Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural design of multi-body heave wave energy conversion system and analysis of energy efficiency of floating body on water surface

Abstract. Due to the defects of the internal structure and energy supply carrier, conventional deep sea unoccupied marine equipment cannot meet the requirements of low power consumption. In this paper, the whole structure of a multi-body heave wave energy conversion system was designed to capture and convert wave energy. The conversion system consists of a floating body, an underwater absorber and a power takeoff system (PTO). The dynamic model of the energy conversion system and the mathematical model of energy efficiency evaluation were established according to the dynamic analysis. Based on the real service environment in the South China Sea, the energy efficiency characteristics of floating bodies with different shapes were simulated, and the amplitude response operator (RAO), radiation damping, added mass and Froude–Krylov force of floating bodies with different shapes were compared. Then, the optimal energy efficiency parameters of surface floating body were explored. Finally, the correctness of the conclusion was verified by the energy efficiency test. The results show that, under the limitation of low power consumption and space scale, the energy conversion system of an axisymmetric rotary body with the same sea conditions, same material and the largest scale can significantly improve the conversion efficiency, and the spherical rotary body performs the best, which makes the unoccupied marine equipment have a broad prospect for development.
Prediction of Neonatal Acidosis Based on the Type of Fetal Hypoxia Observed on the Cardiotocograph (CTG)

Cardiotocograph (CTG) was introduced into clinical practice to promptly recognize the features of intrapartum fetal hypoxic stress, so that timely action could be taken to avoid hypoxic-ischaemic encephalopathy (HIE) and perinatal deaths. However, the current systematic evidence suggests that the introduction of CTG into clinical practice over 50 years has not resulted in improvement in the rates of cerebral palsy or perinatal deaths. This is because most fetuses are able to withstand intrapartum hypoxic stresses without sustaining damage, and if the features of fetal compensatory responses are erroneously considered as “pathological”, “Abnormal” or “Category III” CTG tracing, it would lead to an exponential increase in unnecessary operative interventions without any improvement in perinatal outcomes. Neonatal acidosis at birth, determined by the estimation of pH in the umbilical artery has been considered as a surrogate marker of poor perinatal outcome. This is because significant intrapartum fetal hypoxic stress which leads to fetal decompensation, would lead to the onset of anaerobic metabolism and production of lactic acid in fetal tissues and organs. Entry of lactic acid into the fetal systemic circulation may cause damage to fetal central organs resulting in organ damage and death, and this lactate may lower the pH in the umbilical artery. Understanding the different types of fetal hypoxia on the CTG trace may help practicing clinicians to predict the rate of fall in fetal pH, and therefore, predict the umbilical cord pH at birth. It is important to appreciate that non-hypoxic pathways of fetal compromise such as chorioamnionitis may not be associated with low umbilical arterial pH at birth. Fetal pathophysiological approach to CTG interpretation based on deeper understanding of types of intrapartum hypoxia and features of non-hypoxic pathways of injury may help avoid the onset of neonatal metabolic acidosis and improve perinatal outcomes.
High-throughput generation of aircraft-like soot

Abstract High-throughput, laboratory units for generation of aircraft-like soot are needed to quantify and understand the impact of such emissions on public health and climate change due to the high costs and limited access to aircraft engines. Enclosed spray combustion of jet fuel is used here to generate high soot concentrations, up to 255 mg/m3, three orders of magnitude higher than those typically obtained by widely used soot generators that use vapor-fed flames. This is attributed mostly to the use of real jet fuels. The large mass concentrations enable routine characterization of the soot specific surface area (SSA) and pore size distribution (PSD) by N2 adsorption. The geometric mean mobility diameter, of soot agglomerates was systematically varied from 15 to 180 nm by varying the equivalence ratio (EQR) at constant fuel feed rates, while the organic to total carbon (OC/TC) ratio is low (<20%) at all conditions. The geometric mean primary particle (PP) diameter, standard deviation, σ g,p, and mass-mobility exponent, D fm, were hardly altered in that EQR range. These measured D fm and σ g,p indicate that soot PPs were sinter-bonded by surface growth, in agreement with aircraft emissions literature. Most importantly, soot made at EQR ≤ 1.34 has mainly small pores (<2 nm) and similar morphology (D fm = 2.52 ± 0.17), SSA (160 − 239 m2/g), OC/TC (< 20%), (15 − 61 nm) and (14 nm) with those from high-thrust aircraft emissions.
Long-Term Resilience of Primary Sex Ratios in a Species with Temperature-Dependent Sex Determination after Decades of Climate Warming

Species with environmental sex determination (ESD) have persisted through deep time, despite massive environmental perturbation in the geological record. Understanding how species with temperature-dependent sex determination (TSD), a type of ESD, persist through climate change is particularly timely given the current climate crisis, as highly biased sex ratios and extinction are predicted. Since 1982, we have studied primary sex ratios of a reptile with TSD (Chelydra serpentina). Primary sex ratios remained unchanged over time, despite warming in the environment. Resilience of the primary sex ratio occurred via a portfolio effect, realized through remarkable intra-annual variation in nest-level sex ratios, leading to a relatively consistent mean annual sex ratio. Intra-annual variation in nest-level sex ratios was related to variation in egg burial depth coupled with large clutch sizes, creating thermal gradients in the nest and promoting mixed-sex clutches. Furthermore, both locally and globally, sustained increases in nighttime air temperature contribute more to warming than increases in daily maximum temperature, but development rate was affected more strongly by maximum daily air temperature, conferring additional resilience to overall warming. Our study suggests that some TSD species may be resilient to warming and provides an example of how ESD may persist under environmental change.
Evaluation and Selection Models for Ensemble Intrusion Detection Systems in IoT

Using the Internet of Things (IoT) for various applications, such as home and wearables devices, network applications, and even self-driven vehicles, detecting abnormal traffic is one of the problematic areas for researchers to protect network infrastructure from adversary activities. Several network systems suffer from drawbacks that allow intruders to use malicious traffic to obtain unauthorized access. Attacks such as Distributed Denial of Service attacks (DDoS), Denial of Service attacks (DoS), and Service Scans demand a unique automatic system capable of identifying traffic abnormality at the earliest stage to avoid system damage. Numerous automatic approaches can detect abnormal traffic. However, accuracy is not only the issue with current Intrusion Detection Systems (IDS), but the efficiency, flexibility, and scalability need to be enhanced to detect attack traffic from various IoT networks. Thus, this study concentrates on constructing an ensemble classifier using the proposed Integrated Evaluation Metrics (IEM) to determine the best performance of IDS models. The automated Ranking and Best Selection Method (RBSM) is performed using the proposed IEM to select the best model for the ensemble classifier to detect highly accurate attacks using machine learning and deep learning techniques. Three datasets of real IoT traffic were merged to extend the proposed approach’s ability to detect attack traffic from heterogeneous IoT networks. The results show that the performance of the proposed model achieved the highest accuracy of 99.45% and 97.81% for binary and multi-classification, respectively.
The role of sodium and calcium permeable T-type channels in generating pacemaking currents in invertebrates

T-type calcium channels (Cav3) play a key role in generating rhythmic action potentials in excitable cells such as the heart pacemaker and sleep-wake cycles in the thalamus. T-type channels are low voltage-activated channels, which can be opened near rest to cause an inward current that leads to membrane voltage rising to threshold to generate rhythmic action potential firing. The upstroke of the ventricular action potential in the vertebrate heart is attributed mostly to the cardiac-specific voltage-gated sodium channel, Nav1.5. Sodium channels are not expressed in the invertebrate heart, such as Lymnaea stagnalis. Instead, the major inward conductances of the invertebrate heart are due to voltage-gated calcium channels. The invertebrate pond snail, Lymnaea stagnalis, heart expresses T-type calcium channel, LCav3, which is alternatively-spliced in exon 12, generating a highly sodium selective T-type channel (with exon 12a) compared to a more calcium ion selective channel isoform (with exon 12b). The sodium permeant, LCav3-12a channel is the only T-type channel expressed in the heart, while LCav3-12b is more exclusive to the secretory glands, such as prostate and albumen. Our goal is to understand the functional consequence of differing sodium and calcium ion selectivities of T-type channels for the generation of rhythmic pacemaking in native tissues by conducting elecrophysiology experiments on dissociated ventricle cells and constructing a qualitative mathematical model of the invertebrate ventricular action potential. Mathematical models for both LCav3-12a and LCav3-12b T-type currents were constructed and implemented using current models from the Kyoto model, a guinea pig ventricular action potential model, to reflect the currents found in Lymnaea stagnalis. LCav3-12b had a smaller pacemaking current and showed slower rate of spike generation opposed to LCav3-12a which had a larger current and faster pacemaking activity. Spiking activity was not abolished with simulated T-type current block but had the greatest effect on rate of pacemaking. Simulations involving reduced current conductance or varying ion concentration show that LCav3 was primarily responsible for generating pacemaking activity and initiating the action potential upstroke.
Long-Term Immunogenicity upon Pertussis Booster Vaccination in Young Adults and Children in Relation to Priming Vaccinations in Infancy

Booster vaccinations for pertussis are advised in many countries during childhood or adulthood. In a phase IV longitudinal interventional study, we assessed long-term immunity following an extra pertussis booster vaccination in children and adults. Children (9 years of age) were primed in infancy with either the Dutch whole cell pertussis (wP) vaccine (n = 49) or acellular pertussis (aP) vaccines (n = 59), and all children received a preschool aP booster. Adults (25–29 years, n = 86) were wP-primed in infancy and did not receive a preschool booster. All were followed-up for approximately 6 years. After the additional booster, antibody responses to pertussis were more heterogeneous but generally higher in adults compared with children, and additional modelling showed that antibody concentrations remained higher for at least a decade. Serologic parameters indicative of recent pertussis infection were more often found in aP-primed children (12%) compared with wP-primed individuals (2%) (p = 0.052). This suggests that the aP booster vaccination in aP-primed children offers less long-term protection against pertussis infection and consequently against transmission. Together, these data show that aP priming in combination with aP boosting may not be sufficient to prevent circulation and transmission, while wP-primed adults may benefit from enhanced long-lasting immunity.
Parking Occupancy Prediction Method Based on Multi Factors and Stacked GRU-LSTM

With the development of society and the continuous advancement of urbanization, motor vehicles have increased rapidly, which exacerbates the imbalance between parking supply and demand. Therefore, it is very important to excavate knowledge from historical parking data and forecast the parking volume in different time periods so as to optimize parking resource utilization and improve traffic conditions. This paper proposes a new hybrid model that stacks gated recurrent unit (GRU) and long-short term memory (LSTM). The proposed stacked GRU-LSTM model combines LSTM’s advantage in prediction accuracy and GRU’s advantage in prediction efficiency, and uses multi factors, including occupancy, weather conditions and holiday, as input to predict parking availability. When compared against other predictive models such as stacked simple RNN, stacked LSTM-RNN, and stacked LSTM-Bi-LSTM, our experimental results indicate that the stacked GRU-LSTM model has better performance for parking occupancy prediction as it not only improves prediction accuracy, but also reduces prediction time.
Investigating Factors Associated with Adolescent Vaping and Cessation: A Qualitative Study

Background: Since 2015, electronic-cigarette (e-cigarette) use increased among adolescents, necessitating research on effective cessation strategies. This qualitative research examined adolescents’ vaping behaviour and the perceived effectiveness of existing and proposed cessation resources, particularly Health Canada’s 2019 “Consider the Consequences of Vaping” video. Methods: In 2019, we conducted semi-structured interviews with 14 youth (ages 14-18) with e-cigarette use experience in Calgary, Alberta, Canada. The interviews were analyzed using qualitative description in NVivo. Results: Participants were introduced to vaping through members of their social groups. Parental guardians and friend groups were highly influential in encouraging and discouraging vaping. Almost all participants held the belief that vaping is healthier than smoking and said that Health Canada’s 2019 “Consider the Consequences of Vaping” campaign video required improvement. Many participants desired to cease vaping but lacked the resources to do so. Conclusions: This research suggests that a presentation from an ex-vaper, in addition to counselling might be the most useful resources to aid young people to cease vaping.
The Cross-Sectional Intrinsic Entropy—A Comprehensive Stock Market Volatility Estimator

To take into account the temporal dimension of uncertainty in stock markets, this paper introduces a cross-sectional estimation of stock market volatility based on the intrinsic entropy model. The proposed cross-sectional intrinsic entropy (CSIE) is defined and computed as a daily volatility estimate for the entire market, grounded on the daily traded prices—open, high, low, and close prices (OHLC)—along with the daily traded volume for all symbols listed on The New York Stock Exchange (NYSE) and The National Association of Securities Dealers Automated Quotations (NASDAQ). We perform a comparative analysis between the time series obtained from the CSIE and the historical volatility as provided by the estimators: close-to-close, Parkinson, Garman–Klass, Rogers–Satchell, Yang–Zhang, and intrinsic entropy (IE), defined and computed from historical OHLC daily prices of the Standard & Poor’s 500 index (S&P500), Dow Jones Industrial Average (DJIA), and the NASDAQ Composite index, respectively, for various time intervals. Our study uses an approximate 6000-day reference point, starting 1 January 2001, until 23 January 2022, for both the NYSE and the NASDAQ. We found that the CSIE market volatility estimator is consistently at least 10 times more sensitive to market changes, compared to the volatility estimate captured through the market indices. Furthermore, beta values confirm a consistently lower volatility risk for market indices overall, between 50% and 90% lower, compared to the volatility risk of the entire market in various time intervals and rolling windows.
Ameliorative Effect of Ocimum forskolei Benth on Diabetic, Apoptotic, and Adipogenic Biomarkers of Diabetic Rats and 3T3-L1 Fibroblasts Assisted by In Silico Approach

Diabetes mellitus (DM) is a complicated condition that is accompanied by a plethora of metabolic symptoms, including disturbed serum glucose and lipid profiles. Several herbs are reputed as traditional medicine to improve DM. The current study was designed to explore the chemical composition and possible ameliorative effects of Ocimum forskolei on blood glucose and lipid profile in high-fat diet/streptozotocin-induced diabetic rats and in 3T3-L1 cell lines as a first report of its bioactivity. Histopathological study of pancreatic and adipose tissues was performed in control and treatment groups, along with quantification of glucose and lipid profiles and the assessment of NF-κB, cleaved caspase-3, BAX, and BCL2 markers in rat pancreatic tissue. Glucose uptake, adipogenic markers, DGAT1, CEBP/α, and PPARγ levels were evaluated in the 3T3-L1 cell line. Hesperidin was isolated from total methanol extract (TME). TME and hesperidin significantly controlled the glucose and lipid profile in DM rats. Glibenclamide was used as a positive control. Histopathological assessment showed that TME and hesperidin averted necrosis and infiltration in pancreatic tissues, and led to a substantial improvement in the cellular structure of adipose tissue. TME and hesperidin distinctly diminished the mRNA and protein expression of NF-κB, cleaved caspase-3, and BAX, and increased BCL2 expression (reflecting its protective and antiapoptotic actions). Interestingly, TME and hesperidin reduced glucose uptake and oxidative lipid accumulation in the 3T3-L1 cell line. TME and hesperidin reduced DGAT1, CEBP/α, and PPARγ mRNA and protein expression in 3T3-L1 cells. Moreover, docking studies supported the results via deep interaction of hesperidin with the tested biomarkers. Taken together, the current study demonstrates Ocimum forskolei and hesperidin as possible candidates for treating diabetes mellitus.
An Educational Framework based on Collaborative Reverse Engineering and Active Learning: a Case Study*

This paper presents early results of a novel educational framework, currently in development. Incorporating Active Learning andCollaborative Reverse Engineering techniques, this framework aims to completely re-structure the learning system at Monterrey Tech.,specifically in engineering courses. This is achieved through the assembly of an experimental, true-scale aircraft RV-10, focusing on theactivities of a work-team of students in charge of the aircraft's structural analysis. In addition to obtaining numerical results, thisassessment investigates the practical aspects of the developed methodology, such as the efficiency of the educational strategies inrealising advanced learning concepts. In particular, it is shown how Reverse Engineering and Active Learning concepts can besuccessfully implemented as learning techniques, via carefully planned activities. During the latter, emphasis is given to the developmentof innovative, creative, self-learning, team-work and other desirable skills in students. Several evaluation methods (diagnostic,formative, summative, etc.) are considered, each measuring a distinctive feature of the students' performance. These show that theproposed strategies have a direct and positive impact on the student's ability to generate and synthesize knowledge. The case studydescribed here represents an isolated cell within a larger educational project, thus future work will continue to explore educationalissues arising from the aircraft's assembly, to be reported in forthcoming papers.
A Performance Comparison of Machine Learning Algorithms for Load Forecasting in Smart Grid

With the rapid increase in the world’s population, the global electricity demand has increased drastically. Therefore, it is required to adopt efficient energy management mechanisms. Since the energy consumption trends are rather dynamic. Therefore, precise energy demand estimation and short and/or long-term forecasting results with higher accuracy are required to develop the optimization and control mechanism. Consequently, the machine learning (ML) techniques along with distributed demand response programs are being adopted to predict the future energy demand requirement with satisfactory results. In this paper, different state-of-the-art ML algorithms such as logistic regression (LR), support vector machines (SVM), naive Bayes (NB), decision tree classifier (DTC), K-nearest neighbor (KNN), and neural networks (NNs), have been implemented to analyze their performance. The main objective of this paper is to present a comparative analysis of ML algorithms for short-term load forecasting (STLF) regarding accuracy and forecast error. Based on the implementation and analysis, we have identified that, among other algorithms, the DTC provides comparatively better results. Therefore, we devised the enhanced DTC (EDTC) by integrating fitting function, loss function, and gradient boosting in DTC mathematical model for fine-tuning the control variables. The implementation results show that the proposed EDTC algorithm provides better forecast results (i.e., 99.9%recall, 100% F1, 100% precision, 99.21%training accuracy, and 99.70% testing accuracy.).
p‐Type Redox‐Active Organic Electrode Materials for Next‐Generation Rechargeable Batteries

p‐Type redox‐active organic materials (ROMs) draw increasing attention as a promising alternative to conventional inorganic electrode materials in secondary batteries due to high redox voltage, fast rate capability, environment friendliness, and abundance. First, fundamental properties of the p‐type ROMs regarding the energy levels and the anion‐related chemistry are briefly introduced. Then, the development progress of the p‐type ROMs is outlined in this review by classifying them according to their redox centers. The molecular design strategies employed for improving their electrochemical performance are discussed to guide further research. Finally, a summary of the electrochemical performance achieved, regarding voltage, specific energy with power, and cycle stability, is provided with perspectives.
Design of passive interconnections in tall buildings subject to earthquake disturbances to suppress inter-storey drifts

This paper studies the problem of passive control of a multi-storey building subjected to an earthquake disturbance. The building is represented as a homogeneous mass chain model, i.e., a chain of identical masses in which there is an identical passive connection between neighbouring masses and a similar connection to a movable point. The paper considers passive interconnections of the most general type, which may require the use of inerters in addition to springs and dampers. It is shown that the scalar transfer functions from the disturbance to a given inter-storey drift can be represented as complex iterative maps. Using these expressions, two graphical approaches are proposed: one gives a method to achieve a prescribed value for the uniform boundedness of these transfer functions independent of the length of the mass chain, and the other is for a fixed length of the mass chain. A case study is presented to demonstrate the effectiveness of the proposed techniques using a 10-storey building model. The disturbance suppression performance of the designed interconnection is also verified for a 10-storey building model which has a different stiffness distribution but with the same undamped first natural frequency as the homogeneous model.