Criminological measures to counteract corruption offences in the field of illegal gambling

Purpose Despite all the anti-corruption measures and anti-corruption initiatives, people offer or accept bribes without any hesitation. As anywhere in the world, the negative consequences of corruption lead to a reduction in direct investment, increase inequality and poverty, distort and use public investment and reduce public revenues. The purpose of this article is to study the criminological measures to counteract corruption offences in the field of illegal gambling. Design/methodology/approach The methodological basis of the study is the provisions of the theory of knowledge: the laws of dialectical materialism, philosophical categories and scientific principles of cognition of social and legal reality. Findings Although many components of foreign state anti-corruption programmes are quite problematic to apply in modern realities in the Republic of Kazakhstan, according to legal scholars, through gradual implementation into the legislation of the Republic of Kazakhstan because of the systematic improvement by the state of the content of regulations and responsible implementation of anti-corruption strategies. In this regard, one of the conditions in the fight against corruption is actions aimed at using the best practices of countries that are similar to each other in terms of religion, habits, traditions, ethics and morality. Originality/value Anti-corruption initiatives using information and communication technologies, such as digital public services and e-government, crowdsourcing platforms, tools for exposing, transparency portals, blockchain and artificial intelligence technologies can provide significant assistance in combating manifestations of corruption in the field of illegal gambling on the internet in the Republic of Kazakhstan.

What determine the price of Bonga sheep at the market level in Southwestern Ethiopia? A hedonic price analysis

Background Bonga sheep is a mutton type breed with a long-fat-tail and better body weight at maturity. The breed is especially located in the Southwestern mid and highland land areas of Ethiopia. Currently, the breed was well known in Ethiopia and also its price is higher as compared to other sheep breeds in the country. However, empirical evidence is lacking on price determinants of the breed in Ethiopia. Therefore, this research aims at identifying sheep attributes and other factors that determine the market prices of Bonga sheep in Southwestern Ethiopia. Methodology Data from 300 traded sheep and sheep marketers were collected from five major sheep marketing centers in the Kaffa zone. A hedonic price model adjusted for heteroscedasticity was employed to analyze the observed price data. Results The model result showed that the attributes of the sheep are important guiding criteria in price formulation. Among the attributes age, sex (male), colour (red), body condition (good), tail type (very fat) and absence of horn significantly and positively determine the sheep price. As well, market place (Gojebi and Bonga) near to big cities and season (holiday) and purpose of purchase are also significant determinants of bong sheep price out of the sheep attributes in Southwestern Ethiopia. Conclusion Targeting in systematic improvement of the significant attributes which are demanded by the market and establishment of a breeding program to make sheep improvement in a sustainable way. Besides, effort should be geared to transform the system into a market-oriented system using a value chain framework by improving access to market information.

Efficient learning of discrete graphical models*

Graphical models are useful tools for describing structured high-dimensional probability distributions. Development of efficient algorithms for learning graphical models with least amount of data remains an active research topic. Reconstruction of graphical models that describe the statistics of discrete variables is a particularly challenging problem, for which the maximum likelihood approach is intractable. In this work, we provide the first sample-efficient method based on the interaction screening framework that allows one to provably learn fully general discrete factor models with node-specific discrete alphabets and multi-body interactions, specified in an arbitrary basis. We identify a single condition related to model parametrization that leads to rigorous guarantees on the recovery of model structure and parameters in any error norm, and is readily verifiable for a large class of models. Importantly, our bounds make explicit distinction between parameters that are proper to the model and priors used as an input to the algorithm. Finally, we show that the interaction screening framework includes all models previously considered in the literature as special cases, and for which our analysis shows a systematic improvement in sample complexity.

A Systematic Improvement Model to Optimize Production Systems within Industry 4.0 Environments: A Simulation Case Study

The industrial revolutions and their impact on production systems have increased productivity and quality in manufacturing over time. Lean methods have been the driver of the development of production systems from the 1990s to the rise of the fourth industrial revolution, or Industry 4.0. However, many different approaches and methodologies have been described, applied, and discussed for achieving improvements in production systems. As a result, organizations are often confused in regard to the order, the convenience, and the outcomes intended by the different improvement strategies and techniques. This paper provides a systematic sequence of process optimization steps that can be applied to any organization. A conceptual model was built based on the systematic sequence. In addition, a simulation model was built with the goal of representing and quantifying the sequential steps of the conceptual model. The results of the simulation model show a clear improvement in quality, performance, and economic indicators, with the first two steps in the optimization sequence providing critical initial information, while the third step served as a net contributor to a global production system improvement for demanding market scenarios. Finally, we analyzed the impacts of Industry 4.0 on production systems and developed a methodological sequence to design, select, implement, and control projects, even those that include Industry 4.0 technologies.

Degree of Integrated Water Resources Management Implementation in Context of Climate Change in a Watershed: Case of Oueme Basin, Benin (West Africa)

This study is based on the hypothesis that the implementation of Integrated Water Resources Management (IWRM) tools in the Oueme watershed has not led to a systematic improvement in the degree of IWRM implementation in the Oueme Valley in Benin. Methodologically, data were collected through a semi-structured survey of stakeholder families in the study area using snowball sampling. The tool used was the form developed by the United Nations to collect data to assess the level of IWRM implementation. The four (4) assessment criteria, each covering a key component of IWRM, are the enabling environment, institutions and participation, management tools and financing. The results obtained per criteria according to the stepwise methodology adopted reveal on average a degree of implementation of 40 on a scale of 0 to 100 in the Oueme basin. This value varies according to the geographical area and the factors considered. This study, which is a decision-making tool, provides a guide for governments to monitor the progress of integrated water resources management in their territory.

Operations management on the front line of COVID-19 vaccination: building capability at scale via technology-enhanced learning

The globe is gripped by the COVID-19 pandemic. Mass population vaccination is seen as the solution. As vaccines become available, governments aim to deploy them as rapidly as possible. It is important, therefore, that the efficiency of vaccination processes is optimal.Operations management is concerned with improving processes and comprises systematic approaches such as Lean. Lean focuses explicitly on process efficiency through the elimination of non-value adding steps to optimise processes for those who use and depend on them.Technology-enhanced learning can be a strategy to build improvement capability at scale. A massive online programme to build capability in Lean has been developed by the regulator of England's National Health Service. Beta testing of this programme has been used by some test sites to refine their COVID-19 vaccination processes. The paper presents a case example of massive online learning supporting the use of Lean in the day-to-day operations management of COVID-19 vaccine processes.The case example illustrates the challenges that vaccination processes may present and the need for responsive and effective operations management. Building capability to respond rapidly and systematically in dynamic situations to optimise flow, safety and patient experience may be beneficial.Given the national imperative to achieve mass vaccination as rapidly as possible, systematic improvement methods such as Lean may have a contribution to make. Massive online programmes, such as that described here, may help with this effort by achieving timely knowledge transfer at large scale.

A performance-based tabular approach for joint systematic improvement of risk control and resilience applied to telecommunication grid, gas network, and ultrasound localization system

Organizational and technical approaches have proven successful in increasing the performance and preventing risks at socio-technical systems at all scales. Nevertheless, damaging events are often unavoidable due to a wide and dynamic threat landscape and enabled by the increasing complexity of modern systems. For overall performance and risk control at the system level, resilience can be a versatile option, in particular for reducing resources needed for system development, maintenance, reuse, or disposal. This paper presents a framework for a resilience assessment and management process that builds on existing risk management practice before, during, and after potential and real events. It leverages tabular and matrix correlation methods similar as standardized in the field of risk analysis to fulfill the step-wise resilience assessment and management for critical functions of complex systems. We present data needs for the method implementation and output generation, in particular regarding the assessment of threats and the effects of counter measures. Also included is a discussion of how the results contribute to the advancement of functional risk control and resilience enhancement at system level as well as related practical implications for its efficient implementation. The approach is applied in the domains telecommunication, gas networks, and indoor localization systems. Results and implications are further discussed.

Investigating the seismic imaging of faults using PS data from the Snøhvit field, Barents Sea, and forward seismic modelling

<p>PS seismic data from the Snøhvit field are compared with forward seismic modelling to understand the effect of azimuthal separation and incidence angle on the imaging of faults. Two faults, one oriented oblique to the survey and one approximately parallel to the survey were chosen. Azimuthally separated W (source is W relative to receivers) and E (source E relative to receivers) data demonstrate that fault imaging is more affected by azimuth when the faults are oblique to the survey orientation, and W data image the faults better. Partial stack data show that with increasing incidence angle there is a systematic improvement in the quality of fault imaging in both the E and W data. In addition, the frequency content of seismic waves back-scattered from within and around fault zones is analysed in the Snøhvit data. Low-medium frequencies are dominant within fault zones, compared with higher frequencies in adjacent areas and haloes of medium frequencies surrounding the faults. Two synthetic experiments support the azimuth, incidence angle and frequency observations. In the first experiment, the fault is modelled as a planar discontinuity and the data were processed in the same way as the Snøhvit data (into separate azimuths and incidence angle stacks). The first experiment confirms a strengthening in the seismic signal from faults in the W data. This is due to the interaction of specular waves and diffractions which are more abundant in the W data. The second experiment had three parts modelling the fault zone with different layering complexity. It proved that frequencies in the fault and adjacent areas increase with fault zone complexity, and that the internal architecture of faults can impact the frequencies in the data adjacent to faults. </p>

Sustainable Strategies for Marathon Tourism in the Time of Pandemics

This study established a new assessment framework to explore the mutual influences of the international marathon tourism indices. A hybrid multiple-criteria decision-making model was used to determine solutions. The surveyed expert team revealed that satisfaction with the Kaohsiung International marathon indices requires improvement, especially in the age of pandemic crisis. To make marathon tourism sustainable, we propose the following systematic improvement procedures: Decision-makers should improve the manager’s professional capabilities and ability to use a comprehensive thinking model to solve problems. Next, managers should invite tourism practitioners from neighboring areas to provide more diverse food and performances along the marathon route. Citizens should be invited to join the cheerleaders to create a lively atmosphere. In addition to the full marathon and half marathon, a completion certificate and a better gift should be provided to participants of races of other distances to increase their sense of self-worth and to encourage more runners to participate, thereby ensuring a more robust crowd of runners at the starting line.