Parallel Combination Research Articles

Time-dependent behavior of reinforcement rock unit anchored by energy-absorbing bolt for large deformation control in tunneling

The energy-absorbing bolt is designed to counteract both burst-prone and squeezing rock conditions in rock excavation. In this paper, the mechanical contribution of energy-absorbing bolts installed systematically around tunnels excavated in rock masses was investigated based on the assumptions of the reinforced rock unit (RRU) model. The time-dependent behavior of rock with a large deformation problem was simulated using a Nishihara model, while that of an energy-absorbing bolt was simplified by a rheological model connecting a Hookean elastic element and a Saint-Venant element in series. The mechanical model of RRU anchored by an energy-absorbing bolt was established by connecting the rock mass model and the energy-absorbing bolt model in a parallel combination. The closed-form solution presented the time-dependent behavior of RRU under an instantaneously applied tension stress and an linearly increasing load, respectively. The results showed the reinforcement could reduce the time-dependent deformation of the reinforced region of tunnels significantly. A preliminary estimate of NPR-bolt reinforcement for large-deformation controls in the Muzhailing tunnel was carried out using the proposed model. The results showed that the NPR cables could have critical effects in controlling the large deformation of the tunnel.

Optimization of a solar irradiation forecasting tool based on artificial intelligence

In current electric markets, where many stakeholders can take part, power network operators need accurate predictions of the energy generated by intermittent renewable sources in order to control the whole system. Therefore, the capacity to accurately forecast solar irradiance is key when it comes to the large-scale integration of solar energy generators in the traditional network. One of the challenges, however, consists of providing accurate very short-term predictions (minutes ahead) due to the variability of solar irradiance caused by different meteorological phenomena. This study addresses this need for very short-term forecasts through the development of an irradiance prediction scheme for 10 minutes ahead. The irradiance prediction algorithm is based on a parallel combination of two different layer recurrent networks and has been trained with a two-year historical database of solar irradiance. The accuracy of the proposed tool has been validated through forecasting a whole year using data that is not in the database used in the training step. This tool was then used to forecast the irradiance in two Spanish locations with different weather conditions to analyse whether the accuracy changes. The accuracy between predicted and actual values demonstrates that this tool outperforms similar forecasters.

Fabrication of pouch cell supercapacitors using abundant coal feedstock and their hybridization with Li-ion battery for e-rickshaw application

The environmental concerns and market demands created high demands of electric vehicles (EVs), but still the energy storage systems are lagging behind the fuel-based vehicles. Even though recent development in battery technologies bring forth great opportunities for EVs, the necessity and a good supply chain of electrode materials, does not fulfill the requirement of EVs. In India, battery-operated e-rickshaws face challenges like insufficient charging facility, low driving range, high battery cost, battery replacement, and disposal. To address the power delivery issues, we have designed and developed an indigenous Hybrid Battery Management System (HBMS) using fabricated coal-based supercapacitors and commercial Li-ion batteries for application in an e-rickshaw. For this study, we have fabricated 500 F pouch cell supercapacitors using high quality activated carbon derived from low-grade coal feedstock which shows excellent properties comparable to the commercially available supercapacitors. Five coal-based pouch cell supercapacitors (500 F) were connected through series parallel combination with commercial supercapacitor (Greencap. 500 F) to make a complete supercapacitor pack of 25 F and 54 V suitable for testing in the e-rickshaw (48 V and 1 kW motor). After implementing the developed hybridized battery management system (HBMS) in e-rickshaw, it showed the maximum speed of 45 km/h with additional power gain of 433 W. The analysis of data showed that the application of coal-based supercapacitors along with Li-ion battery had improved the overall performance of the e-rickshaw. Thus, this study has opened up a new venture of using abundant coal powder in fabricating low-cost pouch cell supercapacitor for using in EVs in a sustainable way that can influence the existing commercial battery market.

A novel creep contact model for rock and its implement in discrete element simulation

The creep behavior of rocks significantly impacts projects' structural safety, including mining, tunneling, and hydraulic engineering. Numerous creep models used in numerical calculations incorporate basic elements, such as Burger's model. However, relying solely on series or parallel combinations of these basic components fails to account for the accelerated creep stage, prompting efforts toward developing enhanced creep models. Despite noted progress, a limited number of studies on the full-process simulation of graded creep loading and the consequent generation of cracks. To overcome this problem, an innovative creep model is proposed based on both the Parallel Bond and the Kelvin-Voigt contact models. This proposed creep model simulates the graded loading creep test of rocks under different load conditions and also conducts a parameter sensitivity analysis for the impact of the proportionate discrepancy between the two contact models. The results indicate that this new creep model could successfully simulate the comprehensive creep process, effectively capture crack progression, and represent ultimate failure modes during the creep process. Notably, to ensure the precision of simulations, it is recommended that the proportion of the Kelvin-Voigt contact model within the total contact is at most 25%.

Effect of rotational shear on the dielectric dispersion of a nematic liquid crystal above the Freedericksz threshold field.

Rheo-dielectric studies of soft materials provide important information on the dynamic structure and electric polarization. We study the dielectric dispersion of a nematic liquid crystal by applying a high AC probe field without a DC bias and a low AC probe field with a high DC bias under steady rotational shear. The dielectric anisotropy of the nematic is positive and the applied electric field is parallel to the velocity gradient with a magnitude larger than the Freedericksz threshold field. We find that the dielectric dispersion and the relaxation frequencies are strongly shear rate dependent. The analysis of the results based on a simple physical model shows that the effective dielectric constant of the nematic with non-uniform director tilt in the shear plane can be modelled as a series combination of parallel and perpendicular components. Our experiments demonstrate changes in dielectric dispersion are due to molecular reorientation under the influence of the competing effects of hydrodynamic and dielectric torques.

Comprehensive Molecular Genetic Analysis in Glioma Patients by Next Generation Sequencing.

Glioma is caused by multiple genomic alterations. The evolving classification of gliomas emphasizes the significance of molecular testing. Next generation sequencing (NGS) offers the assessment of parallel combinations of multiple genetic alterations and identifying actionable mutations that guide treatment. This study comprehensively analyzed glioma patients using multi-gene NGS panels, providing powerful insights to inform diagnostic classification and targeted therapies. We conducted a targeted panel-based NGS analysis on formalin-fixed and paraffin-embedded nucleic acids extracted from a total of 147 glioma patients. These samples underwent amplicon capture-based library preparation and sequenced using the Oncomine Comprehensive Assay platform. The resulting sequencing data were then analyzed using the bioinformatics tools. A total of 301 mutations, were found in 132 out of 147 tumors (89.8%). These mutations were in 68 different genes. In 62 tumor samples (42.2%), copy number variations (CNVs) with gene amplifications occurred in 25 genes. Moreover, 25 tumor samples (17.0%) showed gene fusions in 6 genes and intragenic deletion in a gene. Our analysis identified actionable targets in several genes, including 11 with mutations, 8 with CNVs, and 3 with gene fusions and intragenic deletion. These findings could impact FDA-approved therapies, NCCN guideline-based treatments, and clinical trials. We analyzed precisely diagnosing the classification of gliomas, detailing the frequency and co-occurrence of genetic alterations and identifying genetic alterations with potential therapeutic targets by NGS-based molecular analysis. The high-throughput NGS analysis is an efficient and powerful tool to comprehensively support molecular testing in neurooncology.

Microgrids control: AC or DC, that is not the question

In these lecture notes, we delve into the models of the most commonly used DC-DC power converters: namely, the buck converter and the boost converter. Furthermore, we derive models for a DC microgrid consisiting of multiple buck and/or boost converters interconnected via (dynamic) resistive-inductive power lines and supplying the so-called ZIP loads, which are characterized by the parallel combination of constant impedance (Z), current (I), and power (P) load components. Furthermore, we introduce the primary control objectives in DC microgrids, focusing on voltage regulation and current sharing. Finally, we explore the most advanced control techniques to achieve these objectives. Importantly, these lecture notes are not intended to advocate total replacement of Alternating Current (AC) power systems with their Direct Current (DC) counterparts, but rather aim to offer a balanced perspective between them, acknowledging the historical dominance of AC power systems while underscoring the contemporary relevance of DC microgrids, which, with their inherent advantages, represent a viable complement to the existing infrastructure, fostering innovation and resilience in modern power networks.

A multi-layered corrugated resonator acoustic metamaterial with excellent low-frequency broadband sound absorption performance

How to design acoustic metamaterials with excellent low frequency broadband sound absorption and mechanical properties is a concern and bottleneck in the field of structural vibration and noise control. Based on the principle of Helmholtz resonator and lightweight multi-functional sandwich structure, we propose a multi-layered corrugated resonator acoustic metamaterial (MLCRAM) and fabricate a sample using 3D printing technology. Through theoretical and simulated studies of the MLCRAM model, it is presumed that the MLCRAM has excellent sound absorption performance in the frequency range of 500 to 1000 Hz, which is mainly attributed to the parallel combination of resonant cavities. Then the experimental test of the sample is also carried out to verify the reliability of both theoretical and simulated methods. By analyzing the complex frequency plane, the influence of coupling effects between resonant cavities on the sound absorption properties of the MLCRAM. Subsequently, the overall sound absorption mechanism of the MLCRAM is further analyzed in terms of sound velocity, sound pressure amplitude, and power dissipation density. It has been demonstrated that MLCRAM exhibits excellent low-frequency broadband sound absorption performance, providing a reference for the design of novel acoustic metamaterials.

Performance Evaluation of Induction Motor Behaviour on Conventional and PV System Power Sources

The present study explores the integration of photovoltaic (PV) systems for enhancement of power system quality and reliability, particularly when PV system is deployed to power inductive loads which have wide applications in commercial and industrial settings. A mixed approach is deployed using MATLAB/Simulink for the modelling and simulation process. The study investigates the impact of inductive loads on PV system, comparing its performance with a conventional power source of same power willing capacity. The PV system was modelled by connecting PV arrays in series and parallel combinations. The output of the PV arrays was fed into a three phase PWM inverter, the harmonics present at the inverter output were eliminated using properly sized Inductor- capacitor (L-C) filter circuit. Also, a three-phase conventional source of same output capacity was modelled. Comparative analysis of the impacts inductive loads has on both sources were carried out and this was accomplished by integrating three-phase asynchronous motor rated at 15kW(20HP). The induction motors were independently powered by two distinct sources and further subjected to intermittent loading conditions. The major parameters investigated includes the rotor speed, rotor current, and electromagnetic torque of the asynchronous motor. The outcome of the study showed that the conventional source outperforms the PV system. These necessitates the studies on adoption of advance strategies on how the negative impacts and challenges encountered in the course of integrating PV system to power inductive loads will be address.

Free-Cog revisited: the diagnostic contribution of cognitive and executive function.

Aim: To examine Free-Cog, a recently described, hybrid screening instrument, as separate tests of cognitive (Free-Cog-Cog) and executive function (Free-Cog-Exec) to see if this improved screening accuracy for cognitive impairment compared with standard Free-Cog. Materials & methods: Free-Cog-Cog and Free-Cog-Exec were combined using Boolean logical 'AND'and 'OR'operators (serial and parallel combination), and also used to construct a stepwise decision tree. Results: Serial combination improved specificity and positive predictive value whereas parallel combination improved sensitivity, typical findings with these operators. Stepwise application identified groups with high and low probability of cognitive impairment but failed to differentiate adequately those in the intermediate uncertain diagnosis group. Conclusion: Study findings suggest limited benefit from reformulations of Free-Cog compared with the standard instrument.

Differentiation of frontotemporal dementia subtypes using neuroimaging‐based multi‐type parallel feature embedding

AbstractBackgroundFrontotemporal dementia (FTD) represents a collection of neurocognitive syndromes with frontotemporal lobar degeneration (FTLD) neuropathology, and is associated with significant clinical, pathological, and genetic heterogeneity. We trained a deep neural network (DNN) classifier to differentiate behavioral‐variant FTD (bvFTD), semantic variant primary progressive aphasia (svPPA), and non‐fluent variant (nfvPPA) patients using MRI scans drawn from two multi‐site neuroimaging consortiums.MethodBvFTD (N = 173), nfvPPA (N = 63), and svPPA (N = 41) patients with T1‐MRI were extracted from the FTLD Neuroimaging Initiative (FTLDNI) and the ARTFL‐LEFFTDS Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) databases. MRI data were preprocessed with FreeSurfer, with cortical thickness, cortical volume, and subcortical volumes extracted. Cortical measures were parcellated into 360 patches (i.e., ROIs) according to the HCP‐MMP1 atlas. Both the patch‐based cortical thickness and volume features were harmonized to control confounding effects of sex, age, total intracranial volume (TIV), cohort, and scanner. Multi‐type features were parallelly fed into a multi‐layer‐perceptron (MLP)‐based classifier. Weighted cross‐entropy loss function was used to account for unbalanced sample sizes across FTD subtypes. 10‐fold nested cross‐validation was used to evaluate the robustness of the classification model, with data split into 80/10/10 of training/validation/test data sets.ResultVisual evaluation z‐scores of cortical features among FTLDNI and ALLFTD groups revealed site‐specific differences significantly reduced by feature harmonization, especially for volume (Figure 1). The balanced accuracy of the ensembled DNN classifier of each FTD subtype on test sets across all ten folds reached 0.76 ± 0.09 for bvFTD, 0.79 ± 0.07 for nfvPPA, and 0.88 ± 0.07 for svPPA (Figure 2). Figure 3 shows confusion matrices for classification performance in the test set across ten cross‐validation folds.ConclusionWe developed a deep‐learning‐based framework to classify three FTD subtypes: bvFTD, nfvPPA, and svPPA. The combination of feature harmonization and parallel multi‐type feature embedding framework showed promising differentiation power. This work can be used to recognize at‐risk populations for early and precise diagnosis, to aid intervention planning. Future studies will compare classification based on different input features and use visualization methods to identify the most discriminative regions and explore their clinical relevance.

Mechanics and validation tests of a post-tensioned self-centering brace with adjusted stiffness and deformation capacities using disc springs

Self-centering braces have been developed to reduce the residual deformation of steel frames in earthquakes. The brace has a higher elastic axial stiffness before the activation, which may result in a higher base shear for frame design. This work was aimed to develop a new post-tensioned self-centering brace (PT-SCB) with adjusted stiffness and deformation capabilities such that the brace could be designed based on a specified yield or ultimate frame drift. Combining disc springs and one of the compression members together in the original SCB could achieve new structural characteristics, providing an alternative to tune the axial stiffness and deformation. This work first introduces the mechanics and deformation mechanism of the proposed SCB and performs a parametric study to investigate the effect of disc spring stacks on the hysteretic response of SCBs. A test program is then conducted on cyclic tests of disc spring stacks in different serial and parallel combinations and two PT-SCBs. Each 3800 mm-long brace is composed of high-strength steel tendons, compression steel members, disc spring stacks, and bolted friction device. The hysteretic responses of the new SCB with different design parameters are used to validate the mechanics and ability of the axial stiffness and deformation adjustment.

Deep Learning-Based Automatic Modulation Classification Using Robust CNN Architecture for Cognitive Radio Networks

Automatic modulation classification (AMC) is an essential technique in intelligent receivers of non-cooperative communication systems such as cognitive radio networks and military applications. This article proposes a robust automatic modulation classification model based on a new architecture of a convolutional neural network (CNN). The basic building convolutional blocks of the proposed model include asymmetric kernels organized in parallel combinations to extract more meaningful and powerful features from the raw I/Q sequences of the received signals. These blocks are connected via skip connection to avoid vanishing gradient problems. The experimental results reveal that the proposed model performs well in classifying nine different modulation schemes simulated with different real wireless channel impairments, including AWGN, Rician multipath fading, and clock offset. The performance of the proposed system systems shows that it outperforms its best rivals from the literature in recognizing the modulation type. The proposed CNN architecture remarkably improves classification accuracy at low SNRs, which is appropriate in realistic scenarios. It achieves 86.1% accuracy at −2 dB SNR. Furthermore, it reaches an accuracy of 96.5% at 0 dB SNR and 99.8% at 10 dB SNR. The proposed architecture has strong feature extraction abilities that can effectively recognize 16QAM and 64QAM signals, the challenging modulation schemes of the same modulation family, with an overall average accuracy of 81.02%.

Memristor neurons and their coupling networks based on Edge of Chaos Kernel

Chua's theory of local activity shows that local activity is the origin of complexity, and the complexity can only occur on or near the stable locally active domain, referred to as Edge of Chaos (EOC). Very recently, for the voltage-controlled locally active memristors, a new physical concept dubbed “Edge of Chaos Kernel” (EOCK), which consists of a series combination between a negative resistance and a negative inductance and exhibits the EOC phenomenon of being stable yet potentially unstable, was defined and applied to the Hodgkin-Huxley neural circuit model. When an EOCK is coupled to a passive environment, its stability is disrupted, resulting in the emergence of action potentials, chaos and various complex phenomena. This paper proposes the dual version of the EOCK called as R-C EOCK, which consists of the parallel combination between a negative resistance and a negative capacitance. We show that the actual NbO memristor manufactured by NaMLab essentially belongs to a current-controlled locally active memristor which contains a R-C EOCK and gives the signature of its EOCK and EOC. We construct a second-order neuron based on the NbO memristor when connected in parallel with a passive capacitor, and further prove that only memristors endowed with an EOCK can generate action potential. On this basis, we construct a minimum cellular neural network with only 7 components based on two NbO memristor neurons and a passive coupling resistor, in which neuromorphic behaviors of static and dynamic pattern formation may emerge if and only if the single neuron has an EOCK and is poised on the EOC. The analysis in this paper explains the dynamic mechanism of Smale's paradox, in which two mathematically dead neurons coupled by a passive environment may become alive, under the same or different input current excitation, which are more in line with the actual biological neural networks.

Active impedance control of a loudspeaker and its parallel combination with porous materials for broadband sound absorption

In this paper, a new hybrid passive-active sound absorption method is investigated, in which porous materials and a loudspeaker are parallelly combined so that sound absorption over the full frequency band could be expected. The target impedance of the loudspeaker’s diaphragm is theoretically derived under this parallel structure, and a novel active impedance control method is further proposed. In the proposed control method, dual microphones installed on the surface of the composite structure as well as in the back cavity of the loudspeaker are used to pick up the sound pressure and the velocity of the loudspeaker’s diaphragm, respectively. Modal expansion analyses are performed theoretically to find the optimal microphone placement on the structure surface. An error signal is then established with an auxiliary filter corresponding to the target impedance as well as an extra delay. With the help of this extra delay, a stable and causal design result of the auxiliary filter could be obtained by solving a weighted least square problem. Finally, the impedance control of the loudspeaker’s diaphragm is realized by minimizing the energy of the established error signal with the filtered-x least mean square algorithm. Both simulations and experiments are carried out based on a standing wave tube, through which it has been verified that the proposed active impedance control method can effectively enhance the low-frequency sound absorption performance of porous materials. The proposed composite structure with a thickness of 80 mm can exhibit a sound absorption coefficient greater than 0.9 for all the frequencies above 20 Hz.

Analysis of factors with low positive predictive value in the diagnosis of urinary tract infection by flow cytometry.

Culture-negative urine specimens can be rapidly screened by urine flow cytometry (UFC), while low positive predictive value (PPV) limits the clinical application. We explored the factors associated with a low PPV. A total of 5095 urine specimens were analyzed with UFC and culture. Diagnostic performance of leukocytes, bacteria, and BACT-info flags was evaluated by sensitivity, specificity, PPV, and negative predictive value (NPV). The association of contaminated culture and squamous epithelial cell count and BACT-info flag was performed by logistic regression analysis. The NPVs of parallel combination of bacteria and leucocytes were 98.9% in males and 97.9% in females, and PPVs of serial combination were 86.6% and 77.8% in men and women, respectively. The PPV of Gram-negative flag was higher than that of Gram-positive flag. The proportions of contamination in the urine culture results of false positive specimens were 86.9% in males and 98.5% in females at the cutoff points of the serial combination, and these parameters were 53.2% in males and 85.6% in females for the Gram-positive flag. There was a statistically significant association between contaminated cultures and squamous epithelial cells count in females, but not in males. Associations between contaminated cultures and Gram-positive flags or Gram-pos/-neg flags were statistically significant, but there was no association between contaminated cultures and Gram-negative flags. A serial combination of leukocytes and bacteria may maximize PPV in the diagnosis of bacterial urinary tract infection by urine flow cytometry, and contamination is the main reason for a low PPV.

Mechanical Properties and Cushioning Effectiveness of FPUF-EPS Combination Materials.

Based on flexible polyurethane foam (FPUF), which is reversible after compression, and expanded polystyrene foam (EPS), which has a high cushioning energy absorption capacity, the parallel and series combinations of FPUF and EPS are provided. According to experimental data of FPUF and EPS uniaxial compression large deformation, the mechanical properties and cushioning effectiveness of the FPUF-EPS combination materials with different structural scale parameters were investigated by theory analysis and finite element simulation. The mechanical response and the cushioning effectiveness influencing factors of FPUF-EPS parallel (FE-P) and FPUF-EPS series (FE-S) combination materials under single compressive load, single-impact load, and multiple compressive loads were obtained. The differences in mechanical properties and cushioning effectiveness of FE-P, FE-S, FPUF, and EPS are analyzed. The influence law of structural scale parameters and load strength on the mechanical properties and cushioning effectiveness of FE-P and FE-S is provided. It indicates that the cushion properties of combination materials should be adjusted to satisfy product protection requirements. It is beneficial for the design optimization of cushioning and packaging protection.

Design of an adaptive fuzzy-neural inference system-based control approach for robotic manipulators

This paper proposes an adaptive fuzzy-neural inference system (ANFIS)-based control approach for a six degrees of freedom (6-DoF) robotic manipulator. Its main objective is to guarantee the error convergence of the controlled system in the presence of uncertainties and unknown disturbances. The suggested controller is a parallel combination of an ANFIS network with a proportional-integral-derivative (PID) controller. The ANFIS system is used as an estimator to approximate a part of the system and then applied as the feedback linearization in the suggested control structure. The convergence of system errors to zero was proven using Barbalat’s lemma. The suggested control law combines the simplicity and ease of implementation of PID control with the estimation properties of ANFIS networks. The suggested approach was evaluated using a simulation study and further validated experimentally using the 6-DoF IRB-120 robotic manipulator (IRB-120-RM). The obtained results confirmed its superior performance and suitability for practical implementation to industrial actuators.

The functional switching on the operating modes of a piezoelectric semiconductor bipolar junction transistor via mechanical loadings

A nonlinear model was developed on piezoelectric bipolar junction transistors (PS-BJTs) subjected to mechanical loadings by disregarding the assumption of low injection and the approximation of depletion layer. It was found that a PS-BJT subjected to different mechanical loadings can exhibit distinct carrier flow directions, for example, a remarkable transition for an n+pn-type PS-BJT from a transistor unit with amplification capability to two parallel connected PN junctions by applying a pair of lateral tensile or compressive stresses at the emitter and collector interfaces. Further in-depth analysis revealed that shielding effect produced by base region carriers greatly varies according to different loading mode, which leads to changing degrees of mechanical reconstruction on emitter junction barrier. Consequently, an artificial electron pathway (AEP) is formed at the emitter junction by a specific loading configuration. It is just due to the form change of AEP to produce distinct carrier flow directions, which can realize multifunctional switching of electronic devices. In addition, we also found an interesting phenomenon that an AEP can be directly constructed to penetrate the emitter junction by applying mechanical loadings in the longitudinal direction such that electrons in emitter region can travel across the emitter interface directly to the collector region without regulated by the base current. Under that situation, the PS-BJT resembles a parallel combination of a PN junction and an NN junction. This research provides novel insights into the mechanical modulation of piezoelectric electronic devices and the design of novel logic circuits.

Underwater low-frequency sound absorption performance and broadband absorption design of membrane-type acoustic metamaterials

The underwater low-frequency sound absorption performance of membrane-type acoustic metamaterials (MAM) is investigated in this paper. The MAM unit is composed of a tensioned membrane, an upper cavity frame, a lower cavity frame, a steel backing and a cylindrical mass block located at the geometric center of the membrane. The sound absorption mechanism of MAM and the influence of critical structural parameters are analyzed by numerical simulation. The absorption performance of MAM is further optimized by the parallel combination design of multiple MAM units and the acoustical siphon effect. A design of MAM with 19 units is proposed to improve the absorption bandwidth. The calculation result demonstrates that the structure can effectively absorb sound in the low-frequency range of 250–750 Hz, and the average absorption coefficient is above 0.6. Lastly, the corresponding sample is fabricated, and the underwater sound absorption performance is tested in the standing wave tube. The accuracy of numerical simulation is validated by experiment.