Abstract. Time studies represent important tools that are used in forest operations research to produce empirical models or to comparatively assess the performance of two or more operational alternatives with the general aim to predict the performance of operational behavior, choose the most adequate equipment or eliminate the useless time. There is a long tradition in collecting the needed data in a traditional fashion, but this approach has its limitations, and it is likely that in the future the use of professional software would be extended is such preoccupations as this kind of tools have been already implemented. However, little to no information is available in what concerns the performance of data analyzing tasks when using purpose-built professional time studying software in such research preoccupations, while the resources needed to conduct time studies, including here the time may be quite intensive. Our study aimed to model the relations between the variation of time needed to analyze the video-recorded time study data and the variation of some measured independent variables for a complex organization of a work cycle. The results of our study indicate that the number of work elements which were separated within a work cycle as well as the delay-free cycle time and the software functionalities that were used during data analysis, significantly affected the time expenditure needed to analyze the data (α=0.01, p<0.01). Under the conditions of this study, where the average duration of a work cycle was of about 48 seconds and the number of separated work elements was of about 14, the speed that was used to replay the video files significantly affected the mean time expenditure which averaged about 273 seconds for half of the real speed and about 192 seconds for an analyzing speed that equaled the real speed. We argue that different study designs as well as the parameters used within the software are likely to produce different results, a fact that should trigger other studies based on variations of these parameters. However, the results of this study give an initial overview on the time resources needed in processing and analyzing the data, and may help researchers in allocating their resources.

The sessile oak tree represents 10.5% of the forest area in Romania and is the most widespread indigenous oak species. To select the most suitable domain of use for sessile oak wood, certain dimensional and qualitative criteria were taken into consideration. The aim of the present study was to highlight the influence of some log characteristics (wood diameters and quality) on the efficiency in sessile oak veneer cutting. The authors used a group of sessile oak logs purchased from Targoviste in Southeast Romania. The results analysis indicates the influence of sessile oak log diameters on the veneer efficiency comparative with decorative veneer efficiency by estimation of the cumulative density function (CDF). Analyzing the quality of sessile oak logs, it was found that buds and insect holes were the most important defects that appeared. Also, the regression analysis indicates an acceptable level of the present defects and did not have a significant influence to the veneer cutting efficiency, upon the number of obtained veneer sheets and the surface area of special veneer sheets, respectively.

Compared to other types of transport, log transport has its own characteristics related to both the transport route and the means of transport. Because, over time, both the routes used to transport timber and the means of transport have constantly evolved, being adapted to the new requirements, specialists in the field always seek to extend the period of operation of forest roads, especially that, in the case of the present, most of them have been designed and built to withstand lower traffic in terms of intensity and frequency. Thus, in order to behave as well as possible in operation, forest roads must be made more precisely the geometric and constructive elements of forest roads and must take into account the constructive characteristics and the movement of vehicles to travel on these roads. Considering the current situation, a very important one, it was considered opportune to research a forest road from the perspective of the traffic on it, recorded for a longer period of time. Thus, the research was carried out on a valley road from the administration of the Bacău Forestry Department -the Ciobănuș forest road. Following the centralization and interpretation of the data, it resulted that the annual distribution of transported volumes is approximately equal and no significant variations were found between 2014 and 2018 and that annually, on the Ciobănuș forest road, a specific tonnage transits the main forest roads, which supports , once again, the accentuated degree of degradation and the rapidity of degradation on this road, due to an insufficiently dimensioned superstructure, which cannot support the annual volumes transited.

Information on body posture, postural change, and dynamic and static work is essential in understanding biomechanical exposure and has many applications in ergonomics and healthcare. This study aimed at evaluating the possibility of using triaxial acceleration data to classify postures and to differentiate between dynamic and static work of the back in an experimental setup, based on a machine learning (ML) approach. A movement protocol was designed to cover the essential degrees of freedom of the back, and a subject wearing a triaxial accelerometer implemented this protocol. Impulses and oscillations from the signals were removed by median filtering, then the filtered dataset was fed into two ML algorithms, namely a multilayer perceptron with back propagation (MLPBNN) and a random forest (RF), with the aim of inferring the most suitable algorithm and architecture for detecting dynamic and static work, as well as for correctly classifying the postures of the back. Then, training and testing subsets were delimitated and used to evaluate the learning and generalization ability of the ML algorithms for the same classification problems. The results indicate that ML has a lot of potential in differentiating between dynamic and static work, depending on the type of algorithm and its architecture, and the data quantity and quality. In particular, MLPBNN can be used to better differentiate between dynamic and static work when tuned properly. In addition, static work and the associated postures were better learned and generalized by the MLPBNN, a fact that could provide the basis for cheap real-world offline applications with the aim of getting time-scaled postural profiling data by accounting for the static postures. Although it wasn’t the case in this study, on bigger datasets, the use of MLPBPNN may come at the expense of high computational costs in the training phase. The study also discusses the factors that may improve the classification performance in the testing phase and sets new directions of research.

Veneers are used as overlaying material for various types of composite substrates for the production of veneered panels and furniture. There is a strong correlation between color and quality for the acceptability of a product, which is currently an industrial preoccupation. The aim of the present study was to evaluate the color variability of veneers produced from high-quality European oak logs exhibiting the best production yield. Defect-free logs cut from a Quercus spp. forest in Romania were sliced into veneers. Color measurements were made at various locations on veneer sheets. All data were statistically analyzed. As expected, heartwood highly influenced the final color of decorative oak veneers, in which yellow and red were highlighted. The statistical analysis also revealed the homogeneity of lightness and yellow degree within the veneer collectivity. The color homogeneity within the sectors confirmed the wood quality for veneer production. Therefore, individual veneer sheets can be segregated based on color measurement to provide accurate results for sorting pieces of different colors.