bluuebunny/crossref_metadata_2025 huggingface dataset:
Prevalence of Drug–Drug Interactions in Primary Care Prescriptions in Egypt: A Cross-Sectional Retrospective Study
Authors: Abdelkawy Khaled , Kharouba Maged , Shendy Khloud , Abdelmagged Omar , Galal Naira , Tarek Mai , Abdelgaied Mohamed , Zakaria Amr Y. , Mahmoud Sherif Hanafy
Published: June 2023

In clinical practice, drug–drug interactions (DDIs) pose significant risks to a large number of patients. Consequently, healthcare providers are required to diligently identify, monitor, and effectively handle these interactions in order to enhance patient outcomes. In Egypt, DDIs are poorly addressed, with no reports for DDIs in primary care. In our cross-sectional, retrospective, observational study, we collected a total of five thousand, eight hundred and twenty prescriptions across eight major governorates in Egypt. Prescriptions were collected over a span of 15 months between 1 June 2021 and 30 September 2022. These prescriptions were analyzed for potential DDIs using the Lexicomp® drug interactions tool. The prevalence of DDIs was found to be 18%, with 22% of the prescriptions having two or more potential DDIs. Moreover, we found 1447 DDIs of categories C (monitoring therapy recommended), D (therapy modification suggested), and X (avoid combination). The most commonly interacting drugs in our study were diclofenac, aspirin, and clopidogrel, while non-steroidal anti-inflammatory drugs (NSAIDs) were the most reported therapeutic class implicated in pharmacologic DDIs. Pharmacodynamic agonistic activity was the most common mechanism of interaction. Therefore, it is crucial to conduct screenings, detect early signs, and closely monitor drug–drug interactions (DDIs) to enhance patients’ overall health outcomes, medication responses, and safety. In this regard, the clinical pharmacist assumes a vital role in implementing these preventive measures.
Do Antimonite and Silicon Share the Same Root Uptake Pathway by Lsi1 in Sorghum bicolor L. Moench?
Authors: Vidya Chirappurathu Sukumaran Nair , Shetty Rajpal , Bokor Boris , Fialová Ivana , Luxová Miroslava , Jašková Katarína , Vaculík Marek
Published: June 2023

A study was conducted to further develop our understanding of antimony (Sb) uptake in plants. Unlike other metal(loid)s, such as silicon (Si), the mechanisms of Sb uptake are not well understood. However, SbIII is thought to enter the cell via aquaglyceroporins. We investigated if the channel protein Lsi1, which aids in Si uptake, also plays a role in Sb uptake. Seedlings of WT sorghum, with normal silicon accumulation, and its mutant (sblsi1), with low silicon accumulation, were grown in Hoagland solution for 22 days in the growth chamber under controlled conditions. Control, Sb (10 mg Sb L−1), Si (1mM) and Sb + Si (10 mg Sb L−1 + 1 mM Si) were the treatments. After 22 days, root and shoot biomass, the concentration of elements in root and shoot tissues, lipid peroxidation and ascorbate levels, and relative expression of Lsi1 were determined. When mutant plants were exposed to Sb, they showed almost no toxicity symptoms compared to WT plants, indicating that Sb was not toxic to mutant plants. On the other hand, WT plants had decreased root and shoot biomass, increased MDA content and increased Sb uptake compared to mutant plants. In the presence of Sb, we also found that SbLsi1 was downregulated in the roots of WT plants. The results of this experiment support the role of Lsi1 in Sb uptake in sorghum plants.
A Review of Critical Issues in High-Speed Vat Photopolymerization
Authors: Paral Sandeep Kumar , Lin Ding-Zheng , Cheng Yih-Lin , Lin Shang-Chih , Jeng Jeng-Ywan
Published: June 2023

Vat photopolymerization (VPP) is an effective additive manufacturing (AM) process known for its high dimensional accuracy and excellent surface finish. It employs vector scanning and mask projection techniques to cure photopolymer resin at a specific wavelength. Among the mask projection methods, digital light processing (DLP) and liquid crystal display (LCD) VPP have gained significant popularity in various industries. To upgrade DLP and LCC VPP into a high-speed process, increasing both the printing speed and projection area in terms of the volumetric print rate is crucial. However, challenges arise, such as the high separation force between the cured part and the interface and a longer resin refilling time. Additionally, the divergence of the light-emitting diode (LED) makes controlling the irradiance homogeneity of large-sized LCD panels difficult, while low transmission rates of near ultraviolet (NUV) impact the processing time of LCD VPP. Furthermore, limitations in light intensity and fixed pixel ratios of digital micromirror devices (DMDs) constrain the increase in the projection area of DLP VPP. This paper identifies these critical issues and provides detailed reviews of available solutions, aiming to guide future research towards developing a more productive and cost-effective high-speed VPP in terms of the high volumetric print rate.
Nucleophilic Aromatic Substitution of Pentafluorophenyl-Substituted Quinoline with a Functional Perylene: A Route to the Modification of Semiconducting Polymers
Authors: Aivali Stefania , Andrikopoulos Konstantinos C. , Andreopoulou Aikaterini K.
Published: June 2023

A systematic study of the influence of the chemical substitution pattern of semiconducting polymers carrying side chain perylene diimide (PDI) groups is presented. Semiconducting polymers based on perflurophenyl quinoline (5FQ) were modified via a readily accessible nucleophilic substitution reaction. The perfluorophenyl group was studied as an electron-withdrawing reactive functionality on semiconducting polymers that can undergo fast nucleophilic aromatic substitution. A PDI molecule, functionalized with one phenol group on the bay area, was used for the substitution of the fluorine atom at the para position in 6-vinylphenyl-(2-perfluorophenyl)-4-phenyl quinoline. The final product was polymerized under free radical polymerization providing polymers of 5FQ incorporated with PDI side groups. Alternatively, the post-polymerization modification of the fluorine atoms at the para position of the 5FQ homopolymer with the PhOH-di-EH-PDI was also successfully tested. In this case, the PDI units were partially introduced to the perflurophenyl quinoline moieties of the homopolymer. The para-fluoro aromatic nucleophilic substitution reaction was confirmed and estimated via 1H and 19F NMR spectroscopies. The two different polymer architectures, namely, fully or partially modified with PDI units, were studied in terms of their optical and electrochemical properties, while their morphology was evaluated using TEM analysis, revealing polymers of tailor-made optoelectronic and morphological properties. This work provides a novel molecule-designing method for semiconducting materials of controlled properties.
Highly Sensitive Detection of Bacteria by Binder-Coupled Multifunctional Polymeric Dyes
Authors: Kapil Kriti , Xu Shirley , Lee Inseon , Murata Hironobu , Kwon Seok-Joon , Dordick Jonathan S. , Matyjaszewski Krzysztof
Published: June 2023

Infectious diseases caused by pathogens are a health burden, but traditional pathogen identification methods are complex and time-consuming. In this work, we have developed well-defined, multifunctional copolymers with rhodamine B dye synthesized by atom transfer radical polymerization (ATRP) using fully oxygen-tolerant photoredox/copper dual catalysis. ATRP enabled the efficient synthesis of copolymers with multiple fluorescent dyes from a biotin-functionalized initiator. Biotinylated dye copolymers were conjugated to antibody (Ab) or cell-wall binding domain (CBD), resulting in a highly fluorescent polymeric dye-binder complex. We showed that the unique combination of multifunctional polymeric dyes and strain-specific Ab or CBD exhibited both enhanced fluorescence and target selectivity for bioimaging of Staphylococcus aureus by flow cytometry and confocal microscopy. The ATRP-derived polymeric dyes have the potential as biosensors for the detection of target DNA, protein, or bacteria, as well as bioimaging.
Synthesis, Molecular Docking, Molecular Dynamics Studies, and In Vitro Biological Evaluation of New Biofunctional Ketoprofen Derivatives with Different N-Containing Heterocycles
Authors: Manolov Stanimir , Bojilov Dimitar , Ivanov Iliyan , Marc Gabriel , Bataklieva Nadezhda , Oniga Smaranda , Oniga Ovidiu , Nedialkov Paraskev
Published: June 2023

Herein, we report the synthesis of four new hybrid molecules between ketoprofen or 2-(3-benzoylphenyl)propanoic acid and N-containing heterocyclic compounds, such as piperidine, pyrrolidine, 1,2,3,4-tetrahydroquinoline, and 1,2,3,4-tetrahydroisoquinoline. The obtained hybrid compounds were fully characterized using 1H- and 13C-NMR, UV-Vis, and HRMS spectra. Detailed HRMS analysis is provided for all novel hybrid molecules. The compounds were assessed for their in vitro anti-inflammatory and antioxidant activity. The lipophilicity of the hybrids was determined, both theoretically (cLogP) and experimentally (RM). The affinity of the compounds to the human serum albumin was assessed in silico by molecular docking study using two software, and the stability of the predicted complexes was evaluated by molecular dynamics study. All novel hybrids have shown very good HPSA activity, statistically close when compared to the reference—quercetin. The molecular docking confirmed the obtained in vitro results. Tetrahydroquinoline derivative 3c and tetrahydroisoquinoline derivative 3d have the highest affinity for albumin. They show stronger anti-inflammatory action than their predecessor, ketoprofen and the regularly used ibuprofen.
Improved Active Islanding Detection Technique with Different Current Injection Waveform
Authors: Zhang Shaoru , Wang Lijun , Du Xiuju , Zhang Ruiye , Huang Zhanping , Duan Shuchun , Yang Wenxiu , Wang Pingjun , Zhang Jielu
Published: June 2023

The active frequency drift (AFD) method is an effective method to detect islanding in grid-connected photovoltaic systems. However, it has some inherent drawbacks, such as generating higher harmonics. In order to reduce the harmonics and non-detection zone (NDZ), various improved AFD methods have been proposed, but they still suffer from high harmonics and reduced detection speed. To overcome these limitations, this paper proposes an innovative islanding detection technique based on AFD. Analysis reveals that the proposed method reduces harmonics by 68% compared to conventional AFD and has a larger chopping factor. Therefore, this technique offers several distinct advantages, including accelerated detection speed, reduced NDZ and harm caused by disturbances, and improved power quality. Furthermore, to verify the harmonic impact of this proposed islanding detection method, simulations and analyses are conducted using simulation software of Matlab/Simulink. An experimental prototype is set up in Laboratory. The simulation and experimental results demonstrate the superiority of the proposed method.
Psychosocial Workers and Indigenous Religious Leaders: An Integrated Vision for Collaboration in Humanitarian Crisis Response
Authors: Alexander David William , Letovaltseva Tatiana
Published: June 2023

Indigenous religious leaders can be the most trusted organic helping agents within vulnerable communities, but often lack orientation to the language and paradigms of the mental health and psychosocial support (MHPSS) professionals responding to their communities after a crisis. Similarly, MHPSS professionals work within paradigms which do not always match the indigenous world views of the vulnerable people they seek to help and therefore can undermine community stability while attempting to provide a service. In parallel, the spiritual care offered by indigenous religious leaders does not always optimally intersect with evidence-based MPHSS interventions, although it is highly likely that both approaches to care provide important benefits to the community, some of which are missing or underemphasized in one or the other. Training approaches designed to orient religious leaders to the work of MHPSS are usually funded and delivered by MHPSS professionals and tend to leverage MHPSS assumptions and portray MHPSS interventions as the most important lines of effort in care. This may leave religious leaders feeling uncertain of their ability to contribute to multi-disciplinary efforts without migrating away from their own foundational assumptions about humanity, illness, and wellness. Often missing from the field is a parallel effort in training which offers MHPSS professionals insight into the efficacy of indigenous spiritual interventions of various kinds and how working alongside indigenous religious leaders can aid them in protecting against the well-known pathologizing tendencies present in their own models of care. The authors are experienced in working during and after community crisis with both MHPSS professionals and indigenous religious leaders and offer an integrated vision for combined training and combined support planning that may facilitate collaboration after crisis in vulnerable communities.
Defending against Poisoning Attacks in Aerial Image Semantic Segmentation with Robust Invariant Feature Enhancement
Authors: Wang Zhen , Wang Buhong , Zhang Chuanlei , Liu Yaohui , Guo Jianxin
Published: June 2023

The outstanding performance of deep neural networks (DNNs) in multiple computer vision in recent years has promoted its widespread use in aerial image semantic segmentation. Nonetheless, prior research has demonstrated the high susceptibility of DNNs to adversarial attacks. This poses significant security risks when applying DNNs to safety-critical earth observation missions. As an essential means of attacking DNNs, data poisoning attacks destroy model performance by contaminating model training data, allowing attackers to control prediction results by carefully crafting poisoning samples. Toward building a more robust DNNs-based aerial image semantic segmentation model, in this study, we proposed a robust invariant feature enhancement network (RIFENet) that can resist data poisoning attacks and has superior semantic segmentation performance. The constructed RIFENet improves the resistance to poisoning attacks by extracting and enhancing robust invariant features. Specifically, RIFENet uses a texture feature enhancement module (T-FEM), structural feature enhancement module (S-FEM), global feature enhancement module (G-FEM), and multi-resolution feature fusion module (MR-FFM) to enhance the representation of different robust features in the feature extraction process to suppress the interference of poisoning samples. Experiments on several benchmark aerial image datasets demonstrate that the proposed method is more robust and exhibits better generalization than other state-of-the-art methods.
The Mean Moment of Inertia for Irregularly Shaped Phobos and Its Application to the Constraint for the Two-Layer Interior Structure for the Martian Moon
Authors: Zhong Zhen , Wen Qilin , Yan Jianguo , Pang Lijun
Published: June 2023

The interior structure of Phobos has been the subject of debate in recent years, with the moment of inertia being a determining factor. To study this structure, we modeled Phobos with a two-layer structure and calculated its mean density and moment of inertia using updated gravity coefficients of degree-2 and forced libration amplitudes. By minimizing the misfit between modeled and derived moment of inertia, and observed and modeled mean density, we determined the frequency distribution for estimated parameters, including the core radius rc, core density ρc, and density ρm of the outer layer. Our results indicate that the optimized core radius is around 8.2 km for our models, along with a core density compromise of approximately 2500 kg·m−3, and an outer layer density of around 1400 kg·m−3. These values have remarkable sensitivity to the misfit function, implying a higher density likely inside Phobos compared to the outer layer. Given that the large core density was associated with ice content, it suggested that the fractional ice content in the outer layer is approximately 11% with a rock density of 2200 kg·m−3, while the content in the core is lower at 2.4% with a rock density of 3000 kg·m−3. The methodology introduced in this study can be further used to study the interior structure of irregularly shaped asteroids.
Distribution of Grazing Paths and Their Influence on Mountain Vegetation in the Traditional Grazing Area of the Tien-Shan Mountains
Authors: Jia Xiang , Huang Tiecheng , Chen Mengyu , Han Ning , Liu Yihao , Chen Shujiang , Zhang Xiaoli
Published: June 2023

In the Tien-Shan Mountains, Ili Prefecture, Xinjiang, China, the livestock industry has experienced rapid growth in recent decades. However, this expansion has led to increased overgrazing behavior, resulting in the proliferation of grazing paths and a decline in vegetation cover. These factors are considered the main causes of vegetation degradation in the region. To investigate this issue, we conducted a study utilizing unmanned aerial vehicle imagery in the Zollersay Mountains of Ili to examine the distribution of grazing paths and their effects on mountain vegetation, including grassland and Malus sieversii. The results of our study revealed that grazing paths in the area exhibited various formations, including parallel, oblique intersection, and grid. On the hilltop, the grazing paths were not only shorter but also wider, whereas on the hillside, they were denser, indicating a higher concentration of livestock trampling events. It was found that grazing path density played a pivotal role in grassland degradation, with a negative correlation observed between grazing path density and indicators such as the grassland quality index and grass vegetation coverage. As grazing path density increased, the damage inflicted on Malus sieversii by livestock also intensified. However, as the trees grow older, their height surpasses the feeding range of livestock, resulting in reduced grazing impact. The findings of our study carry significant implications for developing scientifically informed livestock policies and promoting the conservation of wild fruit forests.
Ecosystem Resistance and Resilience after Dry and Wet Events across Central Asia Based on Remote Sensing Data
Authors: Zou Jie , Ding Jianli , Huang Shuai , Liu Bohua
Published: June 2023

Climate change forecasts indicate that the frequency and intensity of extreme climate events will increase in the future; these changes will have important effects on ecosystem stability and function. An important arid region of the world, Central Asia has ecosystems that are extremely vulnerable to extreme weather events. However, few studies have investigated the resistance and resilience of this region’s ecosystems to extreme weather events. In this study, first, the extreme drought/wet threshold was calculated based on the 113-year (1901–2013) standardized precipitation–evapotranspiration index (SPEI); second, moderate resolution imaging spectroradiometer (MODIS) remote sensing data were applied to calculate ecosystem water use efficiency (WUE) and quantify ecosystem resistance and resilience after different extreme climate events; and finally, differences in the changes of various ecosystem types before and after climate events were assessed. The results showed the following: (1) The average SPEI was 0.073, and the thresholds of extreme wetness and drought were 0.91 and −0.67, respectively. Central Asia experienced extreme wet periods in 2002 and 2003 and a drought period in 2008. (2) Suitable wetness levels can increase the resistance of an ecosystem; however, continuous wetness reduces ecosystem resistance, as does drought. Wet areas had strong resilience after wet events, and arid areas had strong resilience after drought events. (3) During both wet and drought years, the transition between shrubland and grassland caused changes in ecosystem resistance and resilience. These findings are important for understanding the impact of future climate change on ecosystem stability.
Flood Analysis Using Multi-Scale Remote Sensing Observations in Laos
Authors: Hansana Phonekham , Guo Xin , Zhang Shuo , Kang Xudong , Li Shutao
Published: June 2023

Heavy rains usually hit Laos countrywide and cause serious floods, influencing local agriculture, households, and the economy. Therefore, it is crucial to monitor the flooding in Laos to better understand the flood patterns and characteristics. This paper aims to analyze the influence of the flooding in Laos with multi-source data, e.g., Synthetic Aperture Radar (SAR), optical multi-spectral images, and geographic information system data. First, the flood areas in Laos from 2018 to 2022 are detected using a decision fusion method. Based on the flood areas and the global Land Use/Land Cover (LULC) product, the macro scale global impact of the flood is analyzed. Second, taking the Vientiane Capital as a case study area, a flood forecasting method is applied to estimate the risk of flooding. Finally, optical images before and after the flood event are extracted for a close-up comparison at the micro scale. Based on the above multi-scale analysis, floods in Laos are found to be predominantly concentrated in the flat areas near the Mekong River, with a decreasing trend over time, which could be helpful for flood management and mitigation strategies in Laos. The validation results exhibited notable average indices across a five-year period, with mIoU (0.7782), F1 score (0.7255), and overall accuracy (0.9854), respectively.
Footprint of the 2020 COVID-19 Lockdown on Column-Integrated Aerosol Parameters in Spain
Authors: Obregón María Ángeles , Martín Blanca , Serrano Antonio
Published: June 2023

The lockdown adopted in Spain to combat the global pandemic due to the coronavirus disease (COVID-19) led to a significant reduction in the emission of aerosols produced by road traffic and industry. This study aims to detect changes in column aerosols in Spain due to the COVID-19 lockdown. High-quality AErosol RObotic NETwork (AERONET) measurements of AOD (aerosol optical depth), AE (Ångström exponent) and SSA (single scattering albedo) over the period 2012–2020 are used for this purpose. Ten AERONET stations with available measurements during the lockdown and post-lockdown periods with a long previous data record are selected. The stations are well distributed throughout Spain, covering different areas and population densities. A comprehensive set of three statistical tests are applied to assess general changes in the dataset, the central tendency and low and high values for each parameter. The analyses are conducted for the 2020 lockdown and post-lockdown periods by comparing daily aerosol data with the measurements recorded for the same calendar days during the period 2012–2019. The results indicate a general increase in AOD during the lockdown and a decrease during the post-lockdown. While AE shows no overall behaviour, SSA is the parameter most sensitive to changes in anthropogenic contribution, with an overall significant increase in almost all the stations during both lockdown and post-lockdown periods. The study contributes to addressing the impact of the COVID-19 lockdown and provides methodologies to detect its footprint.
Deep Graph-Convolutional Generative Adversarial Network for Semi-Supervised Learning on Graphs
Authors: Jia Nan , Tian Xiaolin , Gao Wenxing , Jiao Licheng
Published: June 2023

Graph convolutional networks (GCNs) are neural network frameworks for machine learning on graphs. They can simultaneously perform end-to-end learning on the attribute information and the structure information of graph data. However, most existing GCNs inevitably encounter the limitations of non-robustness and low classification accuracy when labeled nodes are scarce. To address the two issues, the deep graph convolutional generative adversarial network (DGCGAN), a model combining GCN and deep convolutional generative adversarial networks (DCGAN), is proposed in this paper. First, the graph data is mapped to a highly nonlinear space by using the topology and attribute information of the graph for symmetric normalized Laplacian transform. Then, through the feature-structured enhanced module, the node features are expanded into regular structured data, such as images and sequences, which are input to DGCGAN as positive samples, thus expanding the sample capacity. In addition, the feature-enhanced (FE) module is adopted to enhance the typicality and discriminability of node features, and to obtain richer and more representative features, which is helpful for facilitating accurate classification. Finally, additional constraints are added to the network model by introducing DCGAN, thus enhancing the robustness of the model. Through extensive empirical studies on several standard benchmarks, we find that DGCGAN outperforms state-of-the-art baselines on semi-supervised node classification and remote sensing image classification.
Real-Time Tomographic Inversion of Truncated Ionospheric GNSS Radio Occultations
Authors: Olivares-Pulido Germán , Hernández-Pajares Manuel , Monte-Moreno Enric , Lyu Haixia , Graffigna Victoria , Cardellach Estel , Hoque Mainul , Prol Fabricio S. , Notarpietro Riccardo , Garcia-Fernandez Miquel
Published: June 2023

This paper presents a new way of combining Abel inversion and the Chapman model with a linearly increasing scale height to retrieve ionospheric electron density vertical profiles from truncated-sounding radio-occultation data. A linear Vary–Chap model is used to cover the blind region due to data truncation, with parameters estimated by enumeration of the possible values in a grid centered around a set of parameters compatible with ionospheric physics. The resulting electron density is estimated with its corresponding error from the linear least-squares solution presenting the smaller post-fit residual on the input GNSS carrier-phase measurements. The results, tested on a set of representative GNSS RO measurements obtained by COSMIC/FORMOSAT-3, show that this method can retrieve EDVPs with a predominant absolute and relative error of 1010e−m−3 and 5%, respectively, and in less than 10 s per profile, which makes this method suitable for near real-time applications in upcoming missions such as EUMETSAT Polar System-Second Generation.
Automated Road Defect and Anomaly Detection for Traffic Safety: A Systematic Review
Authors: Rathee Munish , Bačić Boris , Doborjeh Maryam
Published: June 2023

Recently, there has been a substantial increase in the development of sensor technology. As enabling factors, computer vision (CV) combined with sensor technology have made progress in applications intended to mitigate high rates of fatalities and the costs of traffic-related injuries. Although past surveys and applications of CV have focused on subareas of road hazards, there is yet to be one comprehensive and evidence-based systematic review that investigates CV applications for Automated Road Defect and Anomaly Detection (ARDAD). To present ARDAD’s state-of-the-art, this systematic review is focused on determining the research gaps, challenges, and future implications from selected papers (N = 116) between 2000 and 2023, relying primarily on Scopus and Litmaps services. The survey presents a selection of artefacts, including the most popular open-access datasets (D = 18), research and technology trends that with reported performance can help accelerate the application of rapidly advancing sensor technology in ARDAD and CV. The produced survey artefacts can assist the scientific community in further improving traffic conditions and safety.
Blockchain-Based Federated Learning System: A Survey on Design Choices
Authors: Oktian Yustus Eko , Lee Sang-Gon
Published: June 2023

The vanilla federated learning is made for a trusted environment, while in contrast, its actual use cases require collaborations in an untrusted setting. For this reason, using blockchain as a trusted platform to run federated learning algorithms has gained traction lately and has become a significant research interest. This paper performs a literature survey on state-of-the-art blockchain-based federated learning systems and analyzes several design patterns researchers often take to solve existing issues through blockchain. We find about 31 design item variations throughout the whole system. Each design is further analyzed to find pros and cons, considering fundamental metrics such as robustness, efficiency, privacy, and fairness. The result shows a linear relationship between fairness and robustness in which, if we focus on improving fairness, it will indirectly become more robust. Furthermore, improving all those metrics altogether is not viable because of the efficiency trade-off. Finally, we classify the surveyed papers to spot which designs are popular among researchers and determine which areas require immediate improvements. Our investigation shows that future blockchain-based federated learning systems require more effort regarding model compression, asynchronous aggregation, system efficiency evaluation, and the application for cross-device settings.
A Data-Driven Factor Graph Model for Anchor-Based Positioning
Authors: Moragrega Ana , Fernández-Prades Carles
Published: June 2023

This work presents a data-driven factor graph (FG) model designed to perform anchor-based positioning. The system makes use of the FG to compute the target position, given the distance measurements to the anchor node that know its own position.The aim was to design a hybrid structure (that involves data and modeling approaches) to address positioning models from a Bayesian point of view, customizing them for each technology and scenario. The weighted geometric dilution of precision (WGDOP) metric, which measures the effect on the positioning solution of distance error to the corresponding anchor node and network geometry of the anchor nodes, was taken into account. The presented algorithms were tested with simulated data and also with real-life data collected from IEEE 802.15.4-compliant sensor network nodes with a physical layer based on ultra-wide band (UWB) technology, in scenarios with one target node, three and four anchor nodes, and a time-of-arrival-based range technique. The results showed that the presented algorithm based on the FG technique provided better positioning results than the least squares-based algorithms and even UWB-based commercial systems in various scenarios, with different setups in terms of geometries and propagation conditions.
A Novel Approach to Realize Plasmonic Sensors via Multimode Optical Waveguides: A Review
Authors: Arcadio Francesco , Del Prete Domenico , Zeni Luigi , Cennamo Nunzio
Published: June 2023

In recent decades, the Surface Plasmon Resonance (SPR) phenomenon has been utilized as an underlying technique in a broad range of application fields. Herein, a new measuring strategy which harnesses the SPR technique in a way that is different from the classical methodology was explored by taking advantage of the characteristics of multimode waveguides, such as plastic optical fibers (POFs) or hetero-core fibers. The sensor systems based on this innovative sensing approach were designed, fabricated, and investigated to assess their ability to measure various physical features, such as magnetic field, temperature, force, and volume, and to realize chemical sensors. In more detail, a sensitive patch of fiber was used in series with a multimodal waveguide where the SPR took place, to alter the mode profile of the light at the input of the waveguide itself. In fact, when the changes of the physical feature of interest acted on the sensitive patch, a variation of the incident angles of the light launched in the multimodal waveguide occurred, and, as a consequence, a shift in resonance wavelength took place. The proposed approach permitted the separation of the measurand interaction zone and the SPR zone. This meant that the SPR zone could be realized only with a buffer layer and a metallic film, thus optimizing the total thickness of the layers for the best sensitivity, regardless of the measurand type. The proposed review aims to summarize the capabilities of this innovative sensing approach to realize several types of sensors for different application fields, showing the high performances obtained by exploiting a simple production process and an easy experimental setup.
Human–Robot Collaborations in Smart Manufacturing Environments: Review and Outlook
Authors: Othman Uqba , Yang Erfu
Published: June 2023

The successful implementation of Human–Robot Collaboration (HRC) has become a prominent feature of smart manufacturing environments. Key industrial requirements, such as flexibility, efficiency, collaboration, consistency, and sustainability, present pressing HRC needs in the manufacturing sector. This paper provides a systemic review and an in-depth discussion of the key technologies currently being employed in smart manufacturing with HRC systems. The work presented here focuses on the design of HRC systems, with particular attention given to the various levels of Human–Robot Interaction (HRI) observed in the industry. The paper also examines the key technologies being implemented in smart manufacturing, including Artificial Intelligence (AI), Collaborative Robots (Cobots), Augmented Reality (AR), and Digital Twin (DT), and discusses their applications in HRC systems. The benefits and practical instances of deploying these technologies are showcased, emphasizing the substantial prospects for growth and improvement in sectors such as automotive and food. However, the paper also addresses the limitations of HRC utilization and implementation and provides some insights into how the design of these systems should be approached in future work and research. Overall, this paper provides new insights into the current state of HRC in smart manufacturing and serves as a useful resource for those interested in the ongoing development of HRC systems in the industry.
Noninvasive Fuel Flow Monitoring System for Vented Fuel Oil Heaters
Authors: Bond Baxter , Vilagi Alana , Pride Dominique J.
Published: June 2023

In this work, we present hardware and firmware design and preliminary testing results for a noninvasive device for measuring fuel oil consumption in fuel oil vented heaters. Fuel oil vented heaters are a popular space heating method in northern climates. Monitoring fuel consumption is useful to understanding residential daily and seasonal heating patterns and understanding the thermal characteristics of buildings. The device is a pump monitoring apparatus (PuMA) that employs a magnetoresistive sensor to monitor the activity of solenoid driven positive displacement pumps, which are commonly used in fuel oil vented heaters. PuMA accuracy for calculating fuel oil consumption was evaluated in a lab setting and found to vary up to 7% from the measured consumption value during testing. This variance will be explored more in field testing.
High-Linearity and High-Speed ROIC of Ultra-Large Array Infrared Detectors Based on Adaptive Compensation and Enhancement
Authors: Guo Zhongjie , Wang Bin , Liu Suiyang , Xu Ruiming , Yu Ningmei
Published: June 2023

In order to solve the problem of limited linearity and frame rate in the large array infrared (IR) readout integrated circuit (ROIC), a high-linearity and high-speed readout method based on adaptive offset compensation and alternating current (AC) enhancement is proposed in this paper. The efficient correlated double sampling (CDS) method in pixels is used to optimize the noise characteristics of the ROIC and output CDS voltage to the column bus. An AC enhancement method is proposed to quickly establish the column bus signal, and an adaptive offset compensation method is used at the column bus terminal to eliminate the nonlinearity caused by the pixel source follower (SF). Based on the 55 nm process, the proposed method is comprehensively verified in an 8192 × 8192 IR ROIC. The results show that, compared with the traditional readout circuit, the output swing is increased from 2 V to 3.3 V, and the full well capacity is increased from 4.3 Me- to 6 Me-. The row time of the ROIC is reduced from 20 µs to 2 µs, and the linearity is improved from 96.9% to 99.98%. The overall power consumption of the chip is 1.6 W, and the single-column power consumption of the readout optimization circuit is 33 μW in the accelerated readout mode and 16.5 μW in the nonlinear correction mode.
Fabrication of Carbon Nanofiber Incorporated with CuWO4 for Sensitive Electrochemical Detection of 4-Nitrotoluene in Water Samples
Authors: Meenakshi Ganesh Abinaya , Sakthinathan Subramanian , Chiu Te-Wei
Published: June 2023

In the current work, copper tungsten oxide (CuWO4) nanoparticles are incorporated with carbon nanofiber (CNF) to form CNF/CuWO4 nanocomposite through a facile hydrothermal method. The prepared CNF/CuWO4 composite was applied to the electrochemical detection of hazardous organic pollutants of 4-nitrotoluene (4-NT). The well-defined CNF/CuWO4 nanocomposite is used as a modifier of glassy carbon electrode (GCE) to form CuWO4/CNF/GCE electrode for the detection of 4-NT. The physicochemical properties of CNF, CuWO4, and CNF/CuWO4 nanocomposite were examined by various characterization techniques, such as X-ray diffraction studies, field emission scanning electron microscopy, EDX-energy dispersive X-ray microanalysis, and high-resolution transmission electron microscopy. The electrochemical detection of 4-NT was evaluated using cyclic voltammetry (CV) the differential pulse voltammetry detection technique (DPV). The aforementioned CNF, CuWO4, and CNF/CuWO4 materials have better crystallinity with porous nature. The prepared CNF/CuWO4 nanocomposite has better electrocatalytic ability compared to other materials such as CNF, and CuWO4. The CuWO4/CNF/GCE electrode exhibited remarkable sensitivity of 7.258 μA μM−1 cm−2, a low limit of detection of 86.16 nM, and a long linear range of 0.2–100 μM. The CuWO4/CNF/GCE electrode exhibited distinguished selectivity, acceptable stability of about 90%, and well reproducibility. Meanwhile, the GCE/CNF/CuWO4 electrode has been applied to real sample analysis with better recovery results of 91.51 to 97.10%.
Prediction for Future Yaw Rate Values of Vehicles Using Long Short-Term Memory Network
Authors: Kontos János , Kránicz Balázs , Vathy-Fogarassy Ágnes
Published: June 2023

Currently, electric mobility and autonomous vehicles are of top priority from safety, environmental and economic points of view. In the automotive industry, monitoring and processing accurate and plausible sensor signals is a crucial safety-critical task. The vehicle’s yaw rate is one of the most important state descriptors of vehicle dynamics, and its prediction can significantly contribute to choosing the correct intervention strategy. In this article, a Long Short-Term Memory network-based neural network model is proposed for predicting the future values of the yaw rate. The training, validating and testing of the neural network was conducted based on experimental data gathered from three different driving scenarios. The proposed model can predict the yaw rate value in 0.2 s in the future with high accuracy, using sensor signals of the vehicle from the last 0.3 s in the past. The R2 values of the proposed network range between 0.8938 and 0.9719 in the different scenarios, and in a mixed driving scenario, it is 0.9624.
Transparent Pneumatic Tactile Sensors for Soft Biomedical Robotics
Authors: Zhao Sinuo , Nguyen Chi Cong , Hoang Trung Thien , Do Thanh Nho , Phan Hoang-Phuong
Published: June 2023

Palpation is a simple but effective method to distinguish tumors from healthy tissues. The development of miniaturized tactile sensors embedded on endoscopic or robotic devices is key to achieving precise palpation diagnosis and subsequent timely treatment. This paper reports on the fabrication and characterization of a novel tactile sensor with mechanical flexibility and optical transparency that can be easily mounted on soft surgical endoscopes and robotics. By utilizing the pneumatic sensing mechanism, the sensor offers a high sensitivity of 1.25 mbar and negligible hysteresis, enabling the detection of phantom tissues with different stiffnesses ranging from 0 to 2.5 MPa. Our configuration, combining pneumatic sensing and hydraulic actuating, also eliminates electrical wiring from the functional elements located at the robot end-effector, thereby enhancing the system safety. The optical transparency path in the sensors together with its mechanical sensing capability open interesting possibilities in the early detection of solid tumor as well as in the development of all-in-one soft surgical robots that can perform visual/mechanical feedback and optical therapy.
MARAS: Mutual Authentication and Role-Based Authorization Scheme for Lightweight Internet of Things Applications
Authors: Şeker Özlem , Dalkılıç Gökhan , Çabuk Umut Can
Published: June 2023

The Internet of things (IoT) accommodates lightweight sensor/actuator devices with limited resources; hence, more efficient methods for known challenges are sought after. Message queue telemetry transport (MQTT) is a publish/subscribe-based protocol that allows resource-efficient communication among clients, so-called brokers, and servers. However, it lacks viable security features beyond username/password checks, yet transport-layer security (TLS/HTTPS) is not efficient for constrained devices. MQTT also lacks mutual authentication among clients and brokers. To address the issue, we developed a mutual authentication and role-based authorization scheme for lightweight Internet of things applications (MARAS). It brings mutual authentication and authorization to the network via dynamic access tokens, hash-based message authentication code (HMAC)-based one-time passwords (HOTP), advanced encryption standard (AES), hash chains, and a trusted server running OAuth2.0 along with MQTT. MARAS merely modifies “publish” and “connect” messages among 14 message types of MQTT. Its overhead to “publish” messages is 49 bytes, and to “connect” messages is 127 bytes. Our proof-of-concept showed that the overall data traffic with MARAS remains lower than double the traffic without it, because “publish” messages are the most common. Nevertheless, tests showed that round-trip times for a “connect” message (and its “ack”) are delayed less than a percentile of a millisecond; for a “publish” message, the delays depend on the size and frequency of published information, but we can safely say that the delay is upper bounded by 163% of the network defaults. So, the scheme’s overhead to the network is tolerable. Our comparison with similar works shows that while our communication overhead is similar, MARAS offers better computational performance as it offloads computationally intensive operations to the broker side.
Joint Calibration of a Multimodal Sensor System for Autonomous Vehicles
Authors: Muhovič Jon , Perš Janez
Published: June 2023

Multimodal sensor systems require precise calibration if they are to be used in the field. Due to the difficulty of obtaining the corresponding features from different modalities, the calibration of such systems is an open problem. We present a systematic approach for calibrating a set of cameras with different modalities (RGB, thermal, polarization, and dual-spectrum near infrared) with regard to a LiDAR sensor using a planar calibration target. Firstly, a method for calibrating a single camera with regard to the LiDAR sensor is proposed. The method is usable with any modality, as long as the calibration pattern is detected. A methodology for establishing a parallax-aware pixel mapping between different camera modalities is then presented. Such a mapping can then be used to transfer annotations, features, and results between highly differing camera modalities to facilitate feature extraction and deep detection and segmentation methods.
Transformer-Based Approach to Melanoma Detection
Authors: Cirrincione Giansalvo , Cannata Sergio , Cicceri Giovanni , Prinzi Francesco , Currieri Tiziana , Lovino Marta , Militello Carmelo , Pasero Eros , Vitabile Salvatore
Published: June 2023

Melanoma is a malignant cancer type which develops when DNA damage occurs (mainly due to environmental factors such as ultraviolet rays). Often, melanoma results in intense and aggressive cell growth that, if not caught in time, can bring one toward death. Thus, early identification at the initial stage is fundamental to stopping the spread of cancer. In this paper, a ViT-based architecture able to classify melanoma versus non-cancerous lesions is presented. The proposed predictive model is trained and tested on public skin cancer data from the ISIC challenge, and the obtained results are highly promising. Different classifier configurations are considered and analyzed in order to find the most discriminating one. The best one reached an accuracy of 0.948, sensitivity of 0.928, specificity of 0.967, and AUROC of 0.948.
Defects Prediction Method for Radiographic Images Based on Random PSO Using Regional Fluctuation Sensitivity
Authors: Shang Zhongyu , Li Bing , Chen Lei , Zhang Lei
Published: June 2023

This paper presents an advanced methodology for defect prediction in radiographic images, predicated on a refined particle swarm optimization (PSO) algorithm with an emphasis on fluctuation sensitivity. Conventional PSO models with stable velocity are often beleaguered with challenges in precisely pinpointing defect regions in radiographic images, attributable to the lack of a defect-centric approach and the propensity for premature convergence. The proposed fluctuation-sensitive particle swarm optimization (FS-PSO) model, distinguished by an approximate 40% increase in particle entrapment within defect areas and an expedited convergence rate, necessitates a maximal additional time consumption of only 2.28%. The model, also characterized by reduced chaotic swarm movement, enhances efficiency through the modulation of movement intensity concomitant with the escalation in swarm size. The FS-PSO algorithm’s performance was rigorously evaluated via a series of simulations and practical blade experiments. The empirical findings evince that the FS-PSO model substantially outperforms the conventional stable velocity model, particularly in terms of shape retention in defect extraction.
FCAN–XGBoost: A Novel Hybrid Model for EEG Emotion Recognition
Authors: Zong Jing , Xiong Xin , Zhou Jianhua , Ji Ying , Zhou Diao , Zhang Qi
Published: June 2023

In recent years, artificial intelligence (AI) technology has promoted the development of electroencephalogram (EEG) emotion recognition. However, existing methods often overlook the computational cost of EEG emotion recognition, and there is still room for improvement in the accuracy of EEG emotion recognition. In this study, we propose a novel EEG emotion recognition algorithm called FCAN–XGBoost, which is a fusion of two algorithms, FCAN and XGBoost. The FCAN module is a feature attention network (FANet) that we have proposed for the first time, which processes the differential entropy (DE) and power spectral density (PSD) features extracted from the four frequency bands of the EEG signal and performs feature fusion and deep feature extraction. Finally, the deep features are fed into the eXtreme Gradient Boosting (XGBoost) algorithm to classify the four emotions. We evaluated the proposed method on the DEAP and DREAMER datasets and achieved a four-category emotion recognition accuracy of 95.26% and 94.05%, respectively. Additionally, our proposed method reduces the computational cost of EEG emotion recognition by at least 75.45% for computation time and 67.51% for memory occupation. The performance of FCAN–XGBoost outperforms the state-of-the-art four-category model and reduces computational costs without losing classification performance compared with other models.