This statistic shows the number of bags of potato chips eaten within one month in the United States in 2020. The data has been calculated by Statista based on the U.S. Census data and Simmons National Consumer Survey (NHCS). According to this statistic, ***** million Americans consumed ** or more bags in 2020.

This statistic shows the amount of potato chips consumed in the United States from 2011 to 2020. The data has been calculated by Statista based on the U.S. Census data and Simmons National Consumer Survey (NHCS). According to this statistic, ***** million Americans consumed ** bags or more in 2020.

This statistic shows the consumption of potato chips in the United States from 2011 to 2020 and a forecast thereof until 2024. The data has been calculated by Statista based on the U.S. Census data and Simmons National Consumer Survey (NHCS). According to this statistic, ****** million Americans consumed potato chips in 2020. This figure is projected to increase to ****** million in 2024.

This statistic shows the number of bags of Lay's (regular) potato chips eaten within one month in the United States in 2020. The data has been calculated by Statista based on the U.S. Census data and Simmons National Consumer Survey (NHCS). According to this statistic, **** million Americans consumed * or more bags in 2020.

This statistic shows the number of bags / packages of corn and tortilla chips and cheese snacks eaten within one month in the United States in 2020. The data has been calculated by Statista based on the U.S. Census data and Simmons National Consumer Survey (NHCS). According to this statistic, ***** million Americans consumed ** or more bags / packages of bags / packages of corn and tortilla chips and cheese snacks in 2020.

All potato chips, regardless of style of chips, flavor, packaging type or size."

Activities of Daily Living Object DatasetOverviewThe ADL (Activities of Daily Living) Object Dataset is a curated collection of images and annotations specifically focusing on objects commonly interacted with during daily living activities. This dataset is designed to facilitate research and development in assistive robotics in home environments.Data Sources and LicensingThe dataset comprises images and annotations sourced from four publicly available datasets:COCO DatasetLicense: Creative Commons Attribution 4.0 International (CC BY 4.0)License Link: https://creativecommons.org/licenses/by/4.0/Citation:Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014). Microsoft COCO: Common Objects in Context. European Conference on Computer Vision (ECCV), 740–755.Open Images DatasetLicense: Creative Commons Attribution 4.0 International (CC BY 4.0)License Link: https://creativecommons.org/licenses/by/4.0/Citation:Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Duerig, T., & Ferrari, V. (2020). The Open Images Dataset V6: Unified Image Classification, Object Detection, and Visual Relationship Detection at Scale. International Journal of Computer Vision, 128(7), 1956–1981.LVIS DatasetLicense: Creative Commons Attribution 4.0 International (CC BY 4.0)License Link: https://creativecommons.org/licenses/by/4.0/Citation:Gupta, A., Dollar, P., & Girshick, R. (2019). LVIS: A Dataset for Large Vocabulary Instance Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 5356–5364.Roboflow UniverseLicense: Creative Commons Attribution 4.0 International (CC BY 4.0)License Link: https://creativecommons.org/licenses/by/4.0/Citation: The following repositories from Roboflow Universe were used in compiling this dataset:Work, U. AI Based Automatic Stationery Billing System Data Dataset. 2022. Accessible at: https://universe.roboflow.com/university-work/ai-based-automatic-stationery-billing-system-data (accessed on 11 October 2024).Destruction, P.M. Pencilcase Dataset. 2023. Accessible at: https://universe.roboflow.com/project-mental-destruction/pencilcase-se7nb (accessed on 11 October 2024).Destruction, P.M. Final Project Dataset. 2023. Accessible at: https://universe.roboflow.com/project-mental-destruction/final-project-wsuvj (accessed on 11 October 2024).Personal. CSST106 Dataset. 2024. Accessible at: https://universe.roboflow.com/personal-pgkq6/csst106 (accessed on 11 October 2024).New-Workspace-kubz3. Pencilcase Dataset. 2022. Accessible at: https://universe.roboflow.com/new-workspace-kubz3/pencilcase-s9ag9 (accessed on 11 October 2024).Finespiralnotebook. Spiral Notebook Dataset. 2024. Accessible at: https://universe.roboflow.com/finespiralnotebook/spiral_notebook (accessed on 11 October 2024).Dairymilk. Classmate Dataset. 2024. Accessible at: https://universe.roboflow.com/dairymilk/classmate (accessed on 11 October 2024).Dziubatyi, M. Domace Zadanie Notebook Dataset. 2023. Accessible at: https://universe.roboflow.com/maksym-dziubatyi/domace-zadanie-notebook (accessed on 11 October 2024).One. Stationery Dataset. 2024. Accessible at: https://universe.roboflow.com/one-vrmjr/stationery-mxtt2 (accessed on 11 October 2024).jk001226. Liplip Dataset. 2024. Accessible at: https://universe.roboflow.com/jk001226/liplip (accessed on 11 October 2024).jk001226. Lip Dataset. 2024. Accessible at: https://universe.roboflow.com/jk001226/lip-uteep (accessed on 11 October 2024).Upwork5. Socks3 Dataset. 2022. Accessible at: https://universe.roboflow.com/upwork5/socks3 (accessed on 11 October 2024).Book. DeskTableLamps Material Dataset. 2024. Accessible at: https://universe.roboflow.com/book-mxasl/desktablelamps-material-rjbgd (accessed on 11 October 2024).Gary. Medicine Jar Dataset. 2024. Accessible at: https://universe.roboflow.com/gary-ofgwc/medicine-jar (accessed on 11 October 2024).TEST. Kolmarbnh Dataset. 2023. Accessible at: https://universe.roboflow.com/test-wj4qi/kolmarbnh (accessed on 11 October 2024).Tube. Tube Dataset. 2024. Accessible at: https://universe.roboflow.com/tube-nv2vt/tube-9ah9t (accessed on 11 October 2024). Staj. Canned Goods Dataset. 2024. Accessible at: https://universe.roboflow.com/staj-2ipmz/canned-goods-isxbi (accessed on 11 October 2024).Hussam, M. Wallet Dataset. 2024. Accessible at: https://universe.roboflow.com/mohamed-hussam-cq81o/wallet-sn9n2 (accessed on 14 October 2024).Training, K. Perfume Dataset. 2022. Accessible at: https://universe.roboflow.com/kdigital-training/perfume (accessed on 14 October 2024).Keyboards. Shoe-Walking Dataset. 2024. Accessible at: https://universe.roboflow.com/keyboards-tjtri/shoe-walking (accessed on 14 October 2024).MOMO. Toilet Paper Dataset. 2024. Accessible at: https://universe.roboflow.com/momo-nutwk/toilet-paper-wehrw (accessed on 14 October 2024).Project-zlrja. Toilet Paper Detection Dataset. 2024. Accessible at: https://universe.roboflow.com/project-zlrja/toilet-paper-detection (accessed on 14 October 2024).Govorkov, Y. Highlighter Detection Dataset. 2023. Accessible at: https://universe.roboflow.com/yuriy-govorkov-j9qrv/highlighter_detection (accessed on 14 October 2024).Stock. Plum Dataset. 2024. Accessible at: https://universe.roboflow.com/stock-qxdzf/plum-kdznw (accessed on 14 October 2024).Ibnu. Avocado Dataset. 2024. Accessible at: https://universe.roboflow.com/ibnu-h3cda/avocado-g9fsl (accessed on 14 October 2024).Molina, N. Detection Avocado Dataset. 2024. Accessible at: https://universe.roboflow.com/norberto-molina-zakki/detection-avocado (accessed on 14 October 2024).in Lab, V.F. Peach Dataset. 2023. Accessible at: https://universe.roboflow.com/vietnam-fruit-in-lab/peach-ejdry (accessed on 14 October 2024).Group, K. Tomato Detection 4 Dataset. 2023. Accessible at: https://universe.roboflow.com/kkabs-group-dkcni/tomato-detection-4 (accessed on 14 October 2024).Detection, M. Tomato Checker Dataset. 2024. Accessible at: https://universe.roboflow.com/money-detection-xez0r/tomato-checker (accessed on 14 October 2024).University, A.S. Smart Cam V1 Dataset. 2023. Accessible at: https://universe.roboflow.com/ain-shams-university-byja6/smart_cam_v1 (accessed on 14 October 2024).EMAD, S. Keysdetection Dataset. 2023. Accessible at: https://universe.roboflow.com/shehab-emad-n2q9i/keysdetection (accessed on 14 October 2024).Roads. Chips Dataset. 2024. Accessible at: https://universe.roboflow.com/roads-rvmaq/chips-a0us5 (accessed on 14 October 2024).workspace bgkzo, N. Object Dataset. 2021. Accessible at: https://universe.roboflow.com/new-workspace-bgkzo/object-eidim (accessed on 14 October 2024).Watch, W. Wrist Watch Dataset. 2024. Accessible at: https://universe.roboflow.com/wrist-watch/wrist-watch-0l25c (accessed on 14 October 2024).WYZUP. Milk Dataset. 2024. Accessible at: https://universe.roboflow.com/wyzup/milk-onbxt (accessed on 14 October 2024).AussieStuff. Food Dataset. 2024. Accessible at: https://universe.roboflow.com/aussiestuff/food-al9wr (accessed on 14 October 2024).Almukhametov, A. Pencils Color Dataset. 2023. Accessible at: https://universe.roboflow.com/almas-almukhametov-hs5jk/pencils-color (accessed on 14 October 2024).All images and annotations obtained from these datasets are released under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits sharing and adaptation of the material in any medium or format, for any purpose, even commercially, provided that appropriate credit is given, a link to the license is provided, and any changes made are indicated.Redistribution Permission:As all images and annotations are under the CC BY 4.0 license, we are legally permitted to redistribute this data within our dataset. We have complied with the license terms by:Providing appropriate attribution to the original creators.Including links to the CC BY 4.0 license.Indicating any changes made to the original material.Dataset StructureThe dataset includes:Images: High-quality images featuring ADL objects suitable for robotic manipulation.Annotations: Bounding boxes and class labels formatted in the YOLO (You Only Look Once) Darknet format.ClassesThe dataset focuses on objects commonly involved in daily living activities. A full list of object classes is provided in the classes.txt file.FormatImages: JPEG format.Annotations: Text files corresponding to each image, containing bounding box coordinates and class labels in YOLO Darknet format.How to Use the DatasetDownload the DatasetUnpack the Datasetunzip ADL_Object_Dataset.zipHow to Cite This DatasetIf you use this dataset in your research, please cite our paper:@article{shahria2024activities, title={Activities of Daily Living Object Dataset: Advancing Assistive Robotic Manipulation with a Tailored Dataset}, author={Shahria, Md Tanzil and Rahman, Mohammad H.}, journal={Sensors}, volume={24}, number={23}, pages={7566}, year={2024}, publisher={MDPI}}LicenseThis dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0).License Link: https://creativecommons.org/licenses/by/4.0/By using this dataset, you agree to provide appropriate credit, indicate if changes were made, and not impose additional restrictions beyond those of the original licenses.AcknowledgmentsWe gratefully acknowledge the use of data from the following open-source datasets, which were instrumental in the creation of our specialized ADL object dataset:COCO Dataset: We thank the creators and contributors of the COCO dataset for making their images and annotations publicly available under the CC BY 4.0 license.Open Images Dataset: We express our gratitude to the Open Images team for providing a comprehensive dataset of annotated images under the CC BY 4.0 license.LVIS Dataset: We appreciate the efforts of the LVIS dataset creators for releasing their extensive dataset under the CC BY 4.0 license.Roboflow Universe:

This statistic shows the number of bags of Pringles potato chips eaten within one month in the United States in 2020. The data has been calculated by Statista based on the U.S. Census data and Simmons National Consumer Survey (NHCS). According to this statistic, *** million Americans consumed * or more bags in 2020.

Climate change, including warmer winter temperatures, a shortened snowfall season, and more rain-on-snow events, threatens nordic skiing as a sport. In response, over-summer snow storage, attempted primarily using wood chips as a covering material, has been successfully employed as a climate change adaptation strategy by high-elevation and/or high-latitude ski centers in Europe and Canada. Such storage has never been attempted at a site with both a low altitude and latitude, and few studies have quantified snowmelt repeatedly through the summer. Such data, along with tests of different cover strategies, are prerequisites to optimizing snow storage strategies. Here, we assess the melt rates of two wood-chip covered snow piles (each ~200 m3) emplaced during spring 2018 in Craftsbury, Vermont (45o N and 360 m asl) to develop an optimized snow storage strategy. In 2019, we tested that strategy on a much larger, 9300 m3 pile. In 2018, we continually logged air-to-snow temperature gradients under different cover layers including rigid foam, open cell foam, and wood chips both with and without an underlying insulating blanket and an overlying reflective cover. We also measured ground temperatures to a meter depth both under and adjacent to the snow piles and used a snow tube to measure snow density. During both years, we monitored volume change over the melt season using terrestrial laser scanning. In 2018, snow volume loss ranged from -0.29 to -2.81 m3 day-1 with highest rates in mid-summer and lowest rates in the fall; mean melt rates were 1.24 and 1.50 m3 day-1, 0.6 to 0.7 % of initial pile volume per day. Snow density did increase over time but most volume loss was the result of melting. Wet wood chips underlain by an insulating blanket and covered with a reflective sheet was the most effective cover combination for minimizing melt, likely because the surface reflected incoming shortwave radiation while the wet wood chips provided significant thermal mass, allowing much of the energy absorbed during the day to be lost by long-wave emission at night. The importance of pile surface area to volume ratio is demonstrated by the melt rates of the 9300 m3 pile emplaced in 2019 which lost only <0.16% of its initial volume per day between April and September, retaining 75% of the initial snow volume over summer. Together, these data demonstrate the feasibility of over-summer snow storage at mid-latitudes and low altitudes and suggest efficient cover strategies.

A partial Life Cycle Assessment (LCA) is conducted to investigate the life cycle impacts of a biorefinery designed to convert poplar tree chips into jet fuel via fermentation and subsequent hydrogenation. The goal of producing jet fuel from Populus (poplar) trees (bio-jet) is to create an alternative to petroleum based jet fuel (petro-jet). Currently no jet fuel producing biorefineries are in commercial operation and the results of this LCA will be used to assess a potential environmental impact that could result from scaling up the proposed system. Work is part of the Advanced Hardwood Biofuels Northwest project. The LCA does not include any proprietary information and is suitable for release to the public domain. Life cycle inventory assessment and conclusion are not included in this document. The biorefinery in this LCA is assumed be located somewhere in the continental U.S and will produce jet fuel similar to Jet-A fuel with a high heating value (HHV) of 45.5 mega joule (MJ) / kilogram (kg) and a density of 0.820 kg/ liters (L). The biorefinery is designed to operate using 3200 bonne dry tonnes (BDt) of poplar chips per day (1.1 million BDt/yr). It is predicted to produce 300 L/ BDt (380 million L per year) of bio-jet fuel per year. The biorefinery is simulated in ASPEN-Plus v.2004.1chemical engineering software (Aspen Technology Inc., 2005). The LCA work is performed using SimaPro v.8.0 LCA software (Pre Consultants, 2012). Resources in this dataset:Resource Title: Supporting documentation for the life cycle assessment of bio-jet fuel production from poplar biomass via bioconversion at a biorefinery. File Name: Supporting documentation for the life cycle assessment of bio-jet fuel production from poplar biomass via bioconversion at a biorefinery.pdfResource Title: aviation fuel; bioconversion of poplar biomass; at biorefinery; 45.5 MJkg. File Name: aviation fuel; bioconversion of poplar biomass; at biorefinery; 45.5 MJkg.zipResource Description: Gate to gate unit process of a biorefinery designed to convert poplar tree chips into jet fuel via fermentation and subsequent hydrogenation. The biorefinery is assumed be located somewhere in the continental U.S and will produce jet fuel similar to Jet-A fuel with a high heating value (HHV) of 45.5 mega joule (MJ) / kilogram (kg) and a density of 0.820 kg/ liters (L). The biorefinery is designed to operate using 3200 bonne dry tonnes (BDt) of poplar chips per day (1.1 million BDt/yr). It is predicted to produce 300 L/ BDt (380 million L per year) of bio-jet fuel per year. The biorefinery was simulated in ASPEN-Plus v.2004.1chemical engineering software. Simapro v.8.0 LCA software was used to facilitate the life cycle analysis.\r \r The functional unit used is 1 MJ of bio-jet fuel at the biorefinery gateResource Software Recommended: openLCA,url: http://www.openlca.org/

Days-of-Sales-Outstanding Time Series for C*Core Technology Co Ltd. C*Core Technology Co., Ltd., a chip design company, offers IP authorization, chip customization, and independent chip and module products in China. The company offers automotive electronics and industrial control chips, advanced computing and storage chips, biometric identification chips, information security chips, biometric modules, and information security products. It also provides software and hardware solutions, SD encryption card and SD keys, USB keys, encrypted storage devices, IC card Internet terminals, fingerprint door locks, mpos solutions, and dynamic two-dimensional code terminals. In addition, the company provides design services, such as IP products, including CPU, IP library, and third-party tools; and SoC platforms covering industrial control, automotive electronic, IoT, and information security. Its products are primarily used in information security, automotive electronics and industrial control, edge computing, and network communications applications. The company was founded in 2001 and is based in Suzhou, China.

AI Noise Classification Edge Chip Market Outlook

As per our latest research, the global AI Noise Classification Edge Chip market size in 2024 stands at USD 1.37 billion, reflecting robust momentum driven by the proliferation of edge AI applications. The market is forecasted to grow at a compelling CAGR of 19.8% from 2025 to 2033, reaching an estimated USD 6.72 billion by 2033. This remarkable growth trajectory is underpinned by the increasing integration of edge AI chips in noise-sensitive environments such as smart homes, automotive cabins, and industrial automation, where real-time noise classification is becoming indispensable for enhanced user experience and safety.

A key growth factor propelling the AI Noise Classification Edge Chip market is the rapid advancement in edge computing and artificial intelligence technologies. The demand for real-time noise classification is surging across diverse industries as organizations seek to process audio data locally on devices rather than relying on cloud-based solutions. This shift is primarily motivated by the need for reduced latency, improved data privacy, and lower bandwidth consumption. As AI algorithms become more sophisticated and power-efficient, edge chips are increasingly capable of executing complex noise classification tasks, thereby expanding their applicability in sectors such as healthcare, automotive, and consumer electronics. The ability to filter, identify, and act upon specific noise patterns in real time is transforming how devices interact with their environments, leading to smarter and more responsive systems.

Another significant driver is the exponential growth of smart devices and IoT ecosystems. With billions of connected devices now embedded in everyday environments, the necessity for on-device intelligence has never been greater. AI noise classification edge chips are being integrated into smart speakers, wearables, surveillance systems, and industrial sensors to enable context-aware functionalities. For instance, in smart homes, these chips can distinguish between routine background noise and critical sounds such as alarms or glass breaking, triggering appropriate responses without human intervention. In the automotive sector, edge chips enhance in-cabin experiences by filtering out unwanted noise and improving voice assistant accuracy, contributing to both safety and comfort. The rising consumer expectation for seamless, intuitive, and secure device interactions is fueling sustained investment in this market.

Furthermore, regulatory and industry standards focused on data security and privacy are catalyzing the adoption of edge-based noise classification solutions. As concerns over data breaches and unauthorized access mount, organizations are prioritizing on-device processing to minimize the transmission of sensitive audio data to external servers. This trend is particularly pronounced in healthcare and industrial applications, where compliance with stringent privacy regulations is non-negotiable. The convergence of AI, edge computing, and evolving regulatory frameworks is creating a fertile environment for innovation and market expansion. Vendors are responding by developing customizable, energy-efficient chips tailored to specific industry requirements, further accelerating the deployment of AI noise classification technologies.

The integration of the Edge Voice Assistant Chip is revolutionizing the way we interact with smart devices, particularly in noise-sensitive environments. These chips are designed to enhance the performance of voice assistants by enabling them to process audio commands locally, thereby reducing latency and improving response times. This capability is especially crucial in settings like smart homes and automotive cabins, where quick and accurate voice recognition can significantly enhance user experience and safety. By processing voice commands at the edge, these chips also contribute to improved data privacy, as sensitive audio data does not need to be transmitted to the cloud. As a result, the Edge Voice Assistant Chip is becoming an essential component in the development of more intuitive and secure voice-activated systems.

From a regional perspective, Asia Pacific is emerging as the dominant market for AI Noise Classification Edge Chips, owing to the rapid adoption of smart devices, ro

This Zenodo record consists of:

• 15 benchmark datasets used to evaluate the performance of spatial domain detection methods (https://github.com/SpatialHackathon/SACCELERATOR)
• Resulting domains and calculated metrics wrt the annotation
• Additional data necessary to reproduce the results with https://github.com/SpatialHackathon/SpaceHack2023_study

The following datasets are included:

CosMx human liver liver dataset (cosmx_liver)

The CosMx human liver dataset was obtained from the NanoString website (https://nanostring.com/products/cosmx-spatial-molecular-imager/ffpe-dataset/human-liver-rna-ffpe-dataset). The dataset consists of 2 Formalin-Fixed Paraffin-Embedded (FFPE) samples from 2 patients, one being normal liver and the other from a hepatocellular carcinoma patient with grade G3 cancer. Data was generated on the CosMx platform using the Human Universal Cell Characterization Panel 1000 plex. The ground truth annotations were computationally identified using Mclust clustering on the frequency of each cell type among its 200 nearest neighbors. See NanoString_Data_License_Agreement.pdf for the license terms.

CosMx human non-small-cell lung cancer dataset (cosmx_lung)

The CosMx human non-small-cell lung cancer dataset was obtained from the NanoString website (https://staging.nanostring.com/products/cosmx-spatial-molecular-imager/ffpe-dataset/nsclc-ffpe-dataset). The dataset consists of 8 FFPE samples from 5 patients presenting with non-small-cell lung cancer grade G1-G3. Data was generated on a CosMx prototype instrument using a 960 gene panel [1]. The ground truth annotations were computationally identified using Mclust clustering on the frequency of each cell type among its 200 nearest neighbors. See NanoString_Data_License_Agreement.pdf for the license terms.

MERSCOPE mouse brain thalamus (abc_atlas_wmb_thalamus)

The MERFISH mouse brain thalamus dataset [2] was obtained from the Brain Knowledge Platform (https://alleninstitute.github.io/abc_atlas_access/descriptions/MERFISH-C57BL6J-638850.html). The dataset consists of 59 fresh frozen (FF) serial full coronal sections at 200-µm intervals spanning one entire mouse brain. Data was generated on a Vizgen MERSCOPE instrument using a custom gene panel of 500 genes. The ground truth annotations were identified by aligning the MERFISH data to the CCFv3 coordinate space and labeling cells with the corresponding CCFv3 anatomical parcellation term [3]. Only the thalamus (TH; CCFv3 structure ID 549) and hypothalamic zona incerta (ZI; CCFv3 structure ID 797) were analyzed in this study. Spatially variable genes in the thalamus were identified by differential gene expression analysis on neighboring consensus clusters.

MERFISH human developmental heart dataset (merfish_devheart)

The MERFISH human developmental heart dataset [4] was obtained from Dryad (https://datadryad.org/stash/dataset/doi:10.5061/dryad.w0vt4b8vp). The dataset consists of 4 FF samples from 2 donors at 13 and 15 post-conception weeks (PCW). Data was generated using MERFISH with a custom 238-gene panel. The ground truth annotations (referred to as cellular communities in the original study) were computationally identified using k-means clustering of relative cell-type composition within 150µm of each cell.

STARmap PLUS mouse brain dataset (STARmap_plus)

The STARmap PLUS mouse brain dataset [5] was obtained from Zenodo (https://zenodo.org/records/8327576). The dataset consists of 20 FF samples from 3 mice. Data was generated using STARmap PLUS using a custom 1,022 gene panel. The ground truth annotations were manually identified by aligning the data to the CCFv3.

Xenium human breast cancer dataset (xenium-ffpe-bc-idc)

The Xenium breast cancer dataset was obtained from the 10x website (https://www.10xgenomics.com/datasets/xenium-ffpe-human-breast-with-custom-add-on-panel-1-standard). The dataset consists of 1 FFPE sample from a patient with infiltrating ductal carcinoma breast cancer. Data was generated on a Xenium Analyzer using the Xenium human breast gene expression panel v1 (280 genes) with 100 additional custom genes. The ground truth annotation was manually identified using the matched histopathology image, annotating for eight region types: ductal carcinoma in-situ, invasive tumor, normal ducts, immune cells, cysts, blood vessels, adipose tissue, and stroma [6].

Xenium mouse brain dataset (xenium-mouse-brain-SergioSalas)

The Xenium mouse brain dataset was obtained from the 10x genomics website (https://www.10xgenomics.com/datasets/fresh-frozen-mouse-brain-replicates-1-standard). The dataset consists of 1 FF sample of a full coronal section. Data was generated on a Xenium Analyzer using the v1 mouse brain gene expression panel (247 genes). The ground truth annotation was manually identified using the mouse coronal P56 sample from Allen Brain Atlas [3] to specify anatomical regions [7].

Slide-seqV2 mouse brain olfactory bulb dataset (slideseq2_olfactory_bulb)

The Slide-seqV2 mouse brain olfactory bulb dataset [8] was obtained from the STOmicsDB website (https://db.cngb.org/stomics/datasets/STDS0000172/data). The dataset consists of 20 samples of a mouse olfactory bulb evenly spaced along the anterior-posterior axis. Data was generated using Slide-seqV2 and sequenced using paired-end reads on an Illumina Novaseq6000 instrument, targeting 200 million reads per sample. The ground truth annotations were manually identified based on the expression of marker genes.

Stereo-seq mouse liver dataset (stereoseq_liver)

The Stereo-seq mouse liver dataset [9] was obtained from the STomicsDB website (https://db.cngb.org/stomics/lista/spatial). The dataset consists of 6 FF samples. Data was generated on Stereo-seq chips and sequenced using paired-end reads on a DIPSEQ T1 instrument. The ground truth annotations were computationally identified where zonation layers were annotated based on the differences between the scores of pericentral and periportal hepatocyte landmark genes.

Stereo-seq mouse embryo dataset (stereoseq_mouse_embryo)

The Stereo-seq mouse embryo dataset [10] was obtained from the StOmicsDB website (https://ftp.cngb.org/pub/SciRAID/stomics/STDS0000058/stomics). The dataset consists of 53 FF samples from mouse embryos spanning E9.5–E16.5 with one-day intervals. Data was generated on Stereo-seq chips and sequenced using paired-end reads on a MGI DNBSEQ-Tx sequencer. The ground truth annotations were computationally identified using Spatially Constrained Clustering (SCC), which is built on top of the Leiden clustering algorithm.

Visium human brain LIBD DLPFC dataset 1 (libd_dlpfc)

The Visium human brain LIBD DLPFC dataset 1 [11] was obtained from the spatialLIBD Bioconductor package (https://research.libd.org/spatialLIBD). The dataset consists of 12 FF samples from 3 donors. The data was generated on Visium chips and sequenced using paired-end reads on an Illumina NovaSeq 6000 instrument. The ground truth annotations were manually identified based on cytoarchitecture and selected gene markers.

osmFISH mouse brain somatosensory cortex dataset (osmfish_Ssp)

The osmFISH mouse brain somatosensory cortex dataset [12] was obtained from the Linnarsson Lab website (https://linnarssonlab.org/osmFISH). The dataset consists of a single FF sample from the mouse brain somatosensory cortex. Data was generated using osmFISH using a custom 33-gene panel. The ground truth annotation was computationally identified using an iterative graph-based algorithm.

Visium human breast cancer (visium_breast_cancer_SEDR)

The Visium human breast cancer dataset, originally from 10x Genomics (https://www.10xgenomics.com/resources/datasets/human-breast-cancer-block-a-section-1-1-standard-1-1-0), was obtained from GitHub (https://github.com/JinmiaoChenLab/SEDR_analyses). The dataset consists of a single FF sample of invasive ductal carcinoma breast tissue. The data was generated on a Visium chip and sequenced

Imports of Semiconductors in the United States decreased to 5994.28 USD Million in February from 6525.82 USD Million in January of 2024. This dataset includes a chart with historical data for the United States Imports of Semiconductors.

This statistic shows the number of bags of Utz potato chips eaten within one month in the United States in 2020. The data has been calculated by Statista based on the U.S. Census data and Simmons National Consumer Survey (NHCS). According to this statistic, **** million Americans consumed * or more bags in 2020.

Embedded Storage Chips Market Outlook

The global market size for Embedded Storage Chips was valued at approximately $45 billion in 2023 and is projected to reach around $95 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.5%. The significant growth factor contributing to this market expansion includes the rising demand for high-performance storage solutions across various industries, the increasing adoption of advanced consumer electronics, and the rapid advancements in automotive technologies.

The surge in demand for consumer electronics such as smartphones, tablets, and laptops is a primary driver for the growth of the embedded storage chips market. These devices require highly reliable and efficient storage solutions to support various applications, including high-definition video streaming, gaming, and data-intensive applications. As the number of connected devices continues to grow, the need for robust storage solutions is becoming more critical, propelling market growth.

Another significant growth factor is the automotive industry's increasing reliance on advanced electronic systems. Modern vehicles are equipped with numerous electronic control units (ECUs), infotainment systems, and autonomous driving technologies, all of which require substantial storage capacity. Embedded storage chips provide the necessary storage solutions for these applications, ensuring the smooth functioning of various automotive systems and enhancing the overall driving experience.

The industrial sector is also contributing to the growth of the embedded storage chips market. Industries such as manufacturing, healthcare, and logistics are increasingly adopting IoT (Internet of Things) technologies, which rely on embedded storage solutions to collect, store, and process vast amounts of data. The growing implementation of smart factories, digital healthcare systems, and automated logistics solutions is driving the demand for embedded storage chips, further fueling market growth.

The regional outlook for the embedded storage chips market indicates significant growth potential across various regions. Asia Pacific is expected to dominate the market, driven by the presence of major electronic device manufacturers and the rapid adoption of advanced technologies. North America and Europe follow closely, with substantial investments in automotive and industrial applications. Emerging markets in Latin America and the Middle East & Africa are also anticipated to witness considerable growth, supported by increasing technological advancements and infrastructure development.

Consumer Grade Emmc is gaining traction in the embedded storage market due to its balance between performance and affordability. These eMMCs are designed to meet the needs of consumer electronics, offering sufficient speed and capacity for everyday applications. As consumer electronics continue to evolve, the demand for storage solutions that can handle multimedia content, apps, and system updates efficiently is increasing. Consumer Grade Emmc provides a viable solution for manufacturers looking to offer cost-effective products without compromising on user experience. This segment is particularly important in regions with price-sensitive markets, where affordability is a key factor in consumer purchasing decisions.

Type Analysis

The embedded storage chips market is segmented by type into NAND Flash, NOR Flash, eMMC, UFS, SSD, and others. NAND Flash is expected to hold a significant market share due to its widespread use in various applications, including consumer electronics and data centers. NAND Flash chips offer high storage densities, fast read/write speeds, and cost-effectiveness, making them a preferred choice for high-capacity storage solutions. The continuous advancements in NAND Flash technology, such as 3D NAND and QLC (Quad-Level Cell) NAND, are further enhancing their performance and driving their adoption in the market.

NOR Flash, although primarily used in applications requiring fast read speeds and high reliability, is also gaining traction in the market. NOR Flash chips are widely used in embedded systems, automotive applications, and industrial equipment, where data integrity and quick access times are critical. The growing demand for reliable and secure storage solutio

The objective of the study was to measure changes in sugars and free amino acids in 15 sweetpotato genotypes before curing, after curing, and over 10 months of storage to investigate the effects on acrylamide formation.The data are of the raw sweetpotatoes (sugar and free amino acids) and chip attributes (oil, acrylamide, color).Materials and Processing: Sweetpotatoes grown in duplicate blocks using conventional practices at the NCDA Research Station in Clinton, NC, USA. Harvested roots were cured (30°C and 85-90% RH) for 7 days, stored at 13-16°C and 80-90% RH for 2, 4, 6, 8, and 10 months. At each timepoint, 7 to 12 sweetpotatoes from each genotype were washed, peeled, and sliced 1.5 mm thick then fried at 157 °C in canola oil for 3 minutes. Raw slices and fried chips were stored at -20 °C until analysis. Raw slices were freeze dried then ground into a powder.Dry matter: Dry matter was determined by drying at 105 °C for 24 h.Chip fat and moisture contents: Chip moisture and fat contents were measured with a Maran NMR analyzer (Resonance Instruments Ltd., Witney, UK).Chip color: Hunter L, a, and b* values of crushed chip samples were measured using a DP‐900 Colorimeter (Hunter Associates Lab, Reston, VA, USA)Sugar Contents: One gram of powder was extracted with 20 mL of boiling ethanol, vortexed, incubated held for 15 min, centrifuged, decanted into 50 mL volumetric flask, then repeated. Glucose, fructose, sucrose, and maltose were separated isocratically using 150 mM NaOH at 1 mL/min on a Dionex CarboPac PA-1 (4 x 250 mm) with guard column (4 x 50 mm) and detected with an Antec Scientific (Alphen a/d Rijn, NL) Decade II electrochemical detector. Fried chip sugars were measured in the same matter but after chips were defatted with hexane.Free Amino Acid Analysis: One gram of powder was extracted with 5 mL of 0.1 M HCl, vortexed, incubated at 4 °C for overnight, then centrifuged. The supernatant was used for total free amino group analysis and individual amino acid quantification. For individual quantification, 250 µL of extract was mixed with 250 µL of internal standards in acetonitrile, vortexed, centrifuged, then filtered in a 0.5 mL, 0.22 µm Ultrafree-MC-GV centrifugal filter tube. Amino acids in filtrate were measured using a Shimadzu Nexera-2 UHPLC system with LCMS-8030 plus using electrospray ionization. They were separated using an Atlantis Silica HILIC column (4.6 mm × 100 mm, 3 μm particle size) (Waters Corporation, Midford, MA, USA) at 35 °C and 0.6 mL/min mobile phase A (85% acetonitrile with 0.15% formic acid and 10 mM ammonium formate) and mobile phase B (water with 0.15% formic acid and 10 mM ammonium formate) using the following gradient profile: 0 to 9.6% B from 0 to 3 min; 9.6 to 27% B from 3 to 7 min; 27% B from 7 to 8 min; 27 to 37% B from 9 to 10.5 min; then re-equilibrated with 0% B from 10.5 to 19 min. Glycine was separated isocratically with 0.5% acetonitrile,0.1% acetic acid in water at 0.5 mL/min and 35°C using an Atlantic dc18 column (4.6mm × 100 mm, 3 μm particle size) (Waters Corporation, Midford, MA, USA). Total free amino groups Free amino groups were measured by the o-phthalaldehyde (OPA) method (Church et al., 1983) with and external standard curve of L-leucine dissolved in 0.1 M sodium tetraborate buffer at a pH of 9.0.Acrylamide measurements: One gram of ground chip material was spiked with 0.2 mL of d3-labelled-acrylamide internal working standard (10 μg/ml in 10 mM formic acid) then extracted with 19 mL of 10 mM formic acid by vortexing for 3 min. Carrez I and Carrez II reagents (0.5 mL of each) were added, then centrifuged at 0°C. 1.5 mL of the clarified aqueous supernatant was loaded onto a preconditioned Oasis HLB SPE cartridge followed by a preconditioned BondElut Accucat SPE cartridge. Acrylamide was measured using the same LC-MS/MS system separated isocratically using 10 mM formic acid at a flow rate of 0.3 mL/min at 25 °C on an Atlantis T3 column (150 mm x 4.6 mm, 3 μm).Abbreviations and termsBlock: Growing block; Dup: Replicate from the same block; Acn: Acrylamide, GABA: gamma-aminobutyric acid; RS: Reducing sugars; CbRt: cubic root transform; fw: fresh weight; dw: dry weight

ChatGPT was the chatbot that kickstarted the generative AI revolution, which has been responsible for hundreds of billions of dollars in data centres, graphics chips and AI startups. Launched by...

The Internet of Things (IoT) WiFi chip market is witnessing unprecedented growth as enhanced connectivity becomes essential across various industries. These chips form the backbone of smart devices, enabling seamless communication within the expansive network of everyday objects, appliances, and systems that consume

Ethernet PHY Chip Market Outlook

The global Ethernet PHY chip market size was valued at approximately USD 1.5 billion in 2023 and is projected to reach around USD 2.8 billion by 2032, growing at a compound annual growth rate (CAGR) of about 7.1% during the forecast period. This growth is driven by the increasing demand for high-speed internet connectivity, advancements in telecommunications infrastructure, and the proliferation of connected devices across various sectors.

One of the primary growth factors for the Ethernet PHY chip market is the rising demand for high-speed data transmission. As the world becomes increasingly digital, the need for robust and reliable internet connectivity is paramount. Ethernet PHY chips play a critical role in ensuring seamless data transmission across different devices and networks. Moreover, the proliferation of Internet of Things (IoT) devices has further amplified the need for efficient and high-speed data transmission, thereby driving the demand for Ethernet PHY chips.

Another significant factor contributing to the market's growth is the expansion of data centers globally. With the exponential increase in data generation and consumption, there is a corresponding need for efficient data storage and processing solutions. Data centers, being the backbone of modern digital infrastructure, require high-speed and reliable connectivity solutions, which Ethernet PHY chips provide. The continuous investment in upgrading and expanding data centers is expected to propel the demand for these chips in the coming years.

The increasing adoption of advanced driver-assistance systems (ADAS) and autonomous driving technologies in the automotive sector is also a crucial growth driver for the Ethernet PHY chip market. Modern vehicles are equipped with numerous sensors and electronic control units that generate vast amounts of data, necessitating high-speed data transmission within the vehicle's network. Ethernet PHY chips facilitate this high-speed communication, ensuring the smooth operation of various in-vehicle systems.

Ethernet Switch Chips play a pivotal role in modern networking environments, acting as the backbone for data flow management across various devices and networks. These chips are integral to the functionality of Ethernet switches, which are essential components in both enterprise and consumer networking setups. By facilitating efficient data routing and traffic management, Ethernet Switch Chips ensure that data packets are delivered accurately and swiftly, minimizing latency and enhancing overall network performance. As businesses and individuals increasingly rely on seamless connectivity for operations and daily activities, the demand for advanced Ethernet Switch Chips continues to grow, driving innovation and development in this critical area of networking technology.

From a regional perspective, the Asia Pacific region is expected to witness significant growth in the Ethernet PHY chip market. This growth can be attributed to the rapid industrialization, increasing investments in telecommunications infrastructure, and the proliferation of consumer electronics in the region. Countries such as China, Japan, and South Korea are leading the charge in technological advancements, further driving the demand for Ethernet PHY chips.

Data Rate Analysis

The data rate segment of the Ethernet PHY chip market is diverse, ranging from 10 Mbps to 100 Gbps and beyond. Each data rate serves specific applications and industries, reflecting the varied needs of modern digital infrastructure. For instance, the 10 Mbps and 100 Mbps segments cater to simpler, low-bandwidth applications where high-speed data transfer is not crucial. These segments remain relevant in legacy systems and some consumer electronics where cost-efficiency is prioritized over speed.

On the other hand, the 1 Gbps and 10 Gbps segments are seeing robust growth, driven by their applications in more demanding environments such as data centers, telecommunications, and industrial automation. These data rates offer a balance between speed and cost, making them suitable for a wide range of applications that require moderately high-speed data transfer. The increasing deployment of gigabit Ethernet in enterprise networks is also a significant contributor to the growth of these segments.

The 25 Gbps, 40 Gbps, and 100 Gbps segments represent the high end of the market,