Bioalcohol As Green Energy -A review

Bioethanol has now become a big industry and this industry seems to become much bigger in the near future. People regard bioethanol as renewable and sustainable new energy source, although some contraversies such as the rivalry of bioethanol for human food widely exist. Actually, bioethanol can also be a good source of basic raw materials. In early days, ethylene, the most important organic chemical raw material, was produced from dehydration of ethanol. Later, things reversed as petrochemical industry well developed after World War II, when industrial ethanol was mostly produced mainly via hydration of ethylene. Now that bioethanol has already become an important fuel blender, we should well expect that bioethanol should also be new resources for basic organic raw materials, as well as other more valuable fine and specialty chemicals, instead of merely a fuel blender. Nowadays, countless new bioethanol companies are setting up every day. It should lead to more research on bioethanol also as a starting raw chemical material

3DMODT: Attention-Guided Affinities for Joint Detection & Tracking in 3D Point Clouds

We propose a method for joint detection and tracking of multiple objects in 3D point clouds, a task conventionally treated as a two-step process comprising object detection followed by data association. Our method embeds both steps into a single end-to-end trainable network eliminating the dependency on external object detectors. Our model exploits temporal information employing multiple frames to detect objects and track them in a single network, thereby making it a utilitarian formulation for real-world scenarios. Computing affinity matrix by employing features similarity across consecutive point cloud scans forms an integral part of visual tracking. We propose an attention-based refinement module to refine the affinity matrix by suppressing erroneous correspondences. The module is designed to capture the global context in affinity matrix by employing self-attention within each affinity matrix and cross-attention across a pair of affinity matrices. Unlike competing approaches, our network does not require complex post-processing algorithms, and processes raw LiDAR frames to directly output tracking results. We demonstrate the effectiveness of our method on the three tracking benchmarks: JRDB, Waymo, and KITTI. Experimental evaluations indicate the ability of our model to generalize well across datasets

Study and Evaluation of Groundwater Quality of Malwa Region, Punjab (North India)

Water is the most v aluable, basic human need, prime natural resource and a precious asset. Water is indeed required in all aspect of life and health for domestic purposes, drinking, cooking, bathing, washing clothes, utensils, producing food, agricultural activity, energy generation, maintenance of environment and development for life. Water plays important role in several metabolic, physiological and other activities in human body as well as in other living beings ( m ittal and Arora, 2014)

Femtosecond laser machining of multi-depth microchannel networks onto silicon

Direct writing of multi-depth microchannel branching networks into a silicon wafer with femtosecond pulses at 200 kHz is reported. The silicon wafer with the microchannels is used as the mold for rapid prototyping of microchannels on polydimethylsiloxane. The branching network is designed to serve as a gas exchanger for use in artificial lungs and bifurcates according to Murray's law. In the development of such micro-fluidic structures, processing speed, machining range with quality surface, and precision are significant considerations. The scan speed is found to be a key parameter to reduce the processing time, to expand the machining range, and to improve the surface quality. By fabricating a multi-depth branching network as an example, the utilization of femtosecond pulses in the development of microfluidic devices is demonstrated.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90801/1/0960-1317_21_4_045027.pd

A case of synchronous papillary and clear cell carcinoma in the same kidney

Renal Cell Carcinoma (RCC) comprises 2-3% of all cancers. Bilateral coexistent benign and malignant renal tumors have been defined in many reports. However, unilateral synchronous malignant tumors of different histologic subtypes are very rare and only a few such cases have been reported. Herein we describe a 56 year old male patient with coexistent clear cell RCC and papillary RCC in his left kidney that were successfully treated with radical nephrectomy

Hairy cell leukemia: a rare case report

Hairy cell leukemia (HCL) is a rare chronic B-cell leukemia accounting for 2% of all the leukemias and occurs more frequently in the elderly males. The etiology is unknown but possible relationships to radiation exposure, exposure to benzene, to farm animals and to commercial herbicides and pesticides have been identified. Familial predisposition among first degree relatives has been noted. It is characterized by distinctive cytoplasmic “hair like” projections on the cell surface of lymphoid cells, pancytopenia and splenomegaly. We report a rare case of 29 year old female, farm labourer presenting with fever, fatigue and weakness for 1 month. On examination the patient had hepatomegaly, massive splenomegaly and inguinal lymphadenopathy. After peripheral smear examination diagnosis of HCL was made which was confirmed by bone marrow aspiration examination, bone marrow biopsy and immunohistochemistry (IHC)

Ensemble Modeling for Multimodal Visual Action Recognition

In this work, we propose an ensemble modeling approach for multimodal action recognition. We independently train individual modality models using a variant of focal loss tailored to handle the long-tailed distribution of the MECCANO [21] dataset. Based on the underlying principle of focal loss, which captures the relationship between tail (scarce) classes and their prediction difficulties, we propose an exponentially decaying variant of focal loss for our current task. It initially emphasizes learning from the hard misclassified examples and gradually adapts to the entire range of examples in the dataset. This annealing process encourages the model to strike a balance between focusing on the sparse set of hard samples, while still leveraging the information provided by the easier ones. Additionally, we opt for the late fusion strategy to combine the resultant probability distributions from RGB and Depth modalities for final action prediction. Experimental evaluations on the MECCANO dataset demonstrate the effectiveness of our approach.Comment: 22nd International Conference on Image Analysis and Processing Workshops - Multimodal Action Recognition on the MECCANO Dataset, 202

Egocentric RGB+Depth Action Recognition in Industry-Like Settings

Action recognition from an egocentric viewpoint is a crucial perception task in robotics and enables a wide range of human-robot interactions. While most computer vision approaches prioritize the RGB camera, the Depth modality - which can further amplify the subtleties of actions from an egocentric perspective - remains underexplored. Our work focuses on recognizing actions from egocentric RGB and Depth modalities in an industry-like environment. To study this problem, we consider the recent MECCANO dataset, which provides a wide range of assembling actions. Our framework is based on the 3D Video SWIN Transformer to encode both RGB and Depth modalities effectively. To address the inherent skewness in real-world multimodal action occurrences, we propose a training strategy using an exponentially decaying variant of the focal loss modulating factor. Additionally, to leverage the information in both RGB and Depth modalities, we opt for late fusion to combine the predictions from each modality. We thoroughly evaluate our method on the action recognition task of the MECCANO dataset, and it significantly outperforms the prior work. Notably, our method also secured first place at the multimodal action recognition challenge at ICIAP 2023