Selection Rules Research Articles

Nomogram for the Pathological Complete Response After Neoadjuvant Chemotherapy in Muscle-Invasive Bladder Cancer Patients.

No validated instrument currently exists to predict neoadjuvant chemotherapy (NAC) response in muscle-invasive bladder cancer (MIBC) patients. We aim to develop and validate a nomogram based on clinicopathological factors for predicting who would benefit most from NAC. Between January 2016 and April 2023, 361 consecutive MIBC patients treated with NAC were enrolled in the study. Two hundred sixty patients at the Hu Nan institution comprised the development cohort. The validation cohort (91 patients) was from the Xiang Hua center. Patient clinicopathologic information was documented. Using regression coefficients, a predictive model was constructed using multivariate logistic regression. The likelihood ratio test with Akaike's information criterion was then used as the ending rule for backward stepwise selection. This predictive model's efficacy was evaluated for discrimination, calibration, and clinical utility. Predictors of this model included the origin of MIBC, pathological tumor type, clinical tumor stage, and tumor size. In the validation cohort, the model demonstrated good discrimination with an AUROC of 0.7221 (P < 0.001) and calibration (Unreliability test, P = 0.580). In addition, decision curve analysis revealed that the model was clinically beneficial. This study indicated that primary MIBC, pure UC pathological type, lower clinical tumor stage, and maximum tumor diameter <3 cm were significant predictors of ypCR in MIBC patients after NAC. This nomogram may contribute to the preciousadministration of NAC and the avoidance of chemotherapy toxicity and delayed RC.

A Low Computational Complexity Family of Spline NLMS Adaptive Filter Algorithm

This paper presents novel and low computational complexity spline adaptive filtering (SAF) algorithms based on partial coefficients selection and adaption. They are addressed partial update SAF normalized least mean squares (PU-SAF-NLMS) algorithms. The PU-SAF-NLMS equations are established via the steepest gradient descent constraint to the proposed cost function. In the proposed algorithms, the filter coefficients are updated based on periodic, sequential, and selective rules, leading to reduction in computational complexity. Furthermore, the convergence speed of PU-SAF-NLMS algorithms is close to the conventional SAF-NLMS. Moreover, the convergence analysis of algorithms is studied. Ultimately, the performance of the presented algorithms is appraised through several experimental results.

Tuning the optical absorption and exciton bound states of germanene by chemical functionalization

We present a comprehensive study of buckled honeycomb germanene functionalized with alternately bonded side groups hydroxyl (–H), methyl (–CH3) and trifluoro methyl (–CF3). By means of most modern theoretical and computational methods we determine the atomic geometries versus the functionalizing groups. The quasiparticle excitation effects on the electronic structure are taken into account by means of exchange-correlation treatment within the GW framework. The Bethe–Salpeter equation is solved ab initio to derive optical spectra including excitonic and quasiparticle effects. Band edge excitons are investigated in detail. The binding properties are compared with those resulting from model studies. The functionalization leads to significantly modified band structures compared with pristine germanene. The Dirac bands near the K point are destroyed and direct gaps appear at the \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Gamma$$\\end{document} point. Together with the many-body effects, quasiparticle gaps of 2.3, 1.8 or 1.0 eV result for –H, –CH3 and –CF3 functionalization. Totally different absorption spectra are found for in-plane and out-of-plane light polarization. Strongly bound excitons are visible below the quasiparticle band edge with binding energies of about 0.5, 0.4 or 0.3 eV. The nature of these band-gap excitons is investigated via their wave function, the contribution of various interband combinations and the dipole selection rules.

An Improved Adaptive Grid-Based Progressive Triangulated Irregular Network Densification Algorithm for Filtering Airborne LiDAR Data

Ground filtering is crucial for airborne Light Detection and Ranging (LiDAR) data post-processing. The progressive triangulated irregular network densification (PTD) algorithm and its variants outperform others in accuracy, stability, and robustness, using grid-based seed point selection, TIN construction, and iterative rules for ground point identification. However, these methods still face limitations in removing low points and accurately preserving terrain details, primarily due to their sensitivity to grid size. To overcome this issue, a novel PTD filtering algorithm based on an adaptive grid (AGPTD) was proposed. The main contributions of the proposed method include an outlier removal method using a radius outlier removal algorithm and Kd-tree, a method for establishing an adaptive two-level grid based on point cloud density and terrain slope, and an adaptive selection method for angle and distance thresholds in the iterative densification processing. The performance of the AGPTD algorithm was assessed based on widely used benchmark datasets. Results show that the AGPTD algorithm outperforms the classical PTD algorithm in retaining ground feature points, especially in reducing Type I error and average total error significantly. In comparison with other advanced algorithms developed in recent years, the novel algorithm showed the lowest average Type I error, the minimal average total error, and the greatest average Kappa coefficient, which were 1.11%, 2.28%, and 90.86%, respectively. Additionally, the average accuracy, precision, and recall of AGPTD were 97.69%, 97.52%, and 98.98%, respectively.

Evaluation of the Hematology Analyzer Test Performance Using Six Sigma Metrics: A Cross Sectional Study

Abstract Introduction/Objective The six sigma metric is used to assess the performance of a measurement procedure in comparison to the medical requirement. In clinical laboratories, it can be used to assess the quality of instruments or processes. This study aims to evaluate the performance of hematology analyzers using the six sigma scale. Methods/Case Report This is a descriptive cross-sectional study wherein quality control data from hematology analyzers were obtained. The mean, standard deviation, coefficient of variation, bias and sigma value of the RBC, WBC, hemoglobin, hematocrit, and platelet parameters were determined and the performance of Beckman Coulter DxH 900 was assessed by plotting a normalized OPSpecs chart. Results (if a Case Study enter NA) The WBC and platelet parameters had sigma values of more than 6. Less stringent quality control measures could be applied these parameters which include fewer Westgard rules and fewer control runs. The RBC and hemoglobin parameters had sigma values of at least 3. The hematocrit parameter had a sigma value of less than 3. Improving this parameter could be done by performing optical channel evaluation, using multiple Westgard control rules, increasing the number of control materials and the number of runs. Conclusion This study has demonstrated the analytical performance of hematology analyzers on the six sigma scale, which in turn allows for the selection of the appropriate statistical quality control rule. Ultimately, statistical quality control is only one part of a laboratory’s quality management system. The sigma quality of the testing process, as well as choosing the appropriate control rules and number of controls, may provide the basis for the total quality control strategy of the laboratory. Other pre-analytic and post-analytic factors must also be considered in formulating a laboratory total quality control plan. This would allow the laboratory to provide the best possible quality of services to its clients by increasing efficiency, reducing wastage, and incorporating continuous changes to improve the system.

A Fractional Tikhonov Regularization Method for Identifying a Time-Independent Source in the Fractional Rayleigh–Stokes Equation

The aim of this paper is to identify a time-independent source term in the Rayleigh–Stokes equation with a fractional derivative where additional data are considered at a fixed time point. This inverse problem is proved to be ill-posed in the sense of Hadamard. By using a fractional Tikhonov regularization method, we construct a regularized solution. Then, according to a priori and a posteriori regularization parameter selection rules, we prove the convergence estimates of the regularization method. Finally, we provide some numerical examples to prove the effectiveness of the proposed method.

Impact of Loss Mechanisms on Linear Spectra of Excitonic and Polaritonic Aggregates.

The presence of loss mechanisms governed by empirical timescales can profoundly affect the dynamics in molecular systems, leading to changes in their spectra. However, incorporation of these effects along with the system's interaction with the thermal dissipative environments proves to be challenging. In this work, we demonstrate the possibility of utilizing the recently developed path integral Lindblad dynamics (PILD) method to study the linear spectra of molecular aggregates. PILD presents a uniquely powerful simulation technique for retaining the effects of the vibrations in a numerically exact manner through the Feynman-Vernon influence functional while incorporating the effects of losses in an empirical manner using the Lindbald master equation. As illustrations of this technique, we provide examples taken from chiral excitonic and polaritonic aggregates for which we simulate both the absorption spectra and circular dichroism (CD) spectra. We demonstrate that the effect of loss on particular states can differ not just on the basis of the symmetries of the state but also on the basis of complicated "interactions" of the structured dissipative environments with the system and its loss mechanisms. Due to the different selection rules between the absorption and CD spectra and the relative intensities and broadening of the peaks, the two linear spectra together give an interesting insight into the contributions of the various eigenstates to the correlation functions. While the focus here is on linear spectroscopy, it should be possible in the future to use PILD to study multidimensional spectra under loss mechanisms as well.

Informality and Relations in International Judicial Appointments: Evidence from Sub-regional Courts in Africa

Abstract Informality in international judicial selection practices has been acknowledged but rarely systematically researched. Described as “design innovations” that are hard to grasp, national judicial appointments to international courts (IC s), even in the most established democracies, are permeated by opacity and secretiveness that remains puzzling to researchers and the legal complex alike. This article presents evidence from three African sub-regional courts to reify the informal dimension of international judicial appointments. Drawing on qualitative research that employs two country case studies, Uganda and Malawi, we demonstrate how the particularly thin formal selection rules leave much leverage to informalities at various levels of governance. Through interviews and judicial biographical data, we reconstruct judicial appointment practices and their dynamics to account for relevant informalities and relations that shed light on the selection of judges to IC s. We argue that appointment practices are less ad hoc or arbitrary than they often appear but mirror the states’ commitment to regional integration, political assessment of the respective regional organisation, and perceptions of the courts’ relevance and ambitions in relation to the political appointers and selectors. Moreover, we raise new concerns about the risk of more silent and subtle backlash against bold regional courts.

View Selection of Safety Managers Affects Their Ability to Evaluate Safety Level of Workers

Evaluating the safety level of work behaviors is an important means of safety management through safety managers’ manual visual or video surveillance. This paper aims to grasp the view selection rules of safety managers in correctly observing and analyzing work behavior, which are conducive to safety behavior assessment. Firstly, based on a similarity simulation experiment, the work process of the operators was simulated, and the process was simultaneously monitored from multiple views to obtain surveillance videos. Then, safety managers with different experience levels watched the videos and answered the questionnaire in a questionnaire survey experiment. The authors found the following: (1) Prior experience does not affect the view selection during the observation stage, but it does affect the view selection during the analysis stage. Experienced safety managers perform better in view selection when dealing with complex tasks. (2) The work postures have a significant effect on the view type and their combination order. (3) The view selection of experienced safety managers in the behavior observation and analysis stages is non-constant except for sitting posture operations, while the view selection of amateur managers is always constant. (4) Sitting posture work takes the front view as the main view and the left–right–upper views as auxiliary views; standing posture uses the left and right views as the main views and the front–back–upper views as auxiliary views; and mixed posture takes the left and right views as the main views and the upper–front–back views as auxiliary views. These view selection rules can achieve the highest evaluation performance. These findings can help train or select high-quality safety managers and provide a scientific basis for arranging views during video surveillance.

Research on high voltage circuit breaker multi-sensor signal feature selection method based on GA-Kmeans algorithm

Abstract Currently, in the research on fault information of high-voltage circuit breakers (HVCBs) based on multi-sensor data, issues such as shallow feature assessment and unclear feature selection rules exist, leading to a decrease in fault identification accuracy due to redundant characteristics. To address this, this paper proposes a novel feature selection method based on GA-Kmeans. This method encodes the original feature space into binary format and employs the clustering accuracy of the Kmeans model as the fitness function to obtain a high-quality feature subset that maximally represents and distinguishes faults. By combining different feature selection methods with fault diagnosis models, six typical application scenarios are constructed. Results indicate that compared to traditional methods such as Relief-F, KPCA, GA-SVM for feature selection, and SVM for fault diagnosis, the proposed GA-Kmeans method reduces the dimensionality of the original feature space and employs the Kmeans clustering algorithm as the diagnostic model, achieving a final diagnostic accuracy of 95.29%. This method outperforms others, with a 37.24% higher diagnostic accuracy than SVM under the original feature space, and a decrease of 47.90% in standard deviation. This validates the necessity of feature selection and the superiority of the proposed method, providing a reliable and stable diagnostic basis for subsequent mechanical fault diagnosis of HVCBs.

Atomic electron transitions of hydrogen-like atoms induced by gravitational waves

Abstract As a realistic model of a quantum system of matter, this paper investigates the gravitational-wave effects on a hydrogen-like atom using a first-principles approach. By formulating the tetrad formalism of linearized gravity, we naturally incorporate gravitational-wave effects through minimal coupling in the covariant Dirac equation. The atomic electron transition rates induced by the gravitational wave are calculated using first-order perturbation theory, revealing a distinctive selection rule along with Fermi's golden rule. This rule can be elegantly understood in terms of gravitons as massless spin-2 particles. Our results suggest the existence of gravitons and may lead to a novel approach to probing ultra-high-frequency gravitational waves (UHF-GWs).

Characters are quantum numbers

Abstract Character tables represent an invaluable tool for applications of group theory in physics and chemistry. They are useful, among other applications, to carry out the projection of functions which later on constitute the basis to simplify the calculations and establish the selection rules. However, the physical interpretation of the character tables as quantum numbers is not visible. In this contribution we prove that the characters are indeed conserved quantities associated with the discrete symmetry of a molecule. In the process, the eigenfunction method of symmetry projection emerges in natural form, an approach not discussed in traditional textbooks. A non trivial example involving the octahedral group ${\cal O}_h$ is presented.&amp;#xD;

Vibronic coupling in the energetically six lowest electronic states of oxirane radical cation.

Multi-dimensional quantum mechanical simulations are carried out to understand the multi-state and multi-mode vibronic interactions in the first six low-lying viz., X̃2B1, Ã2A1, B̃2B2, C̃2A2, D̃2A1, and Ẽ2B1 electronic states of c-C2H4O·+. Vibronic coupling theory is applied to study interactions among electronic states using symmetry selection rules. A model 6 × 6 diabatic electronic Hamiltonian is constructed. The parameters of the diabatic Hamiltonian are estimated by performing extensive abinitio electronic structure calculations, using the EOM-IP-CCSD method. The nuclear dynamics calculations are performed with both time-independent and time-dependent quantum mechanical methods. The calculated vibronic structures of six electronic states are found to be in excellent agreement with the available experimental findings. Progressions found in the theoretical spectrum are assigned in terms of vibrational modes. It is found that extremely strong vibronic interactions among the X̃2B1-Ã2A1, B̃2B2-C̃2A2, and D̃2A1-Ẽ2B1 electronic states results into highly overlapping vibronic bands due to multiple multi-state conical intersections. The impact of associated nonadiabatic effects on the vibronic structure and dynamics of the mentioned electronic states is examined at length. Interesting comparison is made with the results obtained for the isomeric acetaldehyde radical cation.

Effects of SiO2 Particle Size in Soggy‐Sand Electrolyte on Electrochemical Performance of Zinc‐Ion Batteries

AbstractThe presence of free water molecules in the aqueous electrolyte leads to serious side reactions at the interface, easy dissolution of the cathode material, and uncontrolled growth of zinc dendrites in Zn‐ion batteries, which hinders their practical applications. Here, we propose a type of SiO2‐based soggy‐sand electrolyte (ZnSO4+MnSO4 electrolyte with SiO2, SiO2‐ZMSO4) and focus on the effect of the SiO2 nanoparticle size on the performance of soggy‐sand electrolyte. It is found that SiO2 with smaller nanoparticle size provides higher porosity, and the SiO2 network‐formed can effectively trap the free water in the electrolyte, which increases the ionic conductivity of electrolyte, widens working voltage window, and decreases the internal resistance of batteries. As a result, the Zn//MnO2 batteries with 20 nm SiO2‐based soggy‐sand electrolyte show stable cycling performance and rate capacities. The specific capacity of the battery can be maintained at 198.5 mAh g−1 after 1200 cycles at 1 A g−1 without capacity degradation. The specific capacity can be increased by 100 mAh g−1 even at a high rate of 5 A g−1. This study provides the rule of particle selection for the development of aqueous soggy‐sand electrolytes used in aqueous rechargeable batteries.

Circularly Polarized High-Harmonic Beams Carrying Self-Torque or Time-Dependent Orbital Angular Momentum.

In the rapidly evolving field of structured light, self-torque has been recently defined as an intrinsic property of light beams carrying time-dependent orbital angular momentum. In particular, extreme-ultraviolet (EUV) beams with self-torque, exhibiting a topological charge that continuously varies on the subfemtosecond time scale, are naturally produced in high-order harmonic generation (HHG) when driven by two time-delayed intense infrared vortex beams with different topological charges. Until now, the polarization state of such EUV beams carrying self-torque has been restricted to linear states due to the drastic reduction in the harmonic up-conversion efficiency with increasing the ellipticity of the driving field. In this work, we theoretically demonstrate how to control the polarization state of EUV beams carrying self-torque, from linear to circular. The extremely high sensitivity of HHG to the properties of the driving beam allows us to propose two different driving schemes to circumvent the current limitations to manipulate the polarization state of EUV beams with self-torque. Our advanced numerical simulations are complemented with the derivation of selection rules of angular momentum conservation, which enable precise tunability over the angular momentum properties of the harmonics with self-torque. The resulting high-order harmonic emission, carrying time-dependent orbital angular momentum with a custom polarization state, can expand the applications of ultrafast light-matter interactions, particularly in areas where dichroic or chiral properties are crucial, such as magnetic materials or chiral molecules.

A distributed dynamic load identification approach for thin plates based on inverse Finite Element Method and radial basis function fitting via strain response

Dynamic load identification is crucial for structural design and health monitoring. Traditional methods for distributed dynamic loads (DDLs) typically involve establishing a transfer matrix, which often leads to ill-posed problems. In this paper, a novel method based on the inverse Finite Element Method (iFEM) and radial basis function (RBF) fitting was proposed to identify DDLs from measured strain responses. The method begins with the reconstruction of the displacement field from discrete strain data using iFEM, followed by applying RBF fitting to obtain a continuous displacement field. To enhance the performance of the RBF fitting, a constrained matrix for force boundary conditions is introduced, along with a selection rule for the RBF shape parameter. The identified loads are subsequently determined by incorporating the well-fitted RBFs into the motion equations of thin plates. This approach eliminates the need for a transfer matrix, thereby avoiding ill-posedness, and enables the simultaneous identification of the spatial distribution and time history of the loads. Three numerical examples for the identification of concentrated loads, uniformly distributed loads, and globally distributed loads demonstrate the method's effectiveness, accuracy, and noise resistance, with additional validation provided through a comparison with the classic time-domain method.

Element Screening Engineering for High-Entropy Alloy Anodes: Achieving Fast and Robust Li-Storage With Optimal Working Potential.

While the high-entropy strategy is highly effective in enhancing the performance of materials across various fields, an optimal methodology for selecting component elements for performance optimization is still lacking. Here the findings on uncovering the element selection rules for rational design of high-entropy alloy anodes with exceptional lithium storage performance are reported. It is investigated high-entropy element screening rules by modifying stable diamond-structured Ge with P to induce a tetrahedrally coordinated sphalerite structure for enhanced metallic conductivity, further stabilized by incorporating Zn and other elements. Moreover, both theoretical and experimental results confirm that Li-storage performance improves with increasing atomic number: BZnGeP3 < AlZnGeP3 < GaZnGeP3 < InZnGeP3. InZnGeP3-based electrodes demonstrate the highest Li-ion affinity, fastest electronic and Li-ion transport, largest Li-storage capacity and reversibility, and best mechanical integrity. Further element screening based on the above criteria leads to high entropy alloy anodes with metallic conductivity like GaCuSnInZnGeP6, GaCu(or Sn)InZnGeP5, CuSnInZnGeP5, InZnGePSeS(or Te), InZnGeP2S(or Se) which show superior Li-storage performances. The excellent phase stability is attributed to their high configurational entropy. This study offers profound insights into element screening for high-entropy alloy-based anodes in Li-ion batteries, providing guidance and reference for the element combination and screening of other high-entropy functional materials.

Interaction between Molnupiravir and noble metal substrates in SERS detection: A DFT method and Raman characteristic study

Molnupiravir, a nucleoside analogue (EIDD-2801), is an RNA polymerase inhibitor that effectively treats infections caused by novel coronaviruses and can be administered orally. Therefore, it is necessary to monitor the distribution of Molnupiravir in vivo after oral administration. Surface-enhanced Raman spectroscopy (SERS) is a suitable detection method, so it is necessary to understand the interaction between Molnupiravir and noble metal nanoparticles in the SERS effect. Therefore, we used density functional theory combined with surface-enhanced Raman spectroscopy to investigate the interaction between Molnupiravir and noble metal/composite nanoparticles in the SERS effect. To study the interaction sites of Molnupiravir and the substrate in the SERS effect, the molecular electrostatic potential (MEP) of Molnupiravir was calculated. Considering the significant role of binding energy in studying molecular docking, the binding energy between Molnupiravir and Metal6 (Au6, Ag6, Au3Ag3) atomic clusters was calculated. The calculated results of the frontier orbitals and relevant molecular parameters of Molnupiravir and Molnupiravir-Metal6 complexes reveal the changes in the molecular properties of Molnupiravir in the SERS effect. Finally, the theoretical Raman spectra differences between Molnupiravir and complexes were compared and analyzed, and the adsorption structure of Molnupiravir on the substrate surface was determined based on the surface selection rules of SERS. The research results will provide insights into the interaction between Molnupiravir and the substrate in the SERS effect, and also offer theoretical support for the application of SERS methods in biomedical detection.

Theoretical basics of the self-learning system of intelligent locomotive decision support systems

Analysis of works in the field of artificial intelligence allows to make an assumption that today there is a sufficiently developed theoretical basis for the development of intelligent control systems for locomotive control. This will minimize the risks associated with the human factor on the railways. The paper presents the theoretical rationale for the development of a knowledge base for intelligent locomotive control systems. The approach and structure of the self-learning system of intelligent DSS is proposed, the advantage of which is the presence of a fuzzy classifier that works according to the set criteria and determines a fuzzy image of the current train situation. Learning a fuzzy classifier consists in finding a vector K that minimizes the distance between the results of logical inference and experimental data from the sample. The knowledge base is implemented using linguistic variables formalized by methods of fuzzy logic. The use of linguistic values makes it possible to design the base using the usual language of communication, which greatly simplifies both the design process itself and the analysis of the system's performance. Also, the knowledge base has the possibility of constant self-improvement. This happens in two ways. The first is by adding new rules to the knowledge base in case the current situation does not match the existing ones in the base, in which case an additional rule is created and checked for adequacy. The second way is a mechanism for ranking rules in the knowledge base. If the control action of the locomotive driver coincided with the recommendation of DSS in the current situation, then the rating of this recommendation (rule) increases, and in the future the rule selection algorithm will choose one or another control action for the current situation that has the highest rating (that is, it has already been verified several times person). The experiment has shown that the use of intelligent DSS has positive results. On average, the DSS made the correct train control decisions faster than the locomotive driver.

Selection Rules of Linear and Nonlinear Polarization-Selective Absorption in Optically Dressed Matter

Dynamical symmetries of laser-dressed matter determine the selection rules that influence its absorption spectrum. We explore selection rules for polarization-sensitive absorption in Floquet matter, using Floquet group theory in synthetic dimensions. We present comprehensive tables of selection rules that polarization-structured light impose on Floquet dark states and Floquet dark bands. Notably, our tables encompass nonlinear absorption for all nonlinear orders, revealing that different nonlinear orders follow distinct polarization selection rules, potentially leading to polarization-tunable optical filters.