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MicroCloud Hologram Inc. announces optimization of stacked sparse autoencoders through DeepSeek model

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SHENZHEN, China, Feb. 14, 2025 /PRNewswire/ — MicroCloud Hologram Inc. (NASDAQ: HOLO), (“HOLO” or the “Company”), a technology service provider, they Announced the deep optimization of stacked sparse autoencoders through the DeepSeek open-source model, injecting new vitality into anomaly detection technology and providing an efficient solution.

Data quality is crucial for model performance, so the behavioral data collected in the data preprocessing stage typically contains multiple features with different dimensions and numerical ranges. In order to eliminate the dimensional influence between different features and improve the effectiveness of model training, HOLO uses normalization processing method.

Normalization is a common data preprocessing technique that scales the data to a specific range, typically between 0 and 1 or -1 and 1. By doing so, data from different features can be compared and analyzed on the same scale, avoiding the situation where certain features dominate model training due to their large value ranges. In HOLO’s detection project, normalization not only improved the efficiency of model training but also laid a solid foundation for subsequent feature extraction. The data processed through normalization is more aligned with the input requirements of deep learning models, enable the model to learn intrinsic patterns more accurately.

After the data preprocessing is completed, the next step is to input the processed data into the stacked sparse autoencoder model. The stacked sparse autoencoder is a powerful deep learning architecture composed of multiple autoencoder layers, with each layer responsible for extracting features at different levels. HOLO utilizes the DeepSeek model to dynamically adjust the strength and manner of the sparsity constraint, ensuring that the features learned by each layer of the autoencoder are sparse and representative. By appropriately setting the sparsity constraint, the model can better capture key information in the data and reduce redundant features. An autoencoder is an unsupervised learning model designed to encode input data into a lower-dimensional feature representation through the encoder, and then reconstruct the original input data as accurately as possible through the decoder. Between the encoder and decoder, the autoencoder learns the feature representation of the data through a hidden layer.

HOLO has innovated and optimized the stacked sparse autoencoder by utilizing the DeepSeek model. This technique employs a greedy, layer-wise training approach, optimizing the parameters of each autoencoder layer step by step. The core of this layered training strategy is to first train the lower layers of the autoencoder to learn the basic features of the input data, then use the output of the lower-layer autoencoder as the input for the next layer, continuing training and progressively extracting deeper features. In this way, the model is able to gradually capture the complex relationships within the data, enhancing its expressive power. Each layer of the autoencoder is constrained by sparsity, ensuring that the learned features are sparse, meaning that only a few neurons are activated, allowing the model to learn more compact and effective feature representations.

HOLO’s stacked sparse autoencoder, trained with the DeepSeek model, adds noise to the input data and requires the model to reconstruct the original input despite the noise interference. This denoising training approach encourages the model to learn more robust feature representations, enabling it to perform accurate anomaly detection even when faced with noisy data in real-world scenarios, thus improving the model’s robustness. Specifically, during training, random noise is added to the input data, and the model is tasked with reconstructing the original input. This process forces the model to learn more resilient feature representations, ensuring high accuracy even in the presence of various types of noise in real-world conditions.

In addition to denoising, HOLO also applies Dropout during the training process. Dropout is a commonly used regularization technique primarily aimed at reducing model overfitting. In deep learning models, overfitting refers to the phenomenon where a model performs well on training data but poorly on unseen samples. To avoid this, HOLO randomly drops a subset of neurons during the training of the stacked sparse autoencoder. In each training iteration, the model randomly selects a portion of neurons and sets their outputs to zero. The benefit of this approach is that the model cannot rely on any specific neuron to learn the features of the data, but must instead learn more general and robust feature representations.

In addition, the DeepSeek model utilizes a distributed computing framework, which allocates training tasks across multiple computational nodes for parallel execution. This significantly shortens training time and improves training efficiency. Using the DeepSeek model, pretraining can first be conducted on the stacked sparse autoencoder to learn general feature representations. This pretraining + fine-tuning strategy can greatly accelerate model convergence and improve performance. By introducing the DeepSeek model, HOLO has injected new vitality into optimizing stacked sparse autoencoders. The DeepSeek model provides comprehensive support in areas such as architecture design, training, strategic feature learning, and generalization ability.

About MicroCloud Hologram Inc.

MicroCloud is committed to providing leading holographic technology services to its customers worldwide. MicroCloud’s holographic technology services include high-precision holographic light detection and ranging (“LiDAR”) solutions, based on holographic technology, exclusive holographic LiDAR point cloud algorithms architecture design, breakthrough technical holographic imaging solutions, holographic LiDAR sensor chip design and holographic vehicle intelligent vision technology to service customers that provide reliable holographic advanced driver assistance systems (“ADAS”). MicroCloud also provides holographic digital twin technology services for customers and has built a proprietary holographic digital twin technology resource library. MicroCloud’s holographic digital twin technology resource library captures shapes and objects in 3D holographic form by utilizing a combination of MicroCloud’s holographic digital twin software, digital content, spatial data-driven data science, holographic digital cloud algorithm, and holographic 3D capture technology. For more information, please visit http://ir.mcholo.com/

Safe Harbor Statement

This press release contains forward-looking statements as defined by the Private Securities Litigation Reform Act of 1995. Forward-looking statements include statements concerning plans, objectives, goals, strategies, future events or performance, and underlying assumptions and other statements that are other than statements of historical facts. When the Company uses words such as “may,” “will,” “intend,” “should,” “believe,” “expect,” “anticipate,” “project,” “estimate,” or similar expressions that do not relate solely to historical matters, it is making forward-looking statements. Forward-looking statements are not guarantees of future performance and involve risks and uncertainties that may cause the actual results to differ materially from the Company’s expectations discussed in the forward-looking statements. These statements are subject to uncertainties and risks including, but not limited to, the following: the Company’s goals and strategies; the Company’s future business development; product and service demand and acceptance; changes in technology; economic conditions; reputation and brand; the impact of competition and pricing; government regulations; fluctuations in general economic; financial condition and results of operations; the expected growth of the holographic industry and business conditions in China and the international markets the Company plans to serve and assumptions underlying or related to any of the foregoing and other risks contained in reports filed by the Company with the Securities and Exchange Commission (“SEC”), including the Company’s most recently filed Annual Report on Form 10-K and current report on Form 6-K and its subsequent filings. For these reasons, among others, investors are cautioned not to place undue reliance upon any forward-looking statements in this press release. Additional factors are discussed in the Company’s filings with the SEC, which are available for review at www.sec.gov. The Company undertakes no obligation to publicly revise these forward-looking statements to reflect events or circumstances that arise after the date hereof.

 

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SOURCE MicroCloud Hologram Inc.

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Technology

Portland General Electric declares dividend

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42 minutes ago

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July 25, 2026

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PORTLAND, Ore., July 24, 2026 /PRNewswire/ — The board of directors of Portland General Electric Company (NYSE: POR) declared a quarterly common stock dividend of $0.55125 per share.

The company’s dividend is evaluated based on capital requirements and financial performance. PGE targets a dividend payout ratio of 60 to 70% over the long term.

The quarterly dividend is payable on or before October 15, 2026, to shareholders of record at the close of business on September 25, 2026.

About Portland General Electric Company
Portland General Electric (NYSE: POR) is an integrated energy company that generates, transmits and distributes electricity to nearly 960,000 customers serving an area of approximately 2 million Oregonians. Since 1889, Portland General Electric (PGE) has been powering economies, delivering safe, affordable and reliable electricity while working to transform energy systems to meet evolving customer needs. PGE continues to make progress towards emissions reduction targets, and customers have set the standard for prioritizing clean energy with the No. 1 voluntary renewable energy program in the country. PGE is ranked a top ten utility in the 2025 Forrester U.S. Customer Experience Index. In 2025, PGE employees and retirees volunteered over 18,300 hours to more than 400 nonprofits organizations. Through the PGE Foundation, along with corporate contributions and the employee matching gift program, more than $5 million was directed to charitable organizations supporting economic growth and community resilience across our service area. For information: portlandgeneral.com/news.

Safe Harbor Statement

This press release contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. Forward-looking statements are based on assumptions about the future, involve risks and uncertainties, and are not guarantees. Future results may differ materially from those expressed or implied in any forward-looking statement. These forward-looking statements represent our estimates and assumptions only as of the date of this press release. We assume no obligation to update or revise any forward-looking statement as a result of new information, future events or otherwise.

Forward-looking statements include statements, other than statements of historical or current fact, regarding the Company’s amount and timing of dividends payable as well as other statements containing words such as “committed to,” “targets,” or similar expressions.

There can be no assurance that future dividends will be declared. The declaration of future dividends is subject to approval of our board of directors and various risks and uncertainties, including, but not limited to: our cash flow and cash needs; the timing or amount of dividends paid; the timing or outcome of various legal and regulatory actions; changes in the Company’s business strategy; increases in capital expenditures; changes in capital and credit market conditions, including volatility of equity markets as well as changes in PGE’s credit ratings and outlook on such credit ratings restrictions on the payment of dividends under existing or future financing arrangements; changes in tax laws relating to corporate dividends; deterioration in our financial condition or results, and those risks, uncertainties, and other factors identified from time-to-time in our filings with the United States Securities and Exchange Commission (SEC), including our annual report on Form 10-K for the year ended December 31, 2025 and subsequent quarterly reports on Form 10-Q. These reports are available through the EDGAR system free-of-charge on the SEC’s website, www.sec.gov and on the Company’s website, investors.portlandgeneral.com. Investors should not rely unduly on any forward-looking statements. The Company assumes no obligation to update or revise any forward-looking statement as a result of new information, future events or other factors.

Media Contact:
Drew Hanson
Corporate Communications
Phone: 503-464-2067

Investor Contact:
Erin Schwartz
Investor Relations
Phone: 503-464-7751

View original content:https://www.prnewswire.com/news-releases/portland-general-electric-declares-dividend-302834503.html

SOURCE Portland General Company

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Care Career Announces Acquisition of MAS Medical Staffing, Completing Its First Acquisition Phase and Expanding Annual Revenue Beyond $150 Million, with a Path to Exceed a Quarter Billion by the End of 2026 Through Additional Acquisitions and Organic Growth

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3 hours ago

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July 24, 2026

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WOODBRIDGE, N.J., July 24, 2026 /PRNewswire/ — Care Career, a rapidly growing healthcare workforce technology organization, today announced the acquisition of MAS Medical Staffing, one of the Northeast’s leading healthcare workforce organizations. Financial terms of the transaction were not disclosed.

The acquisition represents Care Career’s seventh strategic acquisition in the past 24 months, further strengthening the company’s position as one of the largest healthcare workforce organizations in the United States while accelerating its strategy to redefine the future of healthcare workforce management through artificial intelligence, enterprise technology, and workforce innovation.

MAS Medical Staffing has built an outstanding reputation for delivering high-quality workforce solutions through strong client relationships, exceptional clinician engagement, and deep regional expertise throughout the Northeastern United States. The acquisition significantly expands Care Career’s geographic footprint while broadening its access to healthcare professionals, client relationships, workforce data, and regional market intelligence.

Care Career is building a technology-enabled workforce ecosystem powered by its AI-powered workforce platform, where every acquisition contributes not only additional market presence, but also expanded data, enhanced artificial intelligence capabilities, digital innovation, and operational scale that continuously improve the experience for clients and clinicians alike. As the platform grows, every clinician engagement, client interaction, credential, placement, and workforce trend strengthens the intelligence of Career’s technology, creating a continuously improving ecosystem designed to deliver faster, smarter, and more effective workforce solutions.

The acquisition also brings MAS Medical Staffing’s MAESTRA® engagement technology, along with its client relationships and clinician network, directly onto Career’s AI-powered workforce platform. MAESTRA’s scheduling, credentialing, and communication capabilities will be integrated into Care Career’s existing technology stack, further enhancing clinician engagement across onboarding, scheduling, and career management while providing healthcare organizations with greater workforce visibility and operational efficiency.

“Our vision is to build the AI-powered infrastructure that modernizes healthcare workforce management,” said Siva Konatham, Group President and Chief Executive Officer of Care Career. “Under my leadership, Care Career is focused on transforming a fragmented, labor-intensive industry into a data-driven, technology-enabled ecosystem that improves speed, efficiency, and workforce visibility for healthcare providers. Each acquisition strengthens our platform intelligence, expands our scale, and enhances our margin potential. By integrating advanced analytics, AI automation, and digital engagement tools, we are not just growing revenue—we are building a smarter, more scalable model positioned to lead the next era of healthcare workforce solutions.”

The combined organization will leverage expanded recruiting resources, centralized credentialing, advanced workforce analytics, AI-enabled automation, and digital engagement technologies—all powered by Care Career’s AI-powered workforce platform—to deliver broader recruiting capabilities, faster response times, enhanced workforce insights, and expanded national coverage. Clinicians will benefit from a seamless digital experience that simplifies every stage of their careers—from job discovery and credentialing to onboarding, scheduling, communication, and long-term career development.

With seven strategic acquisitions completed in less than two years, representing the first round of acquisitions now totaling more than $150 million in annual revenue, Care Career has rapidly expanded its national presence while executing a disciplined growth strategy focused on technology integration, operational excellence, and workforce innovation. The company has also signed additional Letters of Intent with other entities with expected close dates in the third quarter of 2026. Upon completion of these transactions, coupled with organic growth, Care Career expects consolidated annual revenue to exceed a quarter of a billion dollars by the end of 2026.

The addition of MAS Medical Staffing further strengthens the organization’s ability to serve healthcare systems, hospitals, long-term care providers, outpatient facilities, and other healthcare organizations across an increasingly diverse geographic footprint.

“The healthcare workforce industry is entering a new era where technology, artificial intelligence, and data-driven decision-making will define the market leaders,” Konatham added. “Every acquisition we complete expands the intelligence of our AI-powered workforce platform, enhances the value we deliver to our clients, and creates more opportunities for clinicians. We believe the combination of exceptional people, innovative technology, and strategic scale positions Care Career to lead the next generation of healthcare workforce solutions.”

About Care Career

Care Career is a technology-enabled healthcare workforce solutions company dedicated to transforming how healthcare organizations recruit, engage, credential, deploy, and retain clinical talent. Powered by its proprietary AI-powered workforce platform and supported by advanced artificial intelligence, enterprise technology, and workforce analytics, Care Career is building an intelligent healthcare workforce ecosystem that connects providers and clinicians more efficiently while improving workforce performance, operational effectiveness, and patient care. Following seven strategic acquisitions over the past 24 months the first round of acquisitions totaling more than $150 million in annual revenue and with additional signed LOIs under contract expected to complete shortly, positioning the company to surpass a quarter of a billion dollars in consolidated annual revenue by the end of 2026, Care Career has become one of the nation’s largest and fastest-growing healthcare workforce organizations, serving healthcare providers and clinicians across the United States.

About MAS Medical Staffing

MAS Medical Staffing is a premier healthcare workforce organization recognized for exceptional service, strong client partnerships, and a commitment to connecting healthcare professionals with rewarding career opportunities. With an established presence throughout the Northeastern United States, MAS Medical Staffing has earned a reputation for quality, responsiveness, and delivering workforce solutions that help healthcare providers meet their evolving workforce needs while supporting clinicians throughout every stage of their careers.

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SOURCE Care Career

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PointsKash Demonstrates How Businesses Can Build on Bitcoin Without Burdening the Blockchain

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3 hours ago

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July 24, 2026

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As industry debate surrounding Bitcoin Improvement Proposal (BIP-110) intensifies, PointsKash unveils an architecture designed to work regardless of the proposal’s outcome.

SCOTTSDALE, Ariz., July 24, 2026 /PRNewswire/ — As the global Bitcoin community debates Bitcoin Improvement Proposal 110 (BIP-110) and the future of data stored on the Bitcoin blockchain, PointsKash, Inc. today announced that its next-generation kiosk infrastructure was intentionally designed to operate efficiently under any outcome of the proposal.

Rather than storing operational data directly on the Bitcoin blockchain, PointsKash utilizes a layered architecture that combines Bitcoin‘s unmatched security with modern decentralized communications technology. Every transaction, machine event, system update, and operational record generated across the PointsKash network is cryptographically verified, securely maintained off-chain, and anchored to the Bitcoin blockchain through a single immutable cryptographic proof.

This approach allows thousands of operational events to be permanently verified while utilizing only a minimal amount of blockchain data.

As discussion surrounding BIP-110 has intensified across the digital asset industry, PointsKash believes the debate does not require choosing between innovation and responsible blockchain stewardship.

“The industry has been debating whether businesses can build meaningful applications on Bitcoin without unnecessarily consuming blockchain space,” said Michael Herron, Chief Executive Officer of PointsKash. “We believe we’ve demonstrated that the answer is yes. Bitcoin provides the world’s most trusted immutable timestamp and security layer, while higher-volume operational data belongs on technologies specifically designed to manage it. By combining both, we’ve built an architecture that is scalable, transparent, and future-ready regardless of how the BIP-110 discussion ultimately evolves.”

The company’s infrastructure assigns every kiosk its own unique cryptographic identity, allowing each machine to securely authenticate every transaction and operational event. Those records are then independently verifiable through cryptographic proofs while remaining resistant to alteration or manipulation—even by PointsKash itself.

According to the company, this architecture delivers several significant advantages:

Mathematically verifiable transaction records for regulators, banking partners, auditors, and enterprise customers.Improved network reliability, allowing kiosks to continue operating during temporary connectivity interruptions without losing transaction history.Enhanced cybersecurity, with every machine maintaining its own authenticated identity and secure communications.A scalable blockchain architecture that minimizes on-chain data while preserving complete auditability.

“Bitcoin was created to provide trust, security, and permanence—not to become a storage system for every piece of application data,” Herron added. “Our philosophy has always been simple: use Bitcoin for what it does better than anyone else—creating immutable proof that records have never been altered—and leverage modern decentralized technologies for everything else. We believe that’s the future of enterprise blockchain infrastructure.”

PointsKash believes this architecture positions the company among a new generation of fintech innovators utilizing Bitcoin as a secure trust layer while developing scalable financial applications for enterprise deployment.

The technology also establishes the foundation for future blockchain-based financial products currently under development, including enhanced digital audit capabilities, verifiable financial records, enterprise licensing opportunities, and next-generation digital asset infrastructure.

As the Bitcoin ecosystem continues to mature, PointsKash believes its technology demonstrates that responsible innovation and blockchain scalability can successfully coexist—providing enterprise organizations with the confidence to build on Bitcoin without contributing unnecessary data to the network.

About PointsKash, Inc.

PointsKash, Inc. is a financial technology company developing an integrated ecosystem of AI-enabled self-service financial centers, digital banking, digital payment solutions, cryptocurrency services, loyalty rewards, enterprise merchant technologies, and mobile financial applications. Through proprietary software, Artificial Intelligence, and strategic partnerships, PointsKash is building innovative financial solutions designed to empower consumers, merchants, and enterprise organizations throughout North America.

For more information, visit www.pointskash.com.

Media Contact

PointsKash, Inc.
Investor Relations
info@pointskash.com
www.pointskash.com

Forward-Looking Statements

This press release contains forward-looking statements regarding anticipated technology integrations, Artificial Intelligence initiatives, product development, future commercialization plans, expected operational efficiencies, business strategy, and future growth. These statements are based on current expectations and involve risks and uncertainties that could cause actual results to differ materially from those expressed or implied. Factors that could affect actual results include, but are not limited to, technology development timelines, integration efforts, financing, regulatory developments, market conditions, and other risks facing the Company. PointsKash undertakes no obligation to update any forward-looking statements except as required by applicable law.

View original content to download multimedia:https://www.prnewswire.com/news-releases/pointskash-demonstrates-how-businesses-can-build-on-bitcoin-without-burdening-the-blockchain-302834473.html

SOURCE PointsKash Inc.

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