Industrial Tools Research Articles

Transforming Healthcare: The Role of Artificial Intelligence in Revolutionising Patient Care

Artificial Intelligence (AI) has emerged as a powerful tool in the healthcare industry. It has enhanced patient care, diagnostics, treatment planning, and operational efficiency. This article explores the diverse applications of AI. Machine learning algorithms, natural language processing, and predictive analytics reshape clinical practices. These technologies allow healthcare providers to focus more on patient care by enhancing diagnostic accuracy, personalising treatments, and automating tasks. However, challenges such as data security, algorithmic bias, and ethical concerns must be addressed for AI to be fully integrated into healthcare. Overcoming these obstacles will enable AI to revolutionise patient outcomes and healthcare innovation.

Transforming Healthcare: The Role of Artificial Intelligence in Revolutionising Patient Care

Artificial Intelligence (AI) has emerged as a powerful tool in the healthcare industry. It has enhanced patient care, diagnostics, treatment planning, and operational efficiency. This article explores the diverse applications of AI. Machine learning algorithms, natural language processing, and predictive analytics reshape clinical practices. These technologies allow healthcare providers to focus more on patient care by enhancing diagnostic accuracy, personalising treatments, and automating tasks. However, challenges such as data security, algorithmic bias, and ethical concerns must be addressed for AI to be fully integrated into healthcare. Overcoming these obstacles will enable AI to revolutionise patient outcomes and healthcare innovation.

The role of technology in tourist guiding: future career, service quality, marketing and tourist experiences

Purpose This study aims to investigate the perceptions of tourist guides in Türkiye regarding the role of technology in the tourist guiding profession. Design/methodology/approach This research adopted an exploratory qualitative case study approach, conducting semi-structured interviews with 29 professional tourist guides actively working in different regions of Türkiye. Findings The findings reveal the dual roles of technology in tourist guiding. While it can facilitate service quality and enhance experiences, technologies such as robot tourist guides, augmented reality and virtual reality are perceived as disruptive and potentially damaging service quality and tourist experiences. Originality/value The research offers unique insights into the facilitative and disruptive aspects of technological tools in the tourism industry. These findings have direct implications for destination managers, travel agencies and tourist guides worldwide, equipping them with a comprehensive understanding of the role of technology in tourist guiding and guiding their future actions, strategies and policies.

Parameterized ZNCC Image Correlation for Rotary-angle Measurement

Abstract High-accuracy rotary-angle measurement is critical for precise positioning and control of robots, machine arms, and machine tools in industry. Image-based measurement is a very promising technology for angle positioning owing to its simple system, low cost, and good robustness to environments. Image correlation, such as the zero-mean normalized cross correlation (ZNCC) method, can obtain high accuracy image-positioning results, but their efficiency is too low to directly use in industries. This paper proposes to measure the absolute rotary angle using a parameterized ZNCC (PZNCC) image correlation method. The PZNCC expresses the ZNCC coefficient by the image shift parameters explicitly, so that the precise-match point corresponding to the global ZNCC maximum can be efficiently obtained and no image-resampling process is required. As a result, PZNCC is approximately 5 times faster than the classic inverse compositional Gauss–Newton (IC-GN) algorithm. Experiments show that rotary angle measurement based on PZNCC can obtain absolute angle positions with an accuracy of 1.16 arcsec (RMSE).

R as a Game-Changer in Clinical Trial Reporting: Best Practices for TLF Compliance

In clinical trial reporting, the generation of Tables, Listings, and Figures (TLFs) is crucial for data presentation and regulatory submission. Historically, SAS has been the predominant tool for creating TLFs; however, with the increasing adoption of open-source software in the pharmaceutical industry, R has emerged as a viable alternative. This paper presents a comprehensive approach to creating TLFs using R, focusing on the required R packages, strategies for handling missing values, and methods for rounding numeric values. By leveraging R's capabilities, organizations can achieve greater flexibility and efficiency in their reporting processes, potentially reducing costs associated with software licensing and training. Additionally, the integration of R in clinical workflows can facilitate real-time data analysis and enhance decision-making, positioning R as a powerful tool for the pharmaceutical industry’s evolving landscape. This work demonstrates how R can be effectively used for clinical trial reporting, offering significant advantages compared to traditional tools. Keywords: R packages, ggplot2, dplyr,R Shiny for clinical trials, R in clinical trials, Adverse event tables, Data visualization, Data preparation, Regulatory submission, Tables, Listings, and Figures (TLFs)

A Review of Principles, Applications, and Recent Developments in HPTLC and HPLC

High-Performance Thin Layer Chromatography (HPTLC) and High-Performance Liquid Chromatography (HPLC) represent fundamental analytical techniques in modern chemical analysis. These sophisticated separation methods have revolutionized the field of analytical chemistry through their precision, reliability, and versatility. HPTLC, an enhancement of traditional thin-layer chromatography, offers advantages such as minimal sample preparation, simultaneous analysis of multiple samples, and cost-effectiveness. It has found extensive applications in pharmaceutical analysis, natural product research, and food quality assessment. HPLC, characterized by its high resolution, sensitivity, and automation capabilities, has become an indispensable tool in various industries, including pharmaceutical, environmental, and biochemical analyses. The technique employs different modes such as reverse phase, normal phase, and ion exchange, enabling the separation of complex mixtures with remarkable accuracy. Recent technological advancements have led to the development of ultra-high-performance liquid chromatography (UHPLC), offering enhanced speed and resolution. Both HPTLC and HPLC complement each other in analytical laboratories, with HPTLC providing rapid screening and HPLC offering detailed quantitative analysis. The continuous evolution of these techniques through technological innovations ensures their pivotal role in modern analytical chemistry.

The evolution of digitainability in the fashion industry: a bibliometric analysis

Purpose This study investigates the evolution of digital transformation research trends in the context of the fashion industry. The paper analyzes and addresses the impact of digital innovations on three areas of the industry: products, processes and business models.Design/methodology/approach The study examined 277 peer-reviewed articles using various bibliometric tools and indicators, aiming to identify and organize the contributions of the most influential works. Additionally, a critical review of the ten most cited papers in the field was carried out.Findings The study shows that digitalization is not merely a technological trend; rather, it is a transformative force reshaping the fashion industry. It fosters collaboration, innovation and sustainability, creating an ecosystem where businesses can thrive by aligning with circular principles and leveraging digital tools.Research limitations/implications The paper reveals a gap in the scientific systematization and exploration of the variety of applications of digital tools in the fashion industry. The study’s limitations include the keywords chosen for the research and the period of the research from 1998 to 2023.Originality/value The paper pursues to contribute to the current discussion on sustainable practices within the fashion industry, while also setting the foundation for future progress in digital innovation that supports the increasing need for sustainable and personalized products.

Augmented Reality Enhanced Image Processing Systems for Interactive Worker Training in Industrial Automation

This paper explores the integration of augmented reality (AR) with image processing systems to enhance interactive worker training in industrial automation environments. The proposed system leverages AR technology to overlay real-time visual information, such as instructional graphics and step-by-step guidance, onto physical workspaces, improving the learning experience for workers. By incorporating advanced image processing techniques, the system ensures precise recognition of industrial tools, components and machinery, facilitating context aware training that adapts to the real-time actions of the trainee. This approach aims to accelerate the onboarding process, reduce human error, and increase efficiency in manufacturing settings, providing workers with immersive, hands-on learning opportunities while enhancing safety and operational effectiveness. The paper discusses the design, implementation and potential benefits of AR – enhanced image processing systems in industrial training programs, with a focus on scalability and practical deployment in diverse industrial environments. Keywords— Augmented Reality (AR), Image Processing, algorithms, Interactive Training, Industrial automation.

Next Generation of US Policies for Industrial Innovation

Forecasts project that industry will be the highest emitting sector in the United States by 2030. While recent legislation and programs have boosted efforts to decarbonize and bolster the competitiveness of the U.S. industrial sector, the programs must continue and additional action is needed. This note provides an overview of many industrial policy tools federal and state policymakers can use to secure the future of U.S. industries, ranging from continuation and expansion of existing tax credits, procurement policies, and grant programs to new policies, including demand-side and market-based policies.

A Review of the State of the Art 3D Generative Models and Their Applications

Ever since 2022, there has been a large number of 3D generative models that have been devised and published, such as AvatarGen, CityDreamer, and HOLOFUSION. Generally speaking, these models can perform tasks such as generating a 3D human model, creating an unbounded city scene, and constructing a 3D object. And it is not a surprise that 3D generative models are very popular these years because there has been a witness of huge need for 3D models in the global market and the models themselves also serve as both convenient and productive tools for the relevant industries. For instance, 3D generative models can utilize a combination of Generative Adversarial Network (GAN) and Multi-Layer Perceptron (MLP) or Neural Radiance Field (NeRF) or Diffusion Model to produce 3D human model; Autoregressive Model or Feature Extraction + Volume Rendering to generate 3D scenes; Diffusion Model or GAN + MLP to produce 3D objects. This paper tries to present a taxonomy of the main 3D generative models from the angle of the kinds of outputs and strategies employed by different models.

Adaptive Impedance Control of Multirotor UAV for Accurate and Robust Path Following

Unmanned Aerial Vehicles (UAVs) have become essential tools in various industries for tasks such as inspection, maintenance, and surveillance. An Online Impedance Adaptive Controller (OIAC) is proposed for the online modulating of UAV control gains to obtain better performance and stability of tracking curved trajectories than the traditional methods, Model Reference Adaptive Controller (MRAC) and Proportional–Integral–Derivative (PID). Two UAV path planners with minimal jerk and snap were integrated into OIAC, MRAC, and PID. These six controllers were implemented and compared in a simulated UAV with perceptional noise, which follows curved pipelines and avoids obstacles. Experimental results show that the OIAC controller achieves at least an 80% improvement over the PID controller across all trajectory types in terms of the trajectory tracking error. Additionally, OIAC demonstrates an over 20% improvement in jerk trajectories and a more than 30% improvement in snap trajectories when compared to the MRAC controller. These results indicate that OIAC offers enhanced trajectory tracking accuracy and robustness against perceptual noise. Our work presents an advanced controller of a UAV and its preliminary validation in accurate and robust path tracking.

Transitioning to Electric UTVs: Implications for Assembly Tooling

This case report explores the UTVs (utility terrain vehicles) transition from internal combustion engines to electric drive and how the shift will impact the assembly tooling industry. A multiple-case study at manufacturing plants was complemented by an exploratory survey with key stakeholders in the industry. The findings showed that the transition to electric drive is still in its infancy and is likely to accelerate soon. Electric vehicles were generally found to contain fewer components and thus have fewer applications for tightening tools in their assembly. Much of the difference comes from the fact that electric engines require far fewer tightening operations compared to internal combustion engines. However, the assembly of electric components and battery packs requires new advanced tooling solutions. When transitioning to electric drives, manufacturers were found to source their battery packs and electric engines most commonly from external suppliers. This can displace the tooling industry’s business within the segment. Several opportunities and challenges for assembly tool suppliers were identified. Firstly, the transition to electric drive will likely generate significant tooling needs on the manufacturers side. Electric vehicles tend to require more advanced tools and solutions, which likely will benefit premium tool suppliers with Industry 4.0 solutions. There are, however, long-term challenges as electric UTVs have fewer components and fewer tightenings in their assembly process. One long-term opportunity that could potentially offset the decline in tightenings within final assembly is battery pack assembly. This process does not only require a lot of advanced tightenings, but there are also opportunities for other joining techniques. Thus, the assembly tooling business’ biggest opportunities within the UTV industry are likely to shift from the vehicle’s final- to battery pack assembly.

Innovative Teaching of AI-Based Text Mining and ChatGPT Applications for Trend Recognition in Tourism and Hospitality

This research applies a model-based teaching approach aimed at scrutinizing trends in the leisure, tourism, hospitality, recreation, and sport (LTHRS) field by integrating artificial intelligence (AI) along with ChatGPT, project-based learning (PBL), systems thinking, and industrial analysis tools to foster trend recognition skills. The study employs a quasi-experimental design to compare the efficacy of two instructional approaches (exploratory vs. confirmatory) concerning AI literacy and learning outcomes. Notably, the exploratory group exhibits marked improvements in AI knowledge, while the confirmatory group demonstrates enhanced trend recognition ability. Additionally, the research delves into the application effects of AI-based text mining and ChatGPT (AITM) as content analysis tools through four distinct projects (5G’s impact on tourism industries, travel trends caused by metaverse, daylily tour in Huatan Township, and Taiwanese elements in spectator sports), underscoring the substantial efficacy of AITM in capturing diverse themes, albeit with challenges in discerning subtle and subjective labels. These findings highlight the effectiveness of the model-based teaching approach and the multifaceted utility of AI and automated text mining in augmenting trend recognition skills.

A New Approach to Integrating Renewable Energies in the Design of a Shea Kernel Solar Mill

The development of the agri-food sector in West African countries is limited by energy and technological factors. As a result, the main shea nut producers in these countries, Burkina Faso, Nigeria and Mali, export their production to the benefit of large industries in northern countries. One of the most difficult operations involved in transforming shea nuts into shea butter is grinding. Processing shea kernels into shea butter requires a mill or grinder. The types of energy (diesel and electricity) used by these mills are fossil-based, difficult to access and pose huge environmental issues. The objective of this work is to design a mill, operating with photovoltaic solar energy as an energy source, in a design approach using design rules and industrial engineering tools. To this end, a survey on the practices of the networks of agri-food equipment stakeholders on the use of renewable energy and the characterization of the network of solar mill stakeholders were carried out. The results of the surveys and the network characterization of the stakeholders, combined with those of the literature review, made it possible to define rules for integrating renewable energy into the design of the solar mill. The use of functional analysis and a tool to help choose energy-efficient technological solutions enabled to apply the rules for integrating renewable energy into the design of the shea kernel solar mill for validation purpose. The results of the functional tests of the solar mill showed a production capacity of 270 Kg of shea kernel paste, a solar energy consumption of 11,532 kWh equivalent to an energy consumption of 1.82 USD in 6 hours operational time per day.

Exploring the cognitive underpinnings of early hominin stone tool use through an experimental EEG approach.

Technological innovation has been crucial in the evolution of our lineage, with tool use and production linked to complex cognitive processes. While previous research has examined the cognitive demands of early stone toolmaking, the neurocognitive aspects of early hominin tool use remain largely underexplored. This study relies on electroencephalography to investigate brain activation patterns associated with two distinct early hominin tool-using behaviors: forceful hammerstone percussion, practiced by both humans and non-human primates and linked to the earliest proposed stone tool industries, and precise flake cutting, an exclusive hominin behavior typically associated with the Oldowan. Our results show increased engagement of the frontoparietal regions during both tasks. Furthermore, we observed significantly increased beta power in the frontal and centroparietal areas when manipulating a cutting flake compared to a hammerstone, and increased beta activity over contralateral frontal areas during the aiming (planning) stage of the tool-using process. This original empirical evidence suggests that certain fundamental brain changes during early hominin evolution may be linked to precise stone tool use. These results offer new insights into the complex interplay between technology and human brain evolution and encourage further research on the neurocognitive underpinnings of hominin tool use.

Comparison between consumer-oriented and laboratory benchtop near-infrared spectrometers for total soluble solids measurement in grape

Portable near-infrared (NIR) spectrometers are gaining interest as a non-destructive tool for fruit quality determination in the decade. In this study, the performance of the portable NIR spectrometer for TSS prediction of grapes was evaluated and compared to the benchtop spectrometer. A total of 105 table grapes were individually measured NIR spectra at short-wavelength (740 – 1070 nm) and long-wavelength NIR region (1000 – 2500 nm) using SCiO and NIRFlex N-500 spectrometers, respectively. Partial least square (PLS) regression analysis was performed on berry spectra from both devices afterward significant wavelengths were identified and used to develop multiple linear regression (MLR) models. The best PLS prediction model was obtained from SNV pretreating spectra for both devices and spectral data acquired from SCiO showed better prediction performance (= 0.854, SEP = 0.452°Brix) than NIRFlex N-500 spectra (= 0.667, SEP = 0.675°Brix). Low predicting ability was gained for the MLR calibration model for both device with an RPD of about 1.70. From the results, a pocket-size spectrometer has the potential to be a non-destructive sorting and screening tool in fruit industries.

Comparison of EfficientNetB1 Model Effectiveness in Identifying Fish Diseases in South Asian Fish Diseases and Salmon Fish Diseases

The purpose of this study is to evaluate the effectiveness of the EfficientNetB1 model in identifying fish diseases across two distinct datasets: South Asian Fish Diseases and Salmon Fish Diseases. The South Asian Fish Diseases dataset includes seven categories: red bacterial disease, aeromoniasis, gill bacterial disease, fungal saprolegniasis, parasitic disease, and white tail viral disease. The Salmon dataset is divided into two parts: FreshFish and InfectedFish. Using the EfficientNetB1 algorithm, each dataset was separately trained and tested to predict species and disease. Results showed an accuracy of 98.14% for the South Asian Fish Diseases dataset and 99.18% for the Salmon Diseases dataset. These findings support the argument that the model possesses sufficient capability to detect diseases affecting various fish species. This suggests that the model could be a valuable tool in the aquaculture industry for disease management and detection strategies.

Analysis of Industrial Performance Indicators Effects On Enhance The Competitiveness of The Food Sector and The Possibility Of Support in The Iraqi Economy (2003-2020)

This study was aimed on analyze the competitiveness of the food industry sector, and showed the level of industrial and competitive performance of these industries, and the possibility of supporting and enhancing their competitiveness in the Iraqi economy for the period 2003-2020. Competitiveness is considered one of the most important indicators of the efficiency of the food industry tool in local and foreign markets, as competitiveness represents the ability of productive sectors of different types to distribute their products and increase their sales level. Therefore, the food industry in Iraq is considered one of the most important sectors of the manufacturing industries that suffer from problems and obstacles including a decrease in the level of competitive and industrial performance. It is believed that the problem of the research is the weak competitiveness of the food industry in Iraq due to the decline in the quality of manufactured products and the weak level of competitiveness of these products compared to imported products. The results of the study showed a decrease and fluctuation in the contribution of the manufacturing sector to the gross domestic product (GDP) in Iraq. The economic policy was not providing the possibility of an investment climate to support and enhance the private sector in industrial investment. The indicators of production requirements such as labor productivity, worker productivity, and the value added index of the food industries sector began to fluctuate between decrease and increase, and to measure the extent of the reciprocal effects between all parts of the standard model and the extent of its contribution to developing the competitive capacity of this sector.

Automated programming approaches to enhance computer-aided translation accuracy.

With the continued development of information technology and increased global cultural exchanges, translation has gained significant attention. Traditional manual translation relies heavily on dictionaries or personal experience, translating word by word. While this method ensures high translation quality, it is often too slow to meet the demands of today's fast-paced environment. Computer-assisted translation (CAT) addresses the issue of slow translation speed; however, the quality of CAT translations still requires rigorous evaluation. This study aims to answer the following questions: How do CAT systems that use automated programming fare compared to more conventional methods of human translation when translating English vocabulary? (2) How can CAT systems be improved to handle difficult English words, specialised terminology, and semantic subtleties? The working premise is that CAT systems that use automated programming techniques will outperform traditional methods in terms of translation accuracy. English vocabulary plays a crucial role in translation, as words can have different meanings depending on the context. CAT systems improve their translation accuracy by utilising specific automated programs and building a translation corpus through translation memory technology. This study compares the accuracy of English vocabulary translations produced by CAT based on automatic programming with those produced by traditional manual translation. Experimental results demonstrate that CAT based on automatic programming is 8% more accurate than traditional manual translation when dealing with complex English vocabulary sentences, professional jargon, English acronyms, and semantic nuances. Consequently, compared to conventional human translation, CAT can enhance the accuracy of English vocabulary translation, making it a valuable tool in the translation industry.

Implementing FMEA (Failure Mode and Effect Analysis) in a canned corn manufacturing industry: a case study

Assuring quality and food safety is challenging for many companies, as they affect the sustainability of the business. it was identified lack of studies focused on applying risk assessment tools in the food industry by reviewing the literature. This study aims to describe the implementation of Failure Mode and Effect Analysis (FMEA) to identify potential risks and how to prevent them, to assure quality requirements in a corn food plant. This paper follows a case study approach, based on a qualitative methodology. During the execution of FMEA, the hazards and mitigation controls were identified. The results provide evidences of how FMEA can be effective by showing the vulnerabilities of production flow, controls to implement and how to prioritize actions. FMEA contributed to managing risks found for the company where the case study was conducted. This case study can be followed by other companies and different processing technologies, extrapolating the food industry. Key words: FMEA; quality; hazard; corn; food industry.