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The purpose of this paper is to propose a two-degrees-of-freedom wire-driven 4SPS/U rigid‒flexible parallel trunk joint mechanism based on spring, in order to improve the robot’s athletic ability, load capacity and rigidity, and to ensure the coordination of multi-modal motion.

First, based on the rotation transformation matrix and closed-loop constraint equation of the parallel trunk joint mechanism, the mathematical model of its inverse position solution is constructed. Then, the Jacobian matrix of velocity and acceleration is derived by time derivative method. On this basis, the stiffness matrix of the parallel trunk joint mechanism is derived on the basis of the principle of virtual work and combined with the deformation effect of the rope driving pair and the spring elastic restraint pair. Then, the eigenvalue distribution of the stiffness matrix and the global stiffness performance index are used as the stiffness evaluation index of the mechanism. In addition, the performance index of athletic dexterity is analyzed. Finally, the distribution map of kinematic dexterity and stiffness is drawn in the workspace by numerical simulation, and the influence of the introduced spring on the stiffness distribution of the parallel trunk joint mechanism is compared and analyzed. It is concluded that the stiffness in the specific direction of the parallel trunk joint mechanism can be improved, and the stiffness distribution can be improved by adjusting the spring elastic structure parameters of the rope-driven branch chain.

Studies have shown that the wire-driven 4SPS/U rigid‒flexible parallel trunk joint mechanism based on spring has a great kinematic dexterity, load-carrying capacity and stiffness performance.

The soft-mixed structure is not mature, and there are few new materials for the soft-mixed mixture; the rope and the rigid structure are driven together with a large amount of friction and hindrance factors, etc.

It ensures that the multi-motion mode hexapod mobile robot can meet the requirement of sufficient different stiffness for different motion postures through the parallel trunk joint mechanism, and it ensures that the multi-motion mode hexapod mobile robot in multi-motion mode can meet the performance requirement of global stiffness change at different pose points of different motion postures through the parallel trunk joint mechanism.

The trunk structure is a very critical mechanism for animals. Animals in the movement to achieve smooth climbing, overturning and other different postures, such as centipede, starfish, giant salamander and other multi-legged animals, not only rely on the unique leg mechanism, but also must have a unique trunk joint mechanism. Based on the cooperation of these two mechanisms, the animal can achieve a stable, flexible and flexible variety of motion characteristics. Therefore, the trunk joint mechanism has an important significance for the coordinated movement of the whole body of the multi-sport mode mobile robot (Huang Hu-lin, 2016).

In this paper, based on the idea of combining rigid parallel mechanism with wire-driven mechanism, a trunk mechanism is designed, which is composed of four spring-based wire-driven 4SPS/U rigid‒flexible parallel trunk joint mechanism in series. Its spring-based wire-driven 4SPS/U rigid‒flexible parallel trunk joint mechanism can make the multi-motion mode mobile robot have better load capacity, mobility and stiffness performance (Qi-zhi et al., 2018; Cong-hao et al., 2018), thus improving the environmental adaptability and reliability of the multi-motion mode mobile robot.

This study aims at investigating the impact of the disclosure and the Shariah governance on the financial performance in MENASA (Middle East, North Africa and Southeast Asia) Islamic banks.

We use the Generalized Least Squares (GLS) regression models to check the interdependence relationship between the disclosure, the Shariah governance and the financial performance of 47 Islamic banks (IBs) from ten countries operating in MENASA region. The sample period is from 2012 to 2019. In these regressions models, Return on Assets (ROA) and Return on Equity (ROE) are the dependent variables. The disclosure and the Shariah governance indicators are the independent factors. To measure the Shariah governance, we use the three sub-indices, which are the Board of Directors (BOD), the Audit Committee (AC) and the Shariah Supervisory Board (SSB). Size, Leverage and Age of the bank are used as control variables. We also used The Generalized Method of Moments (GMM) and the three-stage least squares (3SLS) estimations for robustness check.

Result shows a negative relationship between the disclosure and the two performance measures in IBs. Furthermore, as far as the governance indicators are concerned, we found that the BOD and AC, as well as the BOD and SSB, have a positive and significant impact on the ROA and ROE, respectively. This reveals that good governance had a significant association with higher performance in MENASA IBs.

The paper considers both IBs that adopt mandatory as well as voluntary AAOIFI standards and the GLS method to investigate the impact of the AAOIFI disclosure and the Shariah governance on ROA and ROE. Also, it uses the GMM and the 3SLS estimations for robustness check. It is relevant for researchers, policymakers and stakeholders concerned with IBs' performance.

In the course of group analytic psychotherapy, where we discovered the power of the therapeutic effects, there occurred the need of group analytic psychotherapy researches. Psychotherapeutic work in general, and group psychotherapy in particular, are hard to measure and put into some objective frames. Researches, i. e. measuring of changes in psychotherapy is a complex task, and there are large disagreements. For a long time, the empirical-descriptive method was the only way of research in the field of group psychotherapy. Problems of researches in group psychotherapy in general, and particularly in group analytic psychotherapy can be reviewed as methodology problems at first, especially due to unrepeatability of the therapeutic process.

The basic polemics about measuring of changes in psychotherapy is based on the question whether a change is to be measured by means of open measuring of behaviour or whether it should be evaluated more finely by monitoring inner psychological dimensions. Following the therapy results up, besides providing additional information on the patient's improvement, strengthens the psychotherapist's self-respect, as well as his respectability and credibility as a scientist.

Digital resources such as smart applications reviews and online feedback information are important sources to seek customers’ feedback and input. This paper aims to help government entities gain insights on the needs and expectations of their customers. Towards this end, we propose an aspect-based sentiment analysis hybrid approach that integrates domain lexicons and rules to analyse the entities smart apps reviews. The proposed model aims to extract the important aspects from the reviews and classify the corresponding sentiments. This approach adopts language processing techniques, rules, and lexicons to address several sentiment analysis challenges, and produce summarized results. According to the reported results, the aspect extraction accuracy improves significantly when the implicit aspects are considered. Also, the integrated classification model outperforms the lexicon-based baseline and the other rules combinations by 5% in terms of Accuracy on average. Also, when using the same dataset, the proposed approach outperforms machine learning approaches that uses support vector machine (SVM). However, using these lexicons and rules as input features to the SVM model has achieved higher accuracy than other SVM models.

In an era marked by heightened geopolitical uncertainties, such as international conflicts and economic instability, the dynamics of energy markets assume paramount importance. Our study delves into this complex backdrop, focusing on the intricate interplay the between traditional and emerging energy sectors.

This study analyzes the interconnections among green financial assets, renewable energy markets, the geopolitical risk index and cryptocurrency carbon emissions from December 19, 2017 to February 15, 2023. We investigate these relationships using a novel time-frequency connectedness approach and machine learning methodology.

Our findings reveal that green energy stocks, except the PBW, exhibit the highest net transmission of volatility, followed by COAL. In contrast, CARBON emerges as the primary net recipient of volatility, followed by fuel energy assets. The frequency decomposition results also indicate that the long-term components serve as the primary source of directional volatility spillover, suggesting that volatility transmission among green stocks and energy assets tends to occur over a more extended period. The SHapley additive exPlanations (SHAP) results show that the green and fuel energy markets are negatively connected with geopolitical risks (GPRs). The results obtained through the SHAP analysis confirm the novel time-varying parameter vector autoregressive (TVP-VAR) frequency connectedness findings. The CARBON and PBW markets consistently experience spillover shocks from other markets in short and long-term horizons. The role of crude oil as a receiver or transmitter of shocks varies over time.

Green financial assets and clean energy play significant roles in the financial markets and reduce geopolitical risk. Our study employs a time-frequency connectedness approach to assess the interconnections among four markets' families: fuel, renewable energy, green stocks and carbon markets. We utilize the novel TVP-VAR approach, which allows for flexibility and enables us to measure net pairwise connectedness in both short and long-term horizons.

This study aims to analyze the imbalances in the public finance structure of Pakistan’s economy and highlight the need for comprehensive reforms. Specifically, it aims to contribute to the empirical literature by analyzing the relationship between fiscal vulnerability, financial stress and macroeconomic policies in Pakistan’s economy between 1971 and 2020.

The study develops an index of fiscal vulnerability, an index of financial stress and an index of macroeconomic policies. The fiscal vulnerability index is based on the patterns of fiscal indicators resulting from past trends of the selected variables in Pakistan’s economy. The financial stress in Pakistan is caused from the financial disorders that are acknowledged in the composite index, which is based on variables with the potential to indicate periods of stress stemming from the foreign exchange market, the securities market and the monetary policy components. The macroeconomic policies index is developed to analyze the mechanism through which fiscal vulnerability and financial stress have influenced macroeconomic policies in Pakistan. The causal association between fiscal vulnerability, financial stress and macroeconomic policies is analyzed using the auto-regressive distributive lags approach.

There exists a long-run relationship between the three indices, and a bi-directional causality between fiscal vulnerability and macroeconomic policies.

This study contributes to the development of a fiscal monitoring mechanism, which has the basic purpose of analyzing the refinancing risk of public liabilities. Moreover, it focuses on fiscal vulnerability from a macroeconomic perspective. The study tries to develop a framework to assess fiscal vulnerability in light of “The Risk Octagon” theory, which focuses on three risk components: fiscal variables, macroeconomic-disruption-associated shocks and non-fiscal country-specific variables. The initial contribution of this work to the literature is to develop a framework (a fiscal vulnerability index, financial stress index and macroeconomic policies index) for effective and result-oriented macro-fiscal surveillance. Moreover, empirical literature emphasized and advised developing countries to develop their own capacity mechanisms to assess their fiscal vulnerability in light of the IMF guidelines regarding vulnerability assessments. This study thus attempts to fulfill the said gap identified in literature.

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.