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Current single head pick and place robots have reached their practical limit for throughput rates due to impractical speeds and acceleration, which often damage or lose the product being transferred. The purpose of this paper is to present a new system which uses 2 XY motion slides and an indexing flexible conveyor to achieve a more desired motion while achieving a high throughput rate.

An innovative robotic pick and place motion design (the FlowBot) was previously created to address the changing needs of the packaging and automation industry. A full patent has been filed covering this technology. This paper documents a refinement to the FlowBot concept that produces a more compact implementation, entitled the Compact FlowBot.

Tit was found that the motion of smaller steps with limited accelerations does produce higher throughputs without the excessive accelerations that Delta robots produce. The robotics system does require limited Z height so the potential for multiple stacked systems is presented.

This novel robot has been found to be a next generation design, which has been confirmed by an international patent search. Many established consumer packaging goods companies and food processing companies have lauded its merits. The system needs to move into prototype and full development mode.

The study delves into the role played by cutting-edge data analytics, machine learning, and innovative technologies in reshaping traditional insurance practices. The primary goal of this review is to juxtapose findings from the literature sources, enabling a comprehensive analysis of the current state of implementation.

Systematic narrative review methodology has been applied to the present study. Scopus database has been used for the manuscripts ranging from year 2020 to 2024 considering the 5-year rule. 74 manuscripts were reviewed to navigate the landscape of data-driven revolution, unlocking the potential to elevate insurance operations to new heights. Two research questions about the impact of data alchemy on operational efficiency and insights and its contribution to reshaping the future landscape of insurance practices have been answered.

This approach captured the interplay between the theoretical potential for insurance and the practical realities of implementation of advanced practices, drawing upon the collective expertise within the field. By doing so, the article discerned the trajectory of the insurance sector concerning the advanced data alchemy observed in the industry.

The current research contributes to the broader area of data alchemy in the insurance industry. The transformative power of big data analytics lies in its capacity to turn vast and diverse datasets into valuable insights, driving innovation, informed decision-making, and improved business outcomes across various sectors. Notably, the research extends the body of literature exploring the impact of data alchemy on operational efficiency and insights, area where limited studies have been conducted.

Hybrid systems as the next generation of intelligent applications within the field of mass appraisal and valuation are investigated. Motivated by the obvious limitations of paradigms that are being used in isolation or as stand‐alone techniques such as multiple regression analysis, artificial neural networks and expert systems. Clearly, there are distinct advantages in integrating two or more information processing systems that would address some of the discrete problems of individual techniques. Examines first, the strategic development of mass appraisal approaches which have traditionally been based on “stand‐alone” techniques; second, the potential application of an intelligent hybrid system. Highlights possible solutions by investigating various hybrid systems that may be developed incorporating a nearest neighbour algorithm (k‐NN). The enhancements are aimed at two major deficiencies in traditional distance metrics; user dependence for attribute weights and biases in the distance metric towards matching categorical variables in the retrieval of neighbours. Solutions include statistical techniques: mean, coefficient of variation and significant mean. Data mining paradigms based on a loosely coupled neural network or alternatively a tight coupling with genetic algorithms are used to discover attribute weights. The hybrid architectures developed are applied to a property data set and their performance measured based on their predictive value as well as perspicuity. Concludes by considering the application and the relevance of these techniques within the field of computer assisted mass appraisal.

The aim of the article was to focus on various peptides identified in the egg and their probable application as novel ingredients in the development of functional food products. Bioactive peptides of egg origin have attracted increasing interest as one of the prominent candidates for development of various health-promoting functional and designer foods.

Traditionally known as a source of highly valuable proteins in human nutrition, eggs are nowadays also considered as an important source of many bioactive peptides which may find wide application in medicine and food production. These specific protein fragments from egg proteins which, above and beyond their nutritional capabilities, have a positive impact on the body’s function or condition by affecting the digestive, endocrine, cardiovascular, immune and nervous systems, and may ultimately influence health.

Several peptides that are released in vitro or in vivo from egg proteins have been attributed to different health effects, including antihypertensive effects, antimicrobial properties, antioxidant activities, anticancer activity, immunomodulating activity, antiadhesive properties and enhancement of nutrient absorption and/or bioavailability. Extensive research has been undertaken to identify and characterize these biologically active peptides of egg origin which has changed the image of egg as a new source of biologically active ingredients for the development of functional foods with specific benefits for human health and treatment and prevention of diseases.

The paper mainly describes the above-stated properties of bioactive peptides derived from egg proteins.

The purpose of this paper is to identify pathways to improve the performance and competitiveness of Ethiopia's tourism sector using coffee as one essential tourism experience, thereby improving the socio-economic conditions of the local communities who depend on coffee for their livelihoods.

Based upon qualitative focus group discussions undertaken with key informants in both the coffee and tourism sectors in Ethiopia.

Despite the existing tourism development potential, Ethiopia has not yet fully exploited this position. While the country uses coffee to assist its destination marketing strategies, practical interventions to position coffee as a primary tourism product are absent and remain of critical importance.

In this exploratory study key informant participants from government and industry share their experience within this policy domain. It is acknowledged that future research aiming to provide a fuller picture of governance in this domain would also include the perspective of community-level coffee growers.

Paramount among the implications of this study is the need to enhance cross-sectoral planning and collaboration and to establish a bridging organisation that will help integrate the agricultural (coffee) sector and the tourism sector.

This study identifies key governance-related obstacles to addressing rural poverty through coffee-related agri-tourism initiatives in Ethiopia.

This paper addresses, from a governance perspective, the obstacles and opportunities for coffee as a tourism product/experience in Ethiopia. The paper identifies what interventions and innovations in policy and practice are necessary to enhance the role of Ethiopia's coffee culture in the performance of the country's tourism sector.

This paper aims to understand the impact of the job switching behavior on different stages of the communities of practice’s life cycle. Job switching has been viewed from both positive and negative point of views, and its impact on certain organizational factors might be found in literature. Job switching/job hopping behavior of an individual might be fueled by socio-economic factors as well as fun, but it has serious implication for the companies. But an understanding of how this new employee might influence the communities of practice, given which stage is the community in, is something that has not been studied yet. This work is an attempt in that direction.

Using integrative review technique, this paper forwards a conceptual framework based on the literature reviewed and builds a model using an understanding of the nuances of each stage of the life cycle of communities of practice.

The model proposes the impact of switching on each stage of the life cycle of communities of practice. It is observed that at each stage a new entrant who is a “job hopper” might either help or hinder the progress of a community of practice.

This paper gives a new impetus to the research on communities of practice in contemporary perspective. The model proposed could be tested using data from real communities of practice. This paper limits itself to the proposal of the model and does not engage in testing it.

Organizations and managers may use the model to understand how a new entrant to the organization will complement the existing life cycle phase of the communities of practice within.

The conceptual model proposed is unique in its context of job switching behavior and its effect on communities of practice. Research on communities of practice from this contemporary perspective might bring important research directions in future.

The paper aims to investigate the application of particle swarm optimisation and back propagation in weights optimisation and training of artificial neural networks within the mass appraisal industry and to compare the performance with standalone back propagation, genetic algorithm with back propagation and regression models.

The study utilised linear regression modelling before the semi-log and log-log models with a sample of 3,242 single-family dwellings. This was followed by the hybrid systems in the selection of optimal attribute weights and training of the artificial neural networks. Also, the standalone back propagation algorithm was used for the network training, and finally, the performance of each model was evaluated using accuracy test statistics.

The study found that combining particle swarm optimisation with back propagation in global and local search for attribute weights enhances the predictive accuracy of artificial neural networks. This also enhances transparency of the process, because it shows relative importance of attributes.

A robust assessment of the models’ predictive accuracy was inhibited by fewer accuracy test statistics found in the software. The research demonstrates the efficacy of combining two models in the assessment of property values.

This work demonstrated the practicability of combining particle swarm optimisation with back propagation algorithms in finding optimal weights and training of the artificial neural networks within the mass appraisal environment.