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Limited by the types of sensors, the state information available for musculoskeletal robots with highly redundant, nonlinear muscles is often incomplete, which makes the control face a bottleneck problem. The aim of this paper is to design a method to improve the motion performance of musculoskeletal robots in partially observable scenarios, and to leverage the ontology knowledge to enhance the algorithm’s adaptability to musculoskeletal robots that have undergone changes.

A memory and attention-based reinforcement learning method is proposed for musculoskeletal robots with prior knowledge of muscle synergies. First, to deal with partially observed states available to musculoskeletal robots, a memory and attention-based network architecture is proposed for inferring more sufficient and intrinsic states. Second, inspired by muscle synergy hypothesis in neuroscience, prior knowledge of a musculoskeletal robot’s muscle synergies is embedded in network structure and reward shaping.

Based on systematic validation, it is found that the proposed method demonstrates superiority over the traditional twin delayed deep deterministic policy gradients (TD3) algorithm. A musculoskeletal robot with highly redundant, nonlinear muscles is adopted to implement goal-directed tasks. In the case of 21-dimensional states, the learning efficiency and accuracy are significantly improved compared with the traditional TD3 algorithm; in the case of 13-dimensional states without velocities and information from the end effector, the traditional TD3 is unable to complete the reaching tasks, while the proposed method breaks through this bottleneck problem.

In this paper, a novel memory and attention-based reinforcement learning method with prior knowledge of muscle synergies is proposed for musculoskeletal robots to deal with partially observable scenarios. Compared with the existing methods, the proposed method effectively improves the performance. Furthermore, this paper promotes the fusion of neuroscience and robotics.

This study aims to combine two fraud-related streams of the literature on guanxi and overconfidence into an integrated framework, which is the fraud triangle, to interpret the mechanism of fraud commission and detection.

A bivariate probit model with Partial Observability (POBi Probit) is applied. Moreover, the POBi Probit model is adjusted to the Chinese context. The China-specific POBi Probit model is constructed using data of Chinese A-share listed companies from 2008 to 2014, with a total of 15,109 firm-year observations.

Overconfidence induces fraud commission and worsens fraud detection; overconfidence mediates the relationship between fraud and guanxi; the “white side” of guanxi comes from alumni networks, while the “dark side” is derived from relatives-based networks; overconfidence induces fraud commission in accounting and disclosure and benefits the detection of disclosure frauds. Guanxi suppresses fraud commission in management and disclosure, however, it worsens fraud detection given fraud in management and disclosure; overconfidence induces fraud commission in both state-owned enterprises (SOE) and non-SOEs, and benefits fraud detection in SOEs. Guanxi suppresses fraud commission and worsens fraud detection in SOEs and city-owned firms.

There are two drawbacks of the partial observable bivariate probit (POBi-Probit) method that must be mentioned here. On one hand, the ex ante variable selection is one of the most difficult parts of applying the POBi-Probit model and different variables are included in different studies. On the other hand, the POBi-Probit model might not converge if too many variables are included. Thus, many widely accepted factors can be included in the model. Thus, this study initially sets the POBi-Probit model based mainly on Khanna et al. (2015) and then adjusts the model for the Chinese context (e. g. considering government ownership) according to Yiu et al. (2018) and Zhang (2018) and the local study of Meng et al. (2019). Considering the observability of fraud, on one hand, the observability of fraud commission is a widely accepted limitation, especially when accounting opacity comes across with regulatory efficiency (Yiu et al. (2018). On the other hand, the observability of relationships is another obstacle to this study. Future studies can go further by revealing the presently unobservable relationships using Big Data technology.

This paper theoretically and practically contributes to the literature on both corporate fraud and corporate governance. Theoretically, by introducing integrated principal-agent resource-reliance theory (IPRT) and upper echelon theory (UET), this paper broadens the framework of fraud triangle theory (FTT) and testifies the availability of the broaden FTT in the transitional and emerging-market context of China. Practically, this paper provides evidence that guanxi and overconfidence are two of the factors affecting corporate fraud. Thus, this paper provides a governance approach opposing corporate fraud in China, which may help the other emerging economies in transition.

This paper combines the objective information of six mixed-frequency partial-activity indicators with assumptions or beliefs (called priors) regarding the distribution of the parameters that approximate the state of the construction activity cycle. Thus, this paper uses Bayesian inference with Gibbs simulations and the Kalman filter to estimate the parameters of the state-space model, used to design the Imacon.

Unlike other economic sectors of similar importance in aggregate gross domestic product, such as mining and industry, the construction sector lacked a short-term measure that helps to identify its most recent performance.

Indeed, because these priors are susceptible to changes, they provide flexibility to the original Imacon model, allowing for the assessment of risk scenarios and adaption to the greater relative volatility that characterizes the sector's activity.

The classic maximum likelihood method of estimating the monthly construction activity index (Imacon) is rigid to the incorporation of new measures of uncertainty, expectations or different volatility (risks) levels in the state of construction activity. In this context, this paper uses Bayesian inference with 10,000 Gibbs simulations and the Kalman filter to estimate the parameters of the state-space model, used to design the Imacon, inspired by the original works of Mariano and Murasawa (2003) and Kim and Nelson (1998). Thus, this paper consists of a natural extension of the classic method used by Tejada (2006) in the estimation of the old Imacon.

Because of the various advantages of reinforcement learning (RL) mentioned above, this study uses RL to train unmanned aerial vehicles to perform two tasks: target search and cooperative obstacle avoidance.

This study draws inspiration from the recurrent state-space model and recurrent models (RPM) to propose a simpler yet highly effective model called the unmanned aerial vehicles prediction model (UAVPM). The main objective is to assist in training the UAV representation model with a recurrent neural network, using the soft actor-critic algorithm.

This study proposes a generalized actor-critic framework consisting of three modules: representation, policy and value. This architecture serves as the foundation for training UAVPM. This study proposes the UAVPM, which is designed to aid in training the recurrent representation using the transition model, reward recovery model and observation recovery model. Unlike traditional approaches reliant solely on reward signals, RPM incorporates temporal information. In addition, it allows the inclusion of extra knowledge or information from virtual training environments. This study designs UAV target search and UAV cooperative obstacle avoidance tasks. The algorithm outperforms baselines in these two environments.

It is important to note that UAVPM does not play a role in the inference phase. This means that the representation model and policy remain independent of UAVPM. Consequently, this study can introduce additional “cheating” information from virtual training environments to guide the UAV representation without concerns about its real-world existence. By leveraging historical information more effectively, this study enhances UAVs’ decision-making abilities, thus improving the performance of both tasks at hand.

Reviews the latest management developments across the globe and pinpoints practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

It was once de rigeur to state that your corporate strategy had the customer at its heart; that your organization was, indeed, “customer-focused”. Then a customer focus became essential to every living and breathing part of the organization, even to the extent that they became partners and even board members, so as to ensure their every whim was catered for through sales, marketing and product development. If you didn’t have a customer focus, you were so out of touch you practically deserved to fail. Customer focus became as natural for an organization as living and breathing to the human body, which business writers and marketers were so fond of creating analogies with.

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Empirical evidence on the determinants of corporate FX speculation is ambiguous. We note that the conflicting findings of prior studies could be the result of different methodologies in determining speculation. Using a novel approach to defining speculative activities, we seek to help solve the puzzle of the determinants of speculation and examine which firms engage in such activities and why they do so.

This paper examines an unexplored regulatory environment that contains publicly reported FX risk data on the firms' exposures before and after hedging per year and currency. This unprecedented data granularity allows us to use actual reported volumes instead of proxy variables in defining speculation and to examine whether the convexity theories are empirically supported in FX risk management.

We find that frequent speculators are smaller, have more growth opportunities and possess lower internal resources, which indicates unprecedented empirical evidence for the convexity theories in FX risk management. Further, we provide evidence that corporate speculation might be linked to the application of hedge accounting.

We help solve the questions of which and why firms engage in speculative activities. This can provide valuable information to various stakeholders such as financial analysts, investors, or regulators, which can help prevent imperiling corporate losses and curb excessive speculative financial activities.

In order to question the unresolved issue of the determinants of speculation, this paper is the first to use openly available accounting data with actual reported FX exposure information before and after hedging in defining speculation, instead of relying on proxy variables for FX exposure and derivative usage with potential estimation errors.

This study examines empirically the stability of the demand for money under two different financial systems. One system pays interest on money deposited at the bank and charges interest on bank loans; the other does not pay interest on money deposited in the bank, and enters into a profit‐sharing contract with the bank borrower instead of charging interest on bank loans. The first system resembles the western financial system and the second resembles the Islamic financial system. A study by Darrat (1988) studies the behavior of demand for money in Tunisia, and concluded that interest‐free money is more stable than the interest‐bearing money. The behavior of demand for money in fifteen countries has been analyzed in this research in order to find out if the findings by Darrat (1988) are applicable to other countries that practice Islamic banking. This study finds that the velocity of money and its variance are lower for interest‐ free banking system than for interest‐bearing banking system. This result may support the hypothesis that interest‐free money is more stable than interest‐bearing money. The monetary policy implications of interest‐free banking are also analyzed.

The purpose of this paper is to analyze the market of rosé wines in Italy, to outline retail strategies and to investigate to what extent the price is affected by branding these wines.

A survey has been carried out on retailers by collecting data about wines as intrinsic attributes (grape variety, blending, origin, alcohol content, etc.) and extrinsic attributes (brand, price, packaging, etc.) and about outlet and retail environment. The hedonic analysis required a rearrangement of data survey, while a Box-Cox transformation allowed to control the strong heteroskedasticity detected of the data.

Results provide strategies for still, semi-sparkling and sparkling rosé market segments. Still rosé wines are strongly differentiated, while the price is affected by the appellation, grape variety, blending, brand and outlet features. Two main strategies are suggested: the first focuses on appellations endorsing consumer’s brand loyalty; the second is driven by retailers while involving weaker brands. Different pictures emerged for semi-sparkling and sparkling wines, as producers and retailers tend to follow consumer’s preferences for fresh and easy drinking wines as well as to extend the product assortment.

Results for sparkling rosé wines cannot be generalized. The high fragmentation hinders the hedonic model performance in capturing the price effects of brands, appellations, grape variety and wine blend.

The hedonic analysis provides suggestions for rosé wine producers that should reinforce their brand through associations among intrinsic attributes, such as appellation, and extrinsic ones, such as price, while satisfying retailer requirements.

The paper contributes to the knowledge base about the Italian rosé wine market, which is mostly export-oriented. Model results help to understand why the domestic consumption is stagnant with respect to other countries such as France or the USA.

The purpose of the paper is to show that land degradation and desertification are threatening the livelihood of more than a billion dryland inhabitants. The paper aims to present traditional and novel approaches for sustainable agricultural exploitation of the arid drylands in Southern Israel and similar climatic zones, and their potential for rehabilitating degraded drylands and increasing agricultural productivity.

The paper analyses the current agricultural activities on the Abu Rabia farm as well as developing experimental approaches and discusses the expected impact on ecological, economic and social sustainability.

The farm investigated consists of about 120 hectares of semi‐desert land 30 km east of Beer Sheva, divided about 50:50 between rocky hill country and plains with deep loess soil. The area receives an average 200 mm of rain per year. The land is used for raising livestock (about 120 head of sheep and goats), wheat cultivation on high quality soil, and agroforestry, mainly olive cultivation in terraces designed to collect runoff water of seasonal streams. These activities provide a basic income and cover a significant amount of the families' food requirements, but can not provide a full income for a family head in a developed country like Israel. Improving the quality of the grazing land by silvipasture, further investments into high value dryland tree crops and simultaneous production of wood for industry and energy can dramatically increase the farm's income, its resilience to drought and ecological sustainability.

This analysis demonstrates the potential of dryland agroforestry for sustainable development while solving a number of economic and social problems of poor dryland inhabitants, and it contributes to fighting desertification and global warming.

This case study demonstrates that sustainable dryland exploitation by agroforestry can establish significant agricultural production potentials on marginal lands often considered worthless. Because of the establishment of significant and permanent carbon sinks, carbon trading may be mobilized to cover the required investments creating a classical win‐win situation.

Students of tourism have long recognized the Caribbean area as a popular tourist objective. The visitors have come primarily from North America; as Burkart and Medlik ob‐served, “…the principal tourist reception areas in the world lie up to about 1,000 miles or so from the generating source or, to put it another way, at about two hours flying time”. The Caribbean's large and expanding tourist trade may be attributed to, in addition to this obviously significant factor of proximity, the physical geographic factors of tropical climate, sandy beaches and varied topography. The Caribbean's cultural geographic assets include its diversity of colonial heritage and its variety of racial and ethnic populations. The unique economic and social situation of this region of the world, together with the continuing role of the U. S. as a prime generating source, will probably result in continued encouragement of and catering to this impressive tourist trade. Of particular note to students of tourism is the rising volume of cruiseship traffic within the Caribbean. This aspect of tourist trade illustrates some significant interrelationships of transport and tourism. This study's objectives are the analysis and interpretation of contemporary cruise‐ship traffic patterns, specifically in terms of studying the relationships which are apparent among various ports of origin and their related ports of call.