Knowledge graphs, a powerful tool for structuring information through relational triplets, have recently become the new front-runner in enhancing question-answering systems. While traditional Retrieval Augmented Generation (RAG) approaches are proficient in fact-based and local context-based extraction from concise texts, they encounter limitations when addressing the thematic and holistic understanding of complex, extensive texts, requiring a deeper analysis of both text and context. This paper presents a comprehensive technical comparative study of three different methodologies for constructing knowledge graph triplets and integrating them with Large Language Models (LLMs) for question answering: spaCy, Stanford CoreNLP-OpenIE, and GraphRAG, all leveraging open source technologies. We evaluate the effectiveness, feasibility, and adaptability of these methods by analyzing their capabilities, state of development, and their impact on the performance of LLM-based question answering. Experimental results indicate that while OpenIE provides the most comprehensive coverage of triplets, GraphRAG demonstrates superior reasoning abilities among the three. We conclude with a discussion on the strengths and limitations of each method and provide insights into future directions for improving knowledge graph-based question answering.

--Accurate channel estimation is critical for high-performance Orthogonal Frequency-Division Multiplexing systems such as 5G New Radio, particularly under low signal-to-noise ratio and stringent latency constraints. This letter presents HELENA, a compact deep learning model that combines a lightweight convolutional backbone with two efficient attention mechanisms: patch-wise multi-head self-attention for capturing global dependencies and a squeeze-and-excitation block for local feature refinement. Compared to CEViT, a state-of-the-art vision transformer-based estimator, HELENA reduces inference time by 45.0% (0.175 ms vs. 0.318 ms), achieves comparable accuracy ( 16 .78 Ccurate estimation of Channel State Information (CSI) is crucial for the effectiveness of Orthogonal Frequency-Division Multiplexing (OFDM)-based wireless communication systems, such as 5G New Radio (5G-NR), as it enables optimal resource allocation, beamforming, and adaptive modulation, all of which directly impact system capacity and reliability. In this context, Channel Estimation (CE) refers to the process of acquiring or predicting CSI using received signals and known reference signals (e.g., pilot symbols).

In 1979, when Steven Spielberg and George Lucas signed with Paramount Pictures to develop a film series based on classic Hollywood adventure serials, the deal they struck was to make five separate movies. The first, Raiders of the Lost Ark, was released in the summer of 1981 and became that year's top-grossing movie, beating even the long-anticipated Superman II and remaining on screens in some cities for more than a year. By 1984, it was Raiders' sequel, Indiana Jones and the Temple of Doom, that had become the year's most anticipated movie, banking that year's biggest opening weekend, and the third entry in the franchise, Indiana Jones and the Last Crusade, became not only the highest-grossing movie of 1989 but the top-earning Indiana Jones movie yet. Given that track record, and viewed from the perspective of our own IP-crazed times, it seems inconceivable that Spielberg and Lucas decided not to move forward immediately with a fourth Indiana Jones picture (though Lucas did go on to produce a spinoff TV series, The Young Indiana Jones Chronicles). Lucas' idea, a riff on the 1950s sci-fi films that would have been contemporaneous with a middle-aged Indy, was to introduce extraterrestrial beings into the previously earthbound Raiders universe.

The model simulates realistic production environments using a training model from Fischertechnik, the protocols MQTT, OPC UA, as well as Amazon Web Services and a programmable logic controller from Siemens. The AR apps were designed to be platform-independent, so that they run on iPadOS, iOS and Android, as well as HoloLens 1 and 2. The "SmARt Factory" continues to evolve: in the future, for example, a multi-agent system will enable adaptable production.

Renowned Visual Effects industry veteran Helena Packer, currently marking her 30th anniversary year working within the VFX arena, is currently working to enhance the next era of the visual effects field by developing new tools which will utilize the powerful advancements in digital technologies offered by Artificial Intelligence (AI). Packer explains how her new AI path came into play: In 2018, Raja Koduri, Chief Architect at Intel, approached her, asking if she would like to consult with Intel on the company's research and development of AI. For Packer, it felt like a natural fit, as she has been working to bridge technology with art throughout her entire career. During the past two years, Packer has had the opportunity to collaborate with some of the best minds currently working in AI, including Jason Yang, Co-Founder & CTO at Dgene. Packer, who sits on the Executive Committee of The Academy of Motion Picture Arts and Sciences (AMPAS), and also serves as Chair of the Diversity Committee for AMPAS' VFX Branch, is presently seeking new ways to make content creation easier and more gratifying through the incorporation of AI. "On the professional level, there is, at the moment, a huge surge in content production," Packer says.

By the time his play "R.U.R." (which stands for "Rossum's Universal Robots") premiered in Prague in 1921, Karel Čapek was a well-known Czech intellectual. Like many of his peers, he was appalled by the carnage wrought by the mechanical and chemical weapons that marked World War I as a departure from previous combat. He was also deeply skeptical of the utopian notions of science and technology. "The product of the human brain has escaped the control of human hands," Čapek told the London Saturday Review following the play's premiere. "This is the comedy of science." In that same interview, Čapek reflected on the origin of one of the play's characters: The old inventor, Mr. Rossum (whose name translated into English signifies "Mr.

"Automated artificial intelligence systems can look through resumes faster than a human can and flag the ones that might be of interest," says Tammy Cohen, Founder and Chief Visionary Officer of InfoMart. AI takes all the data stored in resumes, staffing agency databases, online job boards, and social media to help shortlist the most fitting applicants. "Companies like Ideal use AI that only looks for hard skills and qualifying experience. It determines which candidates will be best suited for the job without once glancing at where they live or determining how old they are. Another system – Avrio - judges candidates based on their credentials and then gives them a score based on how well they fit the criteria provided," adds Tammy.

The efficiency of the recruitment industry is set by the ability of employers and agents to match candidates to vacancies. With a typical hire taking up to six weeks and costing upwards of $4,000, more accurate matching can clearly lead to reduced waste of time and resources, and an impact on a business's bottom line. One of the inspirations behind Helena – an "AI head hunter" created by recruitment platform Woo – is the recommendation engines developed by online retailers like Amazon. Impressively, according to Woo CEO and founder LiranKotzer, early results show that employers are accepting for interview 52% of candidates put forward by Helena. This compares very favorably to performance of human recruitment agents, where the average figure is around 20%, and far better than relying on respondents to postings on job boards – where just 2.5% of applicants will be suitable for interview.

Everyone is familiar with the common job hunting struggles, but with Woo's newest AI robot, "Helena," job seeking has finally been made easy. The average employment process today is long, difficult, and frustrating; on both sides. With Woo's new technology the future of recruitment will be straightforward and painless. Woo's AI headhunter Helena collects relevant data from Woo's community of job seekers and employers, as well as external online sources. It then uses powerful algorithms to create high quality matches.

Along with that new round, it has also launched a new AI-powered bot, Helena, that has a few impressive tricks up its digital sleeve. But the bot goes further than that. It approaches those candidates once they have been discovered, acting as a corporate headhunter. But Helena also completes the recruitment circle by acting as the job seeker's agent. Effectively, it works on behalf of the company and the passive job seeker at the same time, sparing both parties the need to search for each other actively. Powered by machine learning and artificial intelligence, Helena is more than just a bot.