the pharmacological blockade of specific purinergic pathways reversed the cognitive and behavioral defects caused by maternal immune activation, a validated and now extensively used rodent model for NDDs. Furthermore, Fragile X and Rett syndrome transgenic animal models as well as models of premature birth, have been successfully utilized to investigate purinergic signaling as a potential pharmacological target for these diseases. In this review, we examine results on the role of the P2 receptor signaling in the etiopathogenesis of NDDs. On this basis, we discuss how this evidence could be exploited to develop more receptor-specific ligands for future therapeutic interventions and novel prognostic markers for the early detection of these conditions.
COVID-19 Vaccines and Myocarditis: An Overview of Current Evidence
Authors: Hromić-Jahjefendić Altijana , Sezer Abas , Aljabali Alaa A. A. , Serrano-Aroca Ángel , Tambuwala Murtaza M. , Uversky Vladimir N. , Redwan Elrashdy M. , Barh Debmalya , Lundstrom Kenneth
Published: May 2023

COVID-19 vaccines have been widely used to reduce the incidence and disease severity of COVID-19. Questions have lately been raised about the possibility of an association between COVID-19 vaccines and myocarditis, an inflammatory condition affecting the myocardium, or the middle layer of the heart. Myocarditis can be caused by infections, immune reactions, or toxic exposure. The incidence rate of myocarditis and pericarditis was calculated to be 5.98 instances per million COVID-19 vaccine doses delivered, which is less than half of the incidences after SARS-CoV-2 infection. Myocarditis rates in people aged 12 to 39 years are around 12.6 cases per million doses following the second dose of mRNA vaccination. Adolescent men are more likely than women to develop myocarditis after receiving mRNA vaccines. The objectives of this systematic review and meta-analysis are to find out how often myocarditis occurs after receiving the COVID-19 vaccine, as well as the risk factors and clinical repercussions of this condition. Nevertheless, the causal relationship between vaccination and myocarditis has been difficult to establish, and further research is required. It is also essential to distinguish between suggested cases of myocarditis and those confirmed by endomyocardial biopsy.
Object Grasp Control of a 3D Robot Arm by Combining EOG Gaze Estimation and Camera-Based Object Recognition
Authors: Amri bin Suhaimi Muhammad Syaiful , Matsushita Kojiro , Kitamura Takahide , Laksono Pringgo Widyo , Sasaki Minoru
Published: May 2023

The purpose of this paper is to quickly and stably achieve grasping objects with a 3D robot arm controlled by electrooculography (EOG) signals. A EOG signal is a biological signal generated when the eyeballs move, leading to gaze estimation. In conventional research, gaze estimation has been used to control a 3D robot arm for welfare purposes. However, it is known that the EOG signal loses some of the eye movement information when it travels through the skin, resulting in errors in EOG gaze estimation. Thus, EOG gaze estimation is difficult to point out the object accurately, and the object may not be appropriately grasped. Therefore, developing a methodology to compensate, for the lost information and increase spatial accuracy is important. This paper aims to realize highly accurate object grasping with a robot arm by combining EMG gaze estimation and the object recognition of camera image processing. The system consists of a robot arm, top and side cameras, a display showing the camera images, and an EOG measurement analyzer. The user manipulates the robot arm through the camera images, which can be switched, and the EOG gaze estimation can specify the object. In the beginning, the user gazes at the screen’s center position and then moves their eyes to gaze at the object to be grasped. After that, the proposed system recognizes the object in the camera image via image processing and grasps it using the object centroid. The object selection is based on the object centroid closest to the estimated gaze position within a certain distance (threshold), thus enabling highly accurate object grasping. The observed size of the object on the screen can differ depending on the camera installation and the screen display state. Therefore, it is crucial to set the distance threshold from the object centroid for object selection. The first experiment is conducted to clarify the distance error of the EOG gaze estimation in the proposed system configuration. As a result, it is confirmed that the range of the distance error is 1.8–3.0 cm. The second experiment is conducted to evaluate the performance of the object grasping by setting two thresholds from the first experimental results: the medium distance error value of 2 cm and the maximum distance error value of 3 cm. As a result, it is found that the grasping speed of the 3 cm threshold is 27% faster than that of the 2 cm threshold due to more stable object selection.
MICaFVi: A Novel Magnetic Immuno-Capture Flow Virometry Nano-Based Diagnostic Tool for Detection of Coronaviruses
Authors: Samman Nosaibah , El-Boubbou Kheireddine , Al-Muhalhil Khawlah , Ali Rizwan , Alaskar Ahmed , Alharbi Naif Khalaf , Nehdi Atef
Published: May 2023

COVID-19 has resulted in a pandemic that aggravated the world’s healthcare systems, economies, and education, and caused millions of global deaths. Until now, there has been no specific, reliable, and effective treatment to combat the virus and its variants. The current standard tedious PCR-based tests have limitations in terms of sensitivity, specificity, turnaround time, and false negative results. Thus, an alternative, rapid, accurate, and sensitive diagnostic tool that can detect viral particles, without the need for amplification or viral replication, is central to infectious disease surveillance. Here, we report MICaFVi (Magnetic Immuno-Capture Flow Virometry), a novel precise nano-biosensor diagnostic assay for coronavirus detection which combines the MNP-based immuno-capture of viruses for enrichment followed by flow-virometry analysis, enabling the sensitive detection of viral particles and pseudoviruses. As proof of concept, virus-mimicking spike-protein-coated silica particles (VM-SPs) were captured using anti-spike-antibody-conjugated MNPs (AS-MNPs) followed by detection using flow cytometry. Our results showed that MICaFVi can successfully detect viral MERS-CoV/SARS-CoV-2-mimicking particles as well as MERS-CoV pseudoviral particles (MERSpp) with high specificity and sensitivity, where a limit of detection (LOD) of 3.9 µg/mL (20 pmol/mL) was achieved. The proposed method has great potential for designing practical, specific, and point-of-care testing for rapid and sensitive diagnoses of coronavirus and other infectious diseases.
Label-Free Biosensor
Authors: Zhang Pengfei , Wang Rui
Published: May 2023

Label-free biosensors have become an indispensable tool for analyzing intrinsic molecular properties, such as mass, and quantifying molecular interactions without interference from labels, which is critical for the screening of drugs, detecting disease biomarkers, and understanding biological processes at the molecular level [...]
Shared Mechanisms of GABAergic and Opioidergic Transmission Regulate Corticolimbic Reward Systems and Cognitive Aspects of Motivational Behaviors
Authors: Hosseinzadeh Sahafi Oveis , Sardari Maryam , Alijanpour Sakineh , Rezayof Ameneh
Published: May 2023

The functional interplay between the corticolimbic GABAergic and opioidergic systems plays a crucial role in regulating the reward system and cognitive aspects of motivational behaviors leading to the development of addictive behaviors and disorders. This review provides a summary of the shared mechanisms of GABAergic and opioidergic transmission, which modulate the activity of dopaminergic neurons located in the ventral tegmental area (VTA), the central hub of the reward mechanisms. This review comprehensively covers the neuroanatomical and neurobiological aspects of corticolimbic inhibitory neurons that express opioid receptors, which act as modulators of corticolimbic GABAergic transmission. The presence of opioid and GABA receptors on the same neurons allows for the modulation of the activity of dopaminergic neurons in the ventral tegmental area, which plays a key role in the reward mechanisms of the brain. This colocalization of receptors and their immunochemical markers can provide a comprehensive understanding for clinicians and researchers, revealing the neuronal circuits that contribute to the reward system. Moreover, this review highlights the importance of GABAergic transmission-induced neuroplasticity under the modulation of opioid receptors. It discusses their interactive role in reinforcement learning, network oscillation, aversive behaviors, and local feedback or feedforward inhibitions in reward mechanisms. Understanding the shared mechanisms of these systems may lead to the development of new therapeutic approaches for addiction, reward-related disorders, and drug-induced cognitive impairment.
Institutional Courage in Healthcare: An Improvement Project Exploring the Perspectives of Veterans Exposed to Airborne Hazards
Authors: Bloeser Katharine , McAdams Mikayla , McCarron Kelly K. , Varon Samantha , Pickett Lisa , Johnson Iman
Published: May 2023

Background: Military environmental exposures and care for subsequent health concerns have been associated with institutional betrayal, or a perception on the part of veterans that the US government has failed to adequately prevent, acknowledge, and treat these conditions and in doing so has betrayed its promise to veterans. Institutional courage is a term developed to describe organizations that proactively protect and care for their members. While institutional courage may be useful in mitigating institutional betrayal, there is a lack of definitions of institutional courage in healthcare from the patient perspective. Methods: Using qualitative methods, we sought to explore the notions of institutional betrayal and institutional courage among veterans exposed to airborne hazards (i.e., airborne particulate matter such as open burn pits; N = 13) to inform and improve clinical practice. We performed initial interviews and follow-up interviews with veterans. Results: Veterans’ depictions of courageous institutions contained key themes of being accountable, proactive, and mindful of unique experiences, supporting advocacy, addressing stigma related to public benefits, and offering safety. Veterans described institutional courage as including both individual-level traits and systems or organizational-level characteristics. Conclusions: Several existing VA initiatives already address many themes identified in describing courageous institutions (e.g., accountability and advocacy). Other themes, especially views of public benefits and being proactive, hold particular value for building trauma-informed healthcare.
Relative Risk (RR) Analysis and Prediction as Part of Assessing Occupational Safety and Determining Priorities for Action in Occupational Health and Safety in the Construction Industry in Poland
Authors: Małysa Tomasz
Published: May 2023

Risks are associated with every human activity. Accidental events are recorded in enterprises in the construction industry every day. Those events differ among themselves in the severity of consequences and the number of victims. It is important to reduce them effectively based on the conclusions of accident rate analyses. The study outlines the process of relative risk (RR) analysis and carries out a process of quantitative data prediction to determine priorities for action in the area of accident risk reduction. For the construction industry, being the subject of the analyses, statistical data on the number of persons injured in accidents at work in 2006–2021 were compiled, the relative risk (RR) was determined, and a prediction process using the Brown model and Winters’ model was performed. The relative risk analyses allowed for determining priorities for action in occupational health and safety. Based on the analyses, it was concluded that it is possible to adapt econometric models in the area of relative risk prediction, and the obtained forecast values may be the basis for taking actions regarding occupational health and safety.
Data-Driven Model for Predicting the Compressive Strengths of GFRP-Confined Reinforced Concrete Columns
Authors: Li Haolin , Yang Dongdong , Hu Tianyu
Published: May 2023

This paper focuses on the compressive strength of Glass fiber reinforced polymer (GFRP)-confined reinforced concrete columns. Data from 114 sets of GFRP-confined reinforced concrete columns were collected to evaluate the researchers’ and proposed model. A data-driven machine learning model was used to model the compressive strength of the GFRP-confined reinforced concrete columns and investigate the importance and sensitivity of the parameters affecting the compressive strength. The results show that the researchers’ model facilitates the study of the compressive strength of confined columns but suffers from a large coefficient of variation and too high or conservative estimation of compressive strength. The back propagation (BP) neural network has the best accuracy and robustness in predicting the compressive strength of the confined columns, with the coefficient of variation of only 14.22%, and the goodness of fit for both the training and testing sets above 0.9. The parameters that have an enormous influence on compressive strength are the concrete strength and FRP thickness, and all the parameters, except the fracture strain of FRP, are positively or inversely related to the compressive strength.
Investigating the Effects of Concrete Mix Design on the Environmental Impacts of Reinforced Concrete Structures
Authors: Mostafaei Hasan , Badarloo Baitollah , Chamasemani Niyousha Fallah , Rostampour Muhammad Ali , Lehner Petr
Published: May 2023

This study examines the impact of mix design parameters on the environmental effects of producing concrete and reinforced concrete buildings by conducting a life cycle assessment (LCA) and carbon footprint analysis (CFA). The study is limited to the cradle-to-gate phase, including the extraction and production of raw materials for concrete production, as well as concrete and rebar production, material transportation, and delivery to the construction site for reinforced concrete structures. Three concrete mix designs based on the American Concrete Institute (ACI) 211-09 standard, with compressive strengths of 20, 30, and 40 MPa, were analyzed. The results indicate that cement was the primary contributor to environmental impacts, accounting for approximately 90% of the carbon footprint. Sand, gravel, and admixtures followed cement in their impact on LCA results. Water usage in concrete production had a negligible effect on LCA indicators. Moreover, to determine how mix design parameters impact the carbon footprint of reinforced concrete buildings, three four-story structures were designed. The results show that in reinforced concrete buildings, concrete was a significant contributor to environmental impacts, accounting for over 50% of all indicators in the IMPACT 2002+ and CML baseline 2000 methods, except for resources and acidification. The study underscores the importance of considering mix design parameters in reducing the carbon footprint of reinforced concrete buildings and provides valuable insights into their environmental impacts. The findings indicate that cement is the main driver of environmental impacts in both assessment methods, accounting for around 90% of the carbon footprint. Additionally, concrete plays a substantial role in environmental effects, contributing to over 50% of all indicators measured in the methods used for evaluating environmental impacts.
Temperature- and Pressure-Reducing Regimes in the Growth Cell of HPHT Diamonds, Optimal for Preserving Crystal Integrity after Growth Completion
Authors: Alekseyev Nikolay I. , Broyko Anton P. , Khmelnitskiy Ivan K. , Kolyadin Alexander V. , Aivazyan Vagarshak M. , Oreshko Ivan V.
Published: May 2023

With its exceptional strength characteristics, diamond has some mechanical drawbacks, significant brittleness being among them. In particular, some HPHT-grown diamonds crack when the extreme parameters inherent to the diamond growth process gradually decrease. The cracking is caused by excessive stress due to the poor plastic properties of the diamond growth catalytic medium at certain stages of reducing the pressure and the temperature. An insulating container with the growth cell and heating circuit fragment inside can also make a significant contribution to the probability of cracking. This paper considers the possibility of minimizing the mechanical stress in the growth cell and, consequently, in the diamond crystal by choosing the optimal trajectory for the decrease in the pressure and temperature from diamond growth conditions to normal conditions.
Accuracy of Delirium Screening Tools in Older People with Cancer; A Systematic Review
Authors: Martínez-Arnau Francisco Miguel , Puchades-García Andrea , Pérez-Ros Pilar
Published: May 2023

Background: The increase in life expectancy worldwide has led to a larger population of older people, which in turn entails a rising prevalence of cancer. One of the main complications of cancer is delirium, especially in advanced stages. Objective: To determine which delirium screening instrument is the most accurate in older people with cancer. Methods: A systematic review was designed. A literature search was performed in MEDLINE, EBSCO and SCOPUS; additional records were identified by handsearching. Selection criteria were studies involving people with cancer and a mean sample age of 60 years or older, assessing delirium, and reporting the metric properties of the assessment instrument. Studies with post-surgical patients and substance abuse delirium were excluded. Results: From 2001 to 2021, 14 eligible studies evaluated 13 different assessment tools, reporting an incidence of delirium ranging from 14.3% to 68.3%. The Delirium Observation Screening Scale (DOSS) showed the best metric properties, followed by the Nursing Delirium Screening Scale (NuDESC), Confusion Assessment Method (CAM) and Memorial Delirium Assessment Scale (MDAS). Only two studies were considered to be at low risk of bias using the QUADAS-2 Tool. No study exclusively examined this population group. Conclusions: Screening tools for delirium are heterogeneous for older people with cancer, and there is a need to analyze metric properties exclusively in the older population. Registered on PROSPERO ID: CRD42022303530.