Hasil untuk "Industry"

A. Young

T. James, A. Lucas, J. Harris

Tamara Machinjili, Chikondi Maluwa, Chawanluk Raungsri et al.

Oxidative stress contributes significantly to chronic disease burden, necessitating identification of accessible dietary antioxidant sources. Pigeon peas (<i>Cajanus cajan</i> L.) contain substantial bioactive compounds, yet most exist in bound forms with limited bioavailability. This study evaluated wild fermentation combined with systematic extraction optimization to enhance antioxidant recovery from pigeon peas. Seeds underwent wild fermentation in brine solution, followed by extraction under varying conditions (seven solvent systems, three temperatures, and three-time durations). Multiple complementary assays assessed antioxidant capacity (total phenolic content, DPPH radical scavenging, ferric reducing power, and ABTS activity). Fermentation substantially improved antioxidant properties across all parameters, with particularly pronounced effects on radical scavenging activities. Extraction optimization identified 70% methanol at 40 °C for 24 h as optimal, demonstrating marked improvements over conventional protocols. Strong intercorrelations among assays confirmed coordinated enhancement of multiple antioxidant mechanisms rather than isolated changes. The findings demonstrate that both biotechnological processing and analytical methodology critically influence antioxidant characterization in pigeon peas. This integrated approach offers practical guidance for developing antioxidant-rich functional foods, particularly relevant for resource-limited settings where pigeon peas serve as dietary staples. The study establishes foundation for translating fermentation technology into nutritional interventions, though further research addressing bioavailability, microbiological characterization, and bioactive compound identification remains essential.

Olena Stryzhak, Volodymyr Yermachenko, L’uboš Cibák et al.

The study focuses on the challenges that the gig-economy brings to the labour market. Digitalisation is transforming the nature of labour relations, and the ratio of the employed to the self-employed is changing. By expanding the scope of digital technology and the use of digital platforms, changes are affecting all areas, including the tourism industry. The article assesses the relationship between tourism development, digitalisation, and self-employment indicators. The study covers 112 countries for 2021. The paper uses the Travel & Tourism Development Index, Network Readiness Index and the World Bank’s self-employment indicator. The analysis showed that the features of the relationship between tourism development, digitalisation and self-employment vary across the three groups of countries identified as a result of cluster analysis. The study found that there is a positive relationship between the level of tourism sector development and the level of the country’s digitalisation. The relationship between the level of self-employment and tourism development is variable across groups. The relationship between self-employment and digitalisation was confirmed only for the sample as a whole.

Martyna Zemlik, Beata Białobrzeska, Mateusz Stachowicz et al.

As a result of welding processes in boron-alloyed martensitic armor steels, unfavorable microstructural changes occur, leading to a significant reduction in the mechanical properties of both the weld metal and the base material. The dendritic structure of the weld metal and the partial tempering in the heat-affected zone contribute to the decreased durability of structural components, thereby deteriorating their performance. This issue is particularly important since such steels are widely used not only in the defense industry but also in the mining, construction, transportation, and metallurgical sectors, where they operate under conditions of intensive abrasive wear. For this reason, the authors attempted to improve the mechanical properties of welded joints of boron-alloyed martensitic armor steel (with a nominal hardness of 500 HBW) through post-weld heat treatment. The welded joint was evaluated based on metallographic examinations using light microscopy and scanning electron microscopy, as well as abrasive wear tests carried out on a T-07 tribotester. The conducted investigations demonstrated that, under loose abrasive conditions (using electrofused alumina), heat treatment increased the wear resistance of the joints by 55% compared to the as-welded condition. The obtained results were compared with selected grades of Hardox steel commonly used in industrial applications.

Ho Thi Ngoc Tram, Pham Van Thinh, Truong Ngoc Minh et al.

Gamma-aminobutyric acid (GABA) is a biologically active amino acid with numerous health benefits. This study aimed to optimize the production of GABA-enriched Mang Buk brown rice through fermentation with various Lactobacillus strains. The impact of incubation temperature, duration, pH levels, and Lactobacillus concentration on GABA content were systematically investigated. The results revealed that in the presence of Lactobacillus brevis under specific conditions, GABA concentrations significantly increased to 8.16 mg/100gDW. Additionally, the condition of 35 °C and 30h of incubation consistently resulted in the highest GABA content across different Lactobacillus strains. However, the increase in the concentration of Lactobacillus involved in the fermentation process led to a rapid decline in GABA content. Simultaneously, the most suitable operating environment for yeast was determined to be at a pH range of 5–6. After fermentation under optimal conditions, the GABA content reached 24.01 mg/100g DW, representing a 141.24-fold increase compared to the initial content. These results were validated using the high-performance liquid chromatography (HPLC) method. These findings highlight the potential for tailored fermentation strategies to produce nutritionally rich GABA-enriched rice products with promising health benefits. The optimized process presents an opportunity for expanding the production of functional foods with enhanced nutritional value.

Amund Norland, Lasse Skare, Ole Jakob Viken et al.

The global energy transition toward net-zero emissions by 2050 is expected to increase the share of variable renewable energy sources (VRES) in the energy mix. As a result, industrial actors will encounter more complex market conditions, characterized by volatile electricity prices, rising carbon costs, and stricter regulations. This situation calls for the industry to capitalize on opportunities in both spot-price arbitrage and reserve market participation, while also meeting future regulatory demands. This paper presents a multi-stage optimization framework that supports investment decisions in flexible assets and enables reserve market participation by delivering ancillary services. The framework incorporates investment decisions, spot- and reserve-market bidding, and real-time operation. Uncertainty in market prices and operational conditions is handled through a nodal formulation. A case study of a large industrial site in Norway is performed, comparing the investment decisions with future technology- and carbon pricing scenarios under varying market conditions.

S. Shen, Z. Lin, W. Liu et al.

Industrial dashboards, commonly deployed by organizations such as enterprises and governments, are increasingly crucial in data communication and decision-making support across various domains. Designing an industrial dashboard prototype is particularly challenging due to its visual complexity, which can include data visualization, layout configuration, embellishments, and animations. Additionally, in real-world industrial settings, designers often encounter numerous constraints. For instance, when companies negotiate collaborations with clients and determine design plans, they typically need to demo design prototypes and iterate on them based on mock data quickly. Such a task is very common and crucial during the ideation stage, as it not only helps save developmental costs but also avoids data-related issues such as lengthy data handover periods. However, existing authoring tools of dashboards are mostly not tailored to such prototyping needs, and motivated by these gaps, we propose DashChat, an interactive system that leverages large language models (LLMs) to generate industrial dashboard design prototypes from natural language. We collaborated closely with designers from the industry and derived the requirements based on their practical experience. First, by analyzing 114 high-quality industrial dashboards, we summarized their common design patterns and inject the identified ones into LLMs as reference. Next, we built a multi-agent pipeline powered by LLMs to understand textual requirements from users and generate practical, aesthetic prototypes. Besides, functionally distinct, parallel-operating agents are created to enable efficient generation. Then, we developed a user-friendly interface that supports text-based interaction for generating and modifying prototypes. Two user studies demonstrated that our system is both effective and efficient in supporting design prototyping.

Sotiris Michaelides, Daniel Eguiguren Chavez, Martin Henze

With the ongoing adoption of 5G for communication in industrial systems and critical infrastructure, the security of industrial UEs such as 5G-enabled industrial robots becomes an increasingly important topic. Most notably, to meet the stringent security requirements of industrial deployments, industrial UEs not only have to fully comply with the 5G specifications but also implement and use correctly secure communication protocols such as TLS. To ensure the security of industrial UEs, operators of industrial 5G networks rely on security testing before deploying new devices to their production networks. However, currently only isolated tests for individual security aspects of industrial UEs exist, severely hindering comprehensive testing. In this paper, we report on our ongoing efforts to alleviate this situation by creating an automated security testing framework for industrial UEs to comprehensively evaluate their security posture before deployment. With this framework, we aim to provide stakeholders with a fully automated-method to verify that higher-layer security protocols are correctly implemented, while simultaneously ensuring that the UE's protocol stack adheres to 3GPP specifications.

Hsi-Lung Hsieh, Ming-Chin Yu, Yu-Chia Chang et al.

Gastric inflammation-related disorders are commonly observed digestive system illnesses characterized by the activation of proinflammatory cytokines, particularly tumor necrosis factor-α (TNF-α). This results in the induction of cyclooxygenase-2 (COX-2)/prostaglandin E₂ (PEG₂) and matrix metallopeptidase-9 (MMP-9). These factors contribute to the pathogenesis of gastric inflammation disorders. We examined the preventive effects of <i>Lonicera japonica</i> Thunb. ethanol extract (Lj-EtOH) on gastric inflammation induced by TNF-α in normal human gastric mucosa epithelial cells (GES-1). The GES-1 cell line was used to establish a model that simulated the overexpression of COX-2/PGE₂ and MMP-9 proteins induced by TNF-α to examine the anti-inflammatory properties of Lj extracts. The results indicated that Lj-EtOH exhibits significant inhibitory effects on COX-2/PEG₂ and MMP-9 activity, attenuates cell migration, and provides protection against TNF-α-induced gastric inflammation. The protective effects of Lj-EtOH are associated with the modulation of COX-2/PEG₂ and MMP-9 through the activation of TNFR–ERK 1/2 signaling pathways as well as the involvement of c-Fos and nuclear factor kappa B (NF-κB) signaling pathways. Based on our findings, Lj-EtOH exhibits a preventive effect on human gastric epithelial cells. Consequently, it may represent a novel treatment for the management of gastric inflammation.

Łukiewska Katarzyna

The aim of the research is to determine the impact of innovations and Industry 4.0 solutions on the international competitiveness from the perspectives of representatives of food industry enterprises. The empirical layer used information collected on the basis of a survey using the CATI method conducted on a representative sample of representatives of food industry enterprises. Descriptive statistics, the Kruskal-Wallis test, Mann-Whitney test, multiple comparison test and box-plot plots were used to analyse the data. The study confirmed that implementing certain innovations and solutions, both intangible and tangible, is important for maintaining and improving competitiveness on the international market. This applies particularly innovative, modern ways of reaching the customer, developing innovative products, the use of IT systems and the use of innovative methods in advertising and promotion. The conclusions present direct implications for managers of food enterprises who formulate competitive strategies.

Al Ansari Mohammed Saleh, Krishnakumari A., Saravanan M. et al.

This present research deals with optimizing machining parameters and surface quality improvement of Al2024/SiCp composites which are important materials used in the aerospace industry. The optimal quartet of factors was investigated to achieve the best outcomes using Taguchi design approach and includes cutting speed of 105 m/min, feed rate of 0.15 mm/rev, and depth of cut of 0.35 mm with a minimal level of roughness of 0.9 μm. An ANN model has been trained and validated, and a high level of predictive accuracy with an overall accuracy of 100% after 195 epochs has been achieved. The results indicated that systematic experimentation and the application of advanced modeling approaches, including the beneficial configuration of parameters and validated ANN model, can help to achieve a superior surface quality meeting the requirements of the aerospace industry. As a result, manufacturers can benefit from the proposed solutions to optimize their production practices, enhance the performance of components, and contribute to the field of aerospace engineering.

Vitek Mercedes, Matjaž Mirjam Gosenca

The principal function of skin is to form an effective barrier between the human body and its environment. Impaired barrier function represents a precondition for the development of skin diseases such as atopic dermatitis (AD), which is the most common inflammatory skin disease characterized by skin barrier dysfunction. AD significantly affects patients’ quality of life, thus, there is a growing interest in the development of novel delivery systems that would improve therapeutic outcomes. Herein, eight novel lyotropic liquid crystals (LCCs) were investigated for the first time in a double-blind, interventional, before-after, single-group trial with healthy adult subjects and a twice-daily application regimen. LCCs consisted of constituents with skin regenerative properties and exhibited lamellar micro-structure, especially suitable for dermal application. The short- and long-term effects of LCCs on TEWL, SC hydration, erythema index, melanin index, and tolerability were determined and compared with baseline. LCCs with the highest oil content and lecithin/Tween 80 mixture stood out by providing a remarkable 2-fold reduction in TEWL values and showing the most distinctive decrease in skin erythema levels in both the short- and long-term exposure. Therefore, they exhibit great potential for clinical use as novel delivery systems for AD treatment, capable of repairing skin barrier function.

Erika Džajić Uršič, Tamara Besednjak Valič

The paper addresses a manifestation of University-Industry collaboration - the Technology Transfer Offices (TTO). The University-Industry collaboration is relevant in the age of Open innovation, and TTO serves as the meeting point of two worlds. In this context, we are interested in how three specific cases of TTO operating in three distinct innovation ecosystems understand their role and how they perceive their strengths and weaknesses. The reader of the presented research will get an insight into three innovation ecosystems, each particular in its regard, and will learn that despite the differences among countries and cultures, the questions the TTO are struggling with are less diverse. A qualitative empirical study in three countries included focus group participants and expert representatives of academic-business technology transfer actors. To sum up, the respected countries need to carefully tailor innovation policies and explore the benefits of the TTO in boosting the commercialisation of products developed at universities.

Qiaolin Qin, Heng Li, Ettore Merlo

Data quality is vital for user experience in products reliant on data. As solutions for data quality problems, researchers have developed various taxonomies for different types of issues. However, although some of the existing taxonomies are near-comprehensive, the over-complexity has limited their actionability in data issue solution development. Hence, recent researchers issued new sets of data issue categories that are more concise for better usability. Although more concise, modern data issue labeling's over-catering to the solution systems may sometimes cause the taxonomy to be not mutually exclusive. Consequently, different categories sometimes overlap in determining the issue types, or the same categories share different definitions across research. This hinders solution development and confounds issue detection. Therefore, based on observations from a literature review and feedback from our industry partner, we propose a comprehensive taxonomy of data quality issues from two distinct dimensions: the attribute dimension represents the intrinsic characteristics and the outcome dimension that indicates the manifestation of the issues. With the categories redefined, we labeled the reported data issues in our industry partner's data warehouse. The labeled issues provide us with a general idea of the distributions of each type of problem and which types of issues require the most effort and care to deal with. Our work aims to address a widely generalizable taxonomy rule in modern data quality issue engineering and helps practitioners and researchers understand their data issues and estimate the efforts required for issue fixing.

Neave O'Clery, Juan Chaparro, Andres Gomez-Lievano et al.

What drives formal employment creation in developing cities? We find that larger cities, home to an abundant set of complex industries, employ a larger share of their working age population in formal jobs. We propose a hypothesis to explain this pattern, arguing that it is the organised nature of formal firms, whereby workers with complementary skills are coordinated in teams, that enables larger cities to create more formal employment. From this perspective, the growth of formal employment is dependent on the ability of a city to build on existing skills to enter new complex industries. To test our hypothesis, we construct a variable which captures the skill-proximity of cities' current industrial base to new complex industries, termed 'complexity potential'. Our main result is that complexity potential is robustly associated with subsequent growth of the formal employment rate in Colombian cities.

Francesco Sanfedino, Paolo Iannelli, Daniel Alazard et al.

To overcome the innovation gap of the Guidance, Navigation and Control (GNC) design process between research and industrial practice a benchmark of industrial relevance has been developed and is presented. This initiative is driven as well by the necessity to train future GNC engineers and the GNC space community on a set of identified complex problems. It allows to demonstrate the relevance of state-of-the-art modeling, control and analysis algorithms for future industrial adoption. The modeling philosophy for robust control synthesis, analysis including the control architecture that enables the simulation of the mission, i.e. the acquisition of a high pointing space mission, are provided.

R. Eckhoff

Slimani Ibtissam, Zaarane Abdelmoghit, Atouf Issam

In this work, a day and night time vehicle detection system for traffic surveillance is proposed. Our system is composed of two main processes, day time and night time processes. In the night time, the vehicles are detected based on their taillights and headlights. First of all, the 2D-DWT (Two Dimensional Discrete Wavelet Transform) and the background subtraction are applied to the input image. Then, the connected component technique is used to extract the region of interest. If it is the daytime, the connected component candidates are taken as potential vehicles after applying a pre-processing algorithm to improve the result. If it is the night-time, a filtering operation is used to keep only the bright white and red connected component candidates (which represent potential headlights and taillights, respectively). Finally, potential lamp sets are formed by grouping the extracted components on the basis of their positions, sizes, and colours. The potential extracted vehicles are classified as a vehicle or non-vehicle by using a pre-trained CNN (Convolutional Neural Network) classifier. The proposed system was tested and evaluated using different works from the literature. The experiments show that our proposed system has reached a high accuracy in terms of vehicle detection process whether in day or night time. The experiments were performed using four different videos and were implemented using the C++ language, which facilitates mathematical computation, and its OpenCV library, which is used to run the image processing algorithms used, as well as the TensorFlow library, which facilitates transfer learning of pre-trained CNN models.

Monica Sileo, Domenico Daniele Bloisi, Francesco Pierri

Robots allow industrial manufacturers to speed up production and to increase the product’s quality. This paper deals with the grasping of partially known industrial objects in an unstructured environment. The proposed approach consists of two main steps: (1) the generation of an object model, using multiple point clouds acquired by a depth camera from different points of view; (2) the alignment of the generated model with the current view of the object in order to detect the grasping pose. More specifically, the model is obtained by merging different point clouds with a registration procedure based on the iterative closest point (ICP) algorithm. Then, a grasping pose is placed on the model. Such a procedure only needs to be executed once, and it works even in the presence of objects only partially known or when a CAD model is not available. Finally, the current object view is aligned to the model and the final grasping pose is estimated. Quantitative experiments using a robot manipulator and three different real-world industrial objects were conducted to demonstrate the effectiveness of the proposed approach.