Number Of Complexes Research Articles

Generalized Linear Driving Force Formulas for Diffusion and Reaction in Porous Catalysts

Approximate models are a fast and most often precise tool for determining the effectiveness factor for heterogeneous catalysis processes that are realized in the real world. They are also frequently applied as robust transient models describing the work of a single catalyst pellet or as a part of a more complex model, for example, a reactor model, where mass balances for the gas phase and solid phase are necessary. So far, approximate models for diffusion and reaction processes have been presented for processes described by a single balance equation. In the present work, approximate models without the mentioned limitation are presented and discussed. In addition, simple rules are shown for the development of other complex approximate models without tedious derivation in the complex domain. The formulas considered in this work are typical long-time approximations of the transient process. The accuracy is good, especially in the range of small and intermediate Thiele modulus values.

Deep Learning and Graphical User Interfaces for the Production of Custom Emojis

This study paper digs into the complex domain of human-PC association through looks, investigating different ways to deal with address difficulties in different situations. Presents a novel, right off the bat, multi-characteristic face-to-animation framework for web-based entertainment, creating comparing emoticon in light of facial credits like inclination and glasses. Then handles the impediments of customary CNNs in look acknowledgment by proposing a multi-facet highlight acknowledgment calculation utilizing a three-channel CNN. This calculation separates definite highlights from eyes/eyebrows, mouth, and the whole face, accomplishing higher acknowledgment rates with less organization layers. Perceiving feelings from to some extent concealed faces because of the Coronavirus pandemic is the another concentration, which proposes a SLPPE-based approach that concentrates highlights from the upper face to beat cover impediment. This strategy outflanks normal CNN approaches with great precision on benchmark datasets. A chart convolutional network (GCN)- based calculation for look acknowledgment in the wild is introduced, developing a face diagram in view of key activity units and using a consideration guide to feature essential highlights. This strategy demonstrates viable on both lab-controlled and wild datasets. At last, a two-layered milestone include map (LFM) is proposed for perceiving unobtrusive facial miniature articulations, exhibiting prevalence over customary strategies and demonstrating its freedom of demeanor power and pertinence to true situations. This overview paper exhaustively investigates these imaginative methods, offering significant bits of knowledge into the developing scene of human-PC cooperation through looks.

Application of 1D model for overland flow simulations on 2D complex domains

High computational demand limits the applications of two-dimensional (2D) shallow water equation models for high resolution overland flow simulations, while one-dimensional (1D) models can achieve higher computing efficiency. This study applied a 1D hydrodynamic model to surrogate 2D models for overland flow simulations on complex 2D domains. With one dimension reduced, the surrogate model simulation would have acceptable accuracy with much higher computing efficiency. The surrogating is fulfilled through mimicking 2D models in mesh generations, so that the 1D channel network is generated in such a way that it geometrically covers the whole domain without overlapping and intersection, and hydrologically follows the steepest slopes. Several benchmark cases on 2D domains in both laboratory and field scales with complex geometry, where no 1D models have been ever applied, are used to compare the 1D and 2D model simulations. The comparisons demonstrate that the 1D model does have potentials to efficiently simulate overland flow on 2D complex domains with accuracy comparable to 2D models.

Video‐based modeling examples and comparative self‐explanation prompts for teaching a complex problem‐solving strategy

AbstractBackgroundIn example‐based learning, examples are often combined with generative activities, such as comparative self‐explanations of example cases. Comparisons induce heavy demands on working memory, especially in complex domains. Hence, only stronger learners may benefit from comparative self‐explanations. While static text‐based examples can be compared easily, this is challenging for transient video‐based modelling examples used in complex domains because simultaneous processing of two videos is not feasible.ObjectivesTo allow for such comparisons, we combined video‐based modelling examples with static representations (i.e., summarizing tables) of the observed optimal and a suboptimal solution of the problem‐solving process. A comparative self‐explanation prompt asked learners to compare the different solution approaches. Our study investigated the impact of video‐based modelling examples versus independent problem‐solving on cognitive load and problem‐solving skill development. Moreover, we investigated the effects of comparative versus sequential self‐explanation prompts, depending on learners' prior knowledge.MethodsIn an experiment, 118 automotive apprentices learned a car malfunction diagnosis strategy. Apprentices were divided into three groups: (1) modelling examples with comparative self‐explanation prompts, (2) modelling examples with sequential prompts, and (3) no examples or prompts. Diagnostic knowledge and skills were assessed before and after the intervention. Cognitive load was measured retrospectively.Results and conclusionsDespite no observed effects on cognitive load, modelling examples enhanced diagnostic knowledge and diagnostic skills with scaffolds, though not independent diagnostic skills without scaffolds. The need for more practice opportunities to foster independent diagnostic skills is assumed. Additionally, comparative prompts seem promising for learners with higher prior knowledge.TakeawaysVideo‐based modelling examples were more beneficial for learning than practising to apply the diagnostic strategy. Static representations allow for comparisons of video examples and comparative prompts are promising for learners with higher prior knowledge (cf. expertise‐reversal effect). Further research, especially on the effects on cognitive load, is necessary.

Navigating Legal Responsibilities in Chinese Private Equity Investments: A Comprehensive Study on Human Resource Management Practices

Human Resource Management (HRM) plays a pivotal role in the success and sustainability of ventures, particularly within the complex domain of Chinese private equity investments. This study delves into the multifaceted legal responsibilities inherent in HRM practices within this context, aiming to provide comprehensive insights essential for effective organizational management and compliance. Drawing upon specific Chinese laws, empirical data, and relevant citations, the study scrutinizes crucial areas such as employment law compliance, corporate governance obligations, adherence to industry-specific regulations, and compliance with international labor standards. Through a detailed examination of key legal obligations and regulatory frameworks, the research illuminates the challenges faced by human resource professionals and private equity firms in China, offering valuable insights for practitioners and organizational leaders alike. By emphasizing proactive compliance initiatives and the significance of ethical labor practices, this study contributes to the cultivation of a regulatory-aware and ethically conscious environment conducive to organizational success in the dynamic landscape of Chinese private equity investments.

Bridging the p-Special Functions between the Generalized Hyperbolic and Trigonometric Families

Here, we study the extension of p-trigonometric functions sinp and cosp family in complex domains and p-hyperbolic functions sinhp and the coshp family in hyperbolic complex domains. These functions satisfy analogous relations as their classical counterparts with some unknown properties. We show the relationship of these two classes of special functions viz. p-trigonometric and p-hyperbolic functions with imaginary arguments. We also show many properties and identities related to the analogy between these two groups of functions. Further, we extend the research bridging the concepts of hyperbolic and elliptical complex numbers to show the properties of logarithmic functions with complex arguments.

ENHANCING DECISION-MAKING IN WATER RESOURCES MANAGEMENT: AN INNOVATIVE ASSESSMENT OF EXPERT CONSISTENCY AND COMPETENCE

In the assessment of principles and guidelines to achieve specific goals before strategies in water resources and related sectors are formulated, the quality of experts participating in making group decisions should be evaluated. This paper introduces an innovative approach designed to evaluate the quality of group members based on their consistency and deviations from the group's decision. The group members are considered as alternatives within a multi-criteria framework, employing several typical performance indicators as criteria to assess their competence and compliance with the group. Considering the policy-making, the paper provides a rationale for possibly excluding certain members from the decision-making process to prevent making unsustainable decisions. A case study is presented about the evaluation of the importance of six Ramsar sites in Serbia, facing imminent threats from water regime disturbances and climate change-induced droughts. Seven experts participated in the process and the results revealed that several experts displayed the poorest performance across all three prioritization schemes. This suggests the necessity for re-evaluating their judgments or considering their exclusion from the final decision-making process. The proposed assessment procedure holds promise for enhancing the potential to derive sustainable solutions in any complex and critical domain of water resources policy-making and strategic planning.

A novel quantum Dempster's rule of combination for pattern classification

Dempster's rule of combination (DRC) is widely used for uncertainty reasoning in intelligent information system, which is generalized to complex domain recently. However, as the increase of identification framework elements, the computational complexity of Dempster's rule of combination increases exponentially. To address this issue, we propose a novel quantum Dempster's rule of combination (QDRC) by means of Toffoli gate. The QDRC combination process is completely implemented using quantum circuits. To validate the efficiency of the proposed QDRC, we conduct simulation experiments on IBM and IonQ quantum cloud platforms. Besides, we implement the QDRC algorithm to the real quantum computer of IonQ Harmony. The experiment results show that the result of QDRC is getting more and more accurate as the number of measurements increases. In addition, we propose a quantum multisource information fusion method on the basis of QDRC. Finally, we demonstrate its effectiveness to solve pattern classification problem under several diverse real UCI datasets.

A comprehensive review on zero-shot-learning techniques

Advancements in computational capabilities have enabled the implementation of advanced deep learning models across various domains of knowledge, yet the increasing complexity and scarcity of data in specialized areas pose significant challenges. Zero-shot learning (ZSL), a subset of transfer learning, has emerged as an innovative solution to these challenges, focusing on classifying unseen categories present in the test set but absent during training. Unlike traditional methods, ZSL utilizes semantic descriptions, like attribute lists or natural language phrases, to map intermediate features from the training data to unseen categories effectively, enhancing the model’s applicability across diverse and complex domains. This review provides a concise synthesis of the advancements, methodologies, and applications in the field of zero-shot learning, highlighting the milestones achieved and possible future directions. We aim to offer insights into the contemporary developments in ZSL, serving as a comprehensive reference for researchers exploring the potentials and challenges of implementing ZSL-based methodologies in real-world scenarios.

Dissecting the molecular puzzle of the editosome core in Arabidopsis organelles

Over the last decade, the composition of the C-to-U RNA editing complex in embryophyte organelles has turned out to be much more complex than first expected. While PPR proteins were initially thought to act alone, significant evidences have clearly depicted a sophisticated mechanism with numerous protein-protein interaction involving PPR and non-PPR proteins. Moreover, the identification of specific functional partnership between PPRs also suggests that, in addition to the highly specific PPRs directly involved in the RNA target recognition, non-RNA-specific ones are required. Although some of them, such as DYW1 and DYW2, were shown to be the catalytic domains of the editing complex, the molecular function of others, such as NUWA, remains elusive. It was suggested that they might stabilize the complex by acting as a scaffold. We here performed functional complementation of the crr28–2 mutant with truncated CRR28 proteins mimicking PPR without the catalytic domain and show that they exhibit a specific dependency to one of the catalytic proteins DYW1 or DYW2. Moreover, we also characterized the role of the PPR NUWA in the editing reaction and show that it likely acts as a scaffolding factor. NUWA is no longer required for efficient editing of the CLB19 editing sites once this RNA specific PPR is fused to the DYW catalytic domain of its partner DYW2. Altogether, our results strongly support a flexible, evolutive and resilient editing complex in which RNA binding activity, editing activity and stabilization/scaffolding function can be provided by one or more PPRs.

Counterfactual thoughts in complex causal domain: content, benefits, and implications for their function

The reliability of previous findings on two crucial aspects of counterfactual thinking, namely the content of counterfactual modifications and their impact on future performance, has been questioned for the frequent use of tasks characterised by simple causal domains, that restrict participants’ possibility to consider a broad range of modifications. To overcome this limitation, we utilised a new experimental task featuring a complex causal domain to investigate such key aspects. The results indicated that participants tend to generate counterfactuals about elements outside their control, especially when presented with a challenging version of the task (Study 1a) and, to a lesser extent, when they receive negative feedback on their performance (Study 1b). Moreover, despite occasional implementations of actions mentioned in controllable counterfactuals, being engaged in counterfactual thinking did not lead to subsequent performance improvements (Study 2). The implications of these findings for the debate on the function of counterfactual thinking are discussed.

Solutions of Surface-Wave Dispersion and Attenuation in Stratified Viscoelastic Media Using a Spectral-Element Approach

ABSTRACT Efficient and accurate calculation for the dispersion and attenuation of the surface waves in viscoelastic media is numerically challenging because the eigen wavenumbers are located in the complex domain. In this study, we propose a semianalytical spectral-element method (SASEM), which can determine the complex eigen wavenumbers by solving linear eigenvalue problems. By simplifying the structure of the eigenvalue problem, we significantly improve the calculation efficiency. The implementation of the frequency-dependent automatic discretization, semi-infinite element, and mode filter guarantees the correctness and accuracy of the modal solutions. Because no root-searching schemes are required, the root-skipping problem is naturally avoided. The numerical tests show that the SASEM can provide sufficiently accurate solutions with much less computation cost than traditional Muller’s method. Meanwhile, SASEM exhibits high flexibility when applied to media the parameters for which vary continuously with depth. To demonstrate the effectiveness of SASEM for complicated dispersion features, the dispersion curves and eigen wavefields of the viscoelastic media with a low-velocity layer are also analyzed. The results of numerical tests indicate the versatility, efficiency, and accuracy of our method. With further study, the proposed SASEM has the potential to become a promising tool for the investigation and retrieval of viscoelastic subsurface structures.

Heteromers Formed by GPR55 and Either Cannabinoid CB1 or CB2 Receptors Are Upregulated in the Prefrontal Cortex of Multiple Sclerosis Patients.

Multiple sclerosis (MS) is an autoimmune, inflammatory, and neurodegenerative disease of the central nervous system for which there is no cure, making it necessary to search for new treatments. The endocannabinoid system (ECS) plays a very important neuromodulatory role in the CNS. In recent years, the formation of heteromers containing cannabinoid receptors and their up/downregulation in some neurodegenerative diseases have been demonstrated. Despite the beneficial effects shown by some phytocannabinoids in MS, the role of the ECS in its pathophysiology is unknown. The main objective of this work was to identify heteromers of cell surface proteins receptive to cannabinoids, namely GPR55, CB1 and CB2 receptors, in brain samples from control subjects and MS patients, as well as determining their cellular localization, using In Situ Proximity Ligation Assays and immunohistochemical techniques. For the first time, CB1R-GPR55 and CB2R-GPR55 heteromers are identified in the prefrontal cortex of the human brain, more in the grey than in the white matter. Remarkably, the number of CB1R-GPR55 and CB2R-GPR55 complexes was found to be increased in MS patient samples. The results obtained open a promising avenue of research on the use of these receptor complexes as potential therapeutic targets for the disease.

Metal Complexes in Biology and Medicine: The System Cadmium ((II))/ Iron ((II))/ Zinc ((II)) - α - Aminobutenoic Acid

Metal ions play an important role in biological system. The importance of metal ions to essential functions of living systems and for wellbeing of living organisms is well known. Metal ions are fundamental elements for the maintenance of life spans of the human, animals, and plants. Recent advances in inorganic chemistry have made possible formation of number of transition metal complexes with organic ligand of interest, which can be used as therapeutic agent. In coordination compounds studies, knowledge of the stability constants of complexes is necessary for preliminary quantitative treatment. The present technique involving the use of paper electrophoresis is described for the study of equilibria in binary complex systems in solution. The method is based on the movement of a spot of a metal ion in an electric field at various pH’s of background electrolyte. A graph of pH versus mobility was used to obtained information in the binary complexes and to calculate its stability constants. Using this method , the stability constants of binary complexes metal (II) – α – aminobutenoic acid have been determined to be ( 3.78 ± 0.01, 2.48 ± 0.03 ); ( 3.27 ± 0.04, 2.33 ± 0.07 ); and (4.45 ± 0.02, 2.66 ± 0.05); (logarithm stability constant values) for cadmium (II), iron (II) and zinc (II) complexes, respectively, at ionic strength 0.01 Mol / L and a temperature of 35º C.

The Industry 5.0 (I50) paradigm, blockchain-based applications and the smart city

PurposeAgainst the background of the I50 paradigm, this paper queries in what ways blockchain and blockchain-based applications deployed in the smart city context facilitate the integration of the I50 paradigm in smart urban contexts.Design/methodology/approachA mixed methods approach is applied. First, by means of desk research and thematic literature review, a conceptual model integrating the I50 paradigm, smart city and blockchain-based solutions is built. Second, science mapping bibliometric analysis (SciMat) based on keywords’ co-occurrence is applied to a sample of 491 research articles to identify key domains of blockchain-based applications’ use in smart city. Third, a semi-systematic literature review complements insights gained through SciMat. Fourth, the findings are interpreted through the precepts of the conceptual model devised earlier.FindingsThe key blockchain-based applications in smart cities pertain to two domains, i.e. the foundational, service facilitation-oriented domain, including security (and safety), networks, computing, resource management and the service delivery-oriented domain, including mobility, energy and healthcare. Blockchain serves as the key building block for applications developed to deliver functions specific to each of the thus identified domains. A substantial layering of blockchain-based tools and applications is necessary to advance from the less to the more complex functional domains of the smart city.Originality/valueAt the conceptual level, the intricacies of the (making of the) I50 paradigm are discussed and a case for I50 – smart city – blockchain nexus is made. Easton’s input–output model as well as constructivism is referenced. At the empirical level, the key major domains of blockchain-based applications are discussed; those that bear the prospect of integrating the I50 paradigm in the smart city are highlighted. At the methodological level, a strategic move is made aimed at restoring the literature review’s role as subservient to the key line of exploration, to justify and ultimately support it, rather than to showcase the literature review as the ultimate purpose for itself.

Design of Optimal Control Systems in the Frequency Domain by the Functional of the Generalized Work

The paper considers the problem of analytical design of optimal controllers (ADOC) for one-dimensional linear stationary objects according to the functional of generalized work (FGW) A. A. Krasovsky. The use of FGW in comparison with the quadratic performance functional greatly simplifies the calculation of the optimal controller — the calculation of its matrix of coefficients mainly consists in solving the linear matrix Lyapunov equation, which, in contrast to the nonlinear matrix Riccati equation, fundamentally reduces the amount of calculations. In addition, the use of FGW provides the best stability margins of the designed system in terms of amplitude and phase. This work is devoted to the development of a method for solving the ADOC problem A. A. Krasovsky in the frequency (complex) domain, which reduces the determination of the transfer function coefficients of the optimal controller for an object of order n to the solution of the corresponding system of n linear algebraic equations. In this regard, the proposed method for solving the ADOC problem by A. A. Krasovsky differs by a much smaller amount of calculations in comparison with the standard method, in which the Lyapunov equation is solved with the desired matrix of dimensions n * n. The proposed method for the synthesis of optimal control systems, which has an analytical nature, became the basis for solving the inverse problem ADOC A. A. Krasovsky, which consists in determining the values of the weight coefficients of the FGW, which provide the given primary quality indicators of the synthesized control system. Using its relations, a relatively simple method for calculating the FGW coefficients based on the given values of the error coefficients for the designed dynamic system has been developed.

Enhancing energy‐efficient sleep scheduling for Narrowband Internet of Things devices in coordinated 5G networks within smart environments

SummaryThis research project takes on a crucial role in the quickly changing field of integrated 5G networks inside smart environments by concentrating on the creation of an extremely effective sleep scheduling system designed especially for Narrowband Internet of Things (NB‐IoT) devices. This work introduces a unique method for precisely controlling the nodes' sleep schedules through the use of convolutional neural network (CNN) architecture, which optimizes both energy usage and operating patterns. The principal goal still stands to guarantee the extended lifetime of operation and dependability of NB‐IoT devices in the larger framework of intelligent ecosystems driven by synchronized 5G networks. To achieve its objectives, the research explores a number of complex domains and employs cutting‐edge technologies and techniques, such as CNN‐based pattern recognition. This method's real‐time component is essential since it allows for prompt modifications to sleep schedules in order to optimize energy savings. Further boosting the devices' effectiveness and flexibility is continuous contact with a central server, which guarantees that the devices are updated with the most recent data and instructions. Essentially, the main objective of this study is to greatly increase the energy economy and operational lifetime of NB‐IoT devices, which will allow for stable and long‐lasting IoT deployments in the context of 5G networks in intelligent settings. This advancement is not only a boon for businesses and industries leveraging IoT technology but also a substantial step toward building smarter, more energy‐efficient, and resilient smart ecosystems that benefit society as a whole.

Power Losses Reduction in Three-Phase Unbalanced Distribution Networks Using a Convex Optimization Model in the Complex Domain

This article presents a solution methodology to minimize power losses in three-phase unbalanced distribution networks. This approach involved an efficient complex-domain model that is categorized under mixed-integer convex optimization. The methodology employed consisted of efficient load rotation at each constant power node via a three-phase rotation matrix that allows defining each load connection to minimize the expected power imbalance at the terminals of the substation, as well as the total grid power losses, and improve voltage profile performance at each system phase. The load imbalance, expressed as a percentage, can be defined as a function of the active, reactive, or apparent power. In addition, considering the complex-domain representation of three-phase electrical networks under steady-state conditions, a mixed-integer convex model was formulated to reduce the power imbalances. With the purpose of determining the initial and final power losses of these distribution systems, the successive approximations method was employed to address the three-phase power flow problem. As a result, numerical validations in the IEEE 25-bus system and a 35-node three-phase feeder showed that the final active power losses vary depending on the objective function analyzed. Therefore, for the test feeders studied, it is necessary to evaluate each objective function, with the aim of finding the one that yields the best numerical results. Power losses reductions of about 3.8056 % and 6.8652 % were obtained for both test feeders via the proposed optimization methodology. All numerical validations were performed in the Julia programming environment, using the JuMP optimization tool and the HiGHS solver.

Mental states via possessive predication: the grammar of possessive experiencer complex predicates in Persian

Persian possesses a number of stative complex predicates with dâshtan ‘to have’ that express certain kinds of mental state. I propose that these possessive experiencer complex predicates be given a formal semantic treatment involving possession of a portion of an abstract quality by an individual, as in the analysis of property concept lexemes due to Francez and Koontz-Garboden (Language 91(3):533–563, 2015; Natural Language and Linguistic Theory 34:93–106, 2016; Semantics and morphosyntactic variation: Qualities and the grammar of property concepts, Oxford University Press, 2017). Augmented with an analysis of prepositional phrases introducing the target of the mental state and an approach to gradability in terms of measure functions (Wellwood in Measuring predicates, PhD dissertation, University of Maryland, College Park, 2014), the analysis explains various properties of possessive experiencer complex predicates, including the behavior of target phrases, the ability of the non-verbal element to be modified by a range of adjectives, the direct participation of the non-verbal element in comparative constructions, and the ability of degree expressions to modify both the non-verbal element and the VP containing the complex predicate. Theoretically, the analysis ties transitive mental state expressions to the grammar and semantics of property concept sentences, which are expressed via possessive morphosyntax cross-linguistically, and connects with syntactic proposals that independently argue for a universal underlyingly possessive morphosyntax for mental state predicates (Noonan in Case and syntactic geometry, PhD dissertation, McGill University, 1992; Hale and Keyser in Prolegomenon to a theory of argument structure, MIT Press, 2002). The work here also motivates modifications to Francez and Koontz-Garboden’s original proposal, and opens new questions in the original empirical domain of the analysis of possessive predicating strategies for the expression of property concept sentences.

Structural characterization of methylation-independent PP2A assembly guides alphafold2Multimer prediction of family-wide PP2A complexes

Dysregulation of phosphorylation-dependent signaling is a hallmark of tumorigenesis. Protein phosphatase 2 (PP2A) is an essential regulator of cell growth. One scaffold subunit (A) binds to a catalytic subunit (C) to form a core AC heterodimer, which together with one of many regulatory (B) subunits forms the active trimeric enzyme. The combinatorial number of distinct PP2A complexes is large, which results in diverse substrate specificity and subcellular localization. The detailed mechanism of PP2A assembly and regulation remains elusive and reports about an important role of methylation of the carboxy terminus of PP2A C are conflicting. A better understanding of the molecular underpinnings of PP2A assembly and regulation is critical to dissect PP2A function in physiology and disease. Here, we combined biochemical reconstitution, mass spectrometry, X-ray crystallography and functional assays to characterize the assembly of trimeric PP2A. In vitro studies demonstrated that methylation of the carboxy-terminus of PP2A C was dispensable for PP2A assembly in vitro. To corroborate these findings, we determined the X-ray crystal structure of the unmethylated PP2A Aα-B56ε-Cα trimer complex to 3.1 Å resolution. The experimental structure superimposed well with an Alphafold2Multimer prediction of the PP2A trimer. We then predicted models of all canonical PP2A complexes providing a framework for structural analysis of PP2A. In conclusion, methylation was dispensable for trimeric PP2A assembly and integrative structural biology studies of PP2A offered predictive models for all canonical PP2A complexes.