bluuebunny/crossref_metadata_2025 huggingface dataset:
Decoupled Monte Carlo Tree Search for Cooperative Multi-Agent Planning
Authors: Asik Okan , Aydemir Fatma Başak , Akın Hüseyin Levent
Published: February 2023

The number of agents exponentially increases the complexity of a cooperative multi-agent planning problem. Decoupled planning is one of the viable approaches to reduce this complexity. By integrating decoupled planning with Monte Carlo Tree Search, we present a new scalable planning approach. The search tree maintains the updates of the individual actions of each agent separately. However, this separation brings coordination and action synchronization problems. When the agent does not know the action of the other agent, it uses the returned reward to deduce the desirability of its action. When a deterministic action selection policy is used in the Monte Carlo Tree Search algorithm, the actions of agents are synchronized. Of all possible action combinations, only some of them are evaluated. We show the effect of action synchronization on different problems and propose stochastic action selection policies. We also propose a combined method as a pruning step in centralized planning to address the coordination problem in decoupled planning. We create a centralized search tree with a subset of joint actions selected by the evaluation of decoupled planning. We empirically show that decoupled planning has a similar performance compared to a central planning algorithm when stochastic action selection is used in repeated matrix games and multi-agent planning problems. We also show that the combined method improves the performance of the decoupled method in different problems. We compare the proposed method to a decoupled method in regard to a warehouse commissioning problem. Our method achieved more than 10% improvement in performance.
Special Issue on Natural Hazards Risk Assessment for Disaster Mitigation
Authors: Nikolić Željana , Benvenuti Elena , Chiozzi Andrea
Published: February 2023

Knowledge and awareness of the risks generated by natural hazards are essential requirements for the enhancement of communities’ resilience to disasters [...]
A Method for Translating Automotive Body-Related CAN Messages Based on Labeled Bits
Authors: Bi Zixiang , Xu Guosheng , Wang Chenyu , Xu Guoai , Zhang Sutao
Published: February 2023

Traditional mechanical parts have been increasingly replaced by in-vehicle electronic control units (ECUs) that communicate via control area networks (CAN). For security reasons, the Database CAN (DBC) file, which defines the contents of CAN messages, is deemed confidential by original equipment manufacturers (OEMs). However, confidentiality has severely hindered research on automotive intrusion detection systems (IDS) and automotive control network testing, which makes automotive aftermarket device development difficult. Previous research has used tokenization algorithms, machine learning algorithms, and diagnostic information to obtain coarse CAN message contents. However, there is a large gap between the results obtained with these methods and the information contained in DBC files. In order to reverse CAN messages in a fine-grained manner, we propose a method to reverse a body-related CAN message based on tagged bits. This method tags data bits by collecting CAN traffic in different vehicle states. The test messages are obtained by fuzzing the CAN messages based on the tagging results, and the candidate messages are obtained by combining them with the results of a packet analysis. The final reverse result was based on the column AND bit-by-bit of the candidate messages. The reverse results showed that the method proposed in this paper could accurately locate the bits representing or controlling the body behavior with high reverse accuracy.
Design and Validation of a U-Net-Based Algorithm for Star Sensor Image Segmentation
Authors: Mastrofini Marco , Agostinelli Ivan , Curti Fabio
Published: February 2023

The present work focuses on the investigation of an artificial intelligence (AI) algorithm for brightest objects segmentation in night sky images’ field of view (FOV). This task is mandatory for many applications that want to focus on the brightest objects in an optical sensor image with a particular shape: point-like or streak. The algorithm is developed as a dedicated application for star sensors both for attitude determination (AD) and onboard space surveillance and tracking (SST) tasks. Indeed, in the former, the brightest objects of most concern are stars, while in the latter they are resident space objects (RSOs). Focusing attention on these shapes, an AI-based segmentation approach can be investigated. This will be carried out by designing, developing and testing a convolutional neural network (CNN)-based algorithm. In particular, a U-Net will be used to tackle this problem. A dataset for the design process of the algorithm, network training and tests is created using both real and simulated images. In the end, comparison with traditional segmentation algorithms will be performed, and results will be presented and discussed together with the proposal of an electro-optical payload for a small satellite for an in-orbit validation (IOV) mission.
Lower and Upper Solution Method for Semilinear, Quasi-Linear and Quadratic Singularly Perturbed Neumann Boundary Value Problems
Authors: Vrabel Robert
Published: February 2023

In this paper, by using the method of lower and upper solutions and notion of (Iq)-stability, we established sufficient conditions for the uniform convergence of the solutions of singularly perturbed Neumann boundary value problems for second-order differential equations to the solution of their reduced problems.
A Hybrid Model for Evaluating the Bikeability of Urban Bicycle Systems
Authors: Hsu Chao-Che , Kuo Ya-Wen , Liou James J. H.
Published: February 2023

Improving people’s willingness to ride bicycles has become the main green transportation policy of the government in the world. Bikeability is an important factor affecting the willingness to ride. Since the urban riding environment is more complex than the suburbs, it is necessary to establish a complete urban bikeability evaluation framework. This study applies Bayesian BWM (Best Worst Method) and modified VIKOR to develop an urban bikeability evaluation framework. First, this study collects criteria affecting urban bikeability through literature review and experts’ surveys to develop a novel evaluation framework. Second, the Bayesian BWM was used to evaluate the relative weights of criteria and dimensions. Finally, the modified VIKOR was used to evaluate the riding environment of urban bicycle systems. The modified VIKOR replaces the relatively good concept as the aspiration level, which can effectively reflect the real situation. This study used two cities of Taiwan as case studies to demonstrate the usefulness and effectiveness of the proposed model. The results show that “completeness of facilities” is the most important dimension and “maintenance of bicycle pavements”, “width of bicycle lanes”, and “separation of bicycle lanes and car lanes” are the critical criteria. Based on the findings, some management implications and improving strategies are provided.
Recent Developments in Biopolymer-Based Hydrogels for Tissue Engineering Applications
Authors: Hama Rikako , Ulziibayar Anudari , Reinhardt James W. , Watanabe Tatsuya , Kelly John , Shinoka Toshiharu
Published: February 2023

Hydrogels are being investigated for their application in inducing the regeneration of various tissues, and suitable conditions for each tissue are becoming more apparent. Conditions such as the mechanical properties, degradation period, degradation mechanism, and cell affinity can be tailored by changing the molecular structure, especially in the case of polymers. Furthermore, many high-functional hydrogels with drug delivery systems (DDSs), in which drugs or bioactive substances are contained in controlled hydrogels, have been reported. This review focuses on the molecular design and function of biopolymer-based hydrogels and introduces recent developments in functional hydrogels for clinical applications.
Risk Assessment of Transgender People: Development of Rodent Models Mimicking Gender-Affirming Hormone Therapies and Identification of Sex-Dimorphic Liver Genes as Novel Biomarkers of Sex Transition
Authors: Tassinari Roberta , Tammaro Alessia , Lori Gabriele , Tait Sabrina , Martinelli Andrea , Cancemi Luigia , Frassanito Paolo , Maranghi Francesca
Published: February 2023

Transgender (TG) describes individuals whose gender identity differs from the social norms. TG people undergoing gender-affirming hormone therapy (HT) may be considered a sub-group of the population susceptible to environmental contaminants for their targets and modes of action. The aim of this study is to set appropriate HT doses and identify specific biomarkers to implement TG animal models. Four adult rats/group/sex were subcutaneously exposed to three doses of HT (plus control) selected starting from available data. The demasculinizing-feminizing models (dMF) were β-estradiol plus cyproterone acetate, at 0.09 + 0.33, 0.09 + 0.93 and 0.18 + 0.33 mg, respectively, five times/week. The defeminizing-masculinizing models (dFM) were testosterone (T) at 0.45, 0.95 and 2.05 mg, two times/week. Clitoral gain and sperm count, histopathological analysis of reproductive organs and liver, hormone serum levels and gene expression of sex-dimorphic CYP450 were evaluated. In the dMF model, the selected doses—leading to T serum levels at the range of the corresponding cisgender—induced strong general toxicity and cannot be used in long-term studies. In the dFM model, 0.45 mg of T represents the correct dose. In addition, the endpoints selected are considered suitable and reliable to implement the animal model. The sex-specific CYP expression is a suitable biomarker to set proper (de)masculinizing/(de)feminizing HT and to implement TG animal models.
Single-Cell Analysis of Primary Liver Cancer in Mouse Models
Authors: Suoangbaji Tina , Zhang Vanilla Xin , Ng Irene Oi-Lin , Ho Daniel Wai-Hung
Published: February 2023

Primary liver cancer (PLC), consisting mainly of hepatocellular carcinoma and intrahepatic cholangiocarcinoma, is one of the major causes of cancer-related mortality worldwide. The curative therapy for PLC is surgical resection and liver transplantation, but most PLCs are inoperable at diagnosis. Even after surgery, there is a high rate of tumor recurrence. There is an unmet clinical need to discover more effective treatment options for advanced PLCs. Pre-clinical mouse models in PLC research have played a critical role in identifying key oncogenic drivers and signaling pathways in hepatocarcinogenesis. Furthermore, recent advances in single-cell RNA sequencing (scRNA-seq) have provided an unprecedented degree of resolution in such characterization. In this review, we will summarize the recent studies that utilized pre-clinical mouse models with the combination of scRNA-seq to provide an understanding of different aspects of PLC. We will focus particularly on the potentially actionable targets regarding the cellular and molecular components. We anticipate that the findings in mouse models could complement those in patients. With more defined etiological background, mouse models may provide valuable insights.
The Single and Combined Effects of Prenatal Nonchemical Stressors and Lead Exposure on Neurodevelopmental Outcomes in Toddlers: Results from the CCREOH Environmental Epidemiologic Study in Suriname
Authors: Koendjbiharie Aloysius Ph. , Hindori-Mohangoo Ashna D. , Zijlmans Wilco C. W. R. , Wickliffe Jeffrey K. , Shankar Arti , Covert Hannah H. , Lichtveld Maureen Y. , Grünberg Antoon W. , Drury Stacy S.
Published: February 2023

The primary aim of this prospective study was to examine the single and combined effect of prenatal exposure to perceived stress, probable depression, and lead on toddlers’ neurodevelopment using the Bayley Scales of Infant and Toddler Development, third edition. Data from 363 mother-toddler pairs enrolled in the Caribbean Consortium for Research in Environmental and Occupational Health prospective cohort study were analyzed. A prenatal lead exposure of ≥3.5 µg/dL was associated with significantly lower receptive (p = 0.008) and expressive (p = 0.006) communication scaled scores. Moderate and severe maternal prenatal probable depression scores were associated with significantly lower fine (p = 0.009) and gross (p = 0.009) motor scaled scores. However, a maternal report of prenatal stress was not associated with neurodevelopmental outcomes. After adjusting for maternal demographics, prenatal stress and lead exposure, prenatal probable depression remained predictive of the toddlers’ gross motor scaled scores (β −0.13, 95% CI [−0.24–−0.02]). Similarly, when adjusting for demographics, prenatal stress and probable depression, prenatal lead exposure remained a significant predictor of their receptive communication scaled scores (β −0.26, 95% CI [−0.49–−0.02]). An analysis testing combined exposure to perceived stress, probable depression, and lead exposure, measured using a cumulative risk index, significantly predicted the child fine motor scaled scores after adjusting for other covariates (β −0.74, 95% CI: [−1.41–−0.01]).
Prevalence of Dual Diagnoses among Children and Adolescents with Mental Health Conditions
Authors: Tomáš Jandáč , Lenka Šťastná
Published: February 2023

Background: The World Health Organisation defines dual diagnosis as the co-occurrence in the same individual of a psychoactive substance use disorder and another psychiatric disorder. Children and adolescents with dual diagnoses represent a significant public health burden in social and financial terms. Aims: The objective of the present paper is to provide a review of studies dealing with dual diagnoses and their prevalence among children and adolescents whose primary treatment involves psychiatric conditions. Methods: The PRISMA was used to conduct a systematic search. Articles published between January 2010 and May 2022 were searched for analysis. Results: Eight articles were eventually found eligible for the final content analysis. The analysis of the articles identified the prevalence of dual diagnoses among children and adolescents treated primarily for psychiatric conditions, the gender-specific occurrence of dual diagnoses, methods used to diagnose psychiatric and substance use disorders, types of psychiatric diagnoses involved in dual diagnoses, and prevalence differences contingent on the type of service provision as the main thematic areas. The prevalence of dual diagnoses among the target population ranged from 18.3% to 54% (mean 32.7%). Boys were more likely to experience dual diagnoses and affective disorders were the most frequent psychiatric diagnoses. Conclusion: The importance of the issue and the high prevalence of dual diagnoses make it imperative that this type of research is pursued.
A Novel Computational Model for Traction Performance Characterization of Footwear Outsoles with Horizontal Tread Channels
Authors: Gupta Shubham , Chatterjee Subhodip , Malviya Ayush , Singh Gurpreet , Chanda Arnab
Published: February 2023

Slips and falls are among the most serious public safety hazards. Adequate friction at the shoe–floor contact is necessary to reduce these risks. In the presence of slippery fluids such as water or oil, the footwear outsole is crucial for ensuring appropriate shoe–floor traction. While the influence of flooring and contaminants on footwear traction has been extensively studied across several outsole surfaces, limited studies have investigated the science of outsole design and how it affects footwear traction performance. In this work, the tread channels of a commonly found outsole pattern, i.e., horizontally oriented treads, was varied parametrically across the widths (i.e., 2, 4, 6 mm) and gaps (i.e., 2, 3, 4 mm). Nine outsole designs were developed and their traction, fluid pressures, and fluid flow rates during slipping were estimated using a mechanical slip testing and a CFD-based computational framework. Outsoles which had wider tread (i.e., 6 mm) surfaces showed increased slip risks on wet flooring. Outsoles with large gaps (i.e., 4 mm) exhibited increased traction performance when slipped on wet flooring (R2 = 0.86). These novel results are anticipated to provide valuable insights into the science of footwear traction and provide important guidelines for the footwear manufacturers to optimize outsole surface design to reduce the risk of slips and falls. In addition to this, the presented CFD-based computational framework could help develop better outsole designs to further solve this problem.
Thermal Spring Waters as an Active Ingredient in Cosmetic Formulations
Authors: Figueiredo Ana Carolina , Rodrigues Márcio , Mourelle M. Lourdes , Araujo André R. T. S.
Published: February 2023

Background: Thermal waters have been showing different beneficial effects on the skin due to their physicochemical composition. The beneficial effect of thermal water in the treatment of some skin diseases may thus justify its use as an active ingredient in cosmetic formulations. The main objective of this work was to demonstrate the potential of incorporating thermal water as an active ingredient in cosmetic formulations. (2) Methods: A descriptive literature review was carried out by the analysis of scientific articles in PubMed and Google Scholar databases. Twelve thermal spring waters were found (Avène, Blue Lagoon, Comano, Cró, Dead Sea, La Roche-Posay, Monfortinho, Saint-Gervais, Salies-de-Béarn, São Pedro do Sul, Uriage and Vichy) with potential as an active in cosmetic products, demonstrated through in vitro studies evaluating the different activities/properties and clinical trials in healthy volunteers or with skin pathologies. (3) Results: For these studies, in natura thermal water as well as incorporated in cosmetic formulations were used. In in vitro studies, most thermal waters have been shown to have activities on membrane fluidity, skin barrier repair, antiradical, antioxidant, anti-inflammatory and immunomodulatory properties, proliferative activity, regulation of processes involved in ageing and moisturizing properties. In clinical trials, cosmetic thermal waters reduced skin discomfort through their soothing and exhibited moisturizing and anti-irritant properties. (4) Conclusions: The effect of thermal waters on the skin and the absence of side effects reported in different studies allows them to be used as an adjuvant or in the treatment of various skin disorders and may play an important role in the cosmetics industry. However, further clinical trials are needed to assess their effectiveness and safety.
Oophorectomy in Premenopausal Patients with Estrogen Receptor-Positive Breast Cancer: New Insights into Long-Term Effects
Authors: Khan Fatima , Rojas Kristin , Schlumbrecht Matthew , Jeudin Patricia
Published: February 2023

Approximately 80% of breast cancers are estrogen receptor-positive (ER+), and 68–80% of those occur in premenopausal or perimenopausal women. Since the introduction of tamoxifen for adjuvant endocrine therapy in women with non-metastatic ER+ breast cancer, subsequent trials have demonstrated an oncologic benefit with the addition of ovarian function suppression (OFS) to adjuvant endocrine therapy. Subsequently, therapies to either suppress or ablate ovarian function may be included in the treatment plan for patients that remain premenopausal or perimenopausal after upfront or adjuvant chemotherapy and primary surgery. One strategy for OFS, bilateral salpingo-oophorectomy (BSO), has lasting implications, and the routine recommendation for this strategy warrants a critical analysis in this population. The following is a narrative review of the utility of ovarian suppression or ablation (through either bilateral oophorectomy or radiation) in the context of adjuvant endocrine therapy, including selective estrogen receptor modulators (SERMs) and aromatase inhibitors (AIs). The long-term sequelae of bilateral oophorectomy include cardiovascular and bone density morbidity along with sexual dysfunction, negatively impacting overall quality of life. As gynecologists are the providers consulted to perform bilateral oophorectomies in this population, careful consideration of each patient’s oncologic prognosis, cardiovascular risk, and psychosocial factors should be included in the preoperative assessment to assist in shared decision-making and prevent the lifelong adverse effects that may result from overtreatment.
Narrow Pore Crossing of Active Particles under Stochastic Resetting
Authors: Zhang Weitao , Li Yunyun , Marchesoni Fabio , Misko Vyacheslav R. , Ghosh Pulak K.
Published: February 2023

We propose a two-dimensional model of biochemical activation process, whereby self-propelling particles of finite correlation times are injected at the center of a circular cavity with constant rate equal to the inverse of their lifetime; activation is triggered when one such particle hits a receptor on the cavity boundary, modeled as a narrow pore. We numerically investigated this process by computing the particle mean-first exit times through the cavity pore as a function of the correlation and injection time constants. Due to the breach of the circular symmetry associated with the positioning of the receptor, the exit times may depend on the orientation of the self-propelling velocity at injection. Stochastic resetting appears to favor activation for large particle correlation times, where most of the underlying diffusion process occurs at the cavity boundary.
The Impact of the COVID-19 Pandemic on the Revealed Comparative Advantage of Industries in the Baltic States
Authors: Droždz Jolanta , Burinskas Arūnas , Cohen Viktorija
Published: February 2023

The active spread of COVID-19 and the resulting containment measures have made it clear that both supply and demand and global production networks are facing unprecedented shocks and disruptions. Accordingly, this has resulted in an urgent need to investigate countries’ competitive situation (and its changes) during a prolonged period of uncertainty. This study aims to assess the impact of the COVID-19 pandemic on the revealed comparative advantage (RCA/ Balassa index) of the Baltic states’ industries. The Balassa index was calculated for nine industries in three Baltic States (Lithuania, Latvia, and Estonia). The RCA index calculations were linked to COVID-19 cases in the countries under consideration by forming a regression equation. RCA index values and their changes were evaluated by analysing data before and after the pandemic, covering the period between 2017 and 2021. This study revealed that the COVID-19 pandemic’s impact on each Baltic country’s competitiveness in trade with EU countries is significantly higher than in trade with third countries. The results show that Baltic states did not have a comparative advantage in trade with third countries during this time. However, Lithuania and Latvia proved to be more resilient to the consequences of the pandemic, even though industries with a low RCA were more affected. Meanwhile, in trade with EU countries, many of the Baltic states’ industries appeared to have a comparative advantage, which began to decline a few years before the pandemic’s start. Nevertheless, highly competitive Baltics industries showed remarkable resilience to the impact of the pandemic. However, a short-term decrease in the RCA was observed in individual cases.
Supporting Decision-Making Process on Higher Education Dropout by Analyzing Academic, Socioeconomic, and Equity Factors through Machine Learning and Survival Analysis Methods in the Latin American Context
Authors: Gutierrez-Pachas Daniel A. , Garcia-Zanabria Germain , Cuadros-Vargas Ernesto , Camara-Chavez Guillermo , Gomez-Nieto Erick
Published: February 2023

The prediction of university dropout is a complex problem, given the number and diversity of variables involved. Therefore, different strategies are applied to understand this educational phenomenon, although the most outstanding derive from the joint application of statistical approaches and computational techniques based on machine learning. Student Dropout Prediction (SDP) is a challenging problem that can be addressed following various strategies. On the one hand, machine learning approaches formulate it as a classification task whose objective is to compute the probability of belonging to a class based on a specific feature vector that will help us to predict who will drop out. Alternatively, survival analysis techniques are applied in a time-varying context to predict when abandonment will occur. This work considered analytical mechanisms for supporting the decision-making process on higher education dropout. We evaluated different computational methods from both approaches for predicting who and when the dropout occurs and sought those with the most-consistent results. Moreover, our research employed a longitudinal dataset including demographic, socioeconomic, and academic information from six academic departments of a Latin American university over thirteen years. Finally, this study carried out an in-depth analysis, discusses how such variables influence estimating the level of risk of dropping out, and questions whether it occurs at the same magnitude or not according to the academic department, gender, socioeconomic group, and other variables.
Helping Students Become Proficient Problem Solvers Part I: A Brief Review
Authors: Maries Alexandru , Singh Chandralekha
Published: February 2023

Understanding issues involved in expertise in physics problem solving is important for helping students become good problem solvers. In part 1 of this article, we summarize the research on problem solving relevant for physics education across three broad categories: knowledge organization, information processing and cognitive load, and metacognition and problem-solving heuristics. We also discuss specific strategies discussed in the literature for promoting the development of problem-solving skills in physics. This review article can be valuable in helping instructors develop students’ problem solving, reasoning, and metacognitive skills in physics and other related disciplines. Additionally, this review article is relevant across educational contexts in countries that may have different educational paradigms and challenges.
Student-Engagement Detection in Classroom Using Machine Learning Algorithm
Authors: Alruwais Nuha , Zakariah Mohammed
Published: February 2023

Student engagement is a flexible, complicated concept that includes behavioural, emotional, and cognitive involvement. In order for the instructor to understand how the student interacts with the various activities in the classroom, it is essential to predict their participation. The current work aims to identify the best algorithm for predicting student engagement in the classroom. In this paper, we gathered data from VLE and prepared them using a variety of data preprocessing techniques, including the elimination of missing values, normalization, encoding, and identification of outliers. On our data, we ran a number of machine learning (ML) classification algorithms, and we assessed each one using cross-validation methods and many helpful indicators. The performance of the model is evaluated with metrics like accuracy, precision, recall, and AUC scores. The results show that the CATBoost model is having higher accuracy than the rest. This proposed model outperformed in all the aspects compared to previous research. The results part of this paper indicates that the CATBoost model had an accuracy of approximately 92.23%, a precision of 94.40%, a recall of 100%, and an AUC score of 0.9624. The XGBoost predictive model, the random forest model, and the multilayer perceptron model all demonstrated approximately the same performance overall. We compared the AISAR model with Our model achieved an accuracy of 94.64% compared with AISAR 91% model and it concludes that our results are better. The AISAR model had only around 50% recall compared to our models, which had around 92%. This shows that our models return more relevant results, i.e., if our models predict that a student has high engagement, they are correct 94.64% of the time.
CBCS: A Scalable Consortium Blockchain Architecture Based on World State Collaborative Storage
Authors: Zhou Jiashun , Wang Na , Liu Aodi , Wang Wenjuan , Du Xuehui
Published: February 2023

In the big data environment, data are characterized by a large volume, various types, and rapid changes. The consortium blockchain applied in this environment faces the problem of excessive storage of the ledger, and the ledger handling different types of business needs to be isolated to ensure the ledger’s security. To this end, this paper proposes a scalable consortium blockchain architecture based on world state collaborative storage (CBCS). First, a business world state database update method is designed based on sparse Merkle multiproofs, where the collaborative storage of world state is realized under the premise of mutual isolation of the ledger between business domains. Then, a world state consistency verification method based on the rank B+ tree is designed to verify the consistency of the business world state in business domains by the checking sidechain, and a main-side chain cross-anchoring structure is designed to realize secure anchoring of the mainchain and the checking sidechain. Meanwhile, a blockchain transaction trusted tracing method based on two-level certification is developed to enable business nodes to obtain complete blockchain transactions. Finally, the feasibility and efficiency of the proposed mechanism to solve the storage scalability problem in the consortium blockchain are verified through experiments.
Millimeter-Wave Image Deblurring via Cycle-Consistent Adversarial Network
Authors: Liu Huteng , Wang Shuoguang , Jing Handan , Li Shiyong , Zhao Guoqiang , Sun Houjun
Published: February 2023

Millimeter-wave (MMW) imaging has a tangible prospect in concealed weapon detection for security checks. Typically, a one-dimensional (1D) linear antenna array with mechanical scanning along a perpendicular direction is employed for MMW imaging. To achieve high-resolution imaging, the target under test needs to keep steady enough during the mechanical scanning process since slight movement can induce large phase variation for MMW systems, which will result in a blurred image. However, in the scenario of imaging of a human body, sometimes it is difficult to meet this requirement, especially for the elderly. Such blurred MMW images would reduce the detection accuracy of the concealed weapons. In this paper, we propose a deblurring method based on cycle-consistent adversarial network (Cycle GAN). Specifically, the Cycle GAN can learn the mapping between the blurred MMW images and the focused ones. To minimize the effect of the shaking blur, we introduce an identity loss. Moreover, a mean squared error loss (MSE loss) is utilized to stabilize the training, so as to obtain more refined deblurred results. The experimental results demonstrate that the proposed method can efficiently suppress the blurring effect in the MMW image.
Research Progress of Tumor Big Data Visualization
Authors: Chen Xingyu , Liu Bin
Published: February 2023

Background: As the number of tumor cases significantly increases, so does the quantity of tumor data. The mining and application of large-scale data have promoted the development of tumor big data. Among them, the visualization methods of tumor big data can well show the key information in a large volume of data and facilitate the human brain to receive information. Therefore, tumor big data visualization methods are a key part of the development of tumor big data. Process: This paper first summarizes the connotation, sources, characteristics, and applications of tumor big data, and expounds the current research status of tumor big data visualization at home and abroad. Then, this paper focuses on four mainstream visualization presentation methods of tumor big data, namely the visualization of tumor spatiotemporal data, the visualization of tumor hierarchy and network data, the visualization of tumor text data, and the visualization of multidimensional tumor data, and gives specific application scenarios. After this, the paper introduces the advantages, disadvantages, and scope of the use of five data visualization websites and software that can be easily obtained by readers. Finally, this paper analyzes the problems existing in tumor big data visualization, summarizes the visualization methods, and proposes the future of tumor big data visualization.
Document Image Restore via SPADE-Based Super-Resolution Network
Authors: Kim Jaehun , Choe Yoonsik
Published: February 2023

With the development of deep learning technology, various structures and research methods for the super-resolution restoration of natural images and document images have been introduced. In particular, a number of recent studies have been conducted and developed in image restoration using generative adversarial networks. Super-resolution restoration is an ill-posed problem because of some complex restraints, such as many high-resolution images being restored for the same low-resolution image, as well as difficulty in restoring noises such as edges, light smudging, and blurring. In this study, we applied super-resolution restoration to text images using the spatially adaptive denormalization (SPADE) structure, different from previous methods. This paper used SPADE for document image restoration to solve previous problems such as edge unclearness, hardness to catch features of texts, and the image color transition. As a result of this study, it can be confirmed that the edge of the character and the ambiguous stroke are restored more clearly when contrasting with the other previously suggested methods. Additionally, the proposed method’s PSNR and SSIM scores are 8% and 15% higher compared to the previous methods.
A Bio-Inspired Cluster Optimization Schema for Efficient Routing in Vehicular Ad Hoc Networks (VANETs)
Authors: Husnain Ghassan , Anwar Shahzad , Sikander Gulbadan , Ali Armughan , Lim Sangsoon
Published: February 2023

Vehicular ad hoc networks (VANETs) are vital to many Intelligent Transportation System (ITS)-enabled technologies, including efficient traffic control, media applications, and encrypted financial transactions. Due to an increase in traffic, vehicular network topology is constantly changing, and sparse vehicle distribution (on highways) hinders network scalability. Thus, there is a challenge for all vehicles (in the network) to maintain a stable route, which would increase network instability. Concerning IoT-based network transportation, this study proposes a bio-inspired, cluster-based algorithm for routing, i.e., the intelligent, probability-based, and nature-inspired whale optimization algorithm (p-WOA), which produces cluster formation in vehicular communication. Various parameters, such as communication range, number of nodes, velocity, and route along the highway were considered, and their probaabilities were incorporated into the fitness function, hence resulting in randomness reduction. Results were compared to existing methods such as Ant Lion Optimizer (ALO) and Grey Wolf Optimization (GWO), demonstrating that the developed p-WOA technique produces an optimal number of cluster heads (CH). The results achieved by calculating the Packet Delivery Ratio (PDR), average throughput, and latency demonstrate the superiority of the proposed method over other well-established methodologies (ALO and GWO). This study confirms statistically that VANETs employing ITS applications optimize their clusters by a factor of 75, which has the twin benefits of decreasing communication costs and routing overhead and extending the life of the cluster as a whole.
Evaluation and Solution Suggestions for Engineering and Workmanship Failures during Design and Installation of Solar Power Plants
Authors: Bakır Hale , Merabet Adel
Published: February 2023

Among the various renewable energy generation systems, the solar photovoltaic occupies a leading position today due to its simple structure. However, increasing the efficiency of solar photovoltaic systems is a highly researched topic. In this study, possible connection failures in maximum power inverters and other failures, which decrease the efficiency in solar power plants, are examined. Furthermore, the possible consequences of these losses and their effects on the performance of solar power plants are explained. Some missing-failure processes were identified and corrected in the field analysis of the solar power plant in Turkey. Detected missing failures include connection failures of solar inverters, incorrect network configuration of camera system, fixing lighting time settings. The inverter string connection failure made during the projecting and assembly phase was eliminated and the maximum output was determined as 584.25 kW after the DC string arrangement. An increase of approximately 10% was achieved in production. In the project and application phase, the connection details of the inverters should be drawn and given to the field application personnel as a full-fledged project. In this way, incorrect connections that are not shown in the project and made in the field are prevented. This ensures that the installed power plant operates more efficiently, and the budgetary payback period of the investments made is shortened.
Effects of Fracture Parameters on VAPEX Performance: A Numerical and Experimental Approach Utilizing Reservoir-On-The-Chip
Authors: Rahimbakhsh Aria , Torabi Farshid
Published: February 2023

The present research carries out an in-detail study of the VAPEX process as one of the most recent solvent-based heavy oil recovery techniques in fractured reservoirs to evaluate the effect of fracture parameters on process performance. To achieve this purpose, several fractured patterns with distinct features were designed and engraved on glass pieces to manufacture state-of-the-art microfluidic models mimicking a typical Canadian heavy oil reservoir. A heavy oil sample of viscosity 1514 cP was utilized during the conducted experiments with pure propane and pure carbon dioxide as the injection solvents. A thorough image analysis operation was carried out over the experimental models to determine heavy oil produced, residual oil saturation, ultimate recovery factors, and monitor solvent chamber expansion. Numerical simulations of the same experiments were carried out for history matching and predicting other designed scenarios. Error analysis revealed average absolute errors of below 8%, showing convincing precision. Together with the simulation outcomes, a comprehensive data bank was obtained from the 30 scenarios designed and 18 VAPEX experiments conducted. The effects of fracture orientation, length, width, intensity, and position on process performance were identified and numerically evaluated. It was observed that all fractures, regardless of their properties, enhanced heavy oil recovery in comparison to the base case (no fractures) scenario. Moreover, propane proved more efficient owing primarily to its higher solubility and effective dispersion. The highest recovery factor, 65.81%, was obtained when implementing two wide vertical fractures on either side of the well pair. Almost equal to that, 64.93% was the process efficiency by positioning two long horizontal fractures between the wells.
Analysis and Design of Resonant DC/AC Converters with Energy Dosing for Induction Heating
Authors: Madzharov Nikolay , Hinov Nikolay
Published: February 2023

This article presents an analysis and methodology for designing resonant inverters with energy dosing for induction heating applications. These power topologies are characterized by the fact that the power consumption of the DC power source does not depend on the magnitude and changes of the load but is a function of the operating frequency, the value of the resonant capacitor and the DC supply voltage. Based on a description of the electromagnetic processes in the power circuit, analytical dependencies have been determined that describe the behavior of the studied power electronic devices. The expressions for the current of the AC circuit in the various stages of the converter’s operation are obtained, and on this basis an engineering methodology for design and prototyping is presented. The proposed methodology is verified through two specific numerical examples, simulation and experimental studies. In this way, the possibilities of these power electronic devices for self-adaptation to the needs and changes of the load, which is very important in the implementation of induction technologies, are demonstrated. Furthermore, the creation and testing of engineering methodologies for the design of power electronic devices are very useful for improving power electronics education.
Risk-Averse Stochastic Programming for Planning Hybrid Electrical Energy Systems: A Brazilian Case
Authors: Kitamura Daniel , Willer Leonardo , Dias Bruno , Soares Tiago
Published: February 2023

This work presents a risk-averse stochastic programming model for the optimal planning of hybrid electrical energy systems (HEES), considering the regulatory policy applied to distribution systems in Brazil. Uncertainties associated with variables related to photovoltaic (PV) generation, load demand, fuel price for diesel generation and electricity tariff are considered, through the definition of scenarios. The conditional value-at-risk (CVaR) metric is used in the optimization problem to consider the consumer’s risk propensity. The model determines the number and type of PV panels, diesel generation, and battery storage capacities, in which the objective is to minimize investment and operating costs over the planning horizon. Case studies involving a large commercial consumer are carried out to evaluate the proposed model. Results showed that under normal conditions only the PV system is viable. The PV/diesel system tends to be viable in adverse hydrological conditions for risk-averse consumers. Under this condition, the PV/battery system is viable for a reduction of 87% in the battery investment cost. An important conclusion is that the risk analysis tool is essential to assist consumers in the decision-making process of investing in HEES.
Operational Data Analysis of a Battery Energy Storage System to Support Wind Energy Generation
Authors: Pontes Luana , Costa Tatiane , Souza Amanda , Dantas Nicolau , Vasconcelos Andrea , Rissi Guilherme , Dias Roberto , Mohamed Mohamed A. , Siano Pierluigi , Marinho Manoel
Published: February 2023

The insertion of renewable sources to diversify the energy matrix is one of the alternatives for the energy transition. In this sense, Brazil is one of the largest producers of renewable energy in the world, mainly in wind generation. However, the impact of integrating intermittent sources into the system depends on their penetration level, causing problems in the electrical network. To evaluate this scenario, the present article aims to investigate the power quality problems generated by wind turbines in connection with the electrical system and how battery energy storage systems (BESS) solve or mitigate these disturbances in the network. Knowing the impacts of high generation power variability, the focus of the work is the application of power smoothing. However, results are presented for five applications (factor correction, voltage control, power factor smoothing, frequency control and time shift) that can be carried out at the studied wind farm. This article presents a real BESS, which has a capacity of 1 MW/1.29 MWh, connected in parallel to a group of wind turbines that provides a power of approximately 50.4 MW located in Brazil. In addition to presenting the system simulation in HOMER Pro software, this study validates the effectiveness of this BESS by presenting real operation data for each application.
The Influence of Plant Type, Substrate and Irrigation Regime on Living Wall Performance in a Semi-Arid Climate
Authors: Bustami Rosmina A. , Beecham Simon , Hopeward James
Published: February 2023

Living walls are fast becoming a ubiquitous feature of modern living and are widely implemented in commercial buildings in both internal and external environments. However, there are several challenges associated with maintaining healthy plant growth on these water sensitive urban design systems. This experimental study of an instrumented prototype-scale living wall has found that there is a close relationship between the plants, substrates and adopted irrigation regimes. In this study, plant selection was found to be more critical than either substrate or irrigation regime selection. This research also found that both the location of the plants on the wall and irrigation volume significantly affected the plants’ ultimate total dry weight. In particular, plants were found to grow taller on the upper section of the living wall compared to the middle and lower sections. It is recommended that particular attention should be given to plant location and the amount of irrigation water supplied at different positions on the living wall.
Aerodynamic Interaction of Migratory Birds in Gliding Flight
Authors: Beaumont Fabien , Murer Sébastien , Bogard Fabien , Polidori Guillaume
Published: February 2023

(1) Background: Many studies suggest that migratory bird groups fly in a V-formation to improve their aerodynamic efficiency, the goal being to reduce their energy expenditure to fly longer distances. To further validate this hypothesis, we numerically simulated the aerodynamic interaction of two gliding migratory birds and evaluated the aerodynamic forces as a function of the bird spacing. (2) Methods: Computational Fluid Dynamics (CFD) was used to model the flow pattern in and around the wake of Canada geese flying at an altitude of 1000 m and a speed of 13.9 m/s. (3) Results: The post-processing of the 3D results revealed a complex flow structure composed of two contra-rotating vortices developing at the wing tip. (4) Conclusions: In a plane perpendicular to the main flow direction, we showed that the bird’s wake could be broken down into two distinct zones: the downwash zone and the upwash zone, the latter being used by birds flying in formation to reduce their energy expenditure. The results of our study suggested an optimal wingtip spacing of -26cm to maximize the lift/drag ratio that characterizes aerodynamic efficiency.
Effects of Lactobacillus on the Differentiation of Intestinal Mucosa Immune Cells and the Composition of Gut Microbiota in Soybean-Sensitized Mice
Authors: Yang Chunhua , Zhu Jierui , Bai Jing , Zhang Jie , Wu Zhihua , Li Xin , Tong Ping , Chen Hongbing , Yang Anshu
Published: February 2023

In the early stage of this study, three strains of Lactobacillus with anti-soybean allergy potential were screened: Lactobacillus acidophilus CICC 6081, Lactobacillus delbrueckii subsp. Bulgaricus CICC 6103 and Lactobacillus plantarum subsp. Plantarum CICC 20988. The aim of this study was to analyze the desensitization effect of three strains of Lactobacillus administered by gavage to soybean-allergic mice through the differentiation of immune cells in intestinal lymph nodes and the changes to gut microbiota. The results showed that the three strains of Lactobacillus could stimulate the proliferation of dendritic cells (DCs) and regulate the balance of Th1/Th2 differentiation in the MLNs and PPs of soybean-allergic mice. Furthermore, the Th17/Tregs cell-differentiation ratio in the MLNs of the Lactobacillus-treated mice was significantly lower than that of the allergic mice (p < 0.05). Compared to the control group, the Shannon, Sobs and Ace indexes of intestinal microbiota in the allergic mice were significantly increased (p < 0.05), and the proportion of Clostridiales was significantly higher (p < 0.05), which was reversed by Lactobacillus gavage. In conclusion, the three strains of Lactobacillus can inhibit the intestinal mucosal immune response and regulate gut microbiota balance in soybean-allergic mice.