A unitary coupled-cluster (UCC)-based self-consistent electron propagator theory (EPT) is proposed for the description of electron-detached and electron-attached states. Two practical schemes, termed IP/EA-UCC3 and IP/EA-qUCCSD, are developed and implemented within the UCC singles and doubles (UCCSD) framework using the perturbative and commutator-based truncation strategies for the similarity-transformed Hamiltonian H̅. The numerical performance of these UCC-based EPT methods is evaluated primarily using full configuration interaction (FCI) reference data and compared with established approaches, including IP/EA-ADC(3), IP/EA-ADC(4) and IP/EA-EOM-CCSD. Benchmark calculations demonstrate that IP-qUCCSD achieves the highest overall accuracy among Hermitian ionized-state methods for one-hole (1h)-dominated IPs of closed-shell systems, with a mean absolute deviation (MAD) of 0.19 eV and standard deviation (SD) of 0.13 eV. Remarkably, despite the absence of triple-excitation contributions, IP-qUCCSD outperforms the higher-order ADC(4) method. For one-particle (1p)-dominated EA calculations, all tested methods exhibit comparable accuracy.

Spectroscopic core-to-valence transitions serve as reporters on the valence virtual orbitals, which is especially informative for molecules and materials with open-shell ground states that feature (quasi-)degenerate frontier molecular orbitals. Excited states of open-shell molecules are difficult to model using methods based on single excitations only, a category that includes time-dependent density functional theory, due to severe spin contamination (in both ground and excited states) when a spin-unrestricted reference determinant is used. Extended configuration-interaction singles (XCIS) is a simple, variational, and size-consistent wave function ansatz that augments the usual CIS excitation space with a limited set of doubly substituted determinants in order to recover spin-pure excited states starting from a restricted open-shell Hartree-Fock (ROHF) ground state. XCIS eliminates spin contamination and offers better accuracy as compared to ROHF-based CIS. Here, we report an implementation of XCIS based on the core/valence separation (CVS) approximation, which restricts the orbital active space to a few occupied orbitals so that core-to-valence transitions can be simulated efficiently. In applications of XCIS-CVS to X-ray transitions in a variety of open-shell systems, including 3d transition metal complexes, we find that both K-edge and pre-edge orbital splittings are reproduced semiquantitatively as compared to experiment.

Voltage-sensitive dyes (VSDs) are fluorescent molecules that detect changes in the membrane potential, making them invaluable for studying electrical activity in neurons, cardiac cells, and other excitable tissues. They are widely used in neuroscience and physiology to visualize and measure real-time voltage dynamics in cellular networks and whole tissues. VSDs exhibit a high sensitivity to their surrounding environment, which leads to notable solvation relaxation and a substantial Stokes shift upon excitation. Accurate prediction of their fluorescence spectra requires an advanced solvation model that captures these dynamic effects. In this work, we extend the Similarity Transformed Equation-of-Motion Domain-Based Local Pair Natural Orbital Coupled Cluster with Singles and Doubles (STEOM-DLPNO-CCSD) method, a computationally efficient approach for vertical excitation energies, to predict fluorescence spectra for VSDs. While the default perturbative solvation correction at the Hartree-Fock level has proven effective for some excited state calculations, it fails to account for the electron correlation effects that are crucial for accurate fluorescence spectra predictions. To address this, we incorporate a time-dependent density functional theory-based perturbative solvation correction, which improves the accuracy of the methods by better capturing the necessary correlation effects. The methodology is validated through studies of two carefully selected VSDs, (E)-3-(4-(2-(6-(dibutylamino)naphthalen-2-yl)vinyl)pyridin-1-ium-1-yl)propane-1-sulfonate (di-4-ANEPPS) and 4-((E)-4-((E)-4-(diethylamino)-2-methoxystyryl)styryl)-2-(6-(dimethylamino)-3-(dimethyliminio)-3H-xanthen-9-yl)benzenesulfonate (sRhoVR-1). The developed quantum chemical protocol allows for accurate prediction of fluorescence maxima for dyes with a predominant excited state and can accommodate computational constraints without greatly compromising precision. However, the study also highlights the need for further improvements for the prediction of peak intensity, suggesting that explicit solvent models or hybrid quantum mechanics/molecular mechanics (QM/MM) approaches could be valuable for future work. The proposed method provides a powerful tool for the design of VSDs optimized for specific environments and applications.

In this study, we apply the CNDOL/2SS approximate Fockian with the configuration interaction of singles (CIS) method to explore the excitonic properties of oligothiophene-based materials for organic solar cells. Our calculations of the excited states and charge density distributions of isolated chromophores and a donor-acceptor pair align closely with experimental data. The methodology used is reliable and useful for addressing complex donor-acceptor systems and their eventual design. The prediction of exciton binding energy using the Coulomb and exchange (ECE) term of the CIS energy transitions, combined with charge density difference maps to visualize the electronic structure of excitons, aids in distinguishing charge transfer states between multiple transitions present in the molecular aggregates representing the donor-acceptor pair. Our results indicate that the donor-acceptor blend Tz6T:eC9-4F exhibits strong low-energy light absorption and a state alignment that enables barrier-free charge transport. The sandwich-type arrangement of this pair reveals a charge transfer (CT) state characterized by low exciton binding energy (low ECE term), highlighting its potential for optimizing organic solar cell performance. In contrast, less-ordered arrangements of the donor-acceptor pair show CT states at higher energies, which may compete with other deactivation processes and reduce the efficiency. This study provides a cost-effective approach to predicting and interpreting the feasibility of charge transfer in molecular aggregate designs for solar cells.

Quantum computers have the potential to efficiently solve the electronic structure problem but are currently limited by noise and shallow circuits. We present an enhanced Variational Quantum Eigensolver (VQE) ansatz based on the Qubit Coupled Cluster (QCC) approach that requires optimization of only n parameters, where n is the number of Pauli string generators, rather than the typical n + 2m parameters, where m is the number of qubits. We evaluate the ground state energies and molecular dissociation curves of strongly correlated molecules, namely O3 and Li4, using active spaces of varying sizes in conjunction with our enhanced QCC ansatz, Unitary Coupled Cluster Single-Double (UCCSD) ansatz, and the classical Coupled Cluster Singles and Doubles (CCSD) method. Compared to UCCSD, our approach significantly reduces the number of parameters while maintaining high accuracy. Numerical simulations demonstrate the effectiveness of our approach, and experiments on superconducting and trapped-ion quantum computers showcase its practicality on real hardware. By eliminating the need for symmetry-restoring gates and reducing the number of parameters, our enhanced QCC ansatz enables accurate quantum chemistry calculations on near-term quantum devices for strongly correlated systems.

Modeling L-edge spectra at X-ray wavelengths requires consideration of spin-orbit splitting of the 2p orbitals. We introduce a low-cost tool to compute core-level spectra that combines a spin-orbit mean-field description of the Breit-Pauli Hamiltonian with nonrelativistic excited states computed using the semi-empirical density-functional theory configuration-interaction singles (DFT/CIS) method, within the state-interaction approach. Our version of DFT/CIS was introduced recently for K-edge spectra and includes a semi-empirical correction to the core orbital energies, significantly reducing ad hoc shifts that are typically required when time-dependent (TD-)DFT is applied to core-level excitations. In combination with the core/valence separation approximation and spin-orbit couplings, the DFT/CIS method affords semiquantitative L-edge spectra at CIS cost. Spin-orbit coupling has a qualitative effect on the spectra, as demonstrated for a variety of 3d transition metal systems and main-group compounds. The use of different active orbital spaces helps to facilitate spectral assignments. We find that spin-orbit splitting has a negligible effect on M-edge spectra for 3d transition metal species.

This paper thoroughly examines three recently introduced modifications of the Gower coefficient, which were determined for data with mixed-type variables in hierarchical clustering. On the contrary to the original Gower coefficient, which only recognizes if two categories match or not in the case of nominal variables, the examined modifications offer three different approaches to measuring the similarity between categories. The examined dissimilarity measures are compared and evaluated regarding the quality of their clusters measured by three internal indices (Dunn, silhouette, McClain) and regarding their classification abilities measured by the Rand index. The comparison is performed on 810 generated datasets. In the analysis, the performance of the similarity measures is evaluated by different data characteristics (the number of variables, the number of categories, the distance of clusters, etc.) and by different hierarchical clustering methods (average, complete, McQuitty and single linkage methods). As a result, two modifications are recommended for the use in practice.

Nowadays face recognition still being a hot topics to be discussed especially it’s utility for genderclassification. Gender classification is an easy task for human but it’s a challenging task for computersbecause it doesn’t have capability for recognizing human gender without feature extraction. There arestill many researches about facial image feature extraction for gender classification. Geometryfeatures and Texture Features are well perform features for gender classification. This paper willpresents fusion of those feature in order to find an improvement for gender classifications task. Eachfeatures will be extracted using Viola Jones Algorithm and Compass Local Binary Pattern method.Both features will be combined using concatenated method in dataframe format. Viola Jonesalgorithm has an issues when detecting each facial regions so it causes outliers in each geometryfeatures. The proposed method will be evaluated on color FERET dataset that contains facial images.Classification task will be done using Random Forest and Backpropagation. The result is fusionfeatures present an improvement in gender classification using Backpropagation with 87% accuracy.It prove that proposed method perform better than single feature extraction method.

The development in attosecond physics allows for unprecedented control of atoms and molecules in the time domain. Here, ultrashort pulses are used to prepare atomic ions in specific magnetic states, which may be important for controlling charge migration in molecules. Our work fills the knowledge gap of relativistic hole alignment prepared by femtosecond and attosecond pulses. The research focuses on optimizing the central frequency and duration of pulses to exploit specific spectral features, such as Fano profiles, Cooper minima, and giant resonances. Simulations are performed using the Relativistic Time-Dependent Configuration-Interaction Singles method. Ultrafast hole alignment with large ratios (on the order of one hundred) is observed in the outer-shell hole of argon. An even larger alignment (on the order of one thousand) is observed in the inner-shell hole of xenon.

The current meta-synthesis study investigated the effective strategies for improving critical thinking ability among EFL/ESL learners, and the relationship between language learning skills and critical thinking ability. To achieve this aim, meta-synthesis was selected as the design of the study. Therefore, some databases were searched using the defined key terms, in order to select the related qualitative, quantitative, and mixed-method studies. As a result, 550 articles were found, 43 articles of which were included in the final review. Using thematic analysis, the obtained data from these 43 articles were analyzed in 6 steps and then coded for each research question. Although no single method was proposed in this study as the best to improve critical thinking, it was indicated that a number of them together can be effective when properly implemented. It was shown that all 4 language skills can be improved by enhancing the level of critical thinking ability among EFL/ESL learners.

The involved object of systematic innovation and management is neither a single goal nor a single meth-od. Firstly, the research object is a systematic and organic whole composed of multiple node elements with a certain degree of correlation. Secondly, the research method is multi-dimensional, comprehensive, all round and highly efficient. Innovation should break through the existing thinking framework, change the existing structure, add new components, give new energy and produce new functions. The 5W1H is a questioning approach and a problem-solving method that answers all the basic elements within a problem which are what, who, when, where, why, and how. It aims to view ideas from various perspectives and gain an in-depth understanding of a specific situation. On the basis of 5W1H analysis method, this paper puts forward the 6WHI systematic innovation management method, which increases the query words weather and if of the innovation expansion solution, increases the number of questions from 6 to 8, and expands innovative forward-looking and innovative solutions diversity, with smoother pronunciation, more exciting storm. Furthermore, a systematic innovation method based on 6WHI and total factor analysis is established, and a more general and normative model WIME method is presented. This method can be utilized as a continuous process-improvement technique in an organization. Taking the 17 letters of “if question patents” as the prefix, they are just 17 common key elements in system management and innovation. For each element mentioned above, 8 questions and answers of 6WHI are given to realize systematic management and innovation. The above two types of work are arranged according to the matrix format, and then the weighted evaluation method is introduced to evaluate the above management and innovation work, so as to give a general and standardized systematic management and innovation (WIME) method.

SummarySupercomputers are advanced computing systems interconnected through high‐speed communication networks, consisting of independent computational nodes. During the unfolding of the big data era, the potent computational capabilities of these supercomputers play a pivotal role in scientific computing. Despite executing numerous advanced computational science and engineering tasks on supercomputers, many submitted jobs fail due to various factors, resulting in user inefficiencies. These failures not only consume system resources but also reduce the overall efficiency of the system. Previous research often couples job performance features with a single machine learning method for predicting job failure. However, a primary hurdle emerges from the high cost of gathering these features, complicating their real‐world applicability. To address this challenge, our study establishes correlations among job applications through extensive job log analysis. Leveraging correlations, we propose a predictive framework based on job application sequence correlation (called FP‐JSC). This innovative framework employs multiple machine learning models to offer holistic predictions, selecting the most suitable model based on its learning effectiveness. Moreover, the framework optimizes feature collection expenses without adversely affecting job execution. We determine job applications using both job paths and job names, with the former emerging as a novel feature derived from supplementary monitoring data. Empirical results underscore FP‐JSC's effectiveness, accurately identifying over 89% of jobs with 95% specificity and 89% sensitivity—outperforming single prediction methods employed in related works.

Abstract The five-year survival rate of glioblastoma multiforme (GBM) is 5.8%, and its standard of care has not changed since 2005. This stagnation in therapeutic development is due, in part, to its characteristic heterogeneity–multiforme–highlighting a critical need to understand the biological processes underlying this GBM hallmark. Despite the well-documented individual relationships between extrachromosomal DNA (ecDNA), GBM, and heterogeneity, the interplay between the three is not well understood. The Furnari lab has generated a library of cancer avatars, or human induced pluripotent stem cells (hiPSCs) engineered to harbor cancer driving mutations. Single cell RNA sequencing (scRNA-seq) was used to characterize the longitudinal evolution of the first avatars engineered to have TP53−/−;PDGFRAΔ8-9 and PTEN−/−;NF1−/− genotypes. The TP53−/−;PDGFRAΔ8-9 avatar acquired ecDNA, while the PTEN−/−; NF1−/− avatar did not. Re-analysis of the scRNA-seq data identified a subpopulation of cells in the TP53−/−;PDGFRAΔ8-9 avatar that had high expression of CDK4 and PDGFRA, two genes that are commonly co-amplified, found on ecDNA, and drive cellular states. This subpopulation of cells also had highly expressed genes near the CDK4 locus indicating a structural variant and possibly ecDNA. This population diverged transcriptionally from other cells and was associated with the OPC-like cellular state, which has been shown to be associated with high levels of PDGFRA and, at times, co-expression with CDK4. While TP53−/−;PDGFRAΔ8-9 tumors contained cells across all four GBM cellular states, PTEN−/−;NF1−/− tumors had a significantly less diverse composition of cellular states suggesting that ecDNA may facilitate cellular state diversity. With the eventual goal to directly test whether ecDNA enables transcriptional state heterogeneity compared to chromosomally inherited amplifications, CRISPR-Cas9 and CRISPR-C were implemented to engineer EGFRvIII ecDNA in U87. While inverse PCR confirmed the successful generation of ecDNA in U87, ecDNA was lost following long term in vitro cell passaging. Current follow up work aims to identify different intrinsic (oncogene amplified, genetic background, cell type, cell line) and extrinsic (in vitro conditions, orthotopic engraftment) contexts that promote the selection and amplification of engineered ecDNA, as it would be would be valuable to have genotypically identical cancer models with different classes of amplifications (i.e., engineered ecDNA vs. lentiviral overexpression, which is chromosomally inherited). Together, these results highlight the potential to use hiPSC-derived models and single cell methods to investigate gliomagenesis and cell identity. Citation Format: Brett Taylor, Brandon Jones, Daisuke Kawauchi, Yohei Miyake, Raghav Vadla, Nathan Jameson, Shunichiro Miki, Tomoyuki Koga, Bing Ren, Frank Furnari. Dissecting the interplay of cell identity and extrachromosomal DNA in glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6945.

Accurate detection depression cell model with a dual-locked fluorescence probe in response to noradrenaline and HClO

The most common management of choledocholithiasis involves the two-step method that involves the use of pre-operative endoscopic retrograde cholangiopancreatography (ERCP) followed by laparoscopic cholecystectomy in six weeks’ time. But since the introduction of laparoscopic surgery, laparoscopic common bile duct exploration has been used as a single step method to treat this condition. Another method involves intraoperative endoscopic retrograde cholangiopancreatography (ERCP) and laparoscopic cholecystectomy in the same setting. As there is no consensus on management, we have conducted this review article to look at the various management options for choledocholithiasis.

Research on electronic health record physiological data is common, invariably including artifacts. Traditionally, these artifacts have been handled using simple filter techniques. The authors hypothesized different artifact detection algorithms, including machine learning, may be necessary to provide optimal performance for various vital signs and clinical contexts. In a retrospective single center study, intraoperative OR and ICU electronic health record datasets including heart rate, oxygen saturation, blood pressure, temperature, and capnometry were included. All records were screened for artifacts by at least two human experts. Classical artifact detection methods (cutoff, multiples of standard deviation (z-value), interquartile range, and local outlier factor) and a supervised learning model implementing long short-term memory neural networks were tested for each vital sign against the human expert reference dataset. For each artifact detection algorithm, sensitivity and specificity were calculated. A total of 106 (53 operating room and 53 ICU) patients were randomly selected, resulting in 392,808 data points. Human experts annotated 5,167 (1.3%) data points as artifacts. The artifact detection algorithms demonstrated large variations in performance. The specificity was above 90% for all detection methods and all vital signs. The neural network showed significantly higher sensitivities than the classic methods for: heart rate (ICU: 33.6%, 95% CI: 33.1-44.6), systolic invasive blood pressure (both in the OR (62.2%, 95% CI: 57.5-71.9) and ICU (60.7%, 95% CI: 57.3-71.8), and temperature in the OR (76.1%, 95% CI: 63.6-89.7). The confidence intervals for specificity overlapped for all methods. Generally, sensitivity was low, with only the z-value for oxygen saturation in the operating room reaching 88.9%. All other sensitivities were less than 80%. No single artifact detection method consistently performed well across different vital signs and clinical settings. Neural networks may be a promising artifact detection method for specific vital signs.

Abstract The electricity load prediction is closely related to production and daily life. The electricity load prediction is also a very important task. With the widespread application of smart grids, load data shows an exponential growth trend. The huge amount of data in the load makes power prediction even more difficult. On the basis of traditional prediction algorithms, a power load prediction model based on machine learning and neural networks is designed. Because the single model prediction has the unstable results, a combined model is obtained based on the ensemble learning idea and two single model prediction method. The prediction results are detected by the load data. From the experimental results, the mean absolute percentage error (MAPE) of the AdaBoost‐GRU data fusion model is 0.066%. Compared to the AdaBoost‐GRU data fusion model, the MAPE decreases by 1.59% and 1.12%, respectively. The relative mass scores of the two groups decrease by 132.57% and 89.14%, respectively. The prediction accuracy is improved, which has advantages compared to traditional combination models. It can effectively enhance the accuracy of short‐term power grid load forecasting. It is an important scientific and practical reference for power grid decision‐making.

The renal cells were isolated from the kidney of different week-age of healthy rabbits by a single two-stage pass perfusion method and suspended in Hanks’ balanced salt solution ([Formula: see text]-, [Formula: see text]-free). The field strains of RHDV were inoculated into the suspension and incubated for 4 days at 37∘C. After incubation, the HA titer of each strain of RHDV in supernatant of virus growth was measured using human erythrocyte O type. There were differences in the HA titers of RHDV strains according to the renal cell of different week-age of rabbits, the number of hepatocyte, the strain of virus (RHDV-1, RHDV-2, RHDV-3) and the duration of incubation. The HA titer was highest when the virus was cultured in the renal cells isolated from more than 12 week-aged rabbits. And the HA titer of RHDV propagated in isolated renal cell was higher than hepatocytes. When immunized rabbits by inactivated virus strains (RHDV-1, RHDV-2 and RHDV-3), two strains, RHDV-1 and RHDV-3, showed the same level of HI titers with inactivated homogenate, but RHDV-2 did lower level of titer than the homogenate.

The recalcitrant nature of the industrial dyes poses a significant challenge to existing treatment technologies due to the stringent environmental regulations. This combined with the inefficiency of a single treatment method has led to the implementation of the combination of primary, secondary, and tertiary treatment processes, which fails during complex secondary aeration processes due to variable pH loads of industrial effluent wastewater. This article presents a modified design methodology of a pilot-scale micro-pre-treatment unit using a solar-triggered advanced oxidation process reactor that both effectively controls the influent variability at the source and mitigates textile effluents for making the discharge reusable for different industrial purposes. The proposed modified combination technique of controlled serial processes inclusive of primary, secondary, and tertiary treatment steps with ZnO/ZnO-GO NanoMat-based advanced oxidation process demonstrates complete remediation of industrial grade effluent with effective reuse of the discharge. Further, a reliable prediction model for estimating water quality parameter using machine learning models are proposed. Multi-linear regression and Artificial Neural network modeling provide simple, accurate, and robust prediction capabilities, which are evaluated for the efficiency of the processes. The generated prediction models capture the output parameters within an acceptable level of accuracy (Radj2>0.90)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$({{\\boldsymbol{R}}}_{{adj}}^{{\\bf{2}}}\\, >\\, 0.90)$$\\end{document} and allow compliance with the discharge Inland Water Discharge Standards (IWDS).

The use of affine maneuver control to maintain the desired configuration of unmanned aerial vehicle (UAV) swarms has been widely practiced. Nevertheless, the lack of capability to interact with obstacles and navigate autonomously could potentially limit its extension. To address this problem, we present an innovative formation flight system featuring a virtual leader that seamlessly integrates global control and local control, effectively addressing the limitations of existing methods that rely on fixed configuration changes to accommodate real-world constraints. To enhance the elasticity of an algorithm for configuration change in an obstacle-laden environment, this paper introduces a second-order differentiable virtual force-based metric for planning local trajectories. The virtual field comprises several artificial potential field (APF) forces that adaptively adjust the formation compared to the existing following control. Then, a distributed and decoupled trajectory optimization framework that considers obstacle avoidance and dynamic feasibility is designed. This novel multi-agent agreement strategy can efficiently coordinate the global planning and local trajectory optimizations of the formation compared to a single method. Finally, an affine-based maneuver approach is employed to validate an optimal formation control law for ensuring closed-loop system stability. The simulation results demonstrate that the proposed scheme improves track accuracy by 32.92% compared to the traditional method, while also preserving formation and avoiding obstacles simultaneously.