Benefits Of Technology Research Articles

How to mitigate the risks of deployment of artificial intelligence in medicine?

The aim of this study is to examine the risks associated with the use of artificial intelligence (AI) in medicine and to offer policy suggestions to reduce these risks and optimize the benefits of AI technology. AI is a multifaceted technology. If harnessed effectively, it has the capacity to significantly impact the future of humanity in the field of health, as well as in several other areas. However, the rapid spread of this technology also raises significant ethical, legal, and social issues. This study examines the potential dangers of AI integration in medicine by reviewing current scientific work and exploring strategies to mitigate these risks. Biases in data sets for AI systems can lead to inequities in health care. Educational data that is narrowly represented based on a demographic group can lead to biased results from AI systems for those who do not belong to that group. In addition, the concepts of explainability and accountability in AI systems could create challenges for healthcare professionals in understanding and evaluating AI-generated diagnoses or treatment recommendations. This could jeopardize patient safety and lead to the selection of inappropriate treatments. Ensuring the security of personal health information will be critical as AI systems become more widespread. Therefore, improving patient privacy and security protocols for AI systems is imperative. The report offers suggestions for reducing the risks associated with the increasing use of AI systems in the medical sector. These include increasing AI literacy, implementing a participatory society-in-the-loop management strategy, and creating ongoing education and auditing systems. Integrating ethical principles and cultural values into the design of AI systems can help reduce healthcare disparities and improve patient care. Implementing these recommendations will ensure the efficient and equitable use of AI systems in medicine, improve the quality of healthcare services, and ensure patient safety.

Does digital transformation promote global value chain upgrading? Evidence from Chinese manufacturing firms

Enterprises in developing countries previously entered the lower end of the global value chain (GVC) by leveraging low-cost production factors. Digital transformation offers these enterprises opportunities to move upstream in the GVC. While the literature highlights the benefits of digital technology in enhancing innovation, productivity, and resource utilization, comparatively limited exploration of the relationship between digital transformation and GVC upgrading has occurred. We utilize data on Chinese listed manufacturing firms from 2007 to 2016 to investigate the impact of digital transformation on their positioning in the GVC. The results show that digital transformation significantly enhances manufacturing firms' positions in the GVC, with skill-biased technical change playing a positive role. Our findings indicate the significance of digitalization and talent-management strategies for firms aiming to capture greater value in the GVC.

Pendidikan dan Teknologi: Optimalkan Pembelajaran di Era Digital

The digital era brings significant changes in various aspects of life, including education. This article explores the application of technology in education in the digital era, outlining the positive impacts, challenges faced, and strategic steps to optimize the benefits of technology in learning. The research method used is descriptive qualitative method through literature study from various sources of relevant journals and articles. The results show that technology improves accessibility, interactivity and personalization of learning. However, the access gap and curriculum adaptation are still the main challenges. Proposed solutions include subsidizing technology and internet devices, curriculum adaptation and professional training for teachers. With these measures, technology can be optimally utilized to improve the quality of education and provide a better learning experience for all students

Feasibility of Photovoltaic-Powered Hydrogen Production for Off-Site Refueling Stations in Iraqi Cities: A Techno-Economic Analysis

The study explores the feasibility of using a photovoltaic (PV) energy system to produce hydrogen for off-site hydrogen refueling stations (HRS) in three Iraqi cities (Karbala, Maysan, and Nineveh), focusing on a comprehensive system model consisting of a 558 MWp off-grid photovoltaic system, a 157.5 MWp proton exchange membrane (PEM) electrolyzer, a converter, and a hydrogen storage tank. Utilizing HOMER Pro software for system simulation and MATLAB, with consedaring 28 years from 2022 to 2050 life span, incorporating hourly weather data for 2022 to optimize system performance. The outcomes identify that the Karbala city as the most cost-effective for green hydrogen production, highlighting the economic benefits of PV technology, which presents the most economical option with a levelized energy cost of $5,010/GWh. The project is projected to produce 10.61 million kg of hydrogen annually at a production cost of $2.75/kg, with an overall project cost estimated at $372.77 million. The results are of strategic significance for Iraq transportation sector, supporting the development of a robust green hydrogen infrastructure for HRS. This infrastructure is expected to promote sustainable transportation practices and reduce reliance on fossil fuels, contributing significantly to the energy transition in Iraq. This techno-economic analysis provides a foundational assessment for stakeholders considering investments in renewable hydrogen production and infrastructure development.

Perspectives on microbial fuel cells cathode improvement for bioenergy generation

Studies have shown the unlimited benefits attributed to using microbial fuel cell (MFC) in our scientific societies for the conversion of organic waste materials into bio-energy with the aid of micro-organisms catalyzed anode and enzymatic-microbial influenced cathode via electrochemical reactions. However, numerous shortcomings calling for serious investigations have equally been identified despite the inestimable benefits of this clean energy technology. A major one is the need for MFC cathode improvement to enhance bioenergy generation. The current situations and challenges of using MFC together with loop holes that are needed to be filled by researchers were critically explained. Additionally, this paper examines the importance of microbial fuel cells (MFCs) as a source of renewable energy generation. The major focus was on techniques of improving the output of MFCs via its cathode. The addition of catalyst on cathode surface, upscale integration of MFCs and antifouling of gas diffusion electrodes used in MFCs were discussed. Relevant areas that can be considered for future research on improvement of MFCs cathode for long term operation were stated.

Effect of Blueberry Pomace Addition on Quality Attributes of Buttermilk-Based Fermented Drinks during Cold Storage.

The fruit and beverage industry faces challenges related to waste management and environmental pollution due to rapid industrial expansion. Fruit industry waste, such as blueberry pomace, holds the promise of enhancing gut health and providing valuable antioxidants. Concurrently, buttermilk, a prominent dairy product, offers nutritional and technological benefits but remains underutilized. This study aimed to evaluate the incorporation of blueberry pomace (0%, 2%, 4%, 6%, 8%, and 10%) into buttermilk at varying levels and assess its impact on the physicochemical, antioxidant, microbiological, and sensory characteristics of the buttermilk. Buttermilk samples were supplemented with different concentrations of blueberry pomace and subjected to analysis over a two-week storage period (4 ± 1 °C). The addition of blueberry pomace led to alterations in the pH, dry matter, water holding capacity, color parameters, total phenolic content, and antioxidant activity. Microbiological analysis revealed the absence of Enterobacteriaceae, yeast, or molds. Sensory evaluation indicated significant differences among samples, with the highest scores observed for the buttermilk supplemented with 2% and 4% blueberry pomace. Incorporating blueberry pomace improved the overall acceptability and sensory properties. This research highlights the potential of fruit industry by-products to enhance the functionality and health benefits of dairy products, which is a promising way to effectively utilize waste.

Post-Pandemic Trends in the Hospitality Industry

Purpose: The general objective of this study was to explore post-pandemic trends in the hospitality trends. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the post-pandemic trends in the hospitality trends. Preliminary empirical review revealed that the COVID-19 pandemic brought significant, lasting changes, with technology playing a crucial role in recovery through the adoption of contactless services. The shift towards domestic tourism and staycations provided a boost, supported by targeted local tourism campaigns. Sustainability emerged as a vital trend, with eco-friendly practices leading to higher occupancy rates and guest satisfaction. Flexibility in booking and cancellation policies restored traveler confidence, with adaptable and transparent communication being key to maintaining customer trust and loyalty. Unique Contribution to Theory, Practice and Policy: The Diffusion of Innovations Theory, Resource- Based View (RBV) and Expectancy Theory may be used to anchor future studies on post-pandemic trends in the hospitality trends. The study recommended several key areas for advancement. It emphasized the need for further research on the long-term impacts of technological innovations, suggesting that theories like the Diffusion of Innovations Theory be expanded to incorporate the effects of global crises. Practically, it advised sustained investment in digital infrastructure and training for staff to maximize the benefits of contactless technologies. Policy recommendations included government support for technological adoption, promotion of domestic tourism, and the prioritization of sustainability through green practices and supportive policies. Additionally, the study underscored the importance of maintaining flexible booking and cancellation policies to enhance traveler confidence and loyalty Keywords: Post-Pandemic Trends, Hospitality Industry, Technological Innovations, Contactless Technologies, Digital Infrastructure

Adoption and Impact of Cloud Computing in Small and Medium Enterprises A Systematic Review

This research aims to investigate the adoption and impact of cloud computing on Small and Medium Enterprises (SMEs) through a systematic approach. The research method involves analyzing a number of relevant and verified scientific articles, focusing on technology adoption, benefits gained, and challenges faced by SMEs in adopting cloud computing technology. The analysis results show that cloud computing adoption among SMEs has significantly increased in recent years, with benefits including operational efficiency, scalability, and data accessibility. However, there are some obstacles faced, including data security issues, resource limitations, and challenges in integrating with existing systems. In conclusion, although cloud computing adoption holds great potential benefits for SMEs, associated challenges need to be addressed with careful strategies.

What makes hospitality and tourism executives adopt information and communication technology: Antecedents and outcomes of ICT adoption

While previous hospitality and tourism research has concentrated on the benefits of information and communication technology (ICT) adoption from the perspectives of customers and suppliers, less attention has been placed on this stream of research from an upper echelons perspective. Furthermore, a limited number of studies have explored the relationship between ICT adoption and its financial implications. Using various theoretical foundations (i.e., upper echelons theory, industrial organization theory, resource-based view, and organizational learning theory), this study attempts to provide an overarching conceptual framework that can explain the antecedents and consequences of adopting ICT. More specifically, this paper delves into industry- and firm-specific conditions to better understand the relationship between a top management team’s (TMT) attributes and ICT adoption as well as the relationship between ICT adoption and its financial implications.

Priority-based Multi-feature Vector Model Using Convolution Neural Network for Biometric Authentication

Many societal institutions have increased standards for the efficacy and dependability of identification systems in response to the ever-increasing sophistication of computer technology. Biometric identity systems have mostly supplanted the usage of conventional key and smart card systems in highly secure industries. There has been a dramatic increase in the adoption of biometric authentication systems in the last decade. While many biometric systems have relied on exterior physiological traits like fingerprints, iris scans, palm prints, and faces, very few have investigated the possibility of using interior physiological traits as a biometric. It is challenging for the benefits of unimodal biometric technology to be realized in real-world applications because to its inherent constraints, such as the fact that it only proposes a single piece of information and that environmental factors can impact data verification. While passwords have served their purpose of authenticating users and controlling access, they have also revealed their weaknesses. Concerns about forging or impersonating authentication have prompted the proposal of various Artificial Intelligence (AI)-based solutions. A lot of people are interested in user authentication based on ECGs these days. Unlike other biometrics, an electrocardiogram verifies that the person is actual and alive, making it one of the most trustworthy advanced authentication methods. The P, Q, R, S, and T characteristic points of an ECG signal are responsible for its most salient features. Feature selection is a statistical method that can be implemented in a supervised or unsupervised setting via regression or classification. A Convolution Neural Network (CNN) is a type of neural network that extracts and feeds into another neural network, which then classifies those extracted features. The ECG signal serves as input to a feature extraction network. The neural network performs categorization based on the retrieved feature signals. To overcome the problem that the recognition accuracy of traditional ECG identification methods declines with the rise in the number of testing samples at various moments or throughout different heartbeat cycles, this research offers a multi feature reuse model. In this research, an Associated Priority-based Weighted Multi-Feature Vector model using Convolution Neural Network (APbWMFV-CNN) is proposed for ECG signal-based authentication. The proposed model selects the most appropriate features used for accurate biometric model. The comparison results represent that the proposed model performance in user detection is high.

Digital Virtual Simulation for Cultural Clothing Restoration: Case Study of Tang Dynasty Mural ‘Diplomatic Envoys’ from Crown Prince Zhang Huai’s Tomb

The advent of 3D virtual presentation technology for clothing has led to the gradual popularisation of digital virtual clothing in the modern fashion industry. However, there remains a gap between the application of this technology and the integration of cultural attributes in the field of digital communication of traditional cultural clothing. Consequently, the objective of this paper is to propose the establishment of a fusion system integrating archaeological research on traditional culture with emerging virtual presentation technology. This paper draws inspiration from the replicability and easy dissemination of digital products to combine cultural archaeology and digital technology. The aim is to provide ideas for the diversity of dissemination of cultural heritage. The research object is Diplomatic Envoys, a Chinese mural painting of the Tang Dynasty that depicts friendly exchanges between countries. The research is divided into two research stages. A CLO3D software-based digital restoration test was conducted to reproduce the costumes of officials and foreign envoys depicted in the Tang Dynasty mural. The FAHP model was employed to verify the accuracy of the restoration results. The experiment demonstrated that the digitally reconstructed clothing exhibited a high degree of similarity to the unearthed mural figure clothing object. Furthermore, the restoration result passed the credibility verification, resulting in a ‘credible’ outcome. The application of digital virtual simulation clothing restoration methods offers two key advantages. Firstly, in comparison with traditional clothing restoration methods, digital restoration enables the rapid assessment of the resulting clothing effect, thereby reducing the likelihood of secondary damage to cultural relics due to manual errors. Secondly, the benefits of digital technology facilitate the convenient storage, replication, and dissemination of clothing data information. Data can not only be extended to online exhibition halls but also to game animation, clothing production, and other fields for the purposes of creative redesign and information dissemination. Furthermore, these benefits can penetrate the education industry to disseminate information to the public through all-round display models and explanations.

EXPLORING THE CONFLUENCE OF BIG DATA, ARTIFICIAL INTELLIGENCE, AND DIGITAL MARKETING ANALYTICS: A COMPREHENSIVE REVIEW

The convergence of big data, artificial intelligence (AI), and digital marketing analytics is revolutionizing the field of digital marketing. This paper explores the transformative effects of these technologies on marketing strategies, focusing on their capacity to enhance decision-making, optimize marketing operations, and personalize customer interactions. By integrating big data and AI with digital marketing analytics, businesses can unlock valuable insights from vast datasets, facilitating more targeted and effective marketing campaigns. This research reviews current literature and employs case studies to illustrate this technological integration's practical applications and benefits in various marketing contexts. The findings highlight a significant shift towards data-driven and AI-enhanced marketing approaches, which are proving to be critical in achieving competitive advantage and customer satisfaction in the digital age.

Research on the Application of Blockchain Technology in Global Supply Chain Information Sharing — Enhancing Transparency and Collaboration

The globalization of trade and commerce has introduced complexities in supply chain management (SCM), necessitating effective information sharing for enhanced transparency and collaboration. Traditional SCM systems, characterized by centralized databases and intermediaries, often lead to inefficiencies and information silos. This paper explores the application of blockchain technology in SCM, highlighting its potential to address these challenges by providing a decentralized, immutable, and secure platform for information sharing. Through a comprehensive review of literature and analysis of case studies in the food, pharmaceutical, and retail industries, this study demonstrates how blockchain enhances transparency, traceability, and efficiency. The findings indicate significant improvements in key performance metrics such as transparency, cost reduction, and risk management post-blockchain implementation. However, the study also identifies several challenges, including scalability, integration with existing systems, and regulatory compliance. The practical implications for supply chain managers and businesses are discussed, emphasizing the need for collaborative efforts and supportive regulatory frameworks to fully realize the benefits of blockchain technology in SCM.

Assessing the outcome of mobility assistive technology (OMAT) in daily living: results in an Italian sample

Background Despite the widely recognised benefits of assistive technology (AT) in everyday life, AT abandonment rate due to unmet user needs or expectations is high. World Health Organisation emphasised the importance of evaluating AT outcomes as a top research priority, but comprehensive approaches to assess users’ needs and AT impact are still limited. Aim The research study OMAT (outcomes of mobility assistive technology in rehabilitation pathways) aimed to evaluate the feasibility of a new AT assessment protocol for mobility AT, spanning from an initial evaluation of users’ and relatives’ needs to a follow-up after real-world use of assistive products (APs). Material and Methods An observational single-arm study involving 95 AT users was conducted at Fondazione Don Gnocchi (Milan, Italy). The OMAT protocol encompassed baseline assessment, user training, prescription, authorisation, delivery and follow-up. The evaluations blend standard clinical evaluations with validated AT outcome measures for perceived effectiveness of AT, user satisfaction and changes in quality of life. Results The feasibility of OMAT in clinical settings was established, with 67 user participants completing the protocol. Data showed positive impacts on user satisfaction and perceived AT effectiveness by all participants (users and relatives), with an improvement in the perceived degree of severity in problems. Quality of life outcomes, however, showed mixed results across various domains. Conclusion OMAT is a feasible protocol for assessing mobility APs in clinical settings, underscoring the importance of involving users in the prescription process and the need for comprehensive AT monitoring mechanisms. This approach could enhance AT service delivery, exceeding the current standards of the Italian-National-Health-Service. Implication for rehabilitation Personalise assistive technology (AT) based on user needs for higher effectiveness. Include structured follow-up in rehabilitation for better AT outcomes. Enhance patient involvement in AT selection to improve use. Focus on AT’s role in enhancing overall quality of life. Use validated tools for AT impact assessment and monitoring.

Effect of a novel artificial intelligence (AI) –enabled multi-trial matching system on patient matching using real-world data.

e13501 Background: Currently clinical research teams at sites are manually matching patients to clinical trials, where each trial in our experience takes at least 25 minutes using highly trained dedicated resources. This limits cancer patients’ clinical trial options and enrollment rates. Moreover, the lack of NGS testing results may also negatively impact matching to clinical trials in targeted therapies. PCC, a consortium of 7 of the Top 20 Pharmaceutical companies, collaborated with Massive Bio to demonstrate how cancer patients’ matching rates can be increased if patients are prescreened to multiple trials simultaneously (i.e. multi-trial matching) instead of a single trial and NGS testing results are integrated into the matching process. Methods: Massive Bio has developed a first-in-class AI-enabled matching system to extract 180 structured clinical data from medical records using computer vision and natural language processing and a recommendation system for matching patients to parameterized inclusion/exclusion (I/E) criteria from over 14,000 actively recruiting interventional clinical trials.Our analysis included a sample of 5,707 cancer patients from the Massive Bio Synergy AI real-world database, corresponding to tumor types in 23 selected trials. We developed a decision-tree algorithm for retrospective matching analysis using modeled I/E criteria and compared these results to the theoretical matching rate (TMR) for each trial based on tumor type and biomarker prevalence, extent of disease at diagnosis and prior treatment history. Results: 690 patients matched to at least one trial were matched to 1.82 times more trials in multi-trial matching; 2.10 times more potential matches were obtained with hypothetical 100% NGS testing; and 99.9% matching time was reduced with AI enabled system; key findings are summarized (Table). Conclusions: This study confirms the underutilization of NGS technology and substantial benefits of AI and NGS in cancer trial matching. Their use resulted in a nearly two-fold increase in potential patient eligibility for trials, and an unprecedented reduction in manual effort, underscoring a significant advancement in the efficiency of the clinical trial matching process. A prospective country-wide analysis is under consideration. [Table: see text]

Impact of Perceived ease of use and Perceived usefulness on Behavioral intention to use Blockchain in Food Supply Firms: The Mediating role Employee Attitude

This study aims to investigate the impact of perceived ease of use and perceived usefulness on behavioral intention to use blockchain in food supply and the mediating role of employee attitude—primary data gathered with the help of adopted questionnaire through face-to-face survey from food supply firms. A total of 229 cases were considered for data analysis in order to achieve the research objectives of the present study—gathered data analyzed via structural equation modelling (SEM) in SmartPLS version 3. Findings confirmed the direct effect of perceived ease of use, perceived usefulness, and employee attitude on behavioral intention to use blockchain in the food supply. However, perceived usefulness was found to be more significant on behavioral intention to use blockchain due to higher beta value. Furthermore, the indirect effect of employee attitude on perceived ease of use, perceived usefulness, and behavioral intention to use blockchain positively and significantly suggested a partial mediation effect. For the Blockchain system to gain acceptance, it should be easy to use and be perceived as useful by end users. Therefore, it can be done with the help of user-centred design, initial and advanced staff training, and awareness of the potential benefits of technology.

Problematic use of WhatsApp and adolescents: What educational role do parents play?

The goal of this study is to understand the relationship between the problematic use of WhatsApp among young adolescents and parental mediation. The rise of mobile phone use among young people is a cause for concern, especially among parents themselves. The literature suggests that parents have a certain amount of room for maneuver through different parental strategies (parental mediation) to maximize the benefits of technology and reduce its possible risks, although this has not been sufficiently analyzed in the case of problematic use of the mobile phone and its applications. This study therefore examined three different parental mediation strategies (restrictive, modeling and stimulation) and their possible impact on the problematic use of WhatsApp. The sample comprised 1,144 13- and 14-year-old students during three academic years (2015-2018) in 17 schools in 7 Spanish provinces. One of the novelties of the study was the use of adolescent voices to understand the parental mediation of their parents. The results showed a direct relationship between the level of restrictive mediation and a problematic use of WhatsApp. In addition, the number of mobile phones that students have enjoyed, as well as whether parents use their mobile phone during dinner, also has a positive relationship with the problematic use of WhatsApp. These results are useful for understanding a responsible use of the mobile phone, as well as to guide parents about the problematic use of WhatsApp among adolescents.

Human Action Recognition System Using LRCN

Abstract: The importance of human action recognition is significant across various domains, driving advancements in safety, healthcare, sports analytics, and interactive technologies. Leveraging machine learning techniques like Long Recurrent Convolutional Neural Networks (LRCN) trained on datasets such as UCF50, our project focuses on action prediction from YouTube videos. This technology plays a pivotal role in enhancingsafety and security by enabling the detection of anomalies and suspicious behaviours in surveillance systems. In healthcare, it supports remote patient monitoring, rehabilitation assessment, and personalized care plans based on observed actions. Addi- tionally, in sports analytics and entertainment, human action recognition informs performance evaluation, content creation, and immersive experiences. Our integration of a pre-trained LRCN model into a Flask web application signifies the tangible impact of machine learning on human action recognition, with ongoing efforts to optimize functionality through input validation, error handling, user feedback, security measures, and performance enhancements, illustrating the practical application and societal benefits of this innovative technology

Analysis of the synergistic benefits of typical technologies for pollution reduction and carbon reduction in the iron and steel industry in the Beijing–Tianjin–Hebei region

With its high energy consumption and pollutant emissions, the iron and steel industry is a significant source of air pollution and carbon emissions in the Beijing–Tianjin–Hebei (BTH) region. To improve air quality and reduce greenhouse gas emissions, a series of policies involving ultra-low emission, synergistic reduction of pollution, and carbon application have been implemented in the region. This study has assessed air pollutant and CO2 emission patterns in the iron and steel industry of the region by employing co-control effects coordinate system, marginal abatement cost curve, and numerical modeling, along with the synergistic benefits of typical technologies. The results have demonstrated that: (1) the intensive production activities pertinent to iron and steel enterprises has contributed greatly to the emission in Tangshan and Handan, where the sintering process is the main source of SO2, NOx, PM2.5, and CO, accounting for 64.86%, 55.15%, 29.98%, and 46.43% of the total emissions, respectively. (2) Among the typical pollution control and reduction measures, industrial restructuring and adjustment of the energy-resource structure have led to the greatest effects on emission reduction. Technologies exhibiting great potential in emission reduction and high-cost efficiency such as Blast Furnace Top Gas Recovery Turbine Unit (TRT) need to be promoted. (3) In Tangshan city with the highest level of steel production, the iron and steel production activities contributed to the concentration of 30.51% of PM2.5, 50.67% of SO2, and 42.54% of NO2 during the non-heating period. During the heating period, pollutants pertinent to the combustion of fossil energy for heating have increased, while iron and steel induced emissions have decreased to 23.7%, 34.32%, and 29.13%, respectively. By 2030, it is speculated that the contribution of the iron and steel industry to air quality will be significantly decreased as result of successful implementation of ultra-low emission policies and typical synergistic reduction technologies.

The Effect of Milk Fat Globule Membrane (MFGM) Concentrate Addition on Ultrafiltered Goat Milk Coagulation and Fresh Cheese Rheology

AbstractMilk fat globule membrane (MFGM) is a lipidic tri-layer structure rich in polar lipids and glycoproteins with promising health and technological benefits, which can be isolated from dairy industry by-products. The present research aims to understand the effect of the addition of an enriched MFGM concentrate on the milk coagulation process and on the structural behavior of ultrafiltered fresh cheeses (UFC). To this end, goat milk was ultrafiltered at 3 protein levels (10, 12, and 14%) and three MFGM contents were evaluated (0, 0.4, and 0.8%). UFC batches were manufactured and characterized using rheology. Coagulation kinetics revealed that ultrafiltration and MFGM addition increased the firmness of the cheese gel. Protein content was the primary factor contributing to gel firmness, and among the mixtures tested, those containing 14% proteins exhibited the highest elastic modulus values. Dynamic and static shear analyses showed that the combination of ultrafiltration and enriched MFGM concentrate supplementation had a synergic effect on cheese viscoelastic behavior. Loss factor (tan(δ)) may be a marker that can be monitored in cheesemaking when adding MFGM concentrate. Overall, MFGM concentrate would improve the rheological characteristics of fresh cheeses, being a promising ingredient for the food industry.