Development Of Modeling Approach Research Articles

Predicting Thermal Failures Using an Advanced Data-Driven Modeling Framework in a Cylindrical Li-Ion Battery Pack

Li-ion cells have been widely used in battery electric vehicles (BEV), hybrid electric vehicles (HEV), plug-in hybrid electric vehicles (PHEV), and stationary energy storage units due to their high specific energy and excellent cycle life. However, the safety of Li-ion battery (LIB) packs has become a vital issue, especially under extreme or abusive conditions. Fire and explosion due to the onset of thermal runaway often lead to catastrophic damages. Thermal failures are caused by drastic and successive processes and reactions that feed into one another, eventually leading to fire and explosion. Thus, one of the most crucial current research goals is to steer LIBs’ technology toward long-lasting and safe operation.In this work, we employ data-driven methods to predict and potentially prevent the thermal runaway (TRA) in a LIB battery pack with various cooling systems. Specifically, we use several deep learning (DL) techniques to learn and predict the likelihood of the TRA, using thermal images acquired from the multi-physics modeling of LIBs. The multi-physics modeling approach comprises a coupled thermal, electrochemical (P2D) model1 and degradation due to the solid electrolyte interface (SEI) formation/decomposition sub-models.2 The geometrical and material properties of the battery pack are taken from the 2170 cell as presently used in some BEV. The presented multi-physics model has been formulated as a three-dimensional model to facilitate efficient and accurate numerical simulations in different conditions. Under general working conditions, the cells in a pack are exposed to a continuous charge/discharge load, where heat is generated inside the cells due to several electrochemical and degradation processes. The multi-physics modeling results in the form of the temperature profiles of the battery surface as well as the SEI formation/decomposition rate at various charge/discharge cycles are validated against the prior literature’s experimental data. Thermal images of the battery pack from different views are collected as sequences of video frames during the battery operation from multiple simulations to be used in DL.Based upon the multi-physics modeling results, Convolutional Neural Networks (CNNs) are proposed to predict the TRA-like events.3–5 Hyperparameters optimization has been performed to identify variables for the best performing ML method. The proposed combined multi-physics and machine learning modeling methodology provide interesting insight and predictive capabilities of the TRA. Thus, the developed combined multi-physics and ML modeling approach establishes a basis for 'on-the-fly' prediction of the TRA as well as a framework for extending machine learning methodologies to broad applications in battery failure prediction.References J. Newman and W. Tiedemann, J. Electrochem. Soc., 140, 1–5 (1993).X. Fen, X. He, M. Ouyang, L. Wang, L. Lu, D. Ren and S. Santhanagopalan, J. Electrochem. Soc., 165, A3748–A3765 (2018).J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2016-Decem, 779–788 (2016).K. He, X. Zhang, S. Ren, and J. Sun, Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2016-Decem, 770–778 (2016).K. Simonyan and A. Zisserman, 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., 1–14 (2015).

Professional Development of Christian Education Teachers Based on Scientific Writing Products to Improve Learning Quality and Career Improvement Assisted Module

Research and publication of scientific papers become the main problems in the professional development of teachers. This aims of this research is to develop sustainable professional teacher through research product-based training models and the publication of scientific papers. It analyses the need for a training management model that can increase the professionalism of Christian education subject teachers who are civil servants to produce scientific papers. This research is using qualitative methods with a Research and Development model approach. The data analysis technique used the Mann-Whitney U test technique. Sources of training data are primary data and secondary data. Primary data was obtained from the interview process of all training participants, and secondary data was obtained based on the analysis of the training module directory, training directory and examples of training product administrators. The results showed the validity level of the training module was in a good category (72%), the results of scientific papers using the IHT module are higher than before using the training module; the product of scientific papers produced by the teacher has met the requirements for promotion; the resulting training product in the form of scientific papers is useful for improving school performance and teacher motivation for higher careers.

The rural consumer adoption of sustainable energy: a PLS-SEM-ANN approach of conceptual model development and cross-country validation of Pakistan and Malaysia.

Sustainable and alternative energy sources of biofuel and solar power panel have been revolutionizing the lives and economy of many countries. However, these changes mainly occur in the urban areas and the rural population section has long been ignored by policy makers and government in the provision of energy. It is only recently that solar and biofuel are finally making in road to provide cheap and clean energy sources to rural population. As a result, literatures on consumer behavior of rural population towards sustainable energy sources are still very scarce. The present research aims to fulfill this gap by developing a conceptual model to investigate the adoption of solar power and biofuel energy resources in the cross-cultural setting of Malaysia and Pakistan. The data was collected from the rural areas of Pakistan and Malaysia. The two-stage data analysis method of partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) have been applied to satisfy both linear and non-linear regression assumptions, respectively. The results show that consumer in rural areas of Pakistan are willing and possess intention to adopt both biofuel and solar power for commercial and domestic use. Additionally, the results confirm that branding, economic, and altruistic factors are important in yielding intention to use towards biofuel and solar power panel in Pakistan which are validated by the results obtained in Malaysia. Other factors such as climate change awareness, retailer services quality, and ease of use are also important. The results offer wide-ranging theoretical and managerial implications.

학사경고자를 위한 학업적 정서조절 개입모형 개발

Objectives The purpose of this study is to develop an academic emotion regulation intervention model for academic probation students. Methods The study was conducted according to the ADDIE model. First, in the draft model development stage, the process, details, and approach of academic emotion regulation intervention for academic probation students were derived through literature analysis, and each key element of the model was designed so that it could be systematically sequenced to develop a draft model. Second, in the implementation process, the developed preliminary model was applied to academic probation students of actual university sites to confirm the effectiveness of the model. The results of ANCOVA of 22 academic probation students(11 experiments, 11 controls) showed a significant effect on improving academic emotion regulation. In order to analyze the effect, participants' evaluations were analyzed and reflected in the intervention. Third, the evaluation process verified the usefulness and value of the preliminary model. An expert formation evaluation was conducted in a focus group interview composed of nine learning experts. The output from the implementation was reviewed and the value of the developed model was evaluated. Finally, the final model was developed by modifying and supplementing the model. Results The research results of this study are as follows. First, first, this model organically linked the academic warning overcoming process, academic emotion, and academic emotion regulation process. Second, this model suggests a systematic procedure for learning experts to effectively solve the academic emotional regulation problem of academic warnings. Third, this model presented a 4x3 matrix type of intervention method according to the approach for academic emotion regulation intervention. Fourth, in this model, the academic emotion regulation was cyclically performed in consideration of the academic emotions of the academic warning. Conclusion This study is meaningful in developing a model that can be used by actual learning experts to intervene in academic emotional regulation for academic warnings by fully reflecting the emotional characteristics of academic warnings and the experiences and needs of learning experts. Conclusions This study is significant in developing a model that can be used by actual learning experts to intervene in academic emotional regulation for academic probations by fully reflecting the emotional characteristics of academic probations and the experiences and needs of learning experts.

Comprehensive Evaluation and Comparison of Machine Learning Methods in QSAR Modeling of Antioxidant Tripeptides.

Due to their multiple beneficial effects, antioxidant peptides have attracted increasing interest. Currently, the screening and identification of bioactive peptides, including antioxidative peptides based on wet-chemistry methods are time-consuming and highly rely on many advanced instruments and trained personnel. Quantitative structure–activity relationship (QSAR) analysis as an in silico method can be more efficient and cost-effective. However, model performance of QSAR studies on antioxidant peptides was still poor due to limited attempts in model development approaches. The objective of this study was to compare popular machine learning methods for antioxidant activity modeling and screening of tripeptides and identify the critical amino acid features that determine the antioxidant activity. 533 numerical indices of amino acids were adopted to characterize 130 tripeptides with known antioxidant activity from the published literature, and then 7 feature selection strategies plus pairwise correlation were used to screen the most important indices for antioxidant activity and model building. 14 machine learning methods were used to build models based on the feature selection strategies, respectively. Among the 98 models, non-linear regression methods tended to perform better, and the best model with an R2Test of 0.847 and RMSETest of 0.627 for tripeptide antioxidants was obtained by combining random forest for feature selection and tree-based extreme gradient boost regression for model development. Based on the predicted antioxidant values of 7870 unknown tripeptides, potentially high antioxidant activity tripeptides all have a tyrosine, tryptophan, or cysteine residue at the C-terminal position. Furthermore, the predicted antioxidant activity of six synthesized tripeptides was confirmed through experimental determination, and for the first time, the cysteine or tyrosine residue at the C-terminal was found to be critical to the antioxidant activity based on both QSAR models and experimental observations.

Power System Stability Enhancement Using a Novel Hybrid Algorithm Based on the Water Cycle Moth-Flame Optimization

Poor control of the power grid can lead to a total system collapse, causing significant economic losses and possible damage to security and social peace. Therefore, improving power system stability, particularly transient stability, has become one of the major research topics. This paper proposes a developed modeling approach that provides the optimal stabilizer parameters of the control devices, aiming at improving the electrical network stability by minimizing the angular speed deviation in the presence of a severe disturbance event using a novel hybrid algorithm called Water Cycle-Moth Flame Optimization (WCMFO). The main advantages of the proposed method are the speed of response and its efficient exploration and exploitation ability to attain the best solution quality. This is achieved by imposing a thermodynamic incident (an abrupt change in mechanical torque) on the well-known test model (SMIB), Single Machine Infinite Bus. To test the effectiveness of the proposed method, Power System Stabilizer (PSS), Proportional-Integral-Derivative (PID-based PSS), and Fractional Order-PID (FOPID-based PSS) are implemented to control and ensure the system’s ability to return to a stable state in the presence of this fault. The achieved experimental outcomes have proven the superiority, and efficiency of the developed approach (WCMFO) in terms of damping the oscillations and reducing the overshot, with an improvement of 44% over the Water Cycle Algorithm (WCA), Moth-Flame Optimization (MFO), and Artificial Ecosystem Optimization (AEO). It is envisaged that the proposed method could be very useful in the design of a practical high-performance power system stabilizer.

A Combined Multi-Physics Modelling and Machine Learning to Predict Electro-Thermal Failures of Cylindrical Li-Ion Batteries

Safety aspects of Li-ion batteries (LIBs) operation become increasingly important due to their integration into large-scale systems such as electric vehicles (EVs). Recent events involving many EVs explosions endangering consumers’ lives have shown that electro-thermal aspects of LIBs are far from being adequately managed. Thus, one of the most crucial goals of current research is to steer LIBs’ technology toward long-lasting and safe operation.In this work, we employ data-driven methods to predict and potentially prevent the thermal runaway (TRA) in LIBs. Specifically, we use various machine learning (ML) techniques to learn and predict the likelihood of the TRA, using thermal images acquired from the multi-physics modeling of LIBs. The multi-physics modeling approach is comprised of a coupled thermal, electrochemical (P2D) model1, and degradation due to the solid electrolyte interface (SEI) formation/decomposition sub-models.2 The presented multi-physics model has been formulated as a three-dimensional model to facilitate efficient and accurate numerical simulations in different conditions. Under general working conditions, the cell is exposed to a continuous charge/discharge load, where heat is generated inside the cell due to several electrochemical and degradation processes. Thermal images of the battery surface are collected as sequences of video frames during the battery operation from multiple simulations to be used in ML. LIB’s geometrical and material properties are taken from the 2170 cell as used in the current Tesla vehicles (model S 2021, model 3 2021). Based upon the multi-physics modeling results, two deep learning techniques, i.e., Convolutional Neural Networks (CNN) and object detection using YOLO, are proposed to address the problem of predicting the TRA like-events.3–5 The multi-physics modeling results in the form of the temperature profiles of the battery surface as well as the SEI formation/decomposition rate at various charge/discharge cycles are validated against the prior literature experimental data. Two regimes with single and double hot spots in the cylindrical LIB are investigated for a potential TRA occurrence. The CNN predictions of the TRA-like event on unknown data set show an excellent accuracy reaching up to 90% for all the different CNN architectures used. The object detection technique is found to be very efficient in identifying the location of TRA initiation at various conditions. Thus, the developed combined multi-physics and ML modeling approach establishes a basis for 'on-the-fly' prediction of the TRA as well as a framework for extending machine learning methodologies to broad applications in electrochemistry.References J. Newman and W. Tiedemann, J. Electrochem. Soc., 140, 1–5 (1993).X. Fen, X. He, M. Ouyang, L. Wang, L. Lu, D. Ren and S. Santhanagopalan, J. Electrochem. Soc., 165, A3748–A3765 (2018).J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2016-Decem, 779–788 (2016).K. He, X. Zhang, S. Ren, and J. Sun, Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2016-Decem, 770–778 (2016).K. Simonyan and A. Zisserman, 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., 1–14 (2015).

Digital transformation maturity model development framework based on design science: case studies in manufacturing industry

PurposeThis study aims to propose a novel maturity model development framework based on design science theory utilizing qualitative and quantitative methods for empirical evidence and develops a descriptive digital transformation maturity model by using the proposed framework.Design/methodology/approachDesign science theory is deeply explored and extended to propose a novel maturity model development approach, including robust and rigorous validation processes. Thus, three consecutive discussion sessions and evaluations with experts are carried out iteratively to evolve and saturate the efficiency and utility of the maturity model, and consensus among experts at each session is validated by the intra-class correlation (ICC) coefficient. Furthermore, the Wilcoxon signed rank test is utilized to test whether there is a difference between consecutive sessions. Finally, prototype testing as a pilot study and two case studies in the manufacturing industry are carried out to validate the applicability of the developed maturity model.FindingsA 3-phase maturity model development framework that includes the activities and outcomes in each phase emerge based on the design science theory. The comparative literature analysis and discussion sessions resulted in six dimensions, ten sub-dimensions, 39-capability items that circumscribe the digital transformation concept and five maturity levels that demonstrate conceptual consistency and a measurement tool for self-assessment. In addition, prototype testing and case studies show that the developed maturity model can measure the company's maturity level. Finally, it is proven that the digital transformation maturity model is developed by following the proposed maturity model development framework.Practical implicationsThe maturity model draws a framework for practitioners that facilitate an initial roadmap and enhance the adoption rate, and it motivates the practitioners for frequent and efficient assessments, thus helping the continuous improvement of the digital transformation journey.Originality/valueThe originality of this paper lies in proposing a novel maturity model development framework based on design science and presents the activities and validation methods for this purpose. Furthermore, a comprehensive and rigorously validated digital transformation maturity model is developed based on the proposed framework.

Pengembangan Bahan Ajar Kosakata Dengan Media Kartu di MIN 1 Probolinggo

This research is development research that aims to describe how the process of developing vocabulary teaching materials using card media, measuring the validity and effectiveness of developing vocabulary teaching materials using card media. This research uses qualitative and quantitative research with the ADDIE model development approach. The respondents of this study were 24 students in third grade at Islamic junior high school (MIN) 1 Probolinggo. Data collection techniques using interviews, observation, documentation, questionnaires, and tests. The results of the research on the development of vocabulary teaching materials with this card media are by the needs of students where learning Arabic requires media to support learning. The results of the validation research on the development of vocabulary teaching materials with card media are based on an average of 94.62%, which means that this development book is very feasible to use. Based on the results of the pretest (before using the book) the test results showed 29.17% and the post-test results (after using the book) showed the results of 87.5%, meaning that the module for developing vocabulary teaching materials with card media was effective in learning Arabic at Islamic junior high school (MIN) 1 Probolinggo.

A mesoscopic model for the simulation of dynamic mechanical properties of filled elastomers: Filled binary polymer blends

We extend a recently developed modeling approach which allows to calculate dynamic moduli of filled elastomers using the experimental surface tensions of the material’s components as input. The approach combines a morphology generator, mimicking the post-mixing filler flocculation, with a finite-element-like dynamic mechanical analysis. The minimum length scale is set by the size of the non-breakable filler aggregates, whereas the maximum length scale is in the μm range. In this work we focus on binary polymer mixtures containing high amounts of filler. The polymers are natural and styrene-butadiene rubber, while the filler type is variable. We study the interdependence between the filler surface tension and the post-mixing dispersion of the components, in particular the relative enrichment or depletion of one or the other polymer with filler, and how this couples to the dynamic moduli of the respective compounds. The algorithm ties the chemical identity of the components, via the dispersive and polar parts of the surface tensions, to the amplitude and frequency dependence of the storage and loss moduli, allowing fast numerical screening for desired mechanical properties in filled elastomers.

A Challenge for Engineering Biomimetic Microvascular Models: How do we Incorporate the Physiology?

The gap between in vitro and in vivo assays has inspired biomimetic model development. Tissue engineered models that attempt to mimic the complexity of microvascular networks have emerged as tools for investigating cell-cell and cell-environment interactions that may be not easily viewed in vivo. A key challenge in model development, however, is determining how to recreate the multi-cell/system functional complexity of a real network environment that integrates endothelial cells, smooth muscle cells, vascular pericytes, lymphatics, nerves, fluid flow, extracellular matrix, and inflammatory cells. The objective of this mini-review is to overview the recent evolution of popular biomimetic modeling approaches for investigating microvascular dynamics. A specific focus will highlight the engineering design requirements needed to match physiological function and the potential for top-down tissue culture methods that maintain complexity. Overall, examples of physiological validation, basic science discoveries, and therapeutic evaluation studies will emphasize the value of tissue culture models and biomimetic model development approaches that fill the gap between in vitro and in vivo assays and guide how vascular biologists and physiologists might think about the microcirculation.

Spatio‐temporal assessment of greenhouse gas emission from rapeseed production in China by coupling nutrient flows model with LCA approach

AbstractRapeseed (Brassica napus L.) production experienced a considerable increase during the past decades, and there is growing concern about how to realize the dual objectives of meeting the increasing demand for rapeseed and reducing the associated greenhouse gas (GHG) emissions. Most reported studies on crop‐sourced GHG emissions to date were aimed at optimizing crop management to reduce emission, but often without a detailed spatio‐temporal assessment of GHG emissions for layout management practices. Herein, we used a newly developed modelling approach that integrates the NUtrient Flows in food chains, Environment, and Resource use (NUFER) model with a dynamic reactive nitrogen (Nr) loss module for rapeseed and Life Cycle Assessment (LCA) approach to assess the GHG emissions from rapeseed production in China, where produces 18% of the world's rapeseed. Results showed that the GHG emissions from rapeseed production in China increased by 1.90% yearly from 2004 to 2018, largely due to increased inputs of agricultural materials, especially diesel. Further, carbon footprints of rapeseed production were found vary greatly between production regions, due to diverse climatic conditions, field management practices and the level of mechanization. Scenario analysis showed that adoption of both optimized nutrient and layout management practice is required to mitigate the current resource and environmental burdens, and to achieve edible oil security. Thus, our study provides new insights for policymakers on the formulation of management practices to reduce crop‐sourced GHG emissions, going along with an increase of food demand in the future.

Abstract 639: Rapidly evaluating cancer dependencies by label-free imaging of zero-passage primary cells

Abstract Successful mapping of cancer dependencies requires conducting genetic and pharmacological screens in a diversity of cell models. However, existing model development approaches require long periods of culture time during which evolutionary pressures reduce heterogeneity. It also remains difficult to create long-term models of many cancers, greatly limiting the share of patient samples that can be studied. These limitations make it challenging to create large and diverse datasets for the discovery and validation of biomarker-vulnerability relationships. To enable high-throughput genetic and pharmacological screens in primary, including nondividing, cells without an intermediate model generation step, we are developing a label-free imaging-based platform of early living tissue perturbation. Here, we describe our efforts to establish an ex vivo cell-preservation and imaging system for malignant ascites from patients with advanced gastroesophageal (GE) cancer, whose prognosis remains poor and there is an urgent need for rapid evidence-based therapeutic discovery. First, we optimized a workflow to acquire and perturb cells within 24 hours of sample collection. We observed that incorporating microenvironmental factors by mixing ascites fluid with organoid media extends the preservation of cellular composition and viability. We next hypothesized that label-free microscopy can be a substitute for fluorescence-based labels which fade over time in live-cell imaging. To test this, we created a dataset consisting of over 1.0M cells from 14 samples (10 unique patients). Before seeding, we added fluorescence labels to annotate cell type and viability during imaging. We then trained predictive models to infer cell annotations based on brightfield morphological features only and observed an overall accuracy of 92% and 82% for cell identity and viability, respectively. Prediction of tumor cell fraction from label-free images alone showed strong correlation with tumor fractions estimated by flow cytometry. We also integrated single-cell RNA sequencing data to generate a candidate panel of 28 compounds which are predicted to exhibit antitumor activity via different mechanisms that are of relevance to our study cohort. We observe that label-free inference of compound activity showed strong correlation (R2 > 0.8) with fluorescent-based methods. We are now expanding the scale of our rapid screens and utilizing transcriptomics to link the molecular profile with response. Since our approach couples the timing of drug or genetic perturbation with the preservation of subcellular heterogeneity, it will serve as a strong foundation for preclinical studies. Importantly, our method substantially expands the fraction of samples that can be interrogated and will have application in other areas of need where material is limited or long-term model generation remains unsuccessful as is the case in many rare diseases. Citation Format: Mushriq Al-Jazrawe, Csaba Molnar, Niklas Rindtorff, Steven Blum, William Colgan, Maria Alimova, Sean Misek, Carmen Rios, Moony Tseng, James M. McFarland, Aviv Regev, Beth A. Cimini, Anne E. Carpenter, Adam Bass, Samuel J. Klempner, Jesse Boehm. Rapidly evaluating cancer dependencies by label-free imaging of zero-passage primary cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 639.

Smash Training Model in Badminton Game in College Students of the Faculty of Sports Science, Makassar State University

In general, the purpose of this research and development is to produce a model of smash practice in badminton games for beginners. In addition, this research and development are carried out to obtain in-depth information about the competencies, characteristics, and initial abilities of FIK UNM students in the implementation of lectures, especially in Badminton Learning materials. The design in the research and development of this learning model uses the research and development model approach (Research & Development) from Brog and Gall. While the subjects in this research and development are all students of the Faculty of Sports Science, Makassar State University, which numbers 40 people. The instruments used in this research and development are questionnaires and questionnaires used to collect data at the stage of (1) needs analysis; (2) expert evaluation (initial product evaluation); (3) limited trials (small group trials); and (4) field testing. For the effectiveness test, the model used the badminton smash test and the rubric for assessing the correctness of the smash movement. To see the results of the effectiveness of the model, a statistical test was used using the test-test formula with a signification level of α = 0.05. Based on the development results, it can be concluded that: (1) The smash practice model in the badminton game for beginners can be developed and applied in training, and (2) The smash practice model in the badminton game for beginners has been developed, obtained data on the effectiveness and results of the development of the smash practice model in badminton games for beginners as well as can and deserve to be used in badminton training.

Multi-scale modelling of fluidized bed biomass gasification using a 1D particle model coupled to CFD

For many fluidized bed applications, the particle movement inside the reactor is accompanied by reactions at the particle scale. The current study presents for the first time in literature a multi-scale modelling approach coupling a one-dimensional volumetric particle model with the dense discrete phase model (DDPM) of ANSYS Fluent via user defined functions. To validate the developed modelling approach, the current study uses experimental data of pressure drop, temperature and gas composition obtained with a lab-scale bubbling fluidized bed biomass gasifier. Therefore, a particle model developed previously for pyrolysis was modified implementing a heat transfer model valid for fluidized bed conditions as well as kinetics for char gasification taken from literature. The kinetic theory of granular flow is used to describe particle–particle interactions allowing for feasible calculation times at the reactor level whereas an optimized solver is employed to guarantee a fast solution at the particle level. A newly developed initialization routine uses an initial bed of reacting particles at different states of conversion calculated previously with a standalone version of the particle model. This allows to start the simulation at conditions very close to stable operation of the reactor. A coupled multi-scale simulation of over 30 s of process time employing 300.000 inert bed parcels and about 25.000 reacting fuel parcels showed good agreement with experimental data at a feasible calculation time. Furthermore, the developed approach allows for an in-depth analysis of the processes inside the reactor allowing to track individual reacting particles while resolving gradients inside the particle.

Assessing runoff and erosion on woodland‐encroached sagebrush steppe using the Rangeland Hydrology and Erosion Model

AbstractThe transition of sagebrush‐dominated (Artemisia spp.) shrublands to pinyon (Pinus spp.) and juniper (Juniperus spp.) woodlands markedly alters resource‐conserving vegetation structure typical of these landscapes. Land managers and scientists in the western United States need knowledge and predictive tools for assessment and effective targeting of tree‐removal treatments to conserve or restore sagebrush vegetation and associated hydrologic function. This study developed modeling approaches to quantify the hydrologic vulnerability and erosion potential of sagebrush rangelands in the later stages of woodland encroachment and in response to commonly applied tree‐removal treatments. Using experimental data from multiple sites in the Great Basin Region, USA, and process‐based knowledge from decade‐long vegetation and rainfall simulation studies at those sites, we (1) assessed the capability of the Rangeland Hydrology and Erosion Model (RHEM) to accurately predict patch‐scale (12 m2) measured runoff and erosion from tree canopy and intercanopy hydrologic functional units in untreated and burned woodlands 9 years postfire, and (2) developed and evaluated multiple RHEM approaches/frameworks to model aggregated effects of tree canopy and intercanopy areas on patch‐ and hillslope‐scale (50 m length) runoff and erosion processes in untreated and treated (burned, cut, and masticated) woodlands. The RHEM accurately predicted measured runoff and sediment yield from patch‐scale rainfall simulations as partitioned on untreated and treated tree canopy and intercanopy areas and effectively parameterized the dominant controls on runoff and erosion process in woodlands. With few exceptions, evaluated hillslope‐scale RHEM frameworks similarly predicted reduced hydrologic vulnerability and erosion potential for conditions 9 years following tree removal by burning, cutting, and mastication treatments. Regressions of RHEM‐predicted hillslope runoff, sediment, and hydraulic/erosion parameters with bare ground and ground cover attributes indicate all RHEM frameworks effectively represented the dominant controls on hydrologic and erosion processes for rangelands and woodlands. The results provide RHEM frameworks and recommendations for assessing hydrologic vulnerability and erosion potential on woodland‐encroached sites and predicting the effectiveness of tree removal to reestablish a water and soil resource‐conserving vegetation structure on sagebrush rangelands. We anticipate our RHEM or similar modeling approaches may be applicable to analogous water‐limited landscapes elsewhere subject to woody plant encroachment.

Digital Marketing Training for Fishball MSMEs Group in Malingping District, Lebak Regency, Banten

This activity aims to help solve the problems faced by the Malingping Fishball MSME group. During pandemics, the number of sales decreased. This is a problem that must be solved. Troubleshooting is done through the interview method for the fishball MSME group. Next, the team determined the solutions and things needed by the fish ball MSME group. The method of implementing the activities is carried out using a community development model approach, which is provided in the form of training and assistance related to marketing techniques, namely in the form of digital marketing according to the needs of the fish ball MSME group. This activity is expected to help fishball SMEs expand their marketing network. The results show that this empowerment activity can be well received and the participants are considered ready to adapt to digitalization. Even so, there is an evaluation that the development of this program must be carried out on an ongoing basis because there are still some participants who need further direction and guidance.

Towards a mechanistic understanding of microalgae-bacteria interactions: integration of metabolomic analysis and computational models.

Interactions amongst marine microalgae and heterotrophic bacteria drive processes underlying major biogeochemical cycles and are important for many artificial systems. These dynamic and complex interactions span the range from cooperative to competitive, and it is the diverse and intricate networks of metabolites and chemical mediators that are predicted to principally dictate the nature of the relationship at any point in time. Recent advances in technologies to identify, analyze, and quantify metabolites have allowed for a comprehensive view of the molecules available for exchange and/or reflective of organismal interactions, setting the stage for development of mechanistic understanding of these systems. Here, we (i) review the current knowledge landscape of microalgal-bacterial interactions by focusing on metabolomic studies of selected, simplified model systems; (ii) describe the state of the field of metabolomics, with specific focus on techniques and approaches developed for microalga-bacterial interaction studies; and (iii) outline the main approaches for development of mathematical models of these interacting systems, which collectively have the power to enhance interpretation of experimental data and generate novel testable hypotheses. We share the viewpoint that a comprehensive and integrated series of -omics approaches that include theoretical formulations are necessary to develop predictive and mechanistic understanding of these biological entities.

Virtual deep brain stimulation: Multiscale co-simulation of a spiking basal ganglia model and a whole-brain mean-field model with The Virtual Brain

Deep brain stimulation (DBS) has been successfully applied in various neurodegenerative diseases as an effective symptomatic treatment. However, its mechanisms of action within the brain network are still poorly understood. Many virtual DBS models analyze a subnetwork around the basal ganglia and its dynamics as a spiking network with their details validated by experimental data. However, connectomic evidence shows widespread effects of DBS affecting many different cortical and subcortical areas. From a clinical perspective, various effects of DBS besides the motoric impact have been demonstrated. The neuroinformatics platform The Virtual Brain (TVB) offers a modeling framework allowing us to virtually perform stimulation, including DBS, and forecast the outcome from a dynamic systems perspective prior to invasive surgery with DBS lead placement. For an accurate prediction of the effects of DBS, we implement a detailed spiking model of the basal ganglia, which we combine with TVB via our previously developed co-simulation environment. This multiscale co-simulation approach builds on the extensive previous literature of spiking models of the basal ganglia while simultaneously offering a whole-brain perspective on widespread effects of the stimulation going beyond the motor circuit. In the first demonstration of our model, we show that virtual DBS can move the firing rates of a Parkinson's disease patient's thalamus - basal ganglia network towards the healthy regime while, at the same time, altering the activity in distributed cortical regions with a pronounced effect in frontal regions. Thus, we provide proof of concept for virtual DBS in a co-simulation environment with TVB. The developed modeling approach has the potential to optimize DBS lead placement and configuration and forecast the success of DBS treatment for individual patients.

Manajemen Informasi Persediaan Obat Menggunakan Metode Fefo Di Apotik Semarang

This research was conducted at the Jatingaleh Pharmacy, Semarang, currently the pharmacy is still experiencing difficulties in terms of processing drug supplies, the obstacles experienced include recording drug supplies manually, which is recorded in a ledger then inputted into a computer. The system also has not been able to generate reports of drugs that are about to expire.
 This study aims to provide solutions to problems related to the drug supply system, and can produce reports of drugs that will enter expiration at the Jatingaleh Pharmacy Semarang. This research method uses a Research and Development (R&D) model approach with 6 stages, namely Research and information collecting, planning, developing preliminary form of product, preliminary field testing, main product revision, and main field testing. This system was created using Microsoft Visual Basic 6.0 programming language using SQL Server 2000 database and based on Multiuser.
 With the system design from this study, it is able to handle problems at the pharmacy, and can facilitate the performance of the pharmacy staff so that the work becomes faster and more efficient.