General Approach Research Articles

Tandem Electroreduction of CO2 to C2+ Products based on M-SACs/Cu Catalyst.

Electrochemical CO2 reduction reaction (ECO2RR) is considered a highly promising method to produce high-value chemicals and fuels, contributing significantly the artificial carbon balance. Cu species exhibit a distinct role in the formation of C2+ products characterized by enhanced energy density. The limited selectivity of C2+ products, along with the inferior stability, and high overpotential demonstrated by single-component Cu catalysts, hinders their applicability in industrial-scale production. The implementation of a tandem strategy, which involves coupling the CO2-to-CO pathway using metal single-atom catalysts (M-SACs), etc., with the CO-to-C2+ conversion on Cu species, represents a novel approach for the efficient generation of C2+ products. Given the high cost and restricted availability of noble metals, M-SACs have attracted substantial interest in tandem systems. The systematic analysis of the design principles and structure-activity relationship is essential for the advancement of M-SACs/Cu-based tandem catalysts. Here we first introduce various prevalent design strategies of M-SACs/Cu-based tandem catalysts for ECO2RR and then systematically summarize the latest advancements of M-SACs/Cu-based tandem system, encompassing metal-organic frameworks/Cu (MOFs/Cu), covalent organic frameworks/Cu (COFs/Cu), and nitrogen-doped carbon support transition metal single atomic materials/Cu (M-N-C/Cu). Lastly, we discuss the challenges and opportunities with the design and construction of M-SACs/Cu-based tandem catalysis for ECO2RR.

Optimal Allocation of Urban Water Resources Based on Multi-Objective Nutcracker Optimization Algorithm

The imbalance between water supply and demand (WSD) has been growing noticeable as a result of the economy’s fast expansion which can be effectively alleviated using optimal allocation of water resources. An urban water resources allocation (WRA) model based on the innovative Multi-Objective Nutcracker Optimization Algorithm (MONOA) is proposed in this study. Taking into account economic, social and ecological benefits, a comprehensive multi-objective optimization (MOO) model is established. By introducing the opposite learning strategy, non-dominated sorting approach and crowding distance mechanism to a recently reported intelligent optimization algorithm called the Nutcracker Optimization Algorithm (NOA), the novel nature-inspired metaheuristic algorithm MONOA is proposed to solve the multi-objective optimization model. The MONOA is evaluated on ten benchmark test functions, and it exhibits superior distribution and convergence by comparing with some highly cited algorithms. The proposed model is applied to Handan, China, in order to obtain a reasonable water allocation scheme in the planning year. The simulation results reveal that the economic benefit is in the range CNY [1.36, 1.44] × 1011, water shortage is in the range [0.66, 0.98] × 108 m3 and COD emission is in the range [3.70, 3.91] × 104 t in all the obtained Pareto solutions. The water resources management departments might create customized water allocation plans by balancing different goals and taking preferences into account. Moreover, the proposed method is a general approach that can be applied to many other cities. Hence, it is of great significance to the sustainable development and utilization of urban water resources.

A Research on Two-Stage Facial Occlusion Recognition Algorithm based on CNN

In recent years, pattern recognition has garnered widespread attention, especially in the domain of face recognition. Traditional face recognition methods have certain limitations in unconstrained environments due to factors such as lighting, facial expressions, and poses. Deep learning can be used to address these challenges. This paper proposes a comprehensive approach to face occlusion recognition based on a two-stage Convolutional Neural Network (CNN). Face verification aims at verifying whether two face images belong to the same individual, and it is a more fundamental task compared to face recognition. The process of face recognition essentially involves multiple instances of face verification, sequentially validating different individuals to ultimately determine the corresponding individual for each face. The primary steps in this research include facial detection, image preprocessing, facial landmark localization, facial landmark extraction, feature matching recognition, and 2D image-assisted 3D face reconstruction. A novel two-stage CNN was designed for facial detection and alignment. The first stage of the network is dedicated to the search for facial windows and regressing vector boundaries. The second stage utilizes 2D images to assist in 3D face reconstruction and perform secondary recognition for cases not identified in the first stage. This method demonstrated excellent performance in handling facial occlusions, achieving high accuracy on datasets such as AFW and FDDB. On the test dataset, face recognition accuracy reached 97.3%, surpassing the original network accuracy of 89.1%. This method outperforms traditional algorithms and general CNN approaches. This study achieved efficient face validation and further handling of unrecognized situations, contributing to the enhancement of face recognition system performance.

Secure fuzzy retrieval protocol for multiple datasets

With the diversification of data sources and the massive growth of datasets, data retrieval has become increasingly complex and time-consuming. In the traditional retrieval method, if a user wants to query multiple datasets, the general approach is to retrieve them one by one in order, which may lead to duplication of work and waste of resources. Private set intersection is a specific issue in secure multi-party computation. It allows several participants, each holding different sets, to jointly calculate the intersection of their sets without revealing any information other than the intersection. This method is naturally suitable for data fusion. In this work, we propose a secure fuzzy retrieval protocol for multiple datasets. First, we use private set intersection technology to fuse multiple datasets. Then, we perform secure retrieval based on this fused dataset, effectively avoiding the waste of resources caused by separate retrievals, thereby maximizing resource efficiency. It is worth mentioning that the protocol proposed in this paper can also be used for fuzzy retrieval to improve the user’s search experience. More importantly, the protocol can maximize privacy protection during the retrieval process, including strict protection of sensitive information such as retrieval keywords, ensuring that user data and query intentions will not be leaked during the entire retrieval process. Finally, we provide a rigorous security proof and demonstrate the effectiveness of the protocol through simulation experiments.

Valorization of dairy waste scum oil and rice husk ash-supported CuO nanocatalyst towards cleaner production of biodiesel: A waste-to-energy approach

This study surveys the valorization of dairy waste scum oils (DWSO) to synthesis biodiesel using a novel rice husk ash-supported CuO nanocatalyst. The rice husk ash-supported CuO nanocatalyst was synthesized through a facile impregnation method and characterized by BET, XRD, FTIR, EDX, CO2/TPD, TEM, and FESEM to confirm its structural and morphological features. Transesterification of DWSO was conducted under optimized conditions, achieving a maximum biodiesel yield of 97.42% at temperature of 62.36°C, methanol/DWSO proportion of 11:12, and nanocatalyst loading of 2.76wt.% within 2.85h. Kinetic studies revealed that the transesterification reaction follows a pseudo-first-order model with an activation energy of 100.34kJ/mol. Thermodynamic analysis indicated that the reaction is endothermic (ΔH = 100.26kJ/mol) and non-spontaneous (ΔG = -11043.6kJ/mol) at the studied temperature range, suggesting the requirement of external heat to drive the process. Reusability tests demonstrated that the rice husk ash-supported CuO nanocatalyst retains over 86% of its initial activity after seven cycles, highlighting its potential for practical applications. This study highlights the dual benefits of utilizing agro-industrial waste for both feedstock and catalyst support, promoting a cleaner and economically viable approach for biodiesel generation.

Computer-aided Design of Wide-spectrum Coronavirus Helicase NSP13 Cage Inhibitors: A Molecular Modelling Approach.

The coronavirus helicase NSP13 plays a critical role in its life cycle. The found NSP13 inhibitors have been tested only in vitro but they definitely have the potential to become antiviral drugs. Thus, the search for NSP13 inhibitors is of great importance. The goal of the present work was to develop a general approach to the design of ligands of coronaviral NSP13 helicase and to propose on its basis potential inhibitors. The structure of the NSP13 protein was refined by molecular dynamics and the cavity, responsible for RNA binding, was chosen as the inhibitor binding site. The potential inhibitor structures were identified by molecular docking and their binding was verified by molecular dynamics simulation. A number of potential NSP13 inhibitors were identified and the binding modes and probable mechanism of action of potential inhibitors was clarified. Using the molecular dynamics and molecular docking techniques, we have refined the structure of the coronavirus NSP13 helicase, a number of potential inhibitors, containing cage fragment were proposed and their probable mechanism of action was clarified. The proposed approach is also suitable for the design of ligands interacting with other viral helicases.

EuroCity Persons 2.0: A Large and Diverse Dataset of Persons in Traffic.

We present the EuroCity Persons (ECP) 2.0 dataset, a novel image dataset for person detection, tracking and prediction in traffic. The dataset was collected on-board a vehicle driving through 29 cities in 11 European countries. It contains more than 250K unique person trajectories, in more than 2.0M images and comes with a size of 11 TB. ECP2.0 is about one order of magnitude larger than previous state-of-the-art person datasets in automotive context. It offers remarkable diversity in terms of geographical coverage, time of day, weather and seasons. We discuss the novel semi-supervised approach that was used to generate the temporally dense pseudo ground-truth (i.e., 2D bounding boxes, 3D person locations) from sparse, manual annotations at keyframes. Our approach leverages auxiliary LiDAR data for 3D uplifting and vehicle inertial sensing for ego-motion compensation. It incorporates keyframe information in a three-stage approach (tracklet generation, tracklet merging into tracks, track smoothing) for obtaining accurate person trajectories. We validate our pseudo ground-truth generation approach in ablation studies, and show that it significantly outperforms existing methods. Furthermore, we demonstrate its benefits for training and testing of state-of-the-art tracking methods. Our approach provides a speed-up factor of about 34 compared to frame-wise manual annotation. The ECP2.0 dataset is made freely available for non-commercial research use.

Text-to-Image Generation Using Stack Generative Adversarial Networks (GANs) and Stable Diffusion Models

Text-to-image synthesis is a challenging task in artificial intelligence that involves generating realistic images from textual descriptions. StackGAN, [1] a generative model with a two-stage architecture, has proven to be effective in improving the quality and fidelity of synthesized images. In this study, we implement and evaluate the performance of StackGAN using the CIFAR-10 dataset, demonstrating how a multi-stage generation approach can produce high-quality images from textual labels. The experimental results show that StackGAN successfully captures the underlying features of the dataset categories and provides reasonable image fidelity.

In Silico Integrative scRNA Analysis of Human Colonic Epithelium Indicates Four Tuft Cell Subtypes.

This study integrated and analyzed human single-cell RNA sequencing data from four publicly available datasets to enhance cellular resolution, unveiling a complex landscape of tuft cell heterogeneity within the human colon. Four tuft subtypes (TC1-TC4) emerged as defined by unique gene expression profiles, indicating potentially novel biological function. Tuft cell 1 (TC1) was characterized by an antimicrobial peptide signature; TC2 had an increased transcription machinery gene expression profile consistent with a progenitor-like cell; TC3 expressed genes related to ganglion (neuronal) development; and TC4 expressed genes related to tight junctions. Our analysis of subtype-specific gene expression and pathway enrichment showed variances in tuft cell subtypes between healthy individuals and those with inflammatory bowel disease (IBD). The frequency of TC1 and TC2 differed between healthy controls and IBD. Relative to healthy controls, TC1 and TC2 in IBD-tissue showed an upregulation of gene expression, favouring increased metabolism and immune function. These findings provide foundational knowledge about the complexity of the human colon tuft cell population and hint at their potential contributions to gut health. They provide a basis for future studies to explore the specific roles these cells may play in gut function during homeostasis and disease. We demonstrate the value of in silico approaches for hypothesis generation in relation to the putative functions of low-frequency gut cells for subsequent physiological analyses.

Generalized representative structures for atomistic systems.

A new method is presented to generate atomic structures that reproduce the essential characteristics of arbitrary material systems, phases, or ensembles. Previous methods allow one to reproduce the essential characteristics (e.g., the chemical disorder) of a large random alloy within a small crystal structure. The ability to generate small representations of random alloys, along with the restriction to crystal systems, results from using the fixed-lattice cluster correlations to describe structural characteristics. A more general description of the structural characteristics of atomic systems is obtained using complete sets of atomic environment descriptors. These are used within for generating representative atomic structures without restriction to fixed lattices. A general data-driven approach is provided here utilizing the atomic cluster expansion (ACE) basis. TheN-body ACE descriptors are a complete set of atomic environment descriptors that span both chemical and spatial degrees of freedom and are used within for describing atomic structures. The generalized representative structure (GRS) method presented within generates small atomic structures that reproduce ACE descriptor distributions corresponding to arbitrary structural and chemical complexity. It is shown that systematically improvable representations of crystalline systems on fixed parent lattices, amorphous materials, liquids, and ensembles of atomic structures may be produced efficiently through optimization algorithms. With the GRS method, we highlight reduced representations of atomistic machine-learning training datasets that contain similar amounts of information and small 40-72 atom representations of liquid phases. The ability to use GRS methodology as a driver for informed novel structure generation is also demonstrated. The advantages over other data-driven methods and state-of-the-art methods restricted to high-symmetry systems are highlighted.

Evaluation of Multidentate Ligands Derived from Ethyl 1,2,4-triazine-3-carboxylate Building Blocks as Potential An(III)-Selective Extractants for Nuclear Reprocessing.

Bis-1,2,4-triazine ligands are amongst the most promising soft N-donor ligands for the partitioning of trivalent actinides from trivalent lanthanides; a key separation proposed in the future reprocessing of spent nuclear fuels. In an effort to improve the extraction properties of these benchmark ligands, we propose herein a general ligand design approach that is inspired by the field of drug discovery, and we apply it to a new class of ligands in which the bidentate 3-(2-pyridyl)-1,2,4-triazine unit of the benchmark ligands is replaced by a bidentate 1,2,4-triazine-3-carboxamide unit. A series of nine novel ligands were synthesized by reactions of readily available ethyl 1,2,4-triazine-3-carboxylate building blocks with different polyamine cores and evaluated for their ability to extract and separate Am(III) and Cm(III) from Eu(III). One of the reported ligands can co-extract Am(III) and Eu(III) from nitric acid into cyclohexanone, albeit with no selectivity between the metal ions. NMR titration experiments suggested that ligand 23 b formed a chiral 1 : 1 complex species with La(III) but not Lu(III) or Y(III), suggesting the coordination cavity of the ligand is sensitive to the size of the metal ion. The structures and thermodynamic parameters for the proposed complexes were further supported by DFT calculations.

General Approach to Function Approximation

Applying a predefined set of functionals to a function provides a set of numbers which characterize the function. One can then interpret an approximation very generally as a construction of some function form which reproduces these numbers if the same functionals are used. A large number of existing approximations can be interpreted in this way, and we review some of them. In addition, we construct several new expansion types including three rational approximations.

Multiplane Optimizing Phase Holograms Using Advanced Machine Learning Algorithms and GPU Acceleration

Phase holography is a critical optical imaging and information processing technique with applications ranging from microscopy to optical communications. However, optimizing phase hologram generation remains a significant challenge due to the non-convex nature of the optimization problem. This paper presents a novel multiplane optimization approach for phase hologram generation to minimize the reconstruction error across multiple focal planes. We significantly improve holographic reconstruction quality by integrating advanced machine learning algorithms like RMSprop and Adam with GPU acceleration. The proposed method utilizes TensorFlow to implement custom propagation layers, optimizing the phase hologram to reduce errors at strategically selected distances.

A mixed-methods community needs assessment of Santa Maria and Guadalupe, California

Santa Maria and Guadalupe are neighboring cities in northern Santa Barbara County that have a lower socioeconomic profile than the county overall, are >75% Latino, and have up to 32,000 residents who identify as Indigenous, primarily Mixtec-speaking people from southern Mexico. We conducted a mixed-methods community needs assessment to identify unique health challenges and barriers that Latinx and Mixtec individuals faced. From January to April 2021, targeted and general recruitment approaches were used to recruit a convenience sample of 159 participants (74% Latinx, 72% female, mean age 41.3 years) to complete modified long- and short-form versions of a community health concerns survey. Fifty-four completed the 40-item form and 102 completed the 19-item form. Of these, 24 individuals who expressed interest in further participation took part in structured, open-ended interviews. Among the key issues raised in surveys and interviews were housing, healthcare, and access to recreational resources. However, perspectives and priorities differed depending on the form of data collection (closed-ended survey vs. open-ended interview). For example, interviews echoed survey respondents’ dissatisfaction regarding lack of safe and affordable housing but added perspective on housing conditions and vulnerability to landlords’ decisions. In interviews, expanding existing resources and mobilizing as a community were noted as potential solutions; existing policies, language, and lack of interest by those in power were raised as significant barriers. Our assessment suggests that Santa Maria and Guadalupe communities face concerns about housing, healthcare, and access to recreational resources. Government, community, and healthcare sectors should focus on addressing these basic health needs.

Excitonic-Vibrational Interaction at 2D Material/Organic Molecule Interfaces Studied by Time-Resolved Sum Frequency Generation

The hybrid heterostructures formed between two-dimensional (2D) materials and organic molecules have gained great interest for their potential applications in advanced photonic and optoelectronic devices, such as solar cells and biosensors. Characterizing the interfacial structure and dynamic properties at the molecular level is essential for realizing such applications. Here, we report a time-resolved sum-frequency generation (TR-SFG) approach to investigate the hybrid structure of polymethyl methacrylate (PMMA) molecules and 2D transition metal dichalcogenides (TMDCs). By utilizing both infrared and visible light, TR-SFG can provide surface-specific information about both molecular vibrations and electronic transitions simultaneously. Our setup employed a Bragg grating for generating both a narrowband probe and an ultrafast pump pulse, along with a synchronized beam chopper and Galvo mirror combination for real-time spectral normalization, which can be readily incorporated into standard SFG setups. Applying this technique to the TMDC/PMMA interfaces yielded structural information regarding PMMA side chains and dynamic responses of both PMMA vibrational modes and TMDC excitonic transitions. We further observed a prominent enhancement effect of the PMMA vibrational SF amplitude for about 10 times upon the resonance with TMDC excitonic transition. These findings lay a foundation for further investigation into interactions at the 2D material/organic molecule interfaces.

Embedding equity and inclusion in universities through motivational theory and community-based conservation approaches.

Despite widespread plans to embed justice, equity, decolonization, indigenization, and inclusion (JEDII) into universities, progress toward deeper, systemic change is slow. Given that many community-based conservation (CBC) scholars have experience creating enduring social change in diverse communities, they have transferable skills that could help embed JEDII in universities. We synthesized the literature from CBC and examined it through the lens of self-determination theory to help identify generalizable approaches to create resilient sociocultural change toward JEDII in universities. Fostering autonomous motivation (i.e., behaving because one truly values and identifies with the behavior or finds behavior inherently satisfying) is critical to inspiring enduring change in both CBC and JEDII. Based on theory and our examination of CBC, we provide 5 broad recommendations that helped motivate behavioral change in a way that was self-sustained (i.e., even without external rewards or pressure). Guiding principles support autonomy by creating meaningful choice and different entry points for JEDII; prioritising relationships; designing payment programs that enhance autonomous motivation; developing meaningful educational opportunities that are relevant, timely, relational, and authentic; and creating institutional change by focusing efforts on critical moments.

An Activity-Based Sensing Approach to Multiplex Mapping of Labile Copper Pools by Stimulated Raman Scattering.

Molecular imaging with analyte-responsive probes offers a powerful chemical approach to studying biological processes. Many reagents for bioimaging employ a fluorescence readout, but the relatively broad emission bands of this modality and the need to alter the chemical structure of the fluorophore for different signal colors can potentially limit multiplex imaging. Here, we report a generalizable approach to multiplex analyte imaging by leveraging the comparably narrow spectral signatures of stimulated Raman scattering (SRS) in activity-based sensing (ABS) mode. We illustrate this concept with two copper Raman probes (CRPs), CRP2181 and CRP2153.2, that react selectively with loosely bound Cu(I/II) and Cu(II) ions, respectively, termed the labile copper pool, through copper-directed acyl imidazole (CDAI) chemistry. These reagents label proximal proteins in a copper-dependent manner using a dye scaffold bearing a 13C≡N or 13C≡15N isotopic SRS tag with nearly identical physiochemical properties in terms of shape and size. SRS imaging with the CRP reagents enables duplex monitoring of changes in intracellular labile Cu(I) and Cu(II) pools upon exogenous copper supplementation or copper depletion or genetic perturbations to copper transport proteins. Moreover, CRP imaging reveals reciprocal increases in labile Cu(II) pools upon decreases in activity of the antioxidant response nuclear factor-erythroid 2-related factor 2 (NRF2) in cellular models of lung adenocarcinoma. By showcasing the use of narrow-bandwidth ABS probes for multiplex imaging of copper pools in different oxidation states and identifying alterations in labile metal nutrient pools in cancer, this work establishes a foundation for broader SRS applications in analyte-responsive imaging in biological systems.

Certain aspects of seizure and cancellation of property in criminal proceedings

The subject matter of this article is the analysis of the procedure for imposing and lifting the seizure of property in criminal proceedings, which is considered with due regard to the current provisions of the Criminal Procedure Code of Ukraine. Although the issue of seizure of property is given quite a lot of attention in the national doctrine of criminal procedure law, the author in the course of the analysis highlights the currently controversial provisions relating to the regulation of the procedure for imposing and revoking the seizure of property in criminal proceedings and analyzes the court’s conclusions regarding the interpretation of problematic issues in the course of law enforcement, and examines the general approaches to seizure of property in criminal proceedings through the prism of the European Court of Human Rights (hereinafter – ECHR) case law. Among other things, the author examines the issues related to the procedural powers of special subjects of criminal proceedings (Director of the National Anti-Corruption Bureau of Ukraine/deputy Director, Director of the Bureau of Economic Security of Ukraine/deputy Director) in specific cases determined by the criminal procedure law. At the same time, the author demonstrates that there are gaps in the current version of the Criminal Procedure Law in terms of regulating the powers of special subjects of criminal proceedings with regard to preliminary seizure of property without a court order, and also proposes amendments to the relevant article of the CPC of Ukraine with a view to bringing the provisions of the law in line with the general principles and purpose of criminal proceedings. Particular emphasis is placed on the issues related to the imposition and cancellation of seizure of property of a third party whose property is being considered in criminal proceedings, the status of which is regulated by Article 642 of the CPC of Ukraine. The author proposes amendments and additions to the Criminal Procedure Code of Ukraine aimed at improving the procedures for seizure of property and ensuring the rights and legitimate interests of persons who do not have status in criminal proceedings and whose property becomes subject to seizure during the pre-trial investigation.

On Bayes factors for hypothesis tests.

We develop alternative families of Bayes factors for use in hypothesis tests as alternatives to the popular default Bayes factors. The alternative Bayes factors are derived for the statistical analyses most commonly used in psychological research - one-sample and two-sample ttests, regression, and ANOVA analyses. They possess the same desirable theoretical and practical properties as the default Bayes factors and satisfy additional theoretical desiderata while mitigating against two features of the default priors that we consider implausible. They can be conveniently computed via an R package that we provide. Furthermore, hypothesis tests based on Bayes factors and those based on significance tests are juxtaposed. This discussion leads to the insight that default Bayes factors as well as the alternative Bayes factors are equivalent to test-statistic-based Bayes factors as proposed by Johnson. Journal of the Royal Statistical Society Series B: Statistical Methodology, 67, 689-701. (2005). We highlight test-statistic-based Bayes factors as a general approach to Bayes-factor computation that is applicable to many hypothesis-testing problems for which an effect-size measure has been proposed and for which test power can be computed.

Simulating Non-Markovian Dynamics in Multidimensional Electronic Spectroscopy via Quantum Algorithm.

Including the effect of the molecular environment in the numerical modeling of time-resolved electronic spectroscopy remains an important challenge in computational spectroscopy. In this contribution, we present a general approach for the simulation of the optical response of multichromophore systems in a structured environment and its implementation as a quantum algorithm. A key step of the procedure is the pseudomode embedding of the system-environment problem resulting in a finite set of quantum states evolving according to a Markovian quantum master equation. This formulation is then solved by a collision model integrated into a quantum algorithm designed to simulate linear and nonlinear response functions. The workflow is validated by simulating spectra for the prototypical excitonic dimer interacting with fast (memoryless) and finite-memory environments. The results demonstrate, on the one hand, the potential of the pseudomode embedding for simulating the dynamical features of nonlinear spectroscopy, including lineshape, spectral diffusion, and relaxations along delay times. On the other hand, the explicit synthesis of quantum circuits provides a fully quantum simulation protocol of nonlinear spectroscopy harnessing the efficient quantum simulation of many-body dynamics promised by the future generation of fault-tolerant quantum computers.