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This study evaluates whether a structured organic LinkedIn strategy can strengthen brand identity, improve professional visibility, and generate early lead indicators for Creativeunoia, an independent…

This study evaluates whether a structured organic LinkedIn strategy can strengthen brand identity, improve professional visibility, and generate early lead indicators for Creativeunoia, an independent Indonesian production house. The research used a qualitative single case design supported by descriptive platform analytics. Primary data came from a semi structured interview with the founder and a focus group discussion with three other founding members. Secondary evidence included company records, industry literature, competitor observations, and LinkedIn analytics. Internal analysis applied the Resource Based View, Marketing Mix 7P, and STP, while external and integrated analysis used PESTLE, customer analysis, SWOT, and TOWS. The resulting strategy published three weekly content pillars for six weeks: brand identity, portfolio highlights, and thought leadership, combined with targeted professional outreach. The inactive page generated 3,651 impressions, 1,646 clicks, 65 reactions, 2 reposts, and reached 35 organic followers, approximately 62 percent of whom worked in creative and media related industries. Outreach to 30 professionals produced 18 responses, five high intent discussions, and one scheduled studio visit. The findings indicate that consistent, process oriented, identity driven content can convert internal creative capability into credible professional visibility and early business opportunities without paid advertising.

Additional data 

These are audio recordings taken by an Eclipse Soundscapes (ES) Data Collector during the week of the April 08, 2024 Total Solar Eclipse.  It was decided to include only raw, unprocessed audio da…

These are audio recordings taken by an Eclipse Soundscapes (ES) Data Collector during the week of the April 08, 2024 Total Solar Eclipse.  It was decided to include only raw, unprocessed audio data files in each site-specific Zenodo record. This decision was so that any researcher can independently verify, reproduce, and extend the analysis performed. As a result, some sites have WAV files with 0 bytes of data or timestamps outside the range of probable recording times. Procedures used by the Eclipse Soundscapes team to process audio data for its purposes are outlined in the Data Management reports located in the Eclipse Soundscapes Zenodo community. Data with 0 bytes of data were included for completeness. When possible, all site-specific files, including the audio files, are included in a single zip file for ease of download. If a single zip file upload was not possible due to upload or bandwidth limitations, audio files are included in multiple zip files based on the day of the observation. Data Site location information: Latitude: 46.6848  Longitude:  -67.87248 Local Eclipse Type: Total Solar Eclipse Eclipse Percent (%): 100 WAV files Time & Date Settings: Set with Automated AudioMoth Time Chime (More information on TimeStamp Setting below) Data Collector Start Time Notes: N/A Included Data: Audio files in WAV format with the date and time in UTC within the file name: YYYYMMDD_HHMMSS meaning YearMonthDay_HourMinuteSecondFor example, 20240411_141600.WAV means that this audio file starts on April 11, 2024 at 14:16:00 Coordinated Universal Time (UTC) CONFIG Text file: Includes AudioMoth device setting information, such as sample rate in Hertz (Hz), gain, firmware, etc.  README.md:  Markdown formatted file with information about the recording and recording site. file_list.csv:  A machine and human file that gives the following information on each file in the record: File Name, File Type, Description, File Size in kilobytes, Name of Associated Data Dictionary with the file, calculated SHA-512 Hash of the file as a unique identifier to insure data integrity during transfer and compression. total_eclipse_data.csv:  A machine and human readable file that gives the following information about the site where the audio data recording was taken: ESID#, Latitude, Longitude, Eclipse_type, CoveragePercent, Eclipse Start UTC (1st contact), Totality Start UTC (2nd contact), Totality End UTC (3rd Contact), Eclipse End UTC (4th Contact), Max Eclipse Time UTC License.txt:  A human readable file that explains the terms and conditions under which the data can be used. AudioMoth_Operation_Manual.pdf:  A human readable document that explains the use of an AudioMoth device. The document is current up to the time of the AudioMoth's use in the Eclipse Soundscapes project. file_list_data_dict.csv:  A machine and human data dictionary file that gives information on the variables contained within the file_list.csv file. CONFIG_data_dict.csv:  A machine and human data dictionary file that gives information on the variables contained within the CONFIG.TXT file. eclipse_data_data_dict.csv:  A machine and human data dictionary file that gives information on the variables contained within the total_eclipse_data.csv file. WAV_data_dict.csv:  A machine and human data dictionary file that gives information on the variables contained within the *.WAV files. ES_Data_Management_Pre-Eclipse_Data_Infrastructure_Stage_0.pdf:  PDF document that describes Stage 0 (Pre-Eclipse Infrastructure and Data Stewardship Planning) of the Eclipse Soundscapes (ES) data lifecycle. ES_Data_Management_Receipt_Sorting_and_Metadata_Organization_Stage_1.pdf:  PDF document that describes Stage 1 (Receipt, Sorting, and Metadata Organization) of the Eclipse Soundscapes (ES) data lifecycle. ES_Data_Management_Data_Processing_Stage_2.pdf:  PDF document that describes Stage 2 (Data Processing) of the Eclipse Soundscapes (ES) data Volunteer Scientists. 2023 and 2024 solar eclipse soundscapes audio datalifecycle. ES_Data_Management_Data_Sharing_Stage_3.pdf:  PDF document that describes Stage 3 (Public Data Sharing) of the Eclipse Soundscapes (ES) data lifecycle. Eclipse Information for this location: Eclipse Date: April 08, 2024 Eclipse Start Time (UTC) (1st Contact): 18:22:20 Totality Start Time (UTC) (2nd Contact): [N/A if partial eclipse] 19:32:10 Eclipse Maximum Time [when the most possible amount of the Sun in blocked] (UTC): 19:33:36 Totality End Time (UTC) (3rd Contact): [N/A if partial eclipse] 19:35:03 Eclipse End Time (UTC) (4th Contact): [N/A if partial eclipse] 20:40:45 Audio Data Collection During Eclipse Week ES Data Collectors used AudioMoth devices to record audio data, known as soundscapes, over a 5-day period during the eclipse week: 2 days before the eclipse, the day of the eclipse, and 2 days after. The complete raw audio data collected by the Data Collector at the location mentioned above is provided here. This data may or may not cover the entire requested timeframe due to factors such as availability, technical issues, or other unforeseen circumstances.  ES ID# Information: Each AudioMoth recording device was assigned a unique Eclipse Soundscapes Identification Number (ES ID#). This identifier connects the audio data, submitted via a MicroSD card, with the latitude and longitude information provided by the data collector through an online form. The ES team used the ES ID# to link the audio data with its corresponding location information and then uploaded this raw audio data and location details to Zenodo. This process ensures the anonymity of the ES Data Collectors while allowing them to easily search for and access their audio data on Zenodo.  TimeStamp Information: The ES team and the Data Collectors took care to set the date and time on the AudioMoth recording devices using an AudioMoth time chime before deployment, ensuring that the recordings would have an automatic timestamp. However, participants also manually noted the date and start time as a backup in case the time chime setup failed. The notes above indicate whether the WAV audio files for this site were timestamped manually or with the automated AudioMoth time chime.   Common Timestamp Error: Some AudioMoth devices experienced a malfunction where the timestamp on audio files reverted to a date in 1970 or before, even after initially recording correctly. Despite this issue, the affected data was still included in this ES site's collected raw audio dataset. Latitude & Longitude Information: The latitude and longitude for each site was taken manually by data collectors and submitted to the ES team, either via a web form or on paper. It is shared in Decimal Degrees format. General Project Information: The Eclipse Soundscapes Project is a NASA Volunteer Science project funded by NASA Science Activation that is studying how eclipses affect life on Earth during the October 14, 2023 annular solar eclipse and the April 8, 2024 total solar eclipse. Eclipse Soundscapes revisits an eclipse study from almost 100 years ago that showed that animals and insects are affected by solar eclipses! Like this study from 100 years ago, ES asked for the public's help. ES uses modern technology to continue to study how solar eclipses affect life on Earth!  Eclipse Soundscapes is an enterprise of ARISA Lab, LLC and is supported by NASA award No. 80NSSC21M0008. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Aeronautics and Space Administration.   Eclipse map/figure/table/predictions courtesy of Fred Espenak, NASA/Goddard Space Flight Center, from eclipse.gsfc.nasa.gov.   Eclipse Data Version Definitions {1st digit = year, 2nd digit = Eclipse type (1=Total Solar Eclipse, 9=Annular Solar Eclipse, 0=Partial Solar Eclipse), 3rd digit is unused and in place for future use} 2023.9.0 = Week of October 14, 2023 Annular Eclipse Audio Data, Path of Annularity (Annular Eclipse) 2023.0.0 = Week of October 14, 2023 Annular Eclipse Audio Data, OFF the Path of Annularity (Partial Eclipse) 2024.1.0 = Week of April 8, 2024 Total Solar Eclipse Audio Data, Path of Totality (Total Solar Eclipse) 2024.0.0 =  Week of April 8, 2024 Total Solar Eclipse Audio Data , OFF the Path of Totality (Partial Solar Eclipse) An A at the end of the version number indicates that the record has multiple zip files. Each zip file is an archive of the WAV files recorded on a particular day. The formatting of the name of these zip archives is ESID_NNN_YYYY_MM_DD.zip. *Please note that this dataset's version number is listed below. Eclipse Soundscapes Data Collector Role Training and Implementation Resources Manual (2023-2024) (Archival Copy) This site-level record includes the Eclipse Soundscapes Data Collector Role Training and Implementation Resources Manual (2023-2024). The manual documents the participant training, device setup procedures, metadata submission requirements, ES ID system, timestamp protocols, data return workflow, and public archiving processes used during the October 14, 2023 annular solar eclipse and the April 8, 2024 total solar eclipse. The manual is preserved for transparency and reproducibility and reflects the procedures under which this dataset was collected and processed. (DOI 10.5281/zenodo.18623442) Data Receipt, Processing, and Analysis Methods All programs supporting Stages 1–3 are openly available in the: Eclipse Soundscapes GitHub repository: https://github.com/ARISA-Lab-LLC/ESCSP Data Management Lifecycle The following section documents the relationship of this record to the full Eclipse Soundscapes (ES) data lifecycle, a multi-stage workflow designed to support large-scale participatory science, long-term data stewardship, open science, and scientific reuse. Each stage addressed a different operational need, beginning before eclipse deployment and continuing through validation, public archiving, and scientific analysis. Together, these stages transformed distributed volunteer-submitted audio recordings into structured, documented, publicly accessible NASA-funded research assets. Stage 0: Pre-Eclipse Infrastructure and Deployment Preparation Severino, M., & Winter, H. (2026). Eclipse Soundscapes Data Management: Pre-Eclipse Infrastructure and Deployment Preparation (Stage 0). Zenodo. https://doi.org/10.5281/zenodo.20413370 Stage 0 focused on building the operational foundation required to support geographically distributed eclipse data collection at national scale. This stage included AudioMoth device preparation, accessibility modifications, ES ID # assignment systems, metadata collection workflows, participant training materials, deployment logistics, and planning for downstream data stewardship and archival workflows. The 2023 annular eclipse served as both a scientific investigation and a large-scale operational beta test that informed improvements for the 2024 total solar eclipse campaign. Related Citations and Resources: Severino, M., & Kline, T. (2025, November 24). Eclipse Soundscapes Apprentice Role Curriculum: Solar Eclipses and Multi-Sensory Observing (Informal Education). Zenodo. https://doi.org/10.5281/zenodo.17703003 Severino, M., & Bauer, D. J. (2026). Eclipse Soundscapes Observer Role Training and Resources Manual (2023–2024). Zenodo. https://doi.org/10.5281/zenodo.18633602</li> Severino, M., Winter, H., & Bauer, D. J. (2026). Eclipse Soundscapes Data Collector Role Training and Implementation Manual (2023–2024). Zenodo. https://doi.org/10.5281/zenodo.18623443 Stage 1: Receipt, Sorting, and Metadata Organization Severino, M., & Winter, H. (2026). Eclipse Soundscapes Data Management: Receipt, Sorting, and Metadata Organization (Stage 1). Zenodo. https://doi.org/10.5281/zenodo.19471425 Stage 1 transformed returned participant materials into organized, traceable site-level records. This included receiving mailed microSD cards, consolidating participant-submitted metadata, reconciling handwritten and online records, organizing physical audio media by ES ID #, and deriving eclipse timing and coverage information using NASA eclipse prediction datasets. The outputs of Stage 1 established the structured metadata relationships required for downstream validation, processing, archiving, and analysis workflows. Related Citations and Resources: Winter, H., & Goncalves, J. (2026). EPTT (Eclipse Phase Timing Tool) [Computer software]. GitHub. https://github.com/ARISA-Lab-LLC/ESCSP-Eclipse-Phase-Timing-Tool/li> Espenak, F. (n.d.). Eclipse predictions by Fred Espenak, NASA's GSFC Eclipse Web Site. NASA Goddard Space Flight Center. http://eclipse.gsfc.nasa.gov/eclipse.html Stage 2: Data Processing and Validation Severino, M., & Winter, H. (2026). Eclipse Soundscapes Data Management: Data Processing (Stage 2). Zenodo. https://doi.org/10.5281/zenodo.18683402 Stage 2 focused on centralized audio ingestion, validation, timestamp verification, metadata reconciliation, and preparation of datasets for analysis and public sharing. During this stage, returned audio recordings were processed using custom open-source tools developed by the ES team, including ES WAVES and ES AMES. The project implemented scalable infrastructure capable of processing large volumes of participant-submitted microSD cards while preserving all raw audio data without modification. Stage 2 established the validated dataset structure required for long-term preservation and scientific analysis. Related Citations and Resources: Winter, H., & Goncalves, J. (2026). ES WAVES (Eclipse Soundscapes WAV Audio Validation & Extraction Suite) [Computer software]. GitHub. https://github.com/ARISA-Lab-LLC/ESCSP-ES-WAV-Audio-Validation-Extraction-Suite Winter, H., & Goncalves, J. (2026). ES AMES (Eclipse Soundscapes AudioMoth Metadata Extractor Suite) [Computer software]. GitHub. https://github.com/ARISA-Lab-LLC/ESCSP-ES-AMES-AudioMoth-Metadata-Extractor-Suite Stage 3: Public Data Sharing and Open Archiving Severino, M., & Winter, H. (2026). Eclipse Soundscapes Data Management: Public Audio Data Sharing (Stage 3). Zenodo.https://doi.org/10.5281/zenodo.18683437 Stage 3 transformed validated site-level datasets into publicly archived, DOI-assigned research records published through the Eclipse Soundscapes Zenodo Community. This stage included dataset packaging, metadata standardization, README generation, integrity verification, DOI assignment, and automated repository upload workflows using the Automated Zenodo Upload Software (AZUS). These workflows established the project's long-term open-science infrastructure and ensured that datasets remained findable, accessible, interoperable, reusable, and citable for future scientific and educational use. Related Citations and Resources: Winter, H., & Goncalves, J. (2026). AZUS (Automated Zenodo Upload Software) [Computer software]. GitHub. https://github.com/ARISA-Lab-LLC/AZUS-Automated-Zenodo-Upload-Software Stage 4: Scientific Analysis and Research Use Stage 4 involves the scientific analysis and interpretation of validated eclipse soundscape datasets. Analysis workflows utilized datasets verified during earlier stages to investigate eclipse-related environmental and animal vocalization changes across hundreds of recording sites. This stage also includes broader scientific interpretation, publication development, and continued reuse of Eclipse Soundscapes datasets and infrastructure for future research, education, and open-science applications. Related Citations and Resources: Pease, B., Gilbert, N., & Severino, M. (2026). Eclipse Soundscapes Preliminary Findings – How Eclipses Affect Nature as determined by Sound (Recorded Webinar). Zenodo.https://doi.org/10.5281/zenodo.18613979 Gilbert, N. A., Pease, B. S., Severino, M., & Winter, H. III. (2026). Photic niche explains avian behavioral responses to solar eclipses. Ecology and Evolution, 16(2), e73090. https://doi.org/10.1002/ece3.73090 Analysis code repository: https://github.com/BrentPease1/eclipse-traits Companion Zenodo record archiving structured analysis scripts and derived outputs: https://doi.org/10.5281/zenodo.15790879[r] Public Archiving, Privacy, and Data Transparency The Eclipse Soundscapes Data Collector Role Training and Implementation Manual (2023-2024) includes a detailed explanation of how Eclipse Soundscapes audio data are publicly archived on Zenodo, how participant privacy is protected through the ES ID system, and how transparency and traceability are maintained. It also outlines the criteria for determining which recordings are included in the public archive, as well as the distinction between publicly shared archival data and datasets used for ES-led scientific analyses. Participants and data users can consult this section for full documentation of the project's open science and privacy practices. Severino, M., & Winter, H. (2026). Eclipse Soundscapes Data Collector Role Training and Implementation Manual (2023–2024). Zenodo. https://doi.org/10.5281/zenodo.18623443 Citations Individual Site Citation: APA Citation (7th edition) Winter, H., Severino, M., & Volunteer Scientist. (2026). 2024 solar eclipse soundscapes audio data [Audio dataset, ES ID# 337]. Zenodo.{Insert DOI} Collected by volunteer scientists as part of the Eclipse Soundscapes Project. This project is supported by NASA award No. 80NSSC21M0008. Eclipse Community Citation Winter, H., Severino, M., & Volunteer Scientists. 2023 and 2024 solar eclipse soundscapes audio data [Collection of audio datasets]. Eclipse Soundscapes Community, Zenodo. https://zenodo.org/communities/eclipsesoundscapes/ Collected by volunteer scientists as part of the Eclipse Soundscapes Project This project is supported by NASA award No. 80NSSC21M0008.  

Patch release. Upgrade before recording anything else with a Virtual Hand, and before running migrate_vhi_sessions over a sessions folder. Fixed Session.save_meta now stamps pose_convention into meta…

Patch release. Upgrade before recording anything else with a Virtual Hand, and before running migrate_vhi_sessions over a sessions folder. Fixed Session.save_meta now stamps pose_convention into meta.json. Without it, a session recorded by 2.5.0 was indistinguishable from a pre-2.5 one — migrate_vhi_sessions reads an absent key as legacy — so running the migration over a sessions folder negated the correct recordings into the old convention. The damage was silent and looked exactly like a hand driven backwards, and because the conversion is self-inverse there was no way to tell from the file which parity it was on. myogestic.tools.migrate_vhi_sessions imports POSE_CONVENTION from myogestic.session instead of defining its own copy, so a migrated session and a freshly recorded one cannot drift into disagreeing about what convention they are in. If you recorded with 2.5.0 Those sessions carry no stamp and are already in the standard convention. Do not run the migration on them — it would negate correct data. Either re-pack them under 2.5.1, or add "pose_convention": "standard" to their meta.json by hand. Compatibility Unchanged from 2.5.0: still requires Virtual Hand Interface 2.0 (vocabulary_version 2). No API changes. Full changelog: https://github.com/NsquaredLab/MyoGestic/compare/v2.5.0...v2.5.1

The fast rise of artificial intelligence, along with computer vision tools, is changing how we monitor exams in places like schools. Old-school ways of watching students during tests usually come with…

The fast rise of artificial intelligence, along with computer vision tools, is changing how we monitor exams in places like schools. Old-school ways of watching students during tests usually come with mistakes, personal bias, or gaps in attention - especially when hundreds take the test at once. To fix this, our idea is a smart exam watcher using AI that spots odd actions, keeps tabs on what students do, flags forbidden items, while making sure rules are followed as things happen. It uses advanced neural networks to recognize faces, follow where someone’s eyes move, detect objects, check body positions - all helping make oversight more solid and quicker. This setup combines a smooth front-end made with up-to-date design tools plus a back-end running ML workflows and protected data storage. Live camera feeds get analyzed instantly to catch warning signs: sudden head turns, constant glancing off-screen, phone use, or multiple people showing up unexpectedly. Notifications pop up on their own for supervisors, so they spend less time checking things by hand while tests stay fairer. This smart exam helper works without help from people, runs smooth whether it’s used online or offline, scales easily when needed, keeps data safe, shows what's happening clearly, avoids favoritism, spots odd behavior fast, cuts down errors, saves effort across schools and boards alike.

Pigeon pea (Cajanus cajan) is an important legume crop widely grown in India and other tropical regions, valued for its high protein content (20–24%) and role in food and nutritional security. C…

Pigeon pea (Cajanus cajan) is an important legume crop widely grown in India and other tropical regions, valued for its high protein content (20–24%) and role in food and nutritional security. Commonly consumed as arhar or tur dal, it serves as a staple pulse in Indian diets. The crop improves soil fertility through biological nitrogen fixation and enhances soil structure by adding organic matter. Its deep root system provides drought tolerance, making it suitable for marginal and rainfed areas. Pigeon pea supports sustainable and eco-friendly farming systems and performs well in intercropping with cereals and oilseeds. Besides providing nutritious food for humans, it also supplies fodder for animals, while crop residues are used as fuel and fencing material. Economically, pigeon pea contributes significantly to farmers’ income and rural employment. Overall, pigeon pea plays a vital role in sustainable agriculture, environmental health, and livelihood security.

The development of Artificial Intelligence (AI) technology in healthcare brings two legal consequences: increased efficiency and accuracy of diagnosis, on the one hand, and the potential for AI-based …

The development of Artificial Intelligence (AI) technology in healthcare brings two legal consequences: increased efficiency and accuracy of diagnosis, on the one hand, and the potential for AI-based medical crimes, such as misdiagnosis, negligence in operating systems, and misuse of patient health data. The lack of regulations specifically addressing legal liability for errors involving AI creates legal uncertainty, both for patients as victims and for medical personnel who may be criminalized for system errors beyond their control. This study aims to examine the characteristics of AI-based medical crimes and analyze the relevance of a restorative justice approach as a resolution mechanism that can achieve balanced legal protection for patients and medical personnel. This study uses a normative juridical method with a statutory approach and a conceptual approach. The research findings indicate that restorative justice, as mandated by Article 306 and Article 310 of Law Number 17 of 2023 concerning Health, can be applied to AI-based medical crimes involving minor negligence (culpa levis), but is inappropriate for cases involving intentional negligence (dolus) or gross negligence (culpa lata). The application of restorative justice requires strengthening of technical regulations regarding audit standards, certification, and the division of responsibilities between medical personnel, healthcare facilities, and AI system developers to ensure that the goals of legal certainty, justice, and benefit can be achieved proportionally.

Abstract Renal carcinoma (RCC) accounts for roughly 2% of global cancer diagnoses, with over 430,000 new cases and 150,000+ deaths annually as of 2022. Major contributors include obesity, smoking, hy…

Abstract Renal carcinoma (RCC) accounts for roughly 2% of global cancer diagnoses, with over 430,000 new cases and 150,000+ deaths annually as of 2022. Major contributors include obesity, smoking, hypertension, and occupational exposure, with up to 34% of cases considered preventable. RCC is a malignant neoplasm of the renal tubular epithelium, primarily classified into three main histological subtypes: clear cell (65–75%), papillary (10–15%), and chromophobe (5%) and other histologies, most commonly categorized as clear cell RCC (ccRCC) and non-clear cell RCC (nccRCC), respectively. nccRCC is a heterogeneous group comprising about 20–30% of all kidney cancers, with over 10 recognized, distinct histological subtypes rather than a single type. The therapeutic algorithm for ccRCC is straightforward according to guidelines of every oncologic society even if the use of certain biomarkers in order for certain therapy module to be matched with certain subpopulations. In contrast, the therapeutic algorithm of nccRCC is not such a solid pathway that includes also the possibility of a clinical trial. There have been many advancements in the last year into renal cell carcinoma management that may alter the current status and improve the survival of these patients.

В данной статье анализируется глобальная и региональная (на примере Узбекистана) эпидемиологическая картина проблемы преждевременных родов, происходящих до 37-й недели гестации, а также научно рассмат…

В данной статье анализируется глобальная и региональная (на примере Узбекистана) эпидемиологическая картина проблемы преждевременных родов, происходящих до 37-й недели гестации, а также научно рассматриваются современные методы диагностики и профилактические меры. В статье проводится сравнительная оценка клинической эффективности трансвагинальной ультрасонографии, теста на фетальный фибронектин и других биомаркеров; на основе международных рандомизированных исследований анализируется роль вагинального прогестерона, цервикального серкляжа, акушерского пессария и антенатальных кортикостероидов в профилактике. Также освещаются различия между клиническими протоколами ВОЗ, FIGO и ACOG. Выявляя существующие пробелы в системе здравоохранения Узбекистана, статья обосновывает необходимость внедрения универсального цервикального скрининга на национальном уровне и стандартизированного применения антенатальных кортикостероидов во всех регионах.