Negative Component Research Articles

Vanadate-based Fe-MOFs as promising negative electrode for hybrid supercapacitor device

In the supercapacitor field, negative electrodes are mainly concentrated in carbon-based materials, such as activated carbon, carbon nanotubes, graphene, and so forth. However, materials based on metal–organic frameworks (MOFs) as negative active components are relatively rare. Herein, a series of composite materials based on graphene oxide (GO) and vanadate-based Fe-organic frameworks have been prepared by hydrothermal method namely GO/Fe-VO4-BIPY. The deposition amount of polyoxometalate-based metal–organic frameworks (POMOFs) on the surface of graphene is adjusted by changing the content of POMOFs. Through the deposition, it can effectively reduce the accumulation between graphene, and increase the dispersion of POMOFs. As a result, the charge storage performance of the as-obtained materials is greatly improved. Among these materials, GO/Fe-VO4-BIPY-1 has the most prominent performance, with a specific capacitance of 190 F g−1 at 0.5 A g−1, which is attributed to the excellent synergistic effect between the Faraday chemical reaction and electric double-layer capacitance. In comparison with pristine Fe-VO4-BIPY, GO/Fe-VO4-BIPY-1 delivers more excellent surface area and therefore exhibits abundant redox reaction sites, achieving better electrochemical performance the best. After assembly with the positive Ni(OH)2 electrode, the maximum energy density of 46.84 W h kg−1 at a power density of 850 W kg−1 is achieved.

Efficient signal-to-noise ratio enhancement model for severely contaminated distributed acoustic sensing seismic data based on heterogeneous knowledge distillation

Distributed acoustic sensing (DAS) is an emerging seismic acquisition technique with great practical potential. However, various types of noise seriously corrupt DAS signals, making it difficult to recover signals, particularly in low signal-to-noise ratio (S/N) regions. Existing deep-learning methods address this challenge by augmenting data sets or strengthening the complex architecture, which can cause overdenoising and a computational power burden. Hence, the heterogeneous knowledge distillation (HKD) method is developed to more efficiently address the signal reconstruction under low S/N. HKD uses ResNet 20 as the teacher and student (T-S) model. It uses residual learning and skip connections to facilitate feature representation at deeper levels. The main contribution is the training of the T-S framework with different noise levels. The teacher model that is trained using slightly noisy data serves as a powerful feature extractor to capture more accurate signal features because high-quality data are easy to recover. By minimizing the difference between the outputs of T-S models, the student model that is trained using severely noisy data can distill the absent signal features from the teacher to improve its own signal recovery, which enables heterogeneous feature distillation. Furthermore, simultaneous positive and negative learning (P&NL) is developed to extract more useful features from the teacher, enabling the T-S framework to learn from the predicted signal and noise during training. Consequently, a new loss function that combines student denoising loss and HKD loss weighted by P&NL is developed to alleviate signal leakage. The experimental results demonstrate that the HKD achieves distinct and consistent signal recovery without increasing computational costs.

An adaptive controller for power quality control in high speed railway with electric locomotives with asynchronous traction motors

Introduction. Power quality in an electric railway system pertains to the dependability, consistency, and purity of the electrical power provided to different components and systems within the railway infrastructure. Assessing power quality offers considerable opportunities to improve the efficiency of railway systems. Problem. Managing the flow of active and reactive power effectively, decreasing harmonic currents, and addressing the negative sequence component are all critical parts of improving power quality for electrified rail systems. As a result, flexible AC transmission systems are the major means of minimizing or decreasing these difficulties. Purpose. This study describes a half-bridge reactive power railway power conditioner (HB-RPC) with a novel Ynev balancing transformer. HB-RPC is made up of four switching devices and two DC capacitors and the compensator’s stability is determined by the operating voltage of the DC-link. Any variations or imbalances in the DC voltage might cause the compensator to operate in an unstable manner. Novelty. Of a novel balanced transformer with HB-RPC in a high-speed railway system with two scenarios. Methods. The study utilized MATLAB/Simulink software for simulation purposes. The system integrates a fuzzy logic controller (FLC) and a PI controller to optimize DC voltage, ensuring its constancy and balance, with the objective of improving the overall stability of the system. Results. The simulation outcomes illustrate the efficacy of the control approach. Through a comparison of results between scenarios (two and four trains) with the PI-based-HB-RPC and the FLC-based-HB-RPC, the system exhibits enhanced stability for the proposed railway system when employing the FLC-based-HB-RPC, compared to a controller based on PI. Practical value. The proposed configuration elucidates its role in enhancing both the dynamic performance of the system and the power quality of the three-phase rail traction chain.

Observation of Doppler shift modulated by the internal kink mode using conventional reflectometry in the EAST tokamak

In this paper we present a new experimental observation using a conventional reflectometry technique, poloidal correlation reflectometry (PCR), in the Experimental Advanced Superconducting Tokamak (EAST). The turbulence spectrum detected by the PCR system exhibits an asymmetry and induced Doppler shift during the internal kink mode (IKM) rotation phase. This Doppler shift is the target measurement of Doppler reflectometry, but captured by conventional reflectometry. Results show that the Doppler shift is modulated by the periodic changes in the effective angle between the probing wave and cutoff layer normal, but not by plasma turbulence. The fishbone mode and saturated long-lived mode are typical IKMs, and this modulation phenomenon is observed in both cases. Moreover, the value of the Doppler shift is positively correlated with the amplitude of the IKM, even when the latter is small. However, the positive and negative frequency components of the Doppler shift can be asymmetric, which is related to the plasma configuration. A simulated analysis is performed by ray tracing to verify these observations. These results establish a clear link between and IKM rotation, and are helpful for studying the characteristics of IKM and related physical phenomena.

Twitter sentiment analysis and bitcoin price forecasting: implications for financial risk management

PurposeThe present study distinguishes itself by pioneering an innovative framework that integrates key elements of prospect theory and the fundamental principles of electronic word of mouth (EWOM) to forecast Bitcoin/USD price fluctuations using Twitter sentiment analysis.Design/methodology/approachWe utilized Twitter data as our primary data source. We meticulously collected a dataset consisting of over 3 million tweets spanning a nine-year period, from 2013 to 2022, covering a total of 3,215 days with an average daily tweet count of 1,000. The tweets were identified by utilizing the “bitcoin” and/or “btc” keywords through the snscrape python library. Diverging from conventional approaches, we introduce four distinct variables, encompassing normalized positive and negative sentiment scores as well as sentiment variance. These refinements markedly enhance sentiment analysis within the sphere of financial risk management.FindingsOur findings highlight the substantial impact of negative sentiments in driving Bitcoin price declines, in contrast to the role of positive sentiments in facilitating price upswings. These results underscore the critical importance of continuous, real-time monitoring of negative sentiment shifts within the cryptocurrency market.Practical implicationsOur study holds substantial significance for both risk managers and investors, providing a crucial tool for well-informed decision-making in the cryptocurrency market. The implications drawn from our study hold notable relevance for financial risk management.Originality/valueWe present an innovative framework combining prospect theory and core principles of EWOM to predict Bitcoin price fluctuations through analysis of Twitter sentiment. Unlike conventional methods, we incorporate distinct positive and negative sentiment scores instead of relying solely on a single compound score. Notably, our pioneering sentiment analysis framework dissects sentiment into separate positive and negative components, advancing our comprehension of market sentiment dynamics. Furthermore, it equips financial institutions and investors with a more detailed and actionable insight into the risks associated not only with Bitcoin but also with other assets influenced by sentiment-driven market dynamics.

Self-perception evolution among university student TikTok users: evidence from China.

The effects of short movies on social media platforms are gaining worldwide popularity and are now attracting global academic attention. Employing self-perception theory and qualitative research methodology, the study examines the influence of short video applications (TikTok) on app-user engagement and evaluates the self-perceived cognitive psychological understanding of Chinese university students. The findings show that identity, attitude change, emotional perception, and civic engagement are the most influential aspects of Chinese youths' self-perceptions. Furthermore, the positive and negative correlated components influence the distribution of short video values. Such tactical use of personality construction contributes to the present psychological research of Chinese university students.

Efficacy of Atypical-Atypical and Atypical-Typical Antipsychotic in Indonesia Psychotic Mental Disorder Patients

It is approximately 7% of Indonesian people undergo psychotic mental disorder or schizophrenia. Special Region of Yogyakarta in Indonesian is on the second rank as the area whose high number of schizophrenia; for about 10 occurrences per 1000 citizens and 80% of patients treated at psychiatric hospital are caused by schizophrenia. A response of a medication therapy can be measured through doctor diagnosis based on the measurement parameter such as The Positive and Negative Syndrome Scale-Excited Component (PANSS-EC) and Global Assessment of Functioning (GAF). Those two parameters are important variables that will be shown objectively by the assessment results. This research aimed to find out the different value of PANSS-EC and GAF, before and after the therapy of atypical-typical and atypical-typical on the schizophrenia patients at Psychiatric Hospital of Grhasia Yogyakarta. The research design here is Prospective Cohort. The criteria of inclusive patient was adults (18-65 years old), being hospitalized, undergoing atypical-atypical and atypical-typical therapy, and possessing complete medical record. The research data were gained from the medical records which then analyzed using univariate test to illustrate the patient distribution. Bivariate test was conducted to compare and correlate the value of PANSS-EC and GAF. The number of samples were 32 patients which were divided into 2 groups; atypical-atypical (n=16) and atypical-typical (n=16). The results showed that the value of PANSS-EC after therapy in both groups experienced a significant decrease (p=0.001). However, there was no significant difference between the two groups of patients with psychotic mental disorders or schizophrenia in a mental hospital and there reseach no significant difference in effectiveness between doses and frecuency of antipsychotic therapy regimens in schizophrenic patients with a significant value of p>0.05

Assessment of compassion and empathy in students of health care professions

Compassion is one of the most important qualities expected and anticipated by patients from their healthcare professionals. Empathy is the ability to recognize and respond to others’ emotions. What differentiates compassion from empathy is the creation of a strong desire to reduce suffering. To assess and compare compassion levels in students of MBBS, BSc Nursing and Bachelors in Physiotherapy (BPT) in a medical college. This is a descriptive study done at the NRI Medical College in Guntur district of Andhra Pradesh state in India, The study involved students of 6 Semester MBBS (126), BSc Nursing 4 year (89) and 4 year BPT (48). The study instruments included the Compassion Score questionnaire & Toronto Empathy Questionnaire (TEQ) 3 which are both available freely in the public domain. Compassion scores in male students had a mean of 58.6 (range 22 to 78 and SD 9.2). Compassion scores in female students have a mean of 59.0 (range 29 to 80 and SD 9.1). Overall compassion scores were highest in MBBS students, both males and females when compared to Nursing and Physiotherapy students. Empathy scores also followed the same statistically significant pattern. Among compassion components, kindness was highest in BPT students while common humanity was higher in the medical students.MBBS students were seen to have significantly higher scores in all three negative components; indifference, separation and disengagement. It is proposed that the nature of training of nursing and physiotherapy students involves practical hands-on involvement in day to day care of their patients, while medical students are more involved in academic pursuits and less involvement with their patients during undergraduate training. Efforts must be made to encourage all healthcare students to inculcate empathy and compassion when communicating with patients. Empathy can be enhanced by adding training courses and workshops to the curriculum to teach empathy to develop interpersonal skills. There is a need to use innovative and creative approaches like simulation, role playing, storytelling and reflective discussion.

Theoretical Design and Dynamic Analysis of a Quasi-Zero Stiffness Device Using Precompressed Springs as Negative Stiffness Component

Theoretical Design and Dynamic Analysis of a Quasi-Zero Stiffness Device Using Precompressed Springs as Negative Stiffness Component

Characteristics of symmetrical components for high impedance faults in distribution networks with photovoltaic inverters

This work models a photovoltaic (PV) inverter connected to an IEC microgrid system. The purpose of this study was to find the characteristics of symmetrical components before and after a high impedance single phase short-circuit fault. The IEC microgrid system is tailored to the distribution system in Indonesia, where the applied voltage is 20 kV. Only parameters related to the short-circuit study are included in the model. A second-order generalized integrator frequency-locked-loop is utilized to represent the 3-phase PV inverter. The inverter model, previously treated as an ideal current source, and positive sequence current are no longer employed, as they fail to depict the characteristics of symmetric components during a phase-to-ground short-circuit fault. The IEC microgrid system connected to the PV inverter is simulated using ATPDraw-electro magnetic transient program (EMTP). Simulation results reveal that the changes in the symmetric component values of current after a fault experience a very insignificant increase. Meanwhile, the positive sequence voltage values following the short-circuit fault exhibit a negligible decrease. In contrast, the negative sequence and zero sequence voltage components after the short-circuit fault undergo a significant increase.

A Simple Score to Predict New-Onset Atrial Fibrillation After Ablation of Typical Atrial Flutter

BackgroundNew-onset atrial fibrillation (NeAF) is common after cavotricuspid isthmus-dependent counterclockwise atrial flutter (CCW-AFL) ablation. This study aimed to investigate a simple predictive model of NeAF after CCW-AFL ablation. MethodsFrom January 2013, to December 2017, consecutive patients receiving CCW-AFL ablation were enrolled from 3 centres. Clinical, echocardiographic, and electrocardiographic data were collected and followed. Patients from 2 centres and another centre were assigned into the derivation and validation cohorts, respectively. In the derivation cohort, logistic regression was performed to evaluate the ability of parameters to discriminate those with and without NeAF. A score system was developed and then validated. ResultsTwo hundred seventy-one patients (mean 59.7 ± 13.6 age; 205 male) were analyzed. During follow-up (73.0 ± 6.5 months), 107 patients (39.5%) had NeAF; 190 and 81 patients were detected in the derivation and validation cohorts, respectively. Hypertension, age ≥ 70 years, left atrial diameter ≥ 42 mm, P-wave duration ≥ 120 ms and the negative component of flutter wave in lead II ≥ 120 ms were selected as the final parameters. A weighted score was used to develop the HAD-AF score ranging from 0 to 9. In the derivation cohort, area under the receiver operating characteristic curve (AUC) was 0.938 (95% confidence interval [CI], 0.902-0.974), superior to those of currently used CHA2DS2-VASC (0.679, 95% CI, 0.600-0.757) and HATCH scores (0.651, 95% CI, 0.571-0.730) (P < 0.001). Performance maintained in the validation cohort. ConclusionsSix years after CCW-AFL ablation, 39.5% of patients developed NeAF. HAD-AF score can reliably identify patients likely to develop NeAF after CCW-AFL ablation.

Depolarization at the Electrodeposition of the Negative Component of Eutectic Alloys

Depolarization at the Electrodeposition of the Negative Component of Eutectic Alloys

A numerical assessment to select the optimal geometry of a specific cyclone separator of wheat flour processing

AbstractIn this study, the effect of the cyclone body height on the flow field within the specific cyclone and also on the performance parameters of the cyclone has been investigated. The five heights of the cyclone as various configurations were I = 600, II = 675, III = 750, IV = 825, and V = 900 mm. A pressure‐based solver was used and the turbulent Reynolds stress model was utilized to simulate the flow. Then, the Lagrangian discrete phase model was used to simulate the solid phase. As the height of the cyclone increases, the pressure drop decreases. Also, the best separation efficiency and the highest particle velocity value were achieved at a height of 750 mm. The maximum values of turbulence intensity, skin friction coefficient, and strain rate were obtained as negative components of cyclone performance at the cyclone body height of 900 mm. The height of 750 mm was recognized as the best height of the cyclone's body.Practical ApplicationsApplying numerical simulation in food processing is a novel method to minimize equipment construction and reach a high‐accuracy design. Computational fluid dynamics can provide an accurate and detailed flow analysis in the cyclone separators utilized in the flour processing plant. The dimensional analysis can help the researchers to achieve the optimum design and enhance the cyclone performance by reducing pressure drop and increasing separation efficiency. Also, the maintenance cost would be significantly reduced due to detecting the critical sections and strengthening them.

SOLVING BIPOLAR FULLY FUZZY SYLVESTER MATRIX EQUATIONS WITH NEGATIVE FUZZY NUMBERS

Sylvester matrix equations play a crucial role in control theory for controller design. Bipolar Fully Fuzzy Sylvester Matrix Equations (FFSME), incorporating both positive and negative components, are employed in controller design to address uncertainties that may affect a system’s performance and stability. However, there is not much existing research on combining bipolar fuzzy numbers and FFSME,and most of them mainly deal with positive coefficients. Thus, this paper presents a method that enables solving the negative coefficient of bipolar FFSME in the form of Left-Right (LR) triangular fuzzynumbers using an Associated Bipolar Linear System (ABLS). To obtain the ABLS, bipolar FFSME is transformed into a bipolar Fully Fuzzy Linear System (FFLS) using the Kronecker product and Vecoperator. Subsequently, the solution is derived through the inverse method, and the equation of the ABLS is rearranged as a bipolar fuzzy matrix. Additionally, this paper provides two numerical examples to illustrate the applicability of the constructed method.

Choosing a mortgage lending model in the Kyrgyz Republic

In the modern world, mortgage plays an important role in the life of the citizens of the country due to its important role in providing their housing needs. Thus, it is important to find the most effective model of mortgage lending for the country to maximize the living standards of its citizens. The aim of the work is to choose the most effective model of mortgage lending that can be used in the country and bring the greatest benefit to it. The main methods used in the research were analysis, deduction, induction, and abstraction. The paper examined various aspects of mortgage lending, focusing on the different payment models and their implications for borrowers. Two types of mortgage loans were considered: with annuity and differentiated payments, their main differences in terms of payment rates were described, and mathematical formulae for their calculation were specified. To illustrate the impact on the borrower of each of them, a theoretical example was provided, on the basis of which the differences between the two systems were described. The paper also describes three models for organizing mortgage lending systems: a closed (stand-alone) mortgage system, a truncated open model and an extended open model. The suitability of these models for the development of the Kyrgyz mortgage market was assessed based on their characteristics and features, positive and negative components. The work brings new knowledge in the context of studying the peculiarities of mortgage lending as a whole, as well as allows finding opportunities for improving its functioning in the Kyrgyz Republic

An island detection methodology with protection against cyber attack

Unplanned islanding of micro-grids is a significant barrier to supplying continuous power to key customers. The identification of the islanding moments must be rapid to enable the distributed generators (DG) to perform control measures in the shortest possible period. Micro phasor measuring units (µ-PMU) are gaining popularity in distribution systems and micro grids as a result of their ability to produce high-quality data at a high speed. These µ-PMUs can be utilized to detect islands. However, the µ-PMU relies heavily on the communication system for transmission of data, which is vulnerable to cyberattacks. In consideration of the previous technique, this research provides a smart island detection application with µ-PMU having lowered cyberattack probabilities. This representation is equipped with a µ-PMU implemented on the relevant DG’s bus. The voltage data acquired from these µ-PMUs are processed using the sequence transformation in order to simulate the sequence component angle. The angular sum of the negative and positive sequence components is evaluated and the maximum value is deployed for detection of islanding. MATLAB/Simulink tests the proposed approach through an IEEE-34 node distribution network. Multiple simulations demonstrate the robustness of the technique.

Three‐phase steady‐state models of distributed generators with different control strategies

AbstractThis paper proposes three‐phase steady‐state models for distributed generators (DGs) with different negative control strategies. An augmented rectangular load flow method is used for three‐phase distribution systems. For two types of DGs with power electronic devices: photovoltaic power generators and doubly fed induction generators, the nodal voltage equations and output power equations are derived. Moreover, under unbalanced conditions, the strategies for mitigating the effects caused by negative sequence components are formulated as functions of the nodal voltage and the injected current. Furthermore, the models are incorporated into an augmented rectangular load flow method with nodal voltage and injected current as state variables in three‐phase systems. Case studies are conducted to verify the correctness and accuracy of the proposed models by comparing the results obtained from MATLAB and PSCAD.

Parameter Estimation of Power System Oscillation Signals under Power Swing Based on Clarke–Discrete Fourier Transform

Accurate knowledge of oscillation parameters (i.e., frequency, amplitude, phase, and damping factor) is crucial for control strategies of power systems under power swing. This paper presents a method for the parameter estimation of power system oscillation signals under power swing based on Clarke–DFT. The proposed method provides accurate parameter estimation of damped sinusoidal signals for both balanced and unbalanced systems, which performs well even in the presence of harmonics. In the meantime, the negative frequency components in the spectra of the damped sinusoidal signals, which are caused by system imbalance, are calculated accurately using complex-valued interpolated DFT. To verify the performance of the proposed method, simulations are performed under balanced and unbalanced conditions. The results of the simulations confirm the effectiveness of the proposed method either in unbalanced or harmonic conditions.

A bidirectional quasi-zero stiffness metamaterial for impact attenuation

A reusable bidirectional quasi-zero stiffness metamaterial (BQZS-MM) with quasi-zero stiffness zones under compressive and tensile loads is proposed, which can be employed for bidirectional kinetic energy absorption and impact attenuation. The BQZS-MM is characterized by the parallel connection of positive stiffness components and negative stiffness components to generate the BQZS. At the unit level, the bidirectional BQZS characteristics are analyzed theoretically and validated by simulations. The research further discusses the span and amplitude of the quasi-zero stiffness zone with structural parameters and serial-parallel mechanisms. The effect of quasi-zero stiffness zone’ span and amplitude on dynamic performance is investigated. The physical prototypes are manufactured by selective laser sintering technology. The BQZS and reusability characteristics of BQZS-MM are examined by three repeated tensile and compressive tests. In comparison to existing metamaterials with bidirectional negative stiffness in one principal direction, under compressive and tensile loads, energy absorption, specific energy absorption, energy density, and crushing force efficiency of BQZS-MM have been significantly improved. During dynamic analysis, BQZS-MM attenuates bidirectional response acceleration peaks and achieves bidirectional impact attenuation. However, existing metamaterial with unidirectional quasi-zero stiffness can only achieve unidirectional impact attenuation. The BQZS-MM features novel BQZS mechanical characteristics, serving as a guide for creating brand-new buffer energy absorbers.

Characteristics of different Mandarin pronunciation element perception: evidence based on a multifeature paradigm for recording MMN and P3a components of phonemic changes in speech sounds.

As a tonal language, Mandarin Chinese has the following pronunciation elements for each syllable: the vowel, consonant, tone, duration, and intensity. Revealing the characteristics of auditory-related cortical processing of these different pronunciation elements is interesting. A Mandarin pronunciation multifeature paradigm was designed, during which a standard stimulus and five different phonemic deviant stimuli were presented. The electroencephalogram (EEG) data were recorded with 256-electrode high-density EEG equipment. Time-domain and source localization analyses were conducted to demonstrate waveform characteristics and locate the sources of the cortical processing of mismatch negativity (MMN) and P3a components following different stimuli. Vowel and consonant differences elicited distinct MMN and P3a components, but tone and duration differences did not. Intensity differences elicited distinct MMN components but not P3a components. For MMN and P3a components, the activated cortical areas were mainly in the frontal-temporal lobe. However, the regions and intensities of the cortical activation were significantly different among the components for the various deviant stimuli. The activated cortical areas of the MMN and P3a components elicited by vowels and consonants seemed to be larger and show more intense activation. The auditory processing centers use different auditory-related cognitive resources when processing different Mandarin pronunciation elements. Vowels and consonants carry more information for speech comprehension; moreover, more neurons in the cortex may be involved in the recognition and cognitive processing of these elements.