Phenylacetylene reacts with the binuclear ruthenium(II) organometallics of type [Ru(PPh3)2(CO)Cl]2(μ-κ2C,O-RL) (1) (RL = (C6H2O-1-Me-4-CHNH-6)2R and R = C3H6 and C6H4) furnishing the alkyne inserted products of type [Ru(PPh3)2(CO)Cl]2(μ-κ2C,O–RL′) (2) (RL′ = (C6H2O-1-C8H6–2-Me-4-CHNH-6)2R and R = C3H6 and C6H4) in excellent yield. In this 1 → 2 conversion, phenylacetylene inserts into the Ru-C(aryl) bond of 1 and expands the four-membered Ru(C,O) chelate ring in 1 to six-membered in 2. Single crystal X-ray structure determination of [Ru(PPh3)2(CO)Cl]2(μ-κ2C,O–C6H4L′) (2b) has revealed the presence of two ruthenium(II) centers and a distorted octahedral RuC2P2OCl coordination sphere. The three trans directions are defined by the (C, O), (C, Cl), and (P, P) pairs. The ruthenium⋯ruthenium distance (12.602 Å) in 2b rules out any Ru∙∙∙∙Ru non-bonding interaction. The RuO bond in 2 is appreciably shorter (∼0.18 Å) than that in 1. The spectroscopic and electrochemical characteristics of type 2 complexes are also reported. The redox potential of type 2 complexes are significantly less (∼300 mV) than that in 1. Electronic structure and absorption patterns of the complexes are ascertained with the help of computational studies.

The reaction of the ortho-metalated binuclear ruthenium(II) organometallics [Ru(PPh3)2(CO)Cl]2(& micro;-kappa 2C,O-RL) (2) (where RL = (C6H2O-2-CHNH-3-Me-5)2R and R = C2H4, C3H6 and C6H10) with Liacac (lithium acetylacetonate) furnished the complexes[Ru(PPh3)2(CO)(acac)]2(& micro;-kappa 1C-RL) (3) (where RL = (C6H2OH-2-CHN-3-Me-5)2R and R = C2H4, C3H6 and C6H10). During the reaction, dissociation of ruthenium-oxygen and ruthenium-chlorine bonds and iminium-phenolato to imine-phenol prototropic shift occurred. In the 2 -> 3 conversion, a change in the rotational conformation is noticed which is sterically controlled. This 2 -> 3 conversion is irreversible. Single crystal X-ray structure determination of[Ru(PPh3)2(CO)(acac)]2(& micro;-kappa 1C-C2H4L) (3a) has revealed that the two ruthenium centers are in 2+ oxidation state and are bridged by the RL Schiff base ligand. The complex 3a has a distorted octahedral RuC2O2P2 coordination sphere and the three trans directions are defined by the (C, O), (C, O), and (P, P) pairs. The ruthenium & ctdot;ruthenium distance (14.267 & Aring;) in the complex 3a is outside the range for any Ru & ctdot;Ru non-bonding interaction. The spectroscopic (1H NMR, UV-vis, IR) and electrochemical properties of these complexes are presented here. Density functional theory along with time-dependent density functional theory were used to study the electronic structure and the absorption patterns of the synthesized complexes.

The reaction of Ru(kappa 2C,O-RL)(PPh3)2(CO)(Cl) [kappa 2C,O-RL is C6H2O-2-CHNHC6H4R(p)-3-Me-5 and R = Me, OMe, Cl] with excess sodium p-methylphenolate (p-MeC6H4ONa) or sodium methoxide (MeONa) in dichloromethane-methanol medium afforded the paramagnetic binuclear ruthenium(III) complexes of the type [Ru (mu:kappa 3C,N,O-RL)(PPh3)(CO)(OR ')]2 [kappa 3C,N,O-RL is C6H2O-2-CHNC6H4R(p)-3-Me-5] in moderate yield. Three different R and two different R ' groups have been used in this study: R = Me and R ' is p-MeC6H4, 2(Me); R = OMe and R ' is p-MeC6H4, 2(OMe); R = Cl and R ' is p-MeC6H4, 2(Cl); R and R ' = Me, 3. The binding of the phenolato/ alkoxo ligand is attended with the cleavage of the Ru-C(aryl), Ru-Cl and one of the Ru-P bonds in Ru(kappa 2C,ORL)(PPh3)2(CO)(Cl) and the RL ligand is now coordinated with one of the metal centers in the complexes via the imine nitrogen and the phenolato oxygen atoms whereas the aryl carbon atom of the RL ligand bridges the other ruthenium center. These are the first examples of structurally characterized binuclear ruthenium(III) complexes in which the RL ligand behaves as a bridging tridentate ligand. These complexes have very similar spectral (UV-vis, IR, EPR) and electrochemical properties which are also reported. Structure determination of [Ru(mu:kappa 3C, N,O-MeL)(PPh3)(CO)(OR ')]2 (R ' = p-MeC6H4 and Me) has revealed a distorted octahedral RuC2O2NP coordination sphere with the pairs (C, O), (C, N), and (P, O) defining the three trans directions. The Ru & ctdot;Ru distances in the complexes are clearly outside of the range for a Ru-Ru single bond. The magnetic and X-band EPR spectral measurements indicate the absence of any strong magnetic interaction between the two low-spin Ru(III) centers of the complexes.

2-mercaptopyridine (H-pySH), 5-chloro-2-mercaptopyridine (Cl-pySH) and 5-trifluoromethyl-2-mercaptopyridine (F3C-pySH) ligands react with the multiply-bonded paramagnetic dirhenium(III,II) complex [Re-2(mu-O2CCH3)Cl-4(mu-dppm)(2)] (1) [dppm is Ph2PCH2PPh2] in refluxing ethanol to afford the paramagnetic substitution products of the type [Re-2(mu-dppm)(2)(mu-R-pyS)(2)Cl-2]Cl (2(R)) [R = H, Cl, CF3]. These are the first examples of paramagnetic dirhenium complexes that contain the bridging mercaptopyridine ligand. These complexes have very similar spectral (UV-vis, IR, EPR) and electrochemical properties which are also reported. The identity of 2(H) and 2(CF3) has been established by single-crystal X-ray structure determination (ReRe distance similar to 2.29 & Aring;). The electronic structures and optical properties of the complexes are scrutinized by density functional theory (DFT) and time-dependent DFT studies. DFT calculation shows that the highest occupied molecular orbital (HOMO) corresponds to a delta* interaction between the d-orbitals of rhenium atoms and pi* interaction between the sulphur atoms and the rhenium centers whereas the lowest unoccupied molecular orbital (LUMO) is the Re-2 pi* based orbital.

It is very important to access a rich music dataset that is useful in a wide variety of applications. Currently, available datasets are mostly focused on storing vocal or instrumental recording data and ignoring the requirement of its visual representation and retrieval. This paper attempts to build an XML-based public dataset, called SANGEET, that stores comprehensive information of Hindustani Sangeet (North Indian Classical Music) compositions written by famous musicologist Pt. Vishnu Narayan Bhatkhande. SANGEET preserves all the required information of any given composition including metadata, structural, notational, rhythmic, and melodic information in a standardized way for easy and efficient storage and extraction of musical information. The dataset is intended to provide the ground truth information for music information research tasks, thereby supporting several data-driven analysis from a machine learning perspective. We present the usefulness of the dataset by demonstrating its application on music information retrieval using XQuery, visualization through Omenad rendering system. Finally, we propose approaches to transform the dataset for performing statistical and machine learning tasks for a better understanding of Hindustani Sangeet. The dataset can be found at https://github.com/cmisra/Sangeet.

Complex biological networks, encompassing metabolic pathways, gene regulatory systems, and protein-protein interaction networks, often exhibit scale-free structures characterized by heavy-tailed degree distributions. However, empirical studies reveal significant deviations from ideal power law behavior, underscoring the need for more flexible and accurate probabilistic models. In this work, we propose the Compounded Burr (CBurr) distribution, a novel four parameter family derived by compounding the Burr distribution with a discrete mixing process. This model is specifically designed to capture both the body and tail behavior of real-world network degree distributions with applications to biological networks. We rigorously derive its statistical properties, including moments, hazard and risk functions, and tail behavior, and develop an efficient maximum likelihood estimation framework. The CBurr model demonstrates broad applicability to networks with complex connectivity patterns, particularly in biological, social, and technological domains. Extensive experiments on large-scale biological network datasets show that CBurr consistently outperforms classical power-law, log-normal, and other heavy-tailed models across the full degree spectrum. By providing a statistically grounded and interpretable framework, the CBurr model enhances our ability to characterize the structural heterogeneity of biological networks.

The heterogeneous phase reaction of [Rh(L-Me(sb))(PPh3)(2)Cl-2] (1) with the sodium salt of three different pyridine-2-olato ligands [Na(R-Opy), R = H, Me, NO2] in dichloromethane-acetone-water medium afforded the complexes of type [Rh(L-al)(PPh3)(R-Opy)](2) (2(R)) in excellent yield. These are the first examples of dirhodium(III) acyl complexes with the bridging pyridine-2-olato ligand. The penta coordinated dirhodium(III) acyl complexes are formed by the cleavage of the RhCl and RhPPh3 bonds of the precursor complex 1. The two R-Opy ligands bridge the two rhodium centers through their O-Opy donor centers. The conversion of 1 -> 2(R) is attended with the concomitant hydrolysis of the aldiminium function in 1. The spectral (UV-vis, IR, NMR) data of the complexes are reported. The identity of 2(Me) has been established by single crystal X-ray structure determination which revealed distorted trigonal bipyramidal RhCNO2P coordination sphere. The Rh, C, N and O-phenolato atoms define the equatorial plane whereas Rh, O-Opy and P atoms define the axial plane. The electronic structures and absorption spectra of the complexes are also scrutinized by the density functional theory (DFT) and time-dependent DFT calculations.

The homogeneous phase reaction of [Ru(η2-RL)(PPh3)2(CO)(Cl)] (1) [η2-RL is C6H2O-2-CHNHC6H4R(p)-3-Me-5 and R is OMe, Cl] with the sodium salts of p-nitrobenzoic acid [NaPNB] and m-nitrobenzoic acid [NaMNB] afforded the complexes of the type [Ru(η1-RL)(PPh3)2(CO)(PNB)] (2(R)) and [Ru(η1-RL)(PPh3)2(CO)(MNB)] (3(R)) respectively in excellent yield [η1-RL is C6H2OH-2-CHNC6H4R(p)-3-Me-5]. When 1 was reacted with the nitrobenzoic acid instead of their sodium salt, the Ru─C(aryl) bond cleavage products [Ru(PPh3)2(CO)(Cl)(PNB)] (4) and [Ru(PPh3)2(CO)(Cl)(MNB)] (5) were obtained. The spectral (UV–vis, IR, NMR) and electrochemical data of the complexes are reported. In dichloromethane solution the type 2(R) and 3(R) complexes display two successive quasi-reversible one electron oxidation processes whereas the complexes 4 and 5 display only one oxidation process. The crystal structures of [Ru(η1-ClL)(PPh3)2(CO)(MNB)] (3(Cl)) and [Ru(PPh3)2(CO)(Cl)(MNB)] (5) are reported, which revealed a distorted octahedral RuP2C2O2 coordination sphere for 3(Cl) and RuP2CO2Cl coordination sphere for 5. The electronic structure and absorption spectra of the complexes are scrutinized by DFT and TD-DFT analyses. The complex 3(Cl) was tested for its ability to exhibit DNA-binding activity.

Recommender systems (RSs) are a type of information processing tool that uses a lot of different types of information filtering processes to figure out what a customer wants and to give them relevant information. There are numerous statistical machine-learning techniques that can be utilised to better comprehend the principles and challenges of RSs. This chapter aims to look into these machine-learning techniques and the mechanisms of their involvement in this context. RS techniques are often divided into three categories: collaborative filtering (CF), content-based filtering (CBF), and hybrid. We will mainly talk about the techniques used in the CF method that allow users to discover new content that is different from what they have seen before. This chapter evaluates and discusses the utility of traditional network clustering techniques such as Louvain, Infomap, and label propagation algorithms for the development of neighbourhood-based robust RSs. We also look into and incorporate a nodality-based network clustering method to make another neighbourhood-based robust RS. This chapter mainly discusses a strategy for building robust RSs that integrates a network clustering approach with neighbourhood-based RSs, especially the adsorption algorithm. Extensive experimental assessments on real world datasets demonstrate the utility of the integrated neighbourhood-based RSs.

The four-membered ruthenium(II) organometallics Ru(η2-RL)(PPh3)2(CO)(Cl) (1) where η2-RL = C6H2O-2-CHNHC6H4R(p)-3-Me-5 and R = CH3 reacts with 2-(2-hydroxyphenyl)benzothiazole (Hhpbt) and 2-(2-hydroxyphenyl)benzoxazole (Hhpbo) in refluxing ethanol to afford Ru(PPh3)2(CO)(hpbt)Cl (2) and Ru(PPh3)2(CO)(hpbo)Cl (3) respectively in excellent yield. In the course of these reactions, the Ru − C(aryl) bond in 1 is cleaved, and the RL ligand is no longer coordinated with the metal center in the products. The spectral (UV–vis, IR, 1H NMR) and electrochemical data of the complexes are reported. The identity of complex 2 has been established by single-crystal X-ray structure determination. The electronic structure and the absorption spectra of the complexes are scrutinized by DFT and TD-DFT analyses. The complexes were also tested for their ability to exhibit DNA-binding activity.

In this study, we present a new hybrid model that integrates the anatomical and topological characteristics of a brain network. The aim is to effectively capture the structural and/or topological alterations that take place in networks as individuals transition from a healthy control state to the stage of Alzheimer's disease. The utilisation of a brain atlas allows for the assessment of the Euclidean distance between two specific regions of interest (ROIs) inside the brain. This distance is considered a metric of anatomical distance, providing a quantitative representation of an anatomical characteristic. Conversely, the measurement of topological similarity, which assesses a characteristic of topology, is determined by calculating the cosine distance between nodes following the embedding of the whole real brain network into a vector space of dimensionality d. The empirical findings obtained using real-brain network data indicate that the hybridization approach well captures the observed topological variations during the transition from a healthy cognitive state (HC) to Alzheimer's disease (AD).

Alzheimer's disease (AD) is one of the significant neurocognitive disorders, generally occurring among older population that progressively worsens with time initially showing symptoms of mild cognitive impairment (M C I). People suffering from AD usually lose their thinking skills and eventually fail to manage their daily routine tasks because of decline in successive memory functions. There exist well-documented evidences suggesting constant deterioration of communication among anatomical regions of the brain in individuals with Alzheimer's disease (AD) and mild cognitive impairment (M C I). Recent studies also exhibit the differences in topological properties of brain network between the individual patients suffering from AD and MC I compared to healthy controls (HC). In this work, we propose a novel hybrid model NeuroANATOP by employing both the anatomical and the topological properties of a brain network to capture the structural and/or topological changes occurring in networks during the progression from H C to MC I and H C to AD. Given a brain atlas, the Euclidean distance between two regions of interest (ROIs) in the brain is regarded as a measure of anatomical distance, quantifying an anatomical property. On the other hand, the topological similarity, quantifying a topological property, is defined by taking the cosine distance between nodes after embedding the whole real brain network into a d-dimensional vector space. Empirical results over real-brain network data suggest that the proposed hybridization successfully models the topological differences observed across the transition from HC to MCI and HC to AD.

Quadruply bonded dirhenium(III) complex (Bu4N)2[Re2Cl8] (1) reacts with sodium salts of dimethyldithiocarbamate, diethyldithiocarbamate and pyrrolidinedithiocarbamate in ethanol under stirring condition to afford the dithiocarbamato chelated chloro bridged linear trinuclear rhenium complexes of the type [Re3(μ-Cl)2(η2-LR)6(OH2)2][ReO4] (2(LR)) where LR represents the dithiocarbamato ligands [LR = S2CNMe2, 2(LMe); S2CNEt2, 2(LEt) and S2CN(CH2)4, 2(LPyr)]. These are the first examples of rhenium complexes in which the novel linear [Re–Cl–Re–Cl–Re] unit is present. These complexes have very similar spectral and electrochemical properties which are also reported. Structural identity of the complex 2(LEt) has been established by X-ray crystal structure determination. Density functional theory calculation shows that for 2(LEt), both the HOMO and LUMO are mainly composed of rhenium d-orbitals and LEt ligands p orbitals.

Abstract Thiophenol (HLH), 4-methylthiophenol (HLMe) and 4-chlorothiophenol (HLCl) react with dirhenium(III,II) complex [Re2(µ-O2CCH3)Cl4(µ-dppm)2] (1) (dppm = Ph2PCH2PPh2) in refluxing dry toluene to afford the diamagnetic Re2 6+ complexes of the type [Re2(μ-LR)2Cl4(μ-dppm)2] (2(LR)) [LR = C6H5S–, p-MeC6H4S– and p-ClC6H4S–]. These are the first examples of thiophenolato bridged dirhenium complexes with a Re2 6+ core containing dppm ligand. The spectral properties of the complexes are reported. In the electrochemical experiment, the type 2(LR) complexes show one quasi-reversible one-electron oxidation and one-electron reduction waves. The identity of 2(LMe) has been established by single-crystal X-ray structure determination (Re–Re distance = 2.6178(5) Å) and is shown to have an edge-shared bi-octahedral structure. DFT analysis shows that the highest occupied molecular orbitals are mainly composed of metal δ-based orbitals whereas the lowest unoccupied molecular orbitals are metal π*-based orbitals and the electronic ground state for the 2(LR) complexes is σ2π2δ*2δ2. Time-dependent DFT (TD-DFT) analysis shows that the sharp peak at 455 nm in the experimental UV-vis spectrum is mainly due to the [Cl(π)]→[Re2(π*)] transition. Graphical Abstract

In this study, we consider large-scale network data sets from different disciplines, namely social networks, collaboration networks, web graphs, citation networks, biological networks, product co-purchasing networks, temporal networks, communication networks, ground-truth networks, and brain networks. We study several individual data sets from each discipline. These data sets are publicly available at http://snap.stanford.edu/data/index.html Usage of the proposed probability models for the paper "Searching for a new probability distribution for modeling non-scale-free heavy-tailed real-world networks" is available at https://github.com/tanujit123/nonscalefree

Perhaps the most recent controversial topic in network science research is to determine whether real-world complex networks are scale-free or not. Recently, Broido and Clauset [A.D. Broido, A. Clauset, Nature Communication, 10, 1017 (2019)] asserted that the degree distributions of real-world networks are rarely power law under statistical tests. Such complex networks, including social, biological, information, temporal, and brain networks, are often heavy-tailed where the assumption on the scale-free nature of real-world heavy-tailed networks become insignificant as the complex system evolves over time. The failure of power law distribution in fitting the degree distribution data is mainly due to the presence of an identifiable non-linearity within the entire degree distribution in a log-log scale of a complex heavy-tailed network. In this study, we attempt to address this issue by proposing a new class of heavy-tailed probability distributions for modeling the entire degree distributions of complex networks. We introduce a new family of generalized Lomax models (GLM) to capture the non-linearity of these heavy-tailed networks. These newly introduced GLM-type distributions provide better fitting and greater flexibility to the entire node degree distribution of complex networks. Several statistical properties of the proposed model, such as extreme value and inferential statistical properties, are derived into this context. Interestingly, the GLM family belongs to the basin of attraction of Frechet distribution, a heavy-tailed extreme value distribution. Rigorous experimental analysis showcases the excellent performance of the proposed family of distributions while fitting the heavy-tailed real-world complex networks over fifty real-world datasets in comparison with benchmark probability models. Our results show that GLM-type distributions are not rare, able to model almost 90% of the tested networks accurately compared to benchmark probability models.

Abstract A new method has been proposed to generalize Burr-XII distribution, also called Burr distribution, by adding an extra parameter to an existing Burr distribution for more flexibility. In this method, the exponent of the Burr distribution is modeled using a nonlinear function of the data and one additional parameter. The models of this newly introduced generalized Burr family can significantly increase the flexibility of the former Burr distribution with respect to the density and hazard rate shapes. Families expanded using the method proposed here is heavy-tailed and belongs to the maximum domain of attractions of the Frechet distribution. The method is further applied to yield three-parameter classical Pareto and generalized exponentiated distributions which shows the broader application of the proposed idea of generalization. A relevant model of the new generalized Burr family has been considered in detail, with particular emphasis on the hazard functions, stochastic orders, estimation procedures, and testing methods are derived. Finally, as empirical evidence, the new distribution is applied to the analysis of large-scale heavy-tailed network data and compared with other commonly used distributions available for fitting degree distributions of networks. Experimental results suggest that the proposed Burr distribution with nonlinear exponent better fits the large-scale heavy-tailed networks better than the popularly used Marhsall-Olkin generalization of Burr and exponentiated Burr distributions.

Detecting communities or the modular structure of real-life networks (e.g. a social network or a prod-uct purchase network) is an important task because the way a network functions is often determined by its communities. Traditional approaches to community detection involve modularity-based algorithms, which generally speaking, construct partitions based on heuristics that seek to maximize the ratio of the edges within the partitions to those between them. On the other hand, node embedding approaches represent each node in a graph as a real-valued vector and is thereby able to transform the problem of community detection in a graph to that of clustering a set of vectors. Existing node embedding ap-proaches are primarily based on, first, initiating random walks from each node to construct a context of a node, and then make the vector representation of a node close to its context. However, standard node embedding approaches do not directly take into account the community structure of a network while constructing the context around each node. To alleviate this, we propose a community structure aware node embedding approach, where we incorporate an initial combinatorial approach-based partition infor-mation into the objective function of node embedding. We demonstrate that our proposed combination of the combinatorial and the embedding approaches for community detection outperforms a number of combinatorial-based baselines on a wide range of real-life and synthetic networks of different sizes and densities. (c) 2021 Elsevier B.V. All rights reserved.

Sodium salts of dimethyldithiocarbamate (L-Me), diethyldithiocarbamate (L-Et) and pyrrolidinedithiocarbamate (L-Pyr) react with the double-bonded diamagnetic dirhenium(III,III) complex Re-2(mu-dppm)(2)(mu-Cl)(2)Cl-4 (1) (dppm = Ph2PCH2PPh2) in refluxing ethanol to afford the diamagnetic substitution products of the type [Re-2(mu-dppm) (eta(2)-LR)(2)(mu-eta(2)-LR)(2)](PF6)(2), where LR represents the dithiocarbamato ligands [LR = S2CNMe2, 2(L-Me); S2CNEt2, 2 (L-Et) and S2CN(CH2)(4), 2(L-Pyr)]. The complexes were isolated as hexafluoridophosphate salts. These are the first examples of dirhenium(III,III) complexes that contain bridging dithiocarbamato ligand along with the dppm ligand. The spectral and electrochemical properties of the complexes are reported. The identity of 2(L-Me) and 2 (L-Et) was established by single crystal X-ray structure determination (Re -Re distance similar to 2.55 angstrom) and is shown to have edge-shared bioctahedral structure. The electronic structure and the absorption spectra of the complexes are scrutinized by the density functional theory (DFT) and time-dependent density functional theory (TD-DFT) analyses. DFT analysis shows that the highest occupied molecular orbitals are mainly metal d* based orbitals. The complex 2(L-Et) was tested for its ability to exhibit DNA-binding activity. The antifungal activity of the complex 2 (L-Et) was also determined against the fungal pathogen Colletotrichum gloeosporioides (CG).

Real-world heavy-tailed networks are claimed to be scale-free, meaning that the degree distributions follow the classical power-law. But it is evident from a closer observation that there exists a clearly identifiable non-linear pattern in the entire degree distribution in a log-log scale. Thus, the classical power-law distribution is often inadequate to fit the large-scale complex network data sets. The presence of this non-linearity can also be linked to the recent debate on scarcity versus the universality of scale-free networks. The search, therefore, continues to develop probabilistic models that can efficiently capture the crucial aspect of heavy-tailed and long-tailed behavior of the entire degree distribution of real-world complex networks. This paper proposes a new variant of the popular Lomax distribution, termed as modified Lomax (MLM) distribution, which can efficiently fit the entire degree distribution of real-world networks. The newly introduced MLM distribution arises from a hierarchical family of Lomax distributions and belongs to the basin of attraction of Frechet distribution. Some interesting statistical properties of MLM including characteristics of the maximum likelihood estimates have been studied. Finally, the proposed MLM model is applied over several real-world complex networks to showcase its excellent performance in uncovering the patterns of these heavy-tailed networks.