bluuebunny/crossref_metadata_2025 huggingface dataset:
Asymmetric Pt(II)-Porphyrin Incorporated in a PVC Ion-Selective Membrane for the Potentiometric Detection of Citrate
Authors: Vlascici Dana , Lascu Anca , Fratilescu Ion , Anghel Diana , Epuran Camelia , Birdeanu Mihaela , Chiriac Vlad , Fagadar-Cosma Eugenia
Published: February 2023

A new sensing material, Pt(II)-5-(4-carboxyphenyl)-10,15,20-tris(4-phenoxyphenyl)-porphyrin (Pt(II)-COOH-TPOPP), was synthesized and characterized. Polymeric membranes containing the porphyrin and three different plasticizers were used as an electroactive material for a new anion-selective sensor. The best composition of the membrane was the one plasticized with dioctylsebacate (DOS), the obtained sensor being citrate-selective in a linear range of 5 × 10−7–1 × 10−1 M citrate. The slope was Nernstian (19.73 mV/decade) with good selectivity towards a number of interfering anions and a lifetime of five weeks.
Oral Health Status of Ngäbe-Buglé Children in Panama: A Cross Sectional Study
Authors: Hagens Eliza , Preatoni Sofia , Bazzini Elena , Akam Daniel , McKalip Konrad , LaBrot Ben , Cagetti Maria
Published: February 2023

Background: Poor oral health is often more prevalent in rural and resource-limited areas. Evaluating oral health status in these communities is the first step in ensuring adequate future health care for the population. The aim of this study was to assess the oral health status of children aged 6–12 years living in the indigenous Ngäbe-Buglé communities. Methods: A cross-sectional study was conducted in two rural indigenous communities of Ngäbe-Buglé on San Cristobal Island in Bocas del Toro, Panama. All children between 6 and 12 years of age and attending local schools were invited to participate, and those whose parents provided oral consent were enrolled. Dental examinations were performed by one trained dentist. To describe oral health, plaque index, DMFT/dmft (decayed, missing, and filled for permanent and primary teeth) index, and developmental defects of enamel index were recorded. Orthodontic characteristics were also evaluated, assessing the prevalence of different molar classes and the prevalence of open bite, lateral crossbite, and scissor bite. Results: A total of 106 children, representing 37.3% of the child population in the age range attending local schools, were included in this study. The mean plaque index of the entire population was 2.8 (SD 0.8). Caries lesions were more common in children living in San Cristobal (80.0%) compared to those living in Valle Escondido (78.3%), p = 0.827. The mean DMFT/dmft for the entire population was 3.3 (SD 2.9). Developmental defects of enamel were recorded in 49 children (46.2%). The majority of the population had a class I molar relationship (80.0%). Anterior open bite, lateral crossbite, and anterior crossbite were found in 10.4%, 4.7%, and 2.8% of the participants, respectively. Conclusions: The oral health of children living in Ngäbe-Buglé communities is generally poor. Oral health education programs for children and adults might play a crucial role in improving the oral health status of the Ngäbe-Buglé population. In addition, the implementation of preventative measures, such as water fluoridation as well as regular toothbrushing with fluoridated toothpaste and more accessible dental care, will be essential in improving future generations’ oral health.
Relationship of Population Attributes of a Dominant Macrofaunal Species with Environmental Conditions in a Eutrophic Estuary (Guanabara Bay, Brazil)
Authors: Neves Raquel A. F. , Santos Luciano N. , Figueiredo Gisela M. , Valentin Jean L.
Published: February 2023

Hydrobioid gastropods are abundant in coastal systems and ecologically important for ecosystem functioning. We aimed to unravel the relationship between usual and stress-related population attributes of the dominant macrofaunal species Heleobia australis with indicators of environmental quality and coastal pollution. Using Guanabara Bay (GB, Brazil) as a model of a multi-impacted coastal system, our hypothesis is that increased amounts of rainfall during the warm season reduce the bay’s environmental quality and induce shifts in snail population attributes. A suite of environmental variables, population attributes, and sediment quality descriptors was assessed by combining field and laboratory evaluations with literature compilation. Results indicate high organic pollution levels with environmental degradation and reinforce GB status as a severely contaminated system. Some environmental conditions can be applied as seasonal predictors of changes in warm-rainy (rainfall and salinity), intermediate (silicate), and cold-dry seasons (nitrite and nitrate). Three selected usual population attributes (snail density, fecundity, and recruitment) were not affected by changes in environmental conditions, but significant effects were detected on two stress-related attributes (relative penis length index and shell deformity). For the first time, shell deformity was recorded in H. australis snails. Low variation in usual population attributes highlight the high tolerance of H. australis to shifts in environmental conditions.
On Volatility Transmission between Gold and Silver Markets: Evidence from A Long-Term Historical Period
Authors: Koulis Alexandros , Kyriakopoulos Constantinos
Published: February 2023

Several studies estimate the volatility spillover effects between gold and silver returns, but none of them used the implied volatility to evaluate the long-term relationship between these two metal markets. Our paper aims to fill this gap in the existing literature. This paper investigates the long-term volatility transmission between gold and silver; by using GARCH and VAR modelling, it finds that the volatility transmission from gold to silver is unidirectional. Volatility strategies using options can be designed to take advantage of this especially in times where the volatility transmission is not captured by the markets. Additionally, the results appear to be useful for gaining better portfolio diversification benefits. Investors, for instance, could use the results of this study for making proper investment decisions during the period of economic down-turns or inflation surges.
Explainable AI-Based DDOS Attack Identification Method for IoT Networks
Authors: Kalutharage Chathuranga Sampath , Liu Xiaodong , Chrysoulas Christos , Pitropakis Nikolaos , Papadopoulos Pavlos
Published: February 2023

The modern digitized world is mainly dependent on online services. The availability of online systems continues to be seriously challenged by distributed denial of service (DDoS) attacks. The challenge in mitigating attacks is not limited to identifying DDoS attacks when they happen, but also identifying the streams of attacks. However, existing attack detection methods cannot accurately and efficiently detect DDoS attacks. To this end, we propose an explainable artificial intelligence (XAI)-based novel method to identify DDoS attacks. This method detects abnormal behaviours of network traffic flows by analysing the traffic at the network layer. Moreover, it chooses the most influential features for each anomalous instance with influence weight and then sets a threshold value for each feature. Hence, this DDoS attack detection method defines security policies based on each feature threshold value for application-layer-based, volumetric-based, and transport control protocol (TCP) state-exhaustion-based features. Since the proposed method is based on layer three traffic, it can identify DDoS attacks on both Internet of Things (IoT) and traditional networks. Extensive experiments were performed on the University of Sannio, Benevento Instrution Detection System (USB-IDS) dataset, which consists of different types of DDoS attacks to test the performance of the proposed solution. The results of the comparison show that the proposed method provides greater detection accuracy and attack certainty than the state-of-the-art methods.
Plants of Commercial Importance in Lesotho: Ethnobotanical and Pharmacological Insights
Authors: Seleteng-Kose Lerato Esther , Likoetla Puseletso , Motjotji Lisebo
Published: February 2023

Several plants have been processed in Lesotho to manufacture various commercial prodcts, ranging from cosmetics and beverages (juice, tea) to traditional herbal mixtures and crafts. Even though plants of commercial importance have been documented in different regions and countries, this is not the case in Lesotho. Therefore, the aim of the study is to record plant species commonly used in the country for development of commercial products. A physical survey was undertaken in different places in the Maseru District of Lesotho between January 2019 and October 2021, taking a record of existing commercial products and identifying plants used in their development. A total of 15 plants of commercial importance in the country are recorded in the current study, namely Agave americana, Aloe ferox, Artemisia afra, Cannabis sativa, Helichrysum odoratissimum, Hemannia depressa, Hypoxis hemerocallidea, Leudeboria cooperi, Mentha spp., Merxmuellera spp., Pelargonium sidoides, Opuntia ficus-indica, Rosa rubiginosa and Urtica urens. Most of the plants are used for production of commercial products in the country, whereas some are exported as raw materials, mainly to countries such as Germany, South Africa, Australia and America. The latter are C. sativa, Merxmuellera spp., P. sidoides and R. rubiginosa. Profiling of the plants provides important information about their commercial potential. However, conservation of the plants is encouraged to ensure their sustainable utilisation.
Flow Field Explorations in a Boundary Layer Pump Rotor for Improving 1D Design Codes
Authors: Freschi Rosa , Bakogianni Agapi , Rajendran David , Palma Eduardo , Talluri Lorenzo , Roumeliotis Ioannis
Published: February 2023

Boundary layer pumps, although attractive due to their compactness, robustness and multi-fluid and phase-handling capability, have been reported to have low experimental efficiencies despite optimistic predictions from analytical models. A lower-order flow-physics-based analytical model that can be used as a 1D design code for sizing and predicting pump performance is described. The rotor component is modelled by means of the Navier–Stokes equations as simplified using velocity profiles in the inter-disk gap, while the volute is modelled using kinetic-energy-based coefficients inspired by centrifugal pumps. The code can predict the rotor outlet and overall pump pressure ratio with an around 3% and 10% average error, respectively, compared to the reference experimental data for a water pump. Moreover, 3D RANS flow-field explorations of the rotor are carried out for different inter-disk gaps to provide insights concerning the improvement of the 1D design code for the better prediction of the overall pump performance. Improvements in volute loss modelling through the inclusion of realistic flow properties at the rotor outlet rather than the detailed resolution of the velocity profiles within the rotor are suggested as guidelines for improved predictions. Such improved design codes could close the gap between predictions and experimental values, thereby paving the way for the appropriate sizing of boundary layer pumps for several applications, including aircraft thermal management.
Performance Analysis of the Thermal Automatic Tracking Method Based on the Model of the UAV Dynamic Model in a Thermal and Cubature Kalman Filter
Authors: Li Ke , Chen Xiaodan , Liu Haobo , Wang Shaofan , Li Ke , Li Bo
Published: February 2023

Fixed-wing, solar-powered unmanned aerial vehicles (SUAVs) can use thermals to expand the duration of flight. Nevertheless, due to the demand for calculating the thermal state parameters of the SUAV during flight, the existing methods still have some shortcomings in their practical applications, such as an inaccurate location estimation of the thermal and an insufficient seeking efficiency. In this paper, by integrating the Gaussian distribution model of thermal updraft of the pitching and roll moment of SUAVs, it is demonstrated that the approach introduced is superior to the traditional methods, disregarding the pitching moment. The simulation indicated that the accuracy and convergence speed of the thermal state estimation, performed while employing the cubature Kalman filter (CKF), are significantly improved after the SUAVs pitching moment is considered. The proposed method improves the automaticity and intelligence of SUAVs autonomous thermal search and enhances the cognitive and decision-making capabilities of SUAVs.
Genetic Fuzzy Methodology for Decentralized Cooperative UAVs to Transport a Shared Payload
Authors: Sathyan Anoop , Ma Ou , Cohen Kelly
Published: February 2023

In this work, we train controllers (models) using Genetic Fuzzy Methodology (GFM) for learning cooperative behavior in a team of decentralized UAVs to transport a shared slung payload. The training is done in a reinforcement learning fashion where the models learn strategies based on feedback received from the environment. The controllers in the UAVs are modeled as fuzzy systems. Genetic Algorithm is used to evolve the models to achieve the overall goal of bringing the payload to the desired locations while satisfying the physical and operational constraints. The UAVs do not explicitly communicate with one another, and each UAV makes its own decisions, thus making it a decentralized system. However, during the training, the cost function is defined such that it is a representation of the team’s effectiveness in achieving the overall goal of bringing the shared payload to the target. By including a penalization term for any constraint violation during the training, the UAVs learn strategies that do not require explicit communication to achieve efficient transportation of payload while satisfying all constraints. We also present the performance metrics by testing the trained UAVs on new scenarios with different target locations and with different number of UAVs in the team.
Investigating Student-Generated Questioning in a Technology-Enabled Elementary Science Classroom: A Case Study
Authors: Wu Longkai , Liu Yaohuizhuo , How Meng-Leong , He Sujin
Published: February 2023

This study is aimed at providing solutions to problems in the field of science and technology education, as well as approaches to improve its effectiveness. This study’s specific goal was to ascertain how inquiry-based learning, when aided by instructional technology, raises student success and fosters their capacity for scientific inquiry. In this paper, we investigate a technology-supported intervention that facilitates students to actively generate and solve questions in a cycle of science inquiry in a primary (elementary) school. Through utilizing a question generation technology platform with a guided pedagogical framework, the teachers purposefully leveraged on students’ generated questioning to design and implement a process of creating and presenting their inquiries. The questioning-driven dialogic exchanges took place in the classroom setting, as well as during online interactions outside of the class. Our empirical study, as demonstrated by quantitative and qualitative analysis, connotes a positive causal effect of students’ generated questioning to their cognitive performances, and their noteworthy differences of attitudes towards science between the experimental and control groups. The results uphold the value of fostering students to generate questions for their inquiries and learning. We also highlight the importance of teachers’ awareness of pedagogical design and enactment, enabled by technology, in order to adapt to the profiles of students’ generated questioning for fostering productive cognitive performances.
Blended Engineering Design Process Learning Activities for Secondary School Students during COVID-19 Epidemic: Students’ Learning Activities and Perception
Authors: Precharattana Monamorn , Sanium Soonthareeya , Pongsanon Khemmawadee , Ritthipravat Panrasee , Chuechote Suparat , Kusakunniran Worapan
Published: February 2023

This study aims to present the teaching and learning activities of Engineering Design Processes (EDP) to secondary school students. The proposed teaching technique used was blended learning, which integrated group activities based on online learning and individual hands-on activities through independent study at home. The context of COVID-19 medical mask protection was used in comparison to the current situation. In order to test the effectiveness of the proposed learning activities, a single-group pretest–posttest design was employed to explore (a) the students’ perceptions of their problem-solving confidence before and after they underwent the proposed learning technique and (b) students’ perceptions of the designed course. After they had finished the 4 weeks of learning activities, the students were asked to complete the Students’ Perception on Problem-Solving Skill Questionnaire (SPPSS) and the Students’ Perception towards the Proposed Blended Engineering Design Process learning activities Questionnaire (SPBEDP) in order to gauge how confident they felt in their ability to solve problems and how they felt about the proposed course. There were 30 seventh-grade students enrolled in this course. An increase in the level of problem-solving confidence was found in the students after they were subjected to the proposed activities. Moreover, the students mentioned that, based on the proposed activities, “Identify Problem and Need”, “Design a Solution”, and “Developing Prototype” are the Engineering Design Process learning steps they enjoyed most since they were the steps in which they could use their creativity, and they were hands-on, fun, easy, challenging, and provided them with an opportunity to choose issues in which they are interested.
The Typology of Public Schools in the State of Louisiana and Interventions to Improve Performance: A Machine Learning Approach
Authors: Kaliba Aloyce R. , Andrews Donald R.
Published: February 2023

Extant literature on education research focuses on evaluating schools’ academic performance rather than the performance of educational institutions. Moreover, the State of Louisiana public school system always performs poorly in education outcomes compared to other school systems in the U.S. One of the limiting factors is the stringent standards applied among heterogeneous schools, steaming from the fit-for-all policies. We use a pairwise controlled manifold approximation technique and gradient boosting machine algorithm to typify Louisiana public schools into homogenous clusters and then characterize each identified group. The analyses uncover critical features of failing and high-performing school systems. Results confirm the heterogeneity of the school system, and each school needs tailored support to buoy its performance. Short-term interventions should focus on customized administrative and academic protocols with malleable interpositions addressing individual school shortcomings such as truancy. Long-term policies must discourse authentic economic development programs to foster community engagement and creativity while allocating strategic resources that cultivate resilience at the school and community levels.
Achieving Transfer from Mathematics Learning
Authors: Orón José Víctor , Lizasoain Inmaculada
Published: February 2023

The question of transfer is a special challenge in mathematics teaching because the wide range and fragmentation of the curricula have in many cases fostered an instrumental understanding, which makes transfer difficult for the students. Although promoting a relational learning has been a huge step forward in achieving transfer, understanding usually remains at the technical level of learning. Fostering critical thinking and metacognition raises learning to the psychological level, as students are encouraged to analyse their own thinking. Despite this, our hypothesis is that transfer will only be achieved when students are helped to reach a personal dimension, being encouraged to discover their own way of approaching the global reality of their lives beyond the subject. Learning, for instance, the greatest common divisor should be an opportunity to discover that, as numbers can be presented by their prime factors, people can be recognised by their features and interests. As such, looking for the greatest common divisor should not differ from discovering common interests with friends. Integrating specific and general learning will make transfer no longer unattainable. Personalising learning means discovering how one specific learning impacts on the personal way of understanding reality (oneself, others, and the world), thus making transfer natural.
Factors Associated with COVID-19-Related Stress among Female Primary Caregivers in Vulnerable Families in South Africa
Authors: Engelbrecht Michelle
Published: February 2023

Inequality in South Africa is deeply rooted, and COVID-19 glaringly brought inequalities between families to the forefront. This study aimed to investigate factors associated with the above average stress levels of female primary caregivers in vulnerable families during the COVID-19 pandemic. A cross-sectional survey was undertaken among vulnerable families from October 2021 to February 2022. Above average scores were reported by approximately half of the respondents regarding stress from children/partners and stress related to financial issues. Fear of COVID-19, feeling depressed, COVID-19′s impact on daily life, lower education levels, being in a relationship and living together, and perceiving an increase in domestic violence were statistically significantly associated with above average stress from children/partners. Age, impact of COVID-19 on daily life, being in a relationship and living with a husband/partner, and running out of food during the past 30 days were statistically significantly associated with above average stress concerning finances. These results provide new insights that can assist policy makers and practitioners in supporting low-income families during times of crisis. Support should not just focus on practical aspects, such as the provision of food, but equally importantly, on emotional support and protection for female primary caregivers and their families. Future research should delve more deeply into causes of COVID-19-related stress in vulnerable families.
Applications of the Internet of Medical Things to Type 1 Diabetes Mellitus
Authors: Rodríguez-Rodríguez Ignacio , Rodríguez José-Víctor , Campo-Valera María
Published: February 2023

Type 1 Diabetes Mellitus (DM1) is a condition of the metabolism typified by persistent hyperglycemia as a result of insufficient pancreatic insulin synthesis. This requires patients to be aware of their blood glucose level oscillations every day to deduce a pattern and anticipate future glycemia, and hence, decide the amount of insulin that must be exogenously injected to maintain glycemia within the target range. This approach often suffers from a relatively high imprecision, which can be dangerous. Nevertheless, current developments in Information and Communication Technologies (ICT) and innovative sensors for biological signals that might enable a continuous, complete assessment of the patient’s health provide a fresh viewpoint on treating DM1. With this, we observe that current biomonitoring devices and Continuous Glucose Monitoring (CGM) units can easily obtain data that allow us to know at all times the state of glycemia and other variables that influence its oscillations. A complete review has been made of the variables that influence glycemia in a T1DM patient and that can be measured by the above means. The communications systems necessary to transfer the information collected to a more powerful computational environment, which can adequately handle the amounts of data collected, have also been described. From this point, intelligent data analysis extracts knowledge from the data and allows predictions to be made in order to anticipate risk situations. With all of the above, it is necessary to build a holistic proposal that allows the complete and smart management of T1DM. This approach evaluates a potential shortage of such suggestions and the obstacles that future intelligent IoMT-DM1 management systems must surmount. Lastly, we provide an outline of a comprehensive IoMT-based proposal for DM1 management that aims to address the limits of prior studies while also using the disruptive technologies highlighted before.
A Dual Long Short-Term Memory Model in Forecasting the Number of COVID-19 Infections
Authors: Lai Jung-Pin , Pai Ping-Feng
Published: February 2023

Since the outbreak of the Coronavirus Disease 2019 (COVID-19), the spread of the epidemic has been a major international public health issue. Hence, various forecasting models have been used to predict the infectious spread of the disease. In general, forecasting problems often involve prediction accuracy decreasing as the horizon increases. Thus, to extend the forecasting horizon without decreasing performance or prediction, this study developed a Dual Long Short-Term Memory (LSTM) with Genetic Algorithms (DULSTMGA) model. The model employed predicted values generated by LSTM models in short-forecasting horizons as inputs for the long-term prediction of LSTM in a rolling manner. Genetic algorithms were applied to determine the parameters of LSTM models, allowing long-term forecasting accuracy to increase as long as short-term forecasting was accurate. In addition, the compartment model was utilized to simulate the state of COVID-19 and generate numbers of infectious cases. Infectious cases in three countries were employed to examine the feasibility and performance of the proposed DULSTMGA model. Numerical results indicated that the DULSTMGA model could obtain satisfactory forecasting accuracy and was superior to many previous studies in terms of the mean absolute percentage error. Therefore, the designed DULSTMGA model is a feasible and promising alternative for forecasting the number of infectious COVID-19 cases.
A Study on the Development of a White Light Source Module for a Large-Capacity Searchlight Using a Blue Laser Diode
Authors: Lee Seung-Min , Cho Joon-Ho , Lee Wan-Bum
Published: February 2023

In this paper, a large-capacity white light source module using a high-power blue laser diode and a reflective spaced phosphor was designed. The reflective spacing phosphor ensured thermal stability. The proposed white light module is a reflective phosphor structure with a bi-directional optical system based on a rhombus prism lens. The rhombus prism optical system can greatly narrow the blue laser beam width and a long-wavelength band-pass filter of 500 nm or more is applied to change the movement path of the laser beam and transmit white light excited by the phosphor. A dichroic filter was applied to the fold mirror part and a planar convex lens was designed to focus the blue laser beam so that the phosphor was irradiated. Finally, a high-power white light source is obtained from the pre-optics unit to which the dichroic filter is applied. In order to use the proposed white light source as a searchlight, a divergence angle of 4° or less is required. For this implementation, a large-area collimating lens combining an aspheric condensing lens and an achromatic lens was applied. It was confirmed that the divergence angle of 4 degrees or less was satisfied at the focal length (FL) of 38.5 to 42.5 mm of the optical lens of the laser white light module emitter and the collimating optical system.
Robust Motion Planning in Robot-Assisted Surgery for Nonlinear Incision Trajectory
Authors: Sachan Shailu , Swarnkar Pankaj
Published: February 2023

In the era of digital OTs (operating theatres), the developments in robot-assisted surgery (RAS) can greatly benefit the medical field. RAS is a method of technological advancement that uses robotic articulations to assist in complicated surgeries. Its implementation improves the ability of the specialized doctor to perform surgery to a great extent. The paper addresses the dynamics and control of the highly non-linear 3DOF surgical robot manipulator in the event of external disturbances and uncertainties. The integration of non-linear robust SMC (sliding mode control) with a smoothing mechanism, a FOPID (fractional-order proportional integral derivative) controller, and a fuzzy controller provides a high degree of robustness and minimal chatter. The addition of fuzzy logic to the controller, named intelligent fuzzy-SFOSMC (smoothing fractional order sliding mode controller) improves the system’s performance by ruling out the disturbances and uncertainties. The prototype model is developed in a laboratory and its outcomes are validated on OP5600, a real-time digital simulator. Simulation and experimental results of the proposed fuzzy-SFOSMC are compared with conventional controllers, which illustrates the efficacy and superiority of the proposed controller’s performance during the typical surgical situations. The proposed fuzzy-SFOSMC outperforms conventional controllers by providing greater precision and robustness to time-varying nonlinear multi-incision trajectories.
An Attention-Based Uncertainty Revising Network with Multi-Loss for Environmental Microorganism Segmentation
Authors: Na Hengyuan , Liu Dong , Wang Shengsheng
Published: February 2023

The presence of environmental microorganisms is inevitable in our surroundings, and segmentation is essential for researchers to identify, understand, and utilize the microorganisms; make use of their benefits; and prevent harm. However, the segmentation of environmental microorganisms is challenging because their vague margins are almost transparent compared with those of the environment. In this study, we propose a network with an uncertainty feedback module to find ambiguous boundaries and regions and an attention module to localize the major region of the microorganism. Furthermore, we apply a mid-pred module to output low-resolution segmentation results directly from decoder blocks at each level. This module can help the encoder and decoder capture details from different scales. Finally, we use multi-loss to guide the training. Rigorous experimental evaluations on the benchmark dataset demonstrate that our method achieves higher scores than other sophisticated network models (95.63% accuracy, 89.90% Dice, 81.65% Jaccard, 94.68% recall, 0.59 ASD, 2.24 HD95, and 85.58% precision) and outperforms them.
Memristive Devices and Systems: Modeling, Properties and Applications
Authors: Lai Chun Sing , Dong Zhekang , Qi Donglian
Published: February 2023

The memristor is considered to be a promising candidate for next-generation computing systems due to its nonvolatility, high density, low power, nanoscale geometry, nonlinearity, binary/multiple memory capacity, and negative differential resistance [...]
Promoting Adversarial Transferability via Dual-Sampling Variance Aggregation and Feature Heterogeneity Attacks
Authors: Huang Yang , Chen Yuling , Wang Xuewei , Yang Jing , Wang Qi
Published: February 2023

At present, deep neural networks have been widely used in various fields, but their vulnerability requires attention. The adversarial attack aims to mislead the model by generating imperceptible perturbations on the source model, and although white-box attacks have achieved good success rates, existing adversarial samples exhibit weak migration in the black-box case, especially on some adversarially trained defense models. Previous work for gradient-based optimization either optimizes the image before iteration or optimizes the gradient during iteration, so it results in the generated adversarial samples overfitting the source model and exhibiting poor mobility to the adversarially trained model. To solve these problems, we propose the dual-sample variance aggregation with feature heterogeneity attack; our method is optimized before and during iterations to produce adversarial samples with better transferability. In addition, our method can be integrated with various input transformations. A large amount of experimental data demonstrate the effectiveness of the proposed method, which improves the attack success rate by 5.9% for the normally trained model and 11.5% for the adversarially trained model compared with the current state-of-the-art migration-enhancing attack methods.
Fault Diagnosis for Rolling Bearings Based on Multiscale Feature Fusion Deep Residual Networks
Authors: Wu Xiangyang , Shi Haibin , Zhu Haiping
Published: February 2023

Deep learning, due to its excellent feature-adaptive capture ability, has been widely utilized in the fault diagnosis field. However, there are two common problems in deep-learning-based fault diagnosis methods: (1) many researchers attempt to deepen the layers of deep learning models for higher diagnostic accuracy, but degradation problems of deep learning models often occur; and (2) the use of multiscale features can easily be ignored, which makes the extracted data features lack diversity. To deal with these problems, a novel multiscale feature fusion deep residual network is proposed in this paper for the fault diagnosis of rolling bearings, one which contains multiple multiscale feature fusion blocks and a multiscale pooling layer. The multiple multiscale feature fusion block is designed to automatically extract the multiscale features from raw signals, and further compress them for higher dimensional feature mapping. The multiscale pooling layer is constructed to fuse the extracted multiscale feature mapping. Two famous rolling bearing datasets are adopted to evaluate the diagnostic performance of the proposed model. The comparison results show that the diagnostic performance of the proposed model is superior to not only several popular models, but also other advanced methods in the literature.
Investigation of Source/Drain Recess Engineering and Its Impacts on FinFET and GAA Nanosheet FET at 5 nm Node
Authors: Wang Dawei , Sun Xin , Liu Tao , Chen Kun , Yang Jingwen , Wu Chunlei , Xu Min , Zhang Wei (David)
Published: February 2023

Impacts of source/drain (S/D) recess engineering on the device performance of both the gate-all-around (GAA) nanosheet (NS) field-effect transistor (FET) and FinFET have been comprehensively studied at 5 nm node technology. TCAD simulation results show that the device off-leakage, including subthreshold leakage through the channel (Isub) and punch-through leakage (IPT) in the sub-channel, is strongly related to the S/D recess process. Firstly, device electrical characteristics such as current density distributions, On/Off-state current (Ion, Ioff), subthreshold swing (SS), RC delay, and gate capacitance (Cgg) are investigated quantitatively for DC/AC performance evaluation and comparison according to S/D lateral recess depth (Lrcs) variations. For both device types, larger Lrcs will result in a shorter effective channel length (Leff), so that the Ion and Ioff simultaneously increase. At the constant Ioff, the Lrcs can be optimized to enhance the device’s drivability by ~3% and improve the device’s RC delay by ~1.5% due to a larger Cgg as a penalty. Secondly, S/D over recess depth (Hrcs) in the vertical direction severely affects the punch-through leakage in the Sub-Fin or bottom parasitic channel region. The NSFET exhibits less Ioff sensitivity provided that it can be well controlled under 12 nm since the bottom parasitic channel is still gated. Furthermore, with both Hrcs and Lrcs accounted for in the device fabrication, the NSFET still shows better control of the off-leakage in the intrinsic and bottom parasitic channel regions and ~37% leakage reduction compared with FinFETs, which would be critical to enable further scaling and the low standby power application. Finally, the S/D recess engineering strategy has been given: a certain lateral recess could be optimized to obtain the best drive current and RC delay, while the vertical over-recess should be in tight management to keep the static power dissipation as low as possible.
Fuzzy Controller in the Products Collecting System of the Jig for Minerals Beneficiation
Authors: Jendrysik Sebastian , Kowol Daniel , Matusiak Piotr , Dymarek Andrzej , Kędzia Krzysztof , Polnik Bartosz , Szczygieł Marcin , Trawiński Tomasz , Starak Mariusz
Published: February 2023

The fuzzy controller of the bottom product collecting system of the pulsating jig is presented. The primary purpose of the research work was to design and properly adapt the fuzzy controller so that it would enable the correct operation of the jig with appropriate control properties in all compartments of the jig. The results of industrial tests, in which selected indicators of regulation quality were considered, were analyzed. A comparative analysis of the tested fuzzy controller and the classic PID controller was also performed.
Histological Assessment of Endochondral Ossification and Bone Mineralization
Authors: Hasegawa Tomoka , Hongo Hiromi , Yamamoto Tomomaya , Muneyama Takafumi , Miyamoto Yukina , Amizuka Norio
Published: February 2023

Finely tuned cartilage mineralization, endochondral ossification, and normal bone formation are necessary for normal bone growth. Hypertrophic chondrocytes in the epiphyseal cartilage secrete matrix vesicles, which are small extracellular vesicles initiating mineralization, into the intercolumnar septa but not the transverse partitions of the cartilage columns. Bone-specific blood vessels invade the unmineralized transverse septum, exposing the mineralized cartilage cores. Many osteoblast precursors migrate to the cartilage cores, where they synthesize abundant bone matrices, and mineralize them in a process of matrix vesicle-mediated bone mineralization. Matrix vesicle-mediated mineralization concentrates calcium (Ca) and inorganic phosphates (Pi), which are converted into hydroxyapatite crystals. These crystals grow radially and are eventually get out of the vesicles to form spherical mineralized nodules, leading to collagen mineralization. The influx of Ca and Pi into the matrix vesicle is regulated by several enzymes and transporters such as TNAP, ENPP1, PiT1, PHOSPHO1, annexins, and others. Such matrix vesicle-mediated mineralization is regulated by osteoblastic activities, synchronizing the synthesis of organic bone material. However, osteocytes reportedly regulate peripheral mineralization, e.g., osteocytic osteolysis. The interplay between cartilage mineralization and vascular invasion during endochondral ossification, as well as that of osteoblasts and osteocytes for normal mineralization, appears to be crucial for normal bone growth.
Does Health Literacy Have an Impact on Adherence to Diabetes Mellitus Treatment?
Authors: Ngoatle Charity , Hlahla Lina S. , Mphasha Mabitsela H. , Mothiba Tebogo M. , Themane Mahlapahlapana J.
Published: February 2023

(1) Background: Health literacy is the intersection of general literacy, health, and healthcare, but it can also incorporate elements of other types of literacies to varying degrees. The notion of literacy surfaced from the fear that individuals would require more than general literacy skills to manage the complexities of health and health system issues. There is a substantial overlap between general literacy and health literacy. Diabetes patients frequently misinterpret medication instructions, resulting in non-adherence and poor health outcomes. (2) Aim: This study sought to review the literature on the impacts of health literacy on adherence and compliance to diabetes mellitus treatment. (3) Methods: A Narrative Literature Review method was used to identify, analyze, assess, and interpret the available information on health literacy regarding prescribed medication instructions. The following databases and search engines were used to locate the literature: electronic databases, search engines, and hand searches. Fifty-three (53) quantitative and qualitative studies and two books were reviewed. (4) Result: The review pointed out the following: the importance of health literacy, the implications of health illiteracy versus medication non-adherence, factors influencing health literacy versus medication adherence, and the interventions to improve medication non-adherence. (5) Conclusion: Relatively few studies have been conducted on how people living with diabetes should carry out their treatment. Therefore, more research on how people living with diabetes carry out their treatment daily is required. (6) Contributions: This study has identified that health literacy plays a role in adherence to treatment and contributes to improved health outcomes.
Further Test of Pneumatic Method in Constructing Vulnerability Curves Using Six Tree Species with Contrasting Xylem Anatomy
Authors: Zhao Han , Li Yueyang , Liao Suhui , Jiang Zaimin , Cai Jing
Published: February 2023

The pneumatic method is a novel method determining vulnerability to embolism in plants, yet it remains unclear whether this method is suitable for all species with different xylem anatomy. In this study, using six tree species with contrasting xylem anatomy, including four vessel-bearing species (diffuse-porous wood and ring-porous wood) and two tracheid-bearing species (non-porous wood), we test the reliability of the pneumatic method by comparing to hydraulic methods and also considering turgor loss point and native embolism. Vessel length distribution and cut-open vessel volume were also evaluated using the silicone injection technique. Additionally, we also synthesized published data to find out the consistency between the pneumatic method and hydraulic methods. Results showed that there was a maximum 10-folds difference in mean vessel length and mean vessel diameter varying from 30 to 56 μm among species. The estimated open vessel volume ranges from 0.064 to 0.397 mL, with a maximum of 14% of the tube vacuum reservoir. For four vessel-bearing species, the pneumatic method showed good consistency with hydraulic methods, and this consistency was evidenced by turgor loss point and native embolism. For two tracheid-bearing species, the pneumatic method significantly overestimated vulnerability because of the bad consistencies with hydraulic methods and plant water relations. Data synthesis of 56 species also suggested that the pneumatic method can accurately measure the embolism vulnerability of vessel-bearing species but not for tracheid-bearing species. Our study provided further evidence that the pneumatic method is accurate for most vessel-bearing species and thus has the potential to be widely used in the plant hydraulics field. However, we proposed that the precise calculation of air discharge volume should take into account the volume of open vessels for species with wide and long vessels.
Exploring the Measurement of Regional Forestry Eco-Efficiency and Influencing Factors in China Based on the Super-Efficient DEA-Tobit Two Stage Model
Authors: Tan Junlan , Su Xiang , Wang Rong
Published: February 2023

This paper adopts the super-efficient DEA (data envelopment analysis) model to measure the forestry eco-efficiency (FECO) of 30 Chinese provinces and cities from 2008 to 2021, and then introduces the Tobit model to explore the influencing factors of FECO to better understand the sustainable development level of forestry. It draws the following conclusions: (1) The average value of FECO in China is 0.504, which is still at a low level, and the FECO of each region has significant regional heterogeneity; the provinces with higher FECO are mainly concentrated in the eastern region, while the FECO of the central and western regions is lower; (2) In terms of the main factors affecting FECO in China, the regression coefficients of market-based environmental regulations are significantly positive in the national, eastern and central regions, while they are significantly negative in the western region. The coefficient of impact of scientific research funding investment on forestry industry eco-efficiency is negative and shows a significant promotion effect in the eastern region, but the elasticity coefficient in the central and western regions is negative but not significant. Economic development has a positive but insignificant effect on FECO, with the eastern region showing a positive correlation, while the central and western regions are insignificant. Industrial structure has a significant negative effect on FECO in the national, eastern and central regions, but the effect of industrial structure on FECO in the western region is not significant. The effect of foreign direct investment on FECO was negative for the national, central and western regions, but the central region did not pass the significance test, while the eastern region reflected a significant promotion effect.
Highly Active Astaxanthin Production from Waste Molasses by Mutated Rhodosporidium toruloides G17
Authors: Tran Tuyet Nhung , Tran Ngoc-Tri , Tran Thu-Anh , Pham Dinh-Chuong , Su Chia-Hung , Nguyen Hoang Chinh , Barrow Colin J. , Ngo Dai-Nghiep
Published: February 2023

Astaxanthin is increasingly attracting commercial interest for its application in the nutraceutical and pharmaceutical industries. This study aimed to produce astaxanthin from molasses with our newly mutated strain of Rhodosporidium toruloides G17 and to evaluate biological activities of the produced astaxanthin. To maximize the astaxanthin yield, the response surface methodology was used so as to optimize the culture conditions. A maximum astaxanthin yield of 1262.08 ± 14.58 µg/L was achieved by growing R. toruloides G17 in a molasses-based medium containing 49.39 g/L reducing sugar, 1.00 g/L urea, 4.15 g/L MgSO4·7H2O, and 10.05% inoculum ratio. The produced astaxanthin was then purified and studied for its antioxidant and anticancer activities. This compound exhibited 123-fold higher antioxidant activity than α-tocopherol, with an IC50 value of 0.97 ± 0.01 µg/mL. The astaxanthin also showed a potent inhibitory ability against the following three cancer cell lines: HeLa, A549, and MCF7, with IC50 values of 69.07 ± 2.4 µg/mL, 55.60 ± 2.64 µg/mL, and 56.38 ± 4.1 µg/mL, respectively. This study indicates that astaxanthin derived from our newly mutated R. toruloides G17 is a promising anticancer and antioxidant agent for further pharmaceutical applications.
Development of a Molasses-Based Medium for Agrobacterium tumefaciens Fermentation for Application in Plant-Based Recombinant Protein Production
Authors: Watthanasakphuban Nisit , Nguyen Luan Van , Cheng Yu-Shen , Show Pau-Loke , Sriariyanun Malinee , Koffas Mattheos , Rattanaporn Kittipong
Published: February 2023

The Agrobacterium-mediated transient gene expression system is a rapid and efficient method for heterologous recombinant protein expression in plants. The fermentation of genetically modified Agrobacterium tumefaciens is an important step in increasing the efficiency of recombinant protein production in plants. However, the limitation of this system that makes it economically non-competitive for industrial-scale applications is the Agrobacterium suspension production cost. In this study, the utilization of sugarcane molasses as an alternative low-cost source of carbon at a concentration of 8.7 g/L and nitrogen at a concentration of 2.4 g/L for Agrobacterium cultivation was investigated. Molasses pretreatment using sulfuric acid (SA) was applied before fermentation, and it resulted in a maximum specific growth rate of 0.232 ± 0.0063 h−1 in the A. tumefaciens EHA105 culture. The supplementation of antibiotics in the molasses-based medium was shown to be unnecessary for plasmid maintenance during fermentation in both Agrobacterium strains, which helped to reduce the production cost. We evaluated recombinant protein production using an Agrobacterium culture without antibiotic supplementation in the growth media by demonstrating green fluorescent protein expression in wild-type Nicotiana benthamiana leaves. In the evaluation of the culture medium cost, the molasses-based medium cost was 6.1 times lower than that of LB. Finally, this study demonstrated that the newly developed molasses-based medium for Agrobacterium fermentation is a feasible and effective medium for transient recombinant protein production in plant tissues.