Unequal Weights Research Articles

Statistical Properties of Superpositions of Coherent Phase States with Opposite Arguments

We calculate the second-order moments, the Robertson–Schrödinger uncertainty product, and the Mandel factor for various superpositions of coherent phase states with opposite arguments, comparing the results with similar superpositions of the usual (Klauder–Glauber–Sudarshan) coherent states. We discover that the coordinate variance in the analog of even coherent states can show the most strong squeezing effect, close to the maximal possible squeezing for the given mean photon number. On the other hand, the Robertson–Schrödinger (RS) uncertainty product in superpositions of coherent phase states increases much slower (as function of the mean photon number) than in superpositions of the usual coherent states. A nontrivial behavior of the Mandel factor for small mean photon numbers is discovered in superpositions with unequal weights of two components. An exceptional nature of the even and odd superpositions is demonstrated.

Segmentation of Chinese consumer preference for wine extrinsic attributes based on stratification and weighted clustering algorithm

Chinese consumers are unfamiliar with the sensory attributes of wine and often pay more attention to the extrinsic attributes of wine that can most directly present information. Therefore, it is crucial to study consumer preferences for extrinsic attributes. This paper proposes a consumer segmentation method based on stratification and weighted clustering algorithm, leveraging data from an anonymous online survey (N = 3179). The AFR model is constructed based on the RFM model to segment wine consumers from the perspective of customer value. This model removes Monetary indicator and adds a new indicator Amount to eliminate multicollinearity caused by the Frequency and Monetary indicators of RFM model. In addition, factor analysis is used to assign weights to clustering indicators, addressing the problems of unequal weighting and strong correlation among the indicators. This allows for a simple, efficient, and precise segmentation of wine consumers at different levels. The method proposed in this article segments consumers into two layers and six categories: the potential layer includes product-oriented type, amorphous type, and cheap-fine type, while the mature layer comprises the rational type, reputation-price type and random type. This research will provide wine producers and distributors with a simple and efficient basis for production and marketing decision-making. Additionally, it can serve as a reference for consumer segmentation in food and other fields.

Bio-inspired target detection method of active sonar based on multi-ping perception

Abstract The performance of the active sonar may degrade severely in real ocean environments, influenced by strong reverberation and multiple clutters. Inspired by the observation that dolphins always utilize a train of clicks when detecting underwater, we proposed an active target detection method by making full use of the multi-ping echo information which encompasses the differences between targets and reverberation/clutters. The detection performance of the click trains with different parameters were analysed first to give an insight on signal choice. Then we achieved apparent reverberation/clutter suppression result by utilizing the correlations and differences between multi-ping echoes. Further, the relative motion status between the target and the sonar platform could be perceived within multiple echoes, which strengthened the detection ability of weak target. We proposed two weighting strategies for multi-ping co-utilization. Either equal weights or unequal weights based on the different envelopes of dolphin echolocation click trains were exerted on multiple echoes, which can achieve different perception results for the relative target motion status. Both simulation and lake trials were conducted to verify the performance of the proposed method. The proposed method effectively suppress reveberaton and noise background by around 2-3 dB and achieve bionic detection of weak targets compared to traditional active sonar signal processing method.

Multi-objective optimization of forest structure for broadleaf mixed forest under different CMIP5 scenarios

Climate change is an important global research topic and one of the most pressing issues facing the world today. It significantly impacts forest distribution, species composition, stand structure, growth, and other functions, therefore, it is crucial to simulate the dynamic changes of forest structure under future climate change and develop optimized solutions to adapt to it. Combined with the various stand structural parameters, based on the equal or unequal weights of each parameter at the stand level, to establish a dynamic stand spatial structure multi-objective optimization index (L-index) under different climate changes, and to implement broadleaf mixed forest spatial structure optimization research in Yanan Forest Farm under RCP 2.6, RCP 4.5, RCP 8.5 climate scenarios after 5 years. The results indicated that the L-index showed an increase ranging from 13.8 % to 35.7 % under equal weights in different climate scenarios. Additionally, the optimal harvesting intensity ranged from 13.2 % to 20.5 %, with the highest intensity observed in the RCP4.5 climate scenario; compared to the RCP4.5 and RCP8.5 climate scenarios, the RCP2.6 scenario shows the largest trend and magnitude of change in the indicators, suggesting that the state of stand growth is more sensitive under the RCP2.6 scenario. The L-index showed an increase ranging from 13.7 % to 34.4 % under the unequal weights in different climate scenarios, in which the optimal harvesting intensity ranged from 14.6 % to 19.8 %. More importantly, it should be noted that the values of the increases in the L-index and the relative increased proportion (RIP) were both higher than those when unequal weights were employed. Moreover, it should be highlighted that the increments in the L-index and RIP were both greater than those observed when unequal weights were applied for all three plots under different climate scenarios. Overall, the adjusted the complete mixing degree index (Mc) increased, angle index (W), dominance ratio index (U), and Hegyi competition index (CI) decreased compared to before optimizing with the equal or unequal weights of the stand structural variable, which is evident that the adjustments made to the stand’s structure have resulted in a significant improvement. Thus, the multi-objective optimization for forest spatial dynamics constructed is both reasonable and effective, and it more scientific forest management under different climatic scenarios.

Customised-sampling approach for pipe failure prediction in water distribution networks

This paper presents a new methodology for addressing imbalanced class data for failure prediction in Water Distribution Networks (WDNs). The proposed methodology relies on existing approaches including under-sampling, over-sampling, and class weighting as primary strategies. These techniques aim to treat the imbalanced datasets by adjusting the representation of minority and majority classes. Under-sampling reduces data in the majority class, over-sampling adds data to the minority class, and class weighting assigns unequal weights based on class counts to balance the influence of each class during machine learning (ML) model training. In this paper, the mentioned approaches were used at levels other than “balance point” to construct pipe failure prediction models for a WDN with highly imbalanced data. F1-score, and AUC–ROC, were selected to evaluate model performance. Results revealed that under-sampling above the balance point yields the highest F1-score, while over-sampling below the balance point achieves optimal results. Employing class weights during training and prediction emphasises the efficacy of lower weights than the balance. Combining under-sampling and over-sampling to the same ratio for both majority and minority classes showed limited improvement. However, a more effective predictive model emerged when over-sampling the minority class and under-sampling the majority class to different ratios, followed by applying class weights to balance data.

Correlation of Foot Posture Index and Years of Experience in Nurses with Ankle and Pain

This study aims to determine the correlation between the foot posture index and the years of experience of nurses having foot and ankle pain. Nurses spend a great deal of time on their feet. Due to long standing and unequal weight shifting on the foot the plantar arches become stiff or less flexible. This leads to have deformed or abnormal foot posture. Therefore, by using foot posture index we’re going to assess the foot posture of the nurses and then correlate them with their years of experience.

System reliability analysis of Cable-stayed bridge using pair Copula and ICLHS with unequal weights under seismic loading

Considering the correlation between different components, quantifying the failure probability of a high-level hyper-static structure under non-stationary seismic loading is of great significance in guiding earthquake-resistant design and fortification of bridges. In this study, a high-pier and large-span railway cable-stayed bridge in the mountainous region were employed to evaluate the seismic performance. First, the basic theoretical methods of the non-stationary random seismic motion simulation, equivalent extreme value theory based on extreme value distribution (EVD), improved Latin hypercube sampling (ICLHS) with unequal weighted fractional moment estimation and Pair Copula technique are presented. Second, the finite element analysis model of the cable-stayed bridge was established based on the OpenSees platform, the seismic response samples of the bridge were calculated under three levels of seismic ground motion to determine the first passage failure probability of the main components of the bridge under the design failure modes. Finally, the Pair Copula function was introduced to estimate the seismic system reliability of the cable-stayed bridge. The optimal Copula type that can describe the correlation between the bridge components was selected through goodness-of-fit tests such as AIC, BIC, and the minimum distance method. The failure probability of the entire system was obtained by calculating the selected Copula function type under seismic loading.

Achieving the Fundamental Quantum Limit of Linear Waveform Estimation.

Sensing a classical signal using a linear quantum device is a pervasive application of quantum-enhanced measurement. The fundamental precision limits of linear waveform estimation, however, are not fully understood. In certain cases, there is an unexplained gap between the known waveform-estimation quantum Cramér-Rao bound and the optimal sensitivity from quadrature measurement of the outgoing mode from the device. We resolve this gap by establishing the fundamental precision limit, the waveform-estimation Holevo Cramér-Rao bound, and how to achieve it using a nonstationary measurement. We apply our results to detuned gravitational-wave interferometry to accelerate the search for postmerger remnants from binary neutron-star mergers. If we have an unequal weighting between estimating the signal's power and phase, then we propose how to further improve the signal-to-noise ratio by a factor of sqrt[2] using this nonstationary measurement.

A general purpose parallel Fortran code for grid projected concentration reconstruction from multidimensional particle distributions

Particle-based transport models are widely used for simulating the movement of pollutants in environmental systems. Unlike grid-based methods, particles naturally address advective transport without numerical dispersion. However, these models require concentration reconstruction from the discrete particle information. This is computationally demanding in multidimensional problems, posing a challenge for field-scale models requiring frequent reconstruction. Grid Projected Kernel Density Estimation (GPKDE) is a cell-averaged reconstruction with improvements in computational performance compared to classical KDE. Currently, no programs implementing this method are readily integrated into particle simulators, compatible with three-dimensional domains, and particles with unequal weights. This article introduces a Fortran code for general-purpose GPKDE, with modular functionalities facilitating the integration into external software. The program implements locally adaptive kernel bandwidth selection and alternatives for the reconstruction from particles with non-uniform weights. The code is parallelized with the OpenMP library. Numerical test cases demonstrate the program’s applicability and scalability of the parallel implementation.

Measuring Parallelism to the Ground in Bipedal Stance Phase: Mechanical Versus Kinematic Alignment in Total Knee Arthroplasty.

Introduction The goal of total knee arthroplasty is to replace diseased cartilage and bone with an artificial implant to improve the patient's quality of life. The knee has historically been reconstructed to the patient's mechanical axis (MA). However, kinematically aligned techniques have been increasingly used. Kinematic alignment requires less soft-tissue resection and aligns the knee with what is anatomically natural to the patient, while there is concern that kinematically aligned knees will lead to earlier failure due to potential unequal weight distribution on the implant. The purpose of this study is to compare the parallelism from the floor of the joint-line cuts using kinematic and mechanical alignment and understand if the MA is a proper estimation of the tibial-ankle axis (TA). Methods A retrospective study was conducted by recruiting all high tibial osteotomy and distal femoral osteotomy recipients operated on by two surgeons in two MedStar Health hospitals from 01/2013 to 07/2020 with full-length films in preparation for restorative procedures. Baseline osteoarthritis was graded using the Kellgren-Lawrence classification system with all patients presenting as Grade 0. The TAand the joint-line orientations of the MAand kinematic axis (KA) were measured on 66 legs. The average distance from parallelism to the ground was compared between the MA and the KA and between the MA and the TA using a paired t-test. Results KA joint-line orientation (1.705° deviation) was more parallel to the floor in the bipedal stance phase than the MA (2.316° deviation, p=0.0156). The MA (2.316° deviation) was not a proper estimation of the TA (4.278° deviation, p=0.0001). Conclusion By utilizing the KA technique, the restoration of the natural joint line, as well as a joint that is more parallel to the floor in the stance phase compared to the MA, is achieved.The parallelism to the ground of the KAduring the bipedal stance phase suggests an even load distribution across the knee.In addition, due to its similarity to the KA and anatomical significance in weight-bearing distribution, further investigation into the hip-to-calcaneal axis as an approximation of the joint line is warranted.

A simulation approach to investigate factors influencing the cost of omitted objectives in multiattribute models

AbstractEmpirical evidence suggests that decision‐makers are ill‐equipped to identify all relevant objectives in a decision problem. We examine the effect of an incomplete set of objectives using a Monte Carlo simulation to compare a baseline model to a reduced model incorporating only a subset of objectives. We assess the performance of reduced models varying in the number of objectives, the number of alternatives, the correlations among objectives, and attribute weights. Results suggest that missing objectives will most impact multiattribute models with negative correlations between objectives; similarly, models with equally weighted objectives suffer more than models with unequal weights. Decision problems with more objectives tend to be less impacted by missing objectives, given the same proportion of missing objectives. In contrast, decision problems with more alternatives are more impacted for some performance measures but less on others. However, the variation in model performance due to the number of objectives and alternatives is relatively minor compared to the variation due to the nature of the correlation between objectives.

Molecular organization of neuronal cell types and neuromodulatory systems in the zebrafish telencephalon

The function of neuronal networks is determined not only by synaptic connectivity but also by neuromodulatory systems that broadcast information via distributed connections and volume transmission. To understand the molecular constraints that organize neuromodulatory signaling in the telencephalon of adult zebrafish, we used transcriptomics and additional approaches to delineate cell types, to determine their phylogenetic conservation, and to map the expression of marker genes at high granularity. The combinatorial expression of GPCRs and cell-type markers indicates that all neuronal cell types are subject to modulation by multiple monoaminergic systems and distinct combinations of neuropeptides. Individual cell types were associated with multiple (typically >30) neuromodulatory signaling networks but expressed only a few diagnostic GPCRs at high levels, suggesting that different neuromodulatory systems act in combination, albeit with unequal weights. These results provide a detailed map of cell types and brain areas in the zebrafish telencephalon, identify core components of neuromodulatory networks, highlight the cell-type specificity of neuropeptides and GPCRs, and begin to decipher the logic of combinatorial neuromodulation.

Breakdown modes in nanosecond pulsed micro-discharges at atmospheric pressure

Nanosecond pulse micro-discharges at atmospheric pressure have garnered attention because of their unique physics and numerous applications. In this study, we employed a one-dimensional particle-in-cell/Monte Carlo collision model coupled with an external circuit, using an unequal weight algorithm to investigate the breakdown processes in micro-discharges driven by pulses with voltage ranging from 1 kV to 50 kV at atmospheric pressure. The results demonstrate that nanosecond pulse-driven microplasma discharges exhibit different breakdown modes under various pulse voltage amplitudes. We present the discharge characteristics of two modes: ‘no-breakdown’ when the breakdown does not occur, and ‘runaway breakdown mode’ and ‘normal breakdown mode’ when the breakdown does happen. In the runaway breakdown mode, the presence of runaway electrons leads to a phenomenon in which the electron density drops close to zero during the pulse application phase. Within this mode, three submodes are observed: local mode, transition mode, and gap mode, which arise from different secondary electron generation scenarios. As the pulse voltage amplitude increases, a normal breakdown mode emerges, characterized by the electron density not dropping close to zero during the pulse application phase. Similarly, three sub-modes akin to those in the runaway breakdown mode exist in this mode, also determined by secondary electrons. In these modes, we find that electron loss during the pulse application phase is dominated by boundary absorption, whereas during the afterglow phase, it is dominated by recombination. Ion losses are primarily governed by recombination. These findings contribute to a better understanding of the discharge mechanisms during the breakdown process.

Psychometric sensitivity analyses can identify bias related to measurement properties in trials that use patient-reported outcome measures: a secondary analysis of a clinical trial using the disabilities of the arm, shoulder, and hand questionnaire

ObjectivesDemonstrate psychometric sensitivity analyses for testing the stability of study findings to assumptions made about patient-reported outcome measures. Study Design and SettingWe performed secondary analyses of Disability of Arm, Shoulder, and Hand (DASH) data collected within the Prevention of Shoulder Problems clinical trial, which compared upper limb function scores in women who had undergone breast cancer surgery, randomized to either an exercise program or usual care. We repeated the principal trial analyses after grouping DASH items into subscales suggested by factorial analyses in this dataset and applied item response theory to account for unequal item weighting. We checked for measurement invariance by participant age and response shift bias using established techniques. ResultsOur analyses suggested that the DASH measured two constructs: motor function and sensory symptoms. The majority of the six-month difference in DASH score was driven by motor function. With item response theory scoring, we found differences in both constructs at 12 months (P = 0.019 and P = 0.007), but in neither construct at 6 months, contrary to the original trial results. We found no differential item function by age or between baseline and 12-month measurements. ConclusionsPsychometric sensitivity analyses aid in the interpretation of the Prevention of Shoulder Problems trial’s results.

Quadratic-Droop-Based Distributed Secondary Control of Microgrid With Detail-Balanced Communication Topology

This article proposes a quadratic-droop-based fixed-time distributed secondary voltage control of an islanded ac microgrid. The microgrid consists of inverter-based distributed generators, connected over a detail-balanced communication topology with bidirectional unequal edge weights. The proposed voltage restoration strategy is derived from the quadratic relation between voltage and reactive power for inductive microgrids against their linear dependence, which has been conventionally used in designing voltage droop controllers. It has been illustrated that the proposed quadratic-droop-based control design strategy shows better performance as compared with the linear counterpart. We provide a rigorous Lyapunov stability analysis for the proposed controller and also obtain an upper bound on the convergence time, independent of the system's initial conditions. In addition, frequency restoration and proportional active power sharing among the generators are realized in the fixed time. Simulations are provided to verify the efficacy of the proposed controllers, including a comparative analysis with conventional droop control and finite-time control through a case study.

Design of a lightweight universal talus implant using topology optimization.

Total talus replacement is a promising alternative treatment for talus fractures complicated by avascular necrosis and collapse. This surgical option replaces the human talus bone with a customized talus implant and can maintain ankle joint functionality compared to traditional treatment (e.g., ankle fusion). However, the customized implant is costly and time-consuming due to its customized nature. To circumvent these drawbacks, universal talus implants were proposed. While they showed clinically satisfactory results, existing talus implants are heavier than biological talus bones as they are solid inside. This can lead to unequal weight between the implant and biological talus bone, and therefore leading to other complications. The reduction of the implants' weight without compromising its performance and congruency with surrounding bones is a potential solution. Therefore, this study aims to design a lightweight universal talus implant using topology optimization. This is done through establishing the loading and boundary conditions for three common foot postures: neutral, dorsi- and plantar-flexion. The optimized implant performance in terms of mass, contact characteristics with surrounding joint cartilage and stress distributions is studied using a 3D Finite Element (FE) model of the ankle joint. The mass of the optimized implant is reduced by approximately 66.6% and its maximum stresses do not exceed 70MPa, resulting in a safety factor of 15.7. Moreover, the optimized and solid implants show similar contact characteristics. Both implants produced peak contact pressures that were approximately 19.0%-196% higher than those produced by the biological talus. While further mechanical testing under in-vivo loading conditions is required to determine clinical feasibility, preliminarily, the use of a lightweight universal implant is expected to provide the patient with a more natural feel, and a reduced waiting period until surgery.

PECULIARITIES OF THE BEHAVIOR OF BULLS-CALFS OF UKRAINIAN GRAY BREED AND ITS RELATIONSHIP WITH LIVE WEIGHT GAINS

PECULIARITIES OF THE BEHAVIOR OF BULLS-CALFS OF UKRAINIAN GRAY BREED AND ITS RELATIONSHIP WITH LIVE WEIGHT GAINS

An approach to determine multi-dimensional poverty index for Sri Lanka: a comparative study

Poverty consists of two types: monetary poverty and multi-dimensional poverty. Monetary poverty is measured by determining whether income of a person falls below the poverty line while multidimensional poverty is measured using a method where it measures whether a person satisfies a set of specified basic needs. In 2010, the Oxford Poverty and Human Development Initiative (OPHI) in collaboration with the United Nation’s Development Program’s Human Development Report Office developed the Global Multidimensional Poverty Index (MPI) to measure multidimensional poverty under certain assumptions. Applying the Equal Weighting System by taking unit as the household, to compute MPI is one of the important assumptions which needs to be improved. The aim of this study is to propose an unequal weighting system taking unit as the individual to address the research problem of applying the equal weighting system taking the unit as the household. This study makes a significant contribution by offering a comprehensive exploration of the multidimensional poverty situation in Sri Lanka, both at the national level and within individual districts. The methodology applied in obtaining MPI is the Alkire -Foster Methodology based on Counting Approach. In the context of the Sri Lankan socio-cultural background, a more accurate national MPI was obtained by selecting indicators together with an unequal weighting structure. The Household Income and Expenditure survey – 2016 data were obtained from the Department of Census and Statistics, Sri Lanka for the analysis. This study reveals variations in the weighting system across dimensions and indicators, challenging the equal weighting approach adopted by the OPHI. Furthermore, it highlights that 68.7% of the population aged 10 or above in Sri Lanka experience multidimensional poverty. This would enable policy makers to have more accurate national and regional MPIs to implement efficient poverty reduction strategies covering all the districts in the country.

Genetic algorithms for optimizing two-stage DEA by considering unequal intermediate weights

Genetic algorithms for optimizing two-stage DEA by considering unequal intermediate weights

AHM-FC Model in the Evolution Analysis of Ideological and Political Subjects

Abstract This paper proposes a new fusion neural network (FC) based on attribute hierarchy (AHM). This paper studies the evolution model of ideological and political subjects based on this method. It is abbreviated as the AHM-FC model with unequal weighted features. Then, based on the Bayesian network and least squares feature hierarchy analysis, this paper gives the conditional probability and attribute weight of ideological and political subjects. Furthermore, this paper establishes the theme evolution model of “double fusion and three stages.” In this way, it has been fully applied in ideological and political teaching in colleges and universities. Experimental results show that FC-AHM based on unequal weights can predict the evolution of ideological and political objects with higher accuracy.