Determination of necessary basis for positioning of drainage structures in forest roads (sample of belgrad forest)

Orman yollarında kullanılan drenaj yapıları ve koruyucu yapılar çeşitli tiplerdedir. Bunlar; kenar hendekleri, büz, menfez, köprü, kasis ve duvarlardır. Tüm bu drenaj tesisleri ve duvarların ana görevi orman yolunu dış etkenlere karşı korumaktır. Bu drenaj yapıları sayesinde yolun kullanım ömrü uzamaktadır. Drenaj yapılarının planlanması ve yapımı çok önemlidir. Planlama ve yapım çalışmalarında yapılan hatalar drenaj yapılarının ömrünü kısaltmaktadır. Bu çalışmada örnek bir orman yolu üzerinde yapılan drenaj yapılarının planlama ve yapım çalışmaları incelenmiştir. Sonuç olarak, bazı drenaj yapılarının konumlarının ve yapımının hatalı olduğu belirlenmiş ve hatalar ortaya konmuştur.The drainage structures and protective structures in used forest roads are various types. These are dip drains, pipes, culverts, bridges, water-bars and walls. The main task of all drainage structures and walls is protecting against external aggressors to the forest roads. The useful life of forest road due to drainage structures is prolonged. The planning and constructions operations of drainage structures are very important. The occurring errors by planning and constructions operations are shortened the life of drainage structures. In this study, the planning and constructions operations of building drainage structures on a sample forest road were investigated. In the results, locations and constructions of some drainage structures have been determined to be incorrect and errors have revealed

Content-Based Medical Image Retrieval with Opponent Class Adaptive Margin Loss

Broadspread use of medical imaging devices with digital storage has paved the way for curation of substantial data repositories. Fast access to image samples with similar appearance to suspected cases can help establish a consulting system for healthcare professionals, and improve diagnostic procedures while minimizing processing delays. However, manual querying of large data repositories is labor intensive. Content-based image retrieval (CBIR) offers an automated solution based on dense embedding vectors that represent image features to allow quantitative similarity assessments. Triplet learning has emerged as a powerful approach to recover embeddings in CBIR, albeit traditional loss functions ignore the dynamic relationship between opponent image classes. Here, we introduce a triplet-learning method for automated querying of medical image repositories based on a novel Opponent Class Adaptive Margin (OCAM) loss. OCAM uses a variable margin value that is updated continually during the course of training to maintain optimally discriminative representations. CBIR performance of OCAM is compared against state-of-the-art loss functions for representational learning on three public databases (gastrointestinal disease, skin lesion, lung disease). Comprehensive experiments in each application domain demonstrate the superior performance of OCAM against baselines.Comment: 10 pages, 6 figure

Productivity of hauling by tajfun MOZ 500 GR cable yarder in Turkey

The extraction of timber is very important despite the process is difficult, expensive, time consuming, and has concerns with work safety. The extraction operations are used human, animal, and machine power. The purpose of this study is to investigate the productivity of the Tajfun MOZ 500 GR cable yarder during the extraction of timber from spruce stands in northeast Turkey. The research results imply that some working characteristics of the MOZ 500 GR cable yarder such as load volume, yarding distance, speed of the carriage, and time consumption per phase have an important impact on the productivity of the cable yarder. The results indicated that the productivity of MOZ 500 GR cable yarder was 8.39 m(3)/h for an average yarding distance of 90 m. Besides, the daily productivity of cable yarders was found at 67.12 m(3). The cable yarders seem ideal for use in the steep terrain. The use of cable yarders in wood production works is more ideal than other production techniques (human, animal, and tractor) in terms of productivity, speed, and work safety

HydraViT: Adaptive Multi-Branch Transformer for Multi-Label Disease Classification from Chest X-ray Images

Chest X-ray is an essential diagnostic tool in the identification of chest diseases given its high sensitivity to pathological abnormalities in the lungs. However, image-driven diagnosis is still challenging due to heterogeneity in size and location of pathology, as well as visual similarities and co-occurrence of separate pathology. Since disease-related regions often occupy a relatively small portion of diagnostic images, classification models based on traditional convolutional neural networks (CNNs) are adversely affected given their locality bias. While CNNs were previously augmented with attention maps or spatial masks to guide focus on potentially critical regions, learning localization guidance under heterogeneity in the spatial distribution of pathology is challenging. To improve multi-label classification performance, here we propose a novel method, HydraViT, that synergistically combines a transformer backbone with a multi-branch output module with learned weighting. The transformer backbone enhances sensitivity to long-range context in X-ray images, while using the self-attention mechanism to adaptively focus on task-critical regions. The multi-branch output module dedicates an independent branch to each disease label to attain robust learning across separate disease classes, along with an aggregated branch across labels to maintain sensitivity to co-occurrence relationships among pathology. Experiments demonstrate that, on average, HydraViT outperforms competing attention-guided methods by 1.2%, region-guided methods by 1.4%, and semantic-guided methods by 1.0% in multi-label classification performance

The use of portable winch for forest harvesting operations in Pinus pinaster plantation areas

Ülke nüfusunun artışına paralel olarak odun üretim miktarlarında da son yıllarda önemli artışlar görülmektedir. Odun üretim miktarlarında ortaya çıkan bu artış üretim çalışmalarında da yeniliklere gitmeyi zorunlu kılmaktadır. Özellikle odun üretim çalışmalarında taşınabilir ve düşük maliyetli yeni teknolojilerin ve makinelerin kullanımı önemli bir hal almıştır. Bu makineler arasında el vinçleri son yıllarda ülkemizde ve dünya ormancılık çalışmalarında da kullanılmaya başlanmıştır. Bu vinçler kurulumu ve sökülmesi yanında kullanım açısından da çok kullanışlı makinelerdir. Bu çalışmada, İstanbul Orman Bölge Müdürlüğü’ne bağlı Sarıyer Orman İşletme Şefliği sınırları içerisinde yer alan Sahilçamı plantasyon sahalarında el vincinin kullanımı incelenmiştir. Çalışmalar sonucunda el vincinin ortalama 50 metrelik sürütme mesafesinde saatlik verimi 1.10 m3/sa ve günlük verimi 8.80 m3/gün olarak bulunmuştur. El vincinin saatlik yakıt tüketimi ise 1.4 lt/sa olarak belirlenmiştir.In parallel with the increase in the population in Turkey, there has been a significant increase in the amount of wood production in recent years. This increase, which occurs in the amount of wood production, makes it imperative to innovate in production of forest products. Especially in wood production, the use of portable and low-cost new technologies and machines has become important. Among these machines, portable winches have been used in forest operations in the world and in Turkey as well in recent years. These winches are very useful machines in terms of installation and uninstallation. In this study, the use of portable winch in the Sahilçamı plantation areas within the boundaries of Sarıyer Forest Operation Directorate affiliated to the Istanbul Forest Regional Directorate has been examined. As a result of the studies, the hourly yield of the portable winch at an average skid distance of 50 meters was found as 1.110 m3/hr and the daily yield was found as 8.800 m3/day. Hourly fuel consumption of the hand winch was determined as 1.4 lt/h

Technical features and working principles of valmet 911 harvester

Tomruk üretimi hala pek çok önemli ormancılık aktivitelerinden biridir. Gelişmiş ülkelerde ormancılık çalışmaları içerisinde yer alan orman ürünlerinin üretimi ve taşınmasında teknoloji en iyi şekilde kullanılmaktadır. Orman makinelerinin verimliliği; arazi koşulları ve bitki özelliklerine göre farklı ekolojik durumlara ve onların negatif - pozitif etkilerine bağlıdır. Orman ürünlerinin kalite ve kantite kaybı olmaksızın bölmeden çıkarılması tehlikeli, güç ve masraflı bir süreci oluşturmaktadır. Günümüzde, gelişmiş ülkelerde üretim alanında ağaçların kesilmesi, kabuklarının soyulması, tomruklanması, bölmeden çıkarılması, yüklenmesi, taşınması, boşaltılması ve istiflenmesi çalışmalarının büyük bir bölümü makineler ile yapılmaktadır. Bu çalışmada, Valmet 911 üretim aracının teknik özellikleri ve çalışma prensipleri incelenmiş ve ülkemizde bu makinenin kullanılabilirliği tartışılmıştır.The production of timber is still one of the most important forestry practical. In developed countries, the best way of advanced technologies is used to production and transport of forest products with in the forest activities. The productivity of forest machines depends on various ecological factors as terrain conditions and plant features and their positive and negative effects. The extraction and production process of forest products without loss of quality and quantity is an important subject. A large part of processes as cutting tree, barking, preparing of timber, hauling, loading, transporting, unloading and stacking are done with machines. In this study, the technical features and working principles of Valmet 911 harvester are investigated and the availability of these machines in Turkey is discussed

Investigation of forest road construction technique by bulldozer in Eskisehir region

Bu çalışmada, Eskişehir bölgesindeki ormanlarda orman yol yapım teknikleri, çevresel zararlar ve dozerin yol yapımı sırasındaki verimliliği araştırılmıştır. Çalışmada, yol üzerinde enkesitler alınarak dozerin kazı ve dolgu şevleri incelenmiştir. Yol yapımı esnasında maksimum dolgu şevi uzunluğu %70 eğimde 17 m olarak bulunmuştur. Dozerin maliyeti 8.5 $/m olarak ve verimliliği ortalama 105.8 m3 /saat olarak hesaplanmıştır. Bunun yanında, yol yapımı sırasında ağaçlarda meydana gelen zarar tipleri gözlemlenmiştir. Bu tipler yaralanma, eğilme ve kırılmadır. Yol yapımı esnasında şevinde kalan ağaçların bulldozer were researched in forested lands in Eskisehir region. In this study, cut and fill shapes of bulldozer were investigated. Maximum length of fill slope during road construction was found 17 m on 70gradient. The cost of forest roads for bulldozer was calculated$8.5 per meter and average productivity of bulldozer was found 105.8 m3 /hr. The types of damages on trees are determined. These types are wounded, bending and crushing. 40% of trees under road fill during road construction are occured for 36-70 % ground slop

Simplified Indirect Bonding Technique for Lingual Retainer Fabrication

Aim:The aim of this study was to describe a simplified technique for fabricating an indirectly bonded lingual retainer using a light cure composite resin and clear tape.Subjects and Methods:Lingual retainers, made of dead soft wire, were bended to the lingual contour of the lower incisors on the stone models of patients and placed to the mouth. Fabricating lingual retainers with this method were used in 6 patients who were followed for 6 months.Results:During this period, failures occurred in 2 teeth. One failure was on a canine; the other one is on an incisor.Conclusion:Due to the isolation done with a clear tape in this method, there is no need for an extra procedure for the removal of the remnants of the separating medium from the resin surface. These extra procedures applied can cause the degradation of the resin surface which may lead to bonding failure

Adaptive Diffusion Priors for Accelerated MRI Reconstruction

Deep MRI reconstruction is commonly performed with conditional models that de-alias undersampled acquisitions to recover images consistent with fully-sampled data. Since conditional models are trained with knowledge of the imaging operator, they can show poor generalization across variable operators. Unconditional models instead learn generative image priors decoupled from the imaging operator to improve reliability against domain shifts. Recent diffusion models are particularly promising given their high sample fidelity. Nevertheless, inference with a static image prior can perform suboptimally. Here we propose the first adaptive diffusion prior for MRI reconstruction, AdaDiff, to improve performance and reliability against domain shifts. AdaDiff leverages an efficient diffusion prior trained via adversarial mapping over large reverse diffusion steps. A two-phase reconstruction is executed following training: a rapid-diffusion phase that produces an initial reconstruction with the trained prior, and an adaptation phase that further refines the result by updating the prior to minimize reconstruction loss on acquired data. Demonstrations on multi-contrast brain MRI clearly indicate that AdaDiff outperforms competing conditional and unconditional methods under domain shifts, and achieves superior or on par within-domain performance