Headaches are among the most prevalent and disabling disorders and there are several patients’ unmet needs in current pharmacological options, while a growing interest is focusing on nutritional approaches as non-pharmacological treatments. Among these, the most promising seems to be the ketogenic diet (KD). Exactly 100 years ago, KD was used to treat pediatric forms of drug-resistant epilepsy, but progressively applications of this diet also involved adults and other neurological disorders. Evidence of KD effectiveness in migraine comes from 1928, but in the last years different groups of research and clinicians paid attention to this therapeutic option to treat patients with drug resistant migraine and cluster headache, and/or comorbid with metabolic syndrome. Here we describe all the existing evidence on the potential benefits of KDs in headaches, explore in deep all the potential mechanisms of action involved in the efficacy, and synthesize results of working meetings of an Italian panel of experts on this topic. Aim of the working group is the creation of a consensus on indications and clinical practice to treat with KDs patients with headache. The results here we present are the base for further improvement in the knowledge and application of KDs in the treatment of headaches.

A new treatment option for cluster headache (CH) prevention is needed. Monoclonal antibodies (mABs) against calcitonin gene-related peptide (CGRP) ligands are used as a preventative treatment for migraine. Considering the CGRP’s role in the CH attack’s ignition and upkeep, fremanezumab and galcanezumab have been evaluated for CH preventative treatment. However, only high-dose (300 mg) galcanezumab was proven for episodic CH prevention. We herein report 3 cases of migraine and comorbid CH with previous failures of preventive treatments. The 2 cases were treated with fremanezumab and the one with non-high-dose galcanezumab. All 3 cases showed good results not only on migraine but also on CH attacks. Our report suggested the efficacy of CGRP-mABs for CH prevention. Our cases differed from the cases in the phase 3 trials of CGRP-mABs for CH prevention in the following 2 points. First, the patients had both migraine and comorbid CH. Second, the combined use of CGRP-mABs with preventative drugs for CH, such as verapamil and/or prednisolone, was performed. Future accumulation of real-world data may prove the efficacy of CGRP-mABs for CH prevention.

Cluster-based routing in Mobile Ad-Hoc Networks is considered one of the convenient methods of routing. The existence of Cluster Head (CH) in a group of nodes for data forwarding improves the performance of routing in terms of routing overhead and power consumption. In this paper, a clustering algorithm using hierarchical routing for mobile ad hoc networks is proposed. In the proposed method, first, clusters are formed by the FLOC clustering method with low overlapping. Then, according to the most important network criteria, including hop count, noise ratio, distance, remaining energy, and link expiration time, head clusters are selected based on the weighting algorithm. To transfer data in the proposed method, two types of communication are used, e.g., inter-cluster communication and intra-cluster communication in which the shortest path to the target cluster is selected. The proposed P-METHOD technique is based on hierarchy and clustering as well as using a weighted algorithm that reduces delay in data transmission on the network. Moreover, P-METHOD is evaluated here via extensive simulations carried out in NS-2. The simulation results indicate that the MANET network performance metrics are improved in terms of average throughput, Packet Delivery Ratio (PDR), delay, residual energy, and lifetime.

The stepwise hydrogenation of the C=C bond and C=O group of acrolein on Au₃ and Au₅ model systems is investigated using the density functional theory(DFT) PW91 functional. Our results show that the C=C hydrogenation is more favorable than that of C=O bond on Au₃ with the barriers of the rate-determining step being 0.35 and 0.62 eV respectively. On the other hand, the C=O reduction is preferred over the hydrogenation of the C=C bond on Au₅. The corresponding barriers of the rate-determining steps are 0.45 and 0.54 eV, respectively. This demonstrated that the second hydrogenation step controls the reaction on both Au₃ and Au₅ for C=O and C=C hydrogenation and the C=O hydrogenation on Au₅ is preferred over the hydrogenation of the C=C bond, which is helpful to address the reactivity of small size-selected supported gold clusters.

In this paper, a controlled-phase-flip (P-CPF) gate using the polarisation and orbital angular momentum degrees of freedom for single-photon two-qubit quantum logic is proposed. This is critical to the realisation of quantum cluster states and graph networks using transverse degrees of freedom. A generalisation of the proposed scheme to arbitrary number and kinds of degrees of freedom, for optical systems, as well as arbitrary operations to be conditionally performed is proposed.

Viral variant analysis is a bedrock of the disease surveillance. When combined with temporospatial analysis variant analysis can further the knowledge of disease spread in a study area. This paper suggests a method to perform the analysis in an operational setting which will allow for real-time surveillance of viral variants and allow local public health professionals to rapidly respond to changes in the evolution of the disease. This method includes three main subprocesses: preprocessing, analysis, and rendering. This method can be performed across multiple software platforms. A use case is given in which it was found that this method helped a hospital system understand the spread of SARS-CoV-2 in Northeast, Ohio.

China’s rural e-commerce has been developing rapidly. Taobao Villages are combination of e-commerce and rural industries. When rural e-commerce coverage evolves from Taobao village to Taobao town, the scale of industrial clusters has been expanding synchronously. This paper investigates flower and seedling industrial cluster in Xinhe Town, Yanji Town and Miaotou Town of Shuyang County, China, and conducts the econometric analysis of the expansive determinants of flower and seedling industrial cluster of Taobao Villages. An effective sample of 263 was obtained through a face-to-face survey of e-merchants of flower and seedling in the Shuyang County of Jiangsu Province. Bases on the structural equation modeling, series of test results show that the data can be used to calculate the path regression. The outcomes shows that creation of e-merchants of flower and seedling, integration of e-commerce platform, supply chain friendliness, involvement of e-commerce service providers, and governmental policy guidance are driving expansion of flower and seedling industrial cluster together, moreover, the five forces interact with each other. This implies that expansion of flower and seedling industrial cluster is a systematic process, each stakeholder needs to pay attention to the role of other forces, and five forces achieve a balanced situation in the cluster.

Multiagent technologies give a new way to study and control complex systems. Local interactions between agents often lead to group synchronization also known as clusterization, which usually is a more rapid process in comparison with relatively slow changes in external environment. Usually, the goal of system control is defined by the behaviour of a system on long time intervals. When these time intervals are much longer than the time of cluster formation, clusters may be considered as new variables in a ``slow'' time model. We call such variables ``mesoscopic'' to emphasize their scale laying between the level of the whole system (macroscopic scale) and the level of individual agents (microscopic scale). Thus, it allows us to reduce significantly the dimensionality of a system by omitting considerations of each separated agent, so that we may hope to reduce the required amount of control inputs. Thus, we are often able to consider a system as a collection of ``flowing'' (morphing) clusters emerged form behaviour of a huge amount of individual agents. In this paper, we contrast such approach to the one where a system is considered as a network of elementary agents. We develop a mathematical framework for analysis of cluster flows in multiagent networks and use it to analyze the Kuramoto model as an attracting example of a complex networked system. In this model, a clusterization leads to sparse representation of dynamic trajectories in the whole quantized state space. With that in mind, compressive sensing allows to restore the trajectories in a high-dimensional discrete state space based on significantly lower amount of randomized integral mesoscopic observations. We propose a corresponding algorithm of quantized dynamic trajectory compression. It could allow us to efficiently transmit the state space data to a data center for further control synthesis. The theoretical results are illustrated for a simulated multiagent network with multiple clusters.

The article provides detailed information about the new technologies based on cluster computing Hadoop and Apache Spark. The experimental task of processing logistic regression with the help of these technologies is considered. The findings on the comparison of the performance of cluster computing of Hadoop and Apache Spark are revealed and substantiated.

Abstract: Current models in design of urban water management systems and their corresponding infrastructure using centralized designs have commonly failed from the perspective of cost effectiveness and inability to adapt to the future changes. These challenges are driving cities towards using decentralized systems. While there is great consensus on the benefits of decentralization; currently no methods exist which guide decision-makers to define the optimal boundaries of decentralized water systems. A new clustering methodology and tool to decentralize water supply systems (WSS) into small and adaptable units is presented. The tool includes two major components: (i) minimization of the distance from source to consumer by assigning demand to the closest water source, and (ii) maximization of the intra-cluster homogeneity by defining the cluster boundaries such that the variation in population density, land use, socio-economic level, and topography within the cluster is minimized. The methodology and tool are applied to Arua Town in Uganda. Four random cluster scenarios and a centralized system were created and compared with the optimal clustered WSS. It was observed that the operational cost of the four cluster scenarios is up to 13.9 % higher than the optimal, and the centralized system is 26.6% higher than the optimal clustered WSS, consequently verifying the efficacy of the proposed method to determine an optimal cluster boundary for WSS. In addition, optimal homogeneous clusters improve efficiency by encouraging reuse of wastewater and stormwater within a cluster and by minimizing leakage through reduced pressure variations.

Leucobryum is a moss genus that exhibits various bioactivities. However, the identification of Leucobryum species with morphology alone remains difficult. Nowadays, chemical profile analysis provides additional tools for plant classification, especially in medicinal plants. This method uses chemical similarities to identify the differences among some plants, especially for the different varieties of the same species. The objective of this study was to obtain the chemical profiles and use chemometrics to identify selected Leucobryum species found in Thailand. Lipophilic extracts from 18 samples of five taxa were analyzed with TLC and HPLC. Cluster analysis and PCA were used to investigate the similarities of the chemical profiles. PERMANOVA was performed to determine the difference among taxa. The results showed that the TLC profile could separate only the genus of the sample, while HPLC chromatograms showed separation among the Leucobryum taxa and consistent patterns within the same species. In addition, the chemical profiles of Leucobryum species were separated at the species level in PCA. The PERMANOVA showed significantly different profiles among the species (P = 0.002). Therefore, this work illustrated the potential of chemometrics as supporting evidence for species delimitation in the moss genus Leucobryum.

One of the most important sources of energy is the sun. Taiwan is located at north 22-25° latitude. Due to its proximity to the equator, it experiences only a small angle of sunlight incidence. Its unique geographical location which can obtain sustainable and stable solar resources. This study takes research on the forecast of solar radiation to maximize the benefits of solar power generation, and develops methods that can predict the future solar radiation pattern to help reduce the costs of solar power generation. This study builds supervised machine learning models, known as deep neural network (DNN) and long short-term memory neural network (LSTM). The hybrid supervised and unsupervised model, namely cluster-based artificial neural network (k-means clustering and fuzzy C-means clustering-based models), was developed. After establishing these models, the study evaluated their prediction results. For different prediction periods, the study selected the best-performing model based on the results and proposed combining them to establish a real-time updated solar radiation forecast system capable of predicting the next 12 hours. The study area covered Kaohsiung, Hualien, and Penghu in Taiwan. Data from ground stations of the Central Weather Administration, collected between 1993 and 2021, as well as the solar angle parameters of each station, were used as input data for the model. The results of this study show that different models have their advantages and disadvantages in predicting different future times. Therefore, the hybrid prediction system can predict future solar radiation more accurately than a single model.

This paper considers the problem of predicting earthquakes. It uses a small amount of information to create a descriptive key that can be used as a footprint to describe an event. A frequency grid clusters events that occurred at the same time and then the algorithm averages the history of these events over preceding days, in particular the gaps when the events did not occur. The gaps are measured for the clustered events only and can be used to create a description that is quite unique. Results suggest that seismic events can in fact be traced using this key and subsequently recognised again, if the same conditions reoccur. They also suggest that force direction may be more important than magnitude, after the initial cluster selections are made. Greek and USA datasets have been looked at and the prediction accuracy can be 70% or better. The author therefore suggests that this is an interesting method that deserves attention.

The general theory of relativity (GR) is symmetric under smooth coordinate transformations also known as diffeomorphisms. The general coordinate transformation group has a linear subgroup denoted the Lorentz group of symmetry which is maintained also in the weak field approximation to GR. The dominant operator in the weak field equation of GR is thus the d'Alembert (wave) operator which has a retarded potential solution. Galaxy Clusters are huge physical systems having dimensions of many hundreds of millions of light years. Thus any change at the cluster center will be noticed at the rim only hundreds of millions of years later. Those retardation effects are neglected in present day cluster modelling and in particular are neglected in virial calculations used to relate mass and velocities on the cluster. The significant differences between the predictions of Newtonian instantaneous action at a distance and observed velocities are usually explained by either assuming dark matter or by modifying the laws of gravity (MOND). In this paper we will show that taking general relativity seriously without neglecting retardation effects one can explain the velocities in a galactic cluster without postulating dark matter. It should be stressed that the current approach does not require that velocities, $v$ are high, in fact the vast majority of cluster bodies are substantially subluminal. In other words, the ratio of $\frac{v}{c} \ll 1$. Typical velocities in galaxies are less than ~1000 km/s, which makes this ratio $0.01$ or smaller. However, one should consider the fact that every gravitational system even if it is made of subluminal bodies has a retardation distance, beyond which the retardation effect cannot be neglected. Every natural system such as stars and galaxies and even galactic clusters exchange mass with its environment. For example, the sun losses mass through the solar wind and galaxies accrete gas from the intergalactic medium. This means that all natural gravitational systems have a finite retardation distance. The question is thus quantitative, how large is the retardation distance? The change of mass of the sun is quite small and thus the retardation distance of the solar system is quite large allowing us to neglect retardation effects within the solar system. However, for galaxies and galaxy clusters the retardation distance is within the system itself and cannot be neglected.

Tuberculosis (TB), an infectious airborne disease caused by Mycobacterium tuberculosis (Mtb), is a serious public health threat reported as the leading cause of morbidity and mortality worldwide. South Africa is a high TB burden country with TB being the highest infectious disease killer. The study investigated the distribution and clustering of Mtb mutations and spoligotypes in rural Eastern Cape Province. The Mtb isolates included were 1,157 from DR-TB patients and analysed by LPA followed by spoligotyping of 441 isolates, of these, 36 were whole genome sequenced. Distribution of mutations and spoligotypes was done by spatial analysis and clustering analysis was done by Bayesian model-based clustering of allele frequencies at heterozygous sites, using Mclust package in R. The rpoB gene had highest number of mutations. The distribution of rpoB and katG mutations was more prevalent in four health care facilities, inhA mutations were more prevalent in three healthcare facilities and heteroresistant isolates were more prevalent in five healthcare facilities. The Mtb was genetically diverse with Beijing more prevalent and largely distributed. Spatial analysis and mapping of gene mutations and spoligotypes revealed better picture of distribution. Clustering of isolates indicates that there is transmission of mixed infection in this area.

The reaction of [Co2(CO)8] with an equimolar amount of P(OPh)3] in toluene solution yields in situ generated [Co2(CO)7{P(OPh)3}] 1. Subsequent heating of 1 provides in a condensation reaction an alternative synthetic access to the tetranuclear cluster [{Co4(μ2-CO)3(CO)8{P(OPh)3}] 2. Compound 2 has been characterized by IR and 31P NMR spectroscopy, the tetranuclear cluster framework has be ascertained by a single-crystal X-ray diffraction study performed at 100 K.

Breakthrough cancer pain (BTcP) is a temporary exacerbation of pain that "breaks through" a phase of adequate pain control by an opioid-based therapy. The non-predictable BTcP (NP-BTcP) is a subtype of BTcP that occurs in the absence of any specific activity. Since NP-BTcP has an important clinical impact, this analysis is aimed at characterizing the NP-BTcP phenomenon through a multidimensional statistical technique. This is a secondary analysis based on the Italian Oncologic Pain multiSetting - Multicentric Survey (IOPS-MS) . A correlation analysis was performed to characterize NP-BTcP profile about its intensity, number of episodes per day, and type. The Multidimensional Correspondence Analysis (MCA) determined the identification of 4 groups (Phenotypes). A univariate analysis was performed to assess differences between the 4 Phenotypes and selected covariates. The four phenotypes represent the hierarchical classification according to the status of NP-BTcP: from the best (Phenotype 1) to the worst (Phenotype 4). The univariate analysis found a significant association between the onset time >10 min in the Phenotype 1 (37.3%) vs. the onset ≤ 10 min in Phenotype 4 (74.2%) (p<0.001). The Phenotype 1 was characterized by gastrointestinal type of cancer (26.4%) respect to Phenotype 4 where the most frequent cancer affected the lung (28.8%) (p<0.001). Phenotype 4 was mainly managed with rapid onset opioids, while in Phenotype 1 many patients were treated with oral, subcutaneous, or intravenous morphine (56.4% and 44.4%, respectively; p=0.008). The ability to characterize NP-BTcP can offer enormous benefits for the management of this serious aspect of cancer pain. This strategy can provide many indications for identifying the diagnostic and therapeutic gaps on NP-BTcP management.

The aim of this paper is the evaluation of the success of graduating with distinction in higher education (SGDHE) using the fuzzy DEMATEL method. The observation has been done using cause and effect criteria. 11 cause and 14 effect clusters have been used in this study. The study result of this work shows that all the effects are connected to the given causes and a cause-effect graph has been generated for each connection. This proposed approach is demonstrated with the empirical case of Dire Dawa University students in Dire Dawa Ethiopia.

Islamic banks (IBs) have been criticized as not being genuinely Islamic, and the methods in measuring their performances have been debatable. While the literature on IBs performance has been emerging, such studies precisely assess its recent development remains absent. Therefore, we aim to evaluate the development of scholarly articles that measure IBs’ performances. We employ bibliometric analysis and sample related articles from the Scopus database. We find that the development of IBs performance literature may be understood by 111 peer-reviewed journal articles, 4 conference papers, 1 book, and 1 book series. We analyze these materials based on publication sources, country and institution affiliation, keywords association, and cluster dendrogram. Our model that quantifies the keywords association and cluster dendrogram provides a novelty in assessing IBs performance literature development. Future studies may replicate our model to cluster and identify the keyword associations from the unstructured data sources.

Degussa P25 is a benchmark form of TiO₂ used worldwide in photocatalysis studies. Currently no such benchmark exists for co-catalysts, which are essential for many photocatalytic reactions. Here, we present the preparation of Pt nanocluster co-catalysts on TiO₂ using an unmodified commercial source and equipment that is commonly available. Transmission electron microscopy reveals that the procedure produces TiO₂ decorated with Pt atom and nanoclusters (1-5 atoms). Optical reflectance and X-ray diffraction measurements show that the procedure does not affect the TiO₂ polymorph or UV-Vis absorbance. Gas phase photocatalytic splitting of heavy water (D₂O) shows that the Pt nanocluster decorated TiO₂ outperforms Pt nanoparticle (produced by photodeposition) decorated TiO₂ in D₂ production. Pt nanoclusters, produced directly from a commercial source, with high co-catalyst activity are prime candidates to be used in benchmark photocatalytic reactions.

The patterns of food consumption in general and those of meat, in particular, are constantly changing. These changes are due not only to socio-economic and cultural trends that affect the whole society but also to the specific lifestyles of consumer groups. Due to the importance of consumer lifestyle, the objectives of this study were i) to identify the profiles of lamb meat consumers according to their orientation toward convenience, as defined by their eating and cooking habits; ii) to characterize these profiles according to their socio-economic characteristics and their preferences regarding the intrinsic and extrinsic quality signals of lamb meat; and iii) to analyze the willingness to pay for lamb confit. In this study, four types of consumers have been differentiated according to their lifestyles related to lamb consumption. These groups, due to their characteristics, could be called "Gourmet", "Disinterested", "Conservative" and "Basic". The Gourmet group has characteristics that make it especially interesting to market a product such as lamb confit; however, this group is unaware of this product. Therefore, a possible strategy to expand the commercialization of light lamb and the confit product would be guided marketing to this niche market.

For the decontamination of surfaces in the food production industry, plasma generated compounds (PGCs) such as plasma-treated water (PTW) or plasma processed air (PPA) offer many promising possibilities for future applications. Therefore, the antimicrobial effect of water treated with microwave-induced plasma (MidiPLexc) on Pseudomonas fluorescens biofilms was investigated. 10 ml deionized water was treated with the MidiPLexc plasma source for 100 s, 300 s and 900 s (pre-treatment time) and the bacterial biofilms were exposed to the PTW for 1 min, 3 min and 5 min (post treatment time). To investigate the influence of PTW on P. fluorescens biofilms, microbiological assays (CFU, fluorescence and XTT assay) and imaging techniques (fluorescence microscopy, confocal laser scanning microscopy (CLSM), and atomic force microscopy (AFM)) were used. The CFU showed a maximum reduction of 6 log10 by using 300 s pre-treated PTW for 5 min. Additionally, a maximum reduction of 81 % for the viability of the cells and a 92 % reduction in the metabolic activity of the cells was achieved by using 900 s pre-treated PTW for 5 min. The microscopic images showed evident microbial inactivation within the biofilm even at the shortest pre treatment (100 s) and post-treatment (1 min) times. Moreover, reduction of the biofilm thickness and increased cluster formation within the biofilm was detected. Morphologically, the fusion of cell walls into a uniform dense cell mass. The findings correlated with a decrease in the pH value of the PTW, which forms the basis for the chemically active components of PTW and its antimicrobial effects. These results, provide valuable insights into the mechanisms of inactivation of biofilms by plasma generated compounds (PGCs) such as PTW and thus allow for further parameter adjustment for applications in food industry.

Accurate and fast traffic flow forecasting is vital in intelligent transportation system because many of the advanced features in intelligent transportation systems are based on it. However, existing methods have poor performance regarding accuracy and computational efficiency in long-term traffic flow forecasting under big data. Hence, we propose an improved Long short-term memory (LSTM) Network and its cluster computing implementation in this paper to address the above challenge. We propose a singular point probability LSTM (SDLSTM) algorithm. The method discards the units of the network according to the singular point probability during the training process and amends the SDLSTM by Autoregressive Integrated Moving Average Model (ARIMA) to achieve the accurate prediction of 24-hour traffic flow data. Furthermore, the paper designs a scheme for implementing this method through cluster computing to shorten the calculation time and improve the system's operating speed. Theoretical analysis and experimental results show that SDLSTM gains a higher accuracy rate and better stability in the long-term traffic flow forecasting compared with previous methods.

Forest certification is considered a viable market-based policy instrument to promote forest sustainability. It has an important role of play in meeting the objective of modern forestry development in China, which is to sustain ecological and environmental benefits of forests. To understand differences in attitudes, opinions, and interests in forest certification, this study segmented respondents of a landowner’s survey in Shandong, China based on their level of interest in participating in forest certification under different program requirements. Multivariate cluster analysis revealed three distinct groups: likely-, potential-, and unlikely-landowners. We further examined the heterogeneity of these groups in terms of their demographics, ownership characteristics, management objectives, and perceived benefits and challenges with adopting forest certification. The results suggested the necessity of differentiating landowners in formulating and designing specific motivation-based incentives and tailor outreach efforts and communication strategies to improve their interests in forest certification. Findings are useful and interesting to forest policymakers interested in promoting forest certification among landowners in China and other countries facing similar circumstances.

The review is devoted to the physical, chemical and technological aspects of the breath-figures self-assembly process. Main stages of the process and the impact of the polymer architecture and physical parameters of the breath-figures self-assembly on the eventual pattern are covered. The review is focused on the hierarchy of spatial and temporal scales inherent for the breath-figures self-assembly. Multi-scale patterns arising from the process are addressed. The characteristic spatial lateral scales of patterns vary from nanometers to dozens of micrometers. The temporal scales of the process span from micro-seconds to seconds. The qualitative analysis performed in the paper demonstrates that the process is mainly governed by the interfacial phenomena, whereas the impact of inertia and gravity is negligible. Characterization and applications of polymer films manufactured with breath-figures self-assembly are discussed.

Physics of high-Tc superconducting cuprates is obscured by the effect of strong electronic correlations. One way to overcome the problem is to seek for an exact solution at least within the small cluster and expand it to the whole crystal. Such an approach is in the heart of the cluster perturbation theory (CPT). Here we develop CPT for the dynamic charge and spin susceptibilities (spin-CPT), within which the correlation effects are explicitly taken into account by the exact diagonalization. We apply spin-CPT to the effective two-band Hubbard model for the cuprates obtained from the three-band Emery model and calculate one- and two-particle correlation functions, namely, spectral function and spin and charge susceptibilities. Doping dependence of the spin susceptibility was studied within spin-CPT and CPT-RPA that is the CPT generalization of the random phase approximation (RPA). Both methods produce the low energy response at four incommensurate wave vectors in qualitative agreement to the results of the inelastic neutron scattering on overdoped cuprates.

Obesity has become a rising global health problem affecting adults’ quality of life. The objective of this study was to describe the prevalence of obesity in Indonesian adults based on the cluster of islands. The study was also aimed to identify the risk factors of obesity in each island cluster. This study analysed secondary data of Indonesian Basic Health Research 2018. Our data for analysis comprised 688,638 adults (>=15 years) randomly selected using proportionate to population size throughout Indonesia. We included 20 variables for sociodemographic and obesity-related risk factors for analysis. Obese status was defined using Body Mass Index (BMI) >= 27.5 kg/m2. Our current study defined seven major islands cluster as the unit analysis consisting of 34 provinces in Indonesia. Descriptive analysis was conducted to determine the characteristics of the population and to calculate the prevalence of obesity within provinces in each of the island’s clusters. Multivariate logistic regression analyses to calculate odds ratios (ORs) was performed using R version 3.6.3. The study results showed that all island clusters had at least one province with an obesity prevalence of more than 20%. Six out of twenty variables, comprising four diet factors (consumption of sweet food, high-salt food, meat food, and carbonated drinks) and two other factors (mental health disorders and smoking behaviour), varied across the island clusters. In conclusion, there was a variation of obesity prevalence of the provinces within and between island clusters. Variation of risk factors raised in each cluster island suggested the government rethink and reframe the intervention to address obesity.

Commercial classification is essential to spatially describe commerce. Although most of the proposed classifications have been classic functional analyses, rooted on qualitative studies, some recent classifications efforts are found to rely heavily on more quantitative methods, due eventually to increasing data availability and technological advancements. In this paper, a classification is proposed, using k-means clustering and a minimal set of variables (density, diversity and clustering) to derive the commercial structure of Lisbon. The classification is implemented for 1995, 2002 and 2010, using a 150m-sided square grid. The cross-sectional analysis of the results shows the rise of shopping-malls against city centre decline and gentrification, along with changes in cluster composition considering 9 different commercial categories (6 categories of retail, and restaurants, cafes and bars). These findings are in line with literature, thus supporting the obtained classification. Since the classification can be used to accurately describe the commercial structure of the city in different time periods, it is implied that it may also be generalized to different cities. Furthermore, the potential use of cluster membership in retail location models, which is an advantage of the proposed classification, could help strengthen the relationship between location modelling and commercial classification, thus reinforcing the role of commercial studies in urban planning and policymaking.

Cardiovascular disease is the number one cause of death in the world and Quoting from WHO, around 31% of deaths in the world are caused by cardiovascular diseases and more than 75% of deaths occur in developing countries. The results of patients with cardiovascular disease produce many medical records that can be used for further patient management. This study aims to develop a method of data mining by grouping patients with cardiovascular disease to determine the level of patient complications in the two clusters. The method applied is principal component analysis (PCA) which aims to reduce the dimensions of the large data available and the techniques of data mining in the form of cluster analysis which implements the K-Medoids algorithm. The results of data reduction with PCA resulted in five new components with a cumulative proportion variance of 0.8311. The five new components are implemented for cluster formation using the K-Medoids algorithm which results in the form of two clusters with a silhouette coefficient of 0.35. Combination of techniques of Data reduction by PCA and the application of the K-Medoids clustering algorithm are new ways for grouping data of patients with cardiovascular disease based on the level of patient complications in each cluster of data generated.

Adverse events associated with opioid use in palliative care have been extensively studied. However, predicting the occurrence of adverse events based on the specific opioid used remains unclear. This study aimed to comprehensively analyze the adverse events caused by µ receptor stimulation of opioids approved in Japan and investigate the tendencies of adverse event occur-rence among different opioids.We utilized the FDA Adverse Event Reporting System (FAERS) database to extract reported adverse events of opioids approved in Japan. Cluster analysis was performed on reporting odds ratios (RORs) of adverse event names among opioids to visualize relationships between opioids and adverse events, facilitating a comparative study of their clas-sifications.We calculated the RORs of adverse events for the target opioids. Based on these RORs, we performed a cluster analysis, which resulted in the classification of 11 target opioids into five distinct groups. we were able to comprehensively compare and examine the relationships between opioids and adverse events. This analysis helps in understanding and managing the risks and benefits of each drug in palliative care settings. By analyzing relationships between opioids and adverse events, clinicians can make informed decisions about opioid selection, dosage, and monitoring to maximize patient safety and comfort.

In order to explore the genetic diversity of the extremely small population wild plant Habenaria dentata and protect and optimize its wild resources. The genetic diversity of 133 samples from 6 wild populations of H.dentata was analyzed by ISSR molecular marker technique to provide a scientific basis for the screening of improved germplasm resources. The results show that the average number of alleles (Na) was 1.765, the average number of effective alleles (Ne) was 1.424, the average Nei 's gene diversity index (He) was 0.252, the average Shannon information diversity index (I) was 0.381, and the average percentage of polymorphic loci (PPL) was 76.499%. The variation within populations was 77.34%, and the variation between populations was 22.66%. Gene flow (Nm) was 1.705 8, greater than 1. The results of cluster analysis showed that the six populations were mainly divided into four branches and not classified according to geographical location. There was no significant correlation between geographical location and genetic distance between populations (r=0.557, P>0.05). The genetic diversity of H.dentata is high. Among the six wild populations, the genetic diversity of Mulun population is the highest and this population can be used as a key protection unit.

This study investigates how Electronic Livestock Health Recording Systems (ELHRs) facilitates the detection of disease burden and make cluster analysis by applying data analytics tools and techniques. A sample size of 18333 livestock disease cases reported from 2007-2015 by the Ministry of Agriculture of the Federal Democratic of Ethiopia was used for data collection. The results showed that ELHRs are important as livestock disease data preservers, saving costs, and facilitating the extraction of up-to-date and complete information. Euclidean and Manhattan distance performed well at 98%, while cosine distance measurement metrics performed poorly. Finally, with the application of the selected clustering techniques, metrics, tools, and dataset, it has been attempted to successfully detect an optimal number of disease clusters and meet the objectives of the study.

Kubernetes (K8s) defines standardized APIs for container-based cluster orchestration so it becomes possible for application managers to deploy their applications in a unified manner across different cloud providers. A practical problem is however feature incompatibility between different K8s vendors, who offer commercial K8s products based on the open-source K8s distribution. A large number of documented features in this open-source distribution are optional features that are turned off by default, but can be activated by setting specific combinations of parameters and plug-in components in configuration manifests for the K8s control plane and worker node agents. However, none of these configuration manifests are standardized, giving K8s vendors the freedom to hide the manifests behind a single, more restricted, and proprietary customization interface. Therefore some optional K8s features cannot be activated consistently across K8s vendors and applications that require these features cannot be run on those vendors. In this paper we present a unified, vendor-agnostic feature management approach that bypasses the proprietary customization interface of K8s vendors in order to consistently activate optional K8s features across a federation of clusters hosted by different Kubernetes vendors. We describe vendor-agnostic reconfiguration tactics that are already applied in industry and cover a wide range of optional K8s features. Based on these tactics, we design and implement an autonomic controller for declarative feature compatibility management across a cluster federation. We found that the features configured through our vendor-agnostic approach have no impact on application performance when compared with a cluster where the features are configured using the configuration manifests of the open-source K8s distribution. Moreover, the maximum time to complete reconfiguration of a single feature is within 100 seconds, which is 6 times faster than using proprietary customization interfaces of mainstream K8s vendors such as Google Kubernetes Engine. However, there is a non-negligible disruption to running applications when performing the reconfiguration; this disruption impact does not appear using the proprietary customization methods of the K8s vendors. Therefore, our approach is best applied in the following three use cases: (i) when starting up new K8s clusters, (ii) when optional K8s features of existing clusters must be activated as quickly as possibly and temporary disruption to running applications can be tolerated or (iii) when proprietary customization interfaces do not allow to activate the desired optional feature.

This study examines the relation between ambient air pollution and emergency department (ED) visits due to certain infectious diseases in Toronto, Canada. The National Ambulatory Care Reporting System database was used to draw the corresponding health cases. Daily data on ED visits, ambient air pollution concentration levels, and weather conditions during the period from April 2004 to December 2015 (4,292 days in total) were linked together and used in statistical models. Six air pollutants (fine particulate matter PM2.5, CO, NO2, SO2, ozone O3 as a daily average, and ozone O3-8 hour ozone, as a maximum eight hour average) were investigated. In addition, the Air Quality Health Index (combining NO2, O3, and PM2.5) was also considered. The time-stratified case-crossover technique was applied in the study design. Conditional Poisson models were created using the daily counts of ED visit data. The considered factors, air pollutants and weather, were lagged by the same number of days, from 0 to 14. In the period of the study 339,644 ED visits were identified; 177,619 for females and 162,025 for males. For each air pollutant 270 models were realized (15 lags x 18 strata). Ambient air pollution concentrations lagged by 2, 3, and 5 days have the highest impact on ED visits, with 34, 32, and 35 positive associations, respectively. For all patients and an increase in a one interquartile range (IQR=1.2 ppb) of sulphur dioxide, the following values of the relative risks (RR) were estimated: RR=1.005 (95% confidence interval: 0.998, 1.013), 1.008 (1.001, 1.016), 1.009 (1.001, 1.016), 1.011 (1.004, 1.019), 1.007 (0.987, 1.028), and 1.009 (1.002, 1.016) for lags from 0 to 5, respectively. The results suggest that exposures for certain air pollutants (mainly CO, O3, and SO2) in urban environment affect the number of ED visits related to infectious diseases.

Understanding and evaluating urban investment environment is essential for effectively improving the efficiency of resource allocation between cities and promoting overall development of the regional economy. This paper takes 15 node cities on maritime Silk Road covered by the “Belt and Road” as the research object, establishes a dynamic evaluation index system for investment environment, and uses projection pursuit cluster to analyze and evaluate the investment environment of the cities. It is found that the investment environment potential of a city is directly related to the level of social development, economic development, and the degree of opening to the outside world. It is recommended that node cities should seize the important opportunity of the construction of the Maritime Silk Road, introduce world-wide human, financial and material resources to promote regional resources allocation and flow, and continuously improve and upgrade the investment environment quality.

With the continuous development of urbanization and industrialization in the world, concrete is widely used in various engineering constructions as an engineering material. However, the consequent problem of durability of concrete structures is also becoming increasingly prominent. As an important additional measures, protective coating can effectively improve the durability of concrete performance. Moreover, the uniformity of the concrete surface coating will directly affect its protective effect. Therefore, we propose a nondestructive inspection and evaluation method of coating uniformity based on infrared imaging and cluster analysis for concrete surface coating uniformity detection and evaluation. Based on the obtained infrared images, a series of processing and analysis of the images were carried out using MATLAB software to obtain the characteristics of the infrared images of concrete surface. Finally, by extracting the temperature distribution data of the pixel points on the concrete surface, an evaluation method of concrete surface coating uniformity based on a combination of cluster analysis and hierarchical analysis was established. The evaluation results show that the determination results obtained by this method are consistent with the actual situation. This study has a positive contribution to the testing of concrete surface coating uniformity and its evaluation.

Recently, several reports have emerged describing the long-term consequences of COVID-19 that may affect multiple systems, suggesting its chronicity. As further research is needed, we conducted a longitudinal observational study to report the prevalence and associated risk factors of long-term health consequences of COVID-19 by symptom clusters in patients discharged from the Temporary COVID-19 Hospital (TCH) in Mexico City. Self-reported clinical symptom data were collected via telephone calls over 90 days post-discharge. Among 4670 patients discharged from the TCH, we identified 45 symptoms across eight symptom clusters (neurological; mood disorders; systemic; respiratory; musculoskeletal; ear, nose, and throat; dermatological; and gastrointestinal). We observed that the neurological, dermatological, and mood disorder symptom clusters persisted in >30% of patients at 90 days post-discharge. Although most symptoms decreased in frequency between day 30 and 90, alopecia and the dermatological symptom cluster significantly increased (p<0·00001). Women were more prone than men to develop long-term symptoms and invasive mechanical ventilation also increased the frequency of symptoms at 30-days post-discharge. Overall, we observed that symptoms often persisted regardless of disease severity. We hope these findings will help promote public health strategies that ensure equity in the access to solutions focused on the long-term consequences of COVID-19.

Abstract: Insecure and portable devices in the smart city’s Internet of Things (IoT) network are increasing at an incredible rate. Various distributed and centralized platforms against cyber-attacks have been implemented in recent years, but these platforms are inefficient due to their constrained levels of storage, high energy consumption, the central point of failure, underutilized resources, high latency, and etc. In addition, the current architecture confronts the problems of scalability, flexibility, complexity, monitoring, managing & collecting of IoT data, and defend against cyber-threats. To address these issues, the author presents distributed and decentralized Blockchain-Software Defined Networking (SDN) based energy-optimized architecture for IoT in smart cities. Thus, SDN continuous observing, controlling, managing IoT devices activities and detect possible attacks in the network; Blockchain provides adequate security & privacy against cyber-attacks, reduces the central point of failure issues; Network Function Virtualization (NFV) are used to saving energy, load balancing, as well as increasing the lifetime of the entire network. Also, we introduce a Cluster Head Selection (CHS) algorithm to reduce the energy consumption in the presented model. Finally, we analyze the performance using various parameters (e.g. throughput, response time, gas consumption, communication overhead) and demonstrating the result that provides higher throughput, lower response time, lower gas consumption than existing works for smart cities.

Reliable information on transition matrix elements of various property operators between molecular electronic states is of crucial importance for predicting spectroscopic, electric, magnetic and radiative properties of molecules. The finite-field technique is a simple and rather accurate tool for evaluating transition matrix elements of first-order properties in the frames of the Fock space relativistic coupled cluster approach. We formulate and discuss the extension of this technique to the case of transitions between the electronic states associated with different sectors of the Fock space. Pilot applications to the evaluation of transition dipole moments between the closed-shell-like states (vacuum sector) and those dominated by single excitations of the Fermi vacuum (the $1h1p$ sector) in heavy atoms (Xe, Hg) and simple molecules of heavy element compounds (I${}_2$, TlF) are reported.

Consider a robot that is navigating in a space modeled by a graph, and that wants to know its current location. It can send a signal to determine how far it is from each landmark among a set of fixed landmarks. We study the problem of computing the minimum required number of landmarks, and where they should be placed so that the robot can always determine its location. Since the problem is an NP-complete problem, the robot's responses to the actions are slow. To accelerate this response, we can use the parallel version of this problem. In this work, we introduce a new parallel implementation for determining the metric dimension of a given graph. We run the proposed algorithm on a symmetric multi-processing (SMP) cluster using C programming language and the Message Passing Interface (MPI) library. Finally, we run our implementation on four categories of graphs (the tracks in which the robot moves): a cycle graph Cn, a path graph Pn, a triangular snake graph and a ladder graph Ln. Preliminary computational results indicate that the metric dimension problem is an NP-complete problem and prove the ability of the proposed algorithm to achieve a speedup of 6 for 8 processors.

As Heterogeneous Wireless Sensor Network (HWSN) fulfill the requirements of researchers in the design of real life application to resolve the issues of unattended problem. But, the main constraint face by researchers is energy source available with sensor nodes. To prolong the life of sensor nodes and hence HWSN, it is necessary to design energy efficient operational schemes. One of the most suitable routing scheme is clustering approach, which improves stability and hence enhances performance parameters of HWSN. A novel solution proposed in this article is to design energy efficient clustering protocol for HWSN, to enhance performance parameters by EECPEP-HWSN. Propose protocol is designed with three level nodes namely normal, advance and super node respectively. In clustering process, for selection of cluster head we consider three parameters available with sensor node at run time, i.e., initial energy, hop count and residual energy. This protocol enhance the energy efficiency of HWSN, it improves performance parameters in the form of enhance energy remain in the network, force to enhance stability period, prolong lifetime and hence higher throughput. It is been found that proposed protocol outperforms than LEACH, DEEC and SEP with about 188, 150 and 141 percent respectively.

In this paper, several two-dimensional clustering scenarios are given. In those scenarios, soft partitioning clustering algorithms (Fuzzy C-means (FCM) and Possibilistic c-means (PCM)) are applied. Afterward, VAT is used to investigate the clustering tendency visually, and then in order of checking cluster validation, three types of indices (e.g., PC, DI, and DBI) were used. After observing the clustering algorithms, it was evident that each of them has its limitations; however, PCM is more robust to noise than FCM as in case of FCM a noise point has to be considered as a member of any of the cluster.

Since China’s economic reform launched in the late 1970s, rural industrial parks (RIPs) have played a crucial role in the rise of the manufacturing industry in the Pearl River Delta (PRD), transforming the former agricultural land into a location of world-famous factories. However, as China has put forward higher requirements for economic development and stricter control measures for environmental pollution, RIPs with low-end industries and low efficiency present an urgent need for renovation and upgrade. By analyzing the spatial and socioeconomic cross-sectional dataset of rural industrial parks in Nanhai District of Foshan City, this paper attempts to classify the existing industrial parks into groups and propose corresponding redevelopment strategies. The results of the spatial analysis show that the spatial distribution of rural industrial parks is closely related to their location and transportation conditions. Failure of the market has resulted in large tracts of advantageous land being taken up by inefficient industrial parks. Cluster analysis and overlay analysis are used evaluate the difficulty of redevelopment and divide the industrial parks into three clusters: retained RIPs, medium-term-removed RIPs, and near-term-removed RIPs. It is shown that some of the industrial parks are still in good form while the majority need substantial reconstruction. Taking economic and institutional factors into consideration, the authors put forward that different strategies should be adopted for the future renovation of medium-term-removed and near-term-removed RIPs.

In recent years discrete element method (DEM) has gradually been applied to the traditional fluidization simulation of fine particles in micro fluidized bed (MFB). The application of DEM in simulating fast fluidization of fine particles in MFB has not yet received attention. This article presents a drag model that relies on the surrounding environment of particles, namely particle circumstance-dependent drag modle or PCDD model. Fast fluidization in MFB of fine particles is simulated using DEM based on the PCDD model. It is found that the gas-solid two-phase flow in MFB meets the common law of fast fluidization in circulating fluidized bed (CFB) to a certain extent, and also presents a special law that is significantly different from that in CFB. Simulations indicate that the local structure in MFB exhibits particle aggregation which is a natural property of fast fluidized, forming a structure where continuous dilute phase and dispersed concentrated phase coexist. The formation and fragmentation of particle clusters in different local regions of the bed have time synchronization. There exists strong effect of solid back-mixing in MFB, leading to relatively low outlet solid flux. The gas back-mixing effect is, however, not so distinct. The axial porosity shows a monotonically increasing distribution with the bed height, but does not strictly follow the single exponential distribution. The solid volume fraction at the bottom of the bed is significantly lower than the correlated value in CFB. The axial heterogeneous distribution of the cross-sectional average porosity in the lower half of the bed is also weakened. The radial porosity shows a distribution pattern of higher in the central region and lower in the sidewall region. Compared with the correlation results in CFB, the porosity near the central region in MFB is relatively low, while the porosity near the sidewall region is relatively high. The geometric size of the container, especially the radial size, is much smaller than that of a CFB. The relative area of contact between the MFB wall and particles is larger, and the wall friction factor becomes more significant. This may be the main reason for the time synchronization of particle agglomeration, severe particle back-mixing, low outlet solid flux, monotonically increasing axial porosity and weak core-annular structure of radial porosity.

(1) Background: In order to study the effects of different electric cookers on the nutritional components of millet porridge, five different electric cookers were selected to cook millet porridge, and sensory and nutritional components in millet porridge, millet soup, and millet grains were analyzed. (2) Methods: Using principal component and cluster analysis, a variety of nutritional components were comprehensively compared. (3) Results: The results showed that among the different cooked samples, the content of amylose and reducing sugar was the highest in the samples cooked by electric cooker no. 3. The electric cooker no. 4 samples had the highest sensory evaluation score, crude fat, and protein content. The contents of ash, fatty acids, bound amino acids, and minerals were the highest in the electric cooker no. 5 samples. The sensory evaluation score and content of crude fat, ash, reducing sugar, direct starch, and Cu were higher in millet grains than in millet soup or porridge. The content of fatty acid, protein, amino acid, Zn, Fe, Mg, Mn, and Ca was highest in millet soup. Different electric cookers produced millet porridge with varying nutritional levels. (4) Conclusions: This study provides a reference for the further development of new electric cookers.

The decoding of genomes of a larger number of animal species have provided further insights into the genomic Hox gene organization and with this indicated the evolutionary changes during the radiation of several clades. The expansion of gene expression studies during development and life history stages of more species, complete the picture of the relationship between cluster organisation and temporal and spatial correlation of the Hox activity. Now these results open the opportunity to look deeper into the regulatory pathways that form these patterns and identify what exact changes caused the evolution of the application of this iconical gene set for the evolution of new larval forms and new structures. Here we review recent progress of Hox gene related research in the large clade Spiralia, that comprises Annelida, Mollusca, Lophophorata, Platyhelminthes, Nemertea and others. Albeit their relationship to each other is not resolved yet, there are emerging patterns that indicate that Hox genes are mainly used for patterning late, adult body parts and that Hox genes are often not expressed on the larval stages. Hox genes seem also often recruited for the formation of morphological novelties. Together with the emerging genomic information Hox genes show a much more dynamic evolutionary history than previously assumed.

Indonesia is one of the most biodiverse countries in the world and a promising resource for novel natural compound producers. Actinomycetes produce about two-thirds of all clinically used antibiotics. Thus, exploiting Indonesia’s microbial diversity for actinomycetes may lead to the discovery of novel antibiotics. A total of 422 actinomycete strains were isolated from three different unique areas in Indonesia and tested for their antimicrobial activity. Nine potent bioactive strains were prioritized for further drug screening approaches. The nine strains were cultivated in different solid and liquid media and a combination of genome mining analysis and mass spectrometry (MS)-based molecular networking was employed to identify potential novel compounds. By correlating secondary metabolite gene cluster data with MS-based molecular networking results, we identified several gene cluster-encoded biosynthetic products from the nine strains, including naphthyridinomycin, amicetin, echinomycin, tirandamycin, antimycin, and desferrioxamine B. Besides, eight putative ion clusters and numerous gene clusters were detected that could not be associated with any known compound, indicating that the strains can produce novel secondary metabolites. Our results demonstrate that sampling of actinomycetes from unique and biodiversity-rich habitats, such as Indonesia, along with a combination of gene cluster networking and molecular networking approaches, accelerates natural product identification.

With a longer-term goal of addressing the comparative behavior of the aqueous halides F$^-$, Cl$^-$, Br$^-$, and I$^-$ on the basis of quasi-chemical theory (QCT), here we study structures and free energies of hydration clusters for those anions. We confirm that energetically optimal $(\mathrm{H_2O})_n\mathrm{X}$ clusters, with X = Cl$^-$, Br$^-$, and I$^-$, exhibit \emph{surface} hydration structures. Computed free energies based on optimized surface hydration structures utilizing a harmonic approximation, typically (but not always) disagree with experimental free energies. To remedy the harmonic approximation, we utilize single-point electronic structure calculations on cluster geometries sampled from an AIMD (\emph{ab initio} molecular dynamics) simulation stream. This \emph{rough-landscape} procedure is broadly satisfactory and suggests unfavorable ligand crowding as the physical effect corrected. Nevertheless, this procedure can break down when $n \gtrsim 4$, with the characteristic discrepancy resulting from a relaxed definition of clustering in the identification of $(\mathrm{H_2O})_n\mathrm{X}$ clusters, including ramified structures natural in \emph{physical cluster theories.} With ramified structures, the central equation for the present rough-landscape approach can acquire some inconsistency. Extension of these physical cluster theories in the direction of QCT should remedy that issue, and should be the next step in this research direction.

The world faces two seemingly unrelated challenges—a shortfall in the STEM workforce and increasing antibiotic resistance among bacterial pathogens. We address these two challenges with Tiny Earth, an undergraduate research course that excites students about science and creates a pipeline for antibiotic discovery.

Nowadays, we live in a world in which people are facing with a lot of data that should be stored or displayed. One of the key methods to control and manage this data refers to grouping and classifying them in clusters. Today, clustering has a critical role in information retrieval methods for organizing large collections inside a few significant clusters. One of the main motivations for the use of clustering is to determine and reveal the hidden and inherent structure of a set of data. Ensemble clustering algorithms combine multiple clustering algorithms to finally reach an overall clustering system. Ensemble clustering methods by lack of information fusing utilize several primary partitions of data to find better ways. Since various clustering algorithms look at the different data points, they can produce various partitions from such data. It is possible to create a partition with high performance by combining the partitions obtained from different algorithms, even if the clusters to be very dense from each other. Most studies in this area have examined all the initial clusters. In this study, a new method is used in which the most sustainable clusters are utilized instead of all primary produced clusters. Consensus function based on co-association matrixes used to select more stable clusters. The most stable clusters selection method is done by cluster stability criterion based on F-measure. Optimization functions are used to optimize the obtained final clusters. The genetic algorithm is the optimizer used in this article to find the ultimate clusters participated in a consensus. Experimental results on several datasets show that the output of proposed method is various clusters with high stability.

A mixture of nonlabeled (14N2H4) and 15N labeled hydrazine (15N2H4) in an aqueous solution is oxidized to 15N2, 14N2, and 14N15N molecules, indicating the intermediate existence of the 14NH2-14NH-15NH-15NH2 with subsequent hydrogen transfers and splitting of side N-N bonds. Structures, thermodynamics, and electron characteristics of various N4H6 molecules in aqueous solutions are investigated using theoretical treatment at the CCSD/cc-pVTZ level of theory to explain the crucial part of the hydrazine oxidation reaction. Most N4H6 structures in aqueous solutions are decomposed during geometry optimization. Splitting the bond between central nitrogen atoms is the most frequent, but the breakaway of the side nitrogen is energetically the most preferred. The N-N fissions are enabled by suitable hydrogen rearrangements. Gibbs free energy data indicate the dominant abundance of NH3... N2... NH3 species. The side N atoms have very high negative charges, which should support hydrogen transfers in aqueous solutions. The only stable cyclo-(NH)4…H2 structure has a too high Gibbs energy and breaks the H2 molecule. The remaining initial cyclic structures are split into hydrazine and HN≡NH or H2N≡N species, and their relative abundance in aqueous solutions is vanishing.

Working memory (WM) is a brain system for short-term storage and manipulation of information and plays an important role in complex cognitive tasks. In the synaptic theory of WM memorized elements are stored in the form of short-term potentiated connections in a sample population of neurons. In this paper, we show that such populations can be formed due to the mechanisms of spike-timing-dependent plasticity (STDP) – the phase dependence associated with the ratio of the pulse times of the interacting neurons. We propose a WM model considering two types of plasticity: short-term plasticity and STDP. We have shown formation of neuronal clusters encoding items in the WM model, that can be formed by external stimulation of a group of neurons due to the mechanisms of STDP and hold and reactivated by short-term plasticity mechanisms. The dynamic formation of neuronal clusters instead of pre-formed clusters gives additional flexibility to the model.

Smartphone-based mobility apps enable users to make informed transportation decisions, offering instant access to transport-related information. This development has created a smartphone-enabled ecosystem of mobility services in developed countries while it is slowly picking up pace in the global south, which can contribute towards the decarbonization of urban transport. Work on this has already started in India, and there is considerable evidence indicating the profound impact of these apps on the perceived utility and usage of transport modes, with far-reaching implications for sustainable development goals (SDGs). However, for most users, the use of smartphone apps is a novel trend, and the knowledge of the impacts of usage of existing apps on the usage pattern of transport modes by various user groups is essential for positioning new consolidated app-based services soon. Against this backdrop, the present study uses latent class cluster analysis to empirically investigate the impacts of mobility apps on transport mode usage patterns in Delhi by classifying users into latent classes based on socioeconomic characteristics, attitudes/preferences, smartphone app usage, and mode usage pattern. The characteristics of the latent class and factors affecting the individual’s probability of being classified to these cluster have been discussed, along with some measures to encourage app-based mobility for each cluster.

Objective: To investigate the protein expression levels of cyclin‑dependent kinase subunit 2 (CKS2) and cluster of differentiation (CD) 47 in gastric cancer (GC) and their clinical significances. Methods: A total of 126 GC patients who underwent radical resection were selected as study subjects. Additionally, 32 patients with benign gastric tumor, 42 patients with low-grade intraepithelial neoplasia (LGIEN), and 49 patients with high-grade intraepithelial neoplasia (HGIEN) who underwent surgery were selected as the control groups. Immunohistochemistry was used to detect the expression of CKS2 and CD47 in surgical specimens. We statistically analyzed the clinical significance of the expression of the two factors. Results: (1) The positivity rates for CKS2 in benign gastric tumor tissue, LGIEN tissue, HGIEN tissue, and GC tissue gradually increased, i.e., 6.3% (2/32), 30.9% (13/42), 38.8% (19/49), and 60.3% (76/126), respectively, and the positivity rates for CD47 were 18.8% (6/32), 38.1% (16/42), 46.9% (23/49), and 65.9% (83/126), respectively. (2) High expression of CKS2 and CD47 were associated with tumor diameter, Lauren classification, number of lymph node metastases, and TNM stage. In addition, the immunohistochemical scores for CKS2 and CD47 were positively correlated (r=0.625, P=0.000). (3) The median follow-up time of 126 patients was 46.5 months, and the overall survival rate was 40.5% (51/126). Survival analysis showed that compared with that in the CKS2 (-) group, the overall survival rate for patients in the CKS2 (+) group was significantly worse (25.0% vs 64.0%, 2=15.67, P=0.000) and that compared with the CD47 (-) group, the CD47 (+) group had significantly worse overall survival (30.1% vs 60.5%, 2=15.67, P=0.000). (4) The overall survival rates of CKS2(+)CD47(+) group, CKS2(+)CD47(-) group, CKS2(-)CD47(+) group, and CKS2 (-)CD47 (-) group were 20.0%(13/65), 58.3%(7/12), 57.1%(8/14), 65.7% (23/35), respectively, the prognosis of patients in CKS2(+)CD47(+) group was significantly poor. Conclusion: High expression levels of CKS2 and CD47 were closely related to the occurrence of GC and can be used as independent risk factors to assess the prognosis of patients.

This study explored father involvement as a social determinant of child health within the context of macro-environmental changes in Mongolia. Using data for children aged 3-4 from UNICEF’s Multiple Indicator Cluster Surveys, this cross-sectional analysis examined the association between father presence and engagement with child health and educational outcomes. Multivariate regression modeling was employed to identify associations between father presence, engagement, and child outcomes including fever, respiratory illness, diarrhea and preschool attendance. In unadjusted analyses, father engagement was associated with higher odds of pre-school attendance (OR=1.12; 95% CI 1.04-1.20) but not with child illness (OR=1.04; 95% CI 0.95-1.14). Father engagement was no longer associated with pre-school attendance after controlling for potentially confounding variables (ORadj = 0.95; 95% CI 0.88-1.03). Unadjusted and adjusted analyses showed that father presence was not associated with acute illness or preschool attendance. Results also suggest that a larger proportion of children were engaged in activities by their mother compared to their father or other adults. Data indicate that father presence and engagement were not associated with child illness or pre-school attendance. Factors such as maternal education, household wealth, and region of residence are stronger predictors of preschool attendance and should continue to be considered for promoting child health and development in Mongolia.

Under the big unified framework of quantum gravitational field and quantum repulsive field, the earlier in this series of work has comprehensively expanded the general relativity and loop quantum ring gravitation theory, set up the quantum repulsive field equation corresponding to the quantum gravitational field equation, and then established the quantum hedge-unified field equation describing the interaction of quantum gravitational field and quantum repulsive field; On this basis, the quantum repulsive universe equation corresponding to the quantum gravitational universe equation has be established, thus a set of new equations describing the expansive universe model under the condition of fully considering the hedge effect of quantum gravitational field and quantum repulsion field, which can be called the expansive universe equation of quantum hedge-unified field theory, has be established. Now, let's go deep into the arche-unified physics behind the creation of the universe. Although all kinds of inflationary universe models can solve cosmological problems to a certain extent, there is no natural and reasonable analysis and explanation for all cosmological and physical consequences. For example, it is impossible to predict a large tensor-scale ratio and a relatively large spectral index run at the same time. At present, string theory, membrane theory, loop quantum gravity theory, super-gravitational holography principle and M theory, which are the theoretical basis of cosmology, are basically established by simplification in the case of breaking away from the extreme background conditions of the universe. While cosmology, which is deficient in nature, cannot provide strong support for particle physics to further advance to a wider field and a deeper level. Due to the lack of new ideas, the construction of existing theoretical models is difficult. Therefore, the existing research can only be further promoted by new astronomical observation. Unlike existing studies, we concentrate on the emergence of the big rip-rebound clusters of quantum chaotic-reticulate distribution between the outwards-push of the universe as a whole (related to pseudo-vacuum energy, Higgs field, dark energy, etc.) and the inwards-pull of the universe in local scale (related to dark matter, quantum gravity and initial gravity separated from high-dimensional supergravity, and the initial strong force separated from the grand unified force subsequently). On the one hand, we find a great impetus to make the inflation of the universe into firstly-imposed suppression, then-imposed acceleration and finally-imposed deceleration, which really explains why the universe expands at a critical rate and has the same temperature in different regions, on the other hand, we find a fundamental unit of matter and its emerging clusters evolving in the big bang, inflation and expansion of the universe accompanied by the great rip-rebound clusters of quantum chaos-reticulate distribution, which can be reduced to an arche-pulsator of neither punctiform particle nor linear string. From these two aspects, it can been seen that the evolution of the early universe is much more complicated than people originally thought. The complexity of the super inflation of the universe is mainly reflected in the big rip-rebound cluster emerging behind the gravitational disturbance (the pressure and tensor disturbance related to dark matter), the repulsive disturbance (the pressure and scalar disturbance related to dark energy), the cosmological disturbance (related to the interaction between high-dimensional supergravity and high-dimensional superrepulsion). This is a system of nonlinear stochastic differential dynamics, or a quantum statistical physical process of nonlinear nonequilibrium state. In the new research paradigms established in this series, the arche-conjugation between the high-dimensional supergravity and the high-dimensional superrepulsion, as well as between the quantum gravity and the quantum repulsion, is higher than the supersymmetry, and then becomes the core concept of the new theory. In this series, cluster-inflating configuration, spin network space and cosmic paradigm vectors are proposed, so the model of the super-inflated universe with quantum chaos-reticulate distribution emerged under the great impetus. The new model reveals that the unified inflation of the universe as a whole and the local inflation of the universe emerge together in the great impetus of the big rip-rebound cluster of quantum chaos-reticulate distribution. As a spin network model variable, the spatiotemporal mode variable is a vector composed of a series of variables, which variables include: dimension, curvature, range, expansion speed, dynamics, quantization, chaotic-reticulate distribution (inhomogeneity, concentration, fluctuation), synergy, and so on. Between particle physics and cosmology, this series of papers proposes the cosmic quantum evolution mode, the physical-sphere distributing configuration and the interaction situation variables, organizes the concepts, variables and models of these three levels, and establishes a systematic super-synergy paradigm. In this paradigm, the general Langevan equation and the general Fokker-Planck equation are established. For the quantum cosmic-sphere, a series of trend parameters are introduced to establish the dynamics of quantum gravitational mode and its master equation, and then to establish the operator distribution function and its motion equation of the action mode. For the Tachyon of upheaval in the rip-rebound-inflating of the universe, the hypothesis on the bifurcate-chaos wave of quantum is given, the quantum theory of the early photons in upheaval is set up, and the analysis on the particles of the ultrahigh energy cosmic ray from the γ ray burst is made. Finally, using Wigner-Ville distribution as a nonlinear time-frequency distribution, we establish the ultra-synergistic field equation of quantum-classic unifying action-sphere.

The sensor nodes have limited computation, sensing, communication capabilities and generally operated by batteries in a harsh atmosphere with non-replenish able power sources. These limitations force the sensor network subject to failure because most of the energy is spent on sensing, computing and data transmission. This paper introduces an Energy Efficient Clustering and Shortest-Path Routing Protocol (EECSRP) to assist Wireless Sensor Networks (WSNs) by (a) extending the lifespan of the network (b) effectively using the battery power (c) decreasing the network overhead and (d) ensuring a high packet transmission ratio with minimal delay. The delay time-based Cluster Head (CH) is elected based on the node degree, residual energy and Received Signal Strength (RSS) to accomplish the goal. Additionally, the RSS-based network partitioning is implemented to evaluate the gradient based on demand routing between source (sensing node) and destination (BS). Whenever the current CH residual energy goes under the threshold level, the proposed protocol performs the clustering process, reducing the exchange of control packets. However, the BS periodically gathers the data from every single CH which helps to reduce the collision and Medium Access Control (MAC) layer conflict. From the simulation results, it is the evident that the proposed protocol performance in terms of average end-to-end latency, packet delivery ratio, average energy consumption and control overhead is better than the well-known current protocols.

Sehezar area is located in southern Tonokabon in Mazandaran province, north of Iran, near the Tarom – Hashdtjin belt. The existence of granitoid masses in the region can be important in terms of the potential of mineralization. Geochemical anomaly separation from the background is one of the important steps in mineral exploration. In the past decades, geochemical anomalies have been identified by means of various methods. Some of these separation methods include: statistical analysis methods (like univariate, bivariate, multivariate statistics), spatial statistical methods and fractal and multi-fractal methods. To identify the anomalous area, 71 stream sediment samples were collected from the area and analyzed by the ICP-MS method, and then interpreted. Initially, data were normalized and afterwards, univariate analysis (threshold limit and screening (P.N) methods) was used, in which results of the probable and definite anomaly of the threshold method were confirmed by the P.N screening method. Finally, the maps of the anomal zones were drawn. Then, bivariate analysis (Pearson correlation coefficients) and multivariate analysis on normal data were performed on SPSS software, in which factor analysis and cluster analysis were used for multivariate analysis. As a result of using the factor analysis method, six factors were identified and factor maps were drawn by the Surfer software. Also, by using cluster analysis, the variables were divided into two groups. In order for a better separation of the geochemical anomaly from the background, in addition to the threshold method, the Concentration - Area fractal method was used. Here, the fractal geometry using full-logarithmic graphs of the Concentration - Area obtained is capable of separating the stairs of different sections (background, threshold, and anomaly) with respect to the angle coefficient of the Concentration - Area plot. Then, in conclusion, results of these methods were compared and investigated, and finally, the anomalies area maps of the Au, Ag, Cu, Fe, W elements were drawn by Concentration - Area fractal and threshold methods and anomalous zones were introduced.

The Voronoi entropy is a mathematical tool for quantitative characterization of the orderliness of points distributed on a surface. The tool is useful to study various surface self-assembly processes. We provide the historical background, from Kepler and Descartes to our days, and discuss topological properties of the Voronoi tessellation, upon which the entropy concept is based, and its scaling properties, known as the Lewis and Aboav-Weaire laws. The Voronoi entropy has been successfully applied to recently discovered self-assembled structures, such as patterned micro-porous polymer surfaces obtained by the breath figure method and levitating ordered water micro-droplet clusters.

El Niño is a quasi-periodic pattern of climate variability and extremes often associated with hazards and disease. While El Niño links to individual diseases have been examined, less is known about the cluster of multi-disease risk referred to as an ecosyndemic, which emerges during extreme events. The objective of this study was to explore a mapping approach to represent the spatial distribution of ecosyndemics in Piura, Peru at the district-level during the first few months of 1998. Using geographic information systems and multivariate analysis, two methodologies were employed to map disease overlap of 7 climate-sensitive diseases and construct an ecosyndemic index, which was then mapped and applied to another El Niño period as proof of concept. The main findings showed that many districts across Piura faced multi-disease risk over several weeks in the austral summer of 1998. The distribution of ecosyndemics were spatially clustered in western Piura among 11 districts. Furthermore, the ecosydemic index in 1998 when compared to 1983 showed a strong positive correlation, demonstrating the utility of the index. The study supports PAHO efforts to develop multi-disease based and interprogrammatic approaches to control and prevention, particularly for climate and poverty-related infections in Latin America and the Caribbean.

State Forest Management Organizations (SFMOs) play a crucial role in the European forest sector, managing almost half the forests in the region. SFMOs are often managed for timber production only whereas, being publicly owned, they should play an important role in providing a vast range of public goods (e.g. soil protection, biodiversity conservation). Their management goals depend on the history and current conditions of the forest sector at a national level, as well as different challenges and the potential for development. Although there is a lack of knowledge about the current performance of SFMOs, there have been recent changes to their management goals and practices in response to the new demands expressed by society (e.g. transparency, social inclusion). The main purpose of this study is to analyse the current situation of SFMOs by clustering them according to indicators that reflect three pillars of the common understanding of sustainable forest management (SFM) concept. With the help of Principal Component Analysis (PCA), we grouped countries according to common characteristics of the forest sector at the national level. Results show three main clusters of SFMOs in Europe. The first cluster has rather small but commercially-oriented forestry unit together with other business activities and a strong focus on public services. The second sees itself as the protector of public interest, rather than commercially-oriented organisations. The third is mainly profit-seeking. The existence of diverse SFMO clusters shows the possibility of different approaches for SFM with a focus on different goals (e.g. profit gaining, public service delivery).

This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things computing. Considering the characteristics of IoT data processing, similar to mainstream high performance computing, we use a GPU cluster to achieve better IoT services. Firstly, we present an energy consumption calculation method (ECCM) based on WSN. Then, using the CUDA Programming model, we propose a Two-level Parallel Optimization Model (TLPOM) which exploits reasonable resource planning and common compiler optimization techniques to obtain the best blocks and threads configuration considering the resource constraints of each node. The key to this part is dynamic coupling Thread-Level Parallelism (TLP) and Instruction-Level Parallelism (ILP) to improve the performance of the algorithms without additional energy consumption. Finally, combining the ECCM and the TLPOM, we use the Reliable GPU Cluster Architecture (RGCA) to obtain a high-reliability computing system considering the nodes’ diversity, algorithm characteristics, etc. The results show that the performance of the algorithms significantly increased by 34.1%, 33.96% and 24.07% for Fermi, Kepler and Maxwell on average with TLPOM and the RGCA ensures that our IoT computing system provides low-cost and high-reliability services.

City zones have become increasingly overcrowded as a result of the extensive population widening ratio and the swift relocation of people from villages. The traffic monitoring process is a major issue in these areas due to the massive traffic flow on the roads. This research proposed a cluster-based improved authentication and communication scheme for an Intelligent Transportation System in Vehicular AdHoc Networks (VANETs). Our primary objective is to optimize resource sharing in vehicular communication. We enhanced the reliability, scalability, and stability of fast-moving VANETs by introducing cluster-based routing schemes for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications. We used a third-party certification authority for vehicle authentication to address security and privacy concerns. Our proposed schemes support minimizing the end-to-end (E2E) delay and route request, in addition to reducing link failure. Our schemes’ leading yield includes throughput enhancement, TCP Socket Initialization time minimization, TCP handshake response speedup, and DNS lookup improvement. The schemes are centred on short-range peer-to-peer (P2P) wireless communication in a 400-meter radius cluster. This includes innovative P2P wireless communications on VANET using minimized resources. The proposed schemes deliver a secure authentication mechanism with a securely generated vehicle authentication key provided by a certification authority. Furthermore, we have developed RESTful APIs in vehicular communication for implementation purposes, and also offered and implemented algorithms for resource sharing regarding V2V and V2I communication. Ultimately, we evaluated the performance of our experiments.

Background: The course of obesity and type 2 diabetes (T2D) development is highly dependent on adipose tissue (AT) angiogenesis. Moreover, angiogenic microRNAs (miRNAs) play pivotal role in AT functionality. The aim of this study was to analyze the relationship of the human AT miR-221-3p/222-3p cluster and their regulatory network with obesity and T2D. Methods: miR-221-3p/222-3p and their target genes (TG) expression levels were measured in visceral and subcutaneous ATs from patients classified according to their BMI and to their glycemic status with a high degree of insulin resistance (IR) and T2D. In silico analyses of miR-221-3p/222-3p and their TGs were performed to identify relevant signaling pathways. Results: A multivariate analysis, including the simultaneous expression of miR-221-3p and miR-222-3p as dependent variables, showed significant differences considering the variables; tissue depot, obesity, IR and T2D altogether as independent variables. In addition, miRNAs and their TGs were differentially expressed according to obesity degree, glycemic status, and AT depot type. Our in silico analysis showed that miR-221-3p/222-3p cluster TGs are mostly involved in angiogenesis, WNT signaling pathway and apoptosis. Conclusion: These findings suggest that the miR-221-3p/222-3p cluster and their related regulatory networks could represent tangible targets for the management of obesity and associated metabolic disorders

Hox gene collinearity (HGC) is a multiscalar property of many animal phyla particularly important during embryogenesis. It relates events occurring in Hox clusters inside the chromosome DNA and embryonic tissues. These two entities differ in size by more than four orders of magnitude. HGC is observed as spatial collinearity (SC) where the Hox genes are located in the order H1, H2, H3 … along the 3’ to 5’ direction of the DNA sequence. The corresponding embryonic tissues (E1, E2, E3, …) are activated along the Anterior – Posterior axis in the same order. Besides this collinearity a temporal collinearity (TC) has been also observed in many vertebrates. According to TC first is H1 expressed in E1, later is H2 in E2, followed by H3,… Lately doubt has been raised whether TC really exists. A biophysical model (BM) has been formulated and tested in the last twenty years. According to BM, physical forces are created which pull the Hox genes one after the other driving them to a transcription factory domain where they are transcribed. The existing experiments support this BM description. In the present work two equivalent realizations of BM are presented which explain the recent findings on TC as observed in the vertebrates.

The article presents the methodology for classifying economic regions with regards to selected factors that characterize a region, such as: the economic structure of the region, and thus the share of individual sectors in the economy; employment; the dynamics of the development of individual sectors expressed as an increase or decrease in production value; the population density in the region and also the level of occupational safety. Cluster analysis, which is a method of multidimensional statistical analysis available in Statistica software, was used to solve the task. The proposed methodology was used to group Polish voivodships with regards to the speed of economic development and occupational safety in the construction industry. Data published by the Central Statistical Office was used for this purpose, such as the value of construction and assembly production, the number of people employed in the construction industry, the population of an individual region and the number of people injured in occupational accidents.

The paper proposes a rock classification method using the vibration signal of the rotary drilling process. This proposed method classifies four rocks with a reliability of 100 %, from the vibration signal record of a lasting 1/4 second. For the design of a suitable classification method, several attributes of the vibration signal were calculated for two different signal recording lengths. Cluster dendrogram, ANOVA test, and boxplot were used to determine useful attributes and proper signal length. The classification rule was found using a decision tree, machine learning tool. The publication gradually describes the process of creating the classification method and the results of the reliability verification of the proposed classification method. Rock disintegration by rotary drilling was carried out under standardized experimental conditions. The disintegration rocks were andesite, granite, limestone, and concrete.

Results from an explorative study revealing spatio-temporal patterns of the SARS-CoV-2/COVID-19 epidemic in Germany are presented. We dispense with contestable model assumptions and show the intrinsic spatio-temporal patterns of the epidemic dynamics. The analysis is based on COVID-19 incidence data, which are age-stratified and spatially resolved at the county level, provided by the Federal Government’s Public Health Institute of Germany (RKI) for public use. Although the 400 county-related incidence time series show enormous heterogeneity both with respect to temporal features as well as spatial distributions, the counties’ incidence curves organise into well distinguished clusters that coincide with East and West Germany. The analysis is based on dimensionality reduction, multidimensional scaling, network analysis, and diversity measures. Dynamical changes are captured by means of difference-in-difference methods which are related to fold changes of the effective reproduction numbers. The age-related dynamical patterns suggest a considerably stronger impact of children, adolescents and seniors on the epidemic activity than previously expected. Besides these concrete interpretations, the work mainly aims at providing an atlas for spatio-temporal patterns of the epidemic which serves as a basis to be further explored with the expertise from different disciplines, particularly sociology and policy makers. The study should also be understood as a methodological contribution to getting a handle on the unusual complexity of the COVID-19 pandemic.

Bermudagrass (Cynodon spp.) is one of the drought-resistant warm-season turfgrasses adapted to the southern and transitional zones in the United States. Multiple hybrid varieties have been developed and released for use as a turfgrass and others are in pipeline. Increasing genetic di-versity of commercial varieties is vital to tackle stress tolerance. A DNA profiling study of 21 experimental selections from the Oklahoma State University (OSU) turfgrass breeding program and 11 cultivars was conducted using 51 simple sequence repeat (SSR) primer pairs that spread across the bermudagrass genome. Pairwise genetic relationship analysis among the genotypes using 352 polymorphic bands showed genetic similarity coefficients ranging from 0.59 to 0.89. Cluster analysis using the un-weighted paired group method with arithmetic average (UPGMA) method grouped the entries into six clusters. Correlation analysis identified different levels of pairwise genetic relationship among the entries that largely reflected parental relationship. Di-rectional breeding and selection for cold hardiness or drought resistance created progeny that had distinct genetic diversity in the tested bermudagrasses. It is evident that an increase in genetic diversity of the existing cultivar pool with the release of one or more of the experimental selec-tions for commercial use will strengthen and improve bermudagrass systems.

This study explored intraindividual multidimensional profiles integrating psychosocial factors, namely, body image and satisfaction, weight-related self-stigma, positivity, and happiness, and behavioural-lifestyle factors, namely, adherence to a healthy diet, among Spanish adults with overweight or obesity. We further aimed to investigate the association of excess weight (i.e., measured body mass index, BMI) with the abovementioned multidimensional configurations. A convenience sample of adult individuals with excessive weight completed self-reports regarding the study variables, and their weight and height were measured. With a perspective centered on the individual, a cluster analysis established three distinct intraindividual psychosocial and diet-related profiles: a group of healthy individuals with excess weight; a group of individuals who were negatively affected by their excessive weight and showed the most distressed profile; and a group of dysfunctional individuals who seemed to be excessively unrealistic and optimistic regarding their excessive weight and unhealthy lifestyles. Furthermore, individuals in the affected cluster had higher obesity. The results showed that there are specific psychosocial and lifestyle profiles in the adult population with excess weight and that there are relationships among psychological, behavioural, and body-composition factors. For clinical application purposes, it is important to account for the heterogeneity within individuals who are obese and to individualize the interventions, with a focus from weight change to individual's overall well-being.

This study explored intraindividual multidimensional profiles integrating psychosocial factors, namely, body image and satisfaction, weight-related self-stigma, positivity, and happiness, and behavioural-lifestyle factors, namely, adherence to a healthy diet, among Spanish adults with overweight or obesity. We further aimed to investigate the association of excess weight (i.e., measured body mass index, BMI) with the abovementioned multidimensional configurations. A convenience sample of adult individuals with excessive weight completed self-reports regarding the study variables, and their weight and height were measured. With a perspective centred on the individual, a cluster analysis established three distinct intraindividual psychosocial and diet-related profiles: a group of healthy individuals with excess weight; a group of individuals who were negatively affected by their excessive weight and showed the most distressed profile; and a group of dysfunctional individuals who seemed to be excessively unrealistic and optimistic regarding their excessive weight and unhealthy lifestyles. Furthermore, individuals in the affected cluster had higher obesity. The results showed that there are specific psychosocial and lifestyle profiles in the adult population with excess weight and that there are relationships among psychological, behavioural, and body-composition factors. For clinical application purposes, it is important to account for the heterogeneity within individuals who are obese.

Almost half of the confirmed COVID-19 cases detected so far in the United Kingdom are part of a large cluster of 13 British nationals who tested positive for SARS-CoV-2 in the UK, Spain, and France. Transmissions among this cluster occurred at a ski resort in France, and originated from a single infected traveller returning from a conference in Singapore where he acquired the virus. At least 21 individuals were exposed to the virus, tested, and quarantined, with 13 of those testing positive between the period of 6th Feb and 15th Feb. Here, all publicly available information about the primarily UK/France cluster is consolidated, providing a complete and accessible summary of the cases and their connections. Notable in this cluster are the number of individuals infected, the apparent absence of any severe illness among those infected, and a case of a `delayed positive' test during isolation after initially testing negative, at least 7 days after last possible contact.

The driving force behind adopting the idea of ​​sustainable development are producers and retailers. Unfortunately, when preparing their product range for consumers, many of them still only pay attention to the size of the customers’ earnings, how often they shop and how much they buy when shopping. In consumer segmentation, sustainable values ​​that consumers apply when making their purchasing decisions are rarely taken into account. The aim of this article is to show that values ​​such as environmental protection, the ethical behaviour of producers, fair trade or maximising the utility function of consumption are so important in the purchasing process in the coffee market that they can be used as segmentation variables. The Polish coffee market was selected to carry out segmentation taking into account the consumers' sustainable values. The main source of the article is the results of a standardized online survey conducted on a nationwide sample of 800 coffee consumers in July 2018. Multi-dimensional analyses such as extrapolative factor analysis (EFA) and cluster analysis (CA) were used to describe the obtained results. As a result, six segments of coffee consumers were identified and described: “responsible, aspiring to be connoisseurs”, “loyal coffee enthusiasts”, “pragmatic users”, “coffee laypersons”, “sophisticated connoisseurs”, “consumerists, connoisseurs, but not at any price”. Among the identified segments, the sustainable consumption values ​​most often mentioned refer to "responsible, aspiring to be connoisseurs", and the least often - ”consumerists, connoisseurs, but not at any price”. The conclusions presented in the last part of the article may be used by manufacturing and trade enterprises, operating on the coffee market, in order to respond to the identified needs and expectations of consumers and by governmental and social organisations so as to determine the directions of pro-ecological education of consumers.

An important moment in the establishment of forestry seed orchards is the formation of their optimal composition, avoiding inbreeding depression in the outgoing seed material which occurs due to crossbreeding between closely related plus trees, the clones of which comprise the orchards. It is possible to minimize the negative effect of inbreeding by considering the hereditary aspect of the heterogeneous seed orchard material. The purpose of our work is to provide a comparative assessment of the formation and lignification of xylem in annual shoots of the plus trees of Scots pine. We investigated the formation and lignification of xylem in the annual shoots of the plus trees of Scots pine (Pinus sylvestris L.), found in natural forests in the Nizhny Novgorod region of the Russian Federation. Their vegetative progeny were cultivated in the clone archive stationed in the same region. Analyzing the one-type of the 1-year shoots, the time that the shoots were cut from the branches corresponded to the presence of plants in the characteristic phenological phases of seasonal development. A histochemical study of xylem was conducted, using the qualitative reactions of phloroglucinol to lignin. The power of xylem development was estimated by counting, in the radial direction, the cell rows from the core to the cambium in the microscope’s field of view. A significant differentiation of plus trees was revealed in a complex of signs characterizing the level of xylem development and the degree of xylem cell lignification in the tissues of annual shoots. Phenotypic differences in the physiological state of plus trees appeared given a leveled ecological background, indicating their genotypic determinism. This was confirmed by an analysis of variance. The share of the influence of differences between the proper plus trees was between 25.16% ± 8.91% and 53.98% ± 5.48% of the total phenotypic variance of the signs of the seasonal state of xylem. Factor analysis was used to reduce the number of considered indicators of xylem physiological state. The results allowed a cluster analysis to be carried out on the basis of the normalized values of the initial xylem features as well as the principal components derived from them. The association of plus trees was done on the basis of the similarity of the multidimensional estimates of xylem seasonal condition. The objects that were the most remote from the others were identified. This information provides a reasonable approach to the formation of an assortment of Scots pine seed orchards.

A unified electro-gravity (UEG) theory, which has been successfully used for modeling an elementary particle, is applied in this paper to model gravitation in spiral galaxies. The new UEG model would explain the "flat rotation curves'' commonly observed in the spiral galaxies, without need for any hypothetical dark matter. The UEG theory is implemented in a somewhat different manner for a spiral galaxy, as compared to the simple application of the UEG theory to an elementary particle. This is because the spiral galaxy, unlike the elementary particle, is not spherically symmetric. The UEG constant $\gamma$, required in the new model to support the galaxies' flat rotation speeds, is estimated using measured data from a galaxy survey, as well as for a selected galaxy for illustration. The estimates are compared with the $\gamma$ derived from the UEG model of an elementary particle. The UEG model for the galaxy is shown to explain the empirical Tully-Fisher Relationship (TFR), is consistent with the Modified Newtonian Dynamics (MOND), and is also independently supported by measured trends of galaxy thickness with surface brightness and rotation speed. The UEG theory may similarly be extended to emulate the hypothetical dark matter in galaxy clusters as well as in cosmology.

The understanding of water quality and its underlying processes is important for the protection of aquatic environments enabling the rare opportunity of access to a domain expert. Hence, an explainable AI (XAI) framework is proposed that is applicable to multivariate time series resulting in explanations that are interpretable by a domain expert. The XAI combines in three steps a data-driven choice of a distance measure with explainable cluster analysis through supervised decision trees. The multivariate time series consists of water quality measurements, including nitrate, electrical conductivity, and twelve other environmental parameters. The relationships between water quality and the environmental parameters are investigated by identifying similar days within a cluster and dissimilar days between clusters. The XAI does not depend on prior knowledge about data structure, and its explanations are tendentially contrastive. The relationships in the data can be visualized by a topographic map representing high-dimensional structures. Two comparable decision-based XAIs were unable to provide meaningful and relevant explanations from the multivariate time series data. Open-source code in R for the three steps of the XAI framework is provided.

A radial basis function network (RBFN) method is proposed to reconstruct daily Sea surface temperatures (SSTs) with limited SST samples. For the purpose of evaluating the SSTs using this method, non-biased SST samples in the Pacific Ocean (10°N–30°N, 115°E–135°E) are selected when the tropical storm Hagibis arrived in June 2014, and these SST samples are obtained from the OISST products according to the distribution of AVHRR L2p SST and in-situ SST data. Furthermore, an improved nearest neighbor cluster (INNC) algorithm is designed to search the optimal hidden knots for RBFNs from both the SST samples and the background fields. Then the reconstructed SSTs from the RBFN method are compared with the results from the optimum interpolation (OI) method. The statistical results show that the RBFN method has a better performance of reconstructing SST than the OI method in the study, and the average RMSE is 0.48°C for the RBFN method, which is quite smaller than the value of 0.69°C for the OI method. Additionally, the RBFN methods with different basis functions and clustering algorithms are tested, and we discover that the INNC algorithm with multi-quadric function is quite suitable for the RBFN method to reconstruct SSTs when the SST samples are sparsely distributed.

Next generation sequencing and metabolomics have become very cost and work efficient and are integrated into an ever growing number of life science research projects. Typically, well established software pipelines provide quantitative data informing about gene expression or concentrations of metabolites from the raw data. This data needs to be visualized and further analyzed in order to support scientific hypothesis building and identification of underlying biological patterns. Some tools exist, but require installation or manual programming. We developed “Gene Expression Plotter” (GXP), an RNA-Seq and Metabolomics data visualization and analysis tool entirely running in the user’s web browsers, thus not needing any custom installation, manual programming or upload of confidential data to third party servers. GXP enables the user to generate interactive plots, visually summarize genetic or metabolic responses in scientific sketches (Mapman), carry out cluster and principal component analysis, and conduct overrepresentation analyses. GXP can be used to publish research data along with interactive plots and results of analyses carried out with it. GXP is freely available on GitHub: https://github.com/usadellab/GeneExpressionPlots

Sideroflexins (SLC56 family) are highly conserved multi-spanning transmembrane proteins inserted in the inner mitochondrial membrane in eukaryotes. Few data are available on their molecular function but, since their first description, they were thought to be metabolite transporters probably required for iron utilization inside the mitochondrion. Such as numerous mitochondrial transporters, sideroflexins remain poorly characterized. The prototypic member SFXN1 has been recently identified as the previously unknown mitochondrial transporter of serine. Nevertheless, pending questions on the molecular function of sideroflexins remain unsolved, especially their link with iron metabolism. Here, we review the current knowledge on sideroflexins, their presumed mitochondrial functions and the sparse - but growing - evidence linking sideroflexins to iron homeostasis and iron-sulfur cluster biogenesis. Since an imbalance in iron homeostasis can be detrimental at the cellular and organismal levels, we also investigate the relationship between sideroflexins, iron and physiological disorders. Investigating Sideroflexins’ functions constitutes an emerging research field of great interest and will certainly lead to main discoveries on mitochondrial physiopathology.

Evaluation of agronomic practices, genetic diversity and population structure of Moringa oleifera (Lam.) is crucial to its sustainable utilization to ensure food and nutritional security. Four agronomic practices of field preparation and soil analysis, regeneration, weeding and pruning, as well as harvesting, were adopted. Genetic diversity and population structure of 20 populations were evaluated using amplified fragment length polymorphism (AFLP) primer pairs (E-ACC/M-CAC and E-ACA/M-CAG). The effects of agronomic practices on leaf quantity production were evaluated using standard procedures. The physico-chemical and morphological data were analyzed using descriptive statistics, while genetic diversity, population structure, dendrogram reconstruction, and Principal Component Analysis (PCA) were analyzed from the AFLP genetic data. Significant effects (P ≤ 0.05) of agronomic practices on the quantity of leaf production were recorded across the accessions. The two primer pairs generated a total of 80 alleles with a mean major allele frequency of 0.0250. Gene diversity and polymorphic information content (PIC) values were high with a mean of 0.98 and 0.974, respectively. The Gst value of 0.0490 indicated that 5 % of the total genetic divergence was among the population while 95 % within the population. Dendrogram reconstruction with genetic distance ranging from 0.87 to 0.96 segregated the accessions into eleven clusters while PCA generated six cluster groups. Field preparation and physico-chemical soil properties, regeneration, weeding and pruning, and stand development, as well as harvesting, influenced the leaf yield and genetic diversity observed in this study. The genetic data revealed that some accessions were clustered along eco-geographical lines while others grouped disparately. Identified potential parent genotypes with valuable and desirable genetic traits can be exploited for commercial, breeding and conservation purposes to ensure sustainable utilization of the species in Nigeria.

A cosmological model was developed using the equation of state of photon gas, as well as cosmic time. The primary objective of this model is to see if determining the observed rotation speed of galactic matter is possible, without using dark matter (halo) as a parameter. To do so, a numerical application of the evolution of variables in accordance with cosmic time and a new state equation was developed to determine precise, realistic values for a number of cosmological parameters, such as energy of the universe , cosmological constant Λ, curvature of space k, energy density , age of the universe etc. Some assumptions were put forth in order to solve these equations. The current version of the model partially explains several of the observed phenomena that raise questions. Numerical application of the model has yielded the following results, among others: Initially, during the Planck era, at the very beginning of Planck time, , the universe contained a single photon at Planck temperature Planck energy in the Planck volume. During the photon inflation phase (before characteristic time ~10^-9 ), the number of original photons (alphatons) increased at each unit of Planck time and geometrical progression ~n³, where n is the quotient of cosmic time over Planck time . Then, the primordial number of photons reached a maximum of N~10⁸⁹, where it remained constant. It's primordial photons (alphatons) are still present today and represents the essential of the energy contained in the universe via the cosmological constante . Such geometric growth in the number of photons can bring a solution to the horizon problem through exchange and a photon energy volume that is in phase with that of the the volume energy of the universe. The age of the universe in cosmic time that is in line with positive energy conservation (in terms of conventional thermodynamics) and the creation of proton, neutron, electron, and neutrino masses, is ~76 [Gy] (observed . The predicted total mass (p, n, e and n), based on the Maxwell-Juttner relativistic statistical distribution, is ~7x10⁵⁰ . The predicted cosmic neutrino mass is ≤ 8.69x10^-32 (≤ 48.7 ) if based on observations of SN1987A. The temperature variation of the cosmic microwave background (CMB), as measured by Planck, can be said to be partially due to energy variations in the universe (DU/U) during the primordial baryon synthesis (energy jump from the creation of protons and neutrons). In this model, what is usually referred to as dark energy actually corresponds to the energy of the universe that has not been converted to mass, and which acts on the mass created by the energy-mass equivalence principle and the cosmological gravity field, , associated with the cosmological constant, which is high during primordial formation of the galaxies (<1 [Gy]). A look at the Casimir effect makes it possible to estimate a minimum Casimir pressure and thus determine our possible relative position in the universe at cosmic time 0,1813 (t₀/t_W=13,8 /76,1 ). Therefore, from the observed age of 13,8 [Gy], we can derive a possible cosmic age of 76,1 [Gy]. That energy of the universe, when taken into consideration during the formation of the first galaxies (< 1 [Gy]), provides a relatively adequate explanation of the non-Keplerian rotation of galactic masses. Indeed, such residual, non-baryonic energy, when considered in Newton’s gravity equation, adds the term , which can partially explain, without recourse to dark matter, the rotations of some galaxies, such as M33, UGC12591, UGC2885, NGC3198, NGC253, DDO161, UDG44, the MW and the Coma cluster. Today, in the MW, that cosmological gravity force is in the order of 10²⁶ times smaller than the conventional gravity force. The model predicts an acceleration of the mass in the universe (q~ 0,986); the energy associated with curvature is the driving force behind the expansion of the universe, rather than the energy associated with the cosmological constant . An equation to determine expansion is obtained using the energy form of the Friedmann equation relative to Planck power and cosmic time or Planck force acting at the frontier of the universe moving at c. This constant Planck force, from unknown sources, acts everywhere to the expansion of the universe as a stretching effect on the volume. Finally, the model partly explains the value a₀ of the MOND theory. Indeed, a₀ is not a true constant, but depends on the cosmological constant at the time the great structures were formed (~1 [Gy]), as well as an adjustment of the typical mass and dimension of those great structures, such as galaxies. The constant a₀ is a different expression of the cosmological gravity force as expressed by the cosmological constant, Λ, acting through the energy-mass equivalent during the formation of the structures. It does not put in question the value of G.

Abstract: Hox gene clusters are crucial in Embryogenesis. It was observed that some Hox genes were located in order along the telomeric to centromeric direction of the DNA sequence: Hox1, Hox2, Hox3…. These genes were expressed in the same order in the ontogenetic units of the Drosophila embryo along the Anterior-Posterior axis. The two entities (genome and embryo) differ significantly in linear size and in-between distance. This strange phenomenon was named Spatial Collinearity (SP). Later, it was observed that, particularly in the Vertebrates, a Temporal Collinearity (TC) coexists: first is Hox1 expressed, later Hox2 and even later Hox3,…,. Hox clusters are irreversibly elongated along the force direction. According to a Biophysical Model (BM), pulling forces act at the anterior end of the cluster while a cluster fastening applies at the posterior end. During Evolution, the elongated Hox clusters are broken at variable lengths thus split clusters may be created. An Empirical Rule was formulated distinguishing development due to a complete Hox cluster from development due to split Hox clusters. BM can explain this Empirical Rule. In an accidental mutation where the cluster fastening is dismantled, a minimal pulling force can automatically shift the cluster inside the Hox activation domain. This cluster translocation can probably explain the absence of temporal collinearity in Drosophila.

The integration of deep learning methods, especially Convolutional Neural Networks (CNN), and Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has been widely deployed in the field of radar signal processing. Nevertheless, these methods are frequently regarded as black-box models due to the limited visual interpretation of their internal feature representation and parameter organization. In this paper, we propose an innovative approach named C-RISE, which builds upon the RISE algorithm to provide a post-hoc interpretation technique for black-box models used in SAR Images Target Recognition. C-RISE generates saliency maps that effectively visualize the significance of each pixel. Our algorithm outperforms RISE by clustering masks that capture similar fusion features into distinct groups, enabling more appropriate weight distribution and increased focus on the target area. Furthermore, we employ Gaussian blur to process the masked area, preserving the original image structure with optimal consistency and integrity. C-RISE has been extensively evaluated through experiments, and the results demonstrate superior performance over other interpretation methods based on perturbation when applied to neural networks for SAR image target recognition. Furthermore, our approach is highly robust and transferable compared to other interpretable algorithms, including white-box methods.

Seventeen bacterial strains able to suppress plant pathogens have been isolated from healthy Vietnamese crop plants, and taxonomically assigned as members of the Bacillus cereus group. In order to prove their potential as biocontrol agents, we performed a comprehensive analysis which included whole genome sequencing of selected strains, and mining for genes and gene clusters involved in the synthesis of endo- and exotoxins, and secondary metabolites, such as antimicrobial peptides (AMPs). Kurstakin, thumolycin, and other AMPs were detected and characterized by different mass spectrometric methods, such as MALDI-TOF-MS, and LIFT-MALDI-TOF/TOF fragment analysis. Based on their whole genome sequences, the plant-associated isolates were assigned to the following species and subspecies: B. cereus subsp. cereus (6), B. cereus subsp. bombysepticus (5), Bacillus tropicus (2), and Bacillus pacificus. Three isolates represented novel genomospecies. Genes encoding entomopathogenic crystal and vegetative proteins were detected in B. cereus subsp. bombysepticus TK1. In vitro assays revealed that many plant-associated isolates enhanced plant growth and suppressed plant pathogens. Our findings indicated that plant-associated representatives of the B. cereus group are a rich source of putative antimicrobial compounds with potential in sustainable agriculture. However, the presence of virulence genes might restrict their application as biologicals in agriculture.

This paper proposes an active-reactive power collaborative scheduling model with cluster division for the flexible distributed energy resources (DERs) of smart building system to resolve the high complexity of centralized optimal scheduling of massive dispersed DERs in the distribution network. Specifically, the optimization objective of each cluster is to minimize the operational cost, the power loss cost, and penalty cost for the flexibility deficiency, and the second-order cone-based branch flow method is utilized to convert the power flow equations into the linearized cone constraints, reducing the nonlinearity and heavy computation burden of the scheduling model. Customized virtual battery models for the building-integrated flexible DERs are developed to aggregate the power characteristics of flexible resources while quantifying their regulation capacities with time-shifting power and energy boundaries. Moreover, a cluster division algorithm considering the module degree index based on the electrical distance and flexible balance contribution index is formulated for cluster division to achieve the information exchange and energy interaction in the distribution network with high proportion of building-integrated flexible DERs. Comparative studies have demonstrated the superior performance of the proposed methodology on the economic merits and voltage regulation.

In order to clarify the nature of the halogen bond (XB), we considered the prototype noble gas – dihalogen molecule (Ng-X2) systems, focusing on the nature, range and strength of the interaction. We exploited data gained from molecular beam scattering experiments with the measure of interference effects to obtain a suitable formulation of the interaction potential, with the support of high-level ab initio calculations, and charge displacement analysis. The essential interaction components involved in the Ng-X2 adducts have been characterized, pointing at their critical balance in the definition of the XB. Particular emphasis is devoted to the energy stability of the orientational Ng-X2 isomers, the barrier for the X2 hindered rotation, and the influence of the X2 electronic state. The present integrated study returns reliable force fields for molecular dynamics simulations in Ng-X2 complexes that can be extended to systems with increasing complexity and whose properties depend on the selective formation of XB.

African locust bean (Parkia biglobosa (Jacq.) is a multi-purpose economic tree with genetic potentials in sub-Saharan Africa. Its cultivation and production is declining with increased aging and genetically threatened throughout its natural ranges. Research efforts are needed to change the present scenario to sustainable cultivation and utilization, hence this present study. This study was aimed at evaluating genetic diversity and geographical spread relationships of twenty landraces collected from different ecological zones of Nigeria using simple sequence repeat (SSR) markers. Ten SSR markers were screened and five primers (PbL02, PbL03, PbL04, PbL05 and PbL09) were selected based on clear amplification products and reproducible scorable bands. The SSR primers detected a total of 55 alleles ranged from 10 to 14 alleles with a mean of 11. The percentage polymorphisms were high and ranged from 68.75 % in PbL04 to 84.21 % in PbL05 with a mean of 74.16 %. The polymorphic information content (PIC) was in the range of 0.31 in PbL02 to 0.37 in PbL09. The genetic diversity and heterozygosity values ranged from 0.39 to 0.50 and 0.00 to 0.68 while the average genetic distance for all pair wise comparisons was 0.31.The first five Principal Component (PC) accounted for 70.20 % of the total variation out of which PC1 (31.50%) and PC2 (19.20%) extracted 49.70% molecular similarity. The dendrogram resulted in separation of the 19 landraces into three major clusters based on unweighted pair group method with arithmetic average. Cluster I comprised of five landraces: ABNo130 and BENo023; OYNo11, KANo125 and NiNo262 while cluster II had only one (BANo116). Cluster III was diverse comprising 13 landraces: ZANo188, KNNo162, KENo220, GMNo076 and EbNo260, ADNo64, EdNo164, KANo137, KENo217, KwNo270, NiNo241, OsNo206 and PLNo120. The homogeneity of alleles among the studied landraces suggested suspicion of loss of genetic intra-specific variation among the landraces of P. biglobosa which calls for concerted efforts toward better cultivation, conservation, management, utilization and genetic improvement of the species in Nigeria.

This study explores several global and country cluster sustainable development (herein SD) models. It jointly examines constructs from the “Neoliberal-turn” ((e.g., Institutional Enhancers (e.g., business freedom, property rights, government integrity, and judicial effectiveness), Financial Enhancers (e.g., government spending and monetary, trade, investment, and financial freedoms), Foreign Direct Investment, Global Competitiveness-institutions, and Global Competitiveness-innovation-sophistication)), and the “Social-turn” (five SD pillars (e.g., Planet, People, Peace, Prosperity, and Resources), which synthesize the 17 United Nations SD goals); and Happiness and Life Satisfaction (herein H&amp;LS). The study examines a large and diverse set of relationships encompassing 11 years of data of 108 social, economic, and environmental indicators from 129 countries at the global, country cluster, and country levels. We analyze data using structural equation modeling. Remarkably, Planet, an SD pillar constituted by biophysical variables, is the least of the five SD pillars positively related to the "neoliberal-turn" constructs and H&amp;LS. Results show model configurations and scale effects. Furthermore, findings reveal both synergies between neoliberal and the five SD pillars as well as both negative and non-significant relationships among them. The diversity of results calls for further model integration and specificity. Diverse findings entail caution and difficulties in generalizing knowledge. Similarly, results suggest that it is not advisable, since there may be many different and partly legitimate alternatives, to rely on just one perspective (e.g., giving primacy to economic-based analyses) and or in analyses at only one scale. The perspective effects and the stakes involved in SD urgently call for more collaborative efforts at all levels.

We have studied Juniperus oxycedrus subsp. badia in the central and southern Iberian Peninsula. Here, the macrobioclimate ranges from Mediterranean-pluviseasonal-oceanic and Mediterranean-pluviseasonal-continental, thermotype from thermomediterranean and supramediterranean. The relevés were taken following the Braun-Blanquet phytosociological methodology. A statistical treatment was applied to establish a separation among Juniperus communities. To understand the presence of Juniperus communities in territories dominated by species of the genus Quercus, we applied the formula of Thornthwaite to calculate potential evapotranspiration. The general cluster analysis clearly separates two groups of plant communities and separates the different associations in each group. All plant communities growing on rocky crests and in steeply extreme sloping areas are significantly influenced by the soil. The Ombroclimatic Index does not explain the presence of plant communities influenced by substrate: so, we propose a new Ombroedaphoxeric Index which explains the presence of the Juniperus communities in territories with a thermotype ranging from the thermo to the supramediterranean belt. The areas of distribution of Juniperus species are expanding due to the spread of rocky areas: this phenomenon causes a rise in edaphoxerophilous areas and a decrease in climatophilous one. We propose four new plant associations and with this update on their structures and floristic compositions, it is possible to make an efficient form of conservation in both territories studied (Spain and Portugal) with specific cross-border-cooperation projects.

CD74 is a type II cell surface receptor found to be highly expressed in several hematological and solid cancers, due to its ability to activate pathways associated with tumor cell survival and proliferation. Over the past 16 years, CD74 is emerging as a commonly detected fusion partner in multiple oncogenic fusion proteins. Studies have found CD74 fusion proteins in a range of cancers including lung adenocarcinoma, inflammatory breast cancer, and pediatric acute lymphoblastic leukemia. To date, there are five known CD74 fusion proteins, CD74-ROS1, CD74-NTRK1, CD74-NRG1, CD74-NRG2a, and CD74-PDGFRB, with a total of 16 different variants, each with unique genetic signatures. Importantly, the occurrence of CD74 in the formation of fusion proteins has not been well explored despite the fact that ROS1 and NRG1 families utilize CD74 as the primary partner for the formation of oncogenic fusions. Fusion proteins known to be oncogenic drivers, including those of CD74, are typically detected, and targeted after standard chemotherapeutic plans fail and the disease relapses. The analysis reported herein provides insights into early intervention of CD74 fusions and highlight the need for improved routine assessment methods so that targeted therapies can be applied while they are most effective.

The article presents a new scalable iterative method for linear programming called the “apex method”. The key feature of this method is constructing a path close to optimal on the surface of the feasible region from a certain starting point to the exact solution of linear programming problem. The optimal path refers to a path of minimum length according to the Euclidean metric. The apex method is based on the predictor-corrector framework and proceeds in two stages: quest (predictor) and target (corrector). The quest stage calculates a rough initial approximation of linear programming problem. The target stage refines the initial approximation with a given precision. The main operation used in the apex method is an operation that calculates the pseudoprojection, which is a generalization of the metric projection to a convex closed set. This operation is used both in the quest stage and in the target stage. A parallel algorithm using a Fejér mapping to compute the pseudoprojection is presented. An analytical estimation of the parallelism degree of this algorithm is obtained. Also, an algorithm implementing the target stage is given. The convergence of this algorithm is proven. An experimental study of the scalability of the apex method on a cluster computing system is described. The results of applying the apex method to solve problems from the Netlib-LP repository are presented.

Peptides are promising drug development frameworks thanks to their high target selectivity, tolerability and relatively low production cost. However, despite the fact that several thousand potentially therapeutic peptides reported, only sixty have arrived at the market. This concerning low proportion is partially explained by undesired properties such as peptide-induced hemolytic activity. Hence, we aim to get a better insight into the chemical space of hemolytic peptides using a novel approach based on network science and interactive data mining as an alternative to design more effective peptide drugs with low hemolytic activity. Metadata networks (METNs) were used to characterize and find general patterns associated to hemolytic peptides, whereas Half-Space Proximal Networks (HSPNs), created using five different two-way dissimilarity measures, represented the hemolytic peptide space. Then, using the best candidate HSPNs, we extracted various scaffolds that capture information of almost all the chemical space but avoiding peptide overrepresentation. Such scaffolds can have many applications, such as training accurate ML-based prediction models, constructing one-class multi-query similarity searching models and characterizing the diversity of hemolytic peptides using a manageable set of peptides. Finally, by means of an alignment-free approach, we reported 47 putative hemolytic motifs, which might provide hints about the mechanisms of hemolysis and can also be used as toxic signatures when developing novel peptide-based drugs.

The persistence of geographic inequities in vaccination coverage often evidences the presence of zero-dose and missed communities and vulnerabilities to vaccine-preventable diseases. These inequities were exacerbated in many places during the COVID-19 pandemic, due to severe disruptions to vaccination services. Understanding changes in zero-dose prevalence and associated risk factors in the context of the COVID-19 pandemic is, therefore, critical to designing effective strategies to reach vulnerable populations. Using data from nationally representative household surveys conducted before the COVID-19 pandemic in 2018 and during the pandemic in 2021 in Nigeria, we fitted Bayesian geostatistical models to map the distribution of three vaccination coverage indicators: receipt of DTP1, MCV1 and any of the four basic vaccines (BCG, OPV0, DTP1 and MCV1), and corresponding zero-dose estimates independently at 1x1 km resolution and the district level during both time periods. We also explored changes in the factors associated with non-vaccination at the national and regional levels using multilevel logistic regression models. Our results revealed no increases in zero-dose prevalence due to the pandemic at the national level, although considerable increases were observed in a few districts. We found substantial subnational heterogeneities in vaccination coverage and zero-dose prevalence both before and during the pandemic, showing broadly similar patterns in both time periods. Areas with relatively higher zero-dose prevalence occurred mostly in the north and a few places in the south in both time periods. We also found consistent areas of low coverage and high zero-dose prevalence using all three zero-dose indicators, revealing areas in greatest need. At the national level, risk factors related to socioeconomic/ demographic status (e.g., maternal education), maternal access to and utilization of health services and remoteness were strongly associated with the odds of zero dose in both time periods, while those related to communication were mostly relevant before the pandemic. These associations were also supported at the regional level, but we additionally identified risk factors specific to zero dose in each region; for example, communication and cross-border migration in the northwest. Our findings can help guide tailored strategies to reduce zero-dose prevalence and boost coverage levels in Nigeria.

One of the biggest challenges in characterizing 2-D image topographies is finding a low-dimensional parameter set that can succinctly describe, not so much image patterns themselves, but the nature of these patterns. The 2-D Cluster Variation Method (CVM), introduced by Kikuchi in 1951, can characterize very local image pattern distributions using configuration variables, identifying nearest-neighbor, next-nearest-neighbor, and triplet configurations. Using the 2-D CVM, we can characterize 2-D topographies using just two parameters; the activation enthalpy and the interaction enthalpy. Initial investigations with two different representative topographies (``scale-free-like'' and ``rich club-like'') produce interesting results when brought to a CVM free energy minimum. Additional phase space investigations, where one of these two parameters has been set to zero, identify useful parameter ranges. Careful comparison of the analytically-predicted configuration variables versus those obtained when performing computational free energy minimization on a 2-D grid show that the computational results differ significantly from the analytic solution. The 2-D CVM can potentially function as a secondary free energy minimization within the hidden layer of a neural network, providing a basis for extending node activations over time and allowing temporal correlation of patterns.

Background: Globally, women’s responsibility for unpaid care work (UCW) remains a barrier to gender equality and women’s empowerment. Rwanda, a low-income country, has a legal and policy framework for promoting gender equality but remains a patriarchal society with women responsible for UCW. Reseaux des Femmes, a local NGO, has been delivering a programme targeted at reducing and redistributing the UCW of women with the objective of gender transformational change. However, there has been no impact evaluation of their intervention to date. Methods/Design: The impact evaluation will be a proof-of-concept critical realist cluster control trial (CRcCT) to evaluate for which women, how and under what circumstances the intervention reduces and redistributes women’s UCW, promote gender equality and women’s empowerment, and improves women’s quality of life. Four clusters of villages in each of five districts in Rwanda will be randomly allocated to the intervention or control arms, and all eligible households, those headed by a couple with at least one child under 12 years, will be recruited. This will yield a sample of around 550 intervention households and 550 control. Discussion: This protocol describes the design of mixed-methods research to evaluate an intervention in Rwanda aimed at reducing and redistributing the time women spend on UCW, thereby promoting gender equality and women’s empowerment. This paper will contribute to our understanding of interventions for transforming gender relations from a scholarly perspective. From a policy perspective, it will act as a proof of concept of Reseaux des Femmes’ Programme.

The coronavirus 2019 (COVID-19) pandemic was caused by a positive sense single-stranded RNA (ssRNA) severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, other human coronaviruses (hCoVs) exist, of which Middle East Respiratory Syndrome (MERS) and SARS-CoV (SARS) showed higher mortality rates without causing a pandemic. As of December 2022, SARS-CoV-2 has resulted in over 6.6 million deaths worldwide through an array of acute to chronic pathologies. Historical pandemics include smallpox and influenza with efficacious therapeutics utilized to reduce overall disease burden. Therefore, immune system process analysis is required to compare innate and adaptive immune system interactions. Lymphatic system organs include bone marrow and thymus using a network of nodes throughout which white blood cells traverse glycolipid membranes utilizing cytokines and chemokine gradients that affect cell development, differentiation, proliferation, and migration processes as well as genetic factors affecting cell receptor expression. Innate processes involve antigen-presenting cells and B lymphocyte cellular responses to pathogens relevant to other viral and bacterial infections but also in oncogenic diseases. Such processes utilize cluster of differentiation (CD) marker expression, major histocompatibility complexes (MHC), pleiotropic interleukins (IL) and chemokines. The adaptive immune system consists of Natural Killer (NK) and T cells. Other viruses are also contributory to cancer including human papillomavirus (cervical carcinoma ), Epstein-Barr virus (EBV) ( lymphoma), hepatitis B and C (hepatocellular carcinoma) and human T cell leukemia virus-1 (adult T-cell leukemia). Bacterial infections also increase the risk of developing cancer( e.g. H. pylori). Therefore, as the above factors can cause both morbidity and mortality along-side being transmitted within clinical and community settings, it is appropriate to now examine advances in single cell sequencing, FACS analysis and many other laboratory techniques that allow insights into discoveries of newer cell types. These developments offer improved clarity and understanding that over-lap with known autoimmune conditions that could be affected by innate B cell or T cell responses to SARS-CoV-2 infection. Thus, this review quantifies and outlines the nature of specific receptors and proteins relevant to clinical laboratories and medical research by documenting both innate and adaptive immune system cells within current coronavirus immunology case study data and other pathologies to date.

The COVID-19 pandemic has resulted in a wide range of spatial interventions to slow down the spread of the virus. The spatial limitations of narrow public circulation spaces within informal settlements, which house over one billion people around the world, make it impossible for pedestrians to practice physical distancing (or social distancing). In this paper, we propose a flexible mathematical method, named the Cluster Lane Method, for turning a planar circulation network of any size or complexity into a network of unidirectional lanes, making physical distancing possible in narrow circulation spaces by limiting face-to-face interactions. New notions and theorems about oriented graphs in graph theory are introduced. The paper ends with a discussion of the potential implementation of this cost-efficient, low-tech, sustainable solution, and with the introduction of a novel unidirectional tactile paving for the visually impaired.

Generalized relativistic pseudopotentials (GRPP) of atomic cores implying the use of different potentials for atomic electronic shells with different principal quantum numbers give rise to accurate and reliable relativistic electronic structure models of atoms, molecules, clusters, and solids. These models readily incorporate the effects of Breit electron-electron interactions and one-loop quantum electrodynamics effects. Here we report the computational procedure for evaluating one-electron integrals of GRPP over contracted Gaussian functions. This procedure was implemented in a library of routines named LIBGRPP, which can be integrated into existing quantum chemistry software, thus enabling the application of various methods to solve the many-electron problem with GRPPs. Pilot applications to electronic transitions in the ThO and UO2 molecules using the new library and intermediate-Hamiltonian Fock space relativistic coupled cluster method are presented. The results clearly demonstrate that rather economical tiny-core GRPP models can exceed in accuracy relativistic all-electron models defined by Dirac-Coulomb and Dirac-Coulomb-Gaunt Hamiltonians.

This paper presents an in-depth contextualized tutorial on Agricultural IoT (Agri-IoT), covering the fundamental concepts, assessment of routing architectures and protocols, and performance optimization techniques via systematic survey and synthesis of related literature. The negative impacts of climate change and the increasing global population on food security and unemployment threats have motivated the adoption of the wireless sensor network (WSN)-based Agri-IoT as an indispensable underlying technology in precision agriculture and greenhouses to improve food production capacities and quality. However, most related Agri-IoT testbed solutions have failed to achieve their performance expectations due to the lack of an in-depth and contextualized reference tutorial that provides a holistic overview of communication technologies, routing architectures, and performance optimization modalities based on users’ expectations. Thus, although IoT applications are founded on a common idea, each use case (e.g., Agri-IoT) varies based on specific performance and users expectations as well as its technological, architectural, and deployment requirements. Likewise, the agricultural setting is a unique and hostile area where conventional IoT technologies do not apply, hence the need for this tutorial. Consequently, this tutorial addresses these via the following contributions: (1) a systematic overview of the fundamental concepts, technologies, and architectural standards of WSN-based Agri-IoT, (2) an evaluation of the technical design requirements of a robust, location-independent, and affordable Agri-IoT, (3) a comprehensive survey of the benchmarking fault tolerance techniques, communication standards, routing and medium access control (MAC) protocols, and WSN-based Agri-IoT testbed solutions, and (4) an in-depth case study on how to design a self-healing, energy-efficient, affordable, adaptive, stable, autonomous, and cluster-based WSN-specific Agri-IoT from a proposed taxonomy of multi-objective optimization (MOO) metrics that can guarantee an optimized network performance. Furthermore, this tutorial established new taxonomies of faults, architectural layers, and MOO metrics for cluster-based Agri-IoT (CA-IoT) networks and a 3-tier objective framework with remedial measures for designing an efficient associated supervisory protocol for cluster-based Agri-IoT networks.

The use of Light Amplification by Stimulated Emission of Radiation (i.e., LASERs or lasers) by the U.S. Department of Defense is not new and includes laser weapons guidance, laser-aided measurements, even lasers as weapons (e.g., Airborne Laser). Lasers in support of telecommunications is also not new. The use of laser light in fiber optics shattered thoughts on communications bandwidth and throughput. Even the use of lasers in space is no longer new. Lasers are being used for satellite-to-satellite crosslinking. Laser communication can transmit orders-of-magnitude more data using orders-of-magnitude less power and can do so with minimal risk of exposure to the sending and receiving terminals. What is new is using lasers as the uplink and downlink between the terrestrial segment and the space segment of satellite systems. More so, the use of lasers to transmit and receive data between moving terrestrial segments (e.g., ships at sea, airplanes in flight) and geosynchronous satellites is burgeoning. This manuscript examines the technological maturation of employing lasers as the signal carrier for satellite communications linking terrestrial and space systems. The purpose of the manuscript is to develop key performance parameters (KPPs) to inform U.S. Department of Defense initial capabilities documents (ICDs) for near-future satellite acquisition and development. By appreciating the history and technological challenges of employing lasers rather than traditional radio frequency sources for satellite uplink and downlink signal carrier, this manuscript recommends ways for the U.S. Department of Defense to employ lasers to transmit and receive high bandwidth, large-throughput data from moving platforms that need to retain low probabilities of detection, intercept, and exploitation (e.g., carrier battle group transiting to a hostile area of operations, unmanned aerial vehicle collecting over adversary areas). The manuscript also intends to identify commercial sector early-adopter fields and those fields likely to adapt to laser employment for transmission and receipt.