Hasil untuk "Pharmaceutical industry"

Kushal Chawla, Chenyang Zhu, Pengshan Cai et al.

Summarization of multi-party dialogues is a critical capability in industry, enhancing knowledge transfer and operational effectiveness across many domains. However, automatically generating high-quality summaries is challenging, as the ideal summary must satisfy a set of complex, multi-faceted requirements. While summarization has received immense attention in research, prior work has primarily utilized static datasets and benchmarks, a condition rare in practical scenarios where requirements inevitably evolve. In this work, we present an industry case study on developing an agentic system to summarize multi-party interactions. We share practical insights spanning the full development lifecycle to guide practitioners in building reliable, adaptable summarization systems, as well as to inform future research, covering: 1) robust methods for evaluation despite evolving requirements and task subjectivity, 2) component-wise optimization enabled by the task decomposition inherent in an agentic architecture, 3) the impact of upstream data bottlenecks, and 4) the realities of vendor lock-in due to the poor transferability of LLM prompts.

Puyu Zeng, Zhaoxi Wang, Zhixu Duan et al.

Code generation and comprehension by Large Language Models (LLMs) have emerged as core drivers of industrial intelligence and decision optimization, finding widespread application in fields such as finance, automation, and aerospace. Although recent advancements have demonstrated the remarkable potential of LLMs in general code generation, existing benchmarks are mainly confined to single domains and languages. Consequently, they fail to effectively evaluate the generalization capabilities required for real-world industrial applications or to reflect the coding proficiency demanded by complex industrial scenarios. To bridge this gap, we introduce IndustryCode, the first comprehensive benchmark designed to span multiple industrial domains and programming languages. IndustryCode comprises 579 sub-problems derived from 125 primary industrial challenges, accompanied by rigorous problem descriptions and test cases. It covers a wide range of fields, including finance, automation, aerospace, and remote sensing-and incorporates diverse programming languages such as MATLAB, Python, C++, and Stata. In our evaluation, the top-performing model, Claude 4.5 Opus, achieved an overall accuracy of 68.1% on sub-problems and 42.5% main problems. The benchmark dataset and automated evaluation code will be made publicly available upon acceptance.

Rituraj Singh, Sachin Pawar, Girish Palshikar

Commonsense knowledge bases (KB) are a source of specialized knowledge that is widely used to improve machine learning applications. However, even for a large KB such as ConceptNet, capturing explicit knowledge from each industry domain is challenging. For example, only a few samples of general {\em tasks} performed by various industries are available in ConceptNet. Here, a task is a well-defined knowledge-based volitional action to achieve a particular goal. In this paper, we aim to fill this gap and present a weakly-supervised framework to augment commonsense KB with tasks carried out by various industry groups (IG). We attempt to {\em match} each task with one or more suitable IGs by training a neural model to learn task-IG affinity and apply clustering to select the top-k tasks per IG. We extract a total of 2339 triples of the form $\langle IG, is~capable~of, task \rangle$ from two publicly available news datasets for 24 IGs with the precision of 0.86. This validates the reliability of the extracted task-IG pairs that can be directly added to existing KBs.

Qiuyin Wei, Chenqi Li, Yibing Tang et al.

p53 functions as a critical guardian of the genome, orchestrating tumor suppression pathways and ensuring the integrity of chromosomal stability. Mdm2 and MdmX, homologous proteins, serve as negative feedback regulators of p53. In approximately half of tumor cases, overexpression of Mdm2/MdmX results in the inhibition of p53 activity. Current research focuses on designing Mdm2 and MdmX inhibitors based on the structure of lidless N-terminal forms of these proteins. However, growing evidence suggests that the lid of Mdm2 and MdmX plays a key role in the selective binding of p53 and inhibitors. Therefore, targeting the lid in the screening and design of Mdm2/MdmX inhibitors may offer a novel strategy for developing anti-cancer drugs. This review examines the impact of the Mdm2/MdmX lid on ligand binding, providing valuable insights for future research and guiding new approaches to the screening and design of innovative anti-cancer therapeutics.

Surya N Reddy, Vaibhav Kurrey, Mayank Nagar et al.

Proper use of personal protective equipment (PPE) can save the lives of industry workers and it is a widely used application of computer vision in the large manufacturing industries. However, most of the applications deployed generate a lot of false alarms (violations) because they tend to generalize the requirements of PPE across the industry and tasks. The key to resolving this issue is to understand the action being performed by the worker and customize the inference for the specific PPE requirements of that action. In this paper, we propose a system that employs activity recognition models to first understand the action being performed and then use object detection techniques to check for violations. This leads to a 23% improvement in the F1-score compared to the PPE-based approach on our test dataset of 109 videos.

Ashok Urlana, Charaka Vinayak Kumar, Ajeet Kumar Singh et al.

Large language models (LLMs) have become the secret ingredient driving numerous industrial applications, showcasing their remarkable versatility across a diverse spectrum of tasks. From natural language processing and sentiment analysis to content generation and personalized recommendations, their unparalleled adaptability has facilitated widespread adoption across industries. This transformative shift driven by LLMs underscores the need to explore the underlying associated challenges and avenues for enhancement in their utilization. In this paper, our objective is to unravel and evaluate the obstacles and opportunities inherent in leveraging LLMs within an industrial context. To this end, we conduct a survey involving a group of industry practitioners, develop four research questions derived from the insights gathered, and examine 68 industry papers to address these questions and derive meaningful conclusions. We maintain the Github repository with the most recent papers in the field.

Ghalib Ahmed Tahir, Murtaza Ashraf

Employee turnover remains a pressing issue within high-tech sectors such as IT firms and research centers, where organizational success heavily relies on the skills of their workforce. Intense competition and a scarcity of skilled professionals in the industry contribute to a perpetual demand for highly qualified employees, posing challenges for organizations to retain talent. While numerous studies have explored various factors affecting employee turnover in these industries, their focus often remains on overarching trends rather than specific organizational contexts. In particular, within the software industry, where projectspecific risks can significantly impact project success and timely delivery, understanding their influence on job satisfaction and turnover intentions is crucial. This study aims to investigate the influence of project risks in the IT industry on job satisfaction and employee turnover intentions. Furthermore, it examines the role of both external and internal social links in shaping perceptions of job satisfaction.

Alessandro Massaad, Rene Moawad, Oumaima Nijad Fares et al.

We revisit the long-only trend-following strategy presented in A Century of Profitable Industry Trends by Zarattini and Antonacci, which achieved exceptional historical performance with an 18.2% annualized return and a Sharpe Ratio of 1.39. While the results outperformed benchmarks, practical implementation raises concerns about robustness and evolving market conditions. This study explores modifications addressing reliance on T-bills, alternative fallback allocations, and industry exclusions. Despite attempts to enhance adaptability through momentum signals, parameter optimization, and Walk-Forward Analysis, results reveal persistent challenges. The results highlight challenges in adapting historical strategies to modern markets and offer insights for future trend-following frameworks.

Muhammad Yasir Siddiqui, Alam Gir

The e-commerce systems are being tackled from commerce behavior and internet technologies. Therefore, trust aspect between buyer-seller transactions is a potential element which needs to be addressed in competitive e-commerce industry. The e-commerce industry is currently handling two different trust approaches. First approach consists on centralized mechanism where digital credentials/set of rules assembled, called Policy based trust mechanisms . Second approach consists on decentralized trust mechanisms where reputation, points assembled and shared, called Reputation based trust mechanisms. The difference between reputation and policy based trust mechanism will be analyzed and recommendations would be proposed to increase trust between buyer and seller in e-commerce industry. The integration of trust mechanism is proposed through mapping process, strength of one mechanism with the weakness of other. The proposed model for integrated mechanism will be presented and illustrated how the proposed model will be used in real world e-commerce industry.

Andy Powrie-Smith

Vasiliki Demertzi, Stavros Demertzis, Konstantinos Demertzis

Thanks to rapid technological developments, new innovative solutions and practical applications of the Industrial Internet of Things (IIoT) are being created, upgrading the structures of many industrial enterprises. IIoT brings the physical and digital environment together with minimal human intervention and profoundly transforms the economy and modern business. Data flowing through IIoT feed artificial intelligence tools, which perform intelligent functions such as performance tuning of interconnected machines, error correction, and preventive maintenance. However, IIoT deployments are vulnerable to sophisticated security threats at various levels of the connectivity and communications infrastructure they incorporate. The complex and often heterogeneous nature of chaotic IIoT infrastructures means that availability, confidentiality and integrity are difficult to guarantee. This can lead to potential mistrust of network operations, concerns about privacy breaches or loss of vital personal data and sensitive information of network end-users. This paper examines the privacy requirements of an IIoT ecosystem in industry standards. Specifically, it describes the industry privacy dimensions of the protection of natural persons through the processing of personal data by competent authorities for the prevention, investigation, detection or prosecution of criminal offences or the execution of criminal penalties. In addition, it presents an overview of the state-of-the-art methodologies and solutions for industrial privacy threats. Finally, it analyses the privacy requirements and suggestions for an ideal secure and private IIoT environment.

Mindy Tran, Yasemin Acar, Michel Cucker et al.

Recent years have shown increased cyber attacks targeting less secure elements in the software supply chain and causing fatal damage to businesses and organizations. Past well-known examples of software supply chain attacks are the SolarWinds or log4j incidents that have affected thousands of customers and businesses. The US government and industry are equally interested in enhancing software supply chain security. We conducted six panel discussions with a diverse set of 19 practitioners from industry. We asked them open-ended questions regarding SBOMs, vulnerable dependencies, malicious commits, build and deploy, the Executive Order, and standards compliance. The goal of this summit was to enable open discussions, mutual sharing, and shedding light on common challenges that industry practitioners with practical experience face when securing their software supply chain. This paper summarizes the summit held on September 30, 2022.

Carolina Morais Carriço, Maria Elizabeth Tiritan, Honorina Cidade et al.

The growing threat of climatic crisis and fossil fuel extinction has caused a boom in sustainability trends. Consumer demand for so-called eco-friendly products has been steadily increasing, built upon the foundation of environmental protection and safeguarding for future generations. A natural product that has been used for centuries is cork, resulting from the outer bark of <i>Quercus suber</i> L. Currently, its major application is the production of cork stoppers for the wine industry, a process that, although considered sustainable, generates by-products in the form of cork powder, cork granulates, or waste such as black condensate, among others. These residues possess constituents of interest for the cosmetic and pharmaceutical industries, as they exhibit relevant bioactivities, such as anti-inflammatory, antimicrobial, and antioxidant. This interesting potential brings forth the need to develop methods for their extraction, isolation, identification, and quantification. The aim of this work is to describe the potential of cork by-products for the cosmetic and pharmaceutical industry and to assemble the available extraction, isolation, and analytical methods applied to cork by-products, as well the biological assays. To our knowledge, this compilation has never been done, and it opens new avenues for the development of new applications for cork by-products.

Bakary M Cissé, Hamma Boubacar Maiga, Aïchata Ben Adam Mariko et al.

Le patient est rassuré sur la provenance de son traitement quand il sait que c'est préparé par un professionnel de la santé comme son pharmacien de proximité. Alors que ces préparations ont tendance à disparaitre. L’objectif de notre étude était d’évaluer la problématique de la réalisation des préparations dans les officines de pharmacie du district de Bamako. Nous avons réalisé une étude descriptive transversale du janvier 2022 à janvier 2023. Des données ont été collectées à l’aide de questionnaires auprès de 145 pharmacies de la ville de Bamako. Les données collectées ont été décrite à l’aide de moyenne pour les variables quantitatives et de fréquence pour les variables qualitatives. Les analyses statistiques ont été réalisées à l’aide de logiciel SPSS version 20. Le sexe masculin était le plus représenté avec 59,30%. Le préparatoire n’existait pas dans 64% des officines, et 60% n’étaient pas fonctionnels. Aucun support technique n’était disponible dans 44,1%. L’absence de préparateurs était observable dans 70%. La majorité des officines ne connaissait pas de fournisseurs de matières premières avec 64,83%, et 86,03% ne trouvaient pas leur besoin en matière première chez les fournisseurs. Cette étude a démontré que les officines de pharmacie sont confrontées à d’énormes problèmes pour réaliser des préparations à l’officine

Mariana Santos Melo , Lúcia de Araújo Costa Beisl Noblat, Ney Cristian Amaral Boa Sort

Introdução: As infecções respiratórias de via aérea inferior na pediatria são as principais causas de admissão hospitalar, principalmente por bronquiolite aguda (BA) e pneumonia adquirida da comunidade (PAC). De modo comparável, a etiologia viral é uma das principais causas infecciosas nesta idade e, concorrentemente, vem sendo acompanhada pelo uso crescente de antibióticos. Ao passo que, o uso recorrente e inadvertido de antibióticos têm demonstrado impacto não só na resistência microbiana, mas também na mortalidade, tempo de permanência hospitalar e nos elevados custos em saúde Objetivo: Identificar o perfil de utilização de antibióticos e sua relação com o tempo de internamento e mortalidade, bem como descrever o perfil demográfico, história medicamentosa e comorbidades de pacientes pediátricos em uso de antibioticoterapia em infecções respiratórias de via aérea inferior de um hospital terciário no nordeste do Brasil. Metodologia: Estudo de utilização de medicamentos de caráter observacional, descritivo, unicêntrico realizado a partir da avaliação de prescrições médicas em prontuários eletrônicos de pacientes admitidos via emergência pediátrica com diagnóstico de BA e PAC de janeiro de 2019 a julho de 2019 conforme os critérios de seleção predefinidos. A coleta de dados foi realizada por um pesquisador e revisada por um segundo, sendo as divergências tratadas após discussão. Os diagnósticos foram validados de forma cega e anônima por um médico especialista em pediatra – auditor externo à pesquisa. Como variáveis, o presente estudo identificou o perfil demográfico, comorbidades, os medicamentos de uso prévio contínuo, perfil de antibioticoterapia, mortalidade e tempo de internamento. Este estudo foi aprovado pelo CEP Prof. Dr. Celso Figueiroa do Hospital Santa Izabel em 26/08/2019 (CAAE19067019.0.0000.5520). Resultados e Discussão: Foram incluídos 257 pacientes, sendo 102 elegíveis com bronquiolite aguda e 155 com pneumonia comunitária. Dentre eles 32,29% (83) apresentaram comorbidades prévias e 18,67% (48) referiram uso prévio de medicamentos contínuos. A frequência de uso de antibióticos na BA foi de 59,80% e na pneumonia 97,41%. Os antibióticos mais prescritos foram as penicilinas, cefalosporinas e macrolídeos. Houve diferença no tempo de internamento dos pacientes que receberam antibióticos (p=0,001), não sendo o mesmo evidenciado em termos de mortalidade. Conclusão: Foi identificado o perfil de utilização de antibióticos em pacientes pediátricos hospitalizados com pneumonia e bronquiolite, embora para este último a literatura respalde, principalmente, terapia de suporte. Consequentemente, o uso de antibióticos demonstrou relação com o aumento do tempo de internamento, sem diferença na mortalidade. Diante dos dados identificados e da emergente crise sanitária acerca do uso racional de antimicrobianos, outros estudos são necessários para maior compreensão do contexto local e dos fatores que conduziram a prescrição de antibioticoterapia.  

Galina V. Merkuryeva, Aija Valberga, Alexander Smirnov

Abstract Demand forecasting plays a critical role in logistics and supply chain management. In the paper, state-of-art methods and key challenges in demand forecasting for the pharmaceutical industry are discussed. An integrated procedure for in-market product demand forecasting and purchase order generation in the pharmaceutical supply chain is described. A case study for supply of pharmaceutical products from a wholesaler to a distribution company located in an emerging market is presented. Alternative forecasting scenarios for thebaseline demand calculations using the SMA model, multiple linear regressions and symbolic regression with genetic programming are experimentally investigated, and their practical implicationsare discussed.

Shah Zeb, Aamir Mahmood, Sunder Ali Khowaja et al.

Industry 5.0 vision, a step toward the next industrial revolution and enhancement to Industry 4.0, envisioned the new goals of resilient, sustainable, and human-centric approaches in diverse emerging applications, e.g., factories-of-the-future, digital society. The vision seeks to leverage human intelligence and creativity in nexus with intelligent, efficient, and reliable cognitive collaborating robots (cobots) to achieve zero waste, zerodefect, and mass customization-based manufacturing solutions. However, the vision requires the merging of cyber-physical worlds through utilizing Industry 5.0 technological enablers, e.g., cognitive cobots, person-centric artificial intelligence (AI), cyberphysical systems, digital twins, hyperconverged data storage and computing, communication infrastructure, and others. In this regard, the convergence of the emerging computational intelligence (CI) paradigm and next-generation wireless networks (NGWNs) can fulfill the stringent communication and computation requirements of the technological enablers in the Industry 5.0 vision, which is the aim of this survey-based tutorial. In this article, we address this issue by reviewing and analyzing current emerging concepts and technologies, e.g., CI tools and frameworks, network-in-box architecture, open radio access networks, softwarized service architectures, potential enabling services, and others, essential for designing the objectives of CINGWNs to fulfill the Industry 5.0 vision requirements. Finally, we provide a list of lessons learned from our detailed review, research challenges, and open issues that should be addressed in CI-NGWNs to realize Industry 5.0.

Martijn Oldenhof, Gergely Ács, Balázs Pejó et al.

To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n°831472), which was comprised of 10 pharmaceutical companies, academic research labs, large industrial companies and startups. The MELLODDY platform was the first industry-scale platform to enable the creation of a global federated model for drug discovery without sharing the confidential data sets of the individual partners. The federated model was trained on the platform by aggregating the gradients of all contributing partners in a cryptographic, secure way following each training iteration. The platform was deployed on an Amazon Web Services (AWS) multi-account architecture running Kubernetes clusters in private subnets. Organisationally, the roles of the different partners were codified as different rights and permissions on the platform and administrated in a decentralized way. The MELLODDY platform generated new scientific discoveries which are described in a companion paper.

Hyun-Jung Park, Woojin Jeong, Hyo Jeong Yu et al.

Grounding is a therapeutic technique that involves doing activities that “ground” or electrically reconnect us to the earth. The physiological effects of grounding have been reported from a variety of perspectives such as sleep or pain. However, its anti-stress efficacy is relatively unknown. The present study investigated the stress-related behavioral effects of earthing mat and its neurohormonal mechanisms in the Sprague–Dawley male rat. Rats were randomly divided into four groups: the naïve normal (Normal), the 21 days immobilization stressed (Control), the 21 days stressed + earthing mat for 7 days (A7) or 21 days (A21) group. The depressive-and anxiety like behaviors were measured by forced swimming test (FST), tail suspension test (TST) and elevated plus maze (EPM). Using immunohistochemistry, the expression of corticotrophin-releasing factor (CRF) and c-Fos immunoreactivity were analyzed in the brain. In the EPM, time spent in the open arm of the earthing mat groups was significantly increased compared to the Control group (<i>p</i> < 0.001), even though there were without effects among groups in the FST and TST. The expression of CRF immunoreactive neurons in the earthing mat group was markedly decreased compared to the Control group. Overall, the earthing mat reduced stress-induced behavioral changes and expression of c-Fos and CRF immunoreactivity in the brain. These results suggest that the earthing mat may have the potential to improve stress-related responses via the regulation of the corticotrophinergic system.

Gao Chen, Chang Hong, Zhou Hong-Bing et al.

To reveal the mechanism of anti-renal fibrosis effects of an n-butanol extract from Amygdalus mongolica, renal fibrosis was induced with unilateral ureteral obstruction (UUO) and then treated with an n-butanol extract (BUT) from Amygdalus mongolica (Rosaceae). Sixty male Sprague-Dawley rats were randomly divided into the sham-operated, renal fibrosis (RF) model, benazepril hydrochloride-treated model (1.5 mg kg−1) and BUT-treated (1.75, 1.5 and 1.25 g kg−1) groups and the respective drugs were administered intragastrically for 21 days. Related biochemical indices in rat serum were determined and histopathological morphology observed. Serum metabolomics was assessed with HPLC-Q-TOF-MS. The BUT reduced levels of blood urea nitrogen, serum creatinine and albumin and lowered the content of malondialdehyde and hydroxyproline in tissues. The activity of superoxide dismutase in tissues was increased and an improvement in the severity of RF was observed. Sixteen possible biomarkers were identified by metabolomic analysis and six key metabolic pathways, including the TCA cycle and tyrosine metabolism, were analyzed. After treatment with the extract, 8, 12 and 9 possible biomarkers could be detected in the high-, medium- and low-dose groups, respectively. Key biomarkers of RF, identified using metabolomics, were most affected by the medium dose. A. mongolica BUT extract displays a protective effect on RF in rats and should be investigated as a candidate drug for the treatment of the disease.