bluuebunny/crossref_metadata_2025 huggingface dataset:
Artificial Intelligence-Empowered 3D and 4D Printing Technologies toward Smarter Biomedical Materials and Approaches
Authors: Pugliese Raffaele , Regondi Stefano
Published: July 2022

In the last decades, 3D printing has played a crucial role as an innovative technology for tissue and organ fabrication, patient-specific orthoses, drug delivery, and surgical planning. However, biomedical materials used for 3D printing are usually static and unable to dynamically respond or transform within the internal environment of the body. These materials are fabricated ex situ, which involves first printing on a planar substrate and then deploying it to the target surface, thus resulting in a possible mismatch between the printed part and the target surfaces. The emergence of 4D printing addresses some of these drawbacks, opening an attractive path for the biomedical sector. By preprogramming smart materials, 4D printing is able to manufacture structures that dynamically respond to external stimuli. Despite these potentials, 4D printed dynamic materials are still in their infancy of development. The rise of artificial intelligence (AI) could push these technologies forward enlarging their applicability, boosting the design space of smart materials by selecting promising ones with desired architectures, properties, and functions, reducing the time to manufacturing, and allowing the in situ printing directly on target surfaces achieving high-fidelity of human body micro-structures. In this review, an overview of 4D printing as a fascinating tool for designing advanced smart materials is provided. Then will be discussed the recent progress in AI-empowered 3D and 4D printing with open-loop and closed-loop methods, in particular regarding shape-morphing 4D-responsive materials, printing on moving targets, and surgical robots for in situ printing. Lastly, an outlook on 5D printing is given as an advanced future technique, in which AI will assume the role of the fifth dimension to empower the effectiveness of 3D and 4D printing for developing intelligent systems in the biomedical sector and beyond.
Crystallinity and Gas Permeability of Poly (Lactic Acid)/Starch Nanocrystal Nanocomposite
Authors: Sharafi Zamir Somayeh , Fathi Babak , Ajji Abdellah , Robert Mathieu , Elkoun Said
Published: July 2022

The present work seeks to determine the impact of weight percentage (wt%) of grafted starch nanocrystals (g-SNCs) on the oxygen and water vapour permeability of poly (lactic acid), PLA. Changes in the oxygen and water vapour permeability of PLA due to changes in PLA’s crystalline structures and lamellar thickness were quantified. To this end, 3, 5, and 7 wt% of g-SNC nanoparticles were blended with PLA using the solvent casting method in order to study impact of g-SNC nanoparticles on crystallization behaviour, long spacing period, melting behavior, and oxygen and water barrier properties of PLA nanocomposites. This was achieved by wide-angle X-ray diffraction (WAXD), small-angle X-ray diffraction (SAXD), differential scanning calorimetry (DSC), and oxygen and water vapour permeability machine. The results of the WAXD and SAXD analysis show that the addition of 5 wt% g-SNC in PLA induces α crystal structure at a lower crystallization time, while it significantly increases the α crystal thickness of PLA, in comparison to neat PLA. However, when g-SNC concentrations were altered (i.e., 3 or 7 wt%), the crystallization time was found to increase due to the thermodynamic barrier of crystallization. Finally, the oxygen and water vapour permeability of PLA/SNC-g-LA (5 wt%) nanocomposite film were found to be reduced by ∼70% and ~50%, respectively, when compared to the neat PLA film. This can lead to the development of PLA nanocomposites with high potential for applications in food packaging.
Evaluation of the Effect of Recycled Polypropylene as Fine Aggregate Replacement on the Strength Performance and Chloride Penetration of Mortars
Authors: Alrshoudi Fahed , Abdus Samad Ubair , Alothman Othman Y.
Published: July 2022

Nowadays, the re-use and recycling of industrial wastes to reduce the environmental impact and landfill problems are the main concerns of researchers. Plastics are one of the main waste materials worldwide, with considerable impacts on health and environmental conditions. Recycling plastic wastes in the concrete industry is one of the adopted ways to reduce such impact and increase the economic recyclability of plastics. In this study, the utilization of recycled polypropylene (rPP) as a fine aggregate in the preparation of cement mortars was evaluated. The river sand was replaced with 10, 20, 30, 40, and 50%, volumes of rPP. The results showed that the inclusion of rPP reduced the mortar’s workability and fresh density. Fresh density dropped from 11% to 35% as the rPP content increased. Furthermore, the compressive strength at early and late age was significantly influenced by the rPP content. At 28 days of curing age, the results showed that the inclusion of 50% of rPP in the mortar matrix led to a drop in the compression strength from 40 MPa to 10 MPa. A similar trend of results was obtained for the flexural (from 8.3 MPa to 2.9 MPa) and tensile strengths (from 3.4 MPa to 1.21 MPa). The chloride ion penetration went through a maximum of 5000 Coulombs between 10% and 50 % of rPP. Therefore, it can be concluded that the use of 10% of rPP as a river sand replacement can achieve acceptable strength (25 MPa) for several applications in the construction industry.
Tungsten Oxide Modified V2O5-Sb2O3/TiO2 Monolithic Catalyst: NH3-SCR Activity and Sulfur Resistance
Authors: Liu Liping , Wu Xiaodong , Ma Yue , Wang Jinyi , Ran Rui , Si Zhichun , Weng Duan
Published: July 2022

In this study, a V2O5-Sb2O3/TiO2 monolithic catalyst was modified by introducing WO3. The WO3-modified catalyst exhibited enhanced catalytic activity in the measuring temperature range of 175–320 °C. The changes in dispersion of vanadia species were investigated by ultraviolet-visible (UV-Vis) spectroscopy and H2 temperature-programmed reduction (H2-TPR). A durability test was conducted in a wet SO2-containing atmosphere at 220 °C for 25 h. The sulfate deposition was estimated by temperature-programmed decomposition (TPDC) of sulfates, thermo-gravimetric (TG) analysis, and temperature-programmed desorption (TPD) of NH3. Isothermal SO2 oxidation and temperature-programmed surface reaction (TPSR) of NH4HSO4 with NO were performed. Based on these characterizations, effects of WO3 modification on the sulfate tolerance of the catalyst were explored.
Study on Wellbore Stability Evaluation Method of New Drilled Well in Old Reservoir
Authors: Wang Shiyue , Liao Gaolong , Zhang Zhenwei , Wang Xiaoyun
Published: July 2022

The borehole wall stability of depleted reservoirs is a key problem that restricts the deeper potential exploitation of old oilfields. Based on these, according to the seepage and elastic mechanics theory of porous media, combined with laboratory rock mechanics experiments, the dynamic evaluation model of geomechanical parameters of the old reservoir is established, and on this basis, the evaluation method of wellbore stability of new wells in the old reservoir is established, and the changing laws and influencing factors of the wellbore stability of the old reservoir are quantitatively evaluated. The research shows that reservoir pressure, reservoir porosity, effective stress coefficient, strength, in situ magnitude, and direction were all changed due to long-term exploitation; both collapse and fracture pressure decreased, the loss risk of drilling fluid was increased, and safe drilling azimuth was changed. The results can guide reasonable choices such as well position, track and mud density, and strengthening sealing while drilling in order to ensure wellbore stability effectively.
Analysis of Solar Energy Development Strategies for a Successful Energy Transition in the UAE
Authors: Salimi Mohsen , Hosseinpour Morteza , N.Borhani Tohid
Published: July 2022

The United Arab Emirates (UAE) is making significant progress in improving its economy by attracting tourists and trade. In the short run, however, economic activity will continue to be more based on oil, natural gas, and related industries. Rising demand for natural gas for power plants and industrial users, such as petrochemicals and steelmakers, has made the UAE a net gas importer, prompting the country to launch multibillion-dollar investments in nuclear and renewable energy. This study addresses the trend of solar energy production and consumption in the UAE. The strengths, weaknesses, opportunities, and threats (SWOT) analysis was performed on the different types of solar energy in the UAE, and some strategies were developed based on it. The SWOT analysis reveals promising strategies for the UAE’s solar energy transition that would reduce fossil fuel demand, mitigate greenhouse gas emissions through solar energy production, and transform the UAE into the carbon market centre of the Gulf Cooperation Council countries.
Exploring Health and Premature Mortality of Wheelchair Users from a Medical and a Greek-Orthodox Perspective
Authors: Papanikolaou Konstantinos , Andreopoulos Andreas , Chatzitolios Apostolos , Gianasmidis Athanasios , Goble Colin
Published: July 2022

Wheelchair users have a lower life expectancy compared to the general population. This project aims to provide a deeper understanding of the interaction between physical, psychological, social, and spiritual factors that affect the mortality and health of this target group. The methods used were qualitative. Data from phenomenological interviews, questionnaires, medical reports, and existing literature were composed to develop a grounded theory depicting the overall health of wheelchair users. The research team explored death causes, risk factors of premature death, and contributors that affect risk factors. In the discussion, we explored and analyzed specific patterns of interaction of the factors in the data and other research projects. Moreover, we attempted to analyze these patterns through the viewpoint of the Greek-Orthodox tradition. In general, the project confirmed previous research findings. The most common death causes seemed to be heart attacks and cancer. These seemed to be affected mainly by obesity, physical inactivity, unhealthy lifestyle, and inadequate preventive healthcare. Spiritual practices based on the Greek-Orthodox tradition are suggested as ways of management. The conclusions can be useful not only to health and social care professionals and clerics, but also wheelchair users themselves.
Multiscale Superpixel-Based Fine Classification of Crops in the UAV-Based Hyperspectral Imagery
Authors: Tian Shuang , Lu Qikai , Wei Lifei
Published: July 2022

As an effective approach to obtaining agricultural information, the remote sensing technique has been applied in the classification of crop types. The unmanned aerial vehicle (UAV)-manned hyperspectral sensors provide imagery with high spatial and high spectral resolutions. Moreover, the detailed spatial information, as well as abundant spectral properties of UAV-manned hyperspectral imagery, opens a new avenue to the fine classification of crops. In this manuscript, multiscale superpixel-based approaches are proposed for the fine identification of crops in the UAV-manned hyperspectral imagery. Specifically, the multiscale superpixel segmentation is performed and a series of superpixel maps can be obtained. Then, the multiscale information is integrated into image classification by two strategies, namely pre-processing and post-processing. For the pre-processing strategy, the superpixel is regarded as the minimum unit for image classification, whose feature is obtained by using the average of spectral values of pixels within it. At each scale, the classification is performed on the basis of the superpixel. Then, the multiscale classification results are combined to generate the final map. For the post-processing strategy, the pixel-wise classification is implemented to obtain the label and posterior probabilities of each pixel. Subsequently, the superpixel-based voting is conducted at each scale, and these obtained voting results are fused to generate the multiscale voting result. To evaluate the effectiveness of the proposed approaches, three open-sourced hyperspectral UAV-manned datasets are employed in the experiments. Meanwhile, seven training sets with different numbers of labeled samples and two classifiers are taken into account for further analysis. The results demonstrate that the multiscale superpixel-based approaches outperform the single-scale approaches. Meanwhile, the post-processing strategy is superior to the pre-processing strategy in terms of higher classification accuracies in all the datasets.
Improving Satellite Retrieval of Coastal Aquaculture Pond by Adding Water Quality Parameters
Authors: Hou Yuxuan , Zhao Gang , Chen Xiaohong , Yu Xuan
Published: July 2022

Coastal aquaculture is an important supply of animal proteins for human consumption, which is expanding globally. Meanwhile, extensive aquaculture may increase nutrient loadings and environmental concerns along the coast. Accurate information on aquaculture pond location is essential for coastal management. Traditional methods use morphological parameters to characterize the geometry of surface waters to differentiate artificially constructed conventional aquaculture ponds from other water bodies. However, there are other water bodies with similar morphology (e.g., saltworks, rice fields, and small reservoirs) that are difficult to distinguish from aquaculture ponds, causing a lot of omission/commissioning errors in areas with complex land-use types. Here, we develop an extraction method with shape and water quality parameters to map aquaculture ponds, including three steps: (1) Sharpen normalized difference water index to detect and binarize water pixels by the Otsu method; (2) Connect independent water pixels into water objects through the four-neighbor connectivity algorithm; and (3) Calculate the shape features and water quality features of water objects and input them into the classifier for supervised classification. We selected eight sites along the coast of China to evaluate the accuracy and generalization of our method in an environment with heterogeneous pond morphology and landscape. The results showed that six transfer characteristics including water quality characteristics improved the accuracy of distinguishing aquaculture ponds from salt pans, rice fields, and wetland parks, which typically had F1 scores > 85%. Our method significantly improved extraction efficiency on average, especially when aquaculture ponds are mixed with other morphological similar water bodies. Our identified area was in agreement with statistics data of 12 coastal provinces in China. In addition, our approach can effectively improve water objects when high-resolution remote sensing images are unavailable. This work was applied to open-source remote sensing imagery and has the potential to extract long-term series and large-scale aquaculture ponds globally.
Reflective Tomography Lidar Image Reconstruction for Long Distance Non-Cooperative Target
Authors: Guo Rui , Jiang Zheyi , Jin Zhihan , Zhang Zhao , Zhang Xinyuan , Guo Liang , Hu Yihua
Published: July 2022

In the long-distance space target detection, the technique of laser reflection tomography (LRT) shows great power and attracts more attention for further study and real use. However, space targets are often non-cooperative, and normally a 360° complete view of reflection projections cannot be obtained. Therefore, this article firstly introduces an improved LRT system design with more advanced laser equipment for long-distance non-cooperative detection to ensure the high quality of the lidar beam and the lidar projection data. Then, the LRT image reconstruction is proposed and focused on the laser image reconstruction method utilizing the total variation (TV) minimization approach based on the sparse algebraic reconstruction technique (ART) model, in order to reconstruct the laser image in a sparse or incomplete view of projections. At last, comparative experiments with the system are performed to validate the advantages of this method with the LRT system. In both near and far field experiments, the effectiveness and superiority of the proposed method are verified for different types of projection data through comparison to typical methods.
A New Method for Estimating Soil Fertility Using Extreme Gradient Boosting and a Backpropagation Neural Network
Authors: Peng Yiping , Liu Zhenhua , Lin Chenjie , Hu Yueming , Zhao Li , Zou Runyan , Wen Ya , Mao Xiaoyun
Published: July 2022

Soil fertility affects crop yield and quality. A quick, accurate evaluation of soil fertility is crucial for agricultural production. Few satellite image-based evaluation studies have quantified soil fertility during the crop growth period. Therefore, this study proposes a new approach to the quantitative evaluation of soil fertility. Firstly, the optimal crop spectral variables were selected using the integration of an extreme gradient boosting (XGBoost) algorithm with variance inflation factor (VIF). Then, based on the optimal crop spectral variables where the red-edge indices were introduced for the first time, the estimation models were developed using the backpropagation neural network (BPNN) algorithm to assess soil fertility. The model was finally adopted to map the soil fertility using Sentinel-2 imagery. This study was performed in the Conghua District of Guangzhou, Guangdong Province, China. The results of our research are as follows: (1) five crop spectral variables (inverted red-edge chlorophyll index (IRECI), chlorophyll vegetation index (CVI), normalized green-red difference index (NGRDI), red-edge position (REP), and triangular greenness index (TGI)) were the optimal variables. (2) The BPNN model established with optimal variables provided reliable estimates of soil fertility, with the determination coefficient (R2) of 0.66 and a root mean square error (RMSE) of 0.17. A nonlinear relation was found between soil fertility and the optimal crop spectral variables. (3) The BPNN model provides the potential for soil fertility mapping using Sentinel-2 images, with an R2 of 0.62 and an RMSE of 0.09 for the measured and estimated results. This study suggests that the proposed method is suitable for the estimation of soil fertility in paddy fields.
Improving Image Clustering through Sample Ranking and Its Application to Remote Sensing Images
Authors: Li Qinglin , Qiu Guoping
Published: July 2022

Image clustering is a very useful technique that is widely applied to various areas, including remote sensing. Recently, visual representations by self-supervised learning have greatly improved the performance of image clustering. To further improve the well-trained clustering models, this paper proposes a novel method by first ranking samples within each cluster based on the confidence in their belonging to the current cluster and then using the ranking to formulate a weighted cross-entropy loss to train the model. For ranking the samples, we developed a method for computing the likelihood of samples belonging to the current clusters based on whether they are situated in densely populated neighborhoods, while for training the model, we give a strategy for weighting the ranked samples. We present extensive experimental results that demonstrate that the new technique can be used to improve the state-of-the-art image clustering models, achieving accuracy performance gains ranging from 2.1% to 15.9%. Performing our method on a variety of datasets from remote sensing, we show that our method can be effectively applied to remote sensing images.
The Deep Convolutional Neural Network Role in the Autonomous Navigation of Mobile Robots (SROBO)
Authors: Sadeghi Esfahlani Shabnam , Sanaei Alireza , Ghorabian Mohammad , Shirvani Hassan
Published: July 2022

The ability to navigate unstructured environments is an essential task for intelligent systems. Autonomous navigation by ground vehicles requires developing an internal representation of space, trained by recognizable landmarks, robust visual processing, computer vision and image processing. A mobile robot needs a platform enabling it to operate in an environment autonomously, recognize the objects, and avoid obstacles in its path. In this study, an open-source ground robot called SROBO was designed to accurately identify its position and navigate certain areas using a deep convolutional neural network and transfer learning. The framework uses an RGB-D MYNTEYE camera, a 2D laser scanner and inertial measurement units (IMU) operating through an embedded system capable of deep learning. The real-time decision-making process and experiments were conducted while the onboard signal processing and image capturing system enabled continuous information analysis. State-of-the-art Real-Time Graph-Based SLAM (RTAB-Map) was adopted to create a map of indoor environments while benefiting from deep convolutional neural network (Deep-CNN) capability. Enforcing Deep-CNN improved the performance quality of the RTAB-Map SLAM. The proposed setting equipped the robot with more insight into its surroundings. The robustness of the SROBO increased by 35% using the proposed system compared to the conventional RTAB-Map SLAM.
In Vivo Contrast Imaging of Rat Heart with Carbon Dioxide Foam
Authors: Karalko Anton , Keša Peter , Jelínek Frantisek , Šefc Luděk , Ježek Jan , Zemánek Pavel , Grus Tomáš
Published: July 2022

Widely used classical angiography with the use of iodine contrast agents is highly problematic, particularly in patients with diabetes mellitus, cardiac and pulmonary diseases, or degree III or IV renal insufficiency. Some patients may be susceptible to allergic reaction to the iodine contrast substance. The intravenous injection of a bolus of CO2 (negative contrast) is an alternative method, which is, however, currently only used for imaging blood vessels of the lower limbs. The aim of our project was to design and test on an animal model a methodology for injecting the CO2 foam which would minimize the possibility of embolization of the brain tissue and heart infarction, leading to their damage. This is important research for the further promotion of the use of CO2, which is increasingly important for endovascular diagnosis and treatment, because carbon-dioxide-related complications are extremely rare. CO2 foam was prepared by the rapid mixing in a 2:1 ratio of CO2 and fetal bovine serum (FBS)-enriched Dulbecco’s Modified Eagle Medium (DMEM). Freshly prepared CO2 foam was administered into the catheterized rat tail vein or cannulated rat abdominal aorta and inferior vena cava (IVC). CO2 foam was compared with commercially available microbubbles (lipid shell/gas core). The rat heart in its parasternal long axis was imaged in B-Mode and Non-linear Contrast Mode before/during and after the contrast administration. Samples of the brain, heart and lungs were collected and subjected to histological examination. The non-linear contrast imaging method enables the imaging of micron-sized gas microbubbles inside a rat heart. The significantly shorter lifetime of the prepared CO2 foam is a benefit for avoiding the local ischemia of tissues.
DOA Estimation in B5G/6G: Trends and Challenges
Authors: Ruan Ningjun , Wang Han , Wen Fangqing , Shi Junpeng
Published: July 2022

Direction-of-arrival (DOA) estimation is the preliminary stage of communication, localization, and sensing. Hence, it is a canonical task for next-generation wireless communications, namely beyond 5G (B5G) or 6G communication networks. Both massive multiple-input multiple-output (MIMO) and millimeter wave (mmW) bands are emerging technologies that can be implemented to increase the spectral efficiency of an area, and a number of expectations have been placed on them for future-generation wireless communications. Meanwhile, they also create new challenges for DOA estimation, for instance, through extremely large-scale array data, the coexistence of far-field and near-field sources, mutual coupling effects, and complicated spatial-temporal signal sampling. This article discusses various open issues related to DOA estimation for B5G/6G communication networks. Moreover, some insights on current advances, including arrays, models, sampling, and algorithms, are provided. Finally, directions for future work on the development of DOA estimation are addressed.
Incremental Encoder Speed Acquisition Using an STM32 Microcontroller and NI ELVIS
Authors: Pop Adrian Augustin
Published: July 2022

Precise motor control requires high accuracy of the rotor position through the incremental encoder. The speed and accuracy of the acquisition equipment (microcontroller) play an important element in terms of cost and efficiency. In this paper, the author presents alternative methods for speed acquisition from an incremental encoder. In the first stage of research, the main performances of the STM32 microcontroller, connected with an incremental encoder, will be analyzed and compared with two different acquisition systems, i.e., ELVIS II and a Unidrive M701 power inverter. Using the LabVIEW graphical programming language, a user-friendly, convenient, and flexible human–machine interface is designed. Due to the advantages provided by the STM32 microcontroller in terms of processing power, cost, and programming interface, the obtained results are accurate and consistent. Through experimental testing and analysis, the speed acquisition is stable for both developed software algorithms used for ELVIS II and STM32 platforms. It is the aim of the paper to propose a useful speed acquisition tool in low-cost, high-accuracy prototyping applications.
Development of a Soil Organic Matter Content Prediction Model Based on Supervised Learning Using Vis-NIR/SWIR Spectroscopy
Authors: Kim Min-Jee , Lee Hye-In , Choi Jae-Hyun , Lim Kyoung Jae , Mo Changyeun
Published: July 2022

In the current scenario of anthropogenic climate change, carbon credit security is becoming increasingly important worldwide. Topsoil is the terrestrial ecosystem component with the largest carbon sequestration capacity. Since soil organic matter (SOM), which is mostly composed of organic carbon, and can be affected by rainfall, cultivation, and pollutant inflow, predicting SOM content through regular monitoring is necessary to secure a stable carbon sink. In addition, topsoil in the Republic of Korea is vulnerable to erosion due to climate, topography, and natural and anthropogenic causes, which is also a serious issue worldwide. To mitigate topsoil erosion, establish an efficient topsoil management system, and maximize topsoil utilization, it is necessary to construct a database or gather data for the construction of a database of topsoil environmental factors and topsoil composition. Spectroscopic techniques have been used in recent studies to rapidly measure topsoil composition. In this study, we investigated the spectral characteristics of the topsoil from four major rivers in the Republic of Korea and developed a machine learning-based SOM content prediction model using spectroscopic techniques. A total of 138 topsoil samples were collected from the waterfront area and drinking water protection zone of each river. The reflection spectrum was measured under the condition of an exposure time of 136 ms using a spectroradiometer (Fieldspec4, ASD Inc., Alpharetta, GA, USA). The reflection spectrum was measured three times in wavelengths ranging from 350 to 2500 nm. To predict the SOM content, partial least squares regression and support vector regression were used. The performance of each model was evaluated through the coefficient of determination (R2) and root mean square error. The result of the SOM content prediction model for the total topsoil was R2 = 0.706. Our findings identified the important wavelength of SOM in topsoil using spectroscopic technology and confirmed the predictability of the SOM content. These results could be used for the construction of a national topsoil database.
App-Based Mindfulness Training Predicts Reductions in Smoking Behavior by Engaging Reinforcement Learning Mechanisms: A Preliminary Naturalistic Single-Arm Study
Authors: Taylor Veronique A. , Smith Ryan , Brewer Judson A.
Published: July 2022

Mindfulness training (MT) has been shown to influence smoking behavior, yet the involvement of reinforcement learning processes as underlying mechanisms remains unclear. This naturalistic, single-arm study aimed to examine slope trajectories of smoking behavior across uses of our app-based MT craving tool for smoking cessation, and whether this relationship would be mediated by the attenuating impact of MT on expected reward values of smoking. Our craving tool embedded in our MT app-based smoking cessation program was used by 108 participants upon the experience of cigarette cravings in real-world contexts. Each use of the tool involved mindful awareness to the experience of cigarette craving, a decision as to whether the participant wanted to smoke or ride out their craving with a mindfulness exercise, and paying mindful attention to the choice behavior and its outcome (contentment levels felt from engaging in the behavior). Expected reward values were computed using contentment levels experienced from the choice behavior as the reward signal in a Rescorla–Wagner reinforcement learning model. Multi-level mediation analysis revealed a significant decreasing trajectory of smoking frequency across MT craving tool uses and that this relationship was mediated by the negative relationship between MT and expected reward values (all ps < 0.001). After controlling for the mediator, the predictive relationship between MT and smoking was no longer significant (p < 0.001 before and p = 0.357 after controlling for the mediator). Results indicate that the use of our app-based MT craving tool is associated with negative slope trajectories of smoking behavior across uses, mediated by reward learning mechanisms. This single-arm naturalistic study provides preliminary support for further RCT studies examining the involvement of reward learning mechanisms underlying app-based mindfulness training for smoking cessation.
Shaping and Focusing Magnetic Field in the Human Body: State-of-the Art and Promising Technologies
Authors: Rotundo Sabrina , Brizi Danilo , Flori Alessandra , Giovannetti Giulio , Menichetti Luca , Monorchio Agostino
Published: July 2022

In recent years, the usage of radio frequency magnetic fields for biomedical applications has increased exponentially. Several diagnostic and therapeutic methodologies exploit this physical entity such as, for instance, magnetic resonance imaging, hyperthermia with magnetic nanoparticles and transcranial magnetic stimulation. Within this framework, the magnetic field focusing and shaping, at different depths inside the tissue, emerges as one of the most important challenges from a technological point of view, since it is highly desirable for improving the effectiveness of clinical methodologies. In this review paper, we will first report some of the biomedical practices employing radio frequency magnetic fields, that appear most promising in clinical settings, explaining the underneath physical principles and operative procedures. Specifically, we direct the interest toward hyperthermia with magnetic nanoparticles and transcranial magnetic stimulation, together with a brief mention of magnetic resonance imaging. Additionally, we deeply review the technological solutions that have appeared so far in the literature to shape and control the radio frequency magnetic field distribution within biological tissues, highlighting human applications. In particular, volume and surface coils, together with the recent raise of metamaterials and metasurfaces will be reported. The present review manuscript can be useful to fill the actual gap in the literature and to serve as a guide for the physicians and engineers working in these fields.
Affect of Secondary Beam Non-Uniformity on Plasma Potential Measurements by HIBD with Split-Plate Detector
Authors: Nedzelskiy Igor , Malaquias Artur , Henriques Rafael , Sharma Ridhima
Published: July 2022

In a Heavy Ion Beam Diagnostic (HIBD), the plasma potential is obtained by measuring the energy of the secondary ions resulting from beam-plasma collisions by an electrostatic energy analyzer with split-plate detector (SPD), which relates the secondary ion beam energy variation to its position determined by the difference in currents between the split plates. Conventionally, the data from SPD are analyzed with the assumption that the secondary beam current is uniform. However, the secondary beam presents an effective projection of the primary beam, the current of which, as a rule, has a bell-like non-uniform profile. This paper presents: (i) the general features of the secondary beam profile formation, considered in the simplistic approximation of the circular primary beam and the secondary ions that emerge orthogonal to the primary beam axis, (ii) details of spit-plate detection and the influence of the secondary beam non-uniformity on plasma potential measurements, (iii) supported experimental data from the tokamak ISTTOK HIBD for primary and secondary beam profiles and the SPD transfer characteristic, obtained for the 90° cylindrical energy analyzer (90° CEA) and (iv) the implementation of a multiple cell array detector (MCAD) with dedicated resolution for the measurements of secondary beam profile and MCAD operation in multi-split-plate detection mode for direct measurements of the SPD transfer characteristic.
Low-Altitude Infrared Slow-Moving Small Target Detection via Spatial-Temporal Features Measure
Authors: Mu Jing , Rao Junmin , Chen Ruimin , Li Fanming
Published: July 2022

Robust detection of infrared slow-moving small targets is crucial in infrared search and tracking (IRST) applications such as infrared guidance and low-altitude security; however, existing methods easily cause missed detection and false alarms when detecting infrared small targets in complex low-altitude scenes. In this article, a new low-altitude slow-moving small target detection algorithm based on spatial-temporal features measure (STFM) is proposed. First, we construct a circular kernel to calculate the local grayscale difference (LGD) in a single image, which is essential to suppress low-frequency background and irregular edges in the spatial domain. Then, a short-term energy aggregation (SEA) mechanism with the accumulation of the moving target energy in multiple successive frames is proposed to enhance the dim target. Next, the spatial-temporal saliency map (STSM) is obtained by integrating the two above operations, and the candidate targets are segmented using an adaptive threshold mechanism from STSM. Finally, a long-term trajectory continuity (LTC) measurement is designed to confirm the real target and further eliminate false alarms. The SEA and LTC modules exploit the local inconsistency and the trajectory continuity of the moving small target in the temporal domain, respectively. Experimental results on six infrared image sequences containing different low-altitude scenes demonstrate the effectiveness of the proposed method, which performs better than the existing state-of-the-art methods.
Internet of Things: Development Intelligent Programmable IoT Controller for Emerging Industry Applications
Authors: Chen Ti-An , Chen Shu-Chuan , Tang William , Chen Bo-Tsang
Published: July 2022

The Internet of Things (IoT) has become critical to the implementation of Industry 4.0. The successful operation of smart manufacturing depends on the ability to connect everything together. In this research, we applied the TOC (Theory of Constraints) to develop a wireless Wi-Fi intelligent programmable IoT controller that can be connected to and easily control PLCs. By applying the TOC-focused thinking steps to break through their original limitations, the development process guides the user to use the powerful and simple flow language process control syntax to efficiently connect to PLCs and realize the full range of IoT applications. Finally, this research uses oil–water mixer equipment as the target of continuous improvement and verification. The verification results meet the requirements of the default function. The IoT controller developed in this research uses a marine boiler to illustrate the application. The successful development of flow control language by TOC in this research will enable academic research on PLC-derivative applications. The results of this research will help more SMEs to move into smart manufacturing and the new realm of Industry 4.0.
Image Classification Method Based on Multi-Agent Reinforcement Learning for Defects Detection for Casting
Authors: Liu Chaoyue , Zhang Yulai , Mao Sijia
Published: July 2022

A casting image classification method based on multi-agent reinforcement learning is proposed in this paper to solve the problem of casting defects detection. To reduce the detection time, each agent observes only a small part of the image and can move freely on the image to judge the result together. In the proposed method, the convolutional neural network is used to extract the local observation features, and the hidden state of the gated recurrent unit is used for message transmission between different agents. Each agent acts in a decentralized manner based on its own observations. All agents work together to determine the image type and update the parameters of the models by the stochastic gradient descent method. The new method maintains high accuracy. Meanwhile, the computational time can be significantly reduced to only one fifth of that of the GhostNet.
Hollow-Core Fiber-Based Biosensor: A Platform for Lab-in-Fiber Optical Biosensors for DNA Detection
Authors: Khozeymeh Foroogh , Melli Federico , Capodaglio Sabrina , Corradini Roberto , Benabid Fetah , Vincetti Luca , Cucinotta Annamaria
Published: July 2022

In this paper, a novel platform for lab-in-fiber-based biosensors is studied. Hollow-core tube lattice fibers (HC-TLFs) are proposed as a label-free biosensor for the detection of DNA molecules. The particular light-guiding mechanism makes them a highly sensitive tool. Their transmission spectrum is featured by alternations of high and low transmittance at wavelength regions whose values depend on the thickness of the microstructured web composing the cladding around the hollow core. In order to achieve DNA detection by using these fibers, an internal chemical functionalization process of the fiber has been performed in five steps in order to link specific peptide nucleic acid (PNA) probes, then the functionalized fiber was used for a three-step assay. When a solution containing a particular DNA sequence is made to flow through the HC of the TLF in an ‘optofluidic’ format, a bio-layer is formed on the cladding surfaces causing a red-shift of the fiber transmission spectrum. By comparing the fiber transmission spectra before and after the flowing it is possible to identify the eventual formation of the layer and, therefore, the presence or not of a particular DNA sequence in the solution.
Spherical-Cap Approximation of Vector Quantization for Quantization-Based Combining in MIMO Broadcast Channels with Limited Feedback
Authors: Min Moonsik , Kim Tae-Kyoung
Published: July 2022

The spherical-cap approximation of vector quantization (SCVQ) is an analytical model used for the mathematical analysis of multiple-input multiple-output (MIMO) systems with limited feedback. SCVQ closely emulates the distribution of the quantization error induced by the finite-rate quantization of a channel using a simple and analytically tractable approach. However, the conventional SCVQ model is not applicable when antenna-combining schemes such as quantization-based combining (QBC) are considered. Because QBC is an effective antenna-combining method that minimizes channel quantization errors, it can be adopted for various practical MIMO broadcast systems. Nevertheless, evaluating the performance of QBC-based MIMO systems with an explicit codebook can be extremely difficult, depending on the system complexity. To resolve this, this study generalizes the conventional SCVQ to be compatible with the QBC. The proposed generalized version of the SCVQ effectively emulates the quantization error obtained using QBC, while enabling a simple simulation independent of the number of feedback bits and mathematically tractable analysis. We validate the effectiveness of the proposed model by presenting a wireless communication application based on a dense cellular network.
DRSNFuse: Deep Residual Shrinkage Network for Infrared and Visible Image Fusion
Authors: Wang Hongfeng , Wang Jianzhong , Xu Haonan , Sun Yong , Yu Zibo
Published: July 2022

Infrared images are robust against illumination variation and disguises, containing the sharp edge contours of objects. Visible images are enriched with texture details. Infrared and visible image fusion seeks to obtain high-quality images, keeping the advantages of source images. This paper proposes an object-aware image fusion method based on a deep residual shrinkage network, termed as DRSNFuse. DRSNFuse exploits residual shrinkage blocks for image fusion and introduces a deeper network in infrared and visible image fusion tasks than existing methods based on fully convolutional networks. The deeper network can effectively extract semantic information, while the residual shrinkage blocks maintain the texture information throughout the whole network. The residual shrinkage blocks adapt a channel-wise attention mechanism to the fusion task, enabling feature map channels to focus on objects and backgrounds separately. A novel image fusion loss function is proposed to obtain better fusion performance and suppress artifacts. DRSNFuse trained with the proposed loss function can generate fused images with fewer artifacts and more original textures, which also satisfy the human visual system. Experiments show that our method has better fusion results than mainstream methods through quantitative comparison and obtains fused images with brighter targets, sharper edge contours, richer details, and fewer artifacts.
Optimal LiDAR Data Resolution Analysis for Object Classification
Authors: Darrah Marjorie , Richardson Matthew , DeRoos Bradley , Wathen Mitchell
Published: July 2022

When classifying objects in 3D LiDAR data, it is important to use efficient collection methods and processing algorithms. This paper considers the resolution needed to classify 3D objects accurately and discusses how this resolution is accomplished for the RedTail RTL-450 LiDAR System. We employ VoxNet, a convolutional neural network, to classify the 3D data and test the accuracy using different data resolution levels. The results show that for our data set, if the neural network is trained using higher resolution data, then the accuracy of the classification is above 97%, even for the very sparse testing set (10% of original test data set point density). When the training is done on lower resolution data sets, the classification accuracy remains good but drops off at around 3% of the original test data set point density. These results have implications for determining flight altitude and speed for an unmanned aerial vehicle (UAV) to achieve high accuracy classification. The findings point to the value of high-resolution point clouds for both the training of the convolutional neural network and in data collected from a LiDAR sensor.
Photoplethysmogram Recording Length: Defining Minimal Length Requirement from Dynamical Characteristics
Authors: Sviridova Nina , Zhao Tiejun , Nakano Akimasa , Ikeguchi Tohru
Published: July 2022

Photoplethysmography is a widely used technique to noninvasively assess heart rate, blood pressure, and oxygen saturation. This technique has considerable potential for further applications—for example, in the field of physiological and mental health monitoring. However, advanced applications of photoplethysmography have been hampered by the lack of accurate and reliable methods to analyze the characteristics of the complex nonlinear dynamics of photoplethysmograms. Methods of nonlinear time series analysis may be used to estimate the dynamical characteristics of the photoplethysmogram, but they are highly influenced by the length of the time series, which is often limited in practical photoplethysmography applications. The aim of this study was to evaluate the error in the estimation of the dynamical characteristics of the photoplethysmogram associated with the limited length of the time series. The dynamical properties were evaluated using recurrence quantification analysis, and the estimation error was computed as a function of the length of the time series. Results demonstrated that properties such as determinism and entropy can be estimated with an error lower than 1% even for short photoplethysmogram recordings. Additionally, the lower limit for the time series length to estimate the average prediction time was computed.
A Hybrid Hand-Crafted and Deep Neural Spatio-Temporal EEG Features Clustering Framework for Precise Emotional Status Recognition
Authors: Haq Qazi Mazhar ul , Yao Leehter , Rahmaniar Wahyu , Fawad  , Islam Faizul
Published: July 2022

Human emotions are variant with time, non-stationary, complex in nature, and are invoked as a result of human reactions during our daily lives. Continuously detecting human emotions from one-dimensional EEG signals is an arduous task. This paper proposes an advanced signal processing mechanism for emotion detection from EEG signals using continuous wavelet transform. The space and time components of the raw EEG signals are converted into 2D spectrograms followed by feature extraction. A hybrid spatio-temporal deep neural network is implemented to extract rich features. A differential-based entropy feature selection technique adaptively differentiates features based on entropy, based on low and high information regions. Bag of Deep Features (BoDF) is applied to create clusters of similar features and computes the features vocabularies for reduction of feature dimensionality. Extensive experiments are performed on the SEED dataset, which shows the significance of the proposed method compared to state-of-the-art methods. Specifically, the proposed model achieved 96.7%, 96.2%, 95.8%, and 95.3% accuracy with the SJTU SEED dataset, for SVM, ensemble, tree, and KNN classifiers, respectively.
Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System
Authors: Kumar Sandeep , Rani Shilpa , Jain Arpit , Verma Chaman , Raboaca Maria Simona , Illés Zoltán , Neagu Bogdan Constantin
Published: July 2022

Nowadays, the demand for soft-biometric-based devices is increasing rapidly because of the huge use of electronics items such as mobiles, laptops and electronic gadgets in daily life. Recently, the healthcare department also emerged with soft-biometric technology, i.e., face biometrics, because the entire data, i.e., (gender, age, face expression and spoofing) of patients, doctors and other staff in hospitals is managed and forwarded through digital systems to reduce paperwork. This concept makes the relation friendlier between the patient and doctors and makes access to medical reports and treatments easier, anywhere and at any moment of life. In this paper, we proposed a new soft-biometric-based methodology for a secure biometric system because medical information plays an essential role in our life. In the proposed model, 5-layer U-Net-based architecture is used for face detection and Alex-Net-based architecture is used for classification of facial information i.e., age, gender, facial expression and face spoofing, etc. The proposed model outperforms the other state of art methodologies. The proposed methodology is evaluated and verified on six benchmark datasets i.e., NUAA Photograph Imposter Database, CASIA, Adience, The Images of Groups Dataset (IOG), The Extended Cohn-Kanade Dataset CK+ and The Japanese Female Facial Expression (JAFFE) Dataset. The proposed model achieved an accuracy of 94.17% for spoofing, 83.26% for age, 95.31% for gender and 96.9% for facial expression. Overall, the modification made in the proposed model has given better results and it will go a long way in the future to support soft-biometric based applications.