bluuebunny/crossref_metadata_2025 huggingface dataset:
Electrosprayed Particles Loaded with Kartogenin as a Potential Osteochondral Repair Implant
Authors: Gurgul Sebastian J. , Moreira Anabela , Xiao Yi , Varma Swastina Nath , Liu Chaozong , Costa Pedro F. , Williams Gareth R.
Published: March 2023

The restoration of cartilage damage is a slow and not always successful process. Kartogenin (KGN) has significant potential in this space—it is able to induce the chondrogenic differentiation of stem cells and protect articular chondrocytes. In this work, a series of poly(lactic-co-glycolic acid) (PLGA)-based particles loaded with KGN were successfully electrosprayed. In this family of materials, PLGA was blended with a hydrophilic polymer (either polyethyleneglycol (PEG) or polyvinylpyrrolidone (PVP)) to control the release rate. Spherical particles with sizes in the range of 2.4–4.1 µm were fabricated. They were found to comprise amorphous solid dispersions, with high entrapment efficiencies of >93%. The various blends of polymers had a range of release profiles. The PLGA-KGN particles displayed the slowest release rate, and blending with PVP or PEG led to faster release profiles, with most systems giving a high burst release in the first 24 h. The range of release profiles observed offers the potential to provide a precisely tailored profile via preparing physical mixtures of the materials. The formulations are highly cytocompatible with primary human osteoblasts.
Study of the Compressive Properties of Heavy Calcium Carbonate-Reinforced Epoxy Composite Spheres (HC-R-EMS) Composite Lightweight Concrete
Authors: Ma Rong , Cao Zheng , Jiang Tao , Wang Ying , Shi Shanshan , Li Wenge , Zhao Yuantao , Zhong Ning , Shi Danda , Wu Xinfeng
Published: March 2023

Lightweight concrete is one of the effective means to solve the problems of structural component weight, energy efficiency, and fire safety in modern civil engineering. Heavy calcium carbonate-reinforced epoxy composite spheres (HC-R-EMS) were prepared by the ball milling method, and HC-R-EMS, cement, and hollow glass microspheres (HGMS) were mixed into the mold by the molding method to prepare composite lightweight concrete. The relationship between the HC-R-EMS volumetric fraction, the initial inner diameter of the HC-R-EMS, the number of layers of HC-R-EMS, the HGMS volume ratio, the basalt fiber length and content, and the multi-phase composite lightweight concrete density and compressive strength was studied. The experimental results show that the density of the lightweight concrete ranges between 0.953–1.679 g/cm3 and the compressive strength ranges between 1.59–17.26 MPa, where the volume fraction of HC-R-EMS is 90%, the initial internal diameter is 8–9 mm, and the number of layers of HC-R-EMS is three. The lightweight concrete can meet the requirements of high strength (12.67 MPa) and low density (0.953 g/cm3). In addition, the addition of basalt fiber (BF) can effectively improve the compressive strength of the material without changing the density of the material. From a micro-level perspective, HC-R-EMS is closely combined with the cement matrix, which is conducive to increasing the compressive strength of concrete. Basalt fibers connect the matrix into a network, improving the maximum limit force of the concrete.
Preparation of Superhydrophobic Materials and Establishment of Anticorrosive Coatings on the Tinplate Substrate by Alkylation of Graphene Oxide
Authors: Gu Jiangdong , An Qiufeng , Li Jialong , Ge Ping , Wu Yanyan , Li Yihan
Published: March 2023

Corrosion of structural parts not only reduces the service life of the equipment but also causes safety accidents, so building a long-lasting anti-corrosion coating on its surface is the key to solving this problem. Under the action of alkali catalysis, n-octyltriethoxysilane (OTES), dimethyldimethoxysilane (DMDMS), and perfluorodecyltrimethoxysilane (FTMS) hydrolyzed and polycondensed co-modified graphene oxide (GO), modified to synthesize a self-cleaning superhydrophobic material fluorosilane-modified graphene oxide (FGO). The structure, film morphology, and properties of FGO were systematically characterized. The results showed that the newly synthesized FGO was successfully modified by long-chain fluorocarbon groups and silanes. FGO presented an uneven and rough morphology on the substrate surface, the water contact angle was 151.3°, and the rolling angle was 3.9°, which caused the coating to exhibit excellent self-cleaning function. Meanwhile, the epoxy polymer/fluorosilane-modified graphene oxide (E-FGO) composite coating adhered to the carbon structural steel’s surface, and its corrosion resistance was detected by the Tafel curve and EIS impedance. It was found that the current density of the 10 wt% E-FGO coating (Icorr) was the lowest (1.087 × 10−10 A/cm2), which was approximately 3 orders of magnitude lower than that of the unmodified epoxy coating. This was primarily due to the introduction of FGO, which formed a continuous physical barrier in the composite coating and gave the composite coating excellent hydrophobicity. This method might provide new ideas for advances in steel corrosion resistance in the marine sector.
Phase Equilibria and Structure Formation in the Polylactic-co-Glycolic Acid/Tetraglycol/Water Ternary System
Authors: Algebraistova Polina Yu. , Basko Andrey V. , Ilyasova Anna N. , Lebedeva Tatyana N. , Mironov Anton V. , Pochivalov Konstantin V. , Popov Vladimir K.
Published: March 2023

This paper concerns a detailed study of the phase separation and structure formation processes that occur in solutions of highly hydrophobic polylactic-co-glycolic acid (PLGA) in highly hydrophilic tetraglycol (TG) upon their contact with aqueous media. In the present work, cloud point methodology, high-speed video recording, differential scanning calorimetry, and both optical and scanning electron microscopy were used to analyze the behavior of PLGA/TG mixtures differing in composition when they are immersed in water (the so-called “harsh” antisolvent) or in a nonsolvent consisting of equal amounts of water and TG (a “soft” antisolvent). The phase diagram of the ternary PLGA/TG/water system was designed and constructed for the first time. The PLGA/TG mixture composition with which the polymer undergoes glass transition at room temperature was determined. Our data enabled us to analyze in detail the structure evolution process taking place in various mixtures upon their immersion in “harsh” and “soft” antisolvent baths and gain an insight into the peculiarities of the structure formation mechanism active in the course of antisolvent-induced phase separation in PLGA/TG/water mixtures. This provides intriguing opportunities for the controlled fabrication of a wide variety of bioresorbable structures—from polyester microparticles, fibers, and membranes to scaffolds for tissue engineering.
Characterizing the Impact of Chitosan on the Nucleation and Crystal Growth of Ritonavir from Supersaturated Solutions
Authors: Budiman Arif , Kalina Kalina , Aristawidya Levina , Shofwan Adnan Aly Al , Rusdin Agus , Aulifa Diah Lia
Published: March 2023

The addition of polymeric materials is often used to delay nucleation or crystal growth and maintain the high supersaturation of amorphous drugs. Therefore, this study aimed to investigate the impact of chitosan on the supersaturation behavior of drugs with a low recrystallization tendency and elucidate the mechanism of its crystallization inhibition in an aqueous solution. It was carried out using ritonavir (RTV) as a model of poorly water-soluble drugs categorized as class III of Taylor’s classification, while chitosan was used as a polymer, and hypromellose (HPMC) was used for comparison. The inhibition of the nucleation and crystal growth of RTV by chitosan was examined by measuring the induction time. The interactions of RTV with chitosan and HPMC were evaluated by NMR measurements, FT-IR, and an in silico analysis. The results showed that the solubilities of amorphous RTV with and without HPMC were quite similar, while the amorphous solubility was significantly increased by the chitosan addition due to the solubilization effect. In the absence of the polymer, RTV started to precipitate after 30 min, indicating that it is a slow crystallizer. Chitosan and HPMC effectively inhibited the nucleation of RTV, as reflected by a 48–64-fold enhancement in the induction time. Furthermore, NMR, FT-IR, and in silico analysis demonstrated that the hydrogen bond interaction between the amine group of RTV and a proton of chitosan, as well as the carbonyl group of RTV and a proton of HPMC, was observed. This indicated that the hydrogen bond interaction between RTV and chitosan as well as HPMC can contribute to the crystallization inhibition and maintenance of RTV in a supersaturated state. Therefore, the addition of chitosan can delay nucleation, which is crucial for stabilizing supersaturated drug solutions, specifically for a drug with a low crystallization tendency.
Special Issue on “Biocatalysis, Enzyme and Process Engineering”
Authors: Fernandes Pedro , de Carvalho Carla C. C. R.
Published: March 2023

Biocatalysis entails the use of enzymes, which are catalytically active proteins, to speed up reactions so that products can be obtained swiftly and accurately [...]
An Empirical Analysis of the Aircraft Emissions by Operating from Scheduled Flights within the Domestic Market in Spain
Authors: Martínez Raya Antonio , Segura de la Cal Alejandro , González Díaz Rafael Eugenio
Published: March 2023

Over the past 20 years, civil aviation has substantially reduced its environmental impact to augment sustainable transportation. In Spain, the domestic market has been habitually characterized by a few enterprises providing air transport services linked to scheduled flights on domestic corridors. Because of geographic diversity and the highly concentrated population characterizing this southern European country, many of them could not be supplied by alternative transport modes in terms of both time and distance by comparison with air transportation. For air quality monitoring from 139 national corridors, this paper aims to study related aviation emissions to conduct an economic analysis in terms of positive or negative externalities. For such purposes, the study focused on these domestic routes served by the five most important Spanish airports, specifically on the number of passengers transported from 2011 to 2020. Up to 10 aircraft types representing no more than 89% of regular operations on these flyways were subsequently identified. In addition, certain engine types also were selected as representatives to evaluate their emissions, depending on the great-circle distance in each route. The research findings, though particularly conditioned by aviation peculiarities of such a domestic market, point decisively to significant dependence upon emissions in connection with the seasonality of the demand and the concentration of flights with low occupancy indices from any one of them. Results suggest that airlines would benefit from operating turboprops instead of turbojets on selected routes, especially when oil prices are high. However, it is not always easy to find a balance between uncompromising economic profitability and effective fleet availability, since nowadays air transport undertakings tend to unify their fleets by using a few aircraft families, mostly powered by jet engines, apart from regional carriers.
Assessing Hyperloop Transport Optimizing Cost with Different Designs of Capsule
Authors: Almujibah Hamad
Published: March 2023

Hyperloop technology is a transport mode designed to move passengers anywhere in the world, using electric propulsion to carry passengers through a vacuum/near-vacuum tube for a maximum speed of 1200 km/h. Given this, governments, engineers, researchers, and billionaires have been racing over the past years to obtain the first operational system in the world off the ground and bring it from concept to reality. The paper aimed to maximize the capacity of the Hyperloop’s capsule and identify a suitable design of Hyperloop technology based on the different capacities and speeds of the capsules as well as the assumptions of the initial annual demand. Additionally, significant attention will be paid to the interior design of the capsules in which people travel to make the journey more comfortable and enjoyable. The design will be conducted in AutoCAD and Autodesk Revit models based on the allocation of different components such as capacity, compressor fan, batteries, compressor motor, etc. The Hyperloop is powered by solar panels located on the top of the tube, which will allow the capsule to generate more energy based on its capacity than it needs to run. The optimizing cost of each design of the Hyperloop’s capsule will be considered using an MS Excel sheet. As a result, the Hyperloop capsule with a lower capacity (28 seats) has the highest value of optimizing cost due to the number of acquired capsules (38) compared to 25 capsules and 16 capsules for medium- and high-capacity capsules, respectively. The total annual cost of the Hyperloop’s capsules with different capacities of 28, 40, and 50 seats is EUR 5.6 million, EUR 5.5 million, and EUR 6.2 million, respectively, which is determined through the sum of the purchasing cost, operating cost and maintenance cost of capsules.
Scalable mRNA Machine for Regulatory Approval of Variable Scale between 1000 Clinical Doses to 10 Million Manufacturing Scale Doses
Authors: Hengelbrock Alina , Schmidt Axel , Helgers Heribert , Vetter Florian Lukas , Strube Jochen
Published: March 2023

The production of messenger ribonucleic acid (mRNA) and other biologics is performed primarily in batch mode. This results in larger equipment, cleaning/sterilization volumes, and dead times compared to any continuous approach. Consequently, production throughput is lower and capital costs are relatively high. Switching to continuous production thus reduces the production footprint and also lowers the cost of goods (COG). During process development, from the provision of clinical trial samples to the production plant, different plant sizes are usually required, operating at different operating parameters. To speed up this step, it would be optimal if only one plant with the same equipment and piping could be used for all sizes. In this study, an efficient solution to this old challenge in biologics manufacturing is demonstrated, namely the qualification and validation of a plant setup for clinical trial doses of about 1000 doses and a production scale-up of about 10 million doses. Using the current example of the Comirnaty BNT162b2 mRNA vaccine, the cost-intensive in vitro transcription was first optimized in batch so that a yield of 12 g/L mRNA was achieved, and then successfully transferred to continuous production in the segmented plug flow reactor with subsequent purification using ultra- and diafiltration, which enables the recycling of costly reactants. To realize automated process control as well as real-time product release, the use of appropriate process analytical technology is essential. This will also be used to efficiently capture the product slug so that no product loss occurs and contamination from the fill-up phase is <1%. Further work will focus on real-time release testing during a continuous operating campaign under autonomous operational control. Such efforts will enable direct industrialization in collaboration with appropriate industry partners, their regulatory affairs, and quality assurance. A production scale-operation could be directly supported and managed by data-driven decisions.
Scattering-Point-Guided RPN for Oriented Ship Detection in SAR Images
Authors: Zhang Yipeng , Lu Dongdong , Qiu Xiaolan , Li Fei
Published: March 2023

Ship detection in synthetic aperture radar (SAR) images has attracted widespread attention due to its significance and challenges. In recent years, numerous detectors based on deep learning have achieved good performance in the field of SAR ship detection. However, ship targets of the same type always have various representations in SAR images under different imaging conditions, while different types of ships may have a high degree of similarity, which considerably complicates SAR target recognition. Meanwhile, the ship target in the SAR image is also obscured by background and noise. To address these issues, this paper proposes a novel oriented ship detection method in SAR images named SPG-OSD. First, we propose an oriented two-stage detection module based on the scattering characteristics. Second, to reduce false alarms and missing ships, we improve the performance of the network by incorporating SAR scattering characteristics in the first stage of the detector. A scattering-point-guided region proposal network (RPN) is designed to predict possible key scattering points and make the regression and classification stages of RPN increase attention to the vicinity of key scattering points and reduce attention to background and noise. Third, supervised contrastive learning is introduced to alleviate the problem of minute discrepancies among SAR object classes. Region-of-Interest (RoI) contrastive loss is proposed to enhance inter-class distinction and diminish intra-class variance. Extensive experiments are conducted on the SAR ship detection dataset from the Gaofen-3 satellite, and the experimental results demonstrate the effectiveness of SPG-OSD and show that our method achieves state-of-the-art performance.
Modeling Soil CO2 Efflux in a Subtropical Forest by Combining Fused Remote Sensing Images with Linear Mixed Effect Models
Authors: Ablat Xarapat , Huang Chong , Tang Guoping , Erkin Nurmemet , Sawut Rukeya
Published: March 2023

Monitoring tropical and subtropical forest soil CO2 emission efflux (FSCO2) is crucial for understanding the global carbon cycle and terrestrial ecosystem respiration. In this study, we addressed the challenge of low spatiotemporal resolution in FSCO2 monitoring by combining data fusion and model methods to improve the accuracy of quantitative inversion. We used time series Landsat 8 LST and MODIS LST fusion images and a linear mixed effect model to estimate FSCO2 at watershed scale. Our results show that modeling without random factors, and the use of Fusion LST as the fixed predictor, resulted in 47% (marginal R2 = 0.47) of FSCO2 variability in the Monthly random effect model, while it only accounted for 19% of FSCO2 variability in the Daily random effect model and 7% in the Seasonally random effect model. However, the inclusion of random effects in the model’s parameterization improved the performance of both models. The Monthly random effect model that performed optimally had an explanation rate of 55.3% (conditional R2 = 0.55 and t value > 1.9) for FSCO2 variability and yielded the smallest deviation from observed FSCO2. Our study highlights the importance of incorporating random effects and using Fusion LST as a fixed predictor to improve the accuracy of FSCO2 monitoring in tropical and subtropical forests.
Monthly Ocean Primary Productivity Forecasting by Joint Use of Seasonal Climate Prediction and Temporal Memory
Authors: Xu Lei , Yu Hongchu , Chen Zeqiang , Du Wenying , Chen Nengcheng , Zhang Chong
Published: March 2023

Ocean primary productivity generated by phytoplankton is critical for ocean ecosystems and the global carbon cycle. Accurate ocean primary productivity forecasting months in advance is beneficial for marine management. Previous persistence-based prediction studies ignore the temporal memories of multiple relevant factors and the seasonal forecasting skill drops quickly with increasing lead time. On the other hand, the emerging ensemble climate forecasts are not well considered as new predictability sources of ocean conditions. Here we proposed a joint forecasting model by combining the seasonal climate predictions from ten heterogeneous models and the temporal memories of relevant factors to examine the monthly predictability of ocean productivity from 0.5- to 11.5-month lead times. The results indicate that a total of ~90% and ~20% productive oceans are expected to be skillfully predicted by the combination of seasonal SST predictions and local memory at 0.5- and 4.5-month leads, respectively. The joint forecasting model improves by 10% of the skillfully predicted areas at 6.5-month lead relative to the prediction by productivity persistence. The hybrid data-driven and model-driven forecasting approach improves the predictability of ocean productivity relative to individual predictions, of which the seasonal climate predictions contribute largely to the skill improvement over the equatorial Pacific and Indian Ocean. These findings highlight the advantages of the integration of climate predictions and temporal memory for ocean productivity forecasting and may provide useful seasonal forecasting information for ocean ecosystem management.
Bivariate Landslide Susceptibility Analysis: Clarification, Optimization, Open Software, and Preliminary Comparison
Authors: Li Langping , Lan Hengxing
Published: March 2023

Bivariate data-driven methods have been widely used in landslide susceptibility analysis. However, the names, principles, and correlations of bivariate methods are still confused. In this paper, the names, principles, and correlations of bivariate methods are first clarified based on a comprehensive and in-depth survey. A total of eleven prevalent bivariate methods are identified, nominated, and elaborated in a general framework, constituting a well-structured bivariate method family. We show that all prevalent bivariate methods depend on empirical conditional probabilities of landslide occurrence to calculate landslide susceptibilities, either exclusively or inclusively. It is clarified that those eight “conditional-probability-based” bivariate methods, which exclusively depend on empirical conditional probabilities, are particularly strongly correlated in principle, and therefore are expected to have a very close or even the same performance. It is also suggested that conditional-probability-based bivariate methods apply to a “classification-free” modification, in which factor classifications are avoided and the result is dominated by a single parameter, “bin width”. Then, a general optimization framework for conditional-probability-based bivariate methods, based on the classification-free modification and obtaining optimum results by optimizing the dominant parameter bin width, is proposed. The open software Automatic Landslide Susceptibility Analysis (ALSA) is updated to implement the eight conditional-probability-based bivariate methods and the general optimization framework. Finally, a case study is presented, which confirms the theoretical expectation that different conditional-probability-based bivariate methods have a very close or even the same performance, and shows that optimal bivariate methods perform better than conventional bivariate methods regarding both the prediction rate and the ability to reveal the quasi-continuous varying pattern of sensibilities to landslides for individual predisposing factors. The principles and open software presented in this study provide both theoretical and practical foundations for applications and explorations of bivariate methods in landslide susceptibility analysis.
Comparative Analysis of Striping Noise between FY-3E MWTS-3 and FY-3D MWTS-2
Authors: Mao Jiali , Qin Zhengkun , Li Juan , Liu Guiqing , Huang Jing
Published: March 2023

On 5 July 2021, China launched the world’s first early morning orbit meteorological satellite, which was equipped with China’s newest-generation microwave instrument, the Microwave Temperature Sounder-3 (MWTS-3). MWTS-3 has 17 detection channels, which can detect temperature profiles from near the ground to approximately 2 hPa. However, similar to the newest-generation microwave temperature instruments in other countries, MWTS-3 may also suffer from striping noise. In this paper, a principal component analysis (PCA) is combined with ensemble empirical mode decomposition (EEMD) to extract the striping noise from the brightness temperatures observed by MWTS-3, and a comparative analysis is performed with its predecessor (MWTS-2). This paper analyzes the characteristics of the striping noise of both instruments and its possible impact on their observations. The results show that the striping noise of MWTS-3 has been significantly reduced in most of the 17 channels. In particular, the striping noise of MWTS-3’s channel 3 is reduced by nearly half compared to the channel of MWTS-2 with the same frequency. Furthermore, the interchannel correlations for MWTS-3 are significantly lower than those for MWTS-2. After noise mitigation, the interchannel correlations of both MWTS-2 and MWTS-3 are obviously reduced. In addition, striping noise mitigation can reduce observation errors to some extent, especially for channels above the middle troposphere.
Quantification of Pollutants in Mining Ponds Using a Combination of LiDAR and Geochemical Methods—Mining District of Hiendelaencina, Guadalajara (Spain)
Authors: Martín-Crespo Tomás , Gomez-Ortiz David , Pryimak Vladyslava , Martín-Velázquez Silvia , Rodríguez-Santalla Inmaculada , Ropero-Szymañska Nikoletta , José Cristina de Ignacio-San
Published: March 2023

More than twenty years after the last mining operations were completed in the Hiendelaencina Mining District, it is necessary to carry out a geochemical characterisation of the tailings stored in two contiguous mine ponds. Both have significant amounts of quartz, siderite, barite and muscovite and show significant contents of As, Ba, Pb, Sb and Zn. The tailings show alkaline pH and low electrical conductivity values, which support the visual observation that rules out acid drainage into the environment. The comparison of the National Topographic Map of 1954 with LiDAR data from 2014 has allowed estimating the volume of abandoned waste. Based on the volume of slurry and its average density, the total tonnage of pollutants has been estimated at 279 ± 9 t stored in Pond North and 466 ± 11 t stored in Pond South. Although these are significant quantities that pose a risk to the environment and nearby populations, they are lower than those present in other Spanish districts, such as the Iberian Pyrite Belt or Cartagena-La Unión. The combined use of LiDAR data, aerial imagery and geochemical methods has proven to be very useful for the estimation of the volume of pollutants stored in mine ponds.
Smart Spread Spectrum Modulated Tags for Detection of Vulnerable Road Users with Automotive Radar
Authors: Lazaro Antonio , Lazaro Marc , Villarino Ramon , Girbau David
Published: March 2023

In recent years, there has been a significant increase in the number of collisions between vehicles and vulnerable road users such as pedestrians, cyclists, road workers and more recently scooter riders, especially in urban streets. This work studies the feasibility of enhancing the detection of these users by means of CW radars because they have a low radar cross section. Since the speed of these users is usually low, they can be confused with clutter due to the presence of large objects. To this end, this paper proposes, for the first time, a method based on a spread spectrum radio communication between vulnerable road users and the automotive radar consisting of modulating a backscatter tag, placed on the user. In addition, it is compatible with low-cost radars that use different waveforms such as CW, FSK or FMCW, and hardware modifications are not required. The prototype that has been developed is based on a commercial monolithic microwave integrated circuit (MMIC) amplifier connected between two antennas, which is modulated by switching its bias. Experimental results with a scooter, under static and moving conditions, using a low-power Doppler radar at a 24 GHz band compatible with blind spot radars, are provided.
Accelerations Recorded by Simple Inertial Measurement Units with Low Sampling Frequency Can Differentiate between Individuals with and without Knee Osteoarthritis: Implications for Remote Health Care
Authors: Ghaffari Arash , Rasmussen John , Kold Søren , Lauritsen Rikke Emilie Kildahl , Kappel Andreas , Rahbek Ole
Published: March 2023

Determining the presence and severity of knee osteoarthritis (OA) is a valuable application of inertial measurement units (IMUs) in the remote monitoring of patients. This study aimed to employ the Fourier representation of IMU signals to differentiate between individuals with and without knee OA. We included 27 patients with unilateral knee osteoarthritis (15 females) and 18 healthy controls (11 females). Gait acceleration signals were recorded during overground walking. We obtained the frequency features of the signals using the Fourier transform. The logistic LASSO regression was employed on the frequency domain features as well as the participant’s age, sex, and BMI to distinguish between the acceleration data from individuals with and without knee OA. The model’s accuracy was estimated by 10-fold cross-validation. The frequency contents of the signals were different between the two groups. The average accuracy of the classification model using the frequency features was 0.91 ± 0.01. The distribution of the selected features in the final model differed between patients with different severity of knee OA. In this study, we demonstrated that using logistic LASSO regression on the Fourier representation of acceleration signals can accurately determine the presence of knee OA.
Evaluation of Different Pressure-Based Foot Contact Event Detection Algorithms across Different Slopes and Speeds
Authors: Blades Samuel , Marriott Hunter , Hundza Sandra , Honert Eric C. , Stellingwerff Trent , Klimstra Marc
Published: March 2023

If validated, in-shoe pressure measuring technology allows for the field-based quantification of running gait, including kinematic and kinetic measures. Different algorithmic methods have been proposed to determine foot contact events from in-shoe pressure insole systems, however, these methods have not been evaluated for accuracy, reliability against a gold standard using running data across different slopes, and speeds. Using data from a plantar pressure measurement system, seven different foot contact event detection algorithms based on pressure signals (pressure sum) were compared to vertical ground reaction force data collected from a force instrumented treadmill. Subjects ran on level ground at 2.6, 3.0, 3.4, and 3.8 m/s, six degrees (10.5%) inclined at 2.6, 2.8, and 3.0 m/s, and six degrees declined at 2.6, 2.8, 3.0, and 3.4 m/s. The best performing foot contact event detection algorithm showed maximal mean absolute errors of only 1.0 ms and 5.2 ms for foot contact and foot off, respectively, on level grade, when compared to a 40 N ascending and descending force threshold from the force treadmill data. Additionally, this algorithm was unaffected by grade and had similar levels of errors across all grades.
Interaction with a Virtual Coach for Active and Healthy Ageing
Authors: McTear Michael , Jokinen Kristiina , Alam Mirza Mohtashim , Saleem Qasid , Napolitano Giulio , Szczepaniak Florian , Hariz Mossaab , Chollet Gérard , Lohr Christophe , Boudy Jérôme , Azimi Zohre , Roelen Sonja Dana , Wieching Rainer
Published: March 2023

Since life expectancy has increased significantly over the past century, society is being forced to discover innovative ways to support active aging and elderly care. The e-VITA project, which receives funding from both the European Union and Japan, is built on a cutting edge method of virtual coaching that focuses on the key areas of active and healthy aging. The requirements for the virtual coach were ascertained through a process of participatory design in workshops, focus groups, and living laboratories in Germany, France, Italy, and Japan. Several use cases were then chosen for development utilising the open-source Rasa framework. The system uses common representations such as Knowledge Bases and Knowledge Graphs to enable the integration of context, subject expertise, and multimodal data, and is available in English, German, French, Italian, and Japanese.
Posthoc Interpretability of Neural Responses by Grouping Subject Motor Imagery Skills Using CNN-Based Connectivity
Authors: Collazos-Huertas Diego Fabian , Álvarez-Meza Andrés Marino , Cárdenas-Peña David Augusto , Castaño-Duque Germán Albeiro , Castellanos-Domínguez César Germán
Published: March 2023

Motor Imagery (MI) refers to imagining the mental representation of motor movements without overt motor activity, enhancing physical action execution and neural plasticity with potential applications in medical and professional fields like rehabilitation and education. Currently, the most promising approach for implementing the MI paradigm is the Brain-Computer Interface (BCI), which uses Electroencephalogram (EEG) sensors to detect brain activity. However, MI-BCI control depends on a synergy between user skills and EEG signal analysis. Thus, decoding brain neural responses recorded by scalp electrodes poses still challenging due to substantial limitations, such as non-stationarity and poor spatial resolution. Also, an estimated third of people need more skills to accurately perform MI tasks, leading to underperforming MI-BCI systems. As a strategy to deal with BCI-Inefficiency, this study identifies subjects with poor motor performance at the early stages of BCI training by assessing and interpreting the neural responses elicited by MI across the evaluated subject set. Using connectivity features extracted from class activation maps, we propose a Convolutional Neural Network-based framework for learning relevant information from high-dimensional dynamical data to distinguish between MI tasks while preserving the post-hoc interpretability of neural responses. Two approaches deal with inter/intra-subject variability of MI EEG data: (a) Extracting functional connectivity from spatiotemporal class activation maps through a novel kernel-based cross-spectral distribution estimator, (b) Clustering the subjects according to their achieved classifier accuracy, aiming to find common and discriminative patterns of motor skills. According to the validation results obtained on a bi-class database, an average accuracy enhancement of 10% is achieved compared to the baseline EEGNet approach, reducing the number of “poor skill” subjects from 40% to 20%. Overall, the proposed method can be used to help explain brain neural responses even in subjects with deficient MI skills, who have neural responses with high variability and poor EEG-BCI performance.
Experimental Analysis of Various Blockage Performance for LiDAR Sensor Cleaning Evaluation
Authors: Son SungHo , Lee WoongSu , Jung HyunGi , Lee JungKi , Kim ChaRyung , Lee HyunWoo , Cho SeoungWoo , Jang JeongAh , Lee Michael , Ryu Han-Cheol
Published: March 2023

Autonomous driving includes recognition, judgment, and control technologies, and is implemented using sensors such as cameras, LiDAR, and radar. However, recognition sensors are exposed to the outside environment and their performance may deteriorate because of the presence of substances that interfere with vision, such as dust, bird droppings, and insects, during operation. Research on sensor cleaning technology to solve this performance degradation has been limited. This study used various types and concentrations of blockage and dryness to demonstrate approaches to the evaluation of cleaning rates for selected conditions that afford satisfactory results. To determine the effectiveness of washing, the study used the following criteria: washer, 0.5 bar/s and air, 2 bar/s, with 3.5 g being used three times to test the LiDAR window. The study found that blockage, concentration, and dryness are the most important factors, and in that order. Additionally, the study compared new forms of blockage, such as those caused by dust, bird droppings, and insects, with standard dust that was used as a control to evaluate the performance of the new blockage types. The results of this study can be used to conduct various sensor cleaning tests and ensure their reliability and economic feasibility.
BAuth-ZKP—A Blockchain-Based Multi-Factor Authentication Mechanism for Securing Smart Cities
Authors: Ahmad Md. Onais , Tripathi Gautami , Siddiqui Farheen , Alam Mohammad Afshar , Ahad Mohd Abdul , Akhtar Mohd Majid , Casalino Gabriella
Published: March 2023

The overwhelming popularity of technology-based solutions and innovations to address day-to-day processes has significantly contributed to the emergence of smart cities. where millions of interconnected devices and sensors generate and share huge volumes of data. The easy and high availability of rich personal and public data generated in these digitalized and automated ecosystems renders smart cities vulnerable to intrinsic and extrinsic security breaches. Today, with fast-developing technologies, the classical username and password approaches are no longer adequate to secure valuable data and information from cyberattacks. Multi-factor authentication (MFA) can provide an effective solution to minimize the security challenges associated with legacy single-factor authentication systems (both online and offline). This paper identifies and discusses the role and need of MFA for securing the smart city ecosystem. The paper begins by describing the notion of smart cities and the associated security threats and privacy issues. The paper further provides a detailed description of how MFA can be used for securing various smart city entities and services. A new concept of blockchain-based multi-factor authentication named “BAuth-ZKP” for securing smart city transactions is presented in the paper. The concept focuses on developing smart contracts between the participating entities within the smart city and performing the transactions with zero knowledge proof (ZKP)-based authentication in a secure and privacy-preserved manner. Finally, the future prospects, developments, and scope of using MFA in smart city ecosystem are discussed.
Elastic Provisioning of Network and Computing Resources at the Edge for IoT Services
Authors: Cardoso Patrícia , Moura José , Marinheiro Rui Neto
Published: March 2023

The fast growth of Internet-connected embedded devices demands new system capabilities at the network edge, such as provisioning local data services on both limited network and computational resources. The current contribution addresses the previous problem by enhancing the usage of scarce edge resources. It designs, deploys, and tests a new solution that incorporates the positive functional advantages offered by software-defined networking (SDN), network function virtualization (NFV), and fog computing (FC). Our proposal autonomously activates or deactivates embedded virtualized resources, in response to clients’ requests for edge services. Complementing existing literature, the obtained results from extensive tests on our programmable proposal show the superior performance of the proposed elastic edge resource provisioning algorithm, which also assumes an SDN controller with proactive OpenFlow behavior. According to our results, the maximum flow rate for the proactive controller is 15% higher; the maximum delay is 83% smaller; and the loss is 20% smaller compared to when the non-proactive controller is in operation. This improvement in flow quality is complemented by a reduction in control channel workload. The controller also records the time duration of each edge service session, which can enable the accounting of used resources per session.
Taking a Load Off: User Perceptions of Smart Offloading Walkers for Diabetic Foot Ulcers Using the Technology Acceptance Model
Authors: Finco M. G. , Cay Gozde , Lee Myeounggon , Garcia Jason , Salazar Elia , Tan Tze-Woei , Armstrong David G. , Najafi Bijan
Published: March 2023

People with diabetic foot ulcers (DFUs) are commonly prescribed offloading walkers, but inadequate adherence to prescribed use can be a barrier to ulcer healing. This study examined user perspectives of offloading walkers to provide insight on ways to help promote adherence. Participants were randomized to wear: (1) irremovable, (2) removable, or (3) smart removable walkers (smart boot) that provided feedback on adherence and daily walking. Participants completed a 15-item questionnaire based on the Technology Acceptance Model (TAM). Spearman correlations assessed associations between TAM ratings with participant characteristics. Chi-squared tests compared TAM ratings between ethnicities, as well as 12-month retrospective fall status. A total of 21 adults with DFU (age 61.5 ± 11.8 years) participated. Smart boot users reported that learning how to use the boot was easy (ρ =−0.82, p≤ 0.001). Regardless of group, people who identified as Hispanic or Latino, compared to those who did not, reported they liked using the smart boot (p = 0.05) and would use it in the future (p = 0.04). Non-fallers, compared to fallers, reported the design of the smart boot made them want to wear it longer (p = 0.04) and it was easy to take on and off (p = 0.04). Our findings can help inform considerations for patient education and design of offloading walkers for DFUs.
Numerical Study of Particle Separation through Integrated Multi-Stage Surface Acoustic Waves and Modulated Driving Signals
Authors: Jiang Yingqi , Chen Jin , Xuan Weipeng , Liang Yuhao , Huang Xiwei , Cao Zhen , Sun Lingling , Dong Shurong , Luo Jikui
Published: March 2023

The manipulation of biomedical particles, such as separating circulating tumor cells from blood, based on standing surface acoustic wave (SSAW) has been widely used due to its advantages of label-free approaches and good biocompatibility. However, most of the existing SSAW-based separation technologies are dedicated to isolate bioparticles in only two different sizes. It is still challenging to fractionate various particles in more than two different sizes with high efficiency and accuracy. In this work, to tackle the problems of low efficiency for multiple cell particle separation, integrated multi-stage SSAW devices with different wavelengths driven by modulated signals were designed and studied. A three-dimensional microfluidic device model was proposed and analyzed using the finite element method (FEM). In addition, the effect of the slanted angle, acoustic pressure, and the resonant frequency of the SAW device on the particle separation were systemically studied. From the theoretical results, the separation efficiency of three different size particles based on the multi-stage SSAW devices reached 99%, which was significantly improved compared with conventional single-stage SSAW devices.
Temporal Neural Network Framework Adaptation in Reconfigurable Intelligent Surface-Assisted Wireless Communication
Authors: Sejan Mohammad Abrar Shakil , Rahman Md Habibur , Aziz Md Abdul , You Young-Hwan , Song Hyoung-Kyu
Published: March 2023

A reconfigurable intelligent surface (RIS) has potential for enhancing the performance of wireless communication. A RIS includes cheap passive elements, and the reflecting of signals can be controlled to a specific location of users. In addition, machine learning (ML) techniques are efficient in solving complex problems without explicit programming. Data-driven approaches are efficient in predicting the nature of any problem and can provide a desirable solution. In this paper, we propose a temporal convolutional network (TCN)-based model for RIS-based wireless communication. The proposed model consists of four TCN layers, one fully connected layer, one ReLU layer, and lastly a classification layer. In the input, we provide data in the form of complex numbers to map a specified label under QPSK and BPSK modulation. We consider 2×2 and 4×4 MIMO communication using one base station and two single-antenna users. We have considered three types of optimizers to evaluate the TCN model. For benchmarking, long short-term memory (LSTM) and without ML are compared. The simulation results are conducted in terms of the bit error rate and symbol error rate which show the effectiveness of the proposed TCN model.
Cooktop Sensing Based on a YOLO Object Detection Algorithm
Authors: Azurmendi Iker , Zulueta Ekaitz , Lopez-Guede Jose Manuel , Azkarate Jon , González Manuel
Published: March 2023

Deep Learning (DL) has provided a significant breakthrough in many areas of research and industry. The development of Convolutional Neural Networks (CNNs) has enabled the improvement of computer vision-based techniques, making the information gathered from cameras more useful. For this reason, recently, studies have been carried out on the use of image-based DL in some areas of people’s daily life. In this paper, an object detection-based algorithm is proposed to modify and improve the user experience in relation to the use of cooking appliances. The algorithm can sense common kitchen objects and identify interesting situations for users. Some of these situations are the detection of utensils on lit hobs, recognition of boiling, smoking and oil in kitchenware, and determination of good cookware size adjustment, among others. In addition, the authors have achieved sensor fusion by using a cooker hob with Bluetooth connectivity, so it is possible to automatically interact with it via an external device such as a computer or a mobile phone. Our main contribution focuses on supporting people when they are cooking, controlling heaters, or alerting them with different types of alarms. To the best of our knowledge, this is the first time a YOLO algorithm has been used to control the cooktop by means of visual sensorization. Moreover, this research paper provides a comparison of the detection performance among different YOLO networks. Additionally, a dataset of more than 7500 images has been generated and multiple data augmentation techniques have been compared. The results show that YOLOv5s can successfully detect common kitchen objects with high accuracy and fast speed, and it can be employed for realistic cooking environment applications. Finally, multiple examples of the identification of interesting situations and how we act on the cooktop are presented.
Facile Separation of Cu2+ from Water by Novel Sandwich NaY Zeolite Adsorptive Membrane
Authors: Guo Yuexin , Zhang Xu , Xie Yuesheng , Hu Yacong , Jia Zhiqian , Ma Yafei , Wang Xin
Published: March 2023

Polyethersulfone-sulfonated polyethersulfone (PES-SPES)/NaY zeolite/nylon sandwich structure membranes were prepared and used to adsorb Cu2+ from water. The adsorption kinetics, adsorption isotherm, dynamic adsorption experiment, and reusability were discussed. The experimental data showed that the Langmuir isotherm model, Dubinin–Radushkevich (D-R) isotherm model, and the pseudo-first-order kinetic model can well represent the adsorption of Cu2+ on the membrane, indicating an ion exchange mechanism, with the maximum adsorption capacity of 111.25 mg·g−1. Repeatability experiments show that the sandwich film still has good adsorption performance after five times of adsorption and desorption. The as-prepared membrane showed considerable separation performance in removing Cu2+ from aspirin solution, providing a feasible method to remove heavy metals from drugs.