bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Rolling Circle Amplification-Based Detection of SARS-CoV-2 with Multi-Region Padlock Hybridization
Authors: Kumari Rajesh , Lim Ji Won , Sullivan Matthew Ryan , Malampy Rachel , Baush Connor , Smolina Irina , Robin Howard , Demidov Vadim V. , Ugolini Giovanni Stefano , Auclair Jared R. , Konry Tania
Published: September 2022

SARS-CoV-2 has remained a global health burden, primarily due to the continuous evolution of different mutant strains. These mutations present challenges to the detection of the virus, as the target genes of qPCR, the standard diagnostic method, may possess sequence alterations. In this study, we develop an isothermal one-step detection method using rolling circle amplification (RCA) for SARS-CoV-2. This novel strategy utilizes a multi-padlock (MP-RCA) approach to detect viral-RNA via a simplified procedure with the reliable detection of mutated strains over other procedures. We designed 40 padlock-based probes to target different sequences across the SARS-CoV-2 genome. We established an optimal one-step isothermal reaction protocol utilizing a fluorescent output detected via a plate reader to test a variety of padlock combinations. This method was tested on RNA samples collected from nasal swabs and validated via PCR. S-gene target failure (SGTF)-mutated strains of SARS-CoV-2 were included. We demonstrated that the sensitivity of our assay was linearly proportional to the number of padlock probes used. With the 40-padlock combination the MP-RCA assay was able to correctly detect 45 out 55 positive samples (81.8% efficiency). This included 10 samples with SGTF mutations which we were able to detect as positive with 100% efficiency. We found that the MP-RCA approach improves the sensitivity of the MP-RCA assay, and critically, allows for the detection of SARS-CoV-2 variants with SGTF. Our method offers the simplicity of the reaction and requires basic equipment compared to standard qPCR. This method provides an alternative approach to overcome the challenges of detecting SARS-CoV-2 and other rapidly mutating viruses.
Oral Lichen Planus and Mutated TP53—A Road to Cancer?
Authors: Holbrook William Peter , Ögmundsdottir Helga M.
Published: September 2022

The malignant potential of oral lichen planus (OLP) has been discussed and disputed for decades. The lesions are often characterized by strong expression of the TP53 protein in the basal layer of the mucosa. In 2002, we reported the presence of TP53 mutations in nine out of 27 OLP lesions tested. At follow-up in 2009, one case of oral squamous cell cancer (OSCC) had occurred in a different site six years later. In contrast, in another case, TP53 mutation persisted for years without malignant transformation. In a longitudinal study of eight selected patients with OSCC or different pre-malignant lesions, it was concluded that TP53 mutations could occur early or late in the development of OSCC. A follow-up in the present, almost 20 years later, revealed that one further case of OSCC had occurred in a TP53-mutated case of OLP, 21 years after the first sample was taken, again in a different site. With this second case, this small study now points towards a risk of developing OSCC in TP53-mutated OLP lesions. A review of recent literature indicates a growing consensus that OLP should be regarded as a potentially pre-malignant lesion. Several protein markers have been studied, but none proved useful for prediction of malignant progression. The great majority of published studies are retrospective, and it has been suggested that multi-centre prospective studies will be needed to reach a definitive answer on the malignant potential of OLP, and particularly, to identify contributing factors. Screening for TP53 mutations could help to identify the subgroup of OLP patients that is truly at risk of developing oral cancer.
PV System Failures Diagnosis Based on Multiscale Dispersion Entropy
Authors: Lebreton Carole , Kbidi Fabrice , Graillet Alexandre , Jegado Tifenn , Alicalapa Frédéric , Benne Michel , Damour Cédric
Published: September 2022

Photovoltaic (PV) system diagnosis is a growing research domain likewise solar energy’s ongoing significant expansion. Indeed, efficient Fault Detection and Diagnosis (FDD) tools are crucial to guarantee reliability, avoid premature aging and improve the profitability of PV plants. In this paper, an on-line diagnosis method using the PV plant electrical output is presented. This entirely signal-based method combines variational mode decomposition (VMD) and multiscale dispersion entropy (MDE) for the purpose of detecting and isolating faults in a real grid-connected PV plant. The present method seeks a low-cost design, an ease of implementation and a low computation cost. Taking into account the innovation of applying these techniques to PV FDD, the VMD and MDE procedures as well as parameters identification are carefully detailed. The proposed FFD approach performance is assessed on a real rooftop PV plant with experimentally induced faults, and the first results reveal the MDE approach has good suitability for PV plants diagnosis.
A Note on Representational Understanding
Authors: Jakovác Antal , Telcs András
Published: September 2022

In this paper, we explore a new approach in which understanding is interpreted as a set representation. We prove that understanding/representation, finding the appropriate coordination of data, is equivalent to finding the minimum of the representational entropy. For the control of the search for the correct representation, we propose a loss function as a combination of the representational entropy, type one and type two errors. Computational complexity estimates are presented for the process of understanding and using the representation found.
Effect of Investment Promotion through the Special Economic Zone Mechanism on the Distribution of FDI in Cambodia
Authors: Theot Therith Chuop
Published: September 2022

This study examines the effect of investment promotion through the special economic zone (SEZ) mechanism on foreign direct investment (FDI) inflow across Cambodia. We applied generalized methods of moments (GMM) to panel data constructed from 19 Cambodian provinces during 2015–2019. Our results show that the number of SEZs positively affects both FDI inflow and diversification across the country, while capital invested in developing SEZs increases only the latter. Other SEZ variables, including the presence of SEZ, its intensity, and the age of the first established SEZ in a province, are mostly found to be positively associated with FDI and diversified FDI but not notably significant. Supportably, the existence of SEZ is confirmed to be significant by the t-test method, meaning that the SEZ province can attract more FDI than the non-SEZ one. Some provincial efforts and characteristics, including annual government expenditure, number of public relations, population density, population 18 years old and up, deep-sea ports, and international gates, likely significantly influence FDI inflow into the provinces of Cambodia. All in all, the SEZ mechanism attracts more diversified foreign investment activities, and it has a significant effect on the distribution of FDI in Cambodia.
STEM Project-Based Instruction: An Analysis of Teacher-Developed Integrated STEM PBI Curriculum Units
Authors: Wieselmann Jeanna R. , Sager Marc T. , Price Brynn C.
Published: September 2022

Integrated science, technology, engineering, and mathematics (STEM) and project-based instruction (PBI) have both received increased attention as instructional approaches that allow for deep, authentic student learning. However, there has been little research that explores the overlap of these two related yet distinct approaches. In this case study, eight teacher-developed STEM PBI curriculum units for grades 1–8 were analyzed using content analysis methods. Each unit was scored for integrated STEM and PBI quality. Findings highlight strengths related to an authentic context for learning, opportunities for communication, and the development of a final product that is shared publicly. However, weaknesses were also apparent related to STEM content integration and learning goals, student voice and choice, assessment, and organization. Notably, the content analysis also illustrated that the units developed for elementary grades (1 and 5) were generally stronger than those units developed for middle-school grades (7 and 8). Implications for practice and future research are discussed.
Post-COVID-19 Transition in University Physics Courses: A Case of Study in a Mexican University
Authors: Delgado Francisco
Published: September 2022

The extended confinement imposed by the COVID-19 pandemic lockdowns resulted in the imposition of online education for two years. Many students experienced their transitions to higher educative levels during this time, surely losing some academic learning as a consequence. On the other hand, this context could have promoted different types of competencies, until recently not explored, as a function of students’ personalities or academic profiles. Physics teaching is among those areas which have changed from its traditional methods during this period. The return to school during the ‘New Normal’ has resulted in certain concerns about students’ adaptability due to their possible lack of learning over this time. We analyse, in the current research, the transitions of three generations crossing several pandemic stages during their time participating in university physics courses during the first year, a common entry point for engineering programs. In addition, we analyse several academic traits as causal factors of academic success in order to understand how performance could be affected during online education and during the ‘New Normal’. The results highlight a general high level of adaptation for the most of the students, but still, some of them were affected in terms of the functioning of their learning styles or regarding their personality profiles. Notably, no meaningful losses were detected among the last transition; instead, several interesting aspects were found relating to academic profile appearing to have an effect on the students’ performance during the first transition to online education, and then during the second transition back to face-to-face education in the ‘New Normal’.
Factors Affecting Undergraduate Medical Science Students’ Motivation to Study during the COVID-19 Pandemic
Authors: Mayers Thomas , Mathis Bryan J. , Ho C. Kiong , Morikawa Kazuya , Maki Naoki , Hisatake Koji
Published: September 2022

In the current study, we investigated the motivational status and underlying factors of the motivational changes among Japanese medical science students brought by the COVID-19 pandemic. Two groups of second-year undergraduate medical science students (training to become medical technologists and/or medical science researchers) participated in this study in the summers of 2020 and 2021 by writing essays describing how the pandemic had affected their motivation to study. A content analysis of the motivation status and underlying factors (both motivating and demotivating factors) was conducted before statistical analysis was used to investigate possible differences between the sexes and the two groups. In total, 73 essays were included in the analysis. The students had increased motivation to study in both groups (89% and 62%, respectively); however, in Group 2021, 19% of the students (all women) had decreased motivation. Among the underlying reasons behind the increased motivation, students showed a desire to help/save others, contribute to the development of medical science, increase knowledge, and disseminate correct information. The demotivating factors were largely linked to online learning and the negative emotions associated with lockdown. Our findings suggest that, for Japanese medical science students, the COVID-19 pandemic has been an overall motivating experience for our students. However, the prolonged pandemic and lockdown measures could attenuate this and be particularly disruptive for women.
The Lived Experience of Mindfulness in Adventure-Based Learning
Authors: Stuhr Paul T. , Denny April L.
Published: September 2022

Adventure-based learning (ABL) is an innovative K-12 instructional model that continues to be used in physical education to promote intrapersonal and interpersonal relationship skills. ABL incorporates low initiatives/cooperative activities coupled with periods of reflection to help enhance the likelihood of transferring intrapersonal and interpersonal relationship skills to other areas of K-12 student life. Mindfulness is a way of consciously and nonjudgmentally being aware of the present moment. Mindfulness practices (MPs) hold tremendous potential as a pedagogical tool in helping physical educators generate greater attention, equanimity, and compassion for themselves and their students. To further explore the impact of MPs for students, this study focused on undergraduate perception with regard to comprehension, practice, and demonstration of the possible benefits of MPs in three separate 16-week ABL teacher education courses. Specifically, we wanted to explore how students in the teacher education course conceptualized mindfulness as a phenomenon and how MPs played a part of their lived experience. Three themes were discovered: conceptualizing mindfulness, student interest with mindfulness, and perceived outcomes from mindfulness practices. The findings provide a deeper understanding of the participants’ meaning making of mindfulness, their overall buy-in and openness in using MPs, and the perceived benefits in using these contemplative practices.
STEM Faculty’s Support of Togetherness during Mandated Separation: Accommodations, Caring, Crisis Management, and Powerlessness
Authors: Thacker Ian , Seyranian Viviane , Madva Alex , Beardsley Paul
Published: September 2022

The emergence of the COVID-19 pandemic initiated major disruptions to higher education systems. Physical spaces that previously supported interpersonal interaction and community were abruptly inactivated, and faculty largely took on the responsibility of accommodating classroom structures in rapidly changing situations. This study employed interviews to examine how undergraduate Science, Technology, Engineering, and Mathematics (STEM) instructors adapted instruction to accommodate the mandated transition to virtual learning and how these accommodations supported or hindered community and belonging during the onset of the pandemic. Interviews with 25 STEM faculty at an undergraduate Hispanic Serving Institution revealed a wide range of accommodations they made to their courses and how they managed communication with students. Faculty strived to support student belonging with responses ranging from caring to crisis management, though some faculty expressed feelings of powerlessness when unable to accommodate certain challenges. The case of a responsive and flexible instructor is presented to highlight a productive response to a crisis. These retrospective findings point to strategies to support faculty teaching in virtual learning environments in the future; increasing opportunities for student–student and student–faculty interaction, supporting faculty in learning technologies that support these interactions and addressing faculty’s feelings of powerlessness.
Decoding Emotion in Drug Abusers: Evidence for Face and Body Emotion Recognition and for Disgust Emotion
Authors: Bonfiglio Natale Salvatore , Renati Roberta , Bottini Gabriella
Published: September 2022

Background: Different drugs damage the frontal cortices, particularly the prefrontal areas involved in both emotional and cognitive functions, with a consequence of decoding emotion deficits for people with substance abuse. The present study aimed to explore the cognitive impairments in drug abusers through facial, body and disgust emotion recognition, expanding the investigation of emotions processing, measuring accuracy and response velocity. Methods: We enrolled 13 addicted to cocaine and 12 alcohol patients attending treatment services in Italy, comparing them with 33 matched controls. Facial emotion and body posture recognition tasks, a disgust rating task and the Barrat Impulsivity Scale were included in the experimental assessment. Results: We found that emotional processes are differently influenced by cocaine and alcohol, suggesting that these substances impact diverse cerebral systems. Conclusions: Drug abusers seem to be less accurate on elaboration of facial, body and disgust emotions. Considering that the participants were not cognitively impaired, our data support the hypothesis that emotional impairments emerge independently from the damage of cognitive functions.
ASARIMA: An Adaptive Harvested Power Prediction Model for Solar Energy Harvesting Sensor Networks
Authors: Li Lingsheng , Han Chong
Published: September 2022

Harvesting energy from solar radiation has emerged as an effective approach to prolong the lifetime of outdoor energy harvesting sensor networks. The harvested energy must be carefully managed to ensure that sufficient energy is available when solar energy is scarce. For the prediction problem of solar energy power harvesting, this paper proposes an adaptive seasonal auto-regressive integrated moving average model (ASARIMA) for solar energy harvesting prediction. A training set can be adaptively adjusted by the similarity of historical data, and then we conduct seasonal difference data fitting based on the adjusted training set to obtain the optimal prediction model parameters. Experimental results show that this ASARIMA model performs better than other existing power prediction algorithms. If the weather conditions are stable, the prediction error of the ASARIMA decreases by more than 70%. If the weather conditions change sharply, the prediction error decreases by more than 20% in comparison with those of other algorithms.
Improvement of LSTM-Based Forecasting with NARX Model through Use of an Evolutionary Algorithm
Authors: Cocianu Cătălina Lucia , Uscatu Cristian Răzvan , Avramescu Mihai
Published: September 2022

The reported work aims to improve the performance of LSTM-based (Long Short-Term Memory) forecasting algorithms in cases of NARX (Nonlinear Autoregressive with eXogenous input) models by using evolutionary search. The proposed approach, ES-LSTM, combines a two-membered ES local search procedure (2MES) with an ADAM optimizer to train more accurate LSTMs. The accuracy is measured from both error and trend prediction points of view. The method first computes the learnable parameters of an LSTM, using a subset of the training data, and applies a modified version of 2MES optimization to tune them. In the second stage, all available training data are used to update the LSTM’s weight parameters. The performance of the resulting algorithm is assessed versus the accuracy of a standard trained LSTM in the case of multiple financial time series. The tests are conducted on both training and test data, respectively. The experimental results show a significant improvement in the forecasting of the direction of change without damaging the error measurements. All quality measures are better than in the case of the standard algorithm, while error measures are insignificantly higher or, in some cases, even better. Together with theoretical consideration, this proves that the new method outperforms the standard one.
Constructing a Gene Regulatory Network Based on a Nonhomogeneous Dynamic Bayesian Network
Authors: Zhang Jiayao , Hu Chunling , Zhang Qianqian
Published: September 2022

Since the regulatory relationship between genes is usually non-stationary, the homogeneity assumption cannot be satisfied when modeling with dynamic Bayesian networks (DBNs). For this reason, the homogeneity assumption in dynamic Bayesian networks should be relaxed. Various methods of combining multiple changepoint processes and DBNs have been proposed to relax the homogeneity assumption. When using a non-homogeneous dynamic Bayesian network to model a gene regulatory network, it is inevitable to infer the changepoints of the gene data. Based on this analysis, this paper first proposes a data-based birth move (ED-birth move). The ED-birth move makes full use of the potential information of data to infer the changepoints. The greater the Euclidean distance of the mean of the data in the two components, the more likely this data point will be selected as a new changepoint by the ED-birth move. In brief, the selection of the changepoint is proportional to the Euclidean distance of the mean on both sides of the data. Furthermore, an improved Markov chain Monte Carlo (MCMC) method is proposed, and the improved MCMC introduces the Pearson correlation coefficient (PCCs) to sample the parent node-set. The larger the absolute value of the Pearson correlation coefficient between two data points, the easier it is to be sampled. Compared with other classical models on Saccharomyces cerevisiae data, synthetic data, RAF pathway data, and Arabidopsis data, the PCCs-ED-DBN proposed in this paper improves the accuracy of gene network reconstruction and further improves the convergence and stability of the modeling process.
A Feature-Based Robust Method for Abnormal Contracts Detection in Ethereum Blockchain
Authors: Aljofey Ali , Rasool Abdur , Jiang Qingshan , Qu Qiang
Published: September 2022

Blockchain technology has allowed many abnormal schemes to hide behind smart contracts. This causes serious financial losses, which adversely affects the blockchain. Machine learning technology has mainly been utilized to enable automatic detection of abnormal contract accounts in recent years. In spite of this, previous machine learning methods have suffered from a number of disadvantages: first, it is extremely difficult to identify features that enable accurate detection of abnormal contracts, and based on these features, statistical analysis is also ineffective. Second, they ignore the imbalances and repeatability of smart contract accounts, which often results in overfitting of the model. In this paper, we propose a data-driven robust method for detecting abnormal contract accounts over the Ethereum Blockchain. This method comprises hybrid features set by integrating opcode n-grams, transaction features, and term frequency-inverse document frequency source code features to train an ensemble classifier. The extra-trees and gradient boosting algorithms based on weighted soft voting are used to create an ensemble classifier that balances the weaknesses of individual classifiers in a given dataset. The abnormal and normal contract data are collected by analyzing the open source etherscan.io, and the problem of the imbalanced dataset is solved by performing the adaptive synthetic sampling. The empirical results demonstrate that the proposed individual feature sets are useful for detecting abnormal contract accounts. Meanwhile, combining all the features enhances the detection of abnormal contracts with significant accuracy. The experimental and comparative results show that the proposed method can distinguish abnormal contract accounts for the data-driven security of blockchain Ethereum with satisfactory performance metrics.
A Framework and Method for Surface Floating Object Detection Based on 6G Networks
Authors: Li He , Yang Shuaipeng , Liu Jinjiang , Yang Yang , Kadoch Michel , Liu Tianyang
Published: September 2022

Water environment monitoring has always been an important method of water resource environmental protection. In practical applications, there are problems such as large water bodies, long monitoring periods, and large transmission and processing delays. Aiming at these problems, this paper proposes a framework and method for detecting floating objects on water based on the sixth-generation mobile network (6G). Using satellite remote sensing monitoring combined with ground-truth data, a regression model is established to invert various water parameters. Then, using chlorophyll as the main reference indicator, anomalies are detected, early warnings are given in a timely manner, and unmanned aerial vehicles (UAVs) are notified through 6G to detect targets in abnormal waters. The target detection method in this paper uses MobileNetV3 to replace the VGG16 network in the single-shot multi-box detector (SSD) to reduce the computational cost of the model and adapt to the computing resources of the UAV. The convolutional block attention module (CBAM) is adopted to enhance feature fusion. A small target data enhancement module is used to enhance the network identification capability in the training process, and the key-frame extraction module is applied to simplify the detection process. The network model is deployed in system-on-a-chip (SOC) using edge computing, the processing flow is optimized, and the image preprocessing module is added. Tested in an edge environment, the improved model has a 2.9% increase in detection accuracy and is 55% higher in detection speed compared with SSD. The experimental results show that this method can meet the real-time requirements of video surveillance target detection.
E-Commerce Sales Revenues Forecasting by Means of Dynamically Designing, Developing and Validating a Directed Acyclic Graph (DAG) Network for Deep Learning
Authors: Petroșanu Dana-Mihaela , Pîrjan Alexandru , Căruţaşu George , Tăbușcă Alexandru , Zirra Daniela-Lenuța , Perju-Mitran Alexandra
Published: September 2022

As the digitalization process has become more and more important in our daily lives, during recent decades e-commerce has greatly increased in popularity, becoming increasingly used, therefore representing an extremely convenient alternative to traditional stores. In order to develop and maintain profitable businesses, traders need accurate forecasts concerning their future sales, a very difficult task considering that these are influenced by a wide variety of factors. This paper proposes a novel e-commerce sales forecasting method that dynamically builds a Directed Acyclic Graph Neural Network (DAGNN) for Deep Learning architecture. This will allow for long-term, fine-grained forecasts of daily sales revenue, refined up to the level of product categories. The developed forecasting method provides the e-commerce store owner an accurate forecasting tool for predicting the sales of each category of products for up to three months ahead. The method offers a high degree of scalability and generalization capability due to the dynamically incremental way in which the constituent elements of the DAGNN’s architecture are obtained. In addition, the proposed method achieves an efficient use of data by combining the numerous advantages of its constituent layers, registering very good performance metrics and processing times. The proposed method can be generalized and applied to forecast the sales for up to three months ahead in the case of other e-commerce stores, including large e-commerce businesses.
eXplainable AI (XAI)-Based Input Variable Selection Methodology for Forecasting Energy Consumption
Authors: Sim Taeyong , Choi Seonbin , Kim Yunjae , Youn Su Hyun , Jang Dong-Jin , Lee Sujin , Chun Chang-Jae
Published: September 2022

This research proposes a methodology for the selection of input variables based on eXplainable AI (XAI) for energy consumption prediction. For this purpose, the energy consumption prediction model (R2 = 0.871; MAE = 2.176; MSE = 9.870) was selected by collecting the energy data used in the building of a university in Seoul, Republic of Korea. Applying XAI to the results from the prediction model, input variables were divided into three groups by the expectation of the ranking-score (Fqvar) (10 ≤ Strong, 5 ≤ Ambiguous < 10, and Weak < 5), according to their influence. As a result, the models considering the input variables of the Strong + Ambiguous group (R2 = 0.917; MAE = 1.859; MSE = 6.639) or the Strong group (R2 = 0.916; MAE = 1.816; MSE = 6.663) showed higher prediction results than other cases (p < 0.05 or 0.01). There were no statistically significant results between the Strong group and the Strong + Ambiguous group (R2: p = 0.408; MAE: p = 0.488; MSE: p = 0.478). This means that when considering the input variables of the Strong group (Fqvar: Year = 14.8; E-Diff = 12.8; Hour = 11.0; Temp = 11.0; Surface-Temp = 10.4) determined by the XAI-based methodology, the energy consumption prediction model showed excellent performance. Therefore, the methodology proposed in this study is expected to determine a model that can accurately and efficiently predict energy consumption.
An Efficient Slotted Waveguide Antenna System Integrated with Inside-Grooves and Modified Gaussian Slot Distribution
Authors: Khan Asif Mehmood , Ahmed Muhammad Mansoor , Rafique Umair , Kiyani Arslan , Abbas Syed Muzahir
Published: September 2022

In this work, an efficient slotted waveguide antenna (SWA) system is designed for S-band high power microwave (HPM) applications. The designed SWA comprises of 10-slot elements placed on the broad wall of SWA with a modified Gaussian distribution (MGD), integrated with two inside-grooves and a Gaussian dielectric radome of high-density polyethylene (HDPE) material. The inside-grooves are introduced to suppress the surface current on the waveguide, which results in high gain as well as sidelobe level (SLL) reduction in the E-plane. The MGD controls the SLLs, and the unique Gaussian profile shape radome offers constant radiation characteristics. The proposed antenna system, within existing size constraints, offers a high gain of 20.1 dBi in conjunction with a high-power handling capability of greater than 100 MW. The designed SWA system has compact dimensions of 8.46λ0 × 1.38λ0 × 1.50λ0, with SLLs of −20 dB and −22 dB in the H- and E-plane, respectively. The HPM antenna system, radiating at 3 GHz, is fabricated on aluminium material using the milling process. The simulated SWA system has good agreement with measured results. Moreover, the proposed SWA system offers clear advantages in terms of its robustness, design simplicity, high power handling capability, and high gain.
Evaluation of Pulsed Spark Discharge for Triggering GaAs Photoconductive Semiconductor Switches
Authors: Zhang Qin , Shi Wei , Ma Cheng , Yang Lei
Published: September 2022

In this study, a 3 mm gap GaAs photoconductive semiconductor switch (GaAs PCSS) was triggered by pulsed spark discharge. The typical linear mode of GaAs PCSS was fulfilled at a low bias voltage. The on-state current waveform was similar to that of an optical pulse. The “nonlinear mode” was demonstrated at a bias voltage of 4 kV; concurrently, the peak current and the carriers’ multiplication rate were 33 A and 179, respectively. This study indicates that pulsed spark discharge is a promising candidate light source for the direct triggering of GaAs PCSSs.
System for Non-Contact and Multispectral Examination of Blood Supply to Cutaneous Tissue
Authors: Labuda Michal , Smondrk Maros , Babusiak Branko , Borik Stefan
Published: September 2022

The presented system for non-contact examination of tissue perfusion is one of the tools for complex examination of human body tissues (skin, subcutaneous) and their mutual interactions, including blood flow and activity under various external stimuli. In our system, optical radiation, with wavelengths of 525 nm, 625 nm, and 940 nm, was used to investigate the perfusion and properties of skin tissue. Our work presents that it is possible to obtain comprehensive information about the cardiovascular system and skin tissue perfusion by a suitable combination of wavelengths, light intensity, and homogeneous illumination distribution with a properly chosen sensing device—a camera. The proposed system consists of an illumination device that emits light of the three wavelengths mentioned above and thus makes it possible to investigate the skin tissue structures and their interrelationships in terms of their blood supply and interactions with each other.
Motion Video Recognition in Speeded-Up Robust Features Tracking
Authors: Zhang Jianguang , Li Yongxia , Tai An , Wen Xianbin , Jiang Jianmin
Published: September 2022

Motion video recognition has been well explored in applications of computer vision. In this paper, we propose a novel video representation, which enhances motion recognition in videos based on SURF (Speeded-Up Robust Features) and two filters. Firstly, the detector scheme of SURF is used to detect the candidate points of the video because it is an efficient faster local feature detector. Secondly, by using the optical flow field and trajectory, the feature points can be filtered from the candidate points, which enables a robust and efficient extraction of motion feature points. Additionally, we introduce a descriptor, called MoSURF (Motion Speeded-Up Robust Features), based on SURF (Speeded-Up Robust Features), HOG (Histogram of Oriented Gradient), HOF (Histograms of Optical Flow), MBH(Motion Boundary Histograms), and trajectory information, which can effectively describe motion information and are complementary to each other. We evaluate our video representation under action classification on three motion video datasets namely KTH, YouTube, and UCF50. Compared with state-of-the-art methods, the proposed method shows advanced results on all datasets.
Classification of Roads and Types of Public Roads Using EOG Smart Glasses and an Algorithm Based on Machine Learning While Driving a Car
Authors: Doniec Rafał , Piaseczna Natalia , Li Frédéric , Duraj Konrad , Pour Hawzhin Hozhabr , Grzegorzek Marcin , Mocny-Pachońska Katarzyna , Tkacz Ewaryst
Published: September 2022

Driving a car is an activity that became necessary for exploration, even when living in the present world. Research exploring the topic of safety on the roads has therefore become increasingly relevant. In this paper, we propose a recognition algorithm based on physiological signals acquired from JINS MEME ES_R smart glasses (electrooculography, acceleration and angular velocity) to classify four commonly encountered road types: city road, highway, housing estate and undeveloped area. Data from 30 drivers were acquired in real driving conditions. Hand-crafted statistical features were extracted from the physiological signals to train and evaluate a random forest classifier. We achieved an overall accuracy, precision, recall and F1 score of 87.64%, 86.30%, 88.12% and 87.08% on the test dataset, respectively.
A Voronoi Diagram-Based Grouping Test Localization Scheme in Wireless Sensor Networks
Authors: Li Guangming , Xu Menghui , Teng Gaofei , Yang Wei , Mak Shu-Lun , Li Chun-Yin , Lee Chi-Chung
Published: September 2022

The wireless sensor network (WSN) provides us with a cost-effective way to remotely monitor a large number of objects, locations, and environmental parameters. Taking advantage of WSNs to applications can add new capabilities to existing products and bring out new services. We propose a novel range-free localization scheme, the Voronoi diagram-based grouping test localization (VTL) scheme, to estimate the location efficiently for WSNs. VTL divides the anchor nodes into multiple groups and uses the corresponding closest Voronoi cells to compute the estimated location. Apart from improving the accuracy of location estimation, it also largely simplifies the implementation. Simulation results show that the VTL scheme has better performance compared with other range-free localization schemes. When reaching a certain anchor node density, the VTL scheme will have higher localization accuracy and a larger percentage of localizable nodes. Hence, VTL is likely more appropriate for upcoming WSN scenarios with large ratios of anchor nodes being available.
A Prediction Method for Development Indexes of Waterflooding Reservoirs Based on Modified Capacitance–Resistance Models
Authors: Fu Libing , Zhao Lun , Chen Song , Xu Anzhu , Ni Jun , Li Xuanran
Published: September 2022

Capacitance–resistance models (CRMs) are semi-analytical methods to estimate the production rate of either an individual producer or a group of producers based on historical observed production and injection rates using material balance and signal correlations between injectors and producers. Waterflood performance methods are applied to evaluate the waterflooding performance effect and to forecast the development index on the basis of Buckley–Leverett displacement theory and oil–water permeability curve. In this case study, we propose an approach that combines a capacitance–resistance model (CRM) modified by increasing the influence radius on the constraints and a waterflood performance equation between oil cut and oil accumulative production to improve liquid and oil production prediction ability. By applying the method, we can understand the waterflood performance, inter-well connectivities between injectors and producer, and production rate fluctuation better, in order to re-just the water injection and optimize the producers’ working parameters to maximize gain from the reservoir. The new approach provides an effective way to estimate the conductivities between wells and production rates of a single well or well groups in CRMs. The application results in Kalamkas oilfield show that the estimated data can be in good agreement with the actual observation data with small fitting errors, indicating a good development index forecasting capability.
Experimental Characterization of Hydrodynamic Properties of a Deformable Rock Fracture
Authors: Haghi Amir H. , Chalaturnyk Richard
Published: September 2022

Characterization of stress-dependent single-phase and multiphase fluid transport in fractured geo-materials is essential for a wide range of applications, including CO2 sequestration, energy storage, and geo-energy extraction. However, pivotal studies on capillarity and multiphase fluid flow in deformable rock fractures are surprisingly sparse. In this study, we initially investigated the hydro-mechanical properties of an intact mixed-wet Calumet carbonate from the Waterways formation (Alberta) under isothermal conditions (40 °C). Then, we conducted core-flooding experiments using water and N2 to assess changes in the aperture, absolute permeability, relative permeability, and capillary pressure of an artificially fractured Calumet core in response to changes in effective confining stress during loading (0–10 MPa) and unloading (10–3 MPa). We quantified the fracture’s non-linear closure and hysteresis effect during the cyclic loading–unloading processes. We found that porosity and absolute permeability of the fracture decreased from 1.5% and 19.8 D to 1.18% and 0.22 D, respectively, during the loading. We revealed a systematic rightward shift in the relative permeability and a significant upward shift in the dynamic capillary pressure curves as the fracture deformed. This fundamental study demonstrates the critical role of fracture deformation on fluid flow in fractured rocks, which paves the way for future research in geoscience and engineering.
Innovative Grid-Connected Photovoltaic Systems Control Based on Complex-Vector-Filter
Authors: Mansouri Nouha , Nasri Sihem , Lashab Abderezak , Guerrero Josep M. , Cherif Adnen
Published: September 2022

The research presented in this paper explains how the complex-vector-filter (CVF) method can help in minimizing the current harmonic of a grid-tied photovoltaic system. In fact, the harmonic-free positive sequence (HFPS) load current is used to produce referential sinusoidal currents. This control stabilizes the grid’s currents under unbalanced load currents, as well as mitigates undesirable harmonic load currents, while feeding clean active power to the grid. Thanks to the proposed controller, the performance, such as robustness, as well as the stability and dynamics of the CVF are more effective compared to the proportional-integral (PI) with phase-locked-loop (PLL) controller. Moreover, the CVF ensures robustness and stability during the synchronization between the photovoltaic (PV) generator and the utility grid system. The PI&PLL control presents higher active and reactive power fluctuations during synchronization. On the other hand, the CVF ensures the elimination of the reactive power fluctuations during synchronization. The performance of the proposed CVF is validated by simulation through MATLAB software. Under all conditions, the grid current, considering harmonics, is within the limits set by the IEEE-519 power quality standard, where a total harmonic distortion (THD) of 1.56% was achieved in the case of feeding a non-linear load.
Characteristics of Open Innovation among Polish SMEs in the Context of Sustainable Innovative Development Focused on the Rational Use of Resources (Energy)
Authors: Stanisławski Robert
Published: September 2022

Open innovations (OI) are playing an increasingly important role in the innovative development (RI) of SMEs. This has led to a need to analyze the impact of OI on innovative development serving the implementation of the assumptions of sustainable development, the positive effect of which is to reduce the negative impact on the environment thanks to a more rational use of both natural and produced resources (e.g., energy). This development is described in this article as “sustainable innovative development”. Research was conducted on a sample of 800 SMEs in Poland using the quantitative method (questionnaire). The aim of this study was to identify the impact of OI on sustainable innovation development. This goal was achieved through the verification of three research hypotheses. It turns out that SMEs obtain significant benefits by exploring the environment, i.e., by showing a high level of willingness to cooperate with various entities in the environment for sustainable innovative development. Moreover, the results show that SMEs cooperating with the environment are more developed in terms of sustainable innovative development than those that base their development on their own internal resources (no cooperation). Hence, it follows that OIs have a positive impact on sustainable innovative development.
Modeling Fracture Propagation in a Dual-Porosity System: Pseudo-3D-Carter-Dual-Porosity Model
Authors: Al Hameli Fatima , Suboyin Abhijith , Al Kobaisi Mohammed , Rahman Md Motiur , Haroun Mohammed
Published: September 2022

Despite the significant advancements in geomodelling techniques over the past few decades, it is still quite challenging to obtain accurate assessments of hydraulic fracture propagation. This work investigates the effect of fluid leak-off in a dual-porosity system on the hydraulic fracture propagation geometry, which, in turn, affects hydrocarbon recovery from tight and unconventional reservoirs. Fracture propagation within tight reservoirs was analyzed using the Pseudo Three-Dimensional-Carter II model for single- (P3D-C) and dual-porosity systems (P3D-C-DP). Previous studies have accounted for leak-off in single-porosity models; however, studies within dual-porosity systems are still quite limited. We present a novel approach to coupling fluid leak-off in a dual-porosity system along with a fracture-height growth mechanism. Our findings provide important insights into the complexities within hydraulic fracturing treatment design using our new and pragmatic modeling approach. The simulation results illustrate that fluid leak-off in dual-porosity systems contributes to a confined fracture half-length (xf), that is 31% smaller using the P3D-C-DP model as opposed to the single-porosity model (P3D-C). As for the fracture height growth (hf), the P3D-C-DP model resulted in a 40% shorter fracture height compared to the single-porosity model.
Developing a TEI-Aware PMIC for Ultra-Low-Power System-on-Chips
Authors: Lee Kyu-Bae , Park Jina , Choi Eunjin , Jeon Mingi , Lee Woojoo
Published: September 2022

As the demand for ultra-low-power (ULP) devices has increased tremendously, system-on-chip (SoC) designs based on ultra-low-voltage (ULV) operation have been receiving great attention. Moreover, research has shown the remarkable potential that even more power savings can be achieved in ULV SoCs by exploiting the temperature effect inversion (TEI) phenomenon, i.e., the delay of the ULV SoCs decreases with increasing temperature. However, TEI-aware low-power (TEI-LP) techniques have a critical limitation in practical terms, in that dedicated power management-integrated circuits (PMICs) have not yet been developed. In other words, it is essential to develop PMICs that automatically bring out the full potential of the TEI-LP techniques as the chip temperature changes. With the aim of designing such PMICs, this paper first conducted a study to find the most suitable DC-DC converter for PMICs and then developed a control algorithm to maximize the effectiveness of the TEI-LP techniques. Furthermore, we have developed a compact hardware controller for the algorithm to operate most energy efficiently on ULP-SoCs.
A Study on Inter-Provincial Environmental Pollution Movement in China Based on the Input–Output Method
Authors: Shi Yong , Tang Anda , Yao Tongsheng
Published: September 2022

In China, environmental pollution responsibilities are divided according to administrative regions. However, because of the strong externality of environmental pollution, the movement of environmental pollution undoubtedly increases the complexity of pollution governance. To divide the responsibility of environmental pollution governance in each province, we effectively quantify the unequal relationship between environmental pollution costs and economic benefits in each province to understand the mechanism and characteristics of inter-provincial environmental pollution movement. Based on the regional input–output model and an inventory we compiled of sulfur dioxide(SO2) emissions of air pollutants in 2012, we calculate the implied inter-provincial environmental pollution emissions and economic benefits from trade, based on production and consumption (supply-side and demand-side). In addition, the movement relationship is explored, and the cost and economic value-added indexes of air pollution control are further constructed to provide effective evidence for a reasonable division of responsibility for environmental pollution control. The results show that there are obvious environmental inequities in the regional trade process in each province, indicating that environmental pollution has been moved. Developed provinces have more economic benefits but pay less in the process of trading goods with less-developed provinces due to the advantages of their industrial structure, while the opposite is true for less-developed provinces. Finally, we propose corresponding policy recommendations to change this condition.