Development Of Working Model of 3-D Mixer

Mixing machine based on Paul Schatz mechanism is a very interesting machine that uses only few simple components without cams or gears etc. However full cycle mobility of this machine is only possible when, link lengths are in appropriate proportion. This paper describes a practical approach for developing such wonderful 3D mixer by exploring the overconstrained mechanism & achieving its full cycle mobility

CoCast - SLR

We are currently living in a world surrounded by technology, making us very dependent on the little nitty gritty way of accomplishing tasks efficiently. Being mindful of the of the changes that are occurring upon us, we are compelled to helping where possible. Personally, Sheridan College pilots their very own campus radio station – Sheridan Life Radio (SLR) a member of the National Campus and Community Radio Association, whose main aim is to bring a little bit of everything to the table covering special occasions such as Canadian upcoming holidays, Valentine’s Day, Mother’s Day to motivational and inspirational podcasts. SLR primarily doesn’t only produce content for the college but for podcasting platforms such as Spotify, Google Podcasts and Apple Podcast. Hence, keeping in mind of the wide variety of topics that the SLR tackles, the most troublesome one happens to be the management of podcasts. Where the mode of operation for them currently is to have individuals chase producing – the act of finding guests, stories and angles and collaborative podcasting process. This process can be nerve-wreaking and frustrating. To help SLR overcome this problem a proposed solution is to have procuring audio clips to one stop location for the clip gathering cutting the stress and effect of people personally being out and about in the field. Since the proposed solution aims to make the way of operation smarted and less stressful for the SLR team it is called Co-Cast

Islamic Religious Education on the Technological Developments of the Era-Revolutionary Society 5

The era of society 5.0 emerged with challenges as well as life obstacles. Society 5.0 has a significant impact on the education sector. Educational institutions are expected to be resilient in the face of emerging changes. Islamic religious education has faced big challenges because the era of society 5.0 puts forth 3 aspects, that of human literacy, data literacy, and technological literacy. The concept of learning Islamic religious education in the era of society 5.0, the benefits of learning this education, and the challenges of it in the era of society 5.0 are the background problems this study explores. This research model uses a literature study or what is commonly referred to as library research and the data collected is reviewed from various sources, including scientific publications and books that focus on these concepts, challenges and innovations of Islamic religious education in society 5.0. The data used are from books and journals. The challenges rose from the fact that there were inadequate human resources and teachers who were mostly elderly. The concepts applied in this learning utilize and create new innovations. The innovation of Islamic religious education learning that is carried out is by using a project-based learning model (PjBL) which is a 21st century learning model based on basic concepts and paradigms. This method is considered to be able to increase students’ motivation in conducting analysis aimed at problem-solving activities and critical thinking.  Keywords: Society 5.0, Islamic religious education, Islamic religious studies, Revolution-era technology

Iterative L1 Regularized Limited Memory Stochastic BFGS Algorithm and Numerical Experiments for Big Data Applications

This research is motivated by challenges in addressing optimization models arising in Big Data. Such models are often formulated as large scale stochastic optimization problems. When the probability distribution of the data is unknown, the Sample Average Approximation (SAA) scheme can be employed which results in an Empirical Risk Minimization (EMR) problem. To address this class of problems deterministic solution methods, such as the Broyden, Fletcher, Goldfarb, Shanno (BFGS) method, face high computational cost per iteration and memory requirement issues due to presence of uncertainty and high dimensionality of the solution space. To cope with these challenges, stochastic methods with limited memory variants have been developed recently. However, the solutions generated by such methods might be dense requiring high memory capacity. To generate sparse solutions, in the literature, standard L1 regularization technique is employed, where constant L1 regularization parameter is added to the objective function of the problem which changes the original problem and the solutions obtained by solving the regularized problem are approximate solutions. Moreover, limited information is available in the literature to obtain sparse solutions to the original problem. To address this gap, in this research we develop an iterative L1 Regularized Limited memory Stochastic BFGS (iRLS-BFGS) method in which the L1 regularization parameter and the step-size parameter are simultaneously updated at each iteration. Our goal is to find the suitable decay rates for these two sequences in our algorithm. To address this research question, we first implement the iRLS-BFGS algorithm on a Big Data text classification problem and provide a detailed numerical comparison of the performance of the developed algorithm under different choices of the update rules. Our numerical experiments imply that when both the step-size and the L1 regularization parameter decay at the rate of the order 1/?k , the best convergence is achieved. Later, to support our findings, we apply our method to address a large scale image deblurring problem arising in signal processing using the update rule from the previous application. As a result, we obtain much clear deblurred images compared to the classical algorithm’s deblurred output images when both the step-size and the L1 regularization parameter decay at the rate of the order 1/?k.Industrial Engineering & Managemen

Rethinking Intermediate Layers design in Knowledge Distillation for Kidney and Liver Tumor Segmentation

Knowledge distillation(KD) has demonstrated remarkable success across various domains, but its application to medical imaging tasks, such as kidney and liver tumor segmentation, has encountered challenges. Many existing KD methods are not specifically tailored for these tasks. Moreover, prevalent KD methods often lack a careful consideration of what and from where to distill knowledge from the teacher to the student. This oversight may lead to issues like the accumulation of training bias within shallower student layers, potentially compromising the effectiveness of KD. To address these challenges, we propose Hierarchical Layer-selective Feedback Distillation (HLFD). HLFD strategically distills knowledge from a combination of middle layers to earlier layers and transfers final layer knowledge to intermediate layers at both the feature and pixel levels. This design allows the model to learn higher-quality representations from earlier layers, resulting in a robust and compact student model. Extensive quantitative evaluations reveal that HLFD outperforms existing methods by a significant margin. For example, in the kidney segmentation task, HLFD surpasses the student model (without KD) by over 10pp, significantly improving its focus on tumor-specific features. From a qualitative standpoint, the student model trained using HLFD excels at suppressing irrelevant information and can focus sharply on tumor-specific details, which opens a new pathway for more efficient and accurate diagnostic tools.Comment: Under-review at ISBI-202

Pengembangan Perangkat Pembelajaran IPA Terpadu Berorientasi Pendidikan Karakter Pada Model Pembelajaran Exclusive

The EXCLUSIVE learning model design as learning program on the theme of nature and around us has never been used by teacher whom leading to character education at the curriculum 2013. To understand the concept of integration of integrated, has developed a learning devices in the form of teacher`s book supplement and student`s book supplement so that the student`s learning process becomes easier, effective, and exciting with applying the scientific approach. The development of teacher`s book supplement and student`s book supplement was started with need analysis and identification of source, identification of product specification will be developed, and then the development of product was in the form of teacher`s book supplement and student`s book supplement. The result of test of the effectiveness of learning devices has been developed showed effective as a learning devices The result of field test, in class VII F SMPN 12 Bandar Lampung showed that the percentage of student`s learning outcome by 87 % complete KKMDesain pembelajaran model EXCLUSIVE sebagai program pembelajaran pada tema “alam dan sekitar kita†belum pernah digunakan oleh guru yang mengarah pendidikan karakter pada kurikulum 2013 ini. Untuk memahami konsep keterpaduan IPA Terpadu, telah dikembangkan perangkat pembelajaran berupa suplemen buku guru dan suplemen buku siswa agar proses belajar siswa menjadi lebih mudah, efektif, dan menarik dengan menerapkan pendekatan Scientific Approach. Pengembangan suplemen buku guru dan suplemen buku siswa diawali dengan analisis kebutuhan dan identifikasi sumber daya, identifikasi spesifikasi produk yang akan dikembangkan, kemudian pengembangan produk berupa suplemen buku guru dan suplemen buku siswa. Hasil uji efektifitas perangkat pembelajaran yang telah dikembangkan menunjukkan efektif sebagai perangkat pembelajaran. Hasil uji lapangan, pada siswa kelas VIIF SMP Negeri 12 Bandar Lampung dengan menunjukkan persentase ketuntasan hasil belajar siswa sebesar 87,00% tuntas KKM

SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation

In recent years, continuous latent space (CLS) and discrete latent space (DLS) deep learning models have been proposed for medical image analysis for improved performance. However, these models encounter distinct challenges. CLS models capture intricate details but often lack interpretability in terms of structural representation and robustness due to their emphasis on low-level features. Conversely, DLS models offer interpretability, robustness, and the ability to capture coarse-grained information thanks to their structured latent space. However, DLS models have limited efficacy in capturing fine-grained details. To address the limitations of both DLS and CLS models, we propose SynergyNet, a novel bottleneck architecture designed to enhance existing encoder-decoder segmentation frameworks. SynergyNet seamlessly integrates discrete and continuous representations to harness complementary information and successfully preserves both fine and coarse-grained details in the learned representations. Our extensive experiment on multi-organ segmentation and cardiac datasets demonstrates that SynergyNet outperforms other state of the art methods, including TransUNet: dice scores improving by 2.16%, and Hausdorff scores improving by 11.13%, respectively. When evaluating skin lesion and brain tumor segmentation datasets, we observe a remarkable improvement of 1.71% in Intersection-over Union scores for skin lesion segmentation and of 8.58% for brain tumor segmentation. Our innovative approach paves the way for enhancing the overall performance and capabilities of deep learning models in the critical domain of medical image analysis.Comment: Accepted at WACV 202

A magyar Dosztojevszkij-kultusz Révay Mór János népművelő tevékenységének tükrében

It is a European peculiarity that in times of great crisis, Dostoevsky’s works always gain greater interest.  It was no different in the case of Hungary either, as the peak of the writer’s popularity can be attributed to the period after the First World War, the 1920–30s. What unique events could have led the Russian writer to become an integral part of Hungarian culture by then?  Among other aims, the present study seeks to answer this question and undertakes to map the institutional framework of the Hungarian Dostoevsky cult and to present the awareness raising activities of János Mór Révay, which may have greatly contributed to the Hungarian readers’ knowledge of Dostoevsky’s life and literature of historical significance.Európai sajátosság, hogy a nagy válság-korszakokban mindig megnő a Dosztojevszkij-művek iránti érdeklődés. Nem volt ez másképp Magyarországon sem, hiszen az első világháború utáni időszakra, 1920–30-as évekre tehető az író népszerűségének a csúcsa. Vajon milyen egyedi folyamatok vezettek oda, hogy az orosz író ekkorra a magyar kultúra szerves részévé vált? Jelen tanulmány többek között erre a kérdésre keres választ, és arra vállalkozik, hogy egyrészről feltérképezze a magyar Dosztojevszkij-kultusz intézményi kereteit, másrészről bemutassa Révay Mór János népnevelő tevékenységét, amely nagyban hozzájárulhatott ahhoz, hogy a magyar olvasóközönség megismerje Dosztojevszkij életét és megértse irodalomtörténeti jelentőségét