Suitable Distance Research Articles

Spatial-Interactive Synergistic Confinement in Isoreticular Metal-Organic Frameworks for Facilitating C2H2 Separation from CO2 with Record Packing Density.

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.

Assessment of School Environmental Health Conditions in Governmental Primary Schools

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.

Gentle Gradient-Reduction of Graphene Oxide Membranes by Interfacial Redox Reaction toward Breaking Selectivity-Permeability Trade-off.

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.

Numerical simulation of magnetic drug targeting for lung cancer therapy using a bulk superconducting magnet

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.

Recent Advances in MOF-Based Dual-Atom Catalysts for CO2 Reduction.

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.

Moving Beyond the Hype/Doom Cycles of Generative AI Discourse in Publishing

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.

A cohomology-based Gromov–Hausdorff metric approach for quantifying molecular similarity

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.

Overcoming Model Uncertainty - How Equivalence Tests Can Benefit From Model Averaging.

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.

Ti3C2Tx MXenes as Anodes for Sodium-IonBatteries: the In Situ Comprehensionof the Electrode Reaction

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.

Euclidean Mirrors and Dynamics in Network Time Series

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 over discrete probability measures: on the non-stationarity relation between Euclidean and Wasserstein Squared Exponential Kernels

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.

Improving Localization Accuracy Through Optimal Selection Strategy

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.

Wetland Distribution in the Qinghai‐Tibetan Plateau and Its Responses to Climate Change and Glacial Retreat

ABSTRACTThe Qinghai‐Tibetan Plateau (QTP) experienced noticeable warming and glacial retreat during the past decades. However, it is unclear how these changes affect QTP wetland distribution in the past and future. To this end, this study estimated the potential wetland distribution in the QTP under present and future climate scenarios using five machine learning methods. We further decoupled the sensitivity of wetland area to temperature, precipitation, and glacier changes based on the control experiment, and quantified the environmental niche of QTP wetland distribution. The RusBoost algorithm model has the best performance and shows that the current potential wetland area is about 1.6 × 105 km2, accounting for 6.22% of the land surface. By 2100, QTP wetlands are projected to increase by 9.6% and 77.3% relative to the current potential wetland area under the SSP1‐2.6 and SSP5‐8.5 scenarios, respectively. Climate warming and wetting are positively correlated with the future wetland areas. Each 1°C increase in the warmest season temperature can lead to a 9.0% increase in QTP wetland areas. Glacial retreat to some extent leads to wetland increase, for example, in the southeastern QTP, likely due to glacial meltwater recharge. However, wetlands will decrease due to longer glacial distances in the northeast QTP, because wetlands tend to grow within a suitable distance of 30 km to glaciers. As more current wetlands spread within the recharge range of glacier meltwater, QTP wetlands expect to increase in the near future. This research provides a valuable reference for predicting wetland changes in alpine regions in the context of global warming.

Electrochemical kinetic fingerprinting of single-molecule coordinations in confined nanopores.

Electrochemical kinetic fingerprinting of single-molecule coordinations in confined nanopores.

Deducing the Total Magnetic Intensity Field for Visualizing the Crustal Rocks Setting of Iraq from the World Magnetic Model 2023

This research includes the derivation of an updated total magnetic intensity map of Iraq given from the satellite data. The World Magnetic Model, (WMM) 2023, which was obtained from satellite orbitography adopted to determine the total magnetic intensity field (F) values for each grid point that covered the whole study region of Iraq. The WMM software was used to calculate the F value for 272 grid points that covers the whole study region. This region was gridded with suitable interstitial distances of 50 Km to cover such region representatively. The calculations of F were achieved in two steps. The first step represents the F calculation at sea level (altitude=0), whereas the second one calculated the F by considering the elevation meter value of altitude for each grid point. It was proposed to deduce the residual F by subtracting these values at altitude = 0 field from that altitude field. Then, the deduced residual surface was reflected the magnetic intensity variations across the study region for the rocks overlain the reference ellipsoid. Residual Fsurface was treated mathematically within the 2D domain by using the software. The results were provided a vision of the highest and lowest gradients of F and for interpreting their variations were shown to be coincided with the major tectonic boundaries of Iraq that been detected by previous studies. The given values of F were found to be mostly dependent on the magnetic susceptibility subsurface basement and / or crustal rocks depth. It was concluded that the increase of F values towards the N and NE direction is related to the increasing thickness of crustal rocks or the depth to the Moho discontinuity, and also such thickness or depth is found to be increased towards the E-W direction but with lesser values.

Thermomechanical Behavior of Parallel Multi-U-Shaped Energy Piles Under the Summer Condition

As a green and sustainable geothermal development technology, energy piles have received wide attention in the field of energy underground structure engineering. This work examines the variation rules of the pile-soil temperature, stress-strain, side friction resistance, and pile-top displacement of a full-scale parallel multi-U-type energy pile under the action of temperature loading using a combination of numerical modeling and field testing. As the number of parallel U-type heat exchanger tubes increases, the temperature of the energy pile body rises. However, after a certain number is reached, the effect of additional increases on the temperature rise of the pile body decreases. Additionally, there is a phenomenon known as thermal interference between the heat exchanger tubes, which should be kept at a suitable distance to minimize their mutual influence. The parallel 5U-type energy pile’s temperature is fairly evenly distributed along the depth direction, and the strain of the pile body demonstrates that the two ends of the pile body change in size, with the middle end having the largest restraining stress. When combined with the change in pile side resistance, this location is thought to produce the displacement’s zero point. The pile top displacement increases by 0.65 mm for every 5 °C increase in the water entry temperature.

Characterization of the neutron calibration laboratory in Jordan

ABSTRACT This paper demonstrates the establishment of the first neutron calibration laboratory in Jordan based on a 252Cf neutron source. The paper discusses the neutron scattering influence on the neutron field in terms of international standards. It also represents the laboratory’s design, the neutron source shielding design, and the implemented safety features. Data was acquired using two spherical neutron devices of the same type. The room’s neutron scattering was determined using three methods: shadow cone, semi-empirical, and reduced fitting. The measurements were conducted at distances starting at 30 cm, and ends at ∼ 200 cm. The results of this work recommend a calibration distances of 100–140 cm from the source using the shadow cone method and within 30–140 cm using the semi-empirical and reduced fitting methods, these limits are set by the 40% limit ecommended by the ISO 8529-2:2000 standard. Finally, the source-detector characteristic constant, k, was determined using these models, and the results of the three models agrees within 0.15–6%.

Modulating Aggregation and Deaggregation Based on Assembling Strategy to Switch on NIR-II Light-Excited Fluorescence for Self-Reporting Viability of Eliminating Cancer Cell.

The fabrication of self-reporting photosensitizers (PSs), enabling real-time evaluation of the extent of elimination of cancer cells, holds significant scientific importance in the photodynamic therapy (PDT) process. To address the intrinsic challenge of the short-wavelength light source, this work proposed an innovative approach of rational design second near-infrared (NIR-II, 1000-1700 nm) light-excited fluorescent PS systems (named HOEt-PI, Me-PI, and Et-PI, respectively) through modulating aggregation and deaggregation based on assembling strategy. Therein, the suitable interplanar distance of adjacent Et-PI linked with C-H···π interactions was an idea for relieving compact π···π packing for fluorescent imaging as well as elevating the spin-orbit coupling for reactive oxygen species (ROS) generation. With ROS continuously increasing, Et-PI underwent cell membrane-to-mitochondria migration, ultimately accumulated in nucleoli, symbolizing programmed cell death, thus distinguishing dead/live cells via three-photon fluorescence imaging (excited on 1250 nm) under photogeneration ROS. Meaningfully, the three-photon fluorescence of Et-PI was triggered by RNA of nucleoli, for which the higher signal-to-noise ratio and in-depth fluorescence imaging observed cancer cellular viability. Collectively, the proposed findings presented a constructing strategy for NIR-II light-mediated self-reporting PS for guiding the PDT of deep cancerous tissue in the future.

Health Insurance Provider Selection Through Novel Correlation Measure of Neutrosophic Sets Using TOPSIS

Risk monitoring aims to recognize and control potential threats to our assets or health activities. Insurance is vital, as it compensates for any unexpected loss of property or life. Clients selecting the best health insurance provider must consider various factors and their relative significance to their circumstances. Multi-criteria decision-making (MCDM) helps people analyze and compare policy alternatives to find the best option. This research aims to assist potential health policy purchasers by addressing the selection of health insurance providers as an MCDM problem. The correlation coefficient is useful for identifying the importance of several conflicting criteria. The idea of correlation coefficients is extended in a neutrosophic context to capture the indeterminacy and incomplete information in the relationship among the criteria. The technique for order preference by similarity to an ideal solution (TOPSIS) approach is a useful and straightforward approach to solving MCDM problems. However, it often became ambiguous to researchers due to its involvement in the distance measure technique. The proposed neutrosophic correlation measure may also replace the ambiguity of using a suitable distance measure in the TOPSIS approach. This study extends the TOPSIS method by using the proposed neutrosophic correlation coefficient on single-valued neutrosophic sets (SVNSs). The criteria preferences are computed using a method based on the removal effects of the criteria (MEREC) approach. Some valuable concepts, like the weighted closeness measure of type I and type II and the weighted index parameter, are introduced with their properties to establish the proposed neutrosophic TOPSIS approach. An MCDM approach for health insurance providers has been constructed to illustrate the proposed approach numerically. The proposed method suggest that the health insurance provider ϒ2 is the most beneficial alternative, whereas ϒ1 is the least suitable. The client considers the terms and conditions for non-coverage and the facilities provided for pediatric and maternity care while buying health insurance. The comparative analysis of the suggested technique demonstrates the merit of the research in terms of consistency. The sensitivity analysis demonstrates the flexibility and robustness of the obtained results.

Buoys for marine weather data monitoring and LoRaWAN communication

Buoys for marine weather data monitoring and LoRaWAN communication