Non-ideal Situations Research Articles

Democracy and the ethics of voting

ABSTRACT This paper provides an overview of the ethical challenges facing voters in democratic elections. It starts by examining the assumptions that underpin contemporary claims about the moral and epistemic advantages of lotteries as compared to elections and shows their similarities to arguments for ‘unveiling the vote’, as Brennan and Pettit put it. (G. Brennan & Pettit, 1990) It looks at the empirical and normative difficulties of these claims and highlights the risk of confusing morally misguided voting with injustice, and of supposing that it must be politically consequential. Finally, the paper casts doubt on the claim that democracy implies that voters should be publicly accountable to each other for their vote and that the secret ballot is, therefore, a regrettable, if necessary, concession of principle to sad reality. It concludes by drawing out the implications of its analysis for the ethics of voting in ideal, non-ideal and highly non-ideal situations.

On the non-resiliency of subsequence reduced resilient consensus in multiagent networks

This paper studies resilient distributed consensus in networks lacking the structural robustness necessary for achieving consensus in the presence of misbehaving agents. Existing resilient consensus solutions, including widely adapted weighted mean subsequence reduced (WMSR) resilient consensus algorithm, present robustness conditions guaranteeing consensus among normal agents. However, when the graph is less robust than required, they only inform that agents fail to achieve consensus and do not evaluate the network performance comprehensively in such non-ideal scenarios. To address this limitation, we analyze the performance of resilient consensus in non-ideal situations by introducing the concept of non-convergent nodes. These nodes/agents cannot achieve consensus with any arbitrary agent due to the presence of misbehaving agents in the network. This notion enables ordering graphs that lack required robustness and facilitates the assessment of partial performance. Additionally, we demonstrate that among graphs with the same level of robustness (measured by their (r,s)-robustness), the number of non-convergent nodes varies significantly, indicating differing degrees of non-resilience. We also present numerical evaluation of results. Our approach quantifies the network performance under sub-optimal robustness conditions and offers a comprehensive resilience perspective.

Deriving Controllable Local Optimal Solutions through an Environment Parameter Fixed Algorithm

This paper addresses the challenge of optimizing objective functions in engineering problems influenced by multiple environmental factors, such as temperature and humidity. Traditional modeling approaches often struggle to capture the complexities of non-ideal situations. In this research, we propose a novel approach called the Environment Parameter Fixed Algorithm (EPFA) for optimizing the objective function of a deep neural network (DNN) trained in a specific environment. By fixing the environmental parameters in the DNN defined objective function, we transform the original optimization problem into a control parameter optimization problem. We integrate EPFA-CLS (Controllable local-Optimal Solution) with Gradient Descent and algorithms such as Adagrad to obtain the optimal solution. To demonstrate the concept, we apply our approach to an optimal course model and validate it using optimal course and Boston house price datasets. The results demonstrate the effectiveness of our approach in handling optimization problems in complex environments, offering promising outcomes for practical engineering applications.

Few-Shot Recognition of Multifunction Radar Modes via Refined Prototypical Random Walk Network

Multi-functional radars (MFRs) can generate a variety of working modes for different tasks based on flexible modulation types and programmable parameters. The neural network has been widely used to recognize of these fine-grained MFR modes. However, it requires a large number of samples with expert annotation in advance, which is hardly available in practical applications. Therefore, few-shot learning (FSL) method is used to learn “general information” and transfer it into new tasks where only a small number of labeled samples are provided. In order to improve its effect, unlabeled samples are utilized to provide “manifolds information” that describes the distribution among data. This paper proposes a framework of coding refined prototypical random walk network (C-RPRWN) combining these two kinds of information. The whole framework is divided into three modules: preprocessing module, embedding module and refined prototypical random walk (RPRW) module, which are respectively used to enhance the signal expression to adapt to non-ideal situations, extract distinguishable features to compute prototypes, and utilize “manifolds information” for better classification. The experimental results and analysis show that the proposed method achieves excellent performance for MFR fine-grained modes recognition even under the condition of a small number of samples. In addition, the robustness of the proposed method is verified under the influence of different non-ideal factors.

Survey of Cross-Modal Person Re-Identification from a Mathematical Perspective

Person re-identification (Re-ID) aims to retrieve a particular pedestrian’s identification from a surveillance system consisting of non-overlapping cameras. In recent years, researchers have begun to focus on open-world person Re-ID tasks based on non-ideal situations. One of the most representative of these is cross-modal person Re-ID, which aims to match probe data with target data from different modalities. According to the modalities of probe and target data, we divided cross-modal person Re-ID into visible–infrared, visible–depth, visible–sketch, and visible–text person Re-ID. In cross-modal person Re-ID, the most challenging problem is the modal gap. According to the different methods of narrowing the modal gap, we classified the existing works into picture-based style conversion methods, feature-based modality-invariant embedding mapping methods, and modality-unrelated auxiliary information mining methods. In addition, by generalizing the aforementioned works, we find that although deep-learning-based models perform well, the black-box-like learning process makes these models less interpretable and generalized. Therefore, we attempted to interpret different cross-modal person Re-ID models from a mathematical perspective. Through the above work, we attempt to compensate for the lack of mathematical interpretation of models in previous person Re-ID reviews and hope that our work will bring new inspiration to researchers.

MF-GCN: Motion Flow-Based Graph Network Learning Dynamics for Aerial IR Target Recognition

The primary technology of the infrared imaging seeker optoelectronic countermeasure system includes the automatic recognition and tracking of air targets. New complex infrared interference factors pose severe challenges to the ability of infrared thermal imaging seekers to identify and lock targets accurately. Despite significant recent advances, machine vision systems still lag substantially behind biological vision systems in terms of performance and robustness. The distinguishing feature of the brain is its ability to detect learned objects under various non-ideal situations. Inspired by the sensitivity of the dorsal visual pathway to object motion information, a graph convolutional network learning model is developed for aerial target recognition, called the adaptive graph reasoning method driven by motion flow model, which employs the predicted object optical flow information to promote the keypoint estimation performance. First, an adaptive graph fusion module is designed to establish an adaptation mechanism to generalize keypoint features from the regular network to the target skeleton graph model. Then, the optical flow estimation features are utilized to understand the graph reasoning of the target keypoint motion. Finally, we propose a aerial target pose estimation framework which effectively integrates the temporal cues across frames by combining the motion features of optical flow estimation, and effectively promotes the estimation accuracy of keypoints. Finally, our extensive experiments in the established aerial target flight infrared dataset show the efficiency of the presented model.

Multi-Target Parameter Estimation of the FMCW-MIMO Radar Based on the Pseudo-Noise Resampling Method.

Subspace methods are widely used in FMCW-MIMO radars for target parameter estimations. However, the performances of the existing algorithms degrade rapidly in non-ideal situations. For example, a small number of snapshots may result in the distortion of the covariance matrix estimation and a low signal-to-noise ratio (SNR) can lead to subspace leakage problems, which affects the parameter estimation accuracy. In this paper, a joint DOA-range estimation algorithm is proposed to solve the above issues. Firstly, the improved unitary root-MUSIC algorithm is applied to reduce the influence of non-ideal terms in building the covariance matrix. Subsequently, the least squares method is employed to process the data and obtain paired range estimation. However, in a small number of snapshots and low SNR scenarios, even if the impact of non-ideal terms is reduced, there will still be cases where the estimators sometimes deviate from the true target. The estimators that deviate greatly from targets are regarded as outliers. Therefore, threshold detection is applied to determine whether outliers exist. After that, a pseudo-noise resampling (PR) technology is proposed to form a new data observation matrix, which further alleviates the error of the estimators. The proposed method overcomes performance degradation in a small number of snapshots or low SNRs simultaneously. Theoretical analyses and simulation results demonstrate the effectiveness and superiority.

Determining the solubilities for benzoate, nicotinate, hydrochloride, and malonate salts of bedaquiline

Determining the solubility of a compound is important for predicting its oral bioavailability, the medium to be used for dissolution, and solvents for cleaning during manufacturing. The solubilities of the newly synthesized benzoate, hydrochloride, nicotinate, and malonate salts of bedaquiline were elucidated, and the plausible reasons for the differences observed in their experimental aqueous solubilities were highlighted.The shake flask method was used to determine the experimental solubilities of the bedaquiline free base and all the salts in water, 0.01 N HCl, and pH 6.8 buffer. The molar and mole fraction solubility estimates of the salts were determined using equations for ideal and non-ideal situations. Furthermore, the relative contribution of the lattice and activity coefficient to the overall aqueous solubility of the salts were predicted graphically.The new salts ranked hydrochloride [0.6437 mg/mL] > malonate [0.0268 mg/ml] > nicotinate [0.0024 mg/mL] > benzoate [0.0004 mg/mL], showed improved aqueous solubility over the free base. The general solubility equation [GSE], fairly predicted the solubilities for the benzoate and malonate salts, but the ideal solubility equations provided poor estimates of their experimental values. Based on the ideal solubility estimates, the crystal lattice contributions of all salts were malonate > nicotinate > HCl > benzoate. However, using the activity coefficient values, the order of hydrophobicity of the bedaquiline salts was: benzoate > nicotinate > malonate > HCl.The salts forms of bedaquiline offered additional solubility as a function of their crystallinity and hydrophobicity.

Study on Fine Management of Construction Engineering Projects Based on BIM Technology: Taking the Construction of Group C Project of Zibo Cultural Center as an Example

The roof steel structure of group C project of Zibo cultural center is composed of steel truss, welded H-shaped steel and large-diameter steel pipe embedded parts. The construction of embedded parts of inclined cylinder overhanging steel structure is difficult. The stair square tube ladder beam requires precise angles and radians, complex processing, and construction site setting out is difficult. A three-dimensional visual building information model has been established for this projects which includes several aspects which are as follows: the development of the post anchor construction technology of large steel sleeve embedded parts with inclined cylinders based on Building Information Modeling (BIM) technology, multi discipline synthesis and collision optimization, the net height optimization of electromechanical pipelines, fine reinforcement management, BIM virtual construction template. Through this planning, 14908 pipeline collisions, including 236 major collisions have been eliminated. BIM is used in handling and solving problems with the construction unit and the supervision company. This greatly improves the project work efficiency and saves meeting time. Through optimization of electromechanical line collision and secondary masonry layout, more than RMB 9 million has been saved in terms of personnel and materials, effectively reducing material losses, saving manpower, and significantly improving the implementation efficiency of the project. Based on the on-site simulation, the project planning scheme of the enterprise was adjusted, efficient work allocation with less idle time were ensured, and the correct utilization of project funds and time were ensured. The original extensive annual control and monthly control have been dynamically and accurately controlled to realize the dynamic understanding of the profits and losses of the project, and effectively reduce the occurrence of non-ideal situations.

Aristotle’s Political Theory as a Craft and Science in Politics 4–6

Abstract This article maintains that Aristotle develops his political theory as a craft and science in Politics 4–6. The literature, however, has argued that he views political knowledge as a form of practical wisdom or prudence. This article discusses the way that Aristotle proposes political theory as a skill to help deal with unfavorable circumstances. In Greek political thought, craft and science are characterized as skills of cooperating with nature, taking up opportunities, and coping with uncertainty. Aristotle uses this conception when he develops his political theory in Politics 4–6. He understands that political theorists should advise prudent legislators on practical reforms of constitutions and help them address non-ideal situations. Serious efforts to gain causal knowledge are indispensable for statesmanship. This view of statesmanship better illustrates political theory as part of human efforts and cooperation to resolve uncertainty rather than the one that sharply distinguishes among theoretical, practical, and productive sciences.

Investigation of the Cryogenic Nitrogen and Non-Cryogenic N-Dodecane and Ammonia Injections using a Real-Fluid Modelling Approach

<div class="section abstract"><div class="htmlview paragraph">In modern compression ignition engines, the dense liquid fuel is directly injected into high pressure and temperature atmosphere, so the spray transitions from subcritical to supercritical conditions. To gain better control of the spray-combustion heat release process, it is important to have a physically accurate description of the spray development process. This work explored the effect of real-fluid thermodynamics in the computational prediction of multiphase flow for two non-ideal situations: the cryogenic nitrogen and non-cryogenic n-dodecane and ammonia sprays. Three real-fluid equations of state (EoS) such as the Soave-Redlich-Kwong (SRK), Peng-Robinson (PR), and Redlich-Kwong-Peng-Robinson (RKPR) coupled with the real-fluid Chung transport model were implemented in OpenFoam to predict the real-fluid thermodynamic properties. Validations against the CoolProp database were conducted. The RKPR EoS demonstrated an overall better predictive performance compared to the SRK and PR EoS. Due to miscalculations of the thermodynamic properties under supercritical conditions, the cases using the ideal-gas EoS predicted the significantly distinct spray features from the cases using real-fluid EoS. For the cryogenic nitrogen spray simulations, cases using various real-fluid EoS yielded similar spray features because of the low injection rate and thus the weak ambient entrainment process. The reduction of ambient pressure promoted the turbulent mixing process for the n-dodecane spray due to the smaller ambient density and resistance. Compared to ammonia, n-dodecane had higher density and viscosity under supercritical conditions, which led to its higher jet velocity and more concentrated spray feature.</div></div>

Performance analysis of continuous-variable quantum key distribution using non-Gaussian states

In this study, we analyze the efficiency of a protocol with discrete modulation of continuous variable non-Gaussian states, the coherent states having one photon added and then one photon subtracted (PASCS). We calculate the secure key generation rate against collective attacks using the fact that Eve's information can be bounded based on the protocol with Gaussian modulation, which in turn is unconditionally secure. Our results for a four-state protocol show that the PASCS always outperforms the equivalent coherent states protocol under the same environmental conditions. Interestingly, we find that for the protocol using discrete-modulated PASCS, the noisier the line, the better will be its performance compared to the protocol using coherent states. Thus, our proposal proves to be advantageous for performing quantum key distribution in non-ideal situations.

Should We Take Up the Slack?: Reflections on Non-ideal Theory in Ethics

This article asks whether (and how) our moral duties are created by others’ non-compliance and whether we should fulfill them or not. For example, do we need to donate more of our income to eradicate world poverty because billionaires do not donate? If so, how much should we donate? In short, should we make up for others’ defaulting on their moral duties – and if so, how and to what extent? Such situations are called non-ideal circumstances in political philosophy. With the increasing importance of non-ideal theories of justice and ethics, the question of how we should act in non-ideal situations has received more attention in the ethics literature in recent years. This article poses and addresses the question of whether we should take up the slack when others do not comply with their own moral duty. This article examines how consequentialism and contractualism might respond to non-ideal situations of taking up the slack and then defends a contractualist-constructivist approach to dealing with these situations.

Model-Based Representation and Deinterleaving of Mixed Radar Pulse Sequences With Neural Machine Translation Network

Deinterleaving mixtures of radar pulse sequences is the first and the most vital step for modern electronic reconnaissance systems to intercept and analyze the intentions of noncooperative radars. Currently, these systems are facing great challenges due to the emerging complexity of radar modulations. This article proposes a novel method for deinterleaving mixtures of radar pulse sequences based on the time series characteristics of each source (component pulse sequences). First, the mathematical representations are established to describe the structural characteristics of each source. Then, the deinterleaving problem is formulated as a minimization of a maximum-likelihood cost function that can be solved efficiently through a supervised neural machine translation (NMT) network, i.e., to translate each received pulse in the interleaved pulse sequence to the corresponding source label. A sequence-to-sequence NMT model is proposed to capture the structural relationships among the nonadjacent pulses originated from the same source in the mixtures and assign the corresponding label to each pulse. The proposed method does not require the exact knowledge of each component pulse sequence. The experimental results based on the time of arrival sequence of mixed pulse sequences show that the proposed method outperforms the state-of-the-art deinterleaving methods and achieves satisfactory performance under highly nonideal situations with measuring noise and lost pulse conditions. The proposed method can be directly applied to other fields involving deinterleaving problems and multivariate input conditions.

Parametric model‐based deinterleaving of radar signals with non‐ideal observations via maximum likelihood solution

In modern electromagnetic environments, as the number of radars continuously increasing, the intercepted interleaving signals become more complex and are easily affected by different non-ideal factors. However, the widely used deinterleaving methods based on the pulse repetition interval (PRI) are difficult to effectively deinterleave signals with complex PRI modulation and are very sensitive to the received signal quality. To solve the deinterleaving problem in these complex situations, this paper proposes a parametric model-based deinterleaving solution consisting of two cascaded stages. The proposed method does not require priors such as the number of sources and corresponding modulation types, which makes it more suitable for complex environments in non-cooperative scenarios. In addition, it can adapt to complex non-ideal situations, including parameter measurement noise, missing pulse and spurious pulse conditions. Simulation results show that the deinterleaving precision of the proposed method outperforms baseline methods in non-ideal conditions and achieves 99.33% accuracy.

A large-scale single-mode array laser based on a topological edge mode

Abstract Topological lasers have been intensively investigated as a strong candidate for robust single-mode lasers. A typical topological laser employs a single-mode topological edge state, which appears deterministically in a designed topological bandgap and exhibits robustness to disorder. These properties seem to be highly attractive in pursuit of high-power lasers capable of single mode operation. In this paper, we theoretically analyze a large-scale single-mode laser based on a topological edge state. We consider a sizable array laser consisting of a few hundreds of site resonators, which support a single topological edge mode broadly distributed among the resonators. We build a basic model describing the laser using the tight binding approximation and evaluate the stability of single mode lasing based on the threshold gain difference Δα between the first-lasing edge mode and the second-lasing competing bulk mode. Our calculations demonstrate that stronger couplings between the cavities and lower losses are advantageous for achieving stable operation of the device. When assuming an average coupling of 100 cm−1 between site resonators and other realistic parameters, the threshold gain difference Δα can reach about 2 cm−1, which would be sufficient for stable single mode lasing using a conventional semiconductor laser architecture. We also consider the effects of possible disorders and long-range interactions to assess the robustness of the laser under non-ideal situations. These results lay the groundwork for developing single-mode high-power topological lasers.

Comparison of closed loop performance of consistency process dynamics for various PID controller algorithms

In this work, various algorithms of PID (Proportional-Integral-Derivative) controllers such as series, parallel, series together with derivative filter and parallel together with derivative filter form are implemented for the comparative analysis of the closed loop responses of an important parameter in head box of a paper machine in a paper making process industry called consistency, which has FOPDT (First Order Plus Dead Time) dynamics. Also the comparison is made between the closed loop responses achieved from different values of derivative filter factor for non-ideal situations. The steady state and dynamic characteristics are analyzed and compared and important inferences are derived.

Electrically doped SiGe-heterojunction TFET based biosensor considering non-ideal hybridization issues: a simulation study.

Calibrated simulations are used to study a dielectric modulated, electrically doped, dual metal gate, SiGe heterojunction, double gate TFET biosensor in this work. Use of lower bandgap SiGe at the source side helps in improving the ON current of the biosensor. Electric doping is preferred over physical doping to overcome the random dopant fluctuations and high thermal budget problems. Non-ideal situations having partially and non-uniformly filled cavity regions are analyzed in this work. Partially filled cavity has fill factor less than 100% and is studied by considering 50%, 20%, and 10% fill factors. Different positions of biomolecules inside a partially filled cavity are also studied through extensive simulations and are found to affect the sensitivity largely. Four different non-uniform profiles, decreasing, increasing, convex and concave, are created in the cavity region and their sensitivity values are compared for different dielectric constants (k) and charge densities (Q_{mathrm{Bio}}). Among the different non-uniform profiles considered, maximum sensitivity is obtained for decreasing profile and it improves with an increase in dielectric constant and positive charge density while it decreases when negative charge density increases.

Digital filter‐based grid synchronization for autonomous microgrids

Having a robust and accurate grid synchronization unit for distributed generators (DGs) has been accentuated due to rapidly growing integration of inverter-based DGs to the autonomous microgrids. Although most of the synchronization methods are based on the phased locked loops (PLL) technique, digital filters could result in more salient dynamic performance in non-ideal sinusoidal situations. This paper presents a novel digital filter as a grid synchronization method that overcomes the flaws of conventional PLLs under harmonic distorted and unbalanced cases as well as large delay of window-based digital filters. For the sake of computational advantages, the proposed method is executed in two distinct offline and online stages. In the offline stage, the second-order Taylor series expansion approximation of the bandpass oscillatory signal calculates the state-transition matrices and state vectors. Then, by applying the concept of Kalman filter, the state vectors are updated in the online calculation section much easier and faster compared to all-online approach. Compared to the existing methods, the proposed method generates a dynamic response with less settling time, more accuracy, and immunity against harmonic distortion, imbalances, and noise.

Effectiveness of Mechanical Chest Compression Devices over Manual Cardiopulmonary Resuscitation: A Systematic Review with Meta-analysis and Trial Sequential Analysis.

IntroductionOur goal was to systematically review contemporary literature comparing the relative effectiveness of two mechanical compression devices (LUCAS and AutoPulse) to manual compression for achieving return of spontaneous circulation (ROSC) in patients undergoing cardiopulmonary resuscitation (CPR) after an out-of-hospital cardiac arrest (OHCA).MethodsWe searched medical databases systematically for randomized controlled trials (RCT) and observational studies published between January 1, 2000–October 1, 2020 that compared mechanical chest compression (using any device) with manual chest compression following OHCA. We only included studies in the English language that reported ROSC outcomes in adult patients in non-trauma settings to conduct random-effects metanalysis and trial sequence analysis (TSA). Multivariate meta-regression was performed using preselected covariates to account for heterogeneity. We assessed for risk of biases in randomization, allocation sequence concealment, blinding, incomplete outcome data, and selective outcome reporting.ResultsA total of 15 studies (n = 18474), including six RCTs, two cluster RCTs, five retrospective case-control, and two phased prospective cohort studies, were pooled for analysis. The pooled estimates’ summary effect did not indicate a significant difference (Mantel-Haenszel odds ratio = 1.16, 95% confidence interval, 0.97 to 1.39, P = 0.11, I2 = 0.83) between mechanical and manual compressions during CPR for ROSC. The TSA showed firm evidence supporting the lack of improvement in ROSC using mechanical compression devices. The Z-curves successfully crossed the TSA futility boundary for ROSC, indicating sufficient evidence to draw firm conclusions regarding these outcomes. Multivariate meta-regression demonstrated that 100% of the between-study variation could be explained by differences in average age, the proportion of females, cardiac arrests with shockable rhythms, witnessed cardiac arrest, bystander CPR, and the average time for emergency medical services (EMS) arrival in the study samples, with the latter three attaining statistical significance.ConclusionMechanical compression devices for resuscitation in cardiac arrests are not associated with improved rates of ROSC. Their use may be more beneficial in non-ideal situations such as lack of bystander CPR, unwitnessed arrest, and delayed EMS response times. Studies done to date have enough power to render further studies on this comparison futile.