The impact of experience at sharpening the chainsaw chain on the crosscutting performance and vibration levels

V diplomski nalogi smo raziskovali vpliv nabrušenosti verige motorne žage na jakost tresenja in učinkovitost prežagovanja, z namenom, da poudarimo izkušnje gozdnega delavca na pripravo verige motorne žage. Posebej nas je zanimalo, kako se uporaba ročno brušenih verig razlikuje po učinkovitosti in jakosti tresenja, od uporabe tovarniško nabrušene verigo. Prežagovanje smo izvedli z motorno žago Stihl 462 C-M, ter verigo Rapid Super (RS) s korakom 3/8. Deset verig, ki smo jih skrhali v vedru kremenčkovega peska, je nabrusilo pet delavcev z delovnim stažem, krajšim od enega leta in pet delavcev z delovnim stažem, daljšim od deset let. Ena veriga je ostala v tovarniškem stanju. Z uporabo vsake verige smo izvedli šest prežagovanj prizmiranega sortimenta smreke. Rezultati so pokazali, da je bila jakost tresenja za 2,6-krat večja na vodilnem ročaju, kot na nosilnem ročaju motorne žage. Najmanjša jakost tresenja je bila na vodilnem ročaju izmerjena pri uporabi tovarniške verige, na nosilnem pa pri uporabi verige, ki so jo nabrusili delavci začetniki. Največja učinkovitost prežagovanja je bila dosežena pri uporabi tovarniško nabrušene verige, ki je bila tako za 53% večja, kot pri uporabi verig, ki so jo nabrusili delavci s krajšim delovnim stažem. Rezultati raziskave potrjujejo vpliv izkušenosti na učinkovitost in jakost tresenja pri delu z motorno žago, sočasno pa izpostavljajo tudi pomembnost pravilne priprave, za učinkovito in varno delo v gozdni proizvodnji.In this thesis, we explored the impact of a forestry worker\u27s experience on the sharpening of chainsaw chains, focusing on HA vibration levels and cross-cutting efficiency, with the aim of highlighting the importance of worker experience in preparing chainsaw chains. We were particularly interested in how the use of hand-sharpened chains compares in terms of efficiency and vibration intensity to factory-sharpened chains. For the study, we used a Stihl 462 C-M chainsaw, and a Rapid Super (RS) chain with a pitch of 3/8 inch. Ten chains were dulled in a bucket of quartz sand and sharpened by five workers with less than one year of experience and five workers with over ten years of experience. One chain remained in factory condition. We used 11 different chains in total, performing six cross-cuts with each one on squared spruce timber. The results showed that vibration intensity was 2.6 times greater on the front handle compared to the rear handle for all chains tested. The lowest vibration level on the front handle was recorded with the factory-sharpened chain, while the lowest on the rear handle was with chains sharpened by less experienced workers. The highest cutting the chains sharpened by less experienced workers. The study confirms the impact of experience on cutting efficiency and vibration levels when working with a chainsaw, while also emphasizing the importance of proper chain preparation for efficient and safe work in forestry

"Choice of plausible alternatives" datasets in South Slavic dialects DIALECT-COPA

The DIALECT-COPA datasets comprise Choice of Plausible Alternatives (COPA) datasets for three South Slavic dialects: (1) COPA-SL-CER for the Cerkno dialect of Slovenian, spoken in the Slovenian Littoral region, specifically from the town of Idrija; (2) COPA-HR-CKM for the Chakavian dialect of Croatian from northern Adriatic, specifically from the town of Žminj; (3) COPA-SR-TOR for the Torlak dialect from southeastern Serbia, specifically from the town of Lebane. The datasets were translated from the English COPA dataset (https://people.ict.usc.edu/~gordon/copa.html) by native dialect speakers, following the XCOPA dataset translation methodology (https://arxiv.org/abs/2005.00333). A novelty in the DIALECT-COPA translation approach is that both English and the corresponding standard South Slavic language were at disposal to the translator during the translation process. Each instance consists of a premise (My body cast a shadow over the grass), a question (What is the cause? / What happened as a result?), and two choices (The sun was rising; The grass was cut), with a label encoding which of the choices is more plausible given the annotator or translator (The sun was rising). The datasets follow the same format as the Croatian COPA-HR dataset (http://hdl.handle.net/11356/1404), the Macedonian COPA-MK dataset (http://hdl.handle.net/11356/1687) and the Serbian COPA-SR dataset (http://hdl.handle.net/11356/1708). Each dataset is split into training (400 instances) and validation (100 instances) JSONL files. The test split (500 instances), which is usually a part of the COPA datasets, has been withheld and can be shared upon request. The reason for this is to prevent its inclusion of the test instances in the training data of future large language models, which would invalidate the benchmark measurements. The DIALECT-COPA datasets are published as part of the DIALECT-COPA shared task at the VarDial 2024 workshop where they were used as gold data for evaluation of the performance of large language models on South Slavic dialects (https://sites.google.com/view/vardial-2024/shared-tasks/dialect-copa)