Development of Green-Affective Work System for Food SMEs

Work system of food Small and Medium-sized Enterprises (SMEs) is influenced by various factors as worker performance, characteristics of raw material, value-added process and workplace environmental ergonomics. Green-affective design analyzes properties of work systems and how these technical attributes could be sensible to the environment (Green) and worker (Affective). The research objectives were: 1) To explore the relationship between green and affective parameters in work systems of Food SMEs; 2) To design a green-affective work system for Food SMEs. Six (6) SMEs of different food products were used for the case studies as Crackers, Nuggets, Fish Chips, Bakpia, Tempe and Herbal Instant Beverages. Air conditioner was suggested to set the temperature set points for controlling environmental ergonomics. Green parameters were analyzed using calculation of air conditioner electricity cost at different workplace temperature set point. Affective parameters were analyzed using heart rate, worker energy consumption and rowan incentive plan. Research findings indicated air conditioner could be used to control environmental ergonomics based on the satisfied temperature set points and efficient electricity cost in work system of food SMEs. Keywords: Air Conditioner; Environmental Ergonomics; Heart Rate; Rowan Incentive Pla

Kansei Engineering for Quantification of Indigenous Knowledges in Agro-industrial Technology

The term of indigenous knowledge refers to specific local knowledge in consumer/user which should be incorporated by agro-industry to compete in a globalized worlds. This research highlighted Kansei Engineering as a potential approach to quantify indigenous knowledge in agro-industrial technology. The research objectives were: 1) To review the quantification tools of indigenous knowledges in agro-industrial technology using Kansei Engineering; 2) To characterize indigenous knowledges in Indonesian agro-industry. Case study was demonstrated in Indonesian food product, services and ergonomic technology. Quantification was characterized using widely developed quantification tools for indigenous knowledges. The research results concluded some indigenous knowledges which could be incorporated in indigenous knowledge-based innovations. Keywords: Agro-industry, Ergonomic technology, Product, Services, Technical parameter

Artificial Neural Network Model for Affective Environmental Control System in Food SMEs

This paper presents an affective environmental control system for Small and Medium-sized Enterprises (SMEs). The system is proposed as a technology innovation in appropriate information technology. It is defined that workplace environment set points could be controlled using worker workload. The research objectives are: 1) To design an affective environmental control model for SME; 2) To develop an Artificial Neural Network (ANN) model for predicting affective environment set points. The system consisted of 4 sub-systems as measurement, assessment, control and decision. An ANN model is developed for sub-systems of control. Training and validation data are acquired from 4 (four) samples of SME in Yogyakarta Special Region, Indonesia. The model has been developed successfully to predict temperature and light intensity set points using back-propagation supervised learning method. The research results indicated the satisfied performance of ANN with minimum error. ANN model indicated the closeness of R2 value between training and validation data. The research results could be applied to support the worker productivity in food SMEs by providing a comfort workplace environment and optimum worker workload

An Optimization Model for Environmental Ergonomics Assessment in Bioproduction of Food SMEs

Environmental ergonomics in bioproduction of food Small Medium-sized Enterprises (SMEs) become a concern and need to be optimized. An optimization model was developed using a Genetic Algorithm (GA). The weight of an Artificial Neural Network Model was used as a fitness function for GA. The research objectives were: 1) To design an environmental ergonomic assessment system for bioproduction of Food SMEs, 2) To develop an optimization model for environmental ergonomic assessment using a Genetic Algorithm. GA is utilized to search optimal set points of environmental ergonomics based on the predicted fitness values. Each chromosome of GA represents the environmental ergonomics value. The parameters were heart rate, bioproduction temperature, distribution of bioproduction relative humidity and light intensity. The target of the optimization model was the bioproduction temperature set points. The research result indicated the model generated optimum values of environmental ergonomics parameter in bioproduction of food SMEs. The parameters could be used to provide standard workplace environment for the sustainability of food SMEs

Daily Worker Evaluation Model for SME-scale Food Production System Using Kansei Engineering and Artificial Neural Network

AbstractThis paper highlighted a daily worker evaluation model for small medium-scale food production system. The model consist of worker capacity assessment and worker performance evaluation sub-models. The model measures the relationship between Total Mood Disturbance (TMD), heart rate of worker and workplace parameters using Kansei Engineering approach.However, the rapid measurement of TMD is difficult and full of bias since using the paper-based questionnaire of Profile of Mood States (POMS). Therefore, a rapid measurement method was developed using Artificial Neural Network to support the application of daily evaluation model. The inputs of the model were heart rate, workplace temperature, relative humidity, light intensity and noise level, which were measured before and after working. The output was TMD score.The training and inspection data for ANN was collected from workers of food production system as Tempe, Bakpia, Fish Chips and Crackers industries in Yogyakarta Special Region.ANN model were tested successfully predicted TMD score using back-propagation supervised learning method. The trained ANN model generated satisfied root mean square error value. ANN model is possible to substitute conventional data acquisition of POMS. The daily evaluation model is applicable to assist industrial management for providing the appropriate worker assignment for shift schedulling and environmental set point for the workplace comfortability

Combining Kansei Engineering and Artificial Neural Network to Assess Worker Capacity in Small-Medium Food Industry

This paper highlighted a new method for worker capacity assessment in Indonesian small-medium food industry. The sustainable and productivity of Indonesian food industry should be maintained based on the workers capacity. The status of worker capacity could be categorized as normal, capacity constrained worker and bottleneck. By using Kansei Engineering, worker capacity can be assessed using verbal response of profile of mood states, non-verbal response of heart rate in a given workplace environmental parameters. Fusing various Kansei Engineering parameters of worker capacity requires a robust modeling tool. Artificial Neural Network (ANN) is required to assess worker capacity. The model was demonstrated via a case study of Tempe Industry. The trained ANN model generated satisfied accuracy and minimum error. The research results concluded the possibility to assess worker capacity in Indonesian small-medium food industry by combining Kansei Engineering and ANN

The pro117 to glycine mutation of staphylococcal nuclease simplifies the unfolding folding kinetics

Kinetics of unfolding and refolding of a staphylococcal nuclease mutant, in which Pro117 is replaced by glycine, have been investigated by stopped-flow circular dichroism, and the results are compared with those for the wild-type protein. In contrast to the biphasic unfolding of the wild-type nuclease, the unfolding of the mutant is represented by a single-phase reaction, indicating that the biphasic unfolding for the wild-type protein is caused by cis-trans isomerization about the prolyl peptide bond in the native state. The proline mutation also simplifies the kinetic refolding. Importance of the results in elucidating the folding mechanism is discussed

Combining Kansei Engineering and Artificial Neural Network to Assess Worker Capacity in Small-Medium Food Industry

This paper highlighted a new method for worker capacity assessment in Indonesian small-medium food industry. The sustainable and productivity of Indonesian food industry should be maintained based on the workers capacity. The status of worker capacity could be categorized as normal, capacity constrained worker and bottleneck. By using Kansei Engineering, worker capacity can be assessed using verbal response of profile of mood states, non-verbal response of heart rate in a given workplace environmental parameters. Fusing various Kansei Engineering parameters of worker capacity requires a robust modeling tool. Artificial Neural Network (ANN) is required to assess worker capacity. The model was demonstrated via a case study of Tempe Industry. The trained ANN model generated satisfied accuracy and minimum error. The research results concluded the possibility to assess worker capacity in Indonesian small-medium food industry by combining Kansei Engineering and ANN

The Internal Magnetic Field at Mercury Nucleus in Fe-Au Alloy (A. NATURAL SCIENCE)

The shift of the angular correlation of 675Kev-412Kev gamma-gamma cascade in Hg^ was measured by utilizing the high internal magnetic field at mercury nucleus in iron host material of alloy magnetized up to saturation. The value of the internal magnetic field at mercury nucleus in 2AT.% Fe-Au alloy was determined to be |H|=(0.83±0.35)×10^6 gauss