Hasil untuk "Industry"

E. Veselova

Domestic tourism is one of the fastest growing sectors of the Russian economy, and at the turn of 2014–15, it received a powerful stimulus for further development. The devaluation of the ruble and the turbulent political situation in certain countries where Russians traditionally spent their summer holidays forced the Russian government to pay attention to the domestic tourism industry. Will the industry be able to use this opportunity to make qualitative changes by improving and expanding services? In many ways, this depends on competent and well-planned government policy regarding the regulation and support of the industry.

E. Reid, M. Toffel

Joseph Moses Juran, A. Godfrey, Robert E. Hoogstoel et al.

James C. Williams, E. A. Starke

Tommaso Dorigo, Pietro Vischia, Shahzaib Abbas et al.

The optimization of large experiments in fundamental science, such as detectors for subnuclear physics at particle colliders, shares with the optimization of complex systems for industrial or societal applications the common issue of addressing the inter-relation between parameters describing the hardware used in data production and parameters used to analyse those data. While in many cases this coupling can be ignored -- when the problem can be successfully factored into simpler sub-tasks and the latter addressed serially -- there are situations in which that approach fails to converge to the absolute maximum of expected performance, as it results in a mis-alignment of the optimized hardware and software solutions. In this work we consider a few use cases of interest in fundamental science collected primarily from particle physics and related areas, and a pot-pourri of industrial and societal applications where the matter is similarly of relevance. We discuss the emergence of strong hardware-software coupling in some of those systems, as well as co-design procedures that may be deployed to identify the global maximum of their relevant utility functions. We observe how numerous opportunities exist to advance methods and tools for hardware-software co-design optimization, bridging fundamental science and industry through application- and challenge-driven projects, and shaping the future of scientific experiments and industrial systems.

Martin Bellmann, Berhane Darsene Dimd, Anne‐Karin Søiland et al.

In the ICARUS project, European partners collaborate to develop and scale innovative technologies for recovering and refining secondary raw materials from silicon photovoltaic (PV) manufacturing. The production of photovoltaic modules generates significant quantities of waste, particularly silicon kerf, graphite, and silica residues from ingot and wafer manufacturing. ICARUS aims to transform these waste streams into high‐value secondary materials suitable for reintegration into the PV value chain and other industrial applications. Four industrial pilot‐scale processes are developed, targeting the purification and reuse of these materials. Results from the pilots demonstrate both the technical feasibility and economic potential of substituting these recovered materials for virgin and critical raw materials. This work provides a viable pathway toward a more resource‐efficient and circular PV manufacturing industry.

I. Little, T. Scitovsky, M. Scott

Hayate Iso, Pouya Pezeshkpour, Nikita Bhutani et al.

Large Language Models (LLMs) offer the potential to automate hiring by matching job descriptions with candidate resumes, streamlining recruitment processes, and reducing operational costs. However, biases inherent in these models may lead to unfair hiring practices, reinforcing societal prejudices and undermining workplace diversity. This study examines the performance and fairness of LLMs in job-resume matching tasks within the English language and U.S. context. It evaluates how factors such as gender, race, and educational background influence model decisions, providing critical insights into the fairness and reliability of LLMs in HR applications. Our findings indicate that while recent models have reduced biases related to explicit attributes like gender and race, implicit biases concerning educational background remain significant. These results highlight the need for ongoing evaluation and the development of advanced bias mitigation strategies to ensure equitable hiring practices when using LLMs in industry settings.

Chetan Arora, Fanyu Wang, Chakkrit Tantithamthavorn et al.

Requirements engineering (RE) in the space industry is inherently complex, demanding high precision, alignment with rigorous standards, and adaptability to mission-specific constraints. Smaller space organisations and new entrants often struggle to derive actionable requirements from extensive, unstructured documents such as mission briefs, interface specifications, and regulatory standards. In this innovation opportunity paper, we explore the potential of Retrieval-Augmented Generation (RAG) models to support and (semi-)automate requirements generation in the space domain. We present a modular, AI-driven approach that preprocesses raw space mission documents, classifies them into semantically meaningful categories, retrieves contextually relevant content from domain standards, and synthesises draft requirements using large language models (LLMs). We apply the approach to a real-world mission document from the space domain to demonstrate feasibility and assess early outcomes in collaboration with our industry partner, Starbound Space Solutions. Our preliminary results indicate that the approach can reduce manual effort, improve coverage of relevant requirements, and support lightweight compliance alignment. We outline a roadmap toward broader integration of AI in RE workflows, intending to lower barriers for smaller organisations to participate in large-scale, safety-critical missions.

Chenxi Li, Chenyang Tang, Xiqian Liu et al.

ABSTRACT Membrane-associated RING-CH-type finger (MARCH) proteins, a class of E3 ubiquitin ligases, have been reported to be involved in the infection of multiple viruses and the regulation of type I interferon (IFN) production. However, the specific role and mechanisms by which MARCH proteins influence Japanese encephalitis virus (JEV) infection remain poorly understood. Here, we systematically investigate the functional relevance of MARCH proteins in JEV replication by examining the effects of siRNA-mediated knockdown of MARCHs on viral infection. We identified MARCH5 as a positive regulator of JEV replication. The knockout of MARCH5 dramatically reduced viral yields, whereas its overexpression significantly enhanced JEV replication. Mechanistically, MARCH5 specifically interacts with the JEV envelope (E) protein and promotes its K27-linked polyubiquitination at the lysine (K) residues 136 and 166. This ubiquitination enhances viral attachment to permissive cells. Substituting these lysine residues with arginine (R) attenuated JEV replication in vitro and reduced viral virulence in vivo. Furthermore, JEV infection upregulated the expression of MARCH5. We also discovered that MARCH5 degrades mitochondrial antiviral-signaling protein (MAVS) through the ubiquitin-proteasome pathway by catalyzing its K48-linked ubiquitination, thereby inhibiting type I IFN production in JEV-infected cells. This suppression of type I IFN further facilitates JEV infection. In conclusion, these findings disclosed a novel role of MARCH5 in positively regulating JEV infection and revealed an important mechanism employed by MARCH5 to regulate the innate immune response.IMPORTANCEJEV is the leading cause of viral encephalitis in many countries of Asia with an estimated 100,000 clinical human cases and causes economic loss to the swine industry. Until now, there is no clinically approved antiviral for the treatment of JEV infection. Although vaccination prophylaxis is widely regarded as the most effective strategy for preventing Japanese encephalitis (JE), the incidence of JE cases continues to rise. Thus, a deeper understanding of virus-host interaction will enrich our knowledge of the mechanisms underlying JEV infection and identify novel targets for the development of next-generation live-attenuated vaccines and antiviral therapies. To the best of our knowledge, this study is the first to identify MARCH5 as a pro-viral host factor that facilitates JEV infection. We elucidated two distinct mechanisms by which MARCH5 promotes JEV infection. First, MARCH5 interacts with viral E protein and mediates the K27-linked ubiquitination of E protein at the K136 and K166 residues to facilitate efficient viral attachment. Furthermore, double mutations of K136R-K166R attenuated JEV infection in vitro and reduced viral virulence in mice. Second, the upregulated expression of MARCH5 induced by JEV infection further suppresses the RIG-I-like receptor (RLR) signaling pathway to benefit viral infection. MARCH5 downregulates type I IFN production by conjugating the K48-linked polyubiquitin at the K286 of MAVS, which leads to MAVS degradation through the ubiquitin-proteasome pathway. In summary, this study provides novel insights into the role played by MARCH proteins in JEV infection and identifies specific ubiquitination sites on JEV E protein that could be targeted for viral attenuation and the development of antiviral therapeutics.

Nicola Zanini, Alessio Suman, Mattia Piovan et al.

Non-Newtonian fluids are widespread in industry, e.g., biomedical, food, and oil and gas, and their rheology plays a fundamental role in choosing the processing parameters. Centrifugal pumps are widely employed to ensure the displacement of a huge amount of fluids due to their robustness and reliability. Since the pump performance is usually provided by manufacturers only for water, the selection of a proper pump to handle non-Newtonian fluids may prove very tricky. On-field experiences in pump operations with non-Newtonian slurries report severe head and efficiency drops, especially in part-load operations, whose causes are still not fully understood. Several models are found in the literature to predict the performance of centrifugal pumps with this type of fluids, but a lack of reliability and generality emerges. In this work, an extensive experimental campaign is carried out with an on-purpose test bench to investigate the effect of non-Newtonian shear-thinning fluids on the performance of a small commercial centrifugal pump. A dedicated experimental campaign is conducted to study the causes of performance drops. The results allow to establish a relationship between head and efficiency drops with solid content in the mixture. Sudden performance drops and unstable operating points are detected in part-load operations and the most severe drops are detected with the higher kaolin content in the mixture. Performance drop investigation allows to ascribe performance drop to gas-locking phenomena. Finally, a critical analysis is proposed to relate the resulting performance with both fluids’ rheology and the gas fraction trapped in the fluid. The results here presented can be useful for future numerical validation and predicting performance models.

Valsaraj PAYINI, Giridhar KAMATH, Vasanth V.P. KAMATH et al.

Purpose – With destinations facing ever-increasing challenges due to increasing tourism activities, understanding residents’ perspectives is critical for fostering a harmonious and sustainable coexistence. Therefore, this study systematically reviews the studies on residents’ attitudes toward blue tourism. Methodology/Design/Approach – Based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), this study employs a TCM framework to identify, assess, and synthesize relevant literature, theories, contexts, and methods the researchers adopt.Findings – The findings revealed blue tourism’s increasing attention and significance,highlighting its growing importance in the global tourism discourse. The systematic review of 81 articles suggests that social exchange theory was the most popular and widely usedto measure residents’ attitudes. Further, findings show that 50% were conducted on islands, followed by coastal tourism (25%). Other contexts were cruise tourism (17.5), marine tourism (2.5%), boat tourism (1.25%), and seaside destination (1.25%). Quantitative research designs account for more than two-thirds of the total (80%), with qualitative papers accounting for 15% and mixed method papers for the remainder (5%).Originality of the research – This review article identifies gaps in the current literature and recommends future research directions to enhance our understanding of resident attitudes and support for blue tourism, ultimately developing a resilient and sustainable knowledge landscape.

Kexin XIANG, Bo ZHANG, Han ZHAO et al.

To investigate the migration mechanisms of water in different states and sugars in peach slices during drying, samples were unpretreated or pretreated with either 20% sucrose or 20% oligosaccharide solution impregnation. Each group was then processed using vacuum freeze-drying (FD) or vacuum freeze-drying combined with puffing drying (FP). The drying kinetics, water migration patterns, and sugar transport dynamics were systematically analyzed throughout the process. Results demonstrated that the T2 relaxation curve exhibited a leftward shift during drying, indicating progressive conversion of free water into immobilized and bound water in peach slices. Concurrently, intra-tissue moisture migrated outward, as evidenced by the decreasing free water content and evolving water state distribution. Compared to FD, FP significantly reduced drying duration while enhancing moisture removal efficiency. During FP treatment, initial moisture evaporation occurred preferentially from surface regions, while intracellular immobilized and bound water fractions progressively increased. During FP stage, accelerated accumulation of immobilized water in the peripheral tissue layers enhanced drying kinetics. In sugar-impregnated FP samples, a steep concentration gradient emerged: Sugar content exhibited a sharp decline in peripheral regions versus gradual depletion in medial zones, confirming unidirectional outward sugar migration. The sugar in the internal field of the peach slices presented with little migration change. In this study, migration from internal to external both happened to the water and sugar in the peach slices during the drying process, which was more prominent in the outer field of the peach slices and less prominent in the internal field. These findings can provide theoretical supports for the improvement of drying efficiency and quality control of the dried fruits and vegetables.

Karthik Muthineni, Alexander Artemenko, Josep Vidal et al.

The fifth generation (5G) mobile communication technology integrates communication, positioning, and mapping functionalities as an in-built feature. This has drawn significant attention from industries owing to the capability of replacing the traditional wireless technologies used in industries with 5G infrastructure that can be used for both connectivity and positioning. To this end, we identify the Automated Guided Vehicle (AGV) as a primary use case to benefit from the 5G functionalities. Given that there have been various works focusing on 5G positioning, it is necessary to analyze the existing works about their applicability with AGVs in industrial environments and provide insights to future research. In this paper, we present state of the art in 5G-based positioning, with a focus on key features, such as Millimeter Wave (mmWave) system, Massive Multiple Input Multiple Output (MIMO), Ultra-Dense Network (UDN), Device-to-Device (D2D) communication, and Reconfigurable Intelligent Surface (RIS). Moreover, we present the shortcomings in the current state of the art. Additionally, we propose enhanced techniques that can complement the accuracy of 5G-based positioning in controlled industrial environments.

Ahmed Alhamadah, Muntasir Mamun, Henry Harms et al.

The onset of Industry 4.0 is rapidly transforming the manufacturing world through the integration of cloud computing, machine learning (ML), artificial intelligence (AI), and universal network connectivity, resulting in performance optimization and increase productivity. Digital Twins (DT) are one such transformational technology that leverages software systems to replicate physical process behavior, representing the physical process in a digital environment. This paper aims to explore the use of photogrammetry (which is the process of reconstructing physical objects into virtual 3D models using photographs) and 3D Scanning techniques to create accurate visual representation of the 'Physical Process', to interact with the ML/AI based behavior models. To achieve this, we have used a readily available consumer device, the iPhone 15 Pro, which features stereo vision capabilities, to capture the depth of an Industry 4.0 system. By processing these images using 3D scanning tools, we created a raw 3D model for 3D modeling and rendering software for the creation of a DT model. The paper highlights the reliability of this method by measuring the error rate in between the ground truth (measurements done manually using a tape measure) and the final 3D model created using this method. The overall mean error is 4.97\% and the overall standard deviation error is 5.54\% between the ground truth measurements and their photogrammetry counterparts. The results from this work indicate that photogrammetry using consumer-grade devices can be an efficient and cost-efficient approach to creating DTs for smart manufacturing, while the approaches flexibility allows for iterative improvements of the models over time.

Friedrich Niemann, Christopher Reining

In a social networked industry, the focus is on collaboration between humans and technology. Communication is the basic prerequisite for synergetic collaboration between all players. It includes non-verbal as well as verbal interactions. To enable non-verbal interaction, machines must be able to detect and understand human movements. This article presents the ongoing fundamental research on the analysis of human movements using sensor-based activity recognition and identifies potential for a transfer to industrial applications. The focus is on the practical feasibility of activity recognition by adding further data streams such as the position data of logistical objects and tools, meaning the context in which a certain activity is carried out. -- In der Social Networked Industry steht die Zusammenarbeit von Mensch und Technik im Vordergrund. Grundvoraussetzung für eine synergetische Zusammenarbeit aller Akteure ist die Kommunikation, welche neben verbalen auch nonverbale Interaktionen umfasst. Um eine nonverbale Interaktion zu ermöglichen, müssen Maschinen in der Lage sein, menschliche Bewegungen zu erfassen und zu verstehen. Dieser Beitrag stellt die laufende Grundlagenforschung zur Analyse menschlicher Bewegungen mittels sensorgestützter Aktivitätserkennung vor und zeigt Anknüpfungspunkte für einen Transfer in industrielle Anwendungen. Im Fokus steht die Praxistauglichkeit der Aktivitätserkennung durch die Hinzunahme weiterer Datenströme wie beispielsweise den Positionsdaten logistischer Objekte und Hilfsmitteln, d. h. dem Kontext, in dem eine gewisse Aktivität ausgeführt wird.

Aitor Toichoa Eyam, Jose L. Martinez Lastra

Photo-realistic avatar is a modern term referring to the digital asset that represents a human in computer graphic advanced systems such as video games and simulation tools. These avatars utilize the advances in graphic technologies in both software and hardware aspects. While photo-realistic avatars are increasingly used in industrial simulations, representing human factors such as human workers psychophysiological states, remains a challenge. This article contributes to resolving this issue by introducing the novel concept of MetaStates which are the digitization and representation of the psychophysiological states of a human worker in the digital world. The MetaStates influence the physical representation and performance of a digital human worker while performing a task. To demonstrate this concept, this study presents the development of a photo-realistic avatar enhanced with multi-level graphical representations of psychophysiological states relevant to Industry 5.0. This approach represents a major step forward in the use of digital humans for industrial simulations, allowing companies to better leverage the benefits of the Industrial Metaverse in their daily operations and simulations while keeping human workers at the center of the system.

Kultida Kaewpetch, Saowapa Yolsuriyan, Terd Disayathanoowat et al.

Gelatin is commonly used as a gelling agent in gummy candy. Honey and bee products are valuable and rich sources of biologically active substances. In this study, the influence of gelatin and propolis extract on honey gummy jelly (HGJ) properties was investigated. Honey (28–32%), xylitol (13–17%), and gelatin (6–10%) were utilized to develop HGJ products by mixture design methodology. Subsequently, the optimized formulation of HGJ was fortified with 1% and 2% propolis extract to enhance its phytochemicals and antimicrobial activities. The variation in the ingredients significantly affected the physicochemical, textural, and sensory properties of the HGJ. The optimized HGJ formulation consisted of honey (32%), xylitol (14%), and gelatin (7%) and exhibited 13.35 × 10³ g.force of hardness, −0.56 × 10³ g.sec of adhesiveness, 11.96 × 10³ N.mm of gumminess, 0.58 of resilience, and a moderate acceptance score (6.7–7.5). The fortification of HGJ with propolis extract significantly increased its phytochemical properties. Furthermore, the incorporation of propolis extract (2%) into the HGJ was able to significantly inhibit the growth of Gram-positive (<i>Streptococcus mutans</i> and <i>Staphylococcus aureus</i>) and Gram-negative (<i>Escherichia coli</i>) bacteria. The mixture of gelatin, xylitol, honey, and propolis extract can be utilized to develop a healthy gummy product with acceptable physicochemical, textural, and sensory qualities.

Md Tahmid Rahman Laskar, Xue-Yong Fu, Cheng Chen et al.

This paper studies how to effectively build meeting summarization systems for real-world usage using large language models (LLMs). For this purpose, we conduct an extensive evaluation and comparison of various closed-source and open-source LLMs, namely, GPT-4, GPT- 3.5, PaLM-2, and LLaMA-2. Our findings reveal that most closed-source LLMs are generally better in terms of performance. However, much smaller open-source models like LLaMA- 2 (7B and 13B) could still achieve performance comparable to the large closed-source models even in zero-shot scenarios. Considering the privacy concerns of closed-source models for only being accessible via API, alongside the high cost associated with using fine-tuned versions of the closed-source models, the opensource models that can achieve competitive performance are more advantageous for industrial use. Balancing performance with associated costs and privacy concerns, the LLaMA-2-7B model looks more promising for industrial usage. In sum, this paper offers practical insights on using LLMs for real-world business meeting summarization, shedding light on the trade-offs between performance and cost.

Wenjun Peng, Guiyang Li, Yue Jiang et al.

In the realm of e-commerce search, the significance of semantic matching cannot be overstated, as it directly impacts both user experience and company revenue. Along this line, query rewriting, serving as an important technique to bridge the semantic gaps inherent in the semantic matching process, has attached wide attention from the industry and academia. However, existing query rewriting methods often struggle to effectively optimize long-tail queries and alleviate the phenomenon of "few-recall" caused by semantic gap. In this paper, we present BEQUE, a comprehensive framework that Bridges the sEmantic gap for long-tail QUEries. In detail, BEQUE comprises three stages: multi-instruction supervised fine tuning (SFT), offline feedback, and objective alignment. We first construct a rewriting dataset based on rejection sampling and auxiliary tasks mixing to fine-tune our large language model (LLM) in a supervised fashion. Subsequently, with the well-trained LLM, we employ beam search to generate multiple candidate rewrites, and feed them into Taobao offline system to obtain the partial order. Leveraging the partial order of rewrites, we introduce a contrastive learning method to highlight the distinctions between rewrites, and align the model with the Taobao online objectives. Offline experiments prove the effectiveness of our method in bridging semantic gap. Online A/B tests reveal that our method can significantly boost gross merchandise volume (GMV), number of transaction (#Trans) and unique visitor (UV) for long-tail queries. BEQUE has been deployed on Taobao, one of most popular online shopping platforms in China, since October 2023.