Hasil untuk "Pharmaceutical industry"

Suyash Mishra, Qiang Li, Anubhav Girdhar

Large language models (LLMs) are increasingly used to create content in regulated domains such as pharmaceuticals, where outputs must be scientifically accurate and legally compliant. Manual quality control (QC) is slow, error prone, and can become a publication bottleneck. We introduce LRBTC, a modular LLM and vision language model (VLM) driven QC architecture covering Language, Regulatory, Brand, Technical, and Content Structure checks. LRBTC combines a Student-Teacher dual model architecture, human in the loop (HITL) workflow with waterfall rule filtering to enable scalable, verifiable content validation and optimization. On AIReg-Bench, our approach achieves 83.0% F1 and 97.5% recall, reducing missed violations by 5x compared with Gemini 2.5 Pro. On CSpelling, it improves mean accuracy by 26.7%. Error analysis further reveals that while current models are strong at detecting misspellings (92.5 recall), they fail to identify complex medical grammatical (25.0 recall) and punctuation (41.7 recall) errors, highlighting a key area for future work. This work provides a practical, plug and play solution for reliable, transparent quality control of content in high stakes, compliance critical industries. We also provide access to our Demo under MIT Licenses.

Hailiang Zhao, Ziqi Wang, Daojiang Hu et al.

The convergence of artificial intelligence, cyber-physical systems, and cross-enterprise data ecosystems has propelled industrial intelligence to unprecedented scales. Yet, the absence of a unified trust foundation across data, services, and knowledge layers undermines reliability, accountability, and regulatory compliance in real-world deployments. While existing surveys address isolated aspects, such as data governance, service orchestration, and knowledge representation, none provides a holistic, cross-layer perspective on trustworthiness tailored to industrial settings. To bridge this gap, we present \textsc{Trisk} (TRusted Industrial Data-Service-Knowledge governance), a novel conceptual and taxonomic framework for trustworthy industrial intelligence. Grounded in a five-dimensional trust model (quality, security, privacy, fairness, and explainability), \textsc{Trisk} unifies 120+ representative studies along three orthogonal axes: governance scope (data, service, and knowledge), architectural paradigm (centralized, federated, or edge-embedded), and enabling technology (knowledge graphs, zero-trust policies, causal inference, etc.). We systematically analyze how trust propagates across digital layers, identify critical gaps in semantic interoperability, runtime policy enforcement, and operational/information technologies alignment, and evaluate the maturity of current industrial implementations. Finally, we articulate a forward-looking research agenda for Industry 5.0, advocating for an integrated governance fabric that embeds verifiable trust semantics into every layer of the industrial intelligence stack. This survey serves as both a foundational reference for researchers and a practical roadmap for engineers to deploy trustworthy AI in complex and multi-stakeholder environments.

A. R. Pina, Shams El-Adawy, H. J. Lewandowski et al.

Continued growth of the quantum information science and engineering (QISE) industry has resulted in stakeholders spanning education, industry, and government seeking to better understand the workforce needs. This report presents a framework for the categorization of roles in the QISE industry based on 42 interviews of QISE professionals across 23 companies, as well as a description of the method used in the creation of this framework. The data included information on over 80 positions, which we have grouped into 29 roles spanning four primary categories. For each primary category we provide an overview of what unites the roles within a category, a description of relevant subcategories, and definitions of the individual roles. These roles serve as the basis upon which we generate profiles of these roles, which include information about role critical tasks, necessary knowledge and skills, and educational requirements. Our next report will present such profiles for each of the roles presented herein.

Jake Zappin, Trevor Stalnaker, Oscar Chaparro et al.

This position paper examines the substantial divide between academia and industry within quantum software engineering. For example, while academic research related to debugging and testing predominantly focuses on a limited subset of primarily quantum-specific issues, industry practitioners face a broader range of practical concerns, including software integration, compatibility, and real-world implementation hurdles. This disconnect mainly arises due to academia's limited access to industry practices and the often confidential, competitive nature of quantum development in commercial settings. As a result, academic advancements often fail to translate into actionable tools and methodologies that meet industry needs. By analyzing discussions within quantum developer forums, we identify key gaps in focus and resource availability that hinder progress on both sides. We propose collaborative efforts aimed at developing practical tools, methodologies, and best practices to bridge this divide, enabling academia to address the application-driven needs of industry and fostering a more aligned, sustainable ecosystem for quantum software development.

H. Käck, T. Sjögren

Structure-based drug design has been an integral part of drug discovery for over three decades, contributing to the development of numerous approved drugs. Here we discuss the evolution, as well as the current state, of structure-based drug design within the pharmaceutical industry, using data from AstraZeneca's internal repository for crystal structures to provide additional context. Over the past 20 years, the company has transitioned from a mixed in-house and synchrotron data collection model to a `synchrotron-only' approach, enabled by technological advancements at synchrotron facilities. We provide real-world examples of structure delivery to projects, including a high-throughput project and a case where a single structure was pivotal for discovering a candidate drug. We conclude that, despite recent developments in single-particle cryo-EM and deep-learning structure prediction methods, macromolecular crystallography remains a critical tool for drug discovery.

Kun Ren, Yannian Wang, Yudan Zhao et al.

With the increasing demands for drug quality and safety, the traditional Chinese medicine (TCM) pharmaceutical industry is in urgent need of transformation and upgrading. This paper provides an overview of the current application and prospects of artificial intelligence (AI) in the TCM pharmaceutical field. It delves into the specific applications and advantages of AI in various stages such as the selection and harvesting of TCM materials, processing, extraction and purification, formulation, and quality control. The paper points out new directions for the application and development of AI in the TCM pharmaceutical industry, offering a new perspective and approach for the intelligent upgrade of the TCM industry. The aim is to promote the industry's transition toward intelligence and high-quality development, with the hope of providing valuable insights and references for the innovation and upgrade of the entire TCM industry.

Mariana Andrades Fiorini Monteiro Novo, Vania dos Santos Nunes-Nogueira, Lucas Oliveira Cantadori et al.

Introdução: O bortezomibe é uma das medicações mais utilizadas no tratamento do mieloma múltiplo (MM). Objetivo: Realizar um estudo de macrocusteio do uso do bortezomibe no tratamento do MM na perspectiva do Sistema Único de Saúde (SUS). Métodos: Foram considerados os custos diretos relacionados a aquisição do bortezomibe. A partir dos dados do Departamento de Informática do Sistema Único de Saúde (DATASUS) foram calculadas a incidência anual de casos de MM e a estimativa de pacientes em tratamento. O custo do bortezomibe foi obtido no Banco de Preços de Saúde (BPS). O custo aproximado por paciente foi comparado ao valor reembolsado pela Autorização de Procedimento Ambulatorial (APAC). Foram consideradas duas possibilidades de tratamento: 9 ciclos, utilizados para pacientes candidatos ao transplante autólogo de células-tronco hematopoiéticas (TACTH) (30% da população), e 12 ciclos utilizados para pacientes não candidatos ao TACTH (70%). Resultados: Considerando 1,3mg/m² e 1,5mg/m², e um desperdício de dose de 10%, o custo por paciente em 9 ciclos foi de R$17.678,30 e R$18.023,64, respectivamente. Para 12 ciclos o custo foi R$23.569,94 para dose de 1,3mg/m² e R$24.030,39 para dose de 1,5mg/m². De acordo com o valor da APAC atual, que é de R$ 5.224,65, o valor total pago pelo tratamento de 9 ciclos é de R$ 47.021,85 e para 12 ciclos é de R$62.695,80. Discussão e Conclusão: O custo médio do tratamento com bortezomibe por paciente foi de R$22.100,59, e o valor reembolsado pela APAC R$57.993,62. Entretanto, enfatiza-se que foram computados apenas os custos diretos da aquisição do bortezomibe.

Sudhanshu Kalantri, Anuj N. Nahata, Nandan Godani

Abstract In recent years, microneedle (MN) and biosensor technologies have emerged as innovative solutions for non-invasive drug delivery and real-time disease diagnostics. Microneedles offer numerous advantages, including minimal pain, targeted delivery, improved bioavailability, and enhanced patient compliance. Various types—solid, hollow, dissolving, coated, and hydrogel microneedles—are designed to address specific therapeutic needs, each with unique drug release mechanisms. Advanced fabrication techniques such as 3D printing, laser ablation, photolithography, and micro-stereolithography allow for precise design and scalability. Biosensors, composed of bioreceptors and transducers, detect and quantify biological signals with high sensitivity and specificity. These devices are classified based on bioreceptors (enzymes, antibodies, cells), transduction mechanisms (electrochemical, optical, acoustic), and detection principles (mechanical, electronic). The integration of microneedles with biosensors enables continuous, real-time monitoring of biomarkers for chronic diseases such as diabetes, cancer, neurological disorders like Parkinson’s disease, and renal dysfunction. Several microneedle-based biosensing devices have been developed for glucose, urea, cholesterol, nitric oxide, and carcinoembryonic antigen detection. Powering these biosensors effectively remains crucial. Emerging technologies such as triboelectric, piezoelectric, thermoelectric nanogenerators, and biological fuel cells offer promising self-powered solutions. Moreover, the future scope includes integration with artificial intelligence (AI), Internet of Things (IoT), and biodegradable materials for personalized and sustainable healthcare. This review highlights the synergistic potential of microneedles and biosensors in diagnostics and therapeutics, emphasizing their role in transforming point-of-care medicine and wearable health monitoring. Graphical abstract

Ashok Urlana, Charaka Vinayak Kumar, Bala Mallikarjunarao Garlapati et al.

Large language models (LLMs) are playing a pivotal role in deploying strategic use cases across a range of organizations, from large pan-continental companies to emerging startups. The issues and challenges involved in the successful utilization of LLMs can vary significantly depending on the size of the organization. It is important to study and discuss these pertinent issues of LLM adaptation with a focus on the scale of the industrial concerns and brainstorm possible solutions and prospective directions. Such a study has not been prominently featured in the current research literature. In this study, we adopt a threefold strategy: first, we conduct a case study with industry practitioners to formulate the key research questions; second, we examine existing industrial publications to address these questions; and finally, we provide a practical guide for industries to utilize LLMs more efficiently. We release the GitHub\footnote{\url{https://github.com/vinayakcse/IndustrialLLMsPapers}} repository with the most recent papers in the field.

Srinjoy Mitra, Jean-Pierre Raskin

This paper discusses the importance of reflective and socially conscious education in engineering schools, particularly within the EE/CS sector. While most engineering disciplines have historically aligned themselves with the demands of the technology industry, the lack of critical examination of industry practices and their impact on justice, equality, and sustainability is self-evident. Today, the for-profit engineering/technology companies, some of which are among the largest in the world, also shape the narrative of engineering education and research in universities. As engineering graduates form the largest cohorts within STEM disciplines in Western countries, they become future professionals who will work, lead, or even establish companies in this industry. Unfortunately, the curriculum within engineering education often lacks a deep understanding of social realities, an essential component of a comprehensive university education. Here we establish this unusual connection with the industry that has driven engineering higher education for several decades and its obvious negative impacts to society. We analyse this nexus and highlight the need for engineering schools to hold a more critical viewpoint. Given the wealth and power of modern technology companies, particularly in the ICT domain, questioning their techno-solutionism narrative is essential within the institutes of higher education.

Panos Fitsilis, Paraskevi Tsoutsa, Vyron Damasiotis et al.

This article analyzes around 200 online articles to identify trends within Industry 5.0 using artificial intelligence techniques. Specifically, it applies algorithms such as LDA, BERTopic, LSA, and K-means, in various configurations, to extract and compare the central themes present in the literature. The results reveal a convergence around a core set of themes while also highlighting that Industry 5.0 spans a wide range of topics. The study concludes that Industry 5.0, as an evolution of Industry 4.0, is a broad concept that lacks a clear definition, making it difficult to focus on and apply effectively. Therefore, for Industry 5.0 to be useful, it needs to be refined and more clearly defined. Furthermore, the findings demonstrate that well-known AI techniques can be effectively utilized for trend identification, particularly when the available literature is extensive and the subject matter lacks precise boundaries. This study showcases the potential of AI in extracting meaningful insights from large and diverse datasets, even in cases where the thematic structure of the domain is not clearly delineated.

Sandeep Gupta

Secure and efficient communication to establish a seamless nexus between the five levels of a typical automation pyramid is paramount to Industry 4.0. Specifically, vertical and horizontal integration of these levels is an overarching requirement to accelerate productivity and improve operational activities. Vertical integration can improve visibility, flexibility, and productivity by connecting systems and applications. Horizontal integration can provide better collaboration and adaptability by connecting internal production facilities, multi-site operations, and third-party partners in a supply chain. In this paper, we propose an Edge-computing-based Industrial Gateway for interfacing information technology and operational technology that can enable Industry 4.0 vertical and horizontal integration. Subsequently, we design and develop a working prototype to demonstrate a remote production-line maintenance use case with a strong focus on security aspects and the edge paradigm to bring computational resources and data storage closer to data sources.

Popat Mohite, Shruti Sule, Anil Pawar et al.

Jobish John, Md. Noor-A-Rahim, Aswathi Vijayan et al.

This paper explores the role that 5G, WiFi-7, and Time-Sensitive Networking (TSN) can play in driving smart manufacturing as a fundamental part of the Industry 4.0 vision. The paper provides an in-depth analysis of each technology's application in industrial communications, with a focus on TSN and its key elements that enable reliable and secure communication in industrial networks. In addition, the paper includes a comparative study of these technologies, analyzing them based on a number of industrial use-cases, supported secondary applications, industry adoption, and current market trends. The paper concludes by highlighting the challenges and future directions for the adoption of these technologies in industrial networks and emphasizes their importance in realizing the Industry 4.0 vision within the context of smart manufacturing.

Mahya Karimzadeh khosroshahi, Mohammad Ebrahim Aghababaei

In the past few years, several major domestics and international challenges have emerged, causing global political and economic uncertainty. Economic uncertainty, defined as the difficulty in predicting the economic environment, arises from various factors such as political instability, changes and uncertainties in government policies, natural disasters, and market fluctuations. The presence of such uncertainties significantly affects the efficiency of markets, including the efficiency of the capital market. The aim of this study is to examine the long-term relationship between of economic policies uncertainty (based on the fluctuations of macroeconomic variables using the composite PCA index) and the excess return of eight different industries index (Automobile and parts manufacturing, Pharmaceutical Products and Materials , cement, lime and gypsum, Multidisciplinary industrial companies , basic metals, oil, coke, and nuclear fuel, coke and nuclear fuels, chemical products, Aggregation, properties and real estate).The investigation, conducted using the econometric ARDL approach over the period from 2012 to 2021, demonstrates that economic policies uncertainty is positively and significantly related to the excess returns of the selected industry index Among the various industries, the Automobile and manufacturing parts industry is most affected by the of economic policies uncertainty, while the construction and real estate industry is least affected. Furthermore, the speed of adjustment of Aggregation, properties and real estate the effect of Economic policy uncertainty on the excess returns of the stock market industries is not homogeneous, as indicated by the ECM coefficient. Automobile and manufacturing parts industry index experiences the fastest adjustment, while the Multidisciplinary industrial companies, due to their diverse portfolios, exhibit the slowest adjustment speed compared to others..

Abubakar Sadiq Mohammed, Philipp Reinecke, Pete Burnap et al.

The offshore oil and gas industry has recently been going through a digitalisation drive, with use of `smart' equipment using technologies like the Industrial Internet of Things (IIoT) and Industrial Cyber-Physical Systems (ICPS). There has also been a corresponding increase in cyber attacks targeted at oil and gas companies. Oil production offshore is usually in remote locations, requiring remote access and control. This is achieved by integrating ICPS, Supervisory, Control and Data Acquisition (SCADA) systems, and IIoT technologies. A successful cyber attack against an oil and gas offshore asset could have a devastating impact on the environment, marine ecosystem and safety of personnel. Any disruption to the world's supply of oil and gas (O\&G) can also have an effect on oil prices and in turn, the global economy. This makes it important to secure the industry against cyber threats. We describe the potential cyberattack surface within the oil and gas industry, discussing emerging trends in the offshore sub-sector, and provide a timeline of known cyberattacks. We also present a case study of a subsea control system architecture typically used in offshore oil and gas operations and highlight potential vulnerabilities affecting the components of the system. This study is the first to provide a detailed analysis on the attack vectors in a subsea control system and is crucial to understanding key vulnerabilities, primarily to implement efficient mitigation methods that safeguard the safety of personnel and the environment when using such systems.

Siddhartha Paul Tiwari

Soybean is the most globalized, traded and processed crop commodity. USA, Argentina and Brazil continue to be the top three producers and exporters of soybean and soymeal. Indian soyindustry has also made a mark in the national and global arena. While soymeal, soyoil, lecithin and other soy-derivatives stand to be driven up by commerce, the soyfoods for human health and nutrition need to be further promoted. The changing habitat of commerce in soyderivatives necessitates a shift in strategy, technological tools and policy environment to make Indian soybean industry continue to thrive in the new industrial era. Terms of trade for soyfarming and soy-industry could be further improved. Present trends, volatilities, slowdowns, challenges faced and associated desiderata are accordingly spelt out in the present article.

Aliya Nur Hasanah, Ike Susanti, Mutakin Mutakin

Beta-blockers are antihypertensive drugs and can be abused by athletes in some sport competitions; it is therefore necessary to monitor beta-blocker levels in biological samples. In addition, beta-blocker levels in environmental samples need to be monitored to determine whether there are contaminants from the activities of the pharmaceutical industry. Several extraction methods have been developed to separate beta-blocker drugs in a sample, one of which is molecularly imprinted polymer solid-phase extraction (MIP-SPE). MIPs have some advantages, including good selectivity, high affinity, ease of synthesis, and low cost. This review provides an overview of the polymerization methods for synthesizing MIPs of beta-blocker groups. The methods that are still widely used to synthesize MIPs for beta-blockers are the bulk polymerization method and the precipitation polymerization method. MIPs for beta-blockers still need further development, especially since many types of beta-blockers have not been used as templates in the MIP synthesis process and modification of the MIP sorbent is required, to obtain high throughput analysis.

Lee Cartier, Svan Lembke

The purpose of this study is to measure the benefits and costs of using biochar, a carbon sequestration technology, to reduce the B.C Wine Industry's carbon emissions. An economic model was developed to calculate the value-added for each of the three sectors that comprise the BC Wine industry. Results indicate that each sector of the wine value chain is potentially profitable, with 9,000 tonnes of CO2 sequestered each year. The study is unique in that it demonstrates that using biochar, produced from wine industry waste, to sequester atmospheric CO2 can be both profitable and environmentally sustainable.

Jozef Barunik, Mattia Bevilacqua, Robert Faff

We argue that uncertainty network structures extracted from option prices contain valuable information for business cycles. Classifying U.S. industries according to their contribution to system-related uncertainty across business cycles, we uncover an uncertainty hub role for the communications, industrials and information technology sectors, while shocks to materials, real estate and utilities do not create strong linkages in the network. Moreover, we find that this ex-ante network of uncertainty is a useful predictor of business cycles, especially when it is based on uncertainty hubs. The industry uncertainty network behaves counter-cyclically in that a tighter network tends to associate with future business cycle contractions.