Custom Solutions Research Articles

MoMa: An assistive mobile manipulator with a webcam-based gaze control system

Mobile Manipulators (MoMa) is a category of mobile robots designed to assist people with motor disabilities to perform object retrieval tasks using a webcam-based gaze control system. Using off-the-shelf components such as reproducible acrylic and 3D-printed plates, and a webcam for eye tracking, MoMa serves as an inexpensive, open-source, and customizable solution in assistive robotics. The robotic system consists of a mobile base that can move forward and backward, as well as turn in place; and a 2-axis cartesian arm equipped with a claw gripper that opens and closes. The simple movement of the robot also allows for a simple control method and graphical user interface (GUI). The user receives information about what is in front of the robot through a mounted camera, and, by looking at parts of the screen that correspond to controls, has their gaze predicted by a convolutional neural network and sends commands wirelessly. The performance of the entire system has been validated through testing of the gaze prediction model, the integration of the control system, as well as its task completion capabilities. All the design, construction and software files are freely available online under the CC BY 4.0 license at https://doi.org/10.17632/k7yfn6wdv7.2.

PYDA—A Python Dashboard for Easy and Customizable Visualization and Fitting of Biophysical Data

ABSTRACT Research-integrating teaching offers a collective approach to engage students and researchers in real research questions, introducing them to authentic problem-solving strategies. In experimental research-integrating teaching, various different data are acquired and often analyzed by using specialized software programs. However, learning to operate these programs can sometimes divert attention from the research itself. To keep the focus on the research, we have developed a Python-based dashboard (PYDA) and implemented it successfully in a research-integrating course on intrinsically disordered proteins. PYDA simplifies data analysis by handling experimental data from techniques such as liquid chromatography, far-ultraviolet circular dichroism, and fluorescence spectroscopy in a unified way. Developed in Python by using the Plotly Dash framework and hosted on Heroku, PYDA allows students to visualize, convert, and analyze data without extensive prior knowledge of Python. By providing a streamlined, flexible, and customizable solution, PYDA enables students to focus on research and hypothesis development, fostering digitally competent and inquiry-driven problem solvers. PYDA is supplied with a set of example files that allows the user to test the program. These files can be accessed as the last item in the dropdown menu.

Information Freshness in Service Queues With Strategic Customers

We examine a scenario in which a service provider updates its queue length information to customers at a specific frequency. Customers decide whether to join the queue based on the most recent update. Our objective is to determine the optimal updating frequency that benefits both the service provider and customers. Using a two-dimensional continuous-time Markov process, we model the actual and announced queue length processes. By proving the identical distributions of these two processes under a Poisson updating scheme, we derive closed-form solutions for customers’ utility functions. Our findings demonstrate that customers adopt a generalized mixed-threshold strategy at equilibrium, and their certainty about whether to join the queue or balk does not always increase with fresher information. Furthermore, we reveal that due to customers’ different sensitivities to information freshness, system performance metrics such as throughput and total customer utility exhibit non-monotonic behavior in response to the updating frequency. Consequently, providing fresher information does not necessarily lead to improved system performance. To address this, we propose algorithms to determine the optimal updating frequency for each system performance metric and identify the conditions under which different updating frequencies are optimal. We demonstrate that any positive updating frequency can achieve customer utility no worse than the no-information system. Additionally, in systems with high customer arrival rates, updating with a positive updating frequency can improve throughput. Furthermore, we prove conditions under which a positive and finite updating frequency can yield higher throughput and total customer utility compared to real-time information systems.

Manufacturing floor mapping and presence tracking with a physics-based game engine

The manufacturing shop floor exists in a dynamic environment with constantly changing components regarding position and features. Presence tracking on the manufacturing shop floor is essential to enhance effective operations and safety. Internet of Things technology solutions have been developed to track and monitor machine and operators’ movements and materials on the shop floor to enhance machine availability and worker safety and improve overall operational effectiveness. However, for SMMs with limited financial resources and infrastructure, there is a need for a financially friendly approach to shop floor presence tracking and monitoring. This work presents the use of Internet-of-things and physics-based engines to develop a scalable and customizable solution to track presence on a manufacturing floor. The paper aims to improve and promote the adoption of I-4.0 technologies and digital manufacturing by SMMs with the development of an affordable, repeatable, customizable, open-source solution. The article describes a methodology for mapping, modeling, monitoring, and reporting presence on a manufacturing floor using physics-based engines. The methodology describes mapping the manufacturing floor into zones, the design of the IoT solution, selecting and placing sensors, and presenting the data using dashboards and physics-based engines. The paper investigates the methodology by implementing a case study on the Interdisciplinary Center for Advanced Manufacturing Systems (ICAMS) manufacturing floor using the Unity3D game engine, open-source software, and off-the-shelf motion sensors.

Study on UV-Curable Acrylic Clear Viscoelastic Materials with Grafted Structures

Flexible substrates are crucial for wearable electronic strain sensors, but achieving biocompatibility, low cost, and aesthetic appeal alongside good mechanical performance remains challenging. Acrylic-based clear viscoelastic films (CVFs) are gaining attention due to their excellent transparency and potential for enhanced mechanical properties. This study synthesized copolymers CVF1-4 with varying branch content using soft monomers 2-ethylhexyl acrylate (2-EHA), butyl acrylate (n-BA), and functional monomer acrylic acid (AA). Employing a "grafting-through" strategy, small molecule monomers, ATRP-synthesized oligomer pBA25, and photoinitiator 1,6-hexanediol diacrylate (HDDA) were in-situ polymerized under UV light to form transparent, flexible elastic films CVF1-4. Between -20 to 80 C, CVF1-4 demonstrated excellent mechanical properties such as low modulus (in the kPa range), low glass transition temperature (below -20 C), large deformation (500-600%), and high strain recovery rate (>95%). Among them, CVF3 with 15% branching showed a balanced comprehensive performance, exhibiting primarily elastic behavior at room temperature. Therefore, selecting appropriate molecular composition and grafting structure provides a simple and customizable solution for optimizing CVF properties.

The practical use of artificial intelligence in Transfusion Medicine and Apheresis

BackgroundBlood and plasma volume calculations are a daily part of practice for many Transfusion Medicine and Apheresis practitioners. Though many formulas exist, each facility may have their own modifications to consider. ChatGPT (Generative Pre-trained Transformer) provides a new and exciting pathway for those with no programming experience to create personalized programs to meet the demands of daily practice. Additionally, this pathway creates computer programs that provide accurate and reproducible outputs. Herein, we aimed to create a step-by-step process for clinicians to create customized computer programs for use in everyday practice. MethodsWe created a process of inputs to ChatGPT-40, which generated computer programming code. This code was copied and pasted into Notepad (and saved as a Python file) and Google Colaboratory to verify functionality. We validated the durability of our process by repeating it over a 5-day timeframe and by recruiting volunteers to reproduce our outputs using the suggested process. ResultsComputer code generated by ChatGPT-40 in response to our common language inputs was accurate and durable over time. The code was fully functional in both Python and Colaboratory. Volunteers reproduced our process and outputs with minimal assistance. ConclusionWe analyzed the practical application of ChatGPT-40 and artificial intelligence (AI) to perform daily calculations encountered in Transfusion Medicine. Our results provide a proof of concept that people with no programming experience can create customizable solutions for their own facilities. Our future work will expand to the creation of comprehensive and customizable websites designed for each individual user.

Measured and Predicted Speckle Correlation from Diffractive Metasurface Diffusers

Speckles are inherent in structured laser-based light projection using diffractive optics such as metasurfaces or diffractive optical elements (DOEs). One application of structured light is to provide illumination for machine vision and depth sensing. This is particularly attractive for mobile or low-power applications, where metasurfaces provide a compact, customizable solution, which can furthermore reach extreme field of illuminations. However, the speckles might limit detection capabilities by, e.g., lowering the detection range or providing false results. In this work, we present a series of measurements with matching simulations on a 70 × 50 degrees diffractive diffuser using different light sources (varying divergence angles + VCSEL array) to quantify the impact of speckles. We observe a qualitative agreement in speckle correlation between the measurements and the simulations and explain, in part using cross-correlation for analysis, why we do not observe the same speckle pattern between the measurements and the simulations. By performing extra simulations, we conclude that by only changing the light source, there is a limit to the reduction of the speckle contrast which, we can achieve, and, to reduce it further, alternative approaches such as changing the design method of the diffractive diffuser must be harnessed.

Application of Fuzzy Logic for Horizontal Scaling in Kubernetes Environments within the Context of Edge Computing

This paper presents a fuzzy logic-based approach for replica scaling in a Kubernetes environment, focusing on integrating Edge Computing. The proposed FHS (Fuzzy-based Horizontal Scaling) system was compared to the standard Kubernetes scaling mechanism, HPA (Horizontal Pod Autoscaler). The comparison considered resource consumption, the number of replicas used, and adherence to latency Service-Level Agreements (SLAs). The experiments were conducted in an environment simulating Edge Computing infrastructure, with virtual machines used to represent edge nodes and traffic generated via JMeter. The results demonstrate that FHS achieves a reduction in CPU consumption, uses fewer replicas under the same stress conditions, and exhibits more distributed SLA latency violation rates compared to HPA. These results indicate that FHS offers a more efficient and customizable solution for replica scaling in Kubernetes within Edge Computing environments, contributing to both operational efficiency and service quality.

A dendritic cell-recruiting, antimicrobial blood clot hydrogel for melanoma recurrence prevention and infected wound management

Surgical resection, the mainstay for melanoma treatment, faces challenges due to high tumor recurrence rates and complex postoperative wound healing. Chronic inflammation from residual disease and the risk of secondary infections impede healing. We introduce an innovative, injectable hydrogel system that integrates a multifaceted therapeutic approach. The hydrogel, crosslinked by calcium ions with sodium alginate, encapsulates a blood clot rich in dendritic cells (DCs) chemoattractants and melanoma cell-derived nanovesicles (NVs), functioning as a potent immunostimulant. This in situ recruitment strategy overcomes the limitations of subcutaneous tumor vaccine injections and more effectively achieves antitumor immunity. Additionally, the hydrogel incorporates Chlorella extracts, enhancing its antimicrobial properties to prevent wound infections and promote healing. One of the key findings of our research is the dual functionality of Chlorella extracts; they not only expedite the healing process of infected wounds but also increase the hydrogel's ability to stimulate an antitumor immune response. Given the patient-specific nature of the blood clot and NVs, our hydrogel system offers customizable solutions for individual postoperative requirements. This personalized approach is highlighted by our study, which demonstrates the synergistic impact of the composite hydrogel on preventing melanoma recurrence and hastening wound healing, potentially transforming postsurgical melanoma management.

QRGB+: Advanced QR Code Generator with RGB Color Method in Python to Expand Data Capacity

The present work entitled QRGB, consists of the development of an application in Python for the generation of QR codes using the additive color generation (RGB) method. This innovative method allows increasing the density of information stored in QR codes by using three color layers (red, green and blue), each representing a different set of data. QRGB offers an efficient and secure solution for storing and transmitting large amounts of information in limited spaces, significantly improving the capabilities of traditional black and white QR codes. By using three layers of colors, QRGB codes can store up to three times more information in the same space. This technique not only increases storage capacity but also improves information security, making it difficult to forge or manipulate the code. The overlay of multiple data layers allows redundancy to be implemented, increasing the robustness of the code against damage or reading errors. QRGBs are especially useful in applications that require the transmission of large amounts of data in limited spaces, such as in the packaging industry, digital business cards, and interactive advertising. Additionally, they have great potential in areas such as document and banknote security, where the authenticity and integrity of information are crucial. These points provide a solid foundation for understanding the innovation and advantages of colored QR codes (QRGB) compared to traditional QR codes, highlighting their applicability and potential in various sectors. This article presents a novel method for encoding and decoding information using a QRGB code, which involves the generation of three independent QR codes and their superimposition according to the additive color system (RGB). The research highlights the challenges encountered during the encoding and decoding processes due to the lack of specific libraries in Python, which required the creation of a custom solution using open source tools. The implementation takes advantage of Python and its libraries: qrcode[pil] to generate QR codes with the Pillow dependency for image manipulation, Pillow to open, manipulate and save different image formats, and opencv-python to perform tasks such as image processing and object detection. Despite facing issues with color mixing and accurate information retrieval, the proposed method demonstrates a significant increase in data density within a single QR code. Future work will focus on optimizing the algorithm and exploring potential applications in data security and high-density information storage. This Python script is designed to generate and decode QR codes with a logo overlay using a graphical user interface (GUI) built with Tkinter. The script combines several functionalities, such as creating QR codes, overlaying a logo, combining QR images of different colors, and manually decoding combined QR codes.

Comparing detection accuracy of mountain chickadee (Poecile gambeli) song by two deep-learning algorithms

The use of autonomous recording units (ARUs) has become an increasingly popular and powerful method of data collection for biological monitoring in recent years. However, the large-scale recordings collected using these devices are often nearly impossible for human analysts to parse through, as they require copious amounts of time and resources. Automated recognition techniques have allowed for quick and efficient analysis of these recordings, and machine learning (ML) approaches, such as deep learning, have greatly improved recognition robustness and accuracy. We evaluated the performance of two deep-learning algorithms: 1. our own custom convolutional neural network (CNN) detector (specialist approach) and 2. BirdNET, a publicly available detector capable of identifying over 6,000 bird species (generalist approach). We used audio recordings of mountain chickadees (Poecile gambeli) collected from ARUs and directional microphones in the field as our test stimulus set, with our custom detector trained to identify mountain chickadee songs. Using confidence thresholds of 0.6 for both detectors, we found that our custom CNN detector yielded higher detection compared to BirdNET. Given both ML approaches are significantly faster than a human detector and the custom CNN detector is highly accurate, we hope that our findings encourage bioacoustics practitioners to develop custom solutions for targeted species identification, especially given the availability of open-source toolboxes such as Koogu.

LANTERN: A multichannel light calibration system for cryogenic detectors

Recent advancements in low-energy rare-event searches rely on the use of cryogenic calorimeters in order to reach extremely low energy thresholds. When attempting to accurately characterize their responses within the region of interest (ROI) these detectors face severe challenges due to the fact that traditional radioactive calibration sources produce signals above the ROI and cannot be used in low background environments.LANTERN, an optical calibration system, can instead be used to accurately study the ROI of cryogenic detectors. It utilizes LED pulses to generate photon signals to perform detector calibration. LANTERN’s current LED matrix allows for a fast characterization of up to 64 calorimeters independently, spanning energies from a few eV to hundreds of keV. Its minimal electronics and optics make it a cost-effective and customizable solution and it will be employed by the BULLKID, NUCLEUS and CRAB experiments.

Transfer learning with generative models for object detection on limited datasets

Abstract The availability of data is limited in some fields, especially for object detection tasks, where it is necessary to have correctly labeled bounding boxes around each object. A notable example of such data scarcity is found in the domain of marine biology, where it is useful to develop methods to automatically detect submarine species for environmental monitoring. To address this data limitation, the state-of-the-art machine learning strategies employ two main approaches. The first involves pretraining models on existing datasets before generalizing to the specific domain of interest. The second strategy is to create synthetic datasets specifically tailored to the target domain using methods like copy-paste techniques or ad-hoc simulators. The first strategy often faces a significant domain shift, while the second demands custom solutions crafted for the specific task. In response to these challenges, here we propose a transfer learning framework that is valid for a generic scenario. In this framework, generated images help to improve the performances of an object detector in a few-real data regime. This is achieved through a diffusion-based generative model that was pretrained on large generic datasets. With respect to the state-of-the-art, we find that it is not necessary to fine tune the generative model on the specific domain of interest. We believe that this is an important advance because it mitigates the labor-intensive task of manual labeling the images in object detection tasks. We validate our approach focusing on fishes in an underwater environment, and on the more common domain of cars in an urban setting. Our method achieves detection performance comparable to models trained on thousands of images, using only a few hundreds of input data. Our results pave the way for new generative AI-based protocols for machine learning applications in various domains, for instance ranging from geophysics to biology and medicine.

Governing green change to improve resilience by assessing urban risks for localizing nature based solutions in fast sprawling Dehradun, India

Considering the increasing disaster risks, vulnerability and reduced resilience across Himalayas due to urban sprawling, our study attempts to understand the crucial environmental concerns in the valley town of Dehradun. We assessed the long-term risks due to the rise in population density, changing land use patterns, land surface temperature, groundwater shortage, air pollution concerns, vegetation loss and concretization using remote sensing tools. Results reveal the deleterious impact of urban sprawling affecting different wards of the city, with the burgeoning population pressure in the central part of the city (200 people per hectare (pph)) while, peripheral areas have lesser pressure (4 pph) but projected to rise. Loss of vegetation for development across the city has resulted in insufficient and unequal distribution of green spaces. The builtup area increased from 23.63 to 59.78 % after the city became the state capital has led to the increased heat islands and surface temperature (21.23–25.53 °C). Depleting groundwater levels in erstwhile water sufficient wards of Raipur (01), Shastradhara (02) and Adhoiwala N (28) is alarming. Increasing air pollutants (P.M.10, SO2 and NO2) and the risk of urban floods are severe for 10 wards. Dehradun city seems to be at tipping point about to cross its bearing capacity, any additional pressure may have serious repercussions for city residents. City municipal corporation along with diverse stakeholder groups need to mainstream Nature based Solutions, designing and implementing ward specific customized solutions for addressing societal challenges according to Sendai Framework for Disaster Risk Reduction and the UN SDG 11.

Accessible Ecosystem for Clinical Research (Federated Learning for Everyone): Development and Usability Study.

The integrity and reliability of clinical research outcomes rely heavily on access to vast amounts of data. However, the fragmented distribution of these data across multiple institutions, along with ethical and regulatory barriers, presents significant challenges to accessing relevant data. While federated learning offers a promising solution to leverage insights from fragmented data sets, its adoption faces hurdles due to implementation complexities, scalability issues, and inclusivity challenges. This paper introduces Federated Learning for Everyone (FL4E), an accessible framework facilitating multistakeholder collaboration in clinical research. It focuses on simplifying federated learning through an innovative ecosystem-based approach. The "degree of federation" is a fundamental concept of FL4E, allowing for flexible integration of federated and centralized learning models. This feature provides a customizable solution by enabling users to choose the level of data decentralization based on specific health care settings or project needs, making federated learning more adaptable and efficient. By using an ecosystem-based collaborative learning strategy, FL4E encourages a comprehensive platform for managing real-world data, enhancing collaboration and knowledge sharing among its stakeholders. Evaluating FL4E's effectiveness using real-world health care data sets has highlighted its ecosystem-oriented and inclusive design. By applying hybrid models to 2 distinct analytical tasks-classification and survival analysis-within real-world settings, we have effectively measured the "degree of federation" across various contexts. These evaluations show that FL4E's hybrid models not only match the performance of fully federated models but also avoid the substantial overhead usually linked with these models. Achieving this balance greatly enhances collaborative initiatives and broadens the scope of analytical possibilities within the ecosystem. FL4E represents a significant step forward in collaborative clinical research by merging the benefits of centralized and federated learning. Its modular ecosystem-based design and the "degree of federation" feature make it an inclusive, customizable framework suitable for a wide array of clinical research scenarios, promising to revolutionize the field through improved collaboration and data use. Detailed implementation and analyses are available on the associated GitHub repository.

Analisis Dana Talangan Pendidikan dalam Perspektif Ekonomi Islam: Studi Kasus Dana Cita Universitas Pelita Bangsa

The aim of this research is to analyze the costs of education funds at Dana Cita, Pelita Bangsa University. The research method used is descriptive qualitative research. Primary data sources were obtained through interviews with Dana Cita. The results of this research indicate that the application of funding for education funding is using a qardh ijarah agreement. Talang funds are a form of multi-service transaction through the use of an Ijarah contract which is based on an agreement between the bank and customer financing and requires customer financing to pay off debts and obligations. This educational savings fund product is a product that makes things easier for customers, where this product is a solution for customers who have difficulty paying fees for their children to continue their studies or complete their studies.

Ethical Challenges with Personalized Avatars in Higher Education of the Metaverse Era

One of the first revolutions of Web 3.0 technology has been the metaverse, which is an alternative world formation where people can experience three-dimensional virtual realities through their avatars. The only way for higher education to survive in the metaverse era is to do an in-depth examination of the current situation and create strategies, regulations, and policies for all segments of this novice field considering also the ethical challenges it will bring to humanity. In a metaverse environment, there will be personalized avatars that represent real people, and in a university setting with virtual classes held in the metaverse, many ethical dilemmas and moral concerns can develop due to the utilization of avatars. The study narrowed its attention to examining these ethical issues raised by avatar customization and potential solutions to design a healthy metaverse in higher education. The study conducted an in-depth exploration with fourteen higher education lecturers, who are also key people specialized in the metaverse and web 3.0 technologies. The data obtained from the interviews were clustered under seven thematic nodes including anonymous identities; socially equal & diverse avatars; avatar policy; honesty & transparency; non-resemblance with the physical realities; avatar clothes codes; and avatars’ intellectual property. Key Words: Avatar, Ethics, Metaverse, Higher Education, Metaverse Universities

AN INNOVATIVE IT SOLUTIONS AGENCY (hub of technology innovative solutions)

ABSTRACT : An innovative IT solution company that provides a variety of services, such as software development, cloud computing, cybersecurity, data analytics, and artificial intelligence, is introduced in the proposal. In order to provide unique solutions for challenging business problems, the agency plans to incorporate cutting-edge technologies like blockchain, IoT, and machine intelligence. Delivering individualised solutions through in-depth market research, agile development, strategic alliances, talent development, and all-encompassing support and training will be the agency’s primary focus. A competitive edge, better decision-making, higher levels of security, and sustained growth are among the anticipated results. By offering clients significant value and long-term growth, the agency hopes to become a leader in the IT services sector. Businesses and organisations in the modern digital age rely more and more on cutting-edge IT solutions to boost productivity, improve customer experiences, and keep a competitive edge. The goal of this project is to create a cutting-edge IT solution company that will provide services and solutions specifically designed to fulfil the demands of different industries. Service diversification entails offering a wide range of IT services, such as data analytics, cloud computing, cybersecurity, software development, and artificial intelligence (AI) solutions. Innovation Focus: To develop custom solutions that tackle particular business difficulties, take advantage of cutting-edge technologies like blockchain, the Internet of Things (IoT), and machine learning. A customer-focused strategy: Adopt a client-first approach to guarantee individualised solutions via thorough needs analysis and ongoing involvement. Guarantee of Quality: Put into practice strict quality control procedures. KEYWORDS – STEM education, interdisciplinary learning, project-based instruction, active learning, innovation

Preliminary research on tailored fluid therapy in pigs: comparing customized ionic solutions with Hartmann’s solution

BackgroundFluid therapy in veterinary medicine is pivotal for treating various conditions in pigs; however, standard solutions, such as Hartmann’s solution, may not optimally align with pig physiology. This study explored the development and efficacy of a customized fluid therapy tailored to the ionic concentrations of pig blood, aiming to enhance treatment outcomes and safety in both healthy and diseased pigs.ResultsThe study involved two experiments: the first to assess the safety and stability of customized fluids in healthy pigs, and the second to evaluate the efficacy in pigs with clinical symptoms of dehydration. In healthy pigs, the administration of customized fluids showed no adverse effects, with slight alterations observed in pO2, hematocrit, and glucose levels in some groups. In symptomatic pigs, the customized fluid group did not show any improvement in clinical symptoms, with no significant changes in blood chemistry or metabolite levels compared to controls. The customized fluid group showed a mild increase in some values after administration, yet within normal physiological ranges. The study reported no significant improvements in clinical or dehydration status, attributing the observed variations in blood test results to the limited sample size and anaesthesia effects rather than fluid characteristics.ConclusionsCustomized fluid therapy, tailored to mimic the ionic concentrations of pig blood, appears to be a safe and potentially more effective alternative to conventional solutions such as Hartmann’s solution for treating pigs under various health conditions. Further research with larger sample sizes and controlled conditions is recommended to validate these findings and to explore the full potential of customized fluid therapy in veterinary practice.

Tribology-driven strategies for tool wear reduction and surface integrity enhancement in cryogenic CO2-cooled milling of laser metal deposited Ti64 alloy

Additive manufacturing (AM) is chosen for its ability to streamline production processes and design freedom. This reduces material waste, enables rapid prototyping, and facilitates intricate geometries, ultimately offering cost-effective and customizable solutions for manufacturing complex components in diverse industries. Overlapping melting trajectories result in a low-quality surface (Ra=∼13.34 µm) in the laser metal deposition (LMD) of the Ti64 alloy. Therefore, post-processing is often essential for AMed parts for engineering applications. Milling trials were conducted on AMed specimens under four environmental conditions: dry, flood, minimum quantity lubrication (MQL), and cryogenic medium. The machinability was evaluated in terms of the cutting temperature, machined surface roughness, tool wear, chip morphology, and microhardness. The flank wear under cryogenic CO2 condition is 52.78–54.29 % lower than dry condition, 33.86–36.24 % lower than flood cutting, and 23.64–26.86 % lower than MQL. The outcomes show that cryogenic cooling augments the tool life and the surface integrity of milling LMD parts. Moreover, the hardness under cryogenic CO2 was higher, indicating dimensional stability and maintenance of shape integrity under applied loads.