Points Of Curve Research Articles

Learning curve of consolers and bedside surgeons fused robotic-assisted thoracoscopic segmentectomy: insights from the initial 100 cases.

This study examined the learning curve of segmentectomy using the "fused surgery" approach. We retrospectively collected data from 100 patients who underwent segmentectomy via fused robot-assisted thoracoscopy at our institution between September 2020 and February 2024. The learning curve was evaluated using the cumulative sum of the operative times in all cases and was analyzed separately for simple and complex segmentectomies. After applying the cumulative sum method to all cases, we obtained a graph of the operative time that showed three well-differentiated phases: phase 1 (n = 23), the initial learning phase; phase 2 (n = 28), the increased competence phase; and phase 3 (n = 49), the highest skill phase. Comparing phases 1 and 2 with phase 3, we found significant differences in operative time (P < 0.001); however, no significant differences were observed in bleeding or rate of postoperative complications. We observed a significant reduction in operative time after 25 simple segmentectomies and 22 complex segmentectomies. The data suggested that the inflection point of the learning curve was achieved in 51 cases. Complex segmentectomy requires the same cases to achieve the same level of competence as simple segmentectomy.

Pattern formation from homogeneous states for an elliptic system with mixed interactions

In this paper, we are interested in the asymptotic behavior of a ground state vector solution for the following coupled nonlinear Schrödinger system { Δ u 1 − λ 1 u 1 + β 11 ( u 1 ) 3 + α β 12 u 1 ( u 2 ) 2 − β β 13 u 1 ( u 3 ) 2 = 0 Δ u 2 − λ 2 u 2 + α β 21 ( u 1 ) 2 u 2 + β 22 ( u 2 ) 3 − β β 23 u 2 ( u 3 ) 2 = 0 Δ u 3 − λ 3 u 3 − β β 31 ( u 1 ) 2 u 3 − β β 32 ( u 2 ) 2 u 3 + β 33 ( u 3 ) 3 = 0 in Ω and ∂ u i ∂ n = 0 on ∂Ω when α , β > 0 are very large. The existence of a ground state vector solution for (1) was proved in Byeon et al. [Pattern formation via mixed interactions for coupled Schrödinger equations under Neumann boundary condition. J Fixed Point Theory Appl. 2017;19:559–583] when α , β , α / β 2 − n 2 are large. We prove that if λ 3 is small, as α , β , α / β 2 − n 2 → ∞ , u 3 converges to a constant, u 1 and u 2 develop a small peak on ∂Ω . Under an additional condition α / β 2 − n 2 + δ → ∞ for some δ > 1 , we show that the peak point converges to a maximum point of the mean curvature of ∂Ω .

Deflection characteristics of semi-rigid base asphalt pavement under traffic speed deflectometer loading

The three-dimensional viscoelastic finite element simulation and on-site validation was combined, so as to study the characteristics of the traffic speed deflection slope curve of the semi-rigid base asphalt pavement. Results show that there is an inert point in the deflection slope curve when only the surface or base structural state changes. With the loading center approaching transverse crack, the ‘three extreme values’ attribute transitions gradually to ‘single extreme value’. For the surface-base penetrating longitudinal crack caused by uneven subgrade settlement, the peak value of deflection slope curve increases significantly as the crack approaches the loading center, reaching an order of magnitude difference, and the attribute transforms from ‘three extreme values’ to ‘single extreme value’. Indicators characterizing the layered structural state were proposed, and the indicator change rules and applicability for the pavement with different surface and base layer thicknesses, under different deflectometer speeds, with or without cracks, were discussed.

A fuzzy-predictive current control with real-time hardware for PEM fuel cell systems

This research study presents the application of the FC-PCC (Fuzzy Logic Predictive Current Control) algorithm in the context of maximum power point tracking (MPPT) for a proton exchange membrane fuel cell system employing a three-level boost converter (TLBC). The proposed approach involves the integration of an intelligent fuzzy controller with a predictive current control strategy in order to improve the performance of MPP tracking. Initially, the utilization of fuzzy logic involves the utilization of data values obtained from the PEMFC. The maximum point (P-I) of the PEMFC polarization curve is determined, followed by the selection of the reference current. A predictive current control technique employs the reference current to ensure the voltage balance of the output capacitor in the three-level converter. The hardware-in-the-loop system utilizes a real-time and high-speed simulator, specifically the PLECS RT Box 1, to obtain the findings. The computational cost of the overall system is rather low, making it feasible to construct using PLECS RT Box 1. The new MPPT algorithm quickly finds the maximum power point (MPP) and balances the voltage of capacitors in a number of different proton exchange membrane fuel cells. The suggested MPPT technique has been verified to demonstrate rapid tracking of the maximum power point (MPP) location, as well as precise balancing of capacitor voltage and robustness to environmental variations. This approach was tested and found to outperform conventional MPPT methods like Perturb and Observe (P&O) and Incremental Conductance (IC) in terms of tracking duration, precision, and voltage balancing, achieving a 15% reduction in tracking duration, a 5% deviation from the MPP value for voltage, and superior stability under changing temperature and pressure.

Modeling of the interaction of stressful components agricultural production

There is a completely natural, organic nonlinear relationship between crop production and animal husbandry, which are crucial components of agricultural production in Ukraine. Timely and appropriate management of these subsystems contributes to the stable growth of the country’s agriculture with minimal risk. This approach requires improvements in terms of the digitalization of the economy, particularly in the context of applying computer modeling to the nonlinear processes of interaction between these components. The article develops a modern methodology for computer modeling of an economic management object—a thorough study of a nonlinear dynamic model of the VolterraLotka class, featuring a component that characterizes the increase in livestock profitability due to feed supply. The model takes into account various influencing factors, allowing for the assessment of the efficiency of the interaction between crop production and animal husbandry at different stages of agricultural production. The study examines the behavior of the dynamic model in the vicinity of the leverage point, analyzing the modeled trend dependencies and statistical components: in the vicinity of the equilibrium point, there are oscillatory movements with a certain defined period, and the solutions of the mathematical model, i.e., integral curves, over time reflect the seasonal nature of the interaction between crop production and animal husbandry. The behavior of virtual scenarios—integral curves and phase portraits based on the mathematical model of nonlinear interaction of agricultural production components—wasstudied, showing the location of the equilibrium point, extreme points, and other critical points for the respective curves in a specific coordinate system. A transition to a dimensionless dynamic model and an economic interpretation of its implicit solution were indicated. Additionally, the impact of external economic factors on the stability of the system was analyzed, allowing for better forecasting of potential risks and the preparation of appropriate strategies to minimize them. For the first time, an approximate numerical estimate of the economic risk measure of the evolution of the interaction and interplay of agricultural subsystems over time was carried out in the study of the nonlinear dynamic model of the economic management object. The obtained results can be used to improve management strategies in the agricultural sector, particularly to enhance the stability of production processes and reduce economic risks in the context of market instability and climate change. This research provides new opportunities for effective planning and the implementation of innovations in Ukraine’s agricultural sector.

Pecan (Carya illinoinensis (Wangenh.) K. Koch) nuts as an emerging source of protein: extraction, physicochemical and functional properties.

The increasing demand for sustainable alternatives to traditional protein sources, driven by population growth, underscores the importance of protein in a healthy diet. Pecan (Carya illinoinensis (Wangenh.) K. Koch) nuts are currently underutilized as plant-based proteins but hold great potential in the food industry. However, there is insufficient information available on pecan protein, particularly its protein fractions. This study aimed to explore the physicochemical and functional properties of protein isolate and the main protein fraction glutelin extracted from pecan nuts. The results revealed that glutelin (820.67 ± 69.42 g kg-1) had a higher crude protein content compared to the protein isolate (618.43 ± 27.35 g kg-1), while both proteins exhibited amino acid profiles sufficient for adult requirements. The isoelectric points of protein isolate and glutelin were determined to be pH 4.0 and pH 5.0, respectively. The denaturation temperature of the protein isolate (90.23 °C) was higher than that of glutelin (87.43 °C), indicating a more organized and stable conformation. This is further supported by the fact that the protein isolate had a more stable main secondary structure than glutelin. Both proteins demonstrated improved solubility, emulsifying, and foaming properties at pH levels deviating from their isoelectric points in U-shaped curves. Compared to the protein isolate, glutelin displayed superior water and oil absorption capacity along with enhanced gelling ability. The protein isolate and glutelin from pecan nuts exhibited improved stability and competitive functional properties, respectively. The appropriate utilization of these two proteins will support their potential as natural ingredients in various food systems. © 2024 Society of Chemical Industry.

How do we safely preserve ovaries in patients with cervical adenocarcinoma: risk factors and predictive models.

To study and predict the risk of ovarian metastasis (OM) in patients with cervical adenocarcinoma (ADC). Patients with ADC who received surgical treatment from January 2015 to December 2022 in the Obstetrics and Gynecology Hospital of Fudan University were included in the study. Patients were further divided into OP (ovaries were preserved in surgery) and BSO (bilateral salpingo-oophorectomy) groups. For the patients who accepted BSO, 60% of the patients were randomly grouped into a training cohort, and predictive prognostic models were constructed with 10-fold cross-validation through random forest, LASSO, stepwise, and optimum subset analysis. The model with the highest area under receiver operator curve (AUC) was screened out in the testing cohort. The nomogram and its calibration curve presented the possibility of OM in future patients. The prognoses between the different populations were compared using Kaplan-Meier analysis. All data processing was carried out in R 4.2.0 software. A total of 934 patients were enrolled in our cohort; 266 patients had their ovaries preserved and 668 patients had BSO according to the previous criteria reported The clinical safety with these criteria was secured, while the 5-year overall survival had no significant difference between the BSO and OP groups (p = 0.069), which suggested that the current criteria could be extended and are more precise. Four predictive models for ovarian metastasis by machine learning were constructed in our study, and the random forest model that obtained the highest AUC in both training and testing sets (0.971 for training and 0.962 for testing set) was taken as the best model. The optimal cut-off point of the ROC curve (specificity 99.5% and 90% sensitivity) was utilized to stratify the patients into high- and low-risk OM. Further comparing the survival curves of the high and low-OM risk groups, it was found that both DFS and OS were significantly prolonged in the low-risk group (p < 0.01). On the basis of this random forest model, a nomogram was used to calculate the OM risk, and the results were validated with calibration. The predictive model was further applied to the whole cohort (934 patients), and we identified the OM low-risk population (n = 822) and the patients with high risk who should be recommended for BSO (n = 112). No significant difference was found in the 5-year survival between the low-risk group with our model and the patients who already had ovaries preserved according to the previous criteria (n = 266). The predictive model constructed in our study could identify the low-risk population of OM in patients with ADC, which remarkably extended the number with the previous criteria, for whom we could potentially preserve ovaries to help improve their life quality.

Association of different timepoint and threshold of time-in-target-range and MACEs: insight from the SPRINT study

Abstract Objective To evaluate the prognostic value of different timepoint and threshold of systolic blood pressure (SBP) Time in Target Rang (TTR) in Major Adverse Cardiovascular Events (MACEs). Method This is a post-hoc analysis of the SPIRNT (Systolic Blood Pressure Intervention Trial). Participants with complete record of BP values and no adverse events occurred in the initial 2-years follow-up were enrolled. Different SBP TTR indexes was calculated by different timepoint (3 month,12 month,24 month) and threshold (110-130mmHg;110-140mmHg;110-130mmHg for intensive treatment group and 120-140mmHg for standard treatment group) (Fig 1). The primary outcome was major adverse cardiovascular events (MACEs) occurred 2 years later, defined as the composite of myocardial infarction, stroke, heart failure and cardiovascular death. Results A total of 7134 participants in SPIRNT were enrolled in this study. Those SBP TTR indicators with independent predictive value of MACEs were selected by a multivariable COX model adjusted for traditional cardiovascular risk factors (Fig 2). SBP TTR calculated with 110-140mmHg window in 3 months (3month SBP TTR110-140) was considered to be the best predictor of MACEs occurred 2 years later among selected SBP TTR indexes according to c statistics and relative informativeness (Table1). The restricted cubic spline plot was used to evaluate the nonlinear trend of the TTR indicator. In addition, we obtain the cut-off value by looking for the point in the RCS curve with the largest slope change. 0.65 was considered to be the best truncation for 3month SBP TTR110-140(Fig 3). Patients with 3month TTR110-140 greater than 0.65 had 23.9% lower incidence of MACEs than patients with TTR less than or equal to 0.65 (HR 0.761[0.592-0978], P=0.033).(Table 2) In addition, for patients with early SBP control (3month SBP TTR110-140 &amp;gt;0.65), there was no significant difference in mean SBP between 110-130 mmHg and 130-140 mmHg (Fig4 Group1 Vs Group2). Lasso regression also suggests that TTR110-140 has more informatics advantages than TTR110-130 in cox model at different timepoints (Fig5). Conclusions 3month SBP TTR110-140 was considered to be the best predictor of MACEs occurred 2 years later in different SBP TTR indexes, which indicated the importance of early SBP control. This study also proposed 0.65 as the critical value of 3-month SBP TTR110-140 for prevention of the incident of MACEs. In the future, more cohort studies are needed to expend these findings.

A Discovery of Pressure-Induced New Semiconductor Electronic Phase Transitions by DFT Calculations: Introducing a Glimpse of a Novel Semiconductor Family.

This study introduces a discovery of pressure-induced new semiconductor electronic phase transitions. A novel semiconductor family that exhibits pressure-induced nonmonotonic changes in band gaps was found and meets the definition of phase transitions, challenging the traditional understanding of linear and monotonic band gap modification through pressurization. Our findings suggest a complex interplay of atomic spacing and electron orbital contributions under varying pressure conditions, resulting in the variation of band gaps. This behavior, which includes three distinct steps: first, narrowing, second, broadening, and third, narrowing again and ultimately metalizing; some compounds could bypass step 1, has potential applications in piezoelectric and semiconductor technologies. We propose two new semiconductor electronic phase transitions (SEPT) associated with specific inflection points in the pressure-dependent band gap curve. Our results open avenues for further research into the electronic properties of crystals under high pressure, with the ultimate goal of uncovering the more profound physical principles governing these phenomena.

Equidistribution of non-uniformly stretching translates of shrinking smooth curves and weighted Dirichlet approximation

AbstractConsider the action of $$a_t=\textrm{diag}(e^{nt},e^{-r_1(t)},\ldots ,e^{-r_n(t)})\in \textrm{SL}(n+1,{\mathbb {R}})$$ a t = diag ( e nt , e - r 1 ( t ) , … , e - r n ( t ) ) ∈ SL ( n + 1 , R ) , where $$r_i(t)\rightarrow \infty $$ r i ( t ) → ∞ for each i, on the space of unimodular lattices in $${\mathbb {R}}^{n+1}$$ R n + 1 . We show that $$a_t$$ a t -translates of segments of size $$e^{-t}$$ e - t about all except countably many points of a nondegenerate smooth horospherical curve get equidistributed in the space as $$t\rightarrow \infty $$ t → ∞ . This result implies that the weighted Dirichlet approximation theorem cannot be improved for almost all points on any nondegenerate $$C^{2n}$$ C 2 n curve in $${\mathbb {R}}^n$$ R n . These results extend the corresponding results for translates of fixed pieces of analytic curves due to Shah (2010) as well as those for uniform translates of shrinking curves due to Shah and Yang (2023), and answer some questions inspired by the work of Davenport and Schmidt (1969) and Kleinbock and Weiss (2008).

Can the digital economy empower the transportation sector to improve green total factor productivity?

The digital economy has the potential to facilitate digitization and green transformation within the transportation sector, but further research is warranted on how it can enhance the sector’s green total factor productivity (GTFP). Utilizing provincial data from China spanning 2013 to 2021, this study investigates the impact of the digital economy on the transportation sector’s GTFP. The findings are as follows: (1) The transportation sector exhibits a U-shaped relationship between the digital economy and GTFP. The inflection point of the U-shaped curve is at 0.441, with 81.11% of the samples falling to the left of this point. It is primarily through technological progress and innovation, rather than changes in technological efficiency, that the digital economy influences the improvement of GTFP. (2) The U-shaped relationship exhibits heterogeneity and varies depending on the mode and content of transportation. The U-shaped relationship is more pronounced in regions with lower total traffic volume on highways and railways or higher traffic volume on waterways. For transportation content, DEEI and GTFP show a significant U-shaped relationship in areas with high traffic volumes of passengers and freights. (3) Government intervention can attenuate the U-shaped relationship, which leads to a flatter curve. The market-driven level has the capability to strengthen the U-shaped relationship, resulting in a steeper curve. In conclusion, these findings contribute to deeper insights into the U-shaped relationship between the digital economy and GTFP, offering valuable guidance for policymakers seeking to improve GTFP in the transportation sector.

A privacy protection scheme to resist ring selection attack in peer-to-peer double auction V2V electricity transaction for charging stations

With the development of Electric Vehicles (EVs), the number of public charging piles cannot meet the immediate charging needs of EV users sometimes. The charging stations provide flexible electricity transaction schemes for users by managing charging piles and Vehicle-to-Vehicle (V2V). Users can freely choose transaction partners and submit transaction plans to multiple charging stations. The mobility of EVs results in users randomly selecting rings multiple times, which may cause them to appear in different rings. Attackers can link multiple transaction plans and infer user privacy information. The leakage of transaction information may threaten the fairness of transactions. Aiming at the above situations, a privacy protection scheme to resist ring selection attack in Peer-to-Peer double auction V2V electricity transaction for charging stations is proposed. Firstly, the ring signcryption algorithm is improved to resist ring selection attack. Secondly, a real identity verification algorithm is designed to track malicious users. Theoretical analysis proves that the security of the proposed privacy protection scheme. The elliptic curve point multiplication is used in the scheme to improve computational efficiency. Experiments show that this scheme has low computational and communication costs. Verification cost increases slowly with the increase of the number of ring members.

Predicting vision-threatening diabetic retinopathy in patients with type 2 diabetes mellitus: Systematic review, meta-analysis, and prospective validation study.

Delayed diagnosis and treatment of vision-threatening diabetic retinopathy (VTDR) is a common cause of visual impairment in individuals with type 2 diabetes mellitus (T2DM). Identification of VTDR predictors is the key to early prevention and intervention, but the predictors from previous studies are inconsistent. This study aims to conduct a systematic review and meta-analysis of the existing evidence for VTDR predictors, then to develop a risk prediction model after quantitatively summarising the predictors across studies, and finally to validate the model with two Chinese cohorts. We systematically retrieved cohort studies that reported predictors of VTDR in T2DM patients from PubMed, Ovid, Embase, Scopus, Cochrane Library, Web of Science, and ProQuest from their inception to December 2023. We extracted predictors reported in two or more studies and combined their corresponding relative risk (RRs) using meta-analysis to obtain pooled RRs. We only selected predictors with statistically significant pooled RRs to develop the prediction model. We also prospectively collected two Chinese cohorts of T2DM patients as the validation set and assessed the discrimination and calibration performance of the prediction model by the time-dependent ROC curve and calibration curve. Twenty-one cohort studies involving 622 490 patients with T2DM and 57 107 patients with VTDR were included in the meta-analysis. Age of diabetes onset, duration of diabetes, glycosylated haemoglobin (HbA1c), estimated glomerular filtration rate (eGFR), hypertension, high albuminuria and diabetic treatment were used to construct the prediction model. We validated the model externally in a prospective multicentre cohort of 555 patients with a median follow-up of 52 months (interquartile range = 39-77). The area under the curve (AUC) of the prediction model was all above 0.8 for 3- to 10-year follow-up periods and different cut-off value of each year provided the optimal balance between sensitivity and specificity. The data points of the calibration curves for each year closely surround the corresponding dashed line. The risk prediction model of VTDR has high discrimination and calibration performance based on validation cohorts. Given its demonstrated effectiveness, there is significant potential to expand the utilisation of this model within clinical settings to enhance the detection and management of individuals at high risk of VTDR.

Phase transition behaviors of Sr0.72Ca0.28Fe1-xNixO3-δ perovskite oxygen sorbents in a fixed bed reactor

Perovskite oxides have emerged as promising materials for oxygen storage applications, with their phase transitions being a key area of research. This study reports on the unique oxygen absorption and desorption breakthrough curves observed in the Sr0.72Ca0.28Fe1-xNixO3-δ series (x = 0.05) during fixed-bed experiments, featuring a distinct plateau. Through in situ X-ray diffraction (XRD) analysis, we demonstrated the reversible phase transition between the perovskite and brownmillerite (BM) structures. High Resolution Transmission electron microscopy (HR-TEM) further revealed the coexistence of these two phases within a granule. Focusing on the Sr0.72Ca0.28Fe0.95Ni0.05O3-δ sample at 550 °C, we conducted a detailed analysis of the oxygen absorption curve. Our findings indicate that the plateau arises from the oxygen-deficient BM phase absorbs oxygen to form the oxygen-saturated BM phase, followed by further oxygen absorption, leading to the formation of the perovskite phase. The inflection point in the oxygen absorption curve marks a critical oxygen concentration, signifying the threshold for effective oxygen absorption. This critical concentration is influenced by both material composition and operation temperature. Based on our observations, a phase diagram for x = 0.05 was drawn, encompassing both oxygen concentration and temperature. This diagram allows for the rational selection of operation temperatures and oxygen concentration swing ranges to enhance the performance of the oxygen sorbent. Our study highlights the crucial role of phase transition in oxygen storage, offering insights into the design and development of high-performance perovskite-based materials for various applications.

On bifurcation of non-isolated singular points arising in a problem of two-phase fluid motion in a pipe

The dynamics of gas suspension in a pipe is studied. It is described by a system of three autonomous differential equations, whose equilibrium (singular) points are not isolated, but form several continuous curves. One of these curves belongs to the transonic plane. The dynamics in a neighborhood of this plane is interesting from the physical viewpoint. In this work, the dynamics in a neighborhood of the curves of singular points is investigated. The main tools of this work are the center manifold theorem and the reduction principle. A peculiarity of the system is that the curves of singular points intersect each other. Moreover, at the intersection point a bifurcation occurs. Results of the work are the description of the dynamics in a neighborhood of singular points, the proof that a curve of singular points belongs to a center manifold and the description of the bifurcation, which occurs at the intersection point of the curves of singular points.

Electromagnetic navigation bronchoscopy-guided preoperative lung nodule localization in video-assisted thoracic surgery (VATS): a learning curve analysis.

Electromagnetic navigation bronchoscopy (ENB) has been widely used to mark small peripheral pulmonary nodules (PPNs) in video-assisted thoracic surgery (VATS) resection. This technique offers the advantages of a high accuracy and fewer complications. However, few studies have analyzed the learning curve of ENB-guided preoperative localization. We aimed to describe the learning curve and factors influencing ENB-guided thoracoscopic pulmonary nodule resection. This study included 300 consecutive patients with PPNs who underwent ENB-guided localization by the same endoscopist in our department between November 2019 and December 2021. The cumulative sum (CUSUM) method was used to analyze the learning curve of ENB-guided localization and the learning curve in different lobes, while logistic regression was used to analyze the risk factors affecting ENB operative time (OT). In 184 patients with 300 nodules, three learning phases were identified through turning points of the learning curve: Phase I (the 16th nodule), Phase II (the 17th to the 107th nodule), and Phase III (the 107th to the 300th nodule). No significant difference was found in the success rate of ENB-guided localization in each phase of the learning curve (100%, 96.7%, and 97.9%, P=0.78). The distance from the localization to the pleura in Phase I was statistically significantly shorter than that in Phase II and Phase III (0.6±0.4 vs. 1.1±0.6 vs. 1.0±0.5 cm, P=0.001 and P=0.003). Furthermore, the learning curves for nodules in different lobes were different. The learning curve for the upper lobe nodules was divided into two phases; the learning curve for the middle lobe disclosed more negative values; and the learning curve for the lower lobe nodules displayed no obvious pattern. Significant differences were found in nodule location, distance from the localization to the pleura and learning curve phase (P=0.003, P<0.001, P=0.02). The independent factors for OT included gender, smoking history, nodule type, distance from localization to the pleura, and learning curve phase. ENB OT at the 107th nodule leveled off and showed a downward trend. Different lobes have different learning curves, the middle lobe is the easiest lobe to learn with ENB and can be used as the first lobe of choice for beginners. The learning curve can objectively evaluate the accuracy of ENB location and help endoscopists identify areas for improvement.

An Optimization Design of Piezoelectric Hair Sensor for Oscillatory Flow Detection

Abstract Biological hair is widely found in nature, and they are responsible for sensing and responding to environmental stimuli in living organisms. By simulating biological hair characteristics, they develop hair flow sensor to achieve high sensitivity detection of environmental factors such as small motion and fluid flow field. Output signal is the key indicator of hair flow sensor, and the improvement of output signal is important to the design of hair flow sensor. The existing hair flow sensor sensing structure is generally straight hair, and the output signal is limited by the structure, and the response is small. Using the direct piezoelectric fiber as the initial configuration, we form a new piezoelectric curved fiber by modeling the secondary spline curve and control point. We propose an optimization model for piezoelectric functional hair design using axial strain as a target function. At 100Hz and 500Hz, the output voltage of the optimized model is much higher than that of straight, 10 times and 7 times that of straight, respectively; An optimized curved hair configuration is obtained in a specific frequency band from 1 Hz to 500 Hz, whose average voltage magnitude of 3.1×10−3 V is 4 times greater than that of the straight hair of 7.8×10−4 V with the same size. The curved hair flow sensor breaks the output limitation of traditional straight hair configuration.

Vapor-liquid equilibria of the ternary system of methane + n-decane + n-octacosane at high pressures

Alkane mixture phase equilibria is required for a range of industrial applications, most particularly for the petroleum industries. Mixtures containing light and heavy normal alkanes are of even further significance due to their non-ideal thermodynamic behavior. In this work, for the first time, the ternary system of C1 + C10 + C28 was investigated experimentally to obtain bubble point pressures at various concentrations and temperatures ranging from 373 up to 445 K. The synthetic method of phase equilibrium measurements was utilized, which is known to have high accuracy. The resulting bubble points for the mixtures ranged in pressure from about 3 MPa up to 6 MPa. The data indicated that methane concentrations had the greatest impact, not only on the magnitude of the bubble point pressure, but also on the slope of its temperature trend. The relative concentrations of n-decane and n-octacosane had far less impact on the bubble point curves. The results were also modelled using the Peng–Robinson equation of state, and it was found that this equation has the capability to predict the data with an AARD% of 5.1 %. When temperature-independent binary interaction parameters were optimized to the data, the AARD% reduced to 2.4 %. Although the components vary greatly in size, perhaps the success of the Peng–Robinson equation of state for this mixture could be attributed to the fact that it was developed for hydrocarbon mixtures, and so, a reason for its popularity in the petroleum industries, alongside its simplicity and ease of use.

An autoencoder model based on one-dimensional neural network for epileptic EEG anomaly detection

We propose an autoencoder model based on a one-dimensional convolutional neural network (1DCNN) as the feature extraction network for efficient detection of epileptic EEG anomalies. The local information of normal EEG signals was captured by utilizing the local feature extraction ability of 1DCNN for training of an autoencoder to learn the expression of normal EEG data in low dimensional feature space. With the difference between the input and output as the anomaly score, the threshold was determined by the optimal equilibrium point of the ROC curve, and the EEG signals exceeding the threshold were diagnosed as the seizure data. The performance of the 1DCNN-AE epilepsy detection model was evaluated using the publicly available CHB-MIT scalp EEG dataset and TUH scalp EEG dataset. The AUC of the 1DCNN-AE model reached 0.890 of CHB-MIT and 0.686 of TUH under the average level of patients, and the epilepsy detection rate reached 0.974 and 0.893, and these results were better than the latest epilepsy anomaly detection models LSTM-VAE and GRU-VAE. The 1DCNN model had a parameter quantity of 58.5M, which was at the same level with LSTM-VAE (47.4 M) and GRU-VAE (36.9 M) but with much smaller FLOPs (0.377 G) than LSTM-VAE (21.6 G) and GRU-VAE (16.2 G). The autoencoder model based one-dimensional convolutional neural network can effectively detect abnormal EEG signals in epileptic seizure.

An optimal demand side management for microgrid cost minimization considering renewables

AbstractIn an ordinary microgrid configuration, the required load changes from hour to hour. The power system firms determine the cost of energy at different times of day by considering the highest and lowest points of the consumption curve. This is referred to as time‐of‐use (TOU) pricing for power. The hourly basis load demand is divided into flexible and inflexible categories. Demand side management (DSM) lowers peak demand while rewarding customers for their participation based on load lowering. His rebuilds the whole load model on the pillars of demand cost movement. The research recommends a DSM methodology based on a combined intellect method to lower the total cost of employing loads in a microgrid (MG) structure while considering carbon tax as an unavoidable constraint to lower the emission of pollutants. This is because 40% of microgrid customers are willing to participate in the DSM scheme. The results obtained in each illustration demonstrate that the suggested DSM technique is suitable in terms of cost reduction. The generating cost was decreased from $15,488 to $15,354 when 0%–40% of clients engaged in the DSM programme. With just a 3% compromised increase in generation costs, a carbon price combined with economic emission dispatch reduced the pollutants emitted by up to 78%, from 70 to 15 tons.