Minimum Splitting Research Articles

Using Adiabatic Energy Splitting To Compute Dexter Energy Transfer Couplings.

Dexter energy transfer and transport (DET) are of broad interest in energy science, and DET rates depend on electronic couplings between donor and acceptor species. DET couplings are challenging to compute since they originate from both one- and two-particle interactions, and the strength of this interaction drops approximately exponentially with donor-acceptor distances. Using adiabatic energy splitting to compute DET couplings has advantages because adiabatic states can be calculated directly using conventional quantum chemical methods. We describe a minimum energy splitting method to compute the DET coupling by altering molecular geometries to drive the systems into a T1/T2 energy quasi-degenerate-activated DA complex. We explore the accuracy of various quantum chemical approaches to calculate the Dexter couplings.

METODE RANDOM FOREST UNTUK MEMUDAHKAN KLASIFIKASI DIAGNOSIS PENYAKIT MENTAL

Mental health is important in the development of every individual. A bad mentality can prevent a person from developing, making a person easily stressed, hopeless, and even attempt suicide and commit crimes. Currently there are quite a lot of case related to mental health which are caused by many factors such as economic, social and medical. Reflecting on this fact, there is a need for rapid mental health detection so that immediate intervention can be carried out. This needs to be done so that the patient's condition can improve. This research focuses on diagnosing mental illness by utilizing machine learning. The method used is random forest which in several studies has been proven to produce good accuracy. Random forest performs machine learning on the attributes contained in the dataset combined with K-Fold Cross Validation so that each patient can be evaluated. Next, a tuning process is also carried out to test the parameters contained in the method. After the tuning process was carried out, the best parameters obtained were n-estimator of 30, maximum depth of 4, minimum sample leaf of 2, and minimum sample split of 10. From the combination of these parameters, accuracy is 90.83%, recall is 90.83 %, and precision of 93.25%.

Towards efficient waste management: identification of waste flow chains in micro-regional detail through monitored data

AbstractCountries around the world are gradually implementing the transition to a circular economy in waste management. This effort should be initiated already at the waste producers. It is necessary to plan and monitor waste management in as much detail as possible, i.e. at the level of micro-regions. At present, only indicators at the national level are analysed, as more detailed data at the micro-regional level are often not available or are burdened with significant errors and inconsistencies. The calculation of waste management indicators for micro-regions will allow to identify the potential for increasing material or energy recovery and to plan the necessary infrastructure directly to these locations instead of blanket and often ineffective legislative actions. This paper presents an approach for determining the producer-treatment linkage, i.e., provides information about each produced waste, where it was treated, and in what way. Such information is often not available based on historical waste management data as there are repeated waste transfers and often aggregated within a micro-region. The network flow approach is based on an iterative procedure combining a simulation with multi-criteria optimization. The chosen criteria replicate expert estimates in investigated issue such as minimum flow splitting, and minimum transfer micro-regions. A data reconciliation is performed where the deviation from all simulations is minimized, given that the capacity constraints of nodes and arcs resulting from the database must be satisfied. The approach is tested on a generated sample task to evaluate the precision and time complexity of the developed tool. Finally, the presented approach is applied to address a case study in the Czech Republic, within which it is possible to identify treatment location and methods for waste from individual regions.

MDou: Accelerating DouDiZhu Self-Play Learning Using Monte-Carlo Method With Minimum Split Pruning and a Single Q-Network

Artificial Intelligence (AI) has demonstrated outstanding performance in some perfect- and imperfect-information games, such as Go, Atari, and Texas Hold'em. Even though AI is successful in these games with small action spaces, it does not play well in large-scale multi-player, imperfect-information games like DouDiZhu. DouZero, a DouDizhu AI system, has recently been proposed and beaten all the existing DouDizhu AI programs. This paper introduces Minimum Split Pruning (MSP) and a single Q-network to accelerate the training of DouZero, called MDou. Our experiments show that MDou improved through self-play using limited computational ability (only a 4-core CPU and 1 GPU) and less learning time (30 days) while achieving comparable performance to DouZero.

Prediction of surface roughness and depth of cut in abrasive waterjet milling of alumina ceramic using Machine learning algorithms

In abrasive waterjet (AWJ) milling process, the ability to accurately predict outcomes such as surface roughness (Ra) and depth of cut (DoC) holds immense significance for cost reduction, resource optimization, and minimizing material wastage. However, a major challenge lies in the limited availability of AWJ milling datasets, hindering the development of robust machine learning models. To address the predicament of outcome, the present study employs a bootstrapping data augmentation technique to expand small AWJ milling datasets. Augmented data is used to train machine learning models, and hyperparameter fine-tuning is conducted using grid search and 5-fold cross-validation.In the prediction of surface roughness (Ra) for AWJ milling of alumina ceramic, the study finds that a Multi-Layer Perceptron (MLP) Regression model with a 4–100-100–1 architecture, 'relu' activation function, 'adam' solver, alpha value of 0.0001, and 'adaptive' learning rate outperforms the decision tree and random forest regression models. In the case of prediction of AWJ milling depth of cut (DoC) in alumina ceramic, the random forest regression model surpasses MLP and decision tree regression models. It employs hyperparameters including 100 estimators, 'gini' criterion, 'log2′ for maximum features, a maximum depth of 50, a minimum sample split of 10, a minimum sample leaf of 2, bootstrap as ‘True’, and a minimum impurity decrease of 0.The methodology followed to develop the machine learning models in this study is also tested in wire electrical discharge machining (WEDM) process in the machining aspects of metal matrix composites (MMC) for the prediction of surface roughness (Ra). The experimental dataset was adopted from (Satishkumar et al., 2011). In case of prediction of surface roughness (Ra) in WEDM, the random forest regression model outperforms MLP and decision tree regression models. These developed machine learning models hold the potential to greatly assist manufacturing engineers in selecting optimal input parameters for AWJ milling and WEDM, thereby reducing the wastage of machine resources.

Facial Expression Recognition for Healthcare Monitoring Systems Using Neural Random Forest.

Facial expressions vary with different health conditions, making a facial expression recognition (FER) system valuable within a healthcare framework. Achieving accurate recognition of facial expressions is a considerable challenge due to the difficulty in capturing subtle features. This research introduced an ensemble neural random forest method that utilizes convolutional neural network (CNN) architecture for feature extraction and optimized random forest for classification. For feature extraction, four convolutional layers with different numbers of filters and kernel sizes are used. Further, the maxpooling, batch normalization, and dropout layers are used in the model to expedite the process of feature extraction and avoid the overfitting of the model. The extracted features are provided to the optimized random forest for classification, which is based on the number of trees, criterion, maximum tree depth, maximum terminal nodes, minimum sample split, and maximum features per tree, and applied to the classification process. To demonstrate the significance of the proposed model, we conducted a thorough assessment of the proposed neural random forest through an extensive experiment encompassing six publicly available datasets. The remarkable weighted average recognition rate of 97.3% achieved across these diverse datasets highlights the effectiveness of our approach in the context of FER systems.

Coupling and thermosplasmonic properties of asymmetric hexagonal nanoframes

The interaction of light with asymmetric nanostructures leads to the coupling of plasmon modes. Understanding the nature and tunability of these hybridized plasmon modes is essential for both fundamental studies and the development of new applications. Herein, we systematically examine these coupled plasmon modes and heat generated by the asymmetric hexagonal nanoframes dimers (HNDs). Our results show that there are three plasmon bands in the examined wavelength. One of these bands is related to the coupled plasmon modes, which can be actively controlled by changing the height of nanoparticles. These coupled plasmon modes originate from the interaction of dipoles with a minimum splitting energy of 170 meV. Moreover, the thermoplasmonic studies show that the temperature of the asymmetric HNDs strongly depends on the coupling of nanoframes which shows great promise for highly sensitive thermal imaging.

Explaining the CDF-II W-boson mass anomaly in the Georgi–Machacek extension models

Original Georgi–Machacek model can preserve the custodial symmetry at tree level after the electroweak symmetry breaking. Unless additional SU(2)_c custodial symmetry breaking effects are significant, the new physics contributions to Delta m_W are always very small. Our numerical results show that ordinary GM model can contribute to Delta m_W a maximal amount 0.0012 GeV, which can not explain the new CDF-II anomaly on W boson mass. We propose firstly to introduce small misalignment among the triplet VEVs to increase Delta m_W, which can give large new physics contributions to Delta m_W. Such slightly misaligned triplet VEVs from custodial symmetry preserving scalar potential can still be allowed. Our numerical results indicate that the resulting Delta m_W can easily reach the 1sigma range of CDF-II m_W data and the splitting among the triplet VEVs Delta v(equiv v_xi -v_chi ) can be as small as 0.8 GeV for v_chi lesssim 15 GeV. We also propose to introduce an additional custodial symmetry breaking source by extending the GM model with a low scale RH neutrino sector, which can adopt the leptogenesis mechanism and allow moderately large coupling strength h_{ij} even for triplet VEVs of order GeV. With low scale RH neutrino mass scale of order 10^2–10^4 TeV, the new physics contribution to Delta m_W can reach 0.03 GeV and is much larger than that of ordinary GM model. Combining both custodial SU(2)_c symmetry breaking effects, the small misalignment among the triplet VEVs and the moderately large h_{ij} couplings allowed with RH neutrino sector, the value of Delta m_W can still easily reach the 1sigma range of CDF-II m_W data, with a minimum splitting (among the triplet VEVs) approximately 0.7 GeV for v_chi lesssim 15 GeV.

Evaluation of short day local and exotic onion genotypes

Onion is one of the important spices of daily dishes in Bangladesh and are shortage in production of the crop. Due to high photosensitivity in onion, only short-day types are suitable for cultivation in the particular agroclimatic condition of the country. But lack of high yielding potentiality in the existing cultivar along with limited variability within the available germplasm is the major drawback in onion production. To mitigate the problem, the present investigation was done to identify suitable short-day onion genotypes at the Regional Spices Research Centre, BARI, Gazipur during rabi 2018-19 and 2019-20. Twenty-nine local and exotic short-day onions were evaluated including two local checks BARI Piaz-1 and BARI Piaz-4. δ2p and PCV was higher than δ2g and GCV in almost all the traits studied and higher heritability (h2b) was observed for total bulb yield. Significant variations were found in morphological and physiological traits for bulb production. Considerably higher bulb length and diameter were found from Ac_G_18_379, Ac_B_18_413, Ac_B_18_420, Ac_B_18_428, BARI Piaz-4, Ac_B_18_419 and Ac_B_18_417. Minimum bulb splitting (%) and bolting (%) were obtained from the genotypes Ac_G_18_379, Ac_G_18_381, Ac_B_18_413 and BARI Piaz-4. Higher dry matter content was noted for the genotypes BARI Piaz-4 (17.9 %), Ac_B_18_425 (21.73 %) and Ac_G_18_384 (21.57 %) along with the TSS ranged from 10.5 to 17.780Brix. The maximum bulb yield was obtained from the genotype Ac_B_18_413 (20.69 t/ha), followed by Ac_G_18_383 (20.6 t/ha), Ac_B_18_419 (18.48 t/ha) and Ac_B_18_417 (18.2 t/ha). These genotypes could be recommended for commercial cultivation as well as to use in future onion breeding program. Bangladesh J. Agri. 2022, 47(2): 27-43

Insight Into Recompression Brayton Cycle

AbstractRecompression cycles have the potential to offer high performance when design parameters such as feasibility, performance, and compactness are considered. These cycles have recently gained attention especially in nuclear and concentrating solar power plants because of their high efficiency and environmentally friendly. A study has been done to investigate and learn more about recompression cycles. In this paper, a recompression Brayton cycle has been analyzed by performing parametric studies on the effectiveness of recuperators, pressure ratio, and split ratio as well as other input variables. To understand the relations between these factors and the performances of the cycle, argon was used as a working fluid because of its constant specific heat. The solution to temperatures at each state has been derived analytically, which is presented as a function of independent input variables. Thermal efficiency and exergy efficiency of this cycle have been determined in these analyses. The model indicates following results: entropy generation of recuperators is lower at a minimum split and decreases with increasing effectiveness. When the cycle is optimized for maximum efficiency it does not operate on maximum specific net work. The energy and exergy efficiencies of the cycle increase with increasing pressure ratio reaching a maximum value at the optimum pressure ratio. The effect of split ratio on temperature difference around recuperators shows that energy recovered at low temperature is higher at a minimum split which yields a higher efficiency in the cycle. The performance of the cycle is strongly affected by turbine inlet temperature.

Research of Frequency Splitting Caused by Uneven Mass of Micro-Hemispherical Resonator Gyro.

In practical engineering, the frequency splitting of Hemispherical Resonator Gyro (HRG) caused by uneven mass distribution seriously affects the precision of HRG. So, the inherent frequency is an important parameter of micro-Hemispherical Resonator Gyro (m-HRG). In the processing of hemispherical resonator, there are some morphological errors and internal defects in the hemispherical resonator, which affect the inherent frequency and the working mode of m-HRG, and reduce the precision and performance of m-HRG. In order to improve the precision and performance of m-HRG, the partial differential equation of the hemispherical resonator is solved, and the three-dimensional model using ANSYS software accurately reflected the actual shape is established in this paper. Then, the mode of hemispherical resonator in ideal state and uneven mass distribution state are simulated and analyzed. The frequency splitting mechanism of the hemispherical resonator is determined by calculation and demonstration, and the frequency splitting of the hemispherical resonator is suppressed by partial mass elimination. The results show that the absolute balance of energy can ensure the high-quality factor and the minimum frequency splitting of the hemispherical resonator. Therefore, during the processing of hemispherical resonator, the balance of mass should be achieved as much as possible to avoid various surface damage, internal defects and uneven mass distribution to guarantee the high-quality factor Q and minimum frequency splitting of hemispherical resonator.

CONTROL OF WORKING BODY OF AIRBORNE AIRCRAFT POSITIONING MECHANISMS BY MEANS OF STEPPER MOTOR IN STEP CRUSHING MODE

Step splitting control mode of the electric drives stepper motors working bodies of the positioning mechanisms of onboard aviation equipment is carried out by means of programmable controllers. From their output signals, pulse-wide signals are formed by means of drivers that control power transistors, which are included in the windings of a stepper motor.
 A simpler version of building control systems for stepper motors (SM) involves the use of programmable timers, which requires its reprogramming when changing the step splitting factor.
 In the process of step splitting the shape of the current in the winding of the SM approaches sinusoidal with an increase in the number of sine discretes stored in the controller's memory. However, in this case, the sine samples must follow at a higher frequency, which difficulties in programming them.
 There is a problem of finding new ways of programming in the controller's memory information about the changing shape of the supply voltage SM in the process of step splitting in order to ensure the maximum speed of the code and the minimum consumption of the processor time of the microcontroller.
 To solve this problem, it is proposed to specify information about the changing shape of the SM supply voltage not in the form of sine samples (table method), but the sum of the coefficients of the Walsh-Fourier series, the amplitudes of which depend of value of the step splitting factor.
 The Walsh-Fourier series is a natural basis for approximating the pulsed supply voltage of a stepper motor. Structural diagrams of digital and analog systems for stepper motors in the step splitting are proposed.
 In the digital control system, the Walsh matrix is entered into the permanent memory of the controller, the size of which is determined by the value of the minimum step splitting factor, and the column vectors of the coefficients of the amplitudes of the Walsh functions are entered into the random-access memory, each of which corresponds to its step splitting factor.
 The input controller sets the program for implementing the inverse Walsh transform to the control controller, as a result of which control signals for the driver are generated at the output of the DAC controller. As its output, control signals are generated for power transistors including in the windings of the stepper motor.
 In the analog control system, the main links are the Walsh functions generator and the block of summing coefficients. In the block of summing coefficients of each Walsh function is assigned an amplitude corresponding to a given step splitting factor, and then they are summed. As a result, a pulsed voltage is formed, which is fed to the input of the driver, as in a digital system. It is shown that an analog control system can be used to form low-frequency quasi- sine signals of high stability. They can be used in precision electric drives with high stability.

Remote sensing and machine learning for yield prediction of lowland paddy crops

Background: Paddy is one of the crops with the largest production worldwide, after corn and wheat. In Indonesia, paddy crops play a role as one of the main boosters of national economic growth based on their contribution to Indonesia's gross domestic product (GDP). Therefore, it is imperative to do research aimed at predicting the yield of paddy crops. Methods: This research exploits the technology of remote sensing and machine learning methods (i.e. Gradient Boosting Regressor) to predict the yield of lowland paddy crops. Remote sensing with a Landsat 8 satellite was used to obtain the input data in the form of the vegetation index (i.e. NDVI) value, surface temperature, and total pixels of the observed area. Afterward, the input data was arranged into training data by combining paddy yield data and the paddy harvest period. Results: The obtained training data was modelled to predict the yield of paddy crops using a Gradient Boosting Regressor. The results obtained from experiments conducted in Bandung, Indonesia, showed the scenario with the best parameter combination is an estimator of 2000, a learning rate of 0.001, minimum samples split of 2, and a maximum depth of 4, which has RMSE of 9766.72. Conclusions: This research succeeded in designing a computational model to predict the yield of lowland paddy crops by involving remote sensing and Gradient Boosting Regressor.

Direct Polariton-To-Electron Tunneling in Quantum Cascade Detectors Operating in the Strong Light-Matter Coupling Regime

Modern optoelectronic devices rely on cavity electrodynamics concepts for improved performances, embedding the active medium in an optical cavity to enhance the light-matter coupling. This coupling rate is usually small compared to the electronic and photon energies. Despite several demonstrations, devices operating with much larger light-matter coupling strength, in the so-called strong light-matter coupling regime, are yet to be demonstrated as viable, practical candidates. One of the main technological obstacles that hamper their dissemination is the comprehension of the carrier current extraction and injection from and into strongly coupled light-matter states. Here, we study this fundamental phenomenon in midinfrared quantum cascade detectors (QCDs) operating in the moderate to strong light-matter coupling regime. They operate around $\ensuremath{\lambda}=10\phantom{\rule{0.2em}{0ex}}\ensuremath{\mu}\mathrm{m}$ with a minimum Rabi splitting of 9.3 meV. A simple model based on the usual description of transport in QCDs does not reproduce the polaritonic features in the photocurrent spectra. On the contrary, a more refined approach, based on the semiclassical coupled modes theory, is capable of reproducing both optical and electrical spectra with excellent agreement. By correlating absorption and photoresponse with the simulations, we demonstrate that---in this system---resonant tunneling from the polaritonic states appears to be the predominant extraction mechanism. The dark intersubband states do not have a significant role in the process, contrary to what happens in electrically injected polaritonic emitters.

Nanoscopic live electrooptic imaging

A door to the nanoscopic domain is opened regarding real-time visualization of electric field distributions and dynamics. Through the use of a live electrooptic imaging system with an oil-immersion objective lens and a highly thinned electrooptic sensor film, a minimum linewidth of 330 nm and a minimum peak splitting of 650 nm in real-time electric field video images have been successfully demonstrated. In addition, room to improve the resolution is noted, while a few problems that need to be solved are discussed, including an effect caused by optical interference.

Polariton response in the presence of Brownian dissipation from molecular vibrations.

We study the elastic response of a stationarily driven system of a cavity field strongly coupled with molecular excitons, taking into account the main dissipation channels due to the finite cavity linewidth and molecular vibrations. We show that the frequently used coupled oscillator model fails in describing this response especially due to the non-Lorentzian dissipation of the molecules to their vibrations. Signatures of this failure are the temperature dependent minimum point of the polariton peak splitting, the uneven polariton peak height at the minimum splitting, and the asymmetric shape of the polariton peaks even at the experimentally accessed "zero-detuning" point. Using a rather generic yet representative model of molecular vibrations, we predict the polariton response in various conditions, depending on the temperature, molecular Stokes shift and vibration frequencies, and the size of the Rabi splitting. Our results can be used as a sanity check of the experiments trying to "prove" results originating from strong coupling, such as vacuum-enhanced chemical reaction rate.

Automatic Quantification of Cardiomyocyte Dimensions and Connexin 43 Lateralization in Fluorescence Images

Cardiomyocytes’ geometry and connexin 43 (CX43) amount and distribution are structural features that play a pivotal role in electrical conduction. Their quantitative assessment is of high interest in the study of arrhythmias, but it is usually hampered by the lack of automatic tools. In this work, we propose a software algorithm (Myocyte Automatic Retrieval and Tissue Analyzer, MARTA) to automatically detect myocytes from fluorescent microscopy images of cardiac tissue, measure their morphological features and evaluate the expression of CX43 and its degree of lateralization. The proposed software is based on the generation of cell masks, contouring of individual cells, enclosing of cells in minimum area rectangles and splitting of these rectangles into end-to-end and middle compartments to estimate CX43 lateral-to-total ratio. Application to human ventricular tissue images shows that mean differences between automatic and manual methods in terms of cardiomyocyte length and width are below 4 m. The percentage of lateral CX43 also agrees between automatic and manual evaluation, with the interquartile range approximately covering from 3% to 30% in both cases. MARTA is not limited by fiber orientation and has an optimized speed by using contour filtering, which makes it run hundreds of times faster than a trained expert. Developed for CX43 studies in the left ventricle, MARTA is a flexible tool applicable to morphometric and lateralization studies of other markers in any heart chamber or even skeletal muscle. This open-access software is available online.

Uniform potential difference scheme to evaluate effective electronic couplings for superexchange electron transfer in donor-bridge-acceptor systems.

This article proposes an ab initio quantum chemical method to evaluate the effective electronic coupling that determines the rate of superexchange electron transfer in donor-bridge-acceptor (D-B-A) systems. The method utilizes the fragment charge difference to define electronic diabatic states and to apply an electrostatic potential in a form of a uniform potential difference that mimics solvation effects on the relative energies of the electronic states. The two-state generalized Mulliken-Hush method is used to obtain the effective electronic coupling as the nondiagonal element of the effective Hamiltonian that is derived based on the Green's function approach and the quasi-degenerate perturbation theory. A theoretical basis is provided for the dependence of the calculated effective electronic coupling on the applied potential and for how to find the optimal potential to give the desired effective electronic coupling that coincides with the result of the minimum energy splitting method. The method is applied to typical D-B-A molecules and gives the effective electronic couplings in reasonable agreement with the experimental estimates.

MicroRNA-based biomarkers of the radiation response in prostate cancer.

163 Background: Intermediate and high-risk prostate cancer can be cured with radiation (RT) to the prostate and pelvic lymph nodes with androgen deprivation therapy (ADT), but both acute and late toxicity of the GU and GI systems are common. There are no biomarkers predicting radiation outcomes, limiting the opportunity to best personalize prostate radiation therapy. Methods: A prospectively enrolled single arm trial for locally advanced prostate cancer patients (T1-T4N0-N1M0) treated with definitive RT (74Gy IMRT) plus ADT was studied. Biologic samples were available in 108 of 138 patients. Toxicity was recorded using the RTOG morbidity grading system. We applied a panel of microRNA-based germline mutations shown to predict cancer therapy endpoints. Machine learning techniques were used to simultaneously identify prognostic features and perform classification of the biomarkers. Upsampling nested LOO-CV was used to assess performance and generality. Independent Fisher’s exact tests were performed to identify statistically significant marginal associations. Three classifiers were studied: logistic regression with elastic net regularization (EN-LR), classification trees (CT), and random forests (RF), with corresponding hyper-parameters of regularization weights (EN-LR), minimum split and bucket level sample size (CT), number of trees and mtry (RF). Normalized on the simplex, feature importance was defined as absolute value of regression weights for EN-LR, and cumulative decrease in Gini impurity for primary and surrogate splits at each node/splits for CT and RF. Results: Grade 2 or higher toxicity included acute GI (11%), acute GU (34%), late GI (3%) and late GU (16%). GI and GU toxicity and acute and late toxicity had unique predictive biomarkers. The top three marginal genetic associations for late GU toxicity were microRNA site variants in CD6 and CD274 (PDL1)(p.val < 0.01) and BRCA2 (p.val = 0.014). Using RF, CT and EN_LR we could predict late GU toxicity with up to 70% sensitivity, 96% specificity, and 90% accuracy. Conclusions: We have identified microRNA-based biomarkers that can predict late GU toxicity. Work incorporating patient reported outcomes and to identify biomarkers for additional endpoints is ongoing.

Bidirectional Quasi-Passive Reconfigurable (Bi-QPAR) Remote Node for Future Optical Access Networks

A bidirectional quasi-passive reconfigurable node is proposed and studied in this paper. The bidirectional quasi-passive reconfigurable node is capable of dynamic wavelength and power allocations in both downlink and uplink directions. In the uplink direction, bidirectional quasi-passive reconfigurable nodes can reduce power discrepancies between different users’ signals reaching the central office. Simulation results show that uplink power variations can be significantly reduced. The impact of minimum power splitting ratios, users’ distributions, and numbers of users supported by bidirectional quasi-passive reconfigurable nodes on the system performance is investigated as well. 2 × 2 × 2 and 2 × 4 × 4 bidirectional quasi-passive reconfigurable nodes are experimentally demonstrated. Our results show that the maximum insertion loss of the 2 × 4 × 4 node is about 16.2 dB, and the power penalty of resource sharing and bidirectional operation is smaller than 0.8 dB for both downlink and uplink. In addition, the proposed bidirectional quasi-passive reconfigurable node can be remote powered by sending a pump signal directly from the central office to eliminate the need for local power supplies at the remote node. Remote power operation of bidirectional quasi-passive reconfigurable nodes is experimentally demonstrated as well with a negligible power penalty.