Number Of Clusters Research Articles

The vegetation around the foothills of the Hijaz Mountains, Saudi Arabia

AbstractDespite Saudi Arabia being an interesting area from a floristic perspective, its vegetation is still relatively poorly studied. This certainly applies to the Hijaz Mountains and adjacent coastal zones. We aim to describe the flora and vegetation of the foothills of the Hijaz Mountains regarding the environmental conditions. Hijaz Mountains and adjacent coastal zones, Saudi Arabia. We studied the vegetation at the Hijaz Mountains’ foothills along two transects of vegetation surveys, one along the coast and one more inland. In addition, soil samples were taken and analyzed for soil texture, pH, electrical conductivity, organic matter, calcium carbonate and the concentration of the elements N, Na, K, Ca, Mg, Fe and Mn were determined. We calculated the data clustering tendency (Hopkins’ test analysis) and its optimal number of clusters (Elbow method). We used modified TWINSPAN to cluster the data and validated the resulting communities using the Silhouette algorithm. Seven plant communities resulted, each one dominated by a different plant species, namely: Haloxylon salicornicum, Lycium shawii, Senegalia hamulosa, Vachellia tortilis, Zygophyllum coccineum, Vachellia flavaand Stipagrostis plumosa, and were described based on their floristic composition and could be further grouped into three vegetation clusters. The communities had varying ranges of occurrence in the study area, some dominating certain sections, with latitude and altitude being the biggest distinguishing environmental variables between the communities. We present a description of the vegetation of the Hijaz Mountains foothills. Our study is considered an important basis for decision-making of nature conservation and in-depth surveys of the area. Graphic abstract

SPECT postprocessing for epileptogenic focus localization: SISCOM versus ISAS.

Ictal SPECT can be used as an estimate for the epileptogenic zone in people with focal epilepsy. Subtraction of ictal and interictal SPECT scans reveals the area with significant ictal hyperperfusion. Some methods use a control database to also correct for physiological variance. This control database is ideally scanner specific, but it is not trivial to obtain such a database because of ethical issues. In this study, we used a publicly available control database to compare ictal-interictal SPECT analyzed by SPM (ISAS) with the most commonly used subtraction ictal SPECT co-registered to MRI (SISCOM). Ictal and interictal SPECTs of 26 patients (age range: 7-50 years, 15 adults, 11 children) with focal drug resistant epilepsy in workup for epilepsy surgery were retrospectively analyzed using both SISCOM and ISAS. The control database for ISAS was obtained from the ISAS website. Two groups of blinded reviewers determined the location of ictal hyperperfusion in all datasets. Results were compared between subtraction algorithms and with the resected area (if available) or the suspected epileptogenic zone. The number of significant clusters and the locations of maximum hyperperfusion were compared between algorithms. The location of ISAS and SISCOM hyperperfusion was the same in 14 patients (54%). ISAS localized in 6 patients where SISCOM did not. Compared to the resected area or suspected epileptogenic zone, SISCOM correctly localized in 55%, while ISAS did in 65% (not significantly different). ISAS shows significantly less clusters than SISCOM. The maximum hyperperfusion was in the reviewer's location in 65% for ISAS and 38% for SISCOM. ISAS using a publicly available control database gives comparable or better results than SISCOM. ISAS results are easier to interpret than SISCOM results. We show that ISAS is a reliable alternative for SISCOM, which could easily be implemented in epilepsy surgery clinics. We explored the effectiveness of ISAS as an alternative to the widely used SISCOM for assessing SPECT scans in epilepsy surgery candidates. Utilizing a publicly available control database, we compared the two methods in 26 patients. The results indicate that ISAS might offer increased accuracy and interpretability, making it a promising option, especially for centers without access to a specific control dataset.

On the Performance of the Relay Selection in Multi-hop Cluster-based Wireless Networks with Multiple Eavesdroppers Under Equally Correlated Rayleigh Fading

The performance of multi-hop cluster-based wireless networks under multiple eavesdroppers is investigated in the present work. More precisely, we derive the outage probability (OP) of the considered networks under two relay selection schemes: the channel-gain-based scheme and the random scheme. Although equally correlated Rayleigh fading is taken into consideration, the derived mathematical framework remains tractable. Specifically, we represent the exact expression of the OP under the channel-based scheme in series form, while the OP under the random scheme is computed in a closed-form expression. Additionally, we propose a novel power allocation for each transmitter that strictly satisfies the given intercept probability. Numerical results based on the Monte Carlo method are provided to verify the correctness of the derived framework. These results are also used to identify the influences of various parameters, such as the number of clusters, the number of relays per cluster, and the transmit power.

Learning to cluster person via graph convolution networks for video‐based person re‐identification

SummaryUnsupervised person re‐identification based on video sequences can be applied to surveillance systems and is attracting much more attention. It aims to spot specific person in other scenes captured by different cameras. This work explores an innovative strategy, namely, learning to cluster unlabeled person in the videos through graph convolutional networks. In this article, we find that the possibility of inter‐frame linkage can be inferred from context. Therefore, a pose‐guided topology linkage clustering framework is proposed. Our framework consists of three modules: (i) a pose‐guided representation module; (ii) a pose‐guided embedding module; (iii) a link prediction module. First, the representation coding alone is performed at the level of relational induction bias, embedding the implicit pose structure information in image features. Then, based on the consideration of the topology relationship between adjacent and cross‐frame, graph convolutional network is introduced to infer the likelihood of linkage between frame nodes. Experiments show that the proposed method demonstrates excellent scalability in addition to being an effective response to person clustering in case of changes, and does not need the number of clusters as a prior.

Morphometric analysis of the Late Cretaceous Placenticeras of Alabama, USA: sexual dimorphism, allometry, and implications for taxonomy

Abstract A traditional typological approach to taxonomy often does not adequately account for intraspecific variation and can result in taxonomic oversplitting. For many groups, including ammonoids of the Placenticeras genus, intraspecific variation documented in recent studies (e.g., ontogenetic changes, sexual dimorphism, polymorphism) challenges the historic proliferation of species names. Here, we used a population approach to taxonomy and quantitatively evaluated morphometric variation in a sample of Late Cretaceous (Santonian–Campanian) Placenticeras from Alabama and adjacent counties. We used linear mixed models (LMMs) to characterize how morphological variables scale with conch size across the sample, exploiting mixed longitudinal data to evaluate individual variation in growth and inform interpretations of multivariate analyses. Extended LMMs incorporating geological formation evaluated morphological changes through time. Principal component analysis and clustering analysis were then used to evaluate the number of distinct clusters that emerged in multivariate morphospace independent of previous taxon name assignments. Discontinuous scaling relationships and distinct clusters in multivariate space suggest sexual dimorphism characterized by differences in adult size and, secondarily, shape. Previous Stantonoceras and Placenticeras assignments broadly overlap in our morphospace, failing to justify this historic distinction (as sexual dimorphs or as genera or subgenera). Placenticeras conch morphology and ornament placement changed through time, suggesting a potential utility for coarse (stage-level) biostratigraphy. However, temporal changes were not associated with distinct clusters in morphospace, and our data fail to support the plethora of reported species names. As few as one or two (successive) species may be present in our sample (representing 130 years of collection effort). In addition to highlighting the need for a significant taxonomic revision of the Placenticeras genus, this study demonstrates the utility of LMMs for distinguishing between different sources of morphological variation, improving interpretations of morphospace under a population approach to taxonomy, and maximizing the amount of ontogenetic information that can be obtained nondestructively.

Abstract PO2-15-05: Mesenchymal-like immune-activated: the 4th robust triple-negative breast cancer molecular subtype

Abstract Triple-negative breast cancer (TNBC) accounts for 15-20% of primary BC. This heterogeneous disease is the most aggressive BC subtype. TNBC is characterized by young age at onset, high-grade tumors, high risk of early recurrence and poor prognosis. Until recently, TNBC patients were considered as one group and all patients were treated with conventional chemotherapy. With the emergence of new therapies targeting distinct biological pathways, personalized medicine is now accessible by defining finer TNBC subtypes, e.g. molecular subtypes, and identifying their specific targetable pathways. Several studies based on transcriptomics have identified three to six TNBC molecular subtypes. Today, there is a clear consensus on three TNBC robust subtypes: luminal androgen receptor (LAR), basal-like immune-activated (BLIA) and basal-like immune-suppressed (BLIS). The debate about the robustness of other subtypes is still open, notably mesenchymal-like (M) and mesenchymal stem-like (MSL or MES). The goal of this study was to establish a robust molecular classification of TNBC that allow to determine the best treatment options. To optimize this process, we harvested as much transcriptomic data as possible. Available clinicopathological data linked to these data were also imported. To our knowledge, this is the largest study aimed at subtyping TNBC; it exploits a total of 1942 TNBC patient data collected from 19 datasets: 1243 samples from microarray technology (14 datasets) and 699 from RNAseq technology (five datasets including a new one from our institution [GSE225002]). We performed independent unsupervised analyses on these two large cohorts with consensus k-means clustering including selection of optimal number of clusters and optimal number of variables. Both consensus clustering analyses separated TNBC microarray and RNAseq TNBC cohorts into four optimal clusters. These clusters were then characterized using clinicopathological features, gene ontology enrichment analyses (GOEA), gene set enrichment analysis (GSEA), 70 gene expression signatures (GES) and expression of 47 immune checkpoint genes. We concluded that TNBC are composed of four robust subtypes: LAR, mesenchymal-like immune-activated (MLIA), BLIA and BLIS. Importantly, we propose that MLIA acronym should replace M or MSL, because it highlights the two main biological characteristics of this subtype, i.e. mesenchymal-like features and a high IR comparable to that of BLIA. Furthermore, we showed that each subtype shares common biological characteristics with another one: LAR and MLIA (androgen receptor pathway activation); MLIA and BLIA (immune activation, notably high levels of immune checkpoints); BLIA and BLIS (basal-like features); BLIS and LAR (immune suppression). As regards microarray cohort, LAR included 19.23% (n = 239) of TNBC, MLIA 17.38% (n = 216), BLIA 32.50% (n = 404) and BLIS 30.89% (n = 384). Concerning RNAseq cohort, LAR included 19.60% (n = 137) of TNBC, MLIA 17.31% (n = 121), BLIA 28.47% (n = 199) and BLIS 34.62% (n = 242). Distributions of patients among the four clusters in the two cohorts were similar (p = 0.3583). Distributions were also similar between studies for microarray cohort (p = 0.2479) and RNAseq cohort (p = 0.1775). Pooled data (n = 1942 patients) gave the following distribution: 19.36% for LAR; 17.35% for MLIA; 31.06% for BLIA; 32.23% for BLIS, confirming that TNBC are largely but not exclusively basal-like (63.29%). In brief, according to the TNBC subtype molecular characterisation, anti-androgen therapy could be prescribed for LAR, immune checkpoint inhibitors for MLIA and BLIA, alone or in combination, and TAM-focused and antineurogenic therapies for BLIS. As an extension of this work, TNBC transcriptomic data associated with their clinicopathological features and TNBC subtype annotations were included in bc-GenExMiner v5.0 web tool (http://bcgenex.ico.unicancer.fr). Citation Format: Mario Campone, Pascal Jézéquel, Fadoua Ben Azzouz, Wilfried Gouraud, Agnes Bassevile, Bertrand Michel, Hamza Lasla, Jean Sebastien FRENEL. Mesenchymal-like immune-activated: the 4th robust triple-negative breast cancer molecular subtype [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-15-05.

Adaptive fuzzy weighted C-mean image segmentation algorithm combining a new distance metric and prior entropy

The fuzzy C-mean clustering algorithm (FCM) is effective in image segmentation. However, its sensitivity to initialization settings and the limitation of the Euclidean distance metric in measuring similarity lead to unsatisfactory image segmentation. To overcome these shortcomings, we propose an adaptive fuzzy weighted C-mean image segmentation algorithm that combines a new distance metric and prior entropy. This algorithm significantly contributes in three aspects. First, we employ an improved sparse subspace clustering (SSC) algorithm based on the superpixel image for initialization settings. This step aims to obtain the initial number of clusters and the membership matrix, ensuring the algorithm’s convergence to the optimal solution. Second, to better capture differences between image regions, we introduce a novel distance metric that combines Euclidean and non-Euclidean distances. Simultaneously, the fuzzy weight is introduced to mitigate redundant feature interference, making the clustering result more reasonable. Finally, leveraging prior entropy – a maximum entropy under conditional constraints – we refine the algorithm’s adaptability to unknown features, thereby improving clustering accuracy. Comparative experiments with several state-of-the-art algorithms validate the effectiveness and robustness of our proposed algorithm.

Innovative Cultural Transmission Paths Analysis for Local Opera Based on Data Mining

With the rapid development of society and cultural diversity, local opera, as a representative of traditional art, faces dual challenges of inheritance and innovation. The inheritance of traditional Chinese opera often relies on oral instruction, but in modern society, this method is no longer suitable for the rapidly changing cultural environment. The purpose of this study is to explore in depth the innovative cultural dissemination path of local opera through data mining techniques. This paper uses data mining techniques to explore and analyze the inheritance of innovative culture in local opera, and to study the inheritance paths. This paper proposes a data mining-based method for analyzing and identifying the paths of inheritance of innovative culture in local opera. This paper focuses on two aspects of technology clustering and technology theme association, and the research paths are divided into flexible clustering based on data enhancement and technology evolution path identification based on theme extraction. Firstly, the heritage changes of dating tunes are discussed. Secondly, the unique artistic esthetics of the dating tune is compared with the artistic characteristics of Henan opera and the three-stringed book. Through the research and learning experience of the Dai Yongqu, it is easier to thoroughly understand the artistic essence and cultural connotation of the Dai Yongqu, and that exposure to the Dai Yongqu is a process of touching traditional culture. In the text clustering section, the data co-occurrence frequency matrix is constructed based on the results of multiple clustering, transforming the problem of determining the number of clusters into a co-occurrence frequency problem with semantic information of the text, thus achieving the purpose of flexible clustering on demand. In a comparison with existing methods, the text representation method using data-enhanced coding achieves better semantic coding results. The flexible clustering method can differentiate the data better than the clustering method with a defined number of clusters and obtains clustering results with high intra-class similarity and high inter-class differentiation.

Optimizing waste-to-energy conversion: Unveiling the potential of unsupervised clustering through the new HOM classification system

The correct and informative classification of a given fuel is essential in optimizing the utilization of the fuel. While the physical properties of the fuels provide more complete information about the fuel, conventional fuel classification systems (CSs) are based on the fuels’ source or origin. In this study, the novel HOM CS is proposed from the data of fuels’ ultimate and proximate analysis and HHVs by utilizing k-means clustering where the number of clusters are optimized. The obtained clusters are investigated in comparison with the conventional CSs by using several methods such as contingency matrices, PCAs, ternary plots, and Van-Krevelen diagrams. Finally, the clusters are appropriately named and the HOM CS is distinctly defined. The proposed CS can be used in informative class assignment of any kind of fuel including fuel mixtures or fuels of unknown source or origin which are generally categorized vaguely as “composite-streams”, “wastes”, or “others” in conventional CSs. Thus, by the implementation of the new CS, these materials can now be categorized in a manner that provides insight into their specific application possibilities. This categorization can assist researchers in determining the most efficient conversion methods especially for the waste-to-energy processes including combustion, gasification, and biogas plants.

Rapid spectrophotometric detection for optimized production of landomycins and characterization of their therapeutic potential.

Microbial-derived natural products remain a major source of structurally diverse bioactive compounds and chemical scaffolds that have the potential as new therapeutics to target drug-resistant pathogens and cancers. In particular, genome mining has revealed the vast number of cryptic or low-yield biosynthetic gene clusters in the genus Streptomyces. However, low natural product yields-improvements to which have been hindered by the lack of high throughput methods-have slowed the discovery and development of many potential therapeutics. Here, we describe our efforts to improve yields of landomycins-angucycline family polyketides under investigation as cancer therapeutics-by a genetically modified Streptomyces cyanogenus 136. After simplifying the extraction process from S. cyanogenus cultures, we identified a wavelength at which the major landomycin products are absorbed in culture extracts, which we used to systematically explore culture medium compositions to improve total landomycin titers. Through correlational analysis, we simplified the culture optimization process by identifying an alternative wavelength at which culture supernatants absorb yet is representative of total landomycin titers. Using the subsequently improved sample throughput, we explored landomycin production during the culturing process to further increase landomycin yield and reduce culture time. Testing the antimicrobial activity of the isolated landomycins, we report broad inhibition of Gram-positive bacteria, inhibition of fungi by landomycinone, and broad landomycin resistance by Gram-negative bacteria that is likely mediated by the exclusion of landomycins by the bacterial membrane. Finally, the anticancer activity of the isolated landomycins against A549 lung carcinoma cells agrees with previous reports on other cell lines that glycan chain length correlates with activity. Given the prevalence of natural products produced by Streptomyces, as well as the light-absorbing moieties common to bioactive natural products and their metabolic precursors, our method is relevant to improving the yields of other natural products of interest.

Clusters from chronic conditions in the Danish adult population.

Multimorbidity, the presence of 2 or more chronic conditions in a person at the same time, is an increasing public health concern, which affects individuals through reduced health related quality of life, and society through increased need for healthcare services. Yet the structure of chronic conditions in individuals with multimorbidity, viewed as a population, is largely unmapped. We use algorithmic diagnoses and the K-means algorithm to cluster the entire 2015 Danish multimorbidity population into 5 clusters. The study introduces the concept of rim data as an additional tool for determining the number of clusters. We label the 5 clusters the Allergies, Chronic Heart Conditions, Diabetes, Hypercholesterolemia, and Musculoskeletal and Psychiatric Conditions clusters, and demonstrate that for 99.32% of the population, the cluster allocation can be determined from the diagnoses of 4-5 conditions. Clusters are characterized through most prevalent conditions, absent conditions, over- or under-represented conditions, and co-occurrence of conditions. Clusters are further characterized through socioeconomic variables and healthcare service utilizations. Additionally, geographical variations throughout Denmark are studied at the regional and municipality level. We find that subdivision into municipality levels suggests that the Allergies cluster frequency is positively associated with socioeconomic status, while the subdivision suggests that frequencies for clusters Diabetes and Hypercholesterolemia are negatively correlated with socioeconomic status. We detect no indication of association to socioeconomic status for the Chronic Heart Conditions cluster and the Musculoskeletal and Psychiatric Conditions cluster. Additional spatial variation is revealed, some of which may be related to urban/rural populations. Our work constitutes a step in the process of characterizing multimorbidity populations, leading to increased comprehension of the nature of multimorbidity, and towards potential applications to individual-based care, prevention, the development of clinical guidelines, and population management.

Dialectometry-based classification of the Central–Southern Italian dialects

Abstract This paper provides a new classification of Central–Southern Italian dialects using dialectometric methods. All varieties considered are analyzed and cast in a data set where homogeneous areas are evaluated according to a selected list of phonetic features. Using numerical evaluation of these features and the Manhattan distance, a linguistic distance rule is defined. On this basis, the classification problem is formulated as a clustering problem, and a k-means algorithm is used. Additionally, an ad-hoc rule is set to identify transitional areas, and silhouette analysis is used to select the most appropriate number of clusters. While meaningful results are obtained for each number of clusters, a nine-group classification emerges as the most appropriate. As the results suggest, this classification is less subjective, more precise, and more comprehensive than traditional ones based on selected isoglosses.

Spatial non-parametric Bayesian clustered coefficients

In the field of population health research, understanding the similarities between geographical areas and quantifying their shared effects on health outcomes is crucial. In this paper, we synthesise a number of existing methods to create a new approach that specifically addresses this goal. The approach is called a Bayesian spatial Dirichlet process clustered heterogeneous regression model. This non-parametric framework allows for inference on the number of clusters and the clustering configurations, while simultaneously estimating the parameters for each cluster. We demonstrate the efficacy of the proposed algorithm using simulated data and further apply it to analyse influential factors affecting children’s health development domains in Queensland. The study provides valuable insights into the contributions of regional similarities in education and demographics to health outcomes, aiding targeted interventions and policy design.

A novel scenario generation and forecasting method for multiple future states at source side based on combinatorial models

This paper proposes a novel method for scenario generation and scenario forecasting considering wind-photovoltaic-hydro-thermal power sources. With the rapid development and application prospects of energy internet and grid modernization, accurate source-side multiple output modeling and forecasting is essential to observe and estimate the multiple curves. It is becoming more and more important to optimize the operation efficiency of power grid. The traditional methods mainly focus on multi-scene generation of a single power source. Grid scheduling requirements that could not take into account multiple time correlations and multiple scenario forecasting. The paper proposes a scenario forecasting method considering scenario generation. Firstly, mutual Information (MI) and depth-first traversal (DFS) are combined to analyze the factors associated with power generation and build the MI-DFS-K2-Bayes network to generate multiple scenarios of wind- photovoltaic-hydro-thermal power. Secondly, the improved k-means algorithm is proposed to obtain the center curve of each power source. The algorithm improves the accuracy of the multivariate clustering center curve. It solves the problem that the initial clustering center and the number of clusters are difficult to select, and builds the multivariate running state. Finally, the upper and lower interval of point forecasting constrained by confidence parameters is proposed to make the running state scene match the point forecasting. The future state curves of each power source in short time scale are obtained. The future state scenario is implemented. Numerical examples demonstrate that the proposed method reduces the modeling error of wind- photovoltaic-hydro-thermal power curves by 0.13%–0.4%. The overall forecasting accuracy is significantly better than the state of the study.

On the atypical solutions of the symmetric binary perceptron

We study the random binary symmetric perceptron problem, focusing on the behavior of rare high-margin solutions. While most solutions are isolated, we demonstrate that these rare solutions are part of clusters of extensive entropy, heuristically corresponding to non-trivial fixed points of an approximate message-passing algorithm. We enumerate these clusters via a local entropy, defined as a Franz–Parisi potential, which we rigorously evaluate using the first and second moment methods in the limit of a small constraint density α (corresponding to vanishing margin κ ) under a certain assumption on the concentration of the entropy. This examination unveils several intriguing phenomena: (i) we demonstrate that these clusters have an entropic barrier in the sense that the entropy as a function of the distance from the reference high-margin solution is non-monotone when κ⩽1.429−α/log⁡α , while it is monotone otherwise, and that they have an energetic barrier in the sense that there are no solutions at an intermediate distance from the reference solution when κ⩽1.239−α/log⁡α . The critical scaling of the margin κ in −α/log⁡α corresponds to the one obtained from the earlier work of Gamarnik et al (2022 (arXiv:2203.15667)) for the overlap-gap property, a phenomenon known to present a barrier to certain efficient algorithms. (ii) We establish using the replica method that the complexity (the logarithm of the number of clusters of such solutions) versus entropy (the logarithm of the number of solutions in the clusters) curves are partly non-concave and correspond to very large values of the Parisi parameter, with the equilibrium being reached when the Parisi parameter diverges.

Peripherally-derived LGI1-reactive monoclonal antibodies cause epileptic seizures in vivo.

One striking clinical hallmark in patients with autoantibodies to leucine-rich glioma inactivated 1 (LGI1) is the very frequent focal seizure semiologies, including faciobrachial dystonic seizures (FBDS), in addition to the amnesia. Polyclonal serum IgGs have successfully modelled the cognitive changes in vivo but not seizures. Hence, it remains unclear whether LGI1-autoantibodies are sufficient to cause seizures. We tested this with the molecularly precise monoclonal antibodies directed against LGI1 (LGI1-mAbs), derived from patient circulating B cells. These were directed towards both major domains of LGI1, LRR and EPTP and infused intracerebroventricularly over 7 days into juvenile male Wistar rats using osmotic pumps. Continuous wireless EEG was recorded from a depth electrode placed in hippocampal CA3 plus behavioural tests for memory and hyperexcitability were performed. Following infusion completion (Day 9), post-mortem brain slices were studied for antibody binding and effects on Kv1.1. The LGI1-mAbs bound most strongly in the hippocampal CA3 region and induced a significant reduction in Kv1.1 cluster number in this subfield. By comparison to control-Ab injected rats video-EEG analysis over 9 days revealed convulsive and non-convulsive seizure activity in rats infused with LGI1-mAbs, with a significant number of ictal events. Memory was not impaired in the novel object recognition test. Peripherally-derived human LGI1-mAbs infused into rodent CSF provide strong evidence of direct in vivo epileptogenesis with molecular correlations. These findings fulfill criteria for LGI1-antibodies in seizure causation.

Single-cluster Functionalized TiO2 Nanotube Array for Boosting Water Oxidation and CO2 Photoreduction to CH3OH.

Solar-driven CO2 reduction and water oxidation to liquid fuels represents a promising solution to alleviate energy crisis and climate issue, but it remains a great challenge for generating CH3OH and CH3CH2OH dominated by multi-electron transfer. Single-cluster catalysts with super electron acceptance, accurate molecular structure, customizable electronic structure and multiple adsorption sites, have led to greater potential in catalyzing various challenging reactions. However, accurately controlling the number and arrangement of clusters on functional supports still faces great challenge. Herein, we develop a facile electrosynthesis method to uniformly disperse Wells-Dawson- and Keggin-type polyoxometalates on TiO2 nanotube arrays, resulting in a series of single-cluster functionalized catalysts P2M18O62@TiO2 and PM12O40@TiO2 (M = Mo or W). The single polyoxometalate cluster can be distinctly identified and serves as electronic sponge to accept electrons from excited TiO2 for enhancing surface-hole concentration and promote water oxidation. Among these samples, P2Mo18O62@TiO2-1 exhibits the highest electron consumption rate of 1260 µmol g-1 for CO2-to-CH3OH conversion with H2O as the electron source, which is 11 times higher than that of isolated TiO2 nanotube arrays. This work supplied a simple synthesis method to realize the single-dispersion of molecular cluster to enrich surface-reaching holes on TiO2, thereby facilitating water oxidation and CO2 reduction.

Single‐cluster Functionalized TiO2 Nanotube Array for Boosting Water Oxidation and CO2 Photoreduction to CH3OH

Solar‐driven CO2 reduction and water oxidation to liquid fuels represents a promising solution to alleviate energy crisis and climate issue, but it remains a great challenge for generating CH3OH and CH3CH2OH dominated by multi‐electron transfer. Single‐cluster catalysts with super electron acceptance, accurate molecular structure, customizable electronic structure and multiple adsorption sites, have led to greater potential in catalyzing various challenging reactions. However, accurately controlling the number and arrangement of clusters on functional supports still faces great challenge. Herein, we develop a facile electrosynthesis method to uniformly disperse Wells‐Dawson‐ and Keggin‐type polyoxometalates on TiO2 nanotube arrays, resulting in a series of single‐cluster functionalized catalysts P2M18O62@TiO2 and PM12O40@TiO2 (M = Mo or W). The single polyoxometalate cluster can be distinctly identified and serves as electronic sponge to accept electrons from excited TiO2 for enhancing surface‐hole concentration and promote water oxidation. Among these samples, P2Mo18O62@TiO2‐1 exhibits the highest electron consumption rate of 1260 µmol g−1 for CO2‐to‐CH3OH conversion with H2O as the electron source, which is 11 times higher than that of isolated TiO2 nanotube arrays. This work supplied a simple synthesis method to realize the single‐dispersion of molecular cluster to enrich surface‐reaching holes on TiO2, thereby facilitating water oxidation and CO2 reduction.

Simulating the LOcal Web (SLOW) II. Properties of local galaxy clusters

This is the second paper in a series presenting the results from a 500 $h^ $Mpc large constrained simulation of the local Universe (SLOW). The initial conditions for this cosmological hydro-dynamical simulation are based on peculiar velocities derived from the CosmicFlows-2 catalog. The simulation follows cooling, star formation, and the evolution of super-massive black holes. This allows one to directly predict observable properties of the intracluster medium (ICM) within galaxy clusters, including X-ray luminosity, temperatures, and the Compton-y signal. Comparing the properties of observed galaxy clusters within the local Universe with the properties of their simulated counterparts enables us to assess the effectiveness of the initial condition constraints in accurately replicating the mildly nonlinear properties of the largest, collapsed objects within the simulation. Based on the combination of several, publicly available surveys we compiled a sample of galaxy clusters within the local Universe, of which we were able to cross-identify 46 of them with an associated counterpart within the SLOW simulation. We then derived the probability of the cross identification based on mass, X-ray luminosity, temperature, and Compton-y by comparing it to a random selection. Our set of 46 cross-identified local Universe clusters contains the 13 most massive clusters from the Planck SZ catalog as well as 70<!PCT!> of clusters with $M_ $ larger than $2 $ M$_ odot $. Compared to previous constrained simulations of the local volume, we found in SLOW a much larger amount of replicated galaxy clusters, where their simulation-based mass prediction falls within the uncertainties of the observational mass estimates. Comparing the median observed and simulated masses of our cross-identified sample allows us to independently deduce a hydrostatic mass bias of $(1-b) approx0.87$. The SLOW constrained simulation of the local Universe faithfully reproduces numerous fundamental characteristics of a sizable number of galaxy clusters within our local neighborhood, opening a new avenue for studying the formation and evolution of a large set of individual galaxy clusters as well as testing our understanding of physical processes governing the ICM.

A planar graph cluster‐routing approach for optimizing medical waste collection based on spatial constraint

AbstractMedical Solid Wastes (MSWs) are major hazardous materials containing harmful biological or chemical compounds that present public and environmental health risks. The collection and transportation of waste are usually informed by optimized work‐balanced routing based on comprehensive spatial data in urban traffic networks, called a Vehicle Routing Problem (VRP). This may be unsuitable for MSWs as their special category means they impose additional complexity. The present article develops a planar graph‐based cluster‐routing approach for the optimal collection of MSWs informed by a Geospatial Information System (GIS). The problem is first formulated as a mixed integer linear program in road network spatial data, in the context of Tehran city. The work has two key aims: (i) to minimize the total routing cost of MSW collection and transfer to waste landfills; (ii) to balance workload across waste collectors. There are three main contributions of the proposed approach: (i) to simplify the large search space area by converting the road network to a planar graph based on graph theory, spatial parameters, and topological rules; (ii) to use a modified K‐means algorithm for clustering; (iii) to consider average traffic impacts in the clustering stage and momentary traffic in the route planning stage. A planar graph extraction procedure is applied to capture the network sketch (i.e., a directed graph) from the traffic roadway network. An iterative cluster‐first‐route‐second heuristic is employed to solve the proposed routing problem. This heuristic customizes a K‐means algorithm to determine the optimal number and size of clusters (i.e., routes). A Traveling Salesman Problem (TSP) algorithm is applied to regulate the optimal sequence of visits to medical centers. The experimental results show improvements in balancing collectors' workload (i.e., ~4 min reduction in the standard deviation of average travel time) with reductions in travel time (i.e., an average ~1 h for the entire fleet and ~4 min per route). These findings confirm that the proposed methodology can be considered as an approach for optimizing waste collection routes.