Shear strength in friction welded joint of poplar wood impregnated with copper-based wood preservative

Environmentally friendly processes are of great interest and considerably needed due to the worldwide problem of pollution. Linear vibration welding of timber structural elements provides new opportunities to potentially achieve structural joints. Mechanically induced vibrational wood fusion welding is shown to be due mostly to the melting and flowing of some amorphous, cells-interconnecting polymer material in the structure of wood, mainly lignin, but also hemicelluloses. In this study, poplar (Populus euramericana) samples were impregnated with alkaline copper quat (ACQ) in order to enhance welding performance. Chemical changes of the impregnated and welded specimens were characterized by FT-IR techniques. A decrease in the proportion of unoxidized phenolic groups in the lignin were observed by FT-IR and the decreased joint strength observed is impregnated wood. After impregnation, shear strength decreased by 37 % to 54 %. The X-ray CT-scanning results revealed that the average density of the poplar wood (368 kg/m3) increased to 710 kg/m3 by welding

An automated approach for extracting forest inventory data from individual trees using a handheld mobile laser scanner

Many dendrometric parameters have been estimated by light detection and ranging (LiDAR) technology over the last two decades. Handheld mobile laser scanning (HMLS), in particular, has come into prominence as a cost-effective data collection method for forest inventories. However, most pilot studies were performed in domesticated landscapes, where the environmental settings were far from those presented by (near )natural forest ecosystems. Besides, these studies consisted of numerous data processing steps, which were challenging when employed by manual means. Here we present an automated approach for deriving key inventory data using the HMLS method in natural forest areas. To this end, many algorithms (e.g., cylinder/circle/ellipse fitting) and machine learning models (e.g., random forest classifier) were used in the data processing stage for estimation of the tree diameter at breast height (DBH) and the number of trees. The estimates were then compared against the reference data obtained by field measurements from six forest sample plots. The results showed that correlations between the estimated and reference DBHs were very strong at the plot level (r=0.83-0.99, p> hard plotso << located at rocky terrains with dense undergrowth and irregular trunks. We concluded that area-based forest inventories might hugely benefit from the HMLS method, particularly in "easy plots". By improving the algorithmic performances, the accuracy levels can be further increased by future research

Application of handheld laser scanning technology for forest inventory purposes in the NE Turkey

Forest inventory (FI) is the most challenging stage of forest management and planning process. Therefore, in situ surveys are often reinforced by modern remote sensing (RS) methods for collecting forestry-related data more efficiently. This study tests a state-of-the-art data collection method for practical use in the Turkish FI system for the first time. To this end, forest sampling plots were conventionally measured to collect dendrometric data from 437 trees in Artvin and Saçınka Forest Enterprises. Then, each plot was scanned using a handheld mobile laser scanning (HMLS) instrument. Finally, HMLS data were compared against ground measurements via basic FI measures. Based on statistical tests, no apparent differences were found between the two datasets at the plot level (P 0.97; P < 0.01). Residual analysis showed that both positive and negative errors had a homogeneous distribution, except for plot 8 where tree stems were in irregular shapes due to anthropogenic pressures. When all plots’ data were aggregated, average values for the number of trees, basal area, and timber volume were estimated as 535 trees/ha–1, 49.6 m2/ha–1, and 499.7 m3/ha–1, respectively. Furthermore, secondary measures such as the number of saplings and slope were successfully retrieved using HMLS method. The highest overestimation was in timber volume with less than 10% difference at the landscape level. The differences were attributed to poor data quality of conventional measurements, as well as marginal site conditions in some plots. We concluded that the HMLS method met the accuracy standards for most FI measures, except for stand height. Thus, the Turkish FI system could benefit from this novel technology, which in turn supports the implementation of sound forest management and planning

A modular software architecture for UAVs

There have been several attempts to create scalable and hardware independent software architectures for Unmanned Aerial Vehicles (UAV). In this work, we propose an onboard architecture for UAVs where hardware abstraction, data storage and communication between modules are efficiently maintained. All processing and software development is done on the UAV while state and mission status of the UAV is monitored from a ground station. The architecture also allows rapid development of mission-specific third party applications on the vehicle with the help of the core module

Letter from a Brazilian Supporter to Geraldine Ferraro

Letter from a Brazilian supporter to Geraldine Ferraro.https://ir.lawnet.fordham.edu/vice_presidential_campaign_correspondence_1984_international/1257/thumbnail.jp

EFFECTIVENESS OF FACEBOOK ON STUDENTS’ ACHIEVEMENT IN MATHEMATICS

In the school practices used in Nepal, the students studying math courses are supposed to show all steps used in solving a math problem. A random survey of answer papers submitted by students revealed that all students in a math class solved the questions in similar ways including the mistakes they made in solving them. The researchers assigned different types of questions as homework or assignments. After close observations, the researchers came to know that students were communicating via social networks like Facebook and share solutions. This observation led to this study? The researchers suggested students not to use Facebook and other social networks and focus on studies to address their parents’ concern and worries. As a result of concerns by parents, a short study was conducted using two groups. The participants were divided into two groups. The experimental group was allowed to use Facebook and the control group was taught in the traditional mode (Non-Facebook based instructional method. The two groups were pretested immediately after teaching. The post-test results show that using interactive social media did improve students’ performance on assignments and exams

Finding Feasible Routes with Reinforcement Learning Using Macro-Level Traffic Measurements

The quest for identifying feasible routes holds immense significance in the realm of transportation, spanning a diverse range of applications, from logistics and emergency systems to taxis and public transport services. This research area offers multifaceted benefits, including optimising traffic management, maximising traffic flow, and reducing carbon emissions and fuel consumption. Extensive studies have been conducted to address this critical issue, with a primary focus on finding the shortest paths, while some of them incorporate various traffic conditions such as waiting times at traffic lights and traffic speeds on road segments. In this study, we direct our attention towards historical data sets that encapsulate individuals’ route preferences, assuming they encompass all traffic conditions, real-time decisions and topological features. We acknowledge that the prevailing preferences during the recorded period serve as a guide for feasible routes. The study’s noteworthy contribution lies in our departure from analysing individual preferences and trajectory information, instead focusing solely on macro-level measurements of each road segment, such as traffic flow or traffic speed. These types of macro-level measurements are easier to collect compared to individual data sets. We propose an algorithm based on Q-learning, employing traffic measurements within a road network as positive attractive rewards for an agent. In short, observations from macro-level decisions will help us to determine optimal routes between any two points. Preliminary results demonstrate the agent’s ability to accurately identify the most feasible routes within a short training period