Transparent Model Research Articles

Agro-Blockchain Markets (ABM): A secure and transparent model for smallholder farmers

This paper introduces the concept of Agro-Blockchain Markets (ABM), a system that applies blockchain technology to agricultural trade to enhance transparency, fair pricing, and trust among stakeholders. By leveraging blockchain's decentralized, immutable, and transparent nature, ABM enables smallholder farmers to participate in secure and efficient markets, bypassing intermediaries and gaining improved market access. The paper discusses the design and functionality of ABM, highlighting key components such as smart contracts and decentralized exchanges. It also explores the potential benefits for smallholder farmers, including financial inclusion, price stability, and reduced fraud. However, challenges such as technology adoption barriers, infrastructure limitations, and costs are acknowledged. Recommendations for policymakers, agricultural stakeholders, and tech developers are provided, emphasizing the need for supportive regulations, partnerships, and user-friendly blockchain platforms to ensure the successful implementation of ABM. The paper concludes that ABM can significantly empower smallholder farmers and promote sustainable agricultural practices.

Emerging Trends in Real-time Recommendation Systems: A Deep Dive into Multi-behavior Streaming Processing and Recommendation for E-commerce Platforms

This paper aims to assess the effectiveness of recommendation systems, focusing on multi-behavior streaming processing to enhance accuracy. It studies recommendation system performance using AI and data science, focusing on user behaviour, algorithm refinement, and satisfaction. E-commerce, streaming, and social media datasets with 93 anonymous participants were employed in experiments. These files from 93 anonymized users show typical internet use. Data anonymization and strict data management ensured anonymity. These databases track clicks, views, purchases, and ratings for empirical research and model evaluation. Collaborative filtering, matrix factorization, and TensorFlow/Keras train and assess application-specific recommendation models. Multi-behavior streaming processing and recommendation accounts assess each method's pros and downsides, system correctness, efficacy, and user involvement. The outcome compares domain-wide recommendation system precision, recall, NDCG, and conversion rate. Multi-behavior streaming processing adapts to user preferences and interactions to improve model accuracy and adaptability. Multi-behavior streaming processing improved model accuracy and flexibility by reacting to user inputs and choices. With error margins for all significant metrics, statistical significance was confirmed. The findings suggest that recommendation systems with real-time adaptation and multi-behavior streaming processing can improve user satisfaction and engagement in the changing digital landscape. It encourages algorithm advancement, model interpretation, user-centric evaluation, and ethics to improve information retrieval and personalisation. The study concluded that algorithm refinement, transparent model interpretation, user-centric evaluation, and ethical problems including data protection and bias mitigation increase information retrieval and personalisation. For durable and flexible recommendation systems, research should improve multi-behavior processing and sectoral applications.

APLICABILIDADE DE PROGRAMAS DE ENVIRONMENTAL, SOCIAL AND GOVERNANCE (ESG) NO SETOR DO E-COMMERCE NA INDÚSTRIA 4.0

The rise of Industry 4.0 marks a new phase in the digital transformation of industrial processes, encompassing advanced technologies like the Internet of Things (IoT), big data, artificial intelligence (AI), and intelligent automation. In the e-commerce sector, these innovations have facilitated unprecedented operational efficiency, yet the growth in digital capabilities has also led to heightened expectations for sustainable and ethical business practices, aligned with Environmental, Social, and Governance (ESG) frameworks. This study explores the impact of integrating ESG practices within ecommerce, leveraging Industry 4.0 technologies to enhance operational sustainability and transparency. A systematic review of literature and data was conducted, revealing that robust ESG policies in e-commerce contribute to competitive advantage by attracting investment, fostering consumer loyalty, and meeting regulatory standards. Findings show that Industry 4.0 technologies can aid in environmental management by reducing carbon emissions, waste, and energy consumption through optimized logistics and sustainable packaging. The implementation of circular economy principles further demonstrates how e-commerce companies can reduce their environmental footprint, ensuring alignment with consumer demands for accountability. Moreover, sound governance and digital security play crucial roles in maintaining customer trust and protecting sensitive data, underscoring the broader relevance of ESG practices. This study concludes that the integration of ESG with Industry 4.0 technologies is essential for the long-term success of e-commerce, as it fosters a responsible, transparent, and sustainable business model that balances economic growth with social and environmental stewardship.

Study on the Influence Characteristics of Excavation Face Instability of New Tunnels Orthogonally Crossing Existing Tunnels

This study aims to solve the problem of stability of the excavation surface of a new tunnel crossing an existing tunnel orthogonally. The relative horizontal spacing between the two tunnels is taken as an influencing factor, and transparent soil model tests are conducted and expanded with numerical simulations. Finally, the active destabilization mechanism and influence characteristics of the excavation surface of the two tunnels at different horizontal spacings, vertical spacings, and tunnel diameter ratios are obtained. The results show that when the excavated face is destabilized, the existing tunnel located in front of and above the excavation surface limits the development of the upper “silo” and the transfer of soil stress in the destabilized area, and the ultimate support pressure is reduced by 17.6% and 8.7%, respectively. This effect increases as the vertical spacing between the two tunnels decreases and the tunnel diameter ratio increases. At this point, the deformation trend of the existing tunnel cross-section is reflected as “elliptical-shaped”. This trend is more apparent when the vertical spacing between the two tunnels and the tunnel diameter ratio are decreased. The protection of the existing tunnel should be strengthened at this time.

Orthogonality catastrophe beyond bosonization from post-selection

We show that the dynamics induced by post-selected measurements can serve as a controlled route to access physical processes beyond the boundaries of Luttinger liquid physics. We consider a one-dimensional fermionic wire whose dynamics results from a sequence of weak measurements of the fermionic density at a given site, interspersed with unitary hopping dynamics. This realizes a non-Hermitian variant of the celebrated instance of a local scatterer in a fermionic system and its ensuing orthogonality catastrophe. We observe a distinct crossover in the system's time evolution as a function of the fermion density. In the high-density regime, reminiscent of the Hermitian case, a bosonized version of the model properly describes the dynamics while, as we delve into the low-density regime, the validity of bosonization breaks down, giving rise to irreversible behavior. Notably, this crossover from reversible to irreversible dynamics is nonperturbative in the measurement rate and can manifest itself even with relatively shallow measurement rates, provided that the system's density remains below the crossover threshold. Our results render a conceptually transparent model for exploring nonperturbative effects beyond bosonization, which could be used as a stepping stone to explore novel routes for the control of nonlinear dynamics in low-dimensional quantum systems. Published by the American Physical Society 2024

Mixed-variable graphical modeling framework towards risk prediction of hospital-acquired pressure injury in spinal cord injury individuals

Developing machine learning (ML) methods for healthcare predictive modeling requires absolute explainability and transparency to build trust and accountability. Graphical models (GM) are key tools for this but face challenges like small sample sizes, mixed variables, and latent confounders. This paper presents a novel learning framework addressing these challenges by integrating latent variables using fast causal inference (FCI), accommodating mixed variables with predictive permutation conditional independence tests (PPCIT), and employing a systematic graphical embedding approach leveraging expert knowledge. This method ensures a transparent model structure and an explainable feature selection and modeling approach, achieving competitive prediction performance. For real-world validation, data of hospital-acquired pressure injuries (HAPI) among individuals with spinal cord injury (SCI) were used, where the approach achieved a balanced accuracy of 0.941 and an AUC of 0.983, outperforming most benchmarks. The PPCIT method also demonstrated superior accuracy and scalability over other benchmarks in causal discovery validation on synthetic datasets that closely resemble our real dataset. This holistic framework effectively addresses the challenges of mixed variables and explainable predictive modeling for disease onset, which is crucial for enabling transparency and interpretability in ML-based healthcare.

Experimental method for internal deformation measurement of 3D solids with embedded cracks based on 3D printing and digital speckle techniques

Quantification of the internal deformation of fractured solids such as rock masses under external loads, especially the deformation around internal fractures, is crucial for understanding and predicting the failure of fractured solids. However, it is very difficult to achieve the goal using conventional experimental methods. In this study, we proposed a novel internal deformation measurement method based on three-dimensional (3D) printing and digital speckle techniques. The 3D printing technology was applied to fabricate a 3D transparent model with an embedded elliptical crack and to set a speckle pattern on the internal section of the transparent model. The digital image correlation (DIC) method was used to determine the internal deformation field of the 3D fractured model. The measured displacements and strains of the internal sections in the fractured model were compared with the numerical simulation results. The comparison indicates that the proposed experimental method can well determine the deformation inside the 3D model. This built-in speckle method can realize real-time observation of the internal deformation of a 3D solid model in a non-contact and non-destructive manner.

Thermodynamically Traceable Calorimetric Results for Aqueous Sodium Chloride Solutions from T = (273.15 to 373.15) K up to the Saturated Solutions: Part 2 —The Quantities Associated with the Partial Molar Heat Capacity

AbstractIn previous articles (Partanen and Partanen in J. Chem. Eng. Data 65: 5226 − 5239 (2020), J. Solution Chem. 52: 1352 − 1385 (2023)), we presented a traceable and transparent three-parameter model for thermodynamic activity and enthalpy quantities in aqueous NaCl solutions. The model is based on extended Hückel equations with parameters B, b1, and b2 and it applies from T = 273.15 to 373.15 K up to the saturated solutions. These studies demonstrate that the model explains the literature data of almost all thermodynamic quantities including apparent and partial molar enthalpies within experimental error. In the model, the ion-size parameter in the Debye–Hückel equation, B, is regarded as a constant while the parameters of the coefficients of the linear and quadratic molality terms, b1 and b2, respectively, possess quadratic temperature dependences. In this study, the results obtained for the heat capacity quantities of NaCl(aq) are considered. We show that the available heat capacity literature for these solutions can be predicted at least satisfactorily up to the saturated solutions with our new model. Following this success, we supplement the existing thermodynamic tables with the new values for the relative apparent and partial molar heat capacities for NaCl solutions. It is likely that the new tables contain the most reliable values determined so far even though no calorimetric data were used in the parameter estimation of our model. Graphical Abstract

Real-space perspective on dephasing in solid-state high harmonic generation

We develop and demonstrate a fully real-space perspective on high harmonic generation (HHG) in crystals. Due to the Wannier-Stark localization induced on subcycle timescales in the presence of a strong field, real-space descriptions are natural for strongly driven solids. Our approach allows us to address the origin of the extremely short dephasing times, which appear necessary for agreement between experimental HHG measurements and theoretical calculations generally performed in reciprocal space. We develop a physically transparent model of real-space dephasing which relates its rate to the distance between different sites in a laser-driven lattice. Our approach leads to well-structured high harmonic spectra at the microscopic level, reproduces the results of macroscopic propagation, and demonstrates that the requirement for ultrafast dephasing times stems from the need for suppressing recombination events with large electron-hole separations during radiative recombination. Published by the American Physical Society 2024

Human centered design of AI-powered Digital Therapeutics for stress prevention: Perspectives from multi-stakeholders' workshops about the SHIVA solution

BackgroundAI-powered Digital Therapeutics (DTx) hold potential for enhancing stress prevention by promoting the scalability of P5 Medicine, which may offer users coping skills and improved self-management of mental wellbeing. However, adoption rates remain low, often due to insufficient user and stakeholder involvement during the design phases. ObjectiveThis study explores the human-centered design potentials of SHIVA, a DTx integrating virtual reality and AI with the SelfHelp+ intervention, aiming to understand stakeholder views and expectations that could influence its adoption. MethodsUsing the SHIVA example, we detail design opportunities involving AI techniques for stress prevention across modeling, personalization, monitoring, and simulation dimensions. Workshops with 12 stakeholders—including target users, digital health designers, and mental health experts—addressed four key adoption aspects through peer interviews: AI data processing, wearable device roles, deployment scenarios, and the model's transparency, explainability, and accuracy. ResultsStakeholders perceived AI-based data processing as beneficial for personalized treatment in a secure, privacy-preserving environment. While wearables were deemed essential, concerns about compulsory use and VR headset costs were noted. Initial human facilitation was favored to enhance engagement and prevent dropouts. Transparency, explainability, and accuracy were highlighted as crucial for the stress detection model. ConclusionStakeholders recognized AI-driven opportunities as crucial for SHIVA's adoption, facilitating personalized solutions tailored to user needs. Nonetheless, challenges persist in developing a transparent, explainable, and accurate stress detection model to ensure user engagement, adherence, and trust.

Research on the Pile–Soil Interaction Mechanism of Micropile Groups in Transparent Soil Model Experiments

Micropile groups (MPGs) are typical landslide resistant structures. To investigate the effects of these two factors on the micropile–soil interaction mechanism, seven sets of transparent soil model experiments were conducted on miniature cluster piles. The soil was scanned and photographed, and the particle image velocimetry (PIV) technique was used to obtain the deformation characteristics of the pile and soil during lateral loading. The spatial distribution information of the soil behind the pile was obtained by a 3D reconstruction program. The results showed that a sufficient roughness of the pile surface was a necessary condition for the formation of a soil arch. If the surface of the pile was smooth, stable arch foundation formation was difficult. When the roughness of the pile surface increases, the soil arch range behind the pile and the load-sharing ratio of the pile and soil will increase. After the roughness reaches a certain level, the above indicators hardly change. Pile spacing within the range of 5–7 d (pile diameters) was suitable. The support effect was poor when the pile spacing was too large. No stable soil arch can be formed, and the soil slips out from between the piles.

A Transparent Soil Experiment to Investigate the Influence of Arrangement and Connecting Beams on the Pile-Soil Interaction of Micropile Groups.

The use of a micropile group is an effective method for small and medium-sized slope management. However, there is limited research on the pile-soil interaction mechanism of micropile groups. Based on transparent soil and PIV technology, a test platform for the lateral load testing of slopes was constructed, and eight groups of transparent soil slope model experiments were performed. The changes in soil pressure and pile top displacement at the top of the piles during lateral loading were obtained. We scanned and photographed the slope, and obtained the deformation characteristics of the soil interior based on particle image velocimetry. A three-dimensional reconstruction program was developed to generate the displacement isosurface behind the pile. The impacts of various arrangement patterns and connecting beams on the deformation attributes and pile-soil interaction mechanism were explored, and the pile-soil interaction model of group piles was summarized. The results show that the front piles in a staggered arrangement bore more lateral thrust, and the distribution of soil pressure on each row of piles was more uniform. The connecting beams enhanced the overall stiffness of the pile group, reduced pile displacement, facilitated coordinated deformation of the pile group, and enhanced the anti-sliding effect of the pile-soil composite structure.

Development and reporting of artificial intelligence in osteoporosis management.

An abundance of medical data and enhanced computational power have led to a surge in artificial intelligence (AI) applications. Published studies involving AI in bone and osteoporosis research have increased exponentially, raising the need for transparent model development and reporting strategies. This review offers a comprehensive overview and systematic quality assessment of AI articles in osteoporosis while highlighting recent advancements. A systematic search in the PubMed database, from December 17, 2020 to February 1, 2023 was conducted to identify AI articles that relate to osteoporosis. The quality assessment of the studies relied on the systematic evaluation of 12 quality items derived from the minimum information about clinical artificial intelligence modeling checklist. The systematic search yielded 97 articles that fell into 5 areas; bone properties assessment (11 articles), osteoporosis classification (26 articles), fracture detection/classification (25 articles), risk prediction (24 articles), and bone segmentation (11 articles). The average quality score for each study area was 8.9 (range: 7-11) for bone properties assessment, 7.8 (range: 5-11) for osteoporosis classification, 8.4 (range: 7-11) for fracture detection, 7.6 (range: 4-11) for risk prediction, and 9.0 (range: 6-11) for bone segmentation. A sixth area, AI-driven clinical decision support, identified the studies from the 5 preceding areas that aimed to improve clinician efficiency, diagnostic accuracy, and patient outcomes through AI-driven models and opportunistic screening by automating or assisting with specific clinical tasks in complex scenarios. The current work highlights disparities in study quality and a lack of standardized reporting practices. Despite these limitations, a wide range of models and examination strategies have shown promising outcomes to aid in the earlier diagnosis and improve clinical decision-making. Through careful consideration of sources of bias in model performance assessment, the field can build confidence in AI-based approaches, ultimately leading to improved clinical workflows and patient outcomes.

An Image Processing Tool for Automated Quantification of Bacterial Burdens in Zebrafish Larvae.

Zebrafish larvae are used to model the pathogenesis of multiple bacteria. This transparent model offers the unique advantage of allowing quantification of fluorescent bacterial burdens (fluorescent pixel counts: FPC) in vivo by facile microscopical methods, replacing enumeration of bacteria using time-intensive plating of lysates on bacteriological media. Accurate FPC measurements require laborious manual image processing to mark the outside borders of the animals so as to delineate the bacteria inside the animals from those in the culture medium that they are in. Here, we have developed an automated ImageJ/Fiji-based macro that accurately detect the outside borders of Mycobacterium marinum-infected larvae.

Experimental and numerical investigation of water flow in different media models at multiple scales

ABSTRACT The study of the flow behavior in pores, fractures, and pore-fracture dual media plays a crucial role in understanding the mechanisms of sudden water inrush and sand flow. In this study, laboratory experiments were conducted using a custom-made transparent porous model to investigate the flow characteristics of fluids in complex media. Additionally, finite element simulations were employed to explore the flow features at multiple scales. A detailed analysis at the micrometer scale was conducted to examine the variations in the water velocity, volumetric flow rate, Reynolds number, flow field, and permeability under different porosity and pressure drop conditions. The results indicate that within the studied range of flow velocities and pressures, the fluid flow in all the media models adhered to Darcy’s law. The improvement in structural connectivity in the random pore model has made the flow channels of fluids in the random pore network more diverse, resulting in an increase in the peak water flow velocity from 4.89E–5 m/s to 4.19E–4 m/s. The fluid accelerated while bypassing the protruding solid particles, resulting in the formation of high-velocity zones in the vicinity. As the pressure drop increases, the volume flow rate of the random dual-medium model with a porosity of 0.6 can increase to 1.01E–9 m3/s, with the highest growth rate. We also simulate the fluid flow of a regular dual-medium model at different porosities and find that the permeability rapidly increases from 5.65E–13 m2 to 1.01E–11 m2, demonstrating that the presence of fractures can significantly improve the permeability performance of the medium. The research findings deepen our understanding of the migration behavior of water in complex geological environments and provide theoretical guidance for groundwater resource protection and underground engineering.

Revitalizing Electrochemical Science and Engineering Education to Create a Clean Energy Workforce

Electrochemical science, engineering, and technology education and workforce development are central to the ongoing clean energy transition. In particular, I will highlight battery, hydrogen, and microelectronics industries as drivers. I then highlight a unique Electrochemistry-Technology-focused Graduate program launched through the Oregon Center for Electrochemistry in 2020. At the master’s level, the accelerated program consists of six core electrochemistry science and engineering lecture, laboratory, and project-based courses over either two or three academic quarters. In addition to core electrochemistry knowledge spanning thermodynamics, transport, kinetics, and device technology, emphasis is placed on problem solving, data analysis, programming (particularly in Python) and professional development and this additional content is embedded throughout all the core lecture and laboratory courses. Students are also introduced to continuum-level electrochemical simulations, with an emphasis on understanding the effects of boundary conditions on the simulation output. They also complete a team-based applied project sponsored by industry partners. The program ends with a nine-month paid internship leading to a master’s degree and career in the electrochemistry industry after 15 months.The coursework is also open to PhD students and postdoctoral scholars who go on to serve as near peer mentors and teaching assistants in the program in future years, directly synergistic with their research, and building community around electrochemistry. A new BS-MS program leveraging the above has launched in 2023, allowing University of Oregon students to complete coursework for both BS and MS in just four years yielding an incredibly high return on tuition and time investment in terms of career potential and earnings. Key to the success of the programs is very low administrative overhead with technical faculty/staff leading all efforts and a transparent budget model to fund the program based on tuition return.

Experience-dependent modulation of collective behavior in larval zebrafish.

Complex group behavior can emerge from simple inter-individual interactions. Commonly, these interactions are considered static and hardwired and little is known about how experience and learning affect collective group behavior. Young larvae use well described visuomotor transformations to guide interindividual interactions and collective group structure. Here, we use naturalistic and virtual-reality (VR) experiments to impose persistent changes in population density and measure their effects on future visually evoked turning behavior and the resulting changes in group structure. We find that neighbor distances decrease after exposure to higher population densities, and increase after the experience of lower densities. These adaptations develop slowly and gradually, over tens of minutes and remain stable over many hours. Mechanistically, we find that larvae estimate their current group density by tracking the frequency of neighbor-evoked looming events on the retina and couple the strength of their future interactions to that estimate. A time-varying state-space model that modulates agents' social interactions based on their previous visual-social experiences, accurately describes our behavioral observations and predicts novel aspects of behavior. These findings provide concrete evidence that inter-individual interactions are not static, but rather continuously evolve based on past experience and current environmental demands. The underlying neurobiological mechanisms of experience dependent modulation can now be explored in this small and transparent model organism.

Model test on the effects of shield machine cutterhead vibration on tunnel face stability in sandy ground

Face stability is one of the essential problems in shield tunneling. When tunneling in cobble stratum or mixed face ground conditions, significant cutting-induced cutterhead vibration would occur and affect the face stability. To reveal the mechanism and effect of vibration on the tunnel face stability, a transparent tunnel model with a movable vibration exciter was designed and a series of model tests were performed under different vibration magnitudes Aa and frequencies f. Meanwhile, particle image velocimetry was used to reveal the displacement field and the failure pattern of the tunnel face. The test results indicate that the cutting-induced vibration produces a significant reduction effect on the tunnel face stability, as expressed by the increase of the face support pressure and the failure zone when the vibration magnitude and frequency increase. Compared with the static unloading conditions, the width of the failure wedge Lwt increased by about 5.75% and 35.66% for the loose and dense sand, respectively, under dynamic unloading conditions (Aa = 0.2 g, f = 10 Hz). The limit support pressure increased up to about 0.20γD at a vibration of 0.3 g and 50 Hz, much larger than those of static conditions, which were about 0.08γD–0.09γD. An observable self-stabilizing arch can be formed in dense sand under static unloading conditions, while under dynamic unloading conditions, the long-time stable soil arch would not occur. The contributions of this paper could provide an insightful understanding of the effects of cutterhead vibration on tunnel face stability.

Model Test Study on the Bearing Mechanism of Inclined Variable Cross-Section Piles Using Transparent Soil

In view of the influence of the inclination and variable section on the pile stability and bearing capacity, this paper introduces particle image velocimetry (PIV) technology, and designs a transparent soil visualization model test. The experimental results show that, when the pile has a variable cross-section and inclination angle, the friction resistance on both sides of the pile increases. The vertical-load-carrying capacity of the 2% and 4% inclined piles with a variable cross-section is greater than that of the piles with inclinations greater than 8%. For model piles with the degrees of inclination of 2% and 4%, the variable-section inclined piles with diameters of 17 mm and 15 mm show significantly less settlement than the equal-section inclined piles. For the model pile with an inclination of 8%, the settlement of the inclined piles with a variable cross-section diameter of 17 mm is slightly smaller than that of the equal cross-section inclined piles. The change in variable cross-section and inclination angle has a large effect on the soil displacement around the pile, and the conclusions of this paper can provide guidance for the engineering application of variable cross-section piles.

Expanding model transparency and learning potential through structural and behavioural debriefings

AbstractSimulation‐based learning environments have been recommended by researchers as educational tools to support learning in complex domains. However, studies have evidenced that subjects may nevertheless have great difficulty understanding and managing dynamic systems, with several assertions are being made regarding the impact of employing transparent model simulations on learning and performance outcomes. This research, therefore, examines the moderating impacts of structural and behavioural debriefings, which are concentrated respectively on critical variables and relations, and on the connection between model structure, action patterns and system behaviour. We propose a series of hypotheses regarding the impact of model transparency and prior debriefing conditions on participants' performance and their comprehension of the system dynamics. Three out of the six hypotheses were validated: prior structural debriefing alongside model transparency positively impacts performance; prior behavioural debriefing, when combined with model transparency and prior structural debriefing, positively impacts both the comprehension of model dynamics and performance.