Hasil untuk "Pharmaceutical industry"

Yuheng Huang, Lei Ma, Keizaburo Nishikino et al.

The pre-training paradigm plays a key role in the success of Large Language Models (LLMs), which have been recognized as one of the most significant advancements of AI recently. Building on these breakthroughs, code LLMs with advanced coding capabilities bring huge impacts on software engineering, showing the tendency to become an essential part of developers' daily routines. However, the current code LLMs still face serious challenges related to trustworthiness, as they can generate incorrect, insecure, or unreliable code. Recent exploratory studies find that it can be promising to detect such risky outputs by analyzing LLMs' internal states, akin to how the human brain unconsciously recognizes its own mistakes. Yet, most of these approaches are limited to narrow sub-domains of LLM operations and fall short of achieving industry-level scalability and practicability. To address these challenges, in this paper, we propose PtTrust, a two-stage risk assessment framework for code LLM based on internal state pre-training, designed to integrate seamlessly with the existing infrastructure of software companies. The core idea is that the risk assessment framework could also undergo a pre-training process similar to LLMs. Specifically, PtTrust first performs unsupervised pre-training on large-scale unlabeled source code to learn general representations of LLM states. Then, it uses a small, labeled dataset to train a risk predictor. We demonstrate the effectiveness of PtTrust through fine-grained, code line-level risk assessment and demonstrate that it generalizes across tasks and different programming languages. Further experiments also reveal that PtTrust provides highly intuitive and interpretable features, fostering greater user trust. We believe PtTrust makes a promising step toward scalable and trustworthy assurance for code LLMs.

Wei-hsiang Yen, Lyn Chao-ling Chen

The innovation of the study is that the deep learning method and sentiment analysis are integrated in traditional business model analysis and forecasting, and the research subject is TSMC for industry trend prediction of semiconductor industry in Taiwan. For the rapid market changes and development of wafer technologies of semiconductor industry, traditional data analysis methods not perform well in the high variety and time series data. Textual data and time series data were collected from seasonal reports of TSMC including financial information. Textual data through sentiment analysis by considering the event intervention both from internal events of the company and the external global events. Using the sentiment-enhanced time series data, the LSTM model was adopted for predicting industry trend of TSMC. The prediction results reveal significant development of wafer technology of TSMC and the potential threatens in the global market, and matches the product released news of TSMC and the international news. The contribution of the work performed accurately in industry trend prediction of the semiconductor industry by considering both the internal and external event intervention, and the prediction results provide valuable information of semiconductor industry both in research and business aspects.

Lorenz Brehme, Benedikt Dornauer, Thomas Ströhle et al.

Retrieval-Augmented Generation (RAG) is a well-established and rapidly evolving field within AI that enhances the outputs of large language models by integrating relevant information retrieved from external knowledge sources. While industry adoption of RAG is now beginning, there is a significant lack of research on its practical application in industrial contexts. To address this gap, we conducted a semistructured interview study with 13 industry practitioners to explore the current state of RAG adoption in real-world settings. Our study investigates how companies apply RAG in practice, providing (1) an overview of industry use cases, (2) a consolidated list of system requirements, (3) key challenges and lessons learned from practical experiences, and (4) an analysis of current industry evaluation methods. Our main findings show that current RAG applications are mostly limited to domain-specific QA tasks, with systems still in prototype stages; industry requirements focus primarily on data protection, security, and quality, while issues such as ethics, bias, and scalability receive less attention; data preprocessing remains a key challenge, and system evaluation is predominantly conducted by humans rather than automated methods.

Pankaj Jalote, Y. Raghu Reddy, Vasudeva Varma

Covid has made online teaching and learning acceptable and students, faculty, and industry professionals are all comfortable with this mode. This comfort can be leveraged to offer an online multi-institutional research-level course in an area where individual institutions may not have the requisite faculty to teach and/or research students to enroll. If the subject is of interest to industry, online offering also allows industry experts to contribute and participate with ease. Advanced topics in Software Engineering are ideally suited for experimenting with this approach as industry, which is often looking to incorporate advances in software engineering in their practices, is likely to agree to contribute and participate. In this paper we describe an experiment in teaching a course titled "AI in Software Engineering" jointly between two institutions with active industry participation, and share our and student's experience. We believe this collaborative teaching approach can be used for offering research level courses in any applied area of computer science by institutions who are small and find it difficult to offer research level courses on their own.

Gauthier Roussilhe, Thibault Pirson, David Bol et al.

Growing attention is given to the environmental impacts of the digital sector, exacerbated by the increase of digital products and services in our globalized societies. The materiality of the digital sector is often presented through the environmental impacts of mining activities to point out that digitization does not mean dematerialization. Despite its importance, such a narrative is often restricted to a few minerals (e.g., cobalt, lithium) that have become the symbols of extractive industries. In this paper, we further explore the materiality of the digital sector with an approach based on the diversity of elements and their purity requirements in the semiconductor industry. Semiconductors are responsible for manufacturing the key building blocks of the digital sector, i.e., microchips. Given that the need for ultra-high purity materials is very specific to the semiconductor industry, a few companies around the world have been studied, revealing new critical actors in complex supply chains. This highlights strong dependencies towards other industrial sectors with mass production and the need for a deeper investigation of interactions with the chemical industry, complementary to the mining industry.

Claudio Pirrone, Stefano Fricano, Gioacchino Fazio

The foundation model industry exhibits unprecedented concentration in critical inputs: semiconductors, energy infrastructure, elite talent, capital, and training data. Despite extensive sectoral analyses, no comprehensive framework exists for assessing overall industrial vulnerability. We develop the Artificial Intelligence Industrial Vulnerability Index (AIIVI) grounded in O-Ring production theory, recognizing that foundation model production requires simultaneous availability of non-substitutable inputs. Given extreme data opacity and rapid technological evolution, we implement a validated human-in-the-loop methodology using large language models to systematically extract indicators from dispersed grey literature, with complete human verification of all outputs. Applied to six state-of-the-art foundation model developers, AIIVI equals 0.82, indicating extreme vulnerability driven by compute infrastructure (0.85) and energy systems (0.90). While industrial policy currently emphasizes semiconductor capacity, energy infrastructure represents the emerging binding constraint. This methodology proves applicable to other fast-evolving, opaque industries where traditional data sources are inadequate.

Sunidhi Bachawala, Dominik Tomasz Nasilowski, Marcial Gonzalez

<b>Background/Objectives</b>: Glidants and lubricants are commonly used pharmaceutical excipients that enhance powder flowability and reduce inter-particle friction, respectively, but they also negatively impact critical quality attributes such as tablet tensile strength and drug release rate. Quantifying these effects is essential as the pharmaceutical industry transitions from batch to continuous manufacturing. <b>Methods</b>: This study develops a rational-function-based modeling approach to capture the effects of lubricants and glidants on tableting. The framework automatically identifies upstream critical material attributes and process parameters, such as excipient concentration and mixing time, and describes their coupling to first and second orders. Reduced-order models were constructed to evaluate the influence of these variables on the four stages of powder compaction—die filling, compaction, unloading, and ejection—using formulations composed of 10% acetaminophen, microcrystalline cellulose, and varying small concentrations of magnesium stearate or colloidal silica. Tablets were fabricated across a wide range of relative densities by varying dosing position and turret speed. <b>Results</b>: The modeling approach successfully quantified the effects of lubricant and glidant mixing conditions on each compaction stage, providing mechanistic insight into how upstream conditions propagate through the tableting process and influence critical quality attributes. <b>Conclusions</b>: Overall, the rational-function-based framework offers a systematic approach to quantify and predict the impact of lubricants and glidants on tablet performance, thereby enhancing product and process understanding in continuous manufacturing.

Md Bokhtiar Al Zami, Shaba Shaon, Vu Khanh Quy et al.

Industrial networks are undergoing rapid transformation driven by the convergence of emerging technologies that are revolutionizing conventional workflows, enhancing operational efficiency, and fundamentally redefining the industrial landscape across diverse sectors. Amidst this revolution, Digital Twin (DT) emerges as a transformative innovation that seamlessly integrates real-world systems with their virtual counterparts, bridging the physical and digital realms. In this article, we present a comprehensive survey of the emerging DT-enabled services and applications across industries, beginning with an overview of DT fundamentals and its components to a discussion of key enabling technologies for DT. Different from literature works, we investigate and analyze the capabilities of DT across a wide range of industrial services, including data sharing, data offloading, integrated sensing and communication, content caching, resource allocation, wireless networking, and metaverse. In particular, we present an in-depth technical discussion of the roles of DT in industrial applications across various domains, including manufacturing, healthcare, transportation, energy, agriculture, space, oil and gas, as well as robotics. Throughout the technical analysis, we delve into real-time data communications between physical and virtual platforms to enable industrial DT networking. Subsequently, we extensively explore and analyze a wide range of major privacy and security issues in DT-based industry. Taxonomy tables and the key research findings from the survey are also given, emphasizing important insights into the significance of DT in industries. Finally, we point out future research directions to spur further research in this promising area.

Abhinav Garg, Naman Shukla, Maarten Wormer

Time series forecasting in the air cargo industry presents unique challenges due to volatile market dynamics and the significant impact of accurate forecasts on generated revenue. This paper explores a comprehensive approach to demand forecasting at the origin-destination (O\&D) level, focusing on the development and implementation of machine learning models in decision-making for the air cargo industry. We leverage a mixture of experts framework, combining statistical and advanced deep learning models to provide reliable forecasts for cargo demand over a six-month horizon. The results demonstrate that our approach outperforms industry benchmarks, offering actionable insights for cargo capacity allocation and strategic decision-making in the air cargo industry. While this work is applied in the airline industry, the methodology is broadly applicable to any field where forecast-based decision-making in a volatile environment is crucial.

Quentin Raillard-Cazanove, Thibaut Knibiehly, Robin Girard

The decarbonisation of the energy system is crucial for achieving climate goals and is inherently linked to the decarbonisation of industry. Despite this, few studies explore the simultaneous impacts of decarbonising both sectors. This paper aims to examine how industrial decarbonisation in Europe affects the energy system and vice versa. To address this, an industry model incorporating key heavy industry sectors across six European countries is combined with an energy system model for electricity and hydrogen covering fifteen European regions, refered to as the EU-15, divided into eleven zones. The study evaluates various policy scenarios under different conditions.The results demonstrate that industrial decarbonisation leads to a significant increase in electricity and hydrogen demand. This additional demand for electricity is largely met through renewable energy sources, while hydrogen supply is predominantly addressed by blue hydrogen production when fossil fuels are authorized and the system lacks renewable energy. This increased demand results in higher prices with considerable regional disparities. Furthermore, the findings reveal that, regardless of the scenario, the electricity mix in the EU-15 remains predominantly renewable, exceeding 85%.A reduction in carbon taxes lowers the prices of electricity and hydrogen, but does not increase consumption, as the lower carbon tax makes the continued use of fossil fuels more attractive to industry. In scenarios that enforce a phase-out of fossil fuels, electricity prices rise, leading to a greater reliance on imports of low-carbon hydrogen and methanol. Results also suggest that domestic hydrogen production benefits from synergies between electrolytic hydrogen and blue hydrogen, helping to maintain competitive prices.

Marco Arazzi, Antonino Nocera, Emanuele Storti

Recently, the Industry 5.0 is gaining attention as a novel paradigm, defining the next concrete steps toward more and more intelligent, green-aware and user-centric digital systems. In an era in which smart devices typically adopted in the industry domain are more and more sophisticated and autonomous, the Internet of Things and its evolution, known as the Internet of Everything (IoE, for short), involving also people, robots, processes and data in the network, represent the main driver to allow industries to put the experiences and needs of human beings at the center of their ecosystems. However, due to the extreme heterogeneity of the involved entities, their intrinsic need and capability to cooperate, and the aim to adapt to a dynamic user-centric context, special attention is required for the integration and processing of the data produced by such an IoE. This is the objective of the present paper, in which we propose a novel semantic model that formalizes the fundamental actors, elements and information of an IoE, along with their relationships. In our design, we focus on state-of-the-art design principles, in particular reuse, and abstraction, to build ``SemIoE'', a lightweight ontology inheriting and extending concepts from well-known and consolidated reference ontologies. The defined semantic layer represents a core data model that can be extended to embrace any modern industrial scenario. It represents the base of an IoE Knowledge Graph, on top of which, as an additional contribution, we analyze and define some essential services for an IoE-based industry.

Ranim Saker, Hadi Shammout, Géza Regdon et al.

Recently, titanate nanotubes (TNTs) have been receiving more attention and becoming an attractive candidate for use in several disciplines. With their promising results and outstanding performance, they bring added value to any field using them, such as green chemistry, engineering, and medicine. Their good biocompatibility, high resistance, and special physicochemical properties also provide a wide spectrum of advantages that could be of crucial importance for investment in different platforms, especially medical and pharmaceutical ones. Hydrothermal treatment is one of the most popular methods for TNT preparation because it is a simple, cost-effective, and environmentally friendly water-based procedure. It is also considered as a strong candidate for large-scale production intended for biomedical application because of its high yield and the special properties of the resulting nanotubes, especially their small diameters, which are more appropriate for drug delivery and long circulation. TNTs’ properties highly differ according to the preparation conditions, which would later affect their subsequent application field. The aim of this review is to discuss the factors that could possibly affect their synthesis and determine the transformations that could happen according to the variation of factors. To fulfil this aim, relevant scientific databases (Web of Science, Scopus, PubMed, etc.) were searched using the keywords titanate nanotubes, hydrothermal treatment, synthesis, temperature, time, alkaline medium, post treatment, acid washing, calcination, pharmaceutical applications, drug delivery, etc. The articles discussing TNTs preparation by hydrothermal synthesis were selected, and papers discussing other preparation methods were excluded; then, the results were evaluated based on a careful reading of the selected articles. This investigation and comprehensive review of different parameters could be the answer to several problems concerning establishing a producible method of TNTs production, and it might also help to optimize their characteristics and then extend their application limits to further domains that are not yet totally revealed, especially the pharmaceutical industry and drug delivery.

Mihajlo Bogdanović, Dragana Mladenović, Ljiljana Mojović et al.

Abstract The last decade provided significant advances in the understanding of microbiota and its role in human health. Probiotics are live microorganisms with proven benefits for the host and were mostly studied in the context of gut health, but they can also confer significant benefits for oral health, mainly in the treatment of gingivitis. Postbiotics are cell-free extracts and metabolites of microorganisms which can provide additional preventive and therapeutic value for human health. This opens opportunities for new preventive or therapeutic formulations for oral administration. The microorganisms that colonize the oral cavity, their role in oral health and disease, as well as the probiotics and postbiotics which could have beneficial effects in this complex environment were discussed. The aim of this study was to review, analyse and discuss novel probiotic and postbiotic formulations intended for oral administration that could be of great preventive and therapeutic importance. A special attention has been put on the formulation of the pharmaceutical dosage forms that are expected to provide new benefits for the patients and technological advantages relevant for industry. An adequate dosage form could significantly enhance the efficiency of these products.

Hussain Sadiq Abuwala, Bohan Zhang, Mushi Wang

The goal of our research was to investigate the effects of collaboration between academia and industry on Natural Language Processing (NLP). To do this, we created a pipeline to extract affiliations and citations from NLP papers and divided them into three categories: academia, industry, and hybrid (collaborations between academia and industry). Our empirical analysis found that there is a trend towards an increase in industry and academia-industry collaboration publications and that these types of publications tend to have a higher impact compared to those produced solely within academia.

Andrés Felipe Posada-Moreno, Kai Müller, Florian Brillowski et al.

The industry 4.0 is leveraging digital technologies and machine learning techniques to connect and optimize manufacturing processes. Central to this idea is the ability to transform raw data into human understandable knowledge for reliable data-driven decision-making. Convolutional Neural Networks (CNNs) have been instrumental in processing image data, yet, their ``black box'' nature complicates the understanding of their prediction process. In this context, recent advances in the field of eXplainable Artificial Intelligence (XAI) have proposed the extraction and localization of concepts, or which visual cues intervene on the prediction process of CNNs. This paper tackles the application of concept extraction (CE) methods to industry 4.0 scenarios. To this end, we modify a recently developed technique, ``Extracting Concepts with Local Aggregated Descriptors'' (ECLAD), improving its scalability. Specifically, we propose a novel procedure for calculating concept importance, utilizing a wrapper function designed for CNNs. This process is aimed at decreasing the number of times each image needs to be evaluated. Subsequently, we demonstrate the potential of CE methods, by applying them in three industrial use cases. We selected three representative use cases in the context of quality control for material design (tailored textiles), manufacturing (carbon fiber reinforcement), and maintenance (photovoltaic module inspection). In these examples, CE was able to successfully extract and locate concepts directly related to each task. This is, the visual cues related to each concept, coincided with what human experts would use to perform the task themselves, even when the visual cues were entangled between multiple classes. Through empirical results, we show that CE can be applied for understanding CNNs in an industrial context, giving useful insights that can relate to domain knowledge.

Shusen Wang

Recommender system (RS) is an established technology with successful applications in social media, e-commerce, entertainment, and more. RSs are indeed key to the success of many popular APPs, such as YouTube, Tik Tok, Xiaohongshu, Bilibili, and others. This paper explores the methodology for improving modern industrial RSs. It is written for experienced RS engineers who are diligently working to improve their key performance indicators, such as retention and duration. The experiences shared in this paper have been tested in some real industrial RSs and are likely to be generalized to other RSs as well. Most contents in this paper are industry experience without publicly available references.

Danny Weyns, Thomas Vogel

Despite the vast body of knowledge developed by the self-adaptive systems community and the wide use of self-adaptation in industry, it is unclear whether or to what extent industry leverages output of academics. Hence, it is important for the research community to answer the question: Are the solutions developed by the self-adaptive systems community mature enough for industrial adoption? Leveraging a set of empirically-grounded guidelines for industry-relevant artifacts in self-adaptation, we develop a position to answer this question from the angle of using artifacts for evaluating research results in self-adaptation, which is actively stimulated and applied by the community.

Setareh Akhtarshenas, Hoda Yasini, Hossein Rastegar

There are various pharmaceutical companies in Iran with high potential. Due to the lack of business with international markets for exports, Iranian pharmaceutical products are mostly traded in domestic markets. Despite numerous studies on pharmaceutical exports in the literature, the performance of Iran in pharmaceutical exports to the Organization of Islamic Cooperation (OIC), remains yet to be evaluated. The novelty of the present work lies in filling this research gap and helping develop policies to expand pharmaceutical exports to Iran.

Qilin Xiang, Xin Peng, Chuan He et al.

Microservice architecture advocates a number of technologies and practices such as lightweight container, container orchestration, and DevOps, with the promised benefits of faster delivery, improved scalability, and greater autonomy. However, microservice systems implemented in industry vary a lot in terms of adopted practices and achieved benefits, drastically different from what is advocated in the literature. In this article, we conduct an empirical study, including an online survey with 51 responses and 14 interviews for experienced microservice experts to advance our understanding regarding to microservice practices in industry. As a part of our findings, the empirical study clearly revealed three levels of maturity of microservice systems (from basic to advanced): independent development and deployment, high scalability and availability, and service ecosystem, categorized by the fulfilled benefits of microservices. We also identify 11 practical issues that constrain the microservice capabilities of organizations. For each issue, we summarize the practices that have been explored and adopted in industry, along with the remaining challenges. Our study can help practitioners better position their microservice systems and determine what infrastructures and capabilities are worth investing. Our study can also help researchers better understand industrial microservice practices and identify useful research problems.

Tiago Espinha Gasiba, Ulrike Lechner, Maria Pinto-Albuquerque et al.

Software needs to be secure, in particular, when deployed to critical infrastructures. Secure coding guidelines capture practices in industrial software engineering to ensure the security of code. This study aims to assess the level of awareness of secure coding in industrial software engineering, the skills of software developers to spot weaknesses in software code, avoid them, and the organizational support to adhere to coding guidelines. The approach draws on well-established theories of policy compliance, neutralization theory, and security-related stress and the authors' many years of experience in industrial software engineering and on lessons identified from training secure coding in the industry. The paper presents the questionnaire design for the online survey and the first analysis of data from the pilot study.