Constructing multi-active-site catalysts with a suitable atomic distance to bring the synergistic enhancement catalytic effect into full play is still challenging. Multi-atom clusters with well-defined, uniform, and controllable atomic configurations and distances provide an unprecedented opportunity. Herein, we designed and synthesized a series of atomically precise Pdx supported on TiO2 (Pdx-TiO2, x = 1, 2, 3, and 5) to elucidate the concept of angstrom-scale, distance-dependent synergistic catalysis via atom-by-atom regulation. Benefiting from enhanced light utilization, CO2 adsorption, and photoinduced charge transfer and separation capability, Pd3-TiO2 exhibited the most optimal activity in the pure H2O-mediated photocatalytic CO2 reduction reaction (CO2RR), with a CO/CH4 yield of 86.09/42.94 μmol g-1 h-1, respectively. The combination of precise structural characterization, ex/in situ photoelectrochemical tests, and theoretical calculations provided in-depth insights into the superior CO2RR activity of Pd3-TiO2. The appropriate atomic distance maximized the interaction between Pd atoms and enhanced the synergistic effect, thereby reducing the rate-determining step energy barrier for CO2-to-CO, as well as promoting the formation of the key intermediate *CO for CH4 generation. This work illuminates a novel avenue to fully utilize the synergistic effect between multiple active sites through atomic spacing adjustment for advanced catalyst design.

Control over the spatial arrangement of heterogeneous yet cooperative sites is highly desirable yet challenging to enhance reaction kinetics. This study develops a coordination-induced reconstruction (CIR) strategy that, under a chloride atmosphere, in situ converts Fe nanoparticles into FeN4Cl single atoms (SAs) and Fe atomic clusters (ACs) on nitrogen-doped carbon (NC), enabling several SAs around single ACs with an average distance of about 0.86 ± 0.08nm. This spatial arrangement constitutes heterogeneous yet catalytically cooperative interfaces, enabling an efficient cascade of proton generation, proton transport, and oxygen hydrogenation at favorable sites with suitable distances, as revealed by operando infrared spectra, kinetic isotope effect, local pH measurements, and theoretical calculations. Resultant FeN4ClSAs-FeACs/NC remarkably enhances the oxygen reduction reaction (ORR) and steers it toward a 4-electron pathway with an ORR half-wave potential of 0.934V, and robust stability for 50000 cycles. The CIR strategy is effective for at least Ni- and Mn-based systems.

Construction of mesoporous dual-metal MOFs by a facile in-situ hydrogel-templated method for boosting CO2 conversion.

The existing industrial active noise reduction (ANR) for heating ventilating and air conditioning (HVAC) system has increased in terms of demand. It is a concern due to the amount of noise generated by industrial HVAC system which can be a problem to the surrounding, where noise pollution is always taken into consideration for healthier environment. In this paper, the industrial HVAC system is appraised based on implementation of the system, placement of sensor and actuators in addition there is also review on algorithms. Moreover, the active noise reduction system is introduced to reduce the lower frequency noise range, this helps in reducing the noise pollution created by the industrial HVAC system. Besides, the placement of sensor and actuators plays a role in determining the suitable distance in achieving best noise reduction position. Furthermore, an evaluation is done on Filtered-XLMS and Kalman Filter active noise cancellation algorithm, the results show that the FxLMS algorithm has much better performance rate compared to Kalman filter in terms of convergence. Lastly, multiple ways of implementing the ANR system is also evaluated which indicates that there are many methods in reducing the noise pollution.

We focus on evolution equations on co-evolving infinite graphs and establish a rigorous link with a class of nonlinear continuity equations, whose vector fields depend on the graphs considered. More precisely, weak solutions of the so-called graph-continuity equation are shown to be the push-forward of their initial datum through the flow map solving the associated characteristics' equation, which depends on the co-evolving graph considered. This connection can be used to prove contractions in a suitable distance, although the flow on the graphs requires a too limiting assumption on the overall flux. Therefore, we consider upwinding dynamics on graphs with pointwise and monotonic velocity and prove long-time convergence of the solutions towards the uniform mass distribution.

Clustering is an unsupervised learning technique that categorizes data into groups based on inherent patterns and similarities, with K-means being one of the most common methods. K-means clustering is particularly susceptible to outliers because of its dependence on non-robust distances (such as the most used Euclidean distance). To address this issue, robust distance metrics such as a new Standardized Euclidean Robust distance and Mahalanobis Robust distance have been discussed in this paper, which will reduce the influence of outliers and, at the same time, improve clustering accuracy empirically. The main objective of this study is to investigate the impact of applying robust distance metrics in the K-means clustering and to identify the most suitable distance metric for seismic data containing outliers. The findings indicate that robust distance measures outperform the non-robust distances in accuracy, yielding superior outcomes for minimum-valued indices such as Davies-Bouldin, Xie-Beni, and Ball-Hall indices, as well as maximum-valued indices such as Calinski-Harabasz and Dunn indices.

Abstract We consider the minimization of $$\ell _0$$ ℓ 0 -regularized criteria involving non-quadratic data terms such as the Kullback-Leibler divergence and the logistic regression, possibly combined with an $$\ell _2$$ ℓ 2 regularization. We first prove the existence of global minimizers for such problems and characterize their local minimizers. Then, we propose a new class of continuous relaxations of the $$\ell _0$$ ℓ 0 pseudo-norm, termed as $$\ell _0$$ ℓ 0 Bregman Relaxations (B-rex). They are defined in terms of suitable Bregman distances and lead to exact continuous relaxations of the original $$\ell _0$$ ℓ 0 -regularized problem in the sense that they do not alter its set of global minimizers and reduce its non-convexity by eliminating certain local minimizers. Both features make such relaxed problems more amenable to be solved by standard non-convex optimization algorithms. In this spirit, we consider the proximal gradient algorithm and provide explicit computation of proximal points for the B-rex penalty in several cases. Finally, we report a set of numerical results illustrating the geometrical behavior of the proposed B-rex penalty for different choices of the underlying Bregman distance, its relation with convex envelopes, as well as its exact relaxation properties in 1D/2D and higher dimensions.

The synchronous implementation of precise molecule recognition and efficient gas accumulation in porous materials is highly desirable but challenging for physisorptive separation/storage applications. Here, we demonstrate the feasibility of achieving effective acetylene (C2H2) purification from a C2H2/CO2 mixture with record-high gas packing density by modulating the pore size and interpenetrating symmetry in three isomorphic pillar-layered MOFs (CTGU-41/42/43). The 1D rectangular narrow channels and regularly arranged paired binding sites trigger spatial-interactive synergistic confinement (SISC), enabling suitable molecular orientation and spacing distances during C2H2 adsorption within these MOFs. In particular, CTGU-41 exhibits exceptional adsorption selectivity (41.4) toward the C2H2/CO2 mixture (v/v, 50/50) with a record-high C2H2 storage density of 0.91 g mL-1 at 298 K and 100 kPa, which, to the best of our knowledge, surpasses the density of solid-C2H2 (4.2 K) for the first time. The practical C2H2/CO2 separation ability of CTGU-41/42/43 is further validated by column breakthrough experiments with high purity of C2H2 (>99.0%) and good separation factors (6.7-11.3). The SISC mechanism clarified in this work deepens the fundamental understanding of dense gas arrangement in specific adsorption space, which can be generalized to other challenging gas separation and storage applications.

Ensuring environmental health in schools is vital for promoting student well-being and learning outcomes, particularly in under-resourced rural settings. However, evidence on environmental health conditions in Sudanese schools is limited. This study aimed to assess the school environmental health conditions in governmental primary schools in El-Obeid City, North Kordofan State, Sudan. Sixteen governmental basic schools were surveyed. A cluster random probability sampling technique was used to select schools. A structured checklist form was used for data collection. Data were managed and analyzed using descriptive statistics within a cross-sectional framework. Final results were presented and interpreted in tables.The results showed that 93% of schools were located at a suitable distance from public services, pollution, and noise. All school buildings were deemed acceptable. Fifty percent of schools had poor ventilation. Fifty percent of schools had access to reliable sources of clean and safe water. Latrines were available in 75% of schools. None of the schools had hand-washing facilities or soap for hand washing. Approximately 43.7% of schools burned solid waste directly. About 56.3% of schools had a canteen or cafeteria. There was a complete absence of hand-washing facilities in all schools and a lack of solid waste disposal services provided by local authorities. Urgent improvements are needed in sanitation and waste management.

Graphene oxide membranes hold promising application potential in molecular nanofiltration, but the traditional selectivity-permeability trade-off is still a challenge. Here, an interfacial redox reaction strategy based on poly(m-phenylenediamine)-graphene oxide (PmPD-GO) interaction was successfully designed to achieve the fabrication of an ultrathin graphene oxide membrane (∼28 nm) with a reasonable reduction state (C/O ratio of 11.2) and adequate interlayer spacing stabilized at 8 ± 0.5 Å, which supplies not only a favorable microenvironment for fast water transport but also a suitable interlayer distance for molecules gating. The resultant membrane exhibits a high permeance of 72 LMH/bar and an exceptional dye rejection rate exceeding 98%. Moreover, the gentle gradient-reduced graphene oxide membrane has significantly improved stability under long-term immersion, pH tolerance, and ultrasonic treatment tests. This research provided a simple and effective strategy for fabricating two-dimensional lamellar membranes with promising prospects for practical nanofiltration applications.

Primary bronchus cancer is one kind of lung cancer with a very high mortality rate. Magnetic drug targeting (MDT) technology could concentrate drugs in a specific area, which could have useful application in lung cancer therapy. Due to a bulk superconducting magnet’s ability to generate a superior magnetic field strength and gradient in comparison to conventional permanent magnets, there is great potential for achieving MDT external to the body. However, current research in this area is still in its infancy, and numerical simulations exploring the guidance ability of this technology have been limited to only two-dimensional geometries, which limits further exploration toward clinical transformation. In this work, a three-dimensional lung and bulk superconducting magnet model have been built in the finite-element software package COMSOL Multiphysics. The model is used to simulate the drug delivery process in the lung via the superconducting magnet. The influence of various parameters on the capture efficiency is investigated, including lung-magnet distance, bulk superconductor properties, particle properties, and physiological or tumor structural parameters. The results demonstrate that the bulk superconducting magnet can effectively improve the capture efficiency of magnetic drugs or drug carriers within a suitable distance outside of the body, which could potentially guide the design of a practical, external superconducting MDT system in the near future.

In recent years, the development of efficient catalysts for photo-/electro-catalytic CO2 reduction reaction (CO2RR) has become a major research focus due to growing environmental concerns and energy demands. Dual-atom catalysts (DACs), composed of two metal atoms with suitable metal-metal distance integrated into the supports, have shown great promise in enhancing catalytic performance via the dual-metal synergistic catalysis (DMSC) effect. This review highlights the advancements in Metal-organic framework (MOF)-based DACs, which combine the high atomic efficiency of DACs with tunable and defined structures and high metal loadings. In this review, we summarized the recent developments on the synthesis strategies of MOF-based DACs and their applications in CO2RR, focusing on the role of DMSC effect in improving catalytic activity, stability, and selectivity. Additionally, we also discuss the influence of the local electronic structure, coordination environment, and metal atom interactions on catalytic performance. This review aims to provide a comprehensive understanding of MOF-based DACs and offers insights into their future potential in sustainable energy conversion.

Generative AI has become a buzzword within the publishing industry over the last two years, with responses often falling into either high optimism or an overall sense of doom. We are now at a suitable distance from the launch of ChatGPT to appraise these developments from a more nuanced perspective and begin to explore their connections to the longer history of AI. In this article, I offer some suggestions for how publishers might approach this topic. With the public release of ChatGPT in November 2022, ‘Generative AI’ has been heralded as one of the most significant technological breakthroughs since the printing press. Cutting through the hyperbole and the numerous possible counterexamples, the comparison is useful. The transition from manuscript to print culture was an on-going process rather than a sharp shift and we have not stopped prizing forms of manuscript writing centuries later. One mode of communication does not completely displace another; there’s little signs that generative AI will eradicate our need and desire for human creativity in fields such as publishing.

We introduce a cohomology-based Gromov–Hausdorff ultrametric method to analyze 1-dimensional and higher-dimensional (co)homology groups, focusing on loops, voids, and higher-dimensional cavity structures in simplicial complexes, to address typical clustering questions arising in molecular data analysis. The Gromov–Hausdorff distance quantifies the dissimilarity between two metric spaces. In this framework, molecules are represented as simplicial complexes, and their cohomology vector spaces are computed to capture intrinsic topological invariants encoding loop and cavity structures. These vector spaces are equipped with a suitable distance measure, enabling the computation of the Gromov–Hausdorff ultrametric to evaluate structural dissimilarities. We demonstrate the methodology using organic–inorganic halide perovskite (OIHP) structures. The results highlight the effectiveness of this approach in clustering various molecular structures. By incorporating geometric information, our method provides deeper insights compared to traditional persistent homology techniques.

A common problem in numerous research areas, particularly in clinical trials, is to test whether the effect of an explanatory variable on an outcome variable is equivalent across different groups. In practice, these tests are frequently used to compare the effect between patient groups, for example, based on gender, age, or treatments. Equivalence is usually assessed by testing whether the difference between the groups does not exceed a pre-specified equivalence threshold. Classical approaches are based on testing the equivalence of single quantities, for example, the mean, the area under the curve or other values of interest. However, when differences depending on a particular covariate are observed, these approaches can turn out to be not very accurate. Instead, whole regression curves over the entire covariate range, describing for instance the time window or a dose range, are considered and tests are based on a suitable distance measure of two such curves, as, for example, the maximum absolute distance between them. In this regard, a key assumption is that the true underlying regression models are known, which is rarely the case in practice. However, misspecification can lead to severe problems as inflated type I errors or, on the other hand, conservative test procedures. In this paper, we propose a solution to this problem by introducing a flexible extension of such an equivalence test using model averaging in order to overcome this assumption and making the test applicable under model uncertainty. Precisely, we introduce model averaging based on smooth Bayesian information criterion weights and we propose a testing procedure which makes use of the duality between confidence intervals and hypothesis testing. We demonstrate the validity of our approach by means of a simulation study and illustrate its practical relevance considering a time-response case study with toxicological gene expression data.

Since their appearanceon the scene, MXenes have been recognizedas promising anode materials for rechargeable batteries, thanks tothe combination of structural and electronic features. The layeredstructure with a suitable interlayer distance, good electronic conductivity,and moldability in composition makes MXenes exploitable both as activeand support materials for the fabrication of nanocomposites providingboth capacitive and Faradaic contributions to the final capacity.Although a variety of possibilities has been explored, the fundamentalmechanism of the electrode reaction is still hazy. We herein reportthe investigation of Ti3C2Tx MXenes, the benchmark composition for application in energystorage, through the combined operando X-ray absorption spectroscopy(XAS) and Raman analysis supported by density functional theory (DFT)calculations with the aim of clarifying the origin and nature of capacitywhen the material was cycled vs Na. The electrode reaction determinedwas Ti3C2X2 + 1Na → Na1Ti3C2X2, defining the theoreticalcapacity.

Analyzing changes in network evolution is central to statistical network inference. We consider a dynamic network model in which each node has an associated time-varying low-dimensional latent vector of feature data, and connection probabilities are functions of these vectors. Under mild assumptions, the evolution of latent vectors exhibits low-dimensional manifold structure under a suitable distance. This distance can be approximated by a measure of separation between the observed networks themselves, and there exist Euclidean representations for underlying network structure, as characterized by this distance. These Euclidean representations, called Euclidean mirrors, permit the visualization of network dynamics and lead to methods for change point and anomaly detection in networks. We illustrate our methodology with real and synthetic data, and identify change points corresponding to massive shifts in pandemic policies in a communication network of a large organization. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Gaussian Process regression is a kernel method successfully adopted in many real-life applications. Recently, there is a growing interest on extending this method to non-Euclidean input spaces, like the one considered in this paper, consisting of probability measures. Although a Positive Definite kernel can be defined by using a suitable distance—the Wasserstein distance— the common procedure for learning the Gaussian Process model can fail due to numerical issues, arising earlier and more frequently than in the case of an Euclidean input space and, as demonstrated, impossible to avoid by adding artificial noise (nugget effect) as usually done. This paper uncovers the main reason of these issues, that is a non-stationarity relation between the Wasserstein-based squared exponential kernel and its Euclidean counterpart. As a relevant result, we learn a Gaussian Process model by assuming the input space as Euclidean and then use an algebraic transformation, based on the uncovered relation, to transform it into a non-stationary and Wasserstein-based Gaussian Process model over probability measures. This algebraic transformation is simpler than log-exp maps used on data belonging to Riemannian manifolds and recently extended to consider the pseudo-Riemannian structure of an input space equipped with the Wasserstein distance.

ConspectusThe concept of photoresponsive coordination polymer (CP) single crystal platforms (CPSCPs) is based on photoresponsive olefin CP single crystals, which can undergo photocycloaddition reactions under light irradiation through a single-crystal-to-single-crystal (SCSC) transformation. Taking advantage of the coordination of olefin ligands to metal ions of Zn2+, Cd2+, etc., a pair of C═C double bonds is positioned adjacent to each other in space at a suitable distance and orientation to allow [2 + 2] photocycloaddition triggered by UV–vis irradiation, affording cyclobutanes in the CPs. The single crystal nature of CPs allows their structures to be determined by X-ray diffraction, providing details of the arrangements in space of the C═C double bonds. These CPs are promising platforms for the synthesis of organic molecules, such as cyclobutanes and derivatives, with high regioselectivity and stereoselectivity without any catalyst. The [2 + 2] photocycloaddition reactions may induce structural modifications like expansion or shrinking of unit cells, resulting in macroscopic changes (e.g., cracking, bending, etc.) of the whole CP single crystals and leading to changes in chemical and physical properties. Applications take advantage of their optical properties, including luminescence and absorption, and allow the detection of guest molecules and photomechanical motions. Although much effort has been devoted to such studies, it remains challenging to develop systematic investigations aiming at increasing the diversity of CPs and properties to meet practical needs. Moreover, more efficient methods are desirable to investigate the reaction mechanisms in the solid state and monitor the structural changes occurring during the process.In this Account, we introduce our research on the design and applications of photoresponsive CPSCPs. It is divided into three parts. First, the design and construction of various CPs with different olefin ligands are discussed. Through a suitable and sometimes sophisticated choice of metal ions and auxiliary carboxylate ligands, these olefin ligands meet the requirements to undergo [2 + 2] photocycloaddition reactions in CP structures, allowing for the precise synthesis of cyclobutanes and their derivatives. These compounds could be subsequently extracted from the CPs to give pure organic products. Second, we introduce new strategies, such as a combination of single crystal X-ray diffraction (SCXRD) with thermal/phototreatments of CPs and in situ fluorescence spectroscopy, to monitor the structural changes occurring on the olefin ligands during the reaction. Furthermore, the fast stepwise photoreaction could also be visualized with high resolution. These data significantly strengthen our understanding of solid-state [2 + 2] photocycloaddition reactions in CPs. Third, applications of photoresponsive CPs are described, which focus on optical and photoinduced mechanical properties. Considering the optical properties, the conjugated structures of the olefin ligands change during the reactions, and circular dichroism (CD) and fluorescence were used for their detection and imaging. Furthermore, the photoinduced mechanical properties of CPs could be significantly expanded through the combination of CP crystals with polymers. Lastly, we point out the challenges and directions for future research in the field. We hope this Account will provide an overview of research on photoresponsive CPSCPs, attract more attention from the community, and inspire future research.

A localization system is essential for providing crucial position information in various applications, such as three-dimensional (3D) warehousing, smart cities, uncrewed aerial vehicle (UAV) control, and other services that heavily rely on accurate localization. However, the transmission of wireless signals can be impacted by diverse environmental factors, leading to decreased accuracy in determining localization in scenarios involving multiple signal paths, None Line of Sight (NLOS) situations, and different types of interference. In some cases, this may render the localization system unsuitable for subsequent applications. To enhance the localization accuracy, we propose a 3D localization method using an optimization selection strategy. With this method, we make the following innovations: (1) We utilize an evaluation of feature points to minimize the negative impact of NLOS. (2) Through the backward assessment and the optimal selection of distance estimations, we obtain a more accurate localization result. In more detail, our approach implements a specific strategy for distance estimation, followed by defining the feature points within the localization field and selecting the most optimized one. Subsequently, using the chosen feature points, we evaluate the quality of the distances in reverse. We then select suitable distance estimation outcomes for further localization calculations. Ultimately, by employing the proposed 3D localization technique, we achieve a highly precise localization result. We perform simulations and experiments to assess the presented localization system. More specifically, compared with certain strategies, we improve the localization accuracy by 58.33% and 43.83% using the selection strategy. Compared with the other methods, we enhance the localization accuracy from 17.94% to 32.54%. The results from these evaluations demonstrate that our method significantly enhances 3D localization accuracy.