Field Implementation Research Articles

Needle-induced cavitation: A method to probe the local mechanics of brain tissue

Traditional mechanical characterization of extremely soft tissues is challenging given difficulty extracting tissue, satisfying geometric requirements, keeping tissues hydrated, and securing the tissue in an apparatus without slippage. The heterogeneous nature and structural complexity of brain tissues on small length scales makes it especially difficult to characterize. Needle-induced cavitation (NIC) is a technique that overcomes these issues and can mechanically characterize brain tissues at precise, micrometer-scale locations. This small-scale capability is crucial in order to spatially characterize diseased tissue states like fibrosis or cancer. NIC consists of inserting a needle into a tissue and pressurizing a fluid until a deformation occurs at the tip of the needle at a critical pressure. NIC is a convenient, affordable technique to measure mechanical properties, such as modulus and fracture energy, and to assess the performance of soft materials. Experimental parameters such as needle size and fluid flowrate are tunable, so that the end-user can control the length and time scales, making it uniquely capable of measuring local mechanical properties across a wide range of strain rates. The portable nature of NIC and capability to conduct in vivo experiments makes it a particularly appealing characterization technique compared to traditional methods. Despite significant developments in the technique over the last decade, wide implementation in the biological field is still limited. Here, we address the limitations of the NIC technique specifically when working with soft tissues and provide readers with expected results for brain tissue. Our goal is to assist others in conducting reliable and reproducible mechanical characterization of soft biomaterials and tissues.

AFT Pattimura CSR Pentahelix Collaboration in the Implementation of Waste Management Center Social Innovation Program: NEKAT SA (Negeri Katong Tanpa Sampah)

The Waste Management Center: NEKAT SA (Negeri Katong Tanpa Sampah) program, initiated by PT Pertamina Patra Niaga Aviation Fuel Terminal Pattimura, represents a social innovation aimed at addressing waste management issues in Negeri Laha through a pentahelix collaboration approach. This approach entails the cooperation of five key elements: businesses, government, academia, media, and the community. The program integrates four community empowerment initiatives through CSR AFT Pattimura partners, functioning as community groups that implement waste management programs across three primary stages: upstream (education and waste collection), process (converting waste into valuable products), and downstream (establishing a circular economy). The program emphasizes zero waste and circular economy principles by managing organic, inorganic, and residual waste into valuable products that can enhance the income of beneficiary communities. This study employs a descriptive analytical method to delineate the roles of each stakeholder in the implementation of the NEKAT SA program. Primary data were gathered from field implementation processes, while secondary data were sourced from literature reviews and documentation. The findings indicate that the pentahelix collaboration approach in the NEKAT SA program is effective in mitigating waste issues, increasing community income, and fostering a culture of cleanliness and health. The success of this program underscores the importance of sustained commitment from all involved stakeholders to ensure the ongoing positive environmental and economic impacts in Negeri Laha.

Development of a Gold Nanoparticle-Based Sensor for Authentication of Organic Milk Based on Differential Levels of miRNA.

Dairy production systems significantly impact environmental sustainability, animal welfare, and human health. Intensive farming maximizes output through high-input practices, raising concerns about environmental degradation, animal welfare, and health risks from antibiotic residues. Conversely, organic farming emphasizes sustainable practices, animal welfare, and minimal synthetic inputs, potentially enhancing biodiversity, soil health, and milk quality. MicroRNAs (miRNAs), non-coding RNAs regulating gene expression, are promising biomarkers due to their response to various conditions. In this study, miRNAs bta-miR-103 and bta-miR-155, which are abundant in milk from pasture-fed cows, were selected. Additionally, bta-miR-215, which is abundant in milk fat from intensive systems, was also studied, in order to differentiate dairy production systems. A novel, cost-effective gold nanoparticle (AuNP)-based sensor was developed for miRNA detection, leveraging the unique plasmonic properties of AuNPs for visual detection. The method involves functionalizing AuNPs with complementary RNA probes and detecting miRNA-induced aggregation through colorimetric changes. This rapid, results in 30 min, and sensitive, visual limit of detection of 200 nM, assay requires minimal instrumentation and can be easily interpreted, offering significant advantages for field implementation in characterizing dairy production systems. This study demonstrates the successful application of this sensor in detecting miRNAs in 350 nM miRNA spiked raw milk, highlighting its potential for in situ dairy industry applications.

Effect of silica nanoparticles on crude oil emulsions stabilized by a natural surfactant from Fenugreek Seeds and polyacrylamide for subsurface oil mobilization applications

Emulsions represent a sustainable method for subsurface resource recovery due to their superior performance in pore plugging, mobility control and interfacial tension reduction. However, the stability of emulsion plays a vital role in determining the success of field implementation. In this work, a novel formulation is reported for emulsions wherein they have been stabilized by natural surfactant extracted from fenugreek seed. In this work, two emulsion systems are reported, that is surfactant polymer and nanoparticle-surfactant-polymer emulsion. Surfactant-polymer emulsions are stabilized by natural surfactant whereas nanoparticle-surfactant-polymer emulsion are stabilized in presence of nanoparticle and natural surfactant. Both the emulsions exhibit shear thinning profile as observed from rheological studies. Nanoparticle-surfactant-polymer emulsions were found to be thermally stable than that of Surfactant-polymer emulsions as evident from microscopic and rheological studies. An increase in temperature has the unfavorable impact on surfactant-polymer emulsion as huge drop in viscosity profile was observed. The viscosity profiles of nanoparticle-surfactant-polymer emulsions were found to be best fitted by the Krieger-Dougherty model with the value of coefficient correlation R2 = 0.99. The contact angle studies showed that NSP solution can reduce the contact angle to much lower value, which is 111˚ to 30˚ whereas surfactant-polymer solution was able to reduce from 116˚ to 57˚ only. Adsorption of silica nanoparticle was also observed in presence of sandstone and decrease in 27.32 % of peak absorbance was observed in span of 20 days. Therefore, finding of the study indicates that nanoparticle-surfactant-polymer emulsions has potential to perform better than surfactant-polymer emulsions, even at elevated temperature, and can be a better and cost-effective alternative to the conventional emulsions used in oilfield applications.

Analisis Kandungan Material Campuran Aspal Panas Mix Design AC-WC terhadap Rekonstruksi Jalan Ruas N-029.1 Bts. Kab. Muaro Jambi / Kab. Tanjabtim-Bts. Kab. Tanjabbar

To obtain a good aggregate mixture, efforts are made to keep the gradation of the aggregate mixture in the middle of its specification range. Based on road reconstruction work on section N-029.1 Bts. Regency. Muaro Jambi / Kab. Tanjabtim – Bts. Regency. Tanjabbar is at the point (sta 16+000 – 16+400) where the asphalt that is spread has a soft/liquid texture, which can be seen from the process of compacting and leveling the asphalt layer which is carried out by operating with a number of passes that is not in accordance with what was planned during the trial mix. So it is necessary to carry out an analysis to evaluate the material composition of the hot mix asphalt design layered by Asphalt Concrete-Wearing Course (AC-WC). The method used is to use secondary data analysis methods in the form of DMF, JMF and gradation data. The results of the analysis of the composition of the hot asphalt mix material show that there are differences in the total proportion value of the mixture, where in the DMF the planned proportion is 32.97% for fine aggregate, 28.26% for medium aggregate, 18.84% for coarse aggregate, 1.88 % for filler, and 5.80% for asphalt. At JMF the proportion of results from weighing is 37.68% for Hot Bin I (fine aggregate), 35.80% for Hot Bin II (medium aggregate), 18.84% for Hot Bin III (coarse aggregate), 1.88% for filler, and 5.80% for asphalt. In field implementation, the proportion of the total mixture produced was 17.90% for Hot Bin III (coarse aggregate), 33.91% for Hot Bin II (medium aggregate), 40.51% for Hot Bin I (fine aggregate), 1, 88% for filler, and 5.81% for asphalt.

An Improved Numerical Simulation Method for Rockbolt Fracture and Its Application in Deep Extra-Thick Coal Seam Roadways

An improved method for rockbolt fracture is proposed in this paper to determine the exact fracture position of rockbolts simulated using cable structural elements (cableSELs) in FLAC3D. This method employs the total elongation of the free segment of the rockbolt as the fracture criterion. The maximum deformation position is identified by comparing the length of each cableSEL in the free segment, leading to the fracture. The simulation results validated through a rockbolt tensile test closely match actual conditions. The proposed method was used to optimize the roadway support in deep extra-thick coal seams (DECSs). Optimized parameters were obtained by simulating and analyzing different lengths and spacings of rockbolts and anchor cables. The field implementation conducted shows that the optimized deformation and support strength of the roadway meet safety needs.

Comparing Rover and Helicopter Planetary Mission Architectures in a Mars Analog Setting in Iceland

The Rover–Aerial Vehicle Exploration Network project field-tested planetary mission operations within a Mars analog environment in Iceland using stand-alone rover and helicopter architectures. Mission planning, implementation, and results are reported for the rover mission and briefly summarized for the helicopter mission. The outcomes of both missions are subsequently compared. Field implementation occurred from 2022 July to August at the Holuhraun lava flow. The rover science operations team executed a 14 sol (Martian day) mission that achieved mission, science, and sampling goals, including the contextualization, acquisition, and planned caching of two eolian and two rock samples. The helicopter science operations team executed a plan of comparable length but emphasized different science goals given long-range flight capabilities and landing limitations. The resolution and targetability of the rover payload enabled more detailed analyses, whereas the helicopter was better able to map flow-scale morphologies. The rover’s exploration was limited by daily mobility duration limits and hazardous terrain, whereas the helicopter’s exploration was constrained by landing site hazards. Resource limitations resulted from lengthier rover drives and data-volume-intensive helicopter imaging surveys. Future missions using combined rover–helicopter architectures should account for each spacecraft’s resource needs and acknowledge system strengths in different geologic settings. Both missions served to establish operations strategies and mission outcomes to be applied to future combined rover and helicopter mission architectures, while the helicopter mission also evaluated strategies and outcomes for future stand-alone airborne missions. Findings in this work are relevant to future missions seeking to optimize strategies for planetary mission operations.

STA-SST: Spatio-temporal time series prediction of Moroccan Sea surface temperature

Global Sea Surface Temperature (SST) trends have garnered significant attention in several ocean-related domains, including global warming, marine biodiversity, and environmental protection. This involves having an accurate and efficient forecast of future SST to ensure early detection and response in time to these events. Deep learning algorithms have become popular in SST prediction recently, although directly obtaining optimal prediction results from historical observation data is not simple. In this paper, we propose STA-SST, a new deep learning approach for forecasting SST, by combining the temporal dependencies of SST using the Bidirectional Long Short-Term Memory (BiLSTM) model, spatial features extracted from the convolution layer, and relevant information from the attention mechanism. To assess how well the Attention-BiLSTM with convolution layer predicts SST, we conducted a case study in the Moroccan Sea, concentrating on five different areas. The proposed model was compared against alternative forecasting models, including LSTM, XGBoost, Support Vector Regression (SVR), and Random Forest (RF). The experimental results show that STA-STT produces noticeably the best prediction results and is a solid choice for field implementation.

Optimization of microgrid operations using renewable energy sources

Microgrids, comprising localized energy systems capable of operating independently or in conjunction with the main grid, are increasingly being recognized as vital components of modern energy infrastructure. The integration of renewable energy sources (RES) into microgrids offers significant potential for enhancing operational efficiency, sustainability, and resilience. This paper presents an overview of recent advancements and methodologies for optimizing microgrid operations utilizing renewable energy sources. The optimization of microgrid operations involves the strategic coordination and management of diverse energy resources, including solar photovoltaic (PV) systems, wind turbines, and energy storage systems (ESS). Key objectives include minimizing operational costs, reducing greenhouse gas emissions, ensuring reliable power supply, and maintaining system stability. Advanced optimization techniques, such as model predictive control (MPC), mixed-integer linear programming (MILP), and heuristic algorithms, play a crucial role in achieving these objectives by enabling the dynamic adjustment of energy generation and distribution in response to real-time conditions. A critical aspect of microgrid optimization is the accurate forecasting of renewable energy generation and load demand. Machine learning (ML) and artificial intelligence (AI) algorithms have been effectively employed to enhance prediction accuracy, thereby improving decision-making processes. Furthermore, the integration of ESS, such as batteries and flywheels, helps to address the intermittency of RES, providing a buffer that can store excess energy during periods of high generation and release it during peak demand. The implementation of demand response (DR) strategies within microgrids further contributes to optimization efforts. By incentivizing consumers to adjust their energy usage patterns in response to price signals or grid needs, DR programs help to balance supply and demand, reduce peak loads, and enhance overall grid reliability. Case studies and field implementations demonstrate the practical benefits of optimized microgrid operations. For instance, microgrids incorporating high shares of RES have been shown to achieve significant cost savings, improved energy security, and reduced environmental impacts. These successes underline the importance of ongoing research and development in optimization techniques and the need for supportive policy frameworks to facilitate the broader adoption of microgrids. In conclusion, optimizing microgrid operations using renewable energy sources presents a promising pathway toward a more sustainable and resilient energy future. Continued advancements in optimization algorithms, predictive analytics, and integrated system design are essential for unlocking the full potential of microgrids, ensuring they can effectively meet the evolving energy demands and environmental challenges of the 21st century. Keywords: Optimization, Microgrid, Operations, Renewable Energy, Energy Sources.

Enhancing practicality of deep learning for crop disease identification under field conditions: insights from model evaluation and crop-specific approaches.

Crop diseases can lead to significant yield losses and food shortages if not promptly identified and managed by farmers. With the advancements in convolutional neural networks (CNN) and the widespread availability of smartphones, automated and accurate identification of crop diseases has become feasible. However, although previous studies have achieved high accuracy (>95%) under laboratory conditions (Lab) using mixed data sets of multiple crops, these models often falter when deployed under field conditions (Field). In this study, we aimed to evaluate disease identification accuracy under Lab, Field, and Mixed (Lab and Field) conditions using an assembled data set encompassing 14 diseases of apple (Malus × domestica Borkh.), potato (Solanum tuberosum L.), and tomato (Solanum lycopersicum L.). In addition, we investigated the impact of model architectures, parameter sizes, and crop-specific models (CSMs) on accuracy, using DenseNets, ResNets, MobileNetV3, EfficientNet, and VGG Nets. Our results revealed a decrease in accuracy across all models from Lab (98.22%) to Mixed (91.76%) to Field (71.55%) conditions. Interestingly, disease classification accuracy showed minimal variation across model architectures and parameter sizes: Lab (97.61-98.76%), Mixed (90.76-92.31%), and Field (68.56-73.81%). Although CSMs were found to reduce inter-crop disease misclassifications, they also led to a slight increase in intra-crop misclassifications. Our findings underscore the importance of enriching data representation and volumes over employing new model architectures. Furthermore, the need for more field-specific images was highlighted. Ultimately, these insights contribute to the advancement of crop disease identification applications, facilitating their practical implementation in farmer's fields. © 2024 Society of Chemical Industry.

The Contribution of Technological Innovations to Enhance Education for Sustainable Development

Technological innovations have revolutionized numerous sectors, including education, by providing new tools and methodologies that enhance learning experiences and outcomes. Governments worldwide made teaching and learning educational technology compulsory during the COVID-19 pandemic in all educational fields. Despite the numerous advantages, integrating technological innovations into the educational process is challenging. There remains a significant gap between the potential of these technologies and their actual implementation in classrooms and educational fields. Issues such as inadequate infrastructure, lack of teacher training, and resistance to change hinder the effective use of technology in education. Moreover, the rapid movement of technological advancements often exceeds the ability of educational institutions to adapt, leading to underutilization of available resources and missed opportunities for enhancing the educational process. While there is a growing body of literature on the subject, much of the existing research is fragmented and lacks a comprehensive framework that connects theory with practice. This study employs qualitative analysis to obtain information about the experiences, benefits, and discusses the challenges that may arise using educational advancements and techniques in language learning. It aims to provide teachers and educators with a better understanding of the impacts of using technology in the educational process and the development of language learning. In the paper, the researcher defines technology, in addition to Information Communication Technology (ICT), and reviews previous studies on the effective use of technological tools across educational settings. Furthermore, the study sheds light on the role of using modern technological innovations that can be effectively utilized and integrated into education, not only for learners but also for their instructors who need to achieve professional development, by increasing the engagement of using technology in educational settings, motivation, as well as improving accessibility and convenience.

Adopting COBIT 2019 for the Evaluation of Information Technology Risk Management in a Startup Company

The start-up XYZ operates in tourism and digital agencies, where every business activity relies on IT from the outset. We must implement good risk management to ensure optimal operation of all business processes and minimize risks, particularly in light of the post-pandemic changes. The study uses the COBIT 2019 framework to evaluate the risk management of the company's business processes. The study comprises five stages: a preliminary study, a research planning phase, data collection, data analysis, and a recommendation phase. As a result, the company has successfully identified potential risks, along with their respective impact levels, and gained insights into IT-related issues. However, the company still requires an extensive evaluation for its field implementation. While the company believes it has effectively managed risks, subsequent assessments reveal that it is still in the early stages, necessitating numerous improvements in risk management implementation. This is evident from the evaluation of the EDM03 and APO12 processes; the company's capacity is currently at level 1 with a gap of 2. The overarching recommendation is for companies to document all past risks, standardize SOPs, and regularly evaluate them to ensure continuous improvement in future business processes.

Exploring spin photovoltaics in defective armchair phosphorene nanoribbons

This study explores the spin photovoltaic potential within armchair phosphorene nanoribbons (APNRs) that feature a periodic distribution of monovacancies (MVs) under the influence of light radiation. We investigate spin-semiconducting behavior induced by MV defects by utilizing both the mean-field Hubbard approximation and the self-consistent non-equilibrium Green's function model. This behavior is characterized by localized and anisotropic band structures around the Fermi energy, particularly within the antiferromagnetic phase. The existence of spin-splitting band gaps in defective APNRs not only enables the crafting of spin-optoelectronic nanodevices but also allows for the manipulation of electronic structure behavior with applied electric fields in both the vertical and transverse directions. Notably, the implementation of electric fields, offering tunability in electronic structure, results in varied spin photovoltaic responses encompassing a broad spectrum of photon energies from visible to ultraviolet. This research reveals promising avenues for advancing the field of spin-optoelectronic devices by MVs in APNRs.

Implementation of external magnetic field to improve strength of St37 steel resistance spot weld

The magnetic assist technique involves the interaction between an external magnetic field and electrical current which produces Lorentz force that influences the flow pattern of molten metal and ultimately impacts the appearance and microstructure of the weld. Many parameters may influence on this process such as welding current, time, and force as well as the working magnet distance (MD). In this study, the effects of the distance between the permanent magnet’s MD on the heat-affected zone (HAZ) and weld nugget zone (NZ) were examined through mechanical and macro- and microstructural analyses. The results demonstrated that MD has a strong influence on the magnetic flux density which determines the joint appearance, quality, and microstructure of St37 steel. Results showed that with increasing MD, HAZ increased from 8 to 22 mm2 while NZ decreased from 26 to 19 mm2, and also, the grain size increased with increasing MD and reaching 48 µm at MD was set to 9 cm. Moreover, hardness decreased at both areas through increasing MD from (120–110) HV at HAZ and from (170–150) HV at NZ. Under the action of (electromagnetic filed) EMF, weld tensile shear strength and the cross tensile strength give the highest values equal to 172 MPa and 155 MPa, respectively, when MD is set to 4.5 cm. Besides, soundness joint was obtained at MD = 4.5 cm which confirms that this is the best distance between magnets.

The Future of Environmental Engineering Technology: A Disruptive Innovation Perspective

Scientific and technological revolutions and industrial transformations have accelerated the rate of innovation in environmental engineering technologies. However, few researchers have evaluated the current status and future trends of technologies. This paper summarizes the current research status in eight major subfields of environmental engineering—water treatment, air pollution control, soil/solid waste management, environmental biotechnology, environmental engineering equipment, emerging contaminants, synergistic reduction of pollution and carbon emissions, and environmental risk and intelligent management—based on bibliometric analysis and future trends in greenization, low carbonization, and intelligentization. Disruptive technologies are further identified based on discontinuous transformation, and ten such technologies are proposed, covering general and specific fields, technical links, and value sources. Additionally, the background and key innovations in disruptive technologies are elucidated in detail. This study not only provides a scientific basis for strategic decision-making, planning, and implementation in the environmental engineering field but also offers methodological guidance for the research and determination of breakthrough technologies in other areas.

마음챙김을 활용한 중학교 도덕과 수업의 현장 적합성 검토

Objectives This research endeavors to investigate the nuanced application of mindfulness, particularly concerning its directionality in field implementation. Methods In the course of this study, a mindfulness-infused moral curriculum was administered to 75 middle school students situated in Gyeonggi-do. Subsequently, students' opinions were collected, and meaningful alterations were assessed via pre- and post-testing utilizing scales gauging emotional control and compassionate love. Results While no noteworthy fluctuations were observed in emotional control scores pre- and post-course, a substantial increase was noted in both the overall compassion score and the score taking other's perspectives. Open-ended responses predominantly reflected affirmative sentiments, outweighing negative sentiments. As a positive answer, there were many opinions that it was possible to have a peaceful mind, and as a negative answer, meditation was boring and difficult. Conclusions The outcomes of this investigation advocate for the allocation of adequate temporal resources, the establishment of connections beyond the classroom, and the integration of a humanities-oriented approach as imperative prerequisites for the efficacious incorporation of mindfulness into educational contexts.

EUROPEAN APPROACHES AND REGULATORY MECHANISMS OF ECOLOGICAL AND HYGIENIC MONITORING FROM THE PERSPECTIVE OF POTENTIAL HARMONIZATION AND IMPLEMENTATION INTO DOMESTIC LEGISLATION

Background. The usage of pesticides against pests is also accompanied by potential negative effects on non-target organisms. The risk of harm to biodiversity is quite significant. Implementation of ecological and hygienic monitoring strategies, which is quite effectively applied in the countries of the European Union, will allow to reduce potential threats, financial costs and unforeseen consequences for beneficial insects, birds, aquatic organisms, etc. Aim: analysis of European approaches and regulatory mechanisms of ecological and hygienic monitoring from the standpoint of potential harmonization and implementation in Ukrainian legislation field. Materials and methods. Data from the literature and regulatory documents of the European Union in the field of environmental assessment and ecological and hygienic monitoring were used for the analysis. An array of literary sources was analyzed, which included the most relevant, up-to-date articles over the past 5 years and valid regulatory documents that have the greatest potential for implementation into the national regulatory framework. Results. In the EU and the US, a strict risk assessment process is in place to obtain permission to use pesticides, taking into account the potential impact on non-target organisms. Risk assessment of the use of chemical plant protection on the ecosystem and monitoring of the condition of non-target kinds of objects in the territories of intensive agricultural management is carried out at the stage of pre-registration tests. State regulatory agencies primarily require data on average lethal doses and concentrations for some bird kinds (Colinus virginianus, Anas platrynchos, Perdix perdix, Coturnix japonica). However, there are studies that describe the effects on smaller songbirds that are more sensitive to acute poisoning, therefore, to consider the established standards only for large birds is rather imperfect and needs to be refined in Ukrainian practice in the future. The European rules of environmental risk assessment (ERA) have undergone significant changes in recent decades, which have caused many scientists to worry about insufficient plant protection, slow implementation of scientific developments, simplification of environmental relevance and priority. Also, in many normative documents on pesticides, a political trail is traced, which is sometimes difficult to define and measure. Conclusion. The adoption and implementation of new laws in the field of chemical plant protection in the EU countries are the basis for improving the legislation of Ukraine in this field and ensuring state regulation in the handling of pesticides and agrochemicals.

H/O edge passivated B/N co-doped armchair graphene nanoribbon field-effect transistors, based on first principles

This study employs the nonequilibrium Green’s function method in conjunction with density functional theory to fabricate and analyze a Graphene Nanoribbon Field-Effect Transistor (GNRFET). The co-doping of B and N creates built-in electric fields, thereby reducing leakage current. The results demonstrate effective control performance of planar gates, as evidenced by an increase in IDS with rising gate voltage. Furthermore, a negative differential conductance phenomenon is observed at bias voltages exceeding 0.7 V, exhibiting correlation with transmission spectra and energy band structures. To precisely illustrate the electron distribution within the doped scattering region, calculations involving transport paths, the molecular projection self-consistent Hamiltonian (MPSH), and the emission eigenvalues and eigenstates of the device are conducted. This research provides a reference for exploring and developing smaller and more energy-efficient AGNR field effect transistor designs and implementations. The principal objective of this paper is to investigate the potential applications of these smaller, more energy-efficient devices.

Methodologies for the collection of parameters to estimate dust/soil ingestion for young children.

Heavy metals, pesticides and a host of contaminants found in dust and soil pose a health risk to young children through ingestion. Dust/soil ingestion rates for young children can be estimated using micro-level activity time series (MLATS) as model inputs. MLATS allow for the generation of frequency and duration of children's contact activities, along with sequential contact patterns. Models using MLATS consider contact types, and transfer dynamics to assign mechanisms of contact and appropriate exposure factors for cumulative estimates of ingestion rates. The objective of this study is to describe field implementation, data needs, advanced field collection, laboratory methodologies, and challenges for integrating into and updating a previously validated physical-stochastic MLATS-based model framework called the Child-Specific Aggregate Cumulative Human Exposure and Dose (CACHED) model. The manuscript focuses on describing the methods implemented in the current study. This current multidisciplinary study (Dust Ingestion childRen sTudy [DIRT]) was implemented across three US regions: Tucson, Arizona; Miami, Florida and Greensboro, North Carolina. Four hundred and fifty participants were recruited between August 2021 to June 2023 to complete a 4-part household survey, of which 100 also participated in a field study. The field study focused on videotaping children's natural play using advanced unattended 360° cameras mounted for participants' tracking and ultimately conversion to MLATS. Additionally, children's hand rinses were collected before and after recording, along with indoor dust and outdoor soil, followed by advanced mass analysis. The gathered data will be used to quantify dust/soil ingestion by region, sociodemographic variables, age groups (from 6 months to 6 years), and other variables for indoor/outdoor settings within an adapted version of the CACHED model framework. New innovative approaches for the estimation of dust/soil ingestion rates can potentially improve modeling and quantification of children's risks to contaminants from dust exposure.

Review of Literature Studies Regarding the Implementation of Business Process Management

This research was carried out with a literature review study to find out findings regarding the development of Business Process Management (BPM) and its implementation in business and various fields in the era of increasingly rapid digitalization and globalization. This research was conducted to complement and expand literature sources regarding BPM from previous research. The method used is a study literature review of 30 internationally indexed articles from Google Scholar from 2020-2024. The results of the literature review study analysis explain BPM as a foundation for business processes in various fields, especially digital transformation and digital innovation. BPM also contributes to BCT, CBP, quality management, IT, AI, IoT, PPM, ABPMS and others. However, it does not have a significant effect on measuring financial risk in company performance so that it contributes to the literature and development of this topic for researchers and practitioners in the future.