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Modern meat processing requires automation and robotisation to remain sustainable and adapt to future challenges, including those brought by global infection events. Automation of all or many processes is seen as the way forward, with robots performing various tasks instead of people. Meat cutting is one of these tasks. Smart novel solutions, including smart knives, are required, with the smart knife being able to analyse and predict the meat it cuts. This paper aims to review technologies with the potential to be used as a so-called “smart knife” The criteria for a smart knife are also defined.

This paper reviews various technologies that can be used, either alone or in combination, for developing a future smart knife for robotic meat cutting, with possibilities for their integration into automatic meat processing. Optical methods, Near Infra-Red spectroscopy, electrical impedance spectroscopy, force sensing and electromagnetic wave-based sensing approaches are assessed against the defined criteria for a smart knife.

Optical methods are well established for meat quality and composition characterisation but lack speed and robustness for real-time use as part of a cutting tool. Combining these methods with artificial intelligence (AI) could improve the performance. Methods, such as electrical impedance measurements and rapid evaporative ionisation mass spectrometry, are invasive and not suitable in meat processing since they damage the meat. One attractive option is using athermal electromagnetic waves, although no commercially developed solutions exist that are readily adaptable to produce a smart knife with proven functionality, robustness or reliability.

This paper critically reviews and assesses a range of sensing technologies with very specific requirements: to be compatible with robotic assisted cutting in the meat industry. The concept of a smart knife that can benefit from these technologies to provide a real-time “feeling feedback” to the robot is at the centre of the discussion.

Intelligent and connected vehicle technology is in the ascendant. High-level autonomous driving places more stringent requirements on the accuracy and reliability of environmental perception. Existing research works on multitarget tracking based on multisensor fusion mostly focuses on the vehicle perspective, but limited by the principal defects of the vehicle sensor platform, it is difficult to comprehensively and accurately describe the surrounding environment information.

In this paper, a multitarget tracking method based on roadside multisensor fusion is proposed, including a multisensor fusion method based on measurement noise adaptive Kalman filtering, a global nearest neighbor data association method based on adaptive tracking gate, and a Track life cycle management method based on M/N logic rules.

Compared with fixed-size tracking gates, the adaptive tracking gates proposed in this paper can comprehensively improve the data association performance in the multitarget tracking process. Compared with single sensor measurement, the proposed method improves the position estimation accuracy by 13.5% and the velocity estimation accuracy by 22.2%. Compared with the control method, the proposed method improves the position estimation accuracy by 23.8% and the velocity estimation accuracy by 8.9%.

A multisensor fusion method with adaptive Kalman filtering of measurement noise is proposed to realize the adaptive adjustment of measurement noise. A global nearest neighbor data association method based on adaptive tracking gate is proposed to realize the adaptive adjustment of the tracking gate.

Designing an efficient routing protocol that opportunistically forwards data to the destination node through nearby sensor nodes or devices is significantly important for an effective incidence response and disaster recovery framework. Existing sensor routing protocols are mostly not effective in such disaster recovery applications as the networks are affected (destroyed or overused) in disasters such as earthquake, flood, Tsunami and wildfire. These protocols require a large number of message transmissions to reestablish the clusters and communications that is not energy efficient and result in packet loss. This paper introduces ODCR - an energy efficient and reliable opportunistic density clustered-based routing protocol for such emergency sensor applications. We perform simulation to measure the performance of ODCR protocol in terms of network energy consumptions, throughput and packet loss ratio. Simulation results demonstrate that the ODCR protocol is much better than the existing TEEN, LEACH and LORA protocols in term of these performance metrics.

The purpose of this paper is to accurately capture the risks which are caused by each road user in time.

The authors proposed a novel risk assessment approach based on the multi-sensor fusion algorithm in the real traffic environment. Firstly, they proposed a novel detection-level fusion approach for multi-object perception in dense traffic environment based on evidence theory. This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception. The information of object type, position and velocity was accurately obtained. Then, they conducted several experiments in real dense traffic environment on highways and urban roads, which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios. By analyzing the generation process of traffic risks between vehicles and the road environment, the equivalent forces of vehicle–vehicle and vehicle–road were presented and theoretically calculated. The prediction steering angle and trajectory were considered in the determination of traffic risk influence area.

The results of multi-object perception in the experiments showed that the proposed fusion approach achieved low false and missing tracking, and the road traffic risk was described as a field of equivalent force. The results extend the understanding of the traffic risk, which supported that the traffic risk from the front and back of the vehicle can be perceived in advance.

This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception. The information of object type, position and velocity was used to reduce erroneous data association between tracks and detections. Then, the authors conducted several experiments in real dense traffic environment on highways and urban roads, which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios. By analyzing the generation process of traffic risks between vehicles and the road environment, the equivalent forces of vehicle–vehicle and vehicle–road were presented and theoretically calculated.

Given the instability and volatility of the labour market and the global talent scarcity, placing more attention on job employability is fundamental. In this context, the literature has already extensively examined employability as a crucial individual aspect, identifying some significant antecedents, including the applicability of training on the job. The present study aims to examine the impact that teaching employees to craft their job may have on the levels of applicability of training and if, in turn, this improves self-perceived employability.

The authors involved three private organizations that followed three workshops on job crafting behaviour. To empirically assess the intervention, the authors asked participants of the workshop to complete four quantitative diaries on a weekly basis, i.e. one per week, one before the intervention and three after the intervention. The diaries comprised measures of job crafting behaviours, applicability of training and self-perceived employability.

Multi-level analysis of data collected provided support to the positive associations between job crafting behaviour and self-perceived employability with the mediating effect of applicability of training. Notably, the applicability of training improves when individuals search for challenges, which indirectly affects perceived employability in terms of organizational sense.

In the present study, no control group was used with which the results of our intervention could be compared. However, this does not affect the overall results, given the amount of intraindividual variability.

The paper proposes initial avenues for promoting employability at work via the use of behavioural job crafting intervention.

This paper aims to identify the relationship between participation motivation, satisfaction and exercise adherence intention of golf range users on the basis of self-determination theory.

For this purpose, the authors proposed research questions and a conceptual research model as well. Then, the authors surveyed users of golf ranges located in Seoul Metropolitan City and Gyeonggi-do province.

By applying convenience sampling, the authors received a total of 313 questionnaires. Results were as follows. First, among the participation motivation sub-factors, health-oriented motivation, achievement motivation, pleasure-oriented motivation and self-displayed motivation had a significant effect on emotional satisfaction, while achievement motivation and pleasure-orientation motivation had a significant effect on performance satisfaction. Second, the following participation motivation factors had a significant effect on exercise adherence intention: health-orientation motivation, achievement motivation and pleasure-orientation motivation. Third, among the satisfaction factors, emotional satisfaction and performance satisfaction both had a significant effect on exercise adherence intention.

This is one of the first papers to examine the relationships that exist between golf range users’ participation motivation, satisfaction and exercise adherence intention.

This study aims to propose an offline exploratory method that consists of two stages: first, the authors focus on completing the kinematics model of the system by analyzing the Jacobians in the vicinity of the starting point and deducing a virtual input to effectively navigate the system along the non-holonomic constraint. Second, the authors explore the sensorimotor space in a predetermined pattern and obtain an approximate mapping from sensor space to chained form that facilitates controllability.

In this paper, the authors tackle the controller acquisition problem of unknown sensorimotor model in non-holonomic driftless systems. This feature is interesting to simplify and speed up the process of setting up industrial mobile robots with feedback controllers.

The authors validate the approach for the test case of the unicycle by controlling the system with time-state control policy. The authors present simulated and experimental results that show the effectiveness of the proposed method, and a comparison with the proximal policy optimization algorithm.

This research indicates clearly that feedback control of non-holonomic systems with uncertain kinematics and unknown sensor configuration is possible.

Confidence intervals play a crucial role in economics and finance, providing a credible range of values for an unknown parameter along with a corresponding level of certainty. Their applications encompass economic forecasting, market research, financial forecasting, econometric analysis, policy analysis, financial reporting, investment decision-making, credit risk assessment and consumer confidence surveys. Signal-to-noise ratio (SNR) finds applications in economics and finance across various domains such as economic forecasting, financial modeling, market analysis and risk assessment. A high SNR indicates a robust and dependable signal, simplifying the process of making well-informed decisions. On the other hand, a low SNR indicates a weak signal that could be obscured by noise, so decision-making procedures need to take this into serious consideration. This research focuses on the development of confidence intervals for functions derived from the SNR and explores their application in the fields of economics and finance.

The construction of the confidence intervals involved the application of various methodologies. For the SNR, confidence intervals were formed using the generalized confidence interval (GCI), large sample and Bayesian approaches. The difference between SNRs was estimated through the GCI, large sample, method of variance estimates recovery (MOVER), parametric bootstrap and Bayesian approaches. Additionally, confidence intervals for the common SNR were constructed using the GCI, adjusted MOVER, computational and Bayesian approaches. The performance of these confidence intervals was assessed using coverage probability and average length, evaluated through Monte Carlo simulation.

The GCI approach demonstrated superior performance over other approaches in terms of both coverage probability and average length for the SNR and the difference between SNRs. Hence, employing the GCI approach is advised for constructing confidence intervals for these parameters. As for the common SNR, the Bayesian approach exhibited the shortest average length. Consequently, the Bayesian approach is recommended for constructing confidence intervals for the common SNR.

This research presents confidence intervals for functions of the SNR to assess SNR estimation in the fields of economics and finance.

Building on a stress-related view, this study examines the impact of qualitative job insecurity on three indicators of career sustainability. It also examines the moderating role of employee age in this relationship.

Dutch respondents (N = 398) working in various sectors responded to an online survey. Our hypotheses were tested using path modeling with Mplus.

Qualitative job insecurity was negatively related to job satisfaction, general health, and employability. Moreover, a moderating effect of employee age on the relationship between qualitative job insecurity and job satisfaction was found.

Organizations need to take measures to prevent qualitative job insecurity, as this appears to be an essential hindering factor that might endanger an employee's career sustainability. Especially younger workers (<40 years) seem to suffer from qualitative job insecurity as this decreases their job satisfaction, urging management and HR professionals to protect them against it.

By focusing on qualitative job insecurity, this study helps to close an essential gap in the literature that so far has mainly focused on quantitative job insecurity. Besides, this is the very first empirical work investigating the link between qualitative job insecurity and multiple indicators of career sustainability. Finally, this research adds a developmental approach to sustainable careers by comparing younger and older workers.

The aim of this study is to explore the contextual influences of packaging design and its cues on respondents' preferences.

To explore the contextuality of packaging cues, a multi-attribute valuation technique, conjoint analysis was used for two types of pharmaceutical products (painkiller and sore throat medicine) across seven countries. Data were collected among respondents (N = 461) from Finland, Ghana, Mongolia, Nigeria, Portuguese, South Africa and the USA.

Similarities and dissimilarities were observed between the product types and countries analysed in terms of the impact of packaging cues. The findings demonstrate the global and local nature of brand cues expressed in retail packaging.

The study implies that some cues may serve global markets, while some cues may need to be localised in order to meet the needs of local markets. Understanding these cues and their influences on consumers' brand preferences and choices at the point-of-purchases may enable companies to enter new markets, help them create sustainable and credible global brands.

The study contributes to the existing retail packaging literature and pharmaceutical branding literature by providing empirical evidence of the multidimensional aspects of sensory packaging cues. Second, it contributes by showing the contextual nature of retail packaging and its associated cues for OTC pharmaceuticals.