The Foodservice Industry\u27s Social Responsibility Regarding the Obesity Epidemic, Part II: Incorporating Strategic Corporate Social Responsibility into Foodservice Operations

Just as all types of business firms are now expected to go beyond their profit-oriented activities in boosting the well-being of the community, so, too, is corporate social responsibility (CSR) expected from foodservice firms. The significance of the obesity epidemic, combined with the foodservice industry\u27s role in the development of this epidemic, suggests that the industry has an ethical responsibility to implement CSR activities that will help reduce obesity, particularly among children. CSR should be seen as an efficient management strategy through which a firm voluntarily integrates social and environmental concerns into its business operations and its interactions with stakeholders. Although costs are associated with CSR initiatives, benefits accrue to the firm. Decisions regarding alternative CSR activities should be based on a cost-benefit analysis and calculation of the present value of the revenue stream that can be identified as resulting from the specific CSR activities. CSR initiatives should be viewed as long-term investments that will enhance the firms’ value. Key areas for foodservice firms\u27 CSR activities include marketing practices, particularly practices impacting advertising to children and marketing that will enhance the firms’ visibility; portion-size modification; new-product development; and consistent nutrition labeling on menus

The Foodservice Industry\u27s Social Responsibility Regarding the Obesity Epidemic, Part I: Parallels to Other Public Health Issues and Potential Legal Implications

The incidence of obesity among both children and adults in the United States (U.S.) has reached epidemic level. If not quickly curtailed, it represents significant long-term costs to all facets of the U.S. economy. The foodservice industry has contributed to this major public health issue. Parallels between the obesity epidemic and the public health issues of smoking and foodborne illnesses could influence the foodservice industry\u27s response to obesity concerns. Of particular note are the parallels between the liability litigation and legislative actions related to smoking and the tobacco industry. This industry has a history of taking socially responsible actions regarding public health issues. There is potential for costs to the foodservice industry from similar anti-obesity litigation and legislation if the industry does not once again assume social responsibility relative to the current obesity crisis and is not proactive in efforts to combat obesit

The Social responsibility of the foodservice industry: The need for action regarding the obesity crisis

Abstract: Obesity has reached epidemic proportions. Costs associated with obesity pose a severe threat to the U.S. economy. Evidence indicates the foodservice industry has had a major role contributing to the obesity crisis; thus it is argued that the industry has an ethical and social responsibility to now aggressively adopt socially responsible actions that will help alleviate the increasing incidence of obesity. Such actions might include innovative advertising initiatives, modification of portion sizes, and nutrition labeling so that consumers can make healthful food selections. Even though such actions might result in short-term profit losses, socially responsible actions have the potential to yield long-term economic value for the foodservice industry

Development Of Parameters For Incorporating Security Studies Into Hospitality Education

Worldwide concern with security issues and disaster management within the hospitality industry is creating opportunities for hospitality management education. This study investigated essential curriculum content, student characteristics, instructional modalities, and program competencies potentially important for hospitality industry based security studies programs. Findings indicate that such programs should have non-traditional, flexible structure as initial students will likely hold middle and upper management industry positions. Executive leadership, agencies involved in emergency preparedness, and integration of systems focused on security and disaster management, both as program content and resulting competencies are important program components

Multi-Action Recognition via Stochastic Modelling of Optical Flow and Gradients

In this paper we propose a novel approach to multi-action recognition that performs joint segmentation and classification. This approach models each action using a Gaussian mixture using robust low-dimensional action features. Segmentation is achieved by performing classification on overlapping temporal windows, which are then merged to produce the final result. This approach is considerably less complicated than previous methods which use dynamic programming or computationally expensive hidden Markov models (HMMs). Initial experiments on a stitched version of the KTH dataset show that the proposed approach achieves an accuracy of 78.3%, outperforming a recent HMM-based approach which obtained 71.2%

Kosher Airline Food: A Logistical Challenge

Providing meals to passengers on aircrafts requires a complex logistical system if it is to be done sucessfully. Variations to that system are required if special meals, such as kosher ones, are to be provided since it entails unique system challenges. The authors discuss service requirements, the challenges they pose to the inflight meal service logistical system, and some of the ways in which these challenges are met

Improved vision-based weed classification for robotic weeding – a method for increasing speed while retaining accuracy

In this paper, we demonstrate how a deep convolutional neural network (DCNN) can be deployed in resource limited environments, such as robots, to reduce the inference time by more than an order of magnitude while retaining high classification accuracy and robustness to novel conditions. This is achieved by training a lightweight DCNN, or compressed model, via model distillation. We show that training models using this approach outperform training a similar model from scratch, using the same data, for weed classification. Using model distillation we are able to improve the accuracy from 97.1% to 97.9% for similar conditions (as the training data) and from 86.4% to 89.8% for different conditions (as the training data). This is in comparison to a traditional approach using robust local binary pattern features which achieves 87.7% for classifying in similar conditions and 83.9% for classifying in different conditions. Finally, we compare this compressed model to a complex fine-tuned model which achieves higher accuracy of 99.6% for the same condition and 95.8% for different conditions but has 100.0 times more parameters (larger model size) and is 40.6 times slower at computing the inference

Cross-Cultural Cuisine: Long-Term Trend or Short-Lived Fad

Defining a consumer interest as a long-term trend or short-lived fad has significant implicatiosn for restauranteurs\u27 management decisions. The terms trend and fad can be operationally defined for the food service industry. The authors examine today\u27s popular cross-cultural cuisine to determine its trend or fad status and discuss the catalysts that promoted or hindered its trend/fad status, as well as implications for the food service industry

Lessons learnt from field trials of a robotic sweet pepper harvester for protected cropping systems

In this paper, we present the lessons learnt during the development of a new robotic harvester (Harvey) that can autonomously harvest sweet pepper (capsicum) in protected cropping environments. Robotic harvesting offers an attractive potential solution to reducing labour costs while enabling more regular and selective harvesting, optimising crop quality, scheduling and therefore profit. Our approach combines effective vision algorithms with a novel end-effector design to enable successful harvesting of sweet peppers. We demonstrate a simple and effective vision-based algorithm for crop detection, a grasp selection method, and a novel end-effector design for harvesting. To reduce complexity of motion planning and to minimise occlusions we focus on picking sweet peppers in a protected cropping environment where plants are grown on planar trellis structures. Initial field trials in protected cropping environments, with two cultivars, demonstrate the efficacy of this approach. The results show that the robot harvester can successfully detect, grasp, and detach crop from the plant within a real protected cropping system. The novel contributions of this work have resulted in significant and encouraging improvements in sweet pepper picking success rates compared with the state-of-the-art. Future work will look at detecting sweet pepper peduncles and improving the total harvesting cycle time for each sweet pepper. The methods presented in this paper provide steps towards the goal of fully autonomous and reliable crop picking systems that will revolutionise the horticulture industry by reducing labour costs, maximising the quality of produce, and ultimately improving the sustainability of farming enterprises