Traditional approaches to welder training demand substantial investments in equipment, consumable materials, and workshop facilities, while also exposing novice learners to considerable safety risks. This study investigates the effectiveness of a virtual reality (VR)-based welding training system developed with Unity for the Meta Quest 2 platform, designed to deliver safe and immersive instruction in fundamental welding techniques. A total of twenty participants with no prior welding experience completed structured VR training sessions over two weeks. The program focused on developing competencies in welding machine operation (including start-up procedures and parameter adjustments), controlling shielding gas flow, and accurately regulating torch-to-workpiece distance, torch angle, and travel speed. Real-time feedback was integrated into the system to support accurate control and positioning of the welding torch. Quantitative assessments demonstrated significant improvements in both technical proficiency and trainee confidence and anxiety levels. Knowledge test scores increased from 45.3 to 85.1, while machine adjustment accuracy rose from 28.7 to 92.3. In parallel, participant confidence levels increased substantially, and anxiety scores decreased from 4.0–4.5 to 1.1–1.5 on standardized scales. These findings provide experimental evidence that VR-based training can enhance fundamental welding education by offering a safe, repeatable, and effective practice environment that simultaneously improves technical performance, strengthens learner confidence, and reduces training-related anxiety.

The field of autonomous driving has seen remarkable progress in recent years, with the prediction of steering angles based on varying road conditions emerging as a critical area of focus. While previous efforts have concentrated on lane detection, road object identification, and 3-D reconstruction, our research centers on a vision-based model that leverages deep networks to translate raw camera images into steering angles without the need for predefined feature learning. In our paper, we introduce an end-to-end model that employs deep transfer learning to predict steering angles from image sequences captured by onboard cameras. This model merges two deep learning architectures: a convolutional neural network (CNN) and a long short-term memory (LSTM) network. We utilize the VGG16 network, pre-trained on ImageNet and renowned for its performance, to extract spatial features from the images. Concurrently, the LSTM network processes the temporal information embedded within the image sequences. Our proposed model comprehensively processes spatial-temporal data and adeptly models the nonlinear relationship between the input images and the steering angles. We conducted an experimental study using a publicly available dataset to evaluate the model's effectiveness. The outcomes of our experimental analysis reveal that our model delivers highly efficient and accurate steering angle predictions, effectively emulating human driving patterns. Moreover, it surpasses current models in both performance and training efficiency, achieving a Mean Squared Error (MSE) score of 0.0728 as its loss function.

Yakın gelecekte petrol rezervlerinin tükeneceği öngörülmemektedir, fakat hidrokarbon kökenli yakıtların kullanılması canlılar için sağlığa olumsuz etki eden emisyonlar oluşturmaktadır. Bu nedenle dünya genelinde yenilenebilir, alternatif ve temiz enerji kaynaklarına yönelim söz konusu olmaktadır. Yakıt pilleri, sessiz ve verimli çalışabilen, aynı zamanda zararlı emisyonları olmayan bir elektrik üretecidir. Ticari uygulamaları da bulunan yakıt pilleri, havacılık, otomotiv, askeri, denizaltı yerel ve mobil uygulamalarda kullanılabilmektedir. Bu da kapsamlı bir uygulama alanı olduğunun göstergesidir. Bu çalışmada yakıt pili tiplerinden PEM yakıt pilleri araştırma konusu olarak seçilmiş ve PEM yakıt pili bileşenlerinden bipolar plakaların üretim teknikleri ele alınmıştır. Geleneksel ve yenilikçi üretim tekniklerinin araştırıldığı bu çalışmada, üretim trendinin ne aşamada olduğu tartışılmış ve konunun gelecek perspektifi göz önünde bulundurularak yapılan araştırmalar değerlendirilmiştir.

This study presents a mathematical and kinematic modeling framework for analyzing trajectory behavior in a virtual reality (VR) welding simulator. Twenty novice participants performed repeated welding trials across three sessions, with torch trajectories recorded at 50 Hz in the task space. The proposed framework combines trial-level performance descriptors with derivative-based dynamic features, including spectral arc length (SPARC), log-normalized jerk (LNJ), and the number of velocity peaks (NVP), to characterize movement smoothness, intermittency, and longitudinal trajectory organization in a computer-simulated manual welding task. The results showed that spatial welding error decreased most clearly during the earliest stage of practice, with mean absolute lateral error declining from approximately 2.8 mm in the first trial to approximately 1.7 mm by the third trial. This early improvement was then broadly preserved across subsequent sessions. In contrast, smoothness- and fragmentation-related metrics exhibited more variable temporal patterns, indicating that improvements in task-space accuracy were not necessarily accompanied by uniform reorganization of movement dynamics. Associations between spatial error and kinematic features remained limited, suggesting that geometric task accuracy and dynamic trajectory organization represent complementary aspects of simulated manual performance. Overall, the findings show that high-frequency trajectory analysis in VR provides a useful basis for the mathematical modeling of dynamic behavior in simulated welding systems and supports the use of computer simulation for process-level investigation of manual task execution.

Temiz ve alternatif enerji arayışı, insanlığın sürekli artan enerji talebini karşılama doğrultusunda artış göstermektedir. Yakıt pilleri, yakıtın kimyasal enerjisini kullanarak doğrudan elektriğe dönüştüren yüksek verimli enerji dönüşüm sistemleridir. Klasik elektrik enerjisi üretim çevriminde yakıt önce yakılarak ısı enerjisi elde edilmekte, ardından buhar etkisi ile türbin döndürülmekte, jeneratörde akım elde edilmekte ve depolanmaktadır. Bu enerji dönüşümü oldukça karmaşık ve çok makineli bir sistem gerektirirken, her enerji dönüşümünde enerji kayıpları da meydana gelmektedir. Yakıt pillerinde ise sadece yakıtın elektronlarını devreden geçirerek yüksek elektriksel verimle enerji elde edilmektedir. Bu çalışmada yakıt pili çeşitlerinden PEM yakıt pilleri ele alınarak, PEM yakıt pillerinde kullanılan bipolar plakaların akış alanları incelenmiştir. Literatürde nümerik çalışmalar da bulunmakla birlikte, yakıt pillerinin oldukça değişken çalışma şartlarından dolayı deneysel olarak test etmek büyük önem arz etmektedir. Bu nedenle son on yılda yapılmış olan deneysel çalışmalar, yöntemleriyle sunulmuştur. Günümüzde çok popüler bazı akış kanalları verilmekle beraber, akış kanallarını birbirleriyle karşılaştırma yöntemiyle üstünlüğün belirlendiği anlaşılmış ve günümüzde hala optimum akış kanalına ulaşılamadığı gözlemlenmiştir.

Evidence on whether spreading-factor (SF) usage follows link quality in passively observed urban Long-Range Wide-Area Network (LoRaWAN) traffic remains limited. This study analyzes 54,893 Long-Range (LoRa) frames decoded over 30.2 days by a receive-only gateway in Ankara, Türkiye, using only physical-layer metadata and unencrypted message types. Join requests formed 73.44% of frames and 77.73% of airtime. A gradient-boosting classifier separated SF7 from SF12 frames with area under the precision–recall curve (PR-AUC) = $0.9376$; on a subsequent 24.9-day external test period, the F1 score was 0.7161, alongside a persistent frequency-offset shift. This discrimination relied largely on SF7 frames without a valid LoRaWAN uplink header; within LoRaWAN uplinks, link-quality variables outperformed frame size combined with frequency offset (PR-AUC 0.5010 versus 0.1129), also externally. At a 10 dB margin, 20.81% of SF12 join requests but only 0.31% of SF12 data uplinks had a signal-to-noise ratio sufficient for a lower SF; join requests accounted for 11.08 of 11.14 percentage points of counterfactual airtime reduction. Unsupervised clustering yielded six signal-quality regimes aligned with message class. Data-uplink SF usage is consistent with link-quality-driven assignment, whereas join requests dominate airtime; separating message classes was necessary for passive interpretation.