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MDPI Video Service: Advancing Visual Scholarly Communication

Encyclopedia MDPI is thrilled to announce significant enhancements to its Academic Video Service, which aim to improve its quality, accessibility, and functionality. Since its launch, our video service has enabled numerous scholars to present their research in a dynamic and visually engaging format, greatly enhancing its visibility and impact. Due to the overwhelmingly positive reception this service has received, we have reached a point where the number of orders we are receiving exceeds our current capacity. In order to maintain the quality of these videos and continue optimizing the service, we have made the decision to introduce a fee. However, to ensure that this service is still a cost-effective option, we have set our prices significantly below the market average. 1. Highlights of the Upgrades to the Service Although the service will now be fee-based, we are committed to providing even more professional and comprehensive support, including the following: One-on-one video production guidance Personalized assistance to ensure your needs are fully met. Scriptwriting and English editing Expertly crafted narratives and professional English editing to ensure your research is presented clearly, accurately, and with impact. High-quality animations Visually engaging animations are created to simplify complex research and captivate your audience. Whiteboard Animations: Clean and minimalist, using hand-drawn illustrations to explain ideas step-by-step. Motion Graphics (MG) Animations: Cartoon Style: Bright, colorful, and approachable, ideal for making technical or scientific content more accessible and engaging. Hand-Drawn Style: Unique and artistic, adding a personal touch to your research while maintaining clarity and professionalism. Customized infographics (optional) We can also create tailored infographics to visually summarize key data or findings, enhancing the clarity and appeal of your video. Native voiceover Native speakers provide voiceovers to enhance the accessibility and reach of your research. Multiple rounds of revision To ensure your video accurately represents your work. Social media promotion Expanding your research's visibility and impact. 2. Why Choose Us? The Proven Impact of Video Abstracts Research shows that a well-crafted video abstract can significantly enhance the visibility and impact of your work. It has been shown to do the following: Increase paper views by 120% （Source: 10.1007/ s11192-019-03108-w） Boost citations by 20% （Source: Wiley Online Library） Improve journal rankings by 33% （Source: Research Square） Raise Altmetrics scores by 140% （Source: Research Square） Our Expertise in Academic Research Backed by MDPI, our experienced production team combines deep academic knowledge with creative excellence. We understand the nuances of scholarly communication and ensure that every frame accurately conveys the value of your research, meeting the highest standards of quality and precision. Collaborations with SCI Journals We have partnered with over 60 SCI journals to create exclusive video series, enhancing the dissemination and impact of published research. For example, our collaborations with Entropy, Remote Sensing, Nanomaterials , Animals , Nutrients, Foods , Sustainability, Cancers, etc., have helped authors achieve greater visibility and recognition for their work. Global visibility The videos are linked to your paper's DOI for maximum exposure. Available Video Services and Their Pricing Video Abstract (up to 5 minutes long): Summarizes the key findings, methodology, and significance of your research paper. Regular Price: 600 CHF Short Take (up to 2 minutes long): Uses original animations to explain the specific aspects of your research. Regular Price: 500 CHF Scholar Interview: A face-to-face discussion offering deeper insights into your publication. Regular Price: 400 CHF Scholar Profile: A brief overview of a scholar’s career, highlighting education, research focus, and key achievements. Regular Price: 500 CHF 3. Video Production Service If you want to see some examples of our videos, please visit https://encyclopedia.pub/video. If you would like to apply for the video service, please click https://encyclopedia.pub/video_service. 4. Others If you have any other questions, please contact office@encyclopedia.pub.

Announcement 14 Apr 2026

From Hype to Value: Bridging the Reality Gap in AI Deployment

Last year, a mid-sized European retailer unveiled an AI-powered demand-forecasting engine with palpable excitement. The board had seen the dazzling demos, the promise of 20% stock-out reduction, and a sleek dashboard pulsing with predictive insight. Eight months later, the system was quietly shelved. It turned out the data lakes were full of inconsistent product codes, store managers did not trust the recommendations, and no one had defined how a forecast should actually change a replenishment order. The algorithm, brilliant in a sandbox, met a messy, human organization and lost. This story is far from unique. Across industries, the chasm between a compelling proof-of-concept and sustained, scaled value remains stubbornly wide. Research firm Gartner captured the zeitgeist bluntly, predicting that by 2025 at least 30% of generative AI projects would be abandoned after the pilot phase, not because the technology failed, but because of poor data quality, escalating costs, and unclear business value 1. The question for leaders today is no longer “Can we build it?” but “How do we bridge the reality gap and turn AI hype into durable returns?” (Figure 1). Figure 1. A visual flowchart showing how organizations move from AI hype to sustained value by building four bridges: business alignment, data readiness, last‑mile adoption, and governance. 1. The Hype Machine Artificial intelligence is enjoying its latest and loudest moment in the sun. From boardroom mandates to government task forces, the narrative is one of inevitability: automate everything, personalize every interaction, predict every failure. A McKinsey global survey found that 65% of organizations are now regularly using generative AI, nearly double the figure from just ten months prior 2. Venture capital pours billions into AI startups, and conference halls echo with tales of autonomous enterprises. Yet behind the curtain, the same survey revealed that fewer than a third of companies have managed to shrink the gap between experimentation and enterprise-wide deployment. Most are stuck in “pilot purgatory”, running dozens of small experiments that never accumulate into a meaningful bottom-line shift. The hype has outpaced the operational muscle needed to digest it. 2. The Reality Gap: Where AI Stumbles The reality gap is not a single fault line; it is a collection of cracks that widen under pressure. Firstly, many projects start with technology in search of a problem. An IT team acquires a state-of-the-art model, only to discover that the business team needed a simple rules-based automation. Secondly, data, the lifeblood of any AI system, is often fragmented, biased, or locked in legacy systems. A model trained on pristine historical data can produce nonsense when fed real-time, noisy operational feeds. Thirdly, the human element, the “last mile” of AI deployment, is routinely underestimated. Employees revert to gut instinct if they do not understand why a recommendation was made, or if it threatens their expertise. Finally, governance and ethical safeguards arrive as an afterthought, triggering regulatory or reputational fire alarms that freeze adoption. 3. Bridge #1: Anchor in Business Problems, Not Technology The first bridge back to reality is a disciplined refusal to start with the algorithm. High-performing organizations begin with a sharp, measurable business problem: reducing customer churn in a specific segment, cutting energy consumption on a particular production line, or slashing invoice-processing time by 50%. They then identify the minimum viable prediction, classification, or generation task that would materially move that metric. This demand-led approach forces the team to articulate the expected ROI before a single line of code is written, and it creates a natural scorecard for the project’s success. Equally importantly, it clarifies whether AI is even the right tool. In many cases, a well-designed business rule or a process simplification delivers most of the benefit with zero model risk. By anchoring in value, companies avoid the siren song of “AI for AI’s sake” and preserve credibility with stakeholders who will ultimately fund the next, more ambitious project. 4. Bridge #2: Data, The Unsexy Bedrock If business alignment is the compass, data readiness is the terrain. Bridging the gap requires an honest reckoning with what data actually exists, what state it is in, and who owns it. A model trained on a perfectly curated dataset in a cloud sandbox will fail the moment it encounters a misspelled product entry or a date field typed in three different formats. The unglamorous work of data engineering, building reliable pipelines, establishing master data management, and embedding real-time quality checks, is the single biggest determinant of whether an AI system will survive contact with reality. Forward-thinking companies appoint “data product” owners who are accountable for the availability, accuracy, and accessibility of critical data assets, treating them with the same rigor as physical products. They also invest in robust data labeling and feedback loops, ensuring that the model continues to learn from operational ground truth rather than drifting into irrelevance. 5. Bridge #3: People, Process, and the Last Mile Even a technically perfect model is worthless if a frontline worker ignores its output. The last mile of AI deployment is profoundly human. This necessitates designing an interface that makes the recommendation interpretable, explaining, for example, that the forecast suggests ordering 200 units because the upcoming holiday plus a competitor’s stock-out raises demand probability to 85%. It also means co-creating the system with the people who will use it, not tossing it over a wall from the data science lab. Additionally, it means redesigning the corresponding workflows so that the insight translates seamlessly into action: an alert that triggers an automated purchase order, a suggested next-best-offer that appears inside the customer-relationship-management tool a salesperson already uses. Change management is not a soft accessory; it is the engine of adoption. Davenport and Ronanki 3 emphasized that companies capturing real ROI from cognitive technologies had “redesigned the processes to take advantage of machine learning’s speed and scale, rather than simply overlaying AI on existing workflows”. That redesign often requires a new breed of “translator”, someone who speaks both business and data science, to bridge the two worlds continuously. 6. Bridge #4: Govern for Trust and Scale Without trust, AI stalls. Trust is built on transparency, fairness, and reliability. A governance framework that clearly defines data usage policies, model validation protocols, and human-in-the-loop oversight is not a bureaucratic drag; it is the scaffolding that allows AI to scale safely. When a bank’s credit model exhibits bias, or a hospital’s triage algorithm makes an inexplicable recommendation, the entire program can be mothballed overnight. Organizations that embed ethics and compliance from the outset, through impact assessments, bias audits, and explainability dashboards, avoid the costly stop–start cycles that doom so many initiatives. Moreover, governance includes financial discipline. Tracking actual costs versus benefits, and being willing to kill a project that does not meet its pre-agreed thresholds, keeps the portfolio healthy. McKinsey’s research shows that the organizations deriving the most value from AI are those that have moved beyond ad hoc experimentation to a factory model, where projects are run through a standardized “AI product lifecycle” with clear stage gates 2. 7. From Pilots to Platforms: A New Mindset Ultimately, bridging the reality gap demands a shift in mindset: AI is not a magic box but an operational capability that must be industrialized. That means adopting platforms, reusable components, and MLOps practices that bring the same rigor to machine learning that DevOps brought to software. It means celebrating not just the brilliance of a model’s accuracy, but the grit of making it run reliably at 2 a.m., and the discipline of measuring whether it actually saved money or grew revenue. The retailer that shelved its demand-forecasting engine eventually found its footing, not by building a better algorithm but by fixing product master data, involving store managers in feature selection, and starting with a single product category where the link between forecast and order was unambiguous. The pilot worked, and the proof was in the profit margin, not the PowerPoint slide. The path from hype to value is rarely a straight line. It is a deliberate journey of problem-first thinking, data discipline, human-centric design, and unwavering governance. The organizations that walk it will stop asking rhetorical questions about AI’s potential and start pointing to earnings statements. The gap is real, but so is the bridge. References Gartner. Gartner Predicts 30% of Generative AI Projects Will Be Abandoned After Proof of Concept By End of 2025.Gartner Press Release 2024. Available online: https://www.gartner.com/en/newsroom/press-releases/2024-07-29-gartner-predicts-30-percent-of-generative-ai-projects-will-be-abandoned-after-proof-of-concept-by-end-of-2025 (Accessed on 29 April 2026). McKinsey & Company. The state of AI in early 2024: Gen AI adoption spikes and starts to generate value. McKinsey Digital Available online: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai (Accessed on 29 April 2026). Davenport, T.H.; Ronanki, R. Artificial Intelligence for the Real World. Harv. Bus. Rev. 2018, 96, 108–116. Biography Dr. Hamed Taherdoost is an award-winning researcher, educator, and R&D leader with over two decades of international experience across academia and industry. He is a Professor at University Canada West and holds academic affiliations with Westcliff University (USA), GISMA University of Applied Sciences (Germany), and Victorian Institute of Technology (Australia). He is a GUS Institute Fellow (UK), a Westcliff Faculty Fellow, and a Fellow at the National Kaohsiung University of Science and Technology, Taiwan. His work spans digital transformation, cybersecurity, AI, and technology innovation, with hundreds of high-impact publications. Dr. Taherdoost serves as Book Series Editor for Routledge’s Mastering Academic Excellence and holds editorial roles with leading international journals.

Blog 13 May 2026

How Scholars Access Knowledge Today

Accessing academic knowledge today is easier than ever. Yet as the volume of scholarly information continues to grow, locating, interpreting, and connecting relevant knowledge remains a complex task. From research articles and databases to emerging platforms and structured knowledge resources, scholars rely on multiple approaches to navigate this evolving landscape. 1. Knowledge Access as a Foundation of Scholarly Work Access to academic knowledge involves more than the availability of information. While scholarly content is increasingly accessible through digital platforms, it is often distributed across different systems and presented within highly specialized contexts. As a result, engaging with academic knowledge typically requires navigating multiple sources and connecting insights across publications. This process involves not only locating information, but also interpreting and integrating it within a broader conceptual framework. 2. How Scholars Access Academic Knowledge Scholars access academic knowledge through a range of interconnected systems, each supporting different functions within the research and learning process. These systems can be broadly understood in terms of how they produce, organize, and disseminate knowledge. Peer-reviewed journal articles and conference papers remain the primary sources of original research, presenting new findings and advancing knowledge within specific fields. Review articles provide synthesis of existing research, helping to contextualize individual studies and identify broader trends within a discipline. Citation databases and academic search tools such as Scilit, Google Scholar, Web of Science, and Scopus support the discovery and retrieval of scholarly literature, enabling users to locate relevant publications efficiently. Academic networking and profiling platforms, including SciProfiles and ResearchGate, facilitate visibility, interaction, and the sharing of research outputs within scholarly communities. Preprint servers such as Preprints.org and arXiv enable the early dissemination of research findings prior to formal peer review, supporting more rapid communication of emerging work. In addition, encyclopedias provide a structured way of accessing established knowledge, supporting topic-level understanding and conceptual orientation. Taken together, these systems form a complementary ecosystem, in which different approaches support different stages of knowledge production, discovery, and understanding. 3. Encyclopedias in Knowledge Access Among these approaches, encyclopedias are characterized by their emphasis on the structured organization and synthesis of knowledge. They present information in a concise and accessible form, helping users grasp key concepts, understand relationships between topics, and navigate broader knowledge domains. In practice, encyclopedias can be broadly divided into general and academic forms. General encyclopedias are typically designed to support broad exploration and initial understanding of unfamiliar topics, while academic encyclopedias are more specialized and support engagement with scholarly knowledge at a deeper level. From an information science perspective, encyclopedias are generally classified as tertiary sources. In academic library and information literacy frameworks, tertiary sources are defined as resources that summarize and synthesize information from primary and secondary sources to provide background understanding of a topic. Encyclopedias are widely recognized as representative examples of this category, as they organize existing knowledge rather than present original research. As part of the broader knowledge ecosystem, encyclopedias contribute to knowledge access by offering structured and synthesized representations of existing knowledge.

Blog 06 May 2026

Best Video Abstract Awards

The Encyclopedia platform, together with the journals Biology and Nutrients, launches the Best Video Abstract Awards to increase the visibility and reach of published research and to inspire researchers to explore the power of visual storytelling. Video abstracts have become an increasingly important medium for scientific communication. By integrating narration, visualizations, animations, and experimental footage, they make complex research more accessible, engaging, and memorable. This initiative recognizes video abstracts that are not only scientifically rigorous but also creatively compelling and educational, thereby promoting broader dissemination and deeper community engagement. To learn more about the awards or to participate directly, please visit the event page via the links provided below. https://encyclopedia.pub/best-video-abstract-award 1. Event Duration 9 February 2026 – 2 February 2027 2. Awards Biology Best Video Abstract AwardOpen to video abstracts based on papers published in Biology between 1 January 2024 and 31 December 2025. This award will be granted to two video abstracts based on the evaluation of the Award Evaluation Committee. Nutrients Best Video Abstract AwardOpen to video abstracts based on papers published in Nutrients between 1 January 2024 and 31 December 2025. This award will be granted to two video abstracts based on the evaluation of the Award Evaluation Committee. Prize For each journal award, the winner will receive: CHF 500 A voucher waiving the Article Processing Charges (APCs) for one journal submission (subject to peer review, valid for one year) A free Academic Video Service production (no matter where the paper is published), valid for one year. An electronic certificate Participant Incentive All participants will receive a CHF 100 discount voucher for the Encyclopedia Academic Video Service. 3. Participation The event will be conducted in three stages. Submission Stage 9 February 2026 – 31 August 2026 Independent Submission Authors may create and submit video abstracts independently using their own tools and creative approach. Professional Support Option Authors who do not currently have a video abstract but intend to apply for the award may opt for the Academic Video Service, which offers a one-stop, end-to-end solution covering script development, animation, voiceover recording, and editing. Please submit your video abstract here: https://encyclopedia.pub/user/video_add?activity=b57ab0910b456a5e4eebd960867ce205 Or place your video service order here: https://encyclopedia.pub/user/video_service_order All video abstracts will be assessed by the editorial team for editorial suitability and overall quality. Submissions that meet the guidelines will be assessed equally. Voting Stage 1 November 2026 – 31 December 2026 Public voting will be conducted during this period. Voting results and video performance metrics, including views, likes, shares, and collections, will contribute to the final evaluation. Winner Announcement 2 February 2027 Final winners will be determined based on a combined assessment of public voting results and a comprehensive evaluation by the Award Evaluation Committee, which carries the primary weight in the final decision. Winners will be announced on the Encyclopedia platform and journal websites. 4. Others If you have any other questions, please contact office@encyclopedia.pub

Announcement 09 Feb 2026

Encyclopedia 2026 Outstanding Contributor Award

We are pleased to announce that the “Encyclopedia Outstanding Contributor Award” 2026 is now open for applications. We will award two outstanding contributors to the Encyclopedia platform (https://encyclopedia.pub/) and the Encyclopedia journal in 2026. After a thorough evaluation, the journal award committee, led by Editor-in-Chief Prof. Dr. Raffaele Barretta, will select all eligible contributors. The winners will be announced in March 2027. The Prize:–CHF 300;–An opportunity to publish one paper free of charge in the Encyclopedia journal before 31 December 2027, following peer review for each winner;–An electronic certificate. Application Deadline:15 January 2027. Document for Application:–Curriculum vitae/resume (please contact our office if you require a template of the form). Please send your curriculum vitae to our office before the deadline (encyclopedia@mdpi.com). The winners will be announced on the journal website in March 2027. Kind regards, Encyclopedia Editorial Office

Announcement 07 Apr 2026

See what people are saying about us

Shlomi Agmon

Encyclopedia Video provides potential readers with a tool to quickly understand what the work is about. That is important for casualreaders, whose time is thus spared, and for investedreaders, for whom it makes the decision to say "yes, I want to read the paper" much simpler.

School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel

Ignacio Cea

For the video abstracts, the papers and authors could gain more visibility and increase citations. Also, it means a more diverse and interesting way of communicating research, which is something valuable in itself.

Center for Research, Innovation and Creation, and Faculty of Religious Sciences and Philosophy, Temuco Catholic University

Melvin R. Pete Hayden

Thank the video production crew for making such a wonderful video. The narrations have been significantly added to the video! Congratulations on such an outstanding job of Encyclopedia Video team.

University of Missouri School of Medicine, United States

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Encyclopedia: From Scholars for Scholars

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Encyclopedia:
From Scholars for Scholars