Real-world Relevance Research Articles

Natural Language Processing and Machine Learning-Based Solution of Cold Start Problem Using Collaborative Filtering Approach

In today’s digital era, the abundance of online services presents users with a daunting array of choices, spanning from streaming platforms to e-commerce websites, leading to decision fatigue. Recommendation algorithms play a pivotal role in aiding users in navigating this plethora of options, among which collaborative filtering (CF) stands out as a prevalent technique. However, CF encounters several challenges, including scalability issues, privacy implications, and the well-known cold start problem. This study endeavors to mitigate the cold start problem by harnessing the capabilities of natural language processing (NLP) applied to user-generated reviews. A unique methodology is introduced, integrating both supervised and unsupervised NLP approaches facilitated by sci-kit learn, utilizing benchmark datasets across diverse domains. This study offers scientific contributions through its novel approach, ensuring rigor, precision, scalability, and real-world relevance. It tackles the cold start problem in recommendation systems by combining natural language processing (NLP) with machine learning and collaborative filtering techniques, addressing data sparsity effectively. This study emphasizes reproducibility and accuracy while proposing an advanced solution that improves personalization in recommendation models. The proposed NLP-based strategy enhances the quality of user-generated content, consequently refining the accuracy of Collaborative Filtering-Based Recommender Systems (CFBRSs). The authors conducted experiments to test the performance of the proposed approach on benchmark datasets like MovieLens, Jester, Book-Crossing, Last.fm, Amazon Product Reviews, Yelp, Netflix Prize, Goodreads, IMDb (Internet movie Database) Data, CiteULike, Epinions, and Etsy to measure global accuracy, global loss, F-1 Score, and AUC (area under curve) values. Assessment through various techniques such as random forest, Naïve Bayes, and Logistic Regression on heterogeneous benchmark datasets indicates that random forest is the most effective method, achieving an accuracy rate exceeding 90%. Further, the proposed approach received a global accuracy above 95%, a global loss of 1.50%, an F-1 Score of 0.78, and an AUC value of 92%. Furthermore, the experiments conducted on distributed and global differential privacy (GDP) further optimize the system’s efficacy.

Co-creating the intervention ‘MAMAACT’ for improved care for pregnant migrant women in Denmark

Abstract Background The MAMAACT intervention was developed as a response to documentation of ethnic and social inequities in stillbirth in Denmark, despite free and universal access to antenatal care. The MAMAACT intervention aimed to reduce stillbirth in migrant families by improving communication about pregnancy complication during antenatal care. Methods The intervention consisted of a training program for midwives and health education materials. It was implemented at a structural level (training of midwives) and, through strengthened needs-based communication during antenatal care, the aim was to improve the health literacy of the women. 19 of the 20 Danish maternity wards participated in the study in 2018-19 and a comprehensive evaluation was conducted. The intervention was co-created with midwifery managers, midwives, and migrant women. Results The management and selected clinical midwives participated in the design of the midwifery training and the health education material as well as the intervention’s implementation and recruitment strategies for the evaluation. As representatives of the target group, a partnership with the non-profit organization Bydelsmødre was initiated. Bydelsmødre trains migrant mothers living in socially deprived areas to help vulnerable migrant women. Evening sessions were conducted with a specific group of women in their community, where the intervention components, the recruitment to the evaluation, and the interpretation of evaluation findings were discussed. A significant contribution was a video explaining the migrant experiences of becoming a mother in Denmark used in the midwifery training. MAMAACT researchers were invited to present the evaluation at the annual assembly for Bydelsmødre organization in 2019. Conclusions The partnership with Bydelsmødre provided a strong voice for migrant women, enhancing engagement with the intervention, recruitment for the trial, and its real-world relevance.

Far-UVC light (222 nm) efficiently inactivates clinically significant antibiotic-resistant bacteria on diverse material surfaces.

This study is crucial, providing compelling evidence on Far-ultraviolet radiation C (Far-UVC) light's efficacy against clinically significant antibiotic-resistant bacteria-a pressing issue in microbiology and infection control. Our research employs antibiotic-resistant strains from clinically isolated bacteria, emphasizing real-world relevance. Simultaneously, we assess Far-UVC light (222 nm) across diverse material surfaces commonly found in clinical settings. This dual approach ensures practical applicability and broad relevance. Our comprehensive setup and rigorous methodologies unequivocally demonstrate Far-UVC light's potency in combating antibiotic-resistant bacteria. Since 222 nm far-UVC has a disinfection capability and is harmless to mammalian cells, this dual effectiveness positions Far-UVC as a secure tool for infection control, with potential applications in healthcare settings, mitigating antibiotic-resistant bacteria spread, and reducing healthcare-associated infections.

INNOVATIVE MATHEMATICAL MODELLING IN PREDATOR-PREY DYNAMICS: A SYSTEMATIC REVIEW

Advancements in mathematical ecology have greatly expanded predator-prey models beyond the classical Lotka-Volterra framework, incorporating complex factors such as role reversal, fractional calculus, and the Allee effect. This review highlights innovative models and advanced mathematical techniques that enhance our understanding of predator-prey dynamics and their applications across disciplines. It analysed recent studies introducing new ecological factors, including maturation delays, disease dynamics, and spatial heterogeneity. Techniques like fractional calculus and network theory were examined for their effectiveness in capturing complex behaviours. Models addressing role reversal between adult prey and juvenile predators or incorporating generalized fractional derivatives reveal significant impacts on population stability. Additionally, maturation delays, handling time, and gestation periods markedly influence oscillatory dynamics. The reviewed models demonstrate versatility in guiding pest control, understanding disease spread, and optimizing biotechnological processes. This review shows that modern predator-prey models, enriched by complex ecological factors and advanced mathematics, provide profound insights into system dynamics, with practical applications across various fields. As global challenges grow, these models offer crucial guidance for developing more resilient and sustainable systems, underscoring their potential to addressissues like food security, disease outbreaks, and ecosystem degradation. Future research should integrate emerging factors, such as anthropogenic noise and industrial pollutants, to further enhance the models’ real-world relevance.

The Good and Bad of AI Tools in Novice Programming Education

As AI coding tools become more prevalent in programming, it is essential to understand how they influence programming education. This study, conducted in a first-semester Introduction to Programming course, aimed to determine the positive and negative effects of these tools on students’ learning experiences and their ability to develop essential programming skills. Using a mixed-methods approach, we collected data from 73 teams of engineering students over a 12-week period. Students completed surveys and reported on their AI tool usage. We analyzed this data quantitatively to identify trends in tool familiarity, usage, and student satisfaction. Additionally, qualitative analysis of student reports provided insights into the specific ways AI tools were used and their perceived benefits and drawbacks. The findings revealed a significant increase in AI tool familiarity (from 28% to 100%) and usage among students. Students’ satisfaction with AI tools improved over time. The most prevalent tasks for which novice programmers used AI tools included creating comments (91.7%), identifying and correcting bugs (80.2%), and seeking information (68.5%), while other tasks were less common. While these tools offered benefits like assisting in learning and enhancing real-world relevance, they also raised concerns about cheating, over-reliance on AI tools, and a limited understanding of core programming concepts.

Student-Centered Physical Education and Green School Curriculum

This study examined the effectiveness of student-centered physical education initiatives in Chinese schools, focusing on how they foster health and fitness awareness within the framework of a green school curriculum. It explored respondents' assessments of various principles like positive relationships, real-world relevance, and sustainability, while also evaluating the impact of family and community engagement on physical activity. Additionally, it sought to determine any significant differences in the assessments among respondent groups and to formulate strategies to promote health and fitness consciousness. In this study, the quantitative research method was utilized along with a descriptive correlational research design and utilized 307 students, 61 teachers and 33 administrators (total of 401 respondents) of the 3 selected universities in China. The study found that student-centered physical education initiatives in Chinese schools are effectively implemented, promoting health and fitness awareness, with no significant differences in assessments across respondent groups. However, the Green School Curriculum and these initiatives show very weak correlations, suggesting the need for further examination and the development of targeted strategies to better integrate health and fitness consciousness. School administrators should enhance "Time and Place" flexibility in physical education and strengthen family and community involvement. Additionally, the Green School Curriculum needs better integration with health and fitness initiatives, focusing on sustainability and physical well-being.

Accent Attitudes: A Review Through Affective, Behavioral, and Cognitive Perspectives.

Interacting with speakers of different accents is a prevalent global phenomenon. Given the considerable influence of accents in daily life, it is important to conduct a comprehensive review of listeners' accent attitudes. This paper provides an integrative summary of research on accent attitudes, drawing from the Affective, Behavioral, and Cognitive (ABC) perspectives. We begin by outlining the social meaning of accents and laying out the theoretical foundations of the ABC approach. Then, we organize and integrate existing research findings using the ABC framework. Next, we illustrate how the perspectives intersect by discussing pertinent research findings. Drawing from various sociocultural contexts over many years, this review underscores the significant impact of accents on people's thoughts, feelings, and behaviors. The review concludes by discussing limitations, proposing future directions, highlighting real-world relevance, and suggesting areas for research expansion.

Acoustic shape optimization using energy stable curvilinear finite differences

A gradient-based method for shape optimization problems constrained by the acoustic wave equation is presented. The method makes use of high-order accurate finite differences with summation-by-parts properties on multiblock curvilinear grids to discretize in space. Representing the design domain through a coordinate mapping from a reference domain, the design shape is obtained by inverting for the discretized coordinate map. The adjoint state framework is employed to efficiently compute the gradient of the loss functional. Using the summation-by-parts properties of the finite difference discretization, we prove stability and dual consistency for the semi-discrete forward and adjoint problems. Numerical experiments verify the accuracy of the finite difference scheme and demonstrate the capabilities of the shape optimization method on two model problems with real-world relevance.

Implementation of Student Effectiveness Using Project Based Learning (PJBL) Learning Model to Improve Learning Outcomes in Class X-ATU 1 Students at SMKN 1 Gondang

This research was conducted with the aim of improving student learning outcomes using the Project Based Learning (PJBL) learning model in the subjects of Physics Science students at SMK Negeri 1 Gondang. This type of research uses the Classroom Action Research (PTK) method. The subjects are class X-ATU1 students at SMK Negeri 1 Gondang totaling 32 students. This research has a good impact on educational approaches that emphasize learning through real-world projects, where students are actively involved in exploration, collaboration, and problem solving. Real-world relevance and active student engagement are key focuses in PJBL, which encourages the development of critical thinking skills, creativity, and skills. This can be conveyed through the results of cycle I &; cycle II research, in cycle I with 22 students showing a percentage of 68% and carried out again in cycle II there was an increase in learning outcomes with 30 students showing a percentage of 82%. So in this study in cycles 1&2 experienced an increase in student learning outcomes.

Review of Firm Strategy: Relate to Firm Development

The effective implementation of firm strategies plays an essential role in the achievement and sustained viability of businesses. This review study provides a comprehensive exploration of various dimensions of firm strategies, encompassing both their internal and external determinants of impact. The examination extends to an analysis of positive and negative ramifications that engender at the firm level and lists a practical example illustrating the application of these strategies, shedding light on their real-world relevance. The paper underscores the critical role of well-crafted firm strategies in shaping the fate of organizations. It emphasizes the need for continuous adaptation in response to evolving business environments, thereby providing a roadmap for firms aiming to secure their position and thrive in the contemporary marketplace.

Exploring the depths of Multi-Armed Bandit algorithms: From theoretical foundations to modern applications

This paper provides an in-depth and comprehensive analysis of Multi-Armed Bandit (MAB) algorithms, which are crucial in decision-making under uncertainty. It begins with a detailed explanation of the fundamental scenarios where MAB algorithms are applicable, focusing on their features and key strategies. The paper then introduces and explains the core algorithms: Explore-Then-Commit (ETC), Upper Confidence Bound (UCB), and Thompson Sampling (TS). Utilizing various plots, the paper not only analyzes these classical algorithms but also compares them with several of their advanced versions. Additionally, the paper highlights two practical applications of MAB algorithms - in recommendation systems and in wireless digital twin networks - to illustrate their real-world relevance and potential. However, the paper also acknowledges the challenges posed by the complexity of different bandit settings, which affect the efficiency and scalability of MAB algorithms, indicating the need for ongoing research in this field. This review aims to delve deep into the realm of MAB algorithms, offering a thorough understanding of their theoretical underpinnings as well as practical implications.

American College of Rheumatology and Food and Drug Administration Summit: Summary of the Meeting, May 17-18, 2022.

The American College of Rheumatology and the US Food and Drug Administration co-sponsored a public meeting in May 2022 about challenges in the clinical development of drugs for rheumatoid arthritis (RA) and psoriatic arthritis (PsA), focusing on innovative clinical trial designs, outcome measures, and data collection methods. Recommendations include early dose-ranging studies and use of active comparators. Challenges and opportunities in assessing long-term safety by leveraging real-world data from electronic health records (EHRs) and claims data are discussed, along with insights from European registries and the evolving role of real-world evidence and artificial intelligence in regulatory evaluations. Endpoints for assessing disease activity and outcome measures used in RA and PsA trials are explored, emphasizing challenges in defining remission, assessing clinical response, and evaluating structural progression. The need for outcome measures that better reflect treatment targets and the potential of advanced imaging in future trials are highlighted. Challenges with placebo-controlled trials in RA are discussed and use of non-inferiority clinical trial design, in which new drugs are evaluated with active comparators, is proposed. Pragmatic trials in RA and PsA, employing decentralized approaches, are highlighted for their real-world relevance and administrative efficiencies. Strategies for identifying at-risk populations for RA and the challenges of using EHRs and insurance claims data in drug development are discussed. Registry data and digital health technologies show promise in bridging the gap between clinical trials and real-world effectiveness.

Fortifying Transparency: Enhancing Corporate Governance through Robust Internal Control Mechanisms

Purpose: This study explores how robust internal control mechanisms enhance corporate governance by fostering organizational transparency and accountability. The focus is on the interplay between internal controls and corporate governance practices, emphasizing their collective role in promoting organizational success and sustainability. Research Design and Methodology: The study employs a qualitative approach, conducting a comprehensive literature review of peer-reviewed articles, books, and reports. Using relevant keywords, data were collected from academic databases such as PubMed, Scopus, Web of Science, and Google Scholar. Thematic analysis was used to categorize and synthesize recurring themes and patterns, ensuring objectivity and rigor in data interpretation. Findings and Discussion: The findings of this study have significant practical implications. They reveal that effective corporate governance frameworks, underpinned by robust internal control mechanisms, not only enhance transparency, accountability, and ethical conduct but also mitigate risks, safeguard assets, and ensure compliance with regulations. This underscores the real-world relevance of the research and its potential to guide organizational practices. Implications: This study highlights the need for organizations to comprehensively integrate internal control mechanisms into their governance frameworks. It also emphasizes the importance of fostering a culture of integrity, adopting technological innovations, and maintaining compliance with evolving regulations. However, the study also points to the need for future research, particularly longitudinal studies, and interdisciplinary approaches, to further understand the dynamic governance-control relationship. This opens opportunities for further exploration and the development of more effective corporate governance practices.

Efficient Nonparametric Estimation of Stochastic Policy Effects with Clustered Interference

Interference occurs when a unit’s treatment (or exposure) affects another unit’s outcome. In some settings, units may be grouped into clusters such that it is reasonable to assume that interference, if present, only occurs between individuals in the same cluster, that is, there is clustered interference. Various causal estimands have been proposed to quantify treatment effects under clustered interference from observational data, but these estimands either entail treatment policies lacking real-world relevance or are based on parametric propensity score models. Here, we propose new causal estimands based on modification of the propensity score distribution which may be more relevant in many contexts and are not based on parametric models. Nonparametric sample splitting estimators of the new estimands are constructed, which allow for flexible data-adaptive estimation of nuisance functions and are consistent, asymptotically normal, and efficient, converging at the usual parametric rate. Simulations show the finite sample performance of the proposed estimators. The proposed methods are applied to evaluate the effect of water, sanitation, and hygiene facilities on diarrhea among children in Senegal. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Bridging the theory-practice gap in pharmacy education using an authentic learning approach: A cross-sectional study

Background: Authentic learning environments highlight real-world relevance to students, contributing to readiness for practice. This study evaluated the impact of an educational activity on student pharmacist learning and personal/professional development. Methods: An activity similar to that used by practising pharmacy professionals to record errors during dispensing was included in simulated community pharmacy workshops delivered to Year-2 students as part of a Professional Practice Module. Students were instructed to complete entries when academic staff identified errors during final accuracy checks. Student evaluation surveys were analysed at the end of terms one and two of the academic year. Descriptive statistics were used for closed questions, and thematic analysis for open-ended responses. Results: Of the 75 student pharmacists who completed both evaluation surveys, 64% (n=48) recorded fewer errors in term two. Respondents considered the exercise helpful in highlighting the need for more attention to detail and identifying negative trends in their dispensing process. A positive response to personal/professional development was noted, with student pharmacists commenting that the activity facilitated reflection. Conclusion: This activity has allowed students to experience real-world working situations, extending their learning experience, facilitating personal/professional development, and encouraging best practices.

An EEG Dataset of Neural Signatures in a Competitive Two-Player Game Encouraging Deceptive Behavior

Studying deception is vital for understanding decision-making and social dynamics. Recent EEG research has deepened insights into the brain mechanisms behind deception. Standard methods in this field often rely on memory, are vulnerable to countermeasures, yield false positives, and lack real-world relevance. Here, we present a comprehensive dataset from an EEG-monitored competitive, two-player card game designed to elicit authentic deception behavior. Our extensive dataset contains EEG data from 12 pairs (N = 24 participants with role switching), controlled for age, gender, and risk-taking, with detailed labels and annotations. The dataset combines standard event-related potential and microstate analyses with state-of-the-art decoding approaches of four scenarios: spontaneous/instructed truth-telling and lying. This demonstrates game-based methods’ efficacy in studying deception and sets a benchmark for future research. Overall, our dataset represents a unique resource with applications in cognitive neuroscience and related fields for studying deception, competitive behavior, decision-making, inter-brain synchrony, and benchmarking of decoding frameworks in a difficult, high-level cognitive task.

Constructing and validating authentic assessments: the case of a new technology-based assessment of economic literacy

BackgroundAuthentic situations are considered a source of learning due to their real world relevance. This can encourage learners to acquire new knowledge. Increasing digitisation and associated resources, such as professional development opportunities for teachers, technology tools, or digital equipment for schools enable the development and implementation of authentic assessments. The basic academic principles for acquiring economic literacy are already provided in lower secondary school. This article examines, using the example of a new authentic technology-based assessment (TBA)—Economic Literacy—Assessing the Status Quo in Grade 8 (ECON 2022) -, the processes involved in constructing a TBA. The purpose is to develop a curricular valid measurement instrument for surveying the current state of economic literacy in the 8th grade of a German federal state. This study explores which economic competencies students—typically between 14 and 15 years of age—possess in Grade 8, and what level of competence can therefore be expected of them at the beginning of a vocational training programme. The assessment is geared toward the curriculum of the subject of economics and is based on a domain model. This article presents the background and construction process for the development of ECON 2022 as a TBA.MethodsTo check the validity of test construction with a focus on the implementation of the authentic assessment and an analysis of difficulty-generating characteristics, the ECON 2022 test items were validated with an expert survey (N = 25). The two-stage data analysis comprised a descriptive quantifying analysis of the rating from the difficulty-generating characteristics specificity, cognitive demand and modelling and the design criterion authenticity. A set of experts rated the criteria. The expert survey was then compared with a previously conducted rating by the research team. The analysis of free-text comments on individual items was carried out discursively and qualitatively by the research team. Both sources of information were used to adapt the test items to measured item difficulties from the field test. For this purpose, items of great difficulty were changed to slightly easier items. In this context, the paper focuses on two central research questions: - How does the authenticity of a test environment relate to difficulty-generating criteria at item level? - Does the authenticity of a test environment have an impact on test results?ResultsResults are as follows. (1) The ECON 2022 assessment offers an example of a test design in which the use of TBAs can support innovative and interactive item development. (2) Using the ECON 2022 assessment enabled the implementation of an assessment of economic literacy using authentic situations and the mapping of different facets of economic competence. (3) The validation study showed that the actual item difficulty did not correlate significantly with the authenticity of the assessment, and authenticity thus did not contribute to item difficulty.ConclusionThe results of the study show that we were successful in developing an authentic TBA in ECON 2022. ECON 2022 allows us to examine economic literacy in schools with a high degree of curricular validity and relevance and to analyse what level of competence and knowledge can be expected of students when they enter a vocational training occupation.

Stacked Ranking Feature Cluster Machine Learning (Srfcml): A Novel Method of Career Planning of College Students Based on Career Interest Assessment and Machine Learning

Career interest assessment, powered by machine learning algorithms, revolutionizes the way individuals explore and align with career paths. By analyzing vast datasets encompassing factors such as skills, preferences, personality traits, and job market trends, machine learning models can provide personalized career recommendations tailored to individual strengths and aspirations. These algorithms leverage advanced techniques such as natural language processing (NLP) to interpret self-assessment responses and match them with suitable career options. Additionally, machine learning algorithms continuously refine their recommendations based on user feedback and real-world outcomes, ensuring accuracy and relevance over time. This paper presents a novel approach to career planning for college students, integrating career interest assessment with machine learning techniques, specifically utilizing the Stacked Ranking Feature Cluster Machine Learning (SRFcML) model. The proposed framework leverages large-scale datasets encompassing diverse factors such as academic performance, skills, interests, and industry trends to provide personalized career recommendations. Through the application of machine learning algorithms, including clustering and ranking techniques, the SRFcML model identifies relevant clusters of career options and ranks them based on individual preferences and aptitudes. This approach enables college students to explore and prioritize career paths aligned with their unique strengths and aspirations. Simualtion results demonstrated that the effectiveness of the proposed framework was evaluated, yielding promising numerical results. For instance, based on self-assessment responses and academic performance data, the SRFcML model achieved an average accuracy of 85% in recommending suitable career paths for college students. Furthermore, in a comparison with traditional career planning methods, the proposed framework demonstrated a 30% improvement in the alignment between recommended career options and students' preferences. Additionally, user satisfaction surveys revealed a high level of confidence and trust in the recommendations provided by the SRFcML model, with 90% of participants expressing satisfaction with the accuracy and relevance of the career suggestions.

Traveling of multiple salesmen to dynamically changing locations for satisfying multiple goals

Polymer grade scheduling, maritime surveillance, e-food delivery, e-commerce, and military tactics necessitate multiple agents (e.g., extruders, speed boats, salesmen) capable of visiting (or completing) dynamically changing locations (or tasks) in minimum time and distance. This study proposes a novel methodology based on clustering and local heuristic-based evolutionary algorithms to address the dynamic traveling salesman problem (TSP) and the dynamic multi-salesman problem with multiple objectives. The proposed algorithm is evaluated on 11 benchmark TSP problems and large-scale problems with up to 10,000 instances. The results show the superior performance of the proposed methodology called the dynamic two-stage evolutionary algorithm as compared to the dynamic hybrid local search evolutionary algorithm. Furthermore, the algorithm's applicability is illustrated through various scenarios involving up to four salesmen and three objectives with dynamically changing locations. To demonstrate real-world relevance, a maritime surveillance problem employing a helideck monitoring system is solved, wherein the objective is to minimize the patrolling route while visiting faulty vessels that threaten marine vessels. This study provides a general framework of TSP which finds application in several sectors, including planning and scheduling in chemical and manufacturing industries, the defense sector, and the e-commerce sector. Finally, the results showcase the effectiveness of the proposed methodology in solving the dynamic multiobjective, and multiple salesmen problem, which represents a more generalized version of the TSP.

Programming self-assembling magnetic microrobots with multiple physical and chemical intelligence

Medical microrobots represent the cutting-edge of biomedical research, showcasing their potential as versatile tools. They exhibit promise in acting as carriers for cancer cell therapy, effectively delivering drugs, and as manipulators equipped for biosensing, offering mobility and adaptability. Despite these advancements, the intricate challenge of creating a microrobot that seamlessly integrates various physical and chemical functionalities persists. This includes the fusion of selective sensing, manipulation capabilities, carrier functionality, precise time-based actuators for motion control, and adaptive shaping. Addressing these complexities remains an ongoing endeavor. In this context, our work introduces a pioneering magnetic microrobot founded on CaCO3 microparticles (MPs) synthesized alongside polyethylenimine (CaCO3-PEI), forming the core body. This is combined with Fe3O4 nanoparticles (NPs) enveloped in glutaraldehyde (Fe3O4-Glu), constituting the propulsive engine. The synergy of these elements enables the microrobot to execute multimodal motions, orchestrating its movement with finesse. This dynamic capability follows a “deliver-and-return” pattern for precise targeting applications with real-world relevance. Furthermore, the Fe3O4-Glu/CaCO3-PEI microrobots demonstrated remarkable proficiency in the targeted identification, manipulation, and transportation of cancer cells through the strategic integration of specific antibodies onto their structure. Within the realm of selective cancer cell detection, these microrobots adeptly function as dynamic mobile immunosensors. The versatile utility of the Fe3O4-Glu/CaCO3-PEI microrobots extends to their role as carriers for drugs and imaging agents, facilitated by the mediation of extracellular pH modulation in cancer cells orchestrated by CaCO3. This innovative work introduces a novel “on-the-fly” concept, revolutionizing the landscape of robotics programmed with multifaceted chemical and physical intelligences.