Milena Zlatković, Rialda Kurtić, Igor A. Pašti
et al.
This study explores the use of carbon materials derived from Nocino walnut liqueur pomace residue for the removal of chlorpyrifos, a widely used organophosphate pesticide, from water. Carbon adsorbents were synthesized from young walnut biomass under different thermal and chemical treatment conditions, and their structural and surface properties were characterized using BET analysis, FTIR, SEM-EDX, Boehm titration, and zeta potential measurements. The materials exhibited distinct textural and chemical features, including high surface areas and varied surface functionalizations. Batch adsorption studies revealed that the chlorpyrifos removal followed pseudo-second-order kinetics and was best described by the Freundlich and Langmuir isotherms, indicating a combination of pore filling and physisorption via π-π and van der Waals interactions. The highest adsorption capacity of 45.2 ± 0.2 mg g−1 was achieved at 30 °C. Thermodynamic analysis confirmed the process to be endothermic, spontaneous, and entropy-driven, with desolvation effects enhancing the performance at elevated temperatures. Dynamic filtration experiments validated the practical applicability of the materials, while moderate reusability was achieved through ethanol-based regeneration. These findings demonstrate the potential of walnut pomace-derived carbons as low-cost, renewable, and effective adsorbents for sustainable water decontamination.
The optimal design of robotic actuators is a critical area of research, yet limited attention has been given to optimizing gearbox parameters and automating actuator CAD. This paper introduces COMPAct: Computational Optimization and Automated Modular Design of Planetary Actuators, a framework that systematically identifies optimal gearbox parameters for a given motor across four gearbox types, single-stage planetary gearbox (SSPG), compound planetary gearbox (CPG), Wolfrom planetary gearbox (WPG), and double-stage planetary gearbox (DSPG). The framework minimizes mass and actuator width while maximizing efficiency, and further automates actuator CAD generation to enable direct 3D printing without manual redesign. Using this framework, optimal gearbox designs are explored across a wide range of gear ratios, providing insights into the suitability of different gearbox types while automatically generating CAD models for all four gearbox types with varying gear ratios and motors. Two actuator types are fabricated and experimentally evaluated through power efficiency, no-load backlash, and transmission stiffness tests. Experimental results indicate that the SSPG actuator achieves a mechanical efficiency of 60-80%, a no-load backlash of 0.59 deg, and a transmission stiffness of 242.7 Nm/rad, while the CPG actuator demonstrates 60% efficiency, 2.6 deg backlash, and a stiffness of 201.6 Nm/rad. CODE: https://github.com/singhaman1750/COMPAct.git VIDEO: https://youtu.be/etK6anjXag8?si=jFK7HgAPSBy-GnDR
Miriam Ugarte, Pablo Valle, José Antonio Parejo
et al.
Large Language Models (LLMs) have recently gained attention due to their ability to understand and generate sophisticated human-like content. However, ensuring their safety is paramount as they might provide harmful and unsafe responses. Existing LLM testing frameworks address various safety-related concerns (e.g., drugs, terrorism, animal abuse) but often face challenges due to unbalanced and obsolete datasets. In this paper, we present ASTRAL, a tool that automates the generation and execution of test cases (i.e., prompts) for testing the safety of LLMs. First, we introduce a novel black-box coverage criterion to generate balanced and diverse unsafe test inputs across a diverse set of safety categories as well as linguistic writing characteristics (i.e., different style and persuasive writing techniques). Second, we propose an LLM-based approach that leverages Retrieval Augmented Generation (RAG), few-shot prompting strategies and web browsing to generate up-to-date test inputs. Lastly, similar to current LLM test automation techniques, we leverage LLMs as test oracles to distinguish between safe and unsafe test outputs, allowing a fully automated testing approach. We conduct an extensive evaluation on well-known LLMs, revealing the following key findings: i) GPT3.5 outperforms other LLMs when acting as the test oracle, accurately detecting unsafe responses, and even surpassing more recent LLMs (e.g., GPT-4), as well as LLMs that are specifically tailored to detect unsafe LLM outputs (e.g., LlamaGuard); ii) the results confirm that our approach can uncover nearly twice as many unsafe LLM behaviors with the same number of test inputs compared to currently used static datasets; and iii) our black-box coverage criterion combined with web browsing can effectively guide the LLM on generating up-to-date unsafe test inputs, significantly increasing the number of unsafe LLM behaviors.
With the diminishing return from Moore's Law, system-technology co-optimization (STCO) has emerged as a promising approach to sustain the scaling trends in the VLSI industry. By bridging the gap between system requirements and technology innovations, STCO enables customized optimizations for application-driven system architectures. However, existing research lacks sufficient discussion on efficient STCO methodologies, particularly in addressing the information gap across design hierarchies and navigating the expansive cross-layer design space. To address these challenges, this paper presents Orthrus, a dual-loop automated framework that synergizes system-level and technology-level optimizations. At the system level, Orthrus employs a novel mechanism to prioritize the optimization of critical standard cells using system-level statistics. It also guides technology-level optimization via the normal directions of the Pareto frontier efficiently explored by Bayesian optimization. At the technology level, Orthrus leverages system-aware insights to optimize standard cell libraries. It employs a neural network-assisted enhanced differential evolution algorithm to efficiently optimize technology parameters. Experimental results on 7nm technology demonstrate that Orthrus achieves 12.5% delay reduction at iso-power and 61.4% power savings at iso-delay over the baseline approaches, establishing new Pareto frontiers in STCO.
Concrete workability is essential for construction quality, with the slump test being the most widely used on-site method for its assessment. However, traditional slump testing is manual, time-consuming, and highly operator-dependent, making it unsuitable for continuous or real-time monitoring during placement. To address these limitations, we present SlumpGuard, an AI-powered vision system that analyzes the natural discharge flow from a mixer-truck chute using a single fixed camera. The system performs automatic chute detection, pouring-event identification, and video-based slump classification, enabling quality monitoring without sensors, hardware installation, or manual intervention. We introduce the system design, construct a site-replicated dataset of over 6,000 video clips, and report extensive evaluations demonstrating reliable chute localization, accurate pouring detection, and robust slump prediction under diverse field conditions. An expert study further reveals significant disagreement in human visual estimates, highlighting the need for automated assessment.
The science and clinical practice of medical physics has been integral to the advancement of radiology and radiation therapy for over a century. In parallel, advances in surgery - including intraoperative imaging, registration, and other technologies within the expertise of medical physicists - have advanced primarily in connection to other disciplines, such as biomedical engineering and computer science, and via somewhat distinct translational paths. This review article briefly traces the parallel and convergent evolution of such scientific, engineering, and clinical domains with an eye to a potentially broader, more impactful role of medical physics in research and clinical practice of surgery. A review of image-guided surgery technologies is offered, including intraoperative imaging, tracking / navigation, image registration, visualization, and surgical robotics across a spectrum of surgical applications. Trends and drivers for research and innovation are traced, including federal funding and academic-industry partnership, and some of the major challenges to achieving major clinical impact are described. Opportunities for medical physicists to expand expertise and contribute to the advancement of surgery in the decade ahead are outlined, including research and innovation, data science approaches, improving efficiency through operations research and optimization, improving patient safety, and bringing rigorous quality assurance to technologies and processes in the circle of care for surgery. Challenges abound but appear tractable, including domain knowledge, professional qualifications, and the need for investment and clinical partnership.
The articles in this issue suggest that the future of effective innovation does not lie in the dominance of algorithmic efficiency over human cognition, nor in rejecting automation. Instead, success depends on a "Hybrid Intelligence" model where formal innovation processes are rigorously applied to speed up execution and reduce risk, while simultaneously leveraging linguistic and contextual diversity to create the "cognitive friction" necessary for high-quality decision-making. Consequently, the challenge for leadership is to design organizations that are "ambidextrous"—capable of balancing the "closing behaviors" required for efficiency and execution with the "opening behaviors" needed for exploration and creativity. Together, these aspects provide a comprehensive perspective on the journey of an idea, from a simple spark of cognitive potential to a transformative force that reshapes our world, starting with the most fundamental element: the innovator's mind.
Michael Welsh, Julian Lopez-Rippe, Dana Alkhulaifat
et al.
Large language models (LLMs) show promise in enhancing medical research through domain-specific question answering. However, their clinical application is limited by hallucination risk, limited domain specialization, and privacy concerns. Public LLMs like GPT-4-Consensus pose challenges for use with institutional data, due to the inability to ensure patient data protection. In this work, we present a secure, custom-designed retrieval-augmented generation (RAG) LLM system deployed entirely within our institution and tailored for radiology research. Radiology researchers at our institution evaluated the system against GPT-4-Consensus through a blinded survey assessing factual accuracy (FA), citation relevance (CR), and perceived performance (PP) using 5-point Likert scales. Our system achieved mean ± SD scores of 4.15 ± 0.99 for FA, 3.70 ± 1.17 for CR, and 3.55 ± 1.39 for PP. In comparison, GPT-4-Consensus obtained 4.25 ± 0.72, 3.85 ± 1.23, and 3.90 ± 1.12 for the same metrics, respectively. No statistically significant differences were observed (<i>p</i> = 0.97, 0.65, 0.42), and 50% of participants preferred our system’s output. These results validate that secure, local RAG-based LLMs can match state-of-the-art performance while preserving privacy and adaptability, offering a scalable tool for medical research environments.
Engineering machinery, tools, and implements, Technological innovations. Automation
Various ideation methods, such as morphological analysis and design-by-analogy, have been developed to aid creative problem-solving and innovation. Among them, the Theory of Inventive Problem Solving (TRIZ) stands out as one of the best-known methods. However, the complexity of TRIZ and its reliance on users' knowledge, experience, and reasoning capabilities limit its practicality. To address this, we introduce AutoTRIZ, an artificial ideation system that integrates Large Language Models (LLMs) to automate and enhance the TRIZ methodology. By leveraging LLMs' vast pre-trained knowledge and advanced reasoning capabilities, AutoTRIZ offers a novel, generative, and interpretable approach to engineering innovation. AutoTRIZ takes a problem statement from the user as its initial input, automatically conduct the TRIZ reasoning process and generates a structured solution report. We demonstrate and evaluate the effectiveness of AutoTRIZ through comparative experiments with textbook cases and a real-world application in the design of a Battery Thermal Management System (BTMS). Moreover, the proposed LLM-based framework holds the potential for extension to automate other knowledge-based ideation methods, such as SCAMPER, Design Heuristics, and Design-by-Analogy, paving the way for a new era of AI-driven innovation tools.
Armin Mokhtarian, Jianye Xu, Patrick Scheffe
et al.
Connected and automated vehicles and robot swarms hold transformative potential for enhancing safety, efficiency, and sustainability in the transportation and manufacturing sectors. Extensive testing and validation of these technologies is crucial for their deployment in the real world. While simulations are essential for initial testing, they often have limitations in capturing the complex dynamics of real-world interactions. This limitation underscores the importance of small-scale testbeds. These testbeds provide a realistic, cost-effective, and controlled environment for testing and validating algorithms, acting as an essential intermediary between simulation and full-scale experiments. This work serves to facilitate researchers' efforts in identifying existing small-scale testbeds suitable for their experiments and provide insights for those who want to build their own. In addition, it delivers a comprehensive survey of the current landscape of these testbeds. We derive 62 characteristics of testbeds based on the well-known sense-plan-act paradigm and offer an online table comparing 23 small-scale testbeds based on these characteristics. The online table is hosted on our designated public webpage https://bassamlab.github.io/testbeds-survey, and we invite testbed creators and developers to contribute to it. We closely examine nine testbeds in this paper, demonstrating how the derived characteristics can be used to present testbeds. Furthermore, we discuss three ongoing challenges concerning small-scale testbeds that we identified, i.e., small-scale to full-scale transition, sustainability, and power and resource management.
In the ever-evolving landscape of technology, product innovation thrives on replacing outdated technologies with groundbreaking ones or through the ingenious recombination of existing technologies. Our study embarks on a revolutionary journey by genetically representing products, extracting their chromosomal data, and constructing a comprehensive phylogenetic network of automobiles. We delve deep into the technological features that shape innovation, pinpointing the ancestral roots of products and mapping out intricate product-family triangles. By leveraging the similarities within these triangles, we introduce a pioneering "Product Disruption Index"-inspired by the CD index (Funk and Owen-Smith, 2017)-to quantify a product's disruptiveness. Our approach is rigorously validated against the scientifically recognized trend of decreasing disruptiveness over time (Park et al., 2023) and through compelling case studies. Our statistical analysis reveals a fascinating insight: disruptive product innovations often stem from minor, yet crucial, modifications.
The primary objective of the current research is to identify the impact that COVID-19 had on the Creative Business in Bulgaria and their first steps for recovery from the crisis. Our research is based on official and reliable Bulgarian National Statistical Insti-tute data. Based on the number of employed persons, the sectors most affected by COVID-19 in the creative business are sound recording and music publishing, photography, and advertising. For the period 2017-2022, there has been a slowdown in the growth rate of gross wages in the creative industries. This slowdown is most noticeable in 2019 and mainly in 2020, followed by a smooth and gradual recovery. At the same time, creative business companies in Bulgaria received the most extensive support in terms of public funding in 2020. The most generously supported sectors in 2020 within the Creative Industries are Advertising, followed by the Production and distribution of films and television shows and Computer Programming. The Creative Business companies in Bulgaria demonstrated relatively good sustainability and adaptability during and after the COVID-19 crisis. The rates in profit change confirm the serious negative impact of the pandemic (results in 2019 and 2020). Still, at the same time, it reveals a good level of resilience and recovery of the companies (results for 2021 and 2022).
In this article, I will explore how the underlying research values of ‘openness’ and ‘mutual responsiveness’, which are central to open science practices, can be integrated into a new ethos of science. Firstly, I will revisit Robert Merton's early contribution to this issue, examining whether the ethos of science should be understood as a set of norms for scientists to practice ‘good’ science or as a set of research values as a functional requirement of the scientific system to produce knowledge, irrespective of individual adherence to these norms. Secondly, I will analyse the recent codification of scientific practice in terms of ‘scientific integrity’, a framework that Merton did not pursue. Based on this analysis, and illustrated on the case of COVID-19 as a case in which the institution of science was challenged to deliver urgently on societal desirable outcomes, I will argue that promoting open science and its core norms of collaboration and openness requires broader governance of the institution of science in its relationship with society at large, rather than relying solely on self-governance within the scientific community through a new ethos of science. This conclusion has implications for re-evaluating research assessments, suggesting that the evaluation of the scientific system should take precedence over evaluating individual researchers, and that incentives should be provided to encourage specific research behaviour rather than solely focusing on individual research outputs.
Gabriel Trujillo-Hernández, Wendy Flores-Fuentes, Luis Roberto Ramírez-Hernández
et al.
Individuals’ lifestyles are affected by valgus and varus deformities in the rearfoot, causing pain in the joints and plantar surface due to the misalignment between the tibial and calcaneus. In orthopedics, medical professionals measure this misalignment by using X-ray systems and goniometers. The X-ray emits ionizing radiation that can cause damage through cumulative exposure over a lifetime, whereas the goniometer will produce measurement errors. This patent review conducted a technological search of systems and methods across various databases using inclusion and exclusion criteria. These thirty-five obtained patents provide valuable information about mechanical, electronic, and mechatronic technologies and non-ionizing radiation to evaluate valgus and varus deformities. The patents are classified into stationary mechanisms, stationary electronic devices, dynamic mechanisms, dynamic electronic devices, stationary mechatronic devices, and dynamic mechatronic devices. They are further categorized based on their measurement methods as either visual or automatic. Additionally, the patents are grouped by usage mode into sitting, standing, and walking. This patent review aims to provide medical professionals with little-known techniques for measuring and evaluating the rearfoot alignment.
Engineering machinery, tools, and implements, Technological innovations. Automation
The increasing complexity of healthcare supply chains characterized by fluctuating demand, stringent regulatory requirements, globalized procurement networks, and the critical need for real-time resource availability has accelerated the adoption of Artificial Intelligence (AI) as a transformative operational tool. This systematic review synthesizes emerging trends, empirical findings, and technological innovations in AI-enabled supply chain management within healthcare systems. Drawing on peer-reviewed literature from the past decade, the study examines how AI-driven techniques such as machine learning, predictive analytics, natural language processing, optimization algorithms, and intelligent automation enhance procurement forecasting, inventory management, logistics optimization, clinical resource allocation, and risk mitigation. The review highlights the growing integration of AI with enabling technologies such as digital twins, Internet of Medical Things (IoMT), blockchain, and cloud-based analytics to strengthen supply chain visibility, traceability, and resilience. Evidence shows that AI significantly reduces stock-outs, improves demand prediction accuracy, enhances cold-chain monitoring, and supports decision-making in critical service lines such as pharmaceuticals, surgical supplies, and emergency care. Despite these advancements, major challenges remain, including data fragmentation, interoperability limitations, model transparency concerns, workforce capacity gaps, and ethical issues relating to bias, privacy, and automation risks. The review concludes by outlining future research directions, emphasizing the need for explainable AI (XAI), scalable real-time analytics, integrated data governance frameworks, and hybrid human-AI decision architectures. This study provides a consolidated knowledge base for policymakers, healthcare administrators, and supply chain professionals seeking evidence-based pathways for AI adoption in healthcare operations.
T he growing influence of generative artificial intelligence (GAI) on our personal and professional lives continues to give it the appearance of a truly disruptive innovation. Kivimaa et al noted the characteristics of disruptive innovations to include high-intensity disruption or deletion of entire job markets, resetting of process or business models, and a “technological substitution process.” Artificial intelligence (AI) applications have already been shown to be quite capable of acting as technological substitutions for human processes. Generative AI, though, moves beyond just the automation facets of AI into something more complex and curious that has captured the public's imagination. The impacts of GAI are continuing to unfold within healthcare delivery and education, with both value and cautions yet to be fully realized. Active engagement on the part of all nurses, particularly nurse informaticists, is required in order to shape the technology moving forward and to alleviate potential negative impacts and misuse.
Agriculture is always needed by every human and responsible for the economic growth of a country. Developed countries likewise America, Japan, China are leading and making other countries too dependent on their technologies. But developing countries like India are expecting a lot of new technological innovations in the field of agriculture. Innovations may be in the form of smart machines, automation systems, sensor-based instruments, etc. and an advantage for society. In this paper, we have proposed Recommending and Predicting Crop Yield using Smart Machine Learning Algorithm (SMLA). The proposed algorithm namely SMLA is compared with other traditional algorithms to predict crop yield. In comparison to other algorithms the proposed algorithm works efficiently and produces 95% accuracy.
Over recent years we have seen an unprecedented revival of interest in Artificial Intelligence (AI) due to major technological advances, particularly in the field of machine learning, which extend the capabilities of computers and increase their performance in a large number of domains (language processing, speech understanding, image recognition, robotics, etc.). These advances have opened up vast opportunities in terms of technological innovations and automation in work situations. Therefore, we clearly need regulation to keep essential decision-making to humans and not to mathematical models, whose skills and biases are not controlled. It is therefore necessary to clarify the place of scientific knowledge and expertise in the political decision-making process, which must find a balance between “argued convictions” and the different internal logics. We must promote the duality “science in society, versus society supported by science”. This notion leads us to constantly invent new frameworks that make it possible to compare knowledges and to promote a dialogue between citizens and science. It allows us by the same time to question the integration of scientific expertise during the decision-making process. We must from now on talk of “science in society” and no longer of “science and society”.
Different types of warfare have evolved between nations and states in the modern era, each with its technological breakthroughs and use of cutting-edge technologies. With the help of the latest innovations, technologies and ideas emerging and contributing more to the It sector, making it more advanced and resulting in different technologies used for cyber warfare, information technology has a stronghold, power, and control over many other integrated automated technologies. To identify the various technologies that are primarily used in cyber warfare. This exploratory study used a systematic review technique and a theme analysis approach to examine prior works in information technology relevant to cyber warfare.