Modeling of High-Density Compaction of Pharmaceutical Tablets Using Multi-Contact Discrete Element Method

The purpose of this work is to simulate the powder compaction of pharmaceutical materials at the microscopic scale in order to better understand the interplay of mechanical forces between particles, and to predict their compression profiles by controlling the microstructure. For this task, the new framework of multi-contact discrete element method (MC-DEM) was applied. In contrast to the conventional discrete element method (DEM), MC-DEM interactions between multiple contacts on the same particle are now explicitly taken into account. A new adhesive elastic-plastic multi-contact model invoking neighboring contact interaction was introduced and implemented. The uniaxial compaction of two microcrystalline cellulose grades (Avicel® PH 200 (FMC BioPolymer, Philadelphia, PA, USA) and Pharmacel® 102 (DFE Pharma, Nörten-Hardenberg, Germany) subjected to high confining conditions was studied. The objectives of these simulations were: (1) to investigate the micromechanical behavior; (2) to predict the macroscopic behavior; and (3) to develop a methodology for the calibration of the model parameters needed for the MC-DEM simulations. A two-stage calibration strategy was followed: first, the model parameters were directly measured at the micro-scale (particle level) and second, a meso-scale calibration was established between MC-DEM parameters and compression profiles of the pharmaceutical powders. The new MC-DEM framework could capture the main compressibility characteristics of pharmaceutical materials and could successfully provide predictions on compression profiles at high relative densities.

A New Method of Carbonate Matrix Stimulation Modelling and Optimisation Using Field Experimental Data for Acid Efficiency Calibration and Case Studies in Kazakhstan

Successful carbonate matrix acidizing treatments require addressing pay rock mineralogy, produced fluid flow profile, selection of the best stimulation fluids, and correct placement of these fluids. A unique method of acid and diverter fluid efficiency calibration using field experimental data for treatment modelling and optimization has been implemented successfully in several mid-temperature reservoirs, including giant oil fields in Kazakhstan. Application of the technique led to positive results. Matrix stimulation is selected as the primary method for raising production from many carbonate reservoirs in the region because of the reservoir features. Coreflood testing conducted with candidate acid systems for selection and optimization of treatment fluid formulations and design schedules did not always lead to the desired post-stimulation skin levels, zone coverage, and production results. Hence, large-scale calibration of the acid parameters to the actual reservoir conditions was attempted. Treatment modelling in an advanced matrix acidizing software considered calibration factors derived from field tests. Thereafter, the optimized designs were implemented in the same reservoirs to improve the incremental production. Whenever possible, coreflood testing was carried out as the first step to determine the pore-volume to breakthrough parameters for the candidate acid systems. As the second step, these laboratory-derived data were used for modelling of the offset well stimulation design. Third, the actual treatment downhole pressure was matched with the simulated pressure by means of acid efficiency calibration in the matrix stimulation software. These calibrated parameters were then used for simulation of the following treatments in the same formation in attempt to model the expected reservoir placement and zone coverage more accurately and realistically to maximize the treatment effect. Post-stimulation fluid flow profile surveys have validated the optimized models and applicability of the methodology for improving incremental well productivity in the subject reservoirs. The stimulation approach uses a unique technique of acid efficiency calibration using field experimental data. It requires good knowledge of reservoir lithology and permeability and porosity profiles in the target zones. The initial skin is calibrated using pretreatment production data. Thereafter, acid efficiency is calibrated based on matching the actual stimulation job pressures.

Air-Sea Gas Fluxes and Remineralization From a Novel Combination of pH and O2 Sensors on a Glider

Accurate, low-power sensors are needed to characterize biogeochemical variability on underwater glider missions. However, the needs for high accuracy and low power consumption can be difficult to achieve together. To overcome this difficulty, we integrated a novel sensor combination into a Seaglider, comprising a spectrophotometric lab-on-a-chip (LoC) pH sensor and a potentiometric pH sensor, in addition to the standard oxygen (O2) optode. The stable, but less frequent (every 10 min) LoC data were used to calibrate the high-resolution (1 s) potentiometric sensor measurements. The glider was deployed for a 10-day pilot mission in August 2019. This represented the first such deployment of either type of pH sensor on a glider. The LoC pH had a mean offset of +0.005±0.008 with respect to pH calculated from total dissolved inorganic carbon content, c(DIC), and total alkalinity, AT, in co-located water samples. The potentiometric sensor required a thermal-lag correction to resolve the pH variations in the steep thermocline between surface and bottom mixed layers, in addition to scale calibration. Using the glider pH data and a regional parameterization of AT as a function of salinity, we derived the dissolved CO2 content and glider c(DIC). Glider surface CO2 and O2 contents were used to derive air-sea fluxes, Φ(CO2) and Φ(O2). Φ(CO2) was mostly directed into the ocean with a median of −0.4 mmol m–2 d–1. In contrast, Φ(O2) was always out of the ocean with a median of +40 mmol m–2 d–1. Bottom water apparent oxygen utilization (AOU) was (35±1) μmol kg–1, whereas apparent carbon production (ACP) was (11±1) μmol kg–1, with mostly insignificant differences along the deployment transect. This deployment shows the potential of using pH sensors on autonomous observing platforms such as Seagliders to quantify the interactions between biogeochemical processes and the marine carbonate system at high spatiotemporal resolution.

Improving Birth Weight Measurement and Recording Practices in Kenya and Tanzania: a Prospective Intervention Study With Historical Controls

Background Low birth weight (LBW) is a significant public health concern given its association with early-life mortality and other adverse health consequences that can impact the entire life-cycle. In many countries, accurate estimates of LBW prevalence are lacking due to inaccuracies in collection and gaps in available data. Our study aimed to determine LBW prevalence among facility-born infants in selected areas of Kenya and Tanzania, and to assess whether the introduction of an intervention to improve the accuracy of birth-weight measurement would result in a meaningfully different estimate of LBW prevalence than current practice. Methods We carried out a historically-controlled intervention study in 22 health facilities in Kenya and three health facilities in Tanzania. The intervention included: provision of high-quality digital scales, training of nursing staff on accurate birth weight measurement, recording and scale calibration practices, and quality maintenance support that consisted of enhanced supervision and feedback (prospective arm). The historically-controlled data were birth weights from the same facilities recorded in maternity registers for the same calendar months from the previous year measured using routine practices and manual scales. Results Between October 2019 and February 2020, we prospectively collected birth weights from 8,441 newborns in Kenya and 4,294 in Tanzania. Historical data were available from 9,318 newborns in Kenya and 12,007 in Tanzania. In the prospective sample, the prevalence of LBW was 12.6% (95% confidence intervals [CI]: 10.9%-14.4%) in Kenya and 18.2% (12.2%-24.2%) in Tanzania. In the historical sample, the corresponding prevalence estimates were 7.8% (6.5%-9.2%) and 10.0% (8.6%-11.4%). Compared to the retrospective sample, the LBW prevalence in the prospective sample was 4.8%-points (3.2%-6.4%) higher in Kenya and 8.2%-points (2.3%-14.0%) higher in Tanzania, corresponding to a risk ratio of 1.61 (1.38–1.88) in Kenya and 1.81 (1.30–2.52) in Tanzania. Conclusion Routine birth weight records under-estimate the risk of LBW among facility born infants in Kenya and Tanzania. The quality of birth-weight data can be improved by a simple intervention consisting of provision of digital scales and supportive training.

STUDI IMPLEMENTASI KONSEP HALAL SUPPLY CHAIN PADA PASOKAN DAGING AYAM DI PASAR BARU KARAWANG

Salah satu produk yang dekat dengan masyarakat dan selalu diminati untuk dikonsumsi adalah daging ayam. Oleh karena itu, diperlukan penelitian yang mengkaji mengenai kehalalan daging ayam curah dalam serangkain proses suplly chain-nya dari hulu ke hilir. Penelitian ini mengambil objek yaitu daging ayam yang dijual di Pasar Baru Karawang. Adapun kesimpulan yang dapat ditarik dari penelitian ini adalah ketiga pemasok daging ayam ke pasar baru karawang secara umum berdasarkan observasi secara langsung di lapangan sudah memenuhi syarat syar’i mengenai kehalalan daging ayam dan sampai kepada konsumen akhir tidak terkontaminasi dengan barang haram/najis. Akan tetapi tetap masih diperlukan penyempurnaan sebagai yaitu perlu dipastikan kembali kehalalan bahan dari makanan, minuman, vaksin, dan vitamin yang digunakan, perlu adanya pemastian menghadap kiblat, perlu adanya pemastian bahwa ayam yang disembelih sudah betul-betul mati sebelum proses selanjutnya atau minimal menunggu 3 menit. Risiko yang dapat terjadi dikelompokkan menjadi resiko rendah, resiko sedang dan resiko tinggi. Risiko rendah yaitu daging ayam tercampur dengan bahan najis/haram dan timbangan tidak sesuai. Resiko sedang yaitu penyembelihan tidak menghadap kiblat. Sedangkan resiko tinggi yaitu ayam belum benar-benar mati karena berhubungan langsung dengan kehalalan daging ayam. Hal ini pula yang jarang disadari oleh para pemasok daging ayam mereka kebanyakan tidak memastikan benar-benar mati atau tidak menunggu setidaknya 3 menit. Oleh karena itu, dibutuhkan edukasi, standarisasi, serta kalibrasi timbangan. Kata kunci : supply chain, kehalalan, haram, Pasar Baru Karawang One of the products that are close to the community and always in demand for consumption is chicken. Therefore, it is necessary to study the halal chicken meat in the supply chain process from upstream to downstream. This research took the object, namely chicken meat sold in Pasar Baru Karawang. The conclusions that can be drawn from this study are that the three suppliers of chicken meat to the Karawang new market in general, based on direct observations in the field, have met the syar'i requirements regarding the halalness of chicken meat and until the end consumers are not contaminated with haram / najis goods. However, improvements are still needed as it is necessary to re-confirm the halalness of the ingredients of the food, drinks, vaccines, and vitamins used, it is necessary to ensure facing the Qibla, it is necessary to ensure that the slaughtered chickens have really died before the next process or at least wait 3 minute. The risks that can occur are grouped into low risk, medium risk and high risk. Low risk, namely the chicken meat is mixed with unclean / haram ingredients and the scale is not suitable. The risk is moderate, namely the slaughter does not face the Qibla. Meanwhile, the high risk is that the chicken has not really died because it is directly related to the halal meat of the chicken. It is also something that is rarely realized by most of their chicken meat suppliers who do not ensure that they are really dead or do not wait at least 3 minutes. Therefore, it takes education, standardization, and scale calibration. Keyword : supply chain, halal, haram, Pasar Baru Karawang (traditional market)

Large-scale calibration of conceptual rainfall-runoff models for two-stage probabilistic hydrological post-processing

<p>Probabilistic hydrological modelling methodologies often comprise two-stage post-processing schemes, thereby allowing the exploitation of the information provided by conceptual or physically-based rainfall-runoff models. They might also require issuing an ensemble of rainfall-runoff model simulations by using the rainfall-runoff model with different input data and/or different parameters. For obtaining a large number of rainfall-runoff model parameters in this regard, Bayesian schemes can be adopted; however, such schemes are accompanied by computational limitations (that are well-recognized in the literature). Therefore, in this work, we investigate the replacement of Bayesian rainfall-runoff model calibration schemes by computationally convenient non-Bayesian schemes within probabilistic hydrological modelling methodologies of the above-defined family. For our experiments, we use a methodology of this same family that is additionally characterized by the following distinguishing features: It (a) is in accordance with a theoretically consistent blueprint, (b) allows the exploitation of quantile regression algorithms (which offer larger flexibility than parametric models), and (c) has been empirically proven to harness the “wisdom of the crowd” in terms of average interval score. We also use a parsimonious conceptual rainfall-runoff model and 50-year-long monthly time series observed in 270 catchments in the United States to apply and compare 12 variants of the selected methodology. Six of these variants simulate the posterior distribution of the rainfall-runoff model parameters (conditional on the observations of a calibration period) within a Bayesian Markov chain Monte Carlo framework (first category of variants), while the other six variants use a simple computationally efficient approach instead (second category of variants). Six indicative combinations of the remaining components of the probabilistic hydrological modelling methodology (i.e., its post-processing scheme and its error model) are examined, each being used in one variant from each of the above-defined categories. In this specific context, the two large-scale calibration schemes (each representing a different “modelling culture” in our tests) are compared using proper scores and large-scale benchmarking. Overall, our findings suggest that the compared “modelling cultures” can lead to mostly equally good probabilistic predictions.</p>