Entire Batch Research Articles

Formulation and evaluation of sustained release matrix tablet of ketoprofen

The aim of the study was the formulation of the sustained release matrix tablet of Ketoprofen. The sustained release tablet was prepared by wet-granulation method. The tablets were formulated using hydrophilic polymer HPMC K4M and xanthan gum. Preformulation compatibility studies indicate that there is no interaction between the excipient and the drug. Total seven batches were prepared and powder blends before compression was subjected for evaluation of flow properties. All the parameter was found to be within the limit showing good flow property. After compression, the entire tablet batch was evaluated for thickness, hardness, friability, weight variation, drug content uniformity, swelling index, In-vitro release pattern. The thickness of tablet indicates that die fill was uniform in all the formulation and the formulation possessed sufficient hardness & friability indicating a good mechanical strength of the development formulation. The weight of the all the formulation were found to be with in pharmacopeial limit. The In-Vitro dissolution profile of all the formulation of Ketoprofen was controlled over an extended period of time. The optimized formulation of F7 containing combination of HPMC K4M and xanthan gum was consider as the best formulation with respect to its sustained in vitro drug release for 12 hrs, total sustained release time & improved bioavailability. The developed formulation was found to be stable during the stability studies of 3 month indicating good stability of the tablets.

Real-time quality analysis of baked goods using advanced technologies

Some critical quality variables of baked goods baked in tunnel ovens cannot be monitored in real-time and quickly after baking. Generally, these variables are measured individually and manually. Analysis of some variables can be done in laboratory environments. It is very difficult to continuously and accurately read the quality variables of products, especially at the exits of industrial tunnel ovens operating at high speeds. With current systems, important quality variables of the products are assessed through widely spaced sampling, and most of the time the measurements made do not represent the entire batch. Additionally, interventions in the process may be delayed due to the time required for analysis, allowing defective products to reach consumers. In this study, experiments were carried out to enable real-time and high-speed identification of important quality variables such as product geometry, surface color, image features, and moisture. The developed system aims to monitor products such as cookies, cakes, biscuits, crackers and bread. Through this system, the size of the products that can represent the lot or even the entire batch can be analyzed when desired. Thus, quality oscillations can be kept within very narrow ranges. The developed hybrid system is applied to the exit of tunnel ovens, and the data read through sensors and cameras are transferred to computers using specially developed algorithms through interfaces. It has been observed that process control signals can also be generated by considering the lower and upper control limits of quality variables. With the developed software, changes can be instantly seen on the screens in the form of graphics, audio, and visual warnings can be created, historical data can be recorded, and reports can be obtained. At the end of the study, it was found that important quality variables such as circularity, diameter, thickness, color, image area, and moisture could be read with a sample size of up to 100%. As a result of the experiments, it was seen that the change in product diameters could be defined within the range of 40.30±0.5 mm. The circularity ratio between horizontal and vertical diameters can be monitored within 1.008 ± 0.018%. The change in moisture within the range of 2.741±0.164 g/100g. The light-dark image area ratios within the range of 0.486±0.033%, and the product thickness within the range of 11.56±0.178 mm. Resultly, all critical quality parameters taken into account can be detected within a range that will not affect product and process quality.

Harnessing uncertainty: A deep mechanistic approach for cautious diagnostic and forecast of Bovine Respiratory Disease

Bovine Respiratory Disease (BRD) is a prevalent infectious disease of respiratory tract in cattle, presenting challenges in accurate diagnosis and forecasting due to the complex interactions of multiple risk factors. Common methods, including mathematical epidemiological models and data-driven approaches such as machine learning models, face limitations such as difficult parameter estimation or the need for data across all potential outcomes, which is challenging given the scarcity and noise in observing BRD processes. In response to these challenges, we introduce a novel approach known as the Bayesian Deep Mechanistic method. This method couples a data-driven model with a mathematical epidemiological model while accounting for uncertainties within the processes. By utilising 265 lung ultrasound videos as sensor data from 163 animals across 9 farms in France, we trained a Bayesian deep learning model to predict the infection status (infected or non-infected) of an entire batch of 12 animals, also providing associated confidence levels. These predictions, coupled with their confidence levels, were used to filter out highly uncertain diagnoses and diffuse uncertainties into the parameterisation of a mathematical epidemiological model to forecast the progression of infected animals. Our findings highlight that considering the confidence levels (or uncertainties) of predictions enhances both the diagnosis and forecasting of BRD. Uncertainty-aware diagnosis reduced errors to 32 %, outperforming traditional automatic diagnosis. Forecast relying on veterinarian diagnoses, considered the most confident, had a 23 % error, whilst forecast taking into account diagnosis uncertainties had a close 27.2 % error. Building upon uncertainty-awareness, our future research could explore integrating multiple types of sensor data, such as video surveillance, audio recordings, and environmental parameters, to provide a comprehensive evaluation of animal health, employing multi-modal methods for processing this diverse data.

Design and Development of Mobile Based Application for Competency Oriented Dental Education

Abstract Background: Teaching, Learning and Asssessment in Competency Based Dental Education need to be digitally captured in an user friendly manner which can be accessed by students and mentor which would be useful for continuous monitoring, periodic assessment and maintenance of a digital record. Methods: A novel customised mobile based application MSCORE© developed for competency oriented dental training among undergraduate interns of an institute. Initial perception on the use of the application was recorded using a questionnaire from the entire batch of interns. Factor Analysis was done to check the validity and reliability of the questionnaire. Results: From the 30-item questionnaire, factor analysis extracted four aforementioned factors and reliability was found for 22 questions. Sixty-three percent of students felt that the application is easy to learn. Overall high scores were recorded for 71% of the students. Eighty-five percent of the students scored high on the suitability of the task. Seventy three scored the high on the satisfaction scale. However, advantages over existing written methods scored high only among 49% of the students. Conclusions: Mobile application-based mentoring, monitoring and assessment can be a better alternative to conventional models as it was found to demonstrate good perception by the digital natives of the present generation.

A hierarchical multi-parametric programming approach for dynamic risk-based model predictive quality control

In this work, we present a hierarchical batch quality control strategy with real-time process safety management. It features a multi-time-scale decision-making framework augmenting: (i) Risk-aware model predictive controller for short-term set point tracking and dynamic risk control under disturbances; (ii) Control-aware optimizer for long-term quality and safety optimization over the entire batch operation; (iii) Intermediate surrogate model to bridge the timescale gap by readjusting the optimizer operating decisions for the controller. All of the above problems are solved via multi-parametric mixed-integer quadratic programming with a key advantage to generate offline explicit control/optimization laws as affine functions of process and risk variables. This allows for the design of a fit-for-purpose risk management plan prior to real-time implementation, while reducing the need for repetitive online dynamic optimization. A unified process model is used to underpin the consistency of hierarchical operational optimization. The proposed approach offers a flexible strategy to integrate distinct decision-making time scales which can be selected separately tailored to the process-specific need of control, fault prognosis, and end-batch quality control. A T2 batch reactor case study is presented to showcase this approach to systematically address the interactions and trade-offs of multiple decision layers toward improving process efficiency and safety.

An efficient protocol for commissioning radiophotoluminescence dosimeters for radiotherapy dosimetry audits

PurposeThe aim of this study was to develop an efficient protocol for the commissioning of 1000 radiophotoluminescence dosimeters (RPLDs) for use in postal dosimetry audits in radiotherapy. This involved the determination of correction factors necessary to reduce measurement uncertainty and ensure accurate dose measurements. MethodsThe commissioning process started with the RPLDs subjected to a series of controlled irradiations to determine their individual nominal response. Experiments were also conducted to assess the influence of irradiation position, reading position, and ambient temperature on the dosimeter readings all which were accounted for calculating individual sensitivity correction factors (SCFs) for each dosimeter. Statistical analysis was performed to evaluate the variability of SCFs depending on normalization group. An additional investigation simulating different dosimetry audit batches was conducted to study the effect of SCF variability on dosimetry audit measurements. ResultsThe adopted commissioning protocol required irradiation position correction factors (0.990–0.996), readout tray position correction factors (0.992–1.01) and room temperature corrections (∼0.4 % per ° C). This enabled the calculation of SCFs for a batch of 1000 RPLDs and the analysis found the majority of SCFs falling within the range of 0.985–1.015. The standard deviations of the SCF distributions were approximately 1% for all normalization groups. It was observed that SCFs normalized to the entire batch of 1000 dosimeters could be effectively used for smaller audit batches, with an additional uncertainty contribution of up to 0.2%. This minimal increase in uncertainty is acceptable within the context of dosimetry audits. ConclusionsThe developed protocol for commissioning RPLDs provides a reliable method for ensuring accurate dose measurements in postal radiotherapy dosimetry audits. The correction factors applied during the commissioning process were thoroughly described to effectively minimize measurement uncertainty. The findings support the use of SCFs normalized to large dosimeter batches for smaller audit groups, thereby streamlining the dosimetry audit process. Future research should focus on the long-term stability of SCFs to further enhance the reliability of RPLD-based dosimetry audits.

The Interplay of Chronic Hepatitis C and COVID-19: Implications for Prognosis and Treatment.

Introduction Chronic hepatitis C (CHC) remains a significant public health concern due to both hepatic and extrahepatic manifestations associated with substantial morbidity and mortality. The emergence of SARS-CoV-2 has raised concerns about the outcomes of COVID-19 in CHC patients. Method We conducted a retrospective analysis of patients with CHC and SARS-CoV-2 infection admitted to a tertiary care hospital between 2020 and 2023. We performed a global analysis of the entire batch of patients and, later, we evaluated the patients according to the severity of the SARS-CoV-2 infection Results The cohort included 89 patients (63 females, 26 males) with a median age of 65 years. Most patients were hospitalized in 2021. Common clinical manifestations included fever, cough, digestive symptoms, and headache. The most frequent comorbidities were renal disease, thyroid disorders, and cancer. Univariate logistic regression analysis identified older age, hospitalization in 2021, and respiratory failure as risk factors for severe COVID-19. Elevated lactate dehydrogenaselevels were also associated with an increased risk of severe COVID-19. Regarding CHC, detectablehepatitis C virus viremia was associated with more severe liver disease (p<0.01). Conclusion Patients with CHC and SARS-CoV-2 infection have a substantial risk of severe outcomes. Early identification and management of these patients are crucial to improve their prognosis.

Bulk irradiation of ammonia for polarized target experiments

We present the simulation, design, and experimental results of a straightforward method for irradiating solid ammonia (NH3) and solid deuterated ammonia (ND3) to optimize their use in dynamic nuclear polarization (DNP) for scattering experiments. A prerequisite for DNP is a certain density of electron spins in the target material, provided by the concentration of F-centers generated by electron beam irradiation. We have developed an approach that efficiently irradiates large quantities of NH3 or ND3, producing approximately 25 grams of target material per hour. A key feature of the method is its near-homogeneous dose delivery, ensuring consistent quality across the entire batch. The production of optimized target material is crucial for polarized fixed target scattering experiments at accelerator facilities worldwide.

Stringent criteria needed for germline genome editing of human IVF embryos.

Germline genome editing of IVF embryos is controversial because it is not directly health or lifesaving but is intended to prevent genetic diseases in yet-unborn future offspring. The following criteria are thus proposed for future clinical trials: (i) Due to medical risks, there should be cautious and judicious application while avoiding any non-essential usage, with rigorous patient counseling. (ii) Genome editing should only be performed on the entire batch of IVF embryos without initial PGT screening if all of them are expected to be affected by genetic disease. (iii) When there is a fair chance that some IVF embryos will not be affected by genetic diseases, initial PGT screening must be performed to identify unaffected embryos for transfer. (iv) IVF embryos with carrier status should not undergo germline genome editing. (v) If patients fail to conceive after the transfer of unaffected embryos, they should undergo another fresh IVF cycle rather than opt for genome editing of their remaining affected embryos. (vi) Only if the patient is unable to produce any more unaffected embryos in a fresh IVF cycle due to advanced maternal age or diminished ovarian reserves, can the genome editing of remaining affected embryos be permitted asa last resort.

TINGKAT STRES MAHASISWA FAKULTAS KESEHATAN ANGKATAN 2021 PADA MASA PANDEMI COVID-19

On March 11, 2020, the World Health Organization (WHO) declared the Coronavirus Disease 2019 (Covid-19) a global pandemic. The government provides a policy of limiting activities outside the home, so school activities are suspended. Several studies have shown that this change in teaching and learning habits creates obstacles that will trigger stress in students. New students have their own stress load, such as adjusting to the environment and lecture activities.To describe the stress level of students in the 2021 batch of learning activities at the Faculty of Health Sciences, Duta Bangsa University during the Covid-19 Pandemic. This study used an observational-descriptive method with a cross sectional approach. The data used is primary data, taken from the Indonesian version of the Medical Student Stressor Questionnaire (MSSQ). The research population is the entire batch of 2021 with a total sampling technique of sampling. This study found the results that the majority of students experienced moderate stress as many as 45 people (54.22%), 15 people mild stress (18.07%), 22 people stressed (26.51%) and very stressed 1 person. (1.20%). In this study, most of the 2021 students of the Faculty of Health Sciences, Duta Bangsa University experienced moderate stress.

Abstract 5049: Detecting chromosomal copy number variations and point mutations in Glioma using a single assay; sparing tissue while significantly reducing testing time and cost

Abstract We aimed to develop a single streamlined assay to detect all Glioma biomarkers. The current diagnosis of Gliomas incorporates molecular biomarkers that include whole chromosomal copy number variations (CNVs) and single nucleotide variations (SNVs). Testing CNVs and SNVs are traditionally done using different large platforms and are performed in large genomic facilities that have the suitable infrastructure. This creates delays in testing, where samples undergo lengthy referrals and the tissue is often split between institutions, which also adds substantially to the costs. We have previously used Nanopore sequencing to develop an assay to detect IDH SNVs from formalin-fixed paraffin-embedded (FFPE) tissue. Nanopore sequencing platform uses small inexpensive tools and can test for both CNVs and SNVs. However, combining both targets efficiently in FFPE tissue remains a challenge. While nanopore whole genome sequencing can detect CNVs, it does not allow for enough coverage to accurately detect small SNVs. Also, raw FFPE DNA is not compatible with the nanopore technology. To fix both challenges we used a PCR based approach that allows enrichment of the SNV targets, as well as creates new nanopore compatible DNA copies. To counteract the bias that PCR creates with uneven amplification, we used a SNP based method to call chromosomal gains and losses. Statistical analysis of probability showed that targeting 15 heterozygous SNPs per chr arm is sufficient for accurate CNV detection (error rate &amp;lt;0.1%). A total of 57 amplicons targeting CNVs on Chr 1,7, 10, 19, and EGFR gene; and IDH1 and IDH2 SNVs were pooled together in one assay and tested on 4 Glioma samples. Variant calling was performed with a custom Python script. All CNV targets were accurately detected on all samples. Losses in chr 1p, 19q and chr 10 were evident with a loss of heterozygosity (LOH) pattern on the SNP analysis. The gain in chromosome 7 was also observed as an allele gain 2:1 SNP pattern on the corresponding samples. The SNV statuses of these samples were also accurately detected when compared to reference results. Finally, the SNV and CNV calling analysis were streamlined using custom shell script, further reducing total turnaround time (including sequencing and data analysis) to less than a day for the entire batch of samples. Initial cost analysis shows &amp;lt;50% of the traditional testing costs for the assay. This work is the first to develop a streamlined single test to detect CNVs and SNVs in Gliomas using FFPE DNA on the nanopore sequencing platform. The shorter testing time, streamlined workflow, and low assay/capital cost could positively influence the care of brain cancer patients. Notably, nanopore-based sequencing tools can also be implemented in other types of tumor diagnosis, especially by smaller labs, helping to overcome many of the existing diagnostic challenges. Citation Format: Mashiat L. Mimosa, Jared T. Simpson, Karel Boissinot, Mora Tiab, Ramzi Fattouh, Rola M. Saleeb. Detecting chromosomal copy number variations and point mutations in Glioma using a single assay; sparing tissue while significantly reducing testing time and cost [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5049.

Asymptotic analysis of the sojourn time of a batch in a M [X]/M/1 processor sharing queue

. In this article, we exploit recent results for the Laplace transform of the sojourn time Ω of an entire batch in the M [ X ] / M / 1 processor sharing queue in order to derive the asymptotic behavior of the distribution function of Ω, namely the behavior of ℙ(Ω > x) when x tends to infinity. Specifically, we show that, up to a multiplying factor, ℙ(Ω > x) has the same order of magnitude as ℙ(ω > x) for large x, where ω is the sojourn time of an arbitrary job in the system. From a practical point of view, this means that if a system has to be dimensioned to guarantee the processing time for jobs, then the system can also guarantee processing times for entire batches by introducing a marginal amount of processing capacity.

Overcoming the obstacle of excess acetonitrile content in the final fluorine-18 radiotracers

Abstract Acetonitrile is widely used as a solvent in synthesizing various fluorine-18 positron emission tomography (PET) radiotracers. Acetonitrile is classified as a Class II residual solvent, and due to its inherent toxic properties, the quantity of residual acetonitrile in drug products has to be limited. When working under Good Manufacturing Practices (GMP) during the radiosynthesis of a radiotracer, the aim is to control all solvent concentrations contained in the ready-to-use product. All products must meet predetermined specifications. Rarely, these limits may be exceeded. To avoid eliminating the entire batch, applying a straightforward time-based technique would be desirable to allow the majority of the product to be safely used. This technique should be based on determining a specific time and volume for which the radiotracer can be utilized in the patients after completing quality control analysis. Here, we report a very simple Excel sheet program based on existing mathematical equations that calculates the exact time and volume at which the radiotracer product can be safely administered to a patient.

АНАЛИЗ ЭКСПЛУАТАЦИОННЫХ ХАРАКТЕРИСТИК ПРИБОРОВ УЧЕТА МОЛОКА

The foreign and domestic individual milk yield accounting devices analysis is carried out. The dairy industry development’s main directions for production rate’s and milk quality increasing are given. The development of these areas is impossible without for milk collecting, accounting and primary processing. Milk accounting on modern domestic and foreign farms can be both group and individual. At group accounting, milk from cows enters into a cooling tanker, and then, through a metering device, into the tanks of vehicles for transportation for enterprises and plants processing. This is a simple and convenient method using only one milk meter, however, by this accounting method, the milking process control impossible. The individual milk yield counters’ using is important for particular cow milk yield predicting for cow health preserving due to milking cups’ absence overexposing. Also, individual milk accounting allows to separate mastitis infected milk from the healthy cows’ milk, that allows to reject only part of farmer milk producing, and not the entire batch, as it would be in the case with group accounting. A brief description of the types of sensors for individual and group milk metering from foreign and domestic producers is presented. An advantages and disadvantages of the considered milk metering devices comparative assessment is given. It is concluded that there is a necessity developing for a system that will meet the following requirements: the ability with a milk-and-air mixture working; device’s high autonomy; construction’s simplicity; high accuracy. Based on the analysis of milk meter designs, it can be argued that the most design promising should be based on the capacitive principle, when the milk-and-air mixture flow will change the electrical capacitance

Merge Loss Calculation Method for Highly Imbalanced Data Multiclass Classification.

In real classification scenarios, the number distribution of modeling samples is usually out of proportion. Most of the existing classification methods still face challenges in comprehensive model performance for imbalanced data. In this article, a novel theoretical framework is proposed that establishes a proportion coefficient independent of the number distribution of modeling samples and a general merge loss calculation method independent of class distribution. The loss calculation method of the imbalanced problem focuses on both the global and batch sample levels. Specifically, the loss function calculation introduces the true-positive rate (TPR) and the false-positive rate (FPR) to ensure the independence and balance of loss calculation for each class. Based on this, global and local loss weight coefficients are generated from the entire dataset and batch dataset for the multiclass classification problem, and a merge weight loss function is calculated after unifying the weight coefficient scale. Furthermore, the designed loss function is applied to different neural network models and datasets. The method shows better performance on imbalanced datasets than state-of-the-art methods.

A Bayesian statistical method for large-scale MEMS-based sensors calibration: a case study on 100 digital accelerometers

Low-cost sensors and in particular micro-electro-mechanical systems (MEMS) devices are widely used in many applications, including consumer electronics, healthcare, automotive, and industrial automation. Their large-scale production (typically in the order of millions per week in a single factory) would require the calibration of a huge number of devices that would be costly and time-consuming. A solution can be found in the use of statistical methods in order to (at least partially) substitute for the typical calibration procedures. In this work, we propose a Bayesian method to statistically calibrate large batches of sensors using probabilistic models and prior knowledge. The method involves experimentally calibrating only a small sample of sensors, then infer the number of reliable sensors in the entire batch and assign an appropriate uncertainty to all the sensors. Therefore, it can be considered as a statistical calibration of the batch. The Bayesian nature of this approach allows reducing the number of experimental calibrations by incorporating the prior knowledge coming from the previous calibration of a ‘benchmark’ batch, which is performed ‘once and for all’ and is representative of the whole production process. The application and validation of the method are performed through the calibration of 100 digital MEMS accelerometers. Validation results showed an acceptable agreement between experimental-based bootstrap and theoretical values, with relative differences within ±7%.

Statistical Approaches for the Reduction of Measurement Errors in Metrology

Abstract Metrology is extensively used in the manufacturing industry to determine whether the dimensions of parts are within their tolerance interval. However, errors cannot be avoided. Metrology experts are of course aware of it, and able to identify the different sources that contribute to making errors. In this paper, the probability density function of measurement errors is assumed to be given as an input. Very little research has been made in metrology to develop methods that take into account such data. This work deals with a batch of measures and its statistical properties. A first method is proposed to correct the effects of the measurement errors on the distribution that characterizes the entire batch. Then a second method is proposed to estimate the true value that is hidden behind each single measure, by removing the measurement error statistically. The second method is based on the output knowledge of the first, which is integrated with Bayesian statistics. The relevance of these two methods is shown through two examples applied on simulated data.

Fracture mechanics-based analysis of the fatigue limit of Ti6Al4V alloy specimens manufactured by SLM in as-built surface conditions by means of areal measurements

The fatigue limit of additively manufactured Ti6Al4V alloy specimens in as-built surface conditions is estimated using a fracture mechanics analysis involving different definitions of an effective crack size related to the areal parameter, Sv, of the surface texture, on which the fatigue limit is made to depend. The approach adopts the Extreme Value Statistics (EVS) to determine the largest Sv of the individual as well as the entire batch of specimens and is validated by comparison with the experimental results. Finally, the applicability of the approach by using the 1D roughness profile parameter, Rv, is discussed using the EVS with appropriate hypotheses.

Samian Ware in Early Roman Contexts at Capidava

The present paper is part of a series of publications related to the research carried out in the last decade in the early Roman contexts from Capidava (Constanța County), as well. The subject of the article is a batch of 17 fragmentary ceramic vessels, belonging to the terra sigillata category. Of these, the first 16 items (Cat. nos. 1-16) are fragments of imported vessels produced in the famous ceramic production centre of La Graufesenque, in South Gaul. The last vessel, Cat. no. 17, is of local production, from the category called Pontic sigillata decorated by the barbotine technique. The entire batch of ceramic material was discovered during the archaeological excavation carried out at the Late Roman headquarters building (principia), during which the foundations of this building were also excavated. Beneath these foundations were discovered the ruins of an early Roman habitation level represented by military barracks-type buildings, a stone-paved street, a well, a rubbish pit and a waste drain that led to that pit. The material presented by us comes from this earliest context, respectively from all the urban planning elements listed above, especially from the waste drain and the rubbish pit. The dating of the material is early, namely the Flavian era until the end of Trajan’s reign. From this point of view, the ceramic material presented here completes the picture provided by other categories of material previously published from the same archaeological context.

A modelling workflow for quantification of photobioreactor performance

In this work we have developed a comprehensive modelling workflow for the quantification of photobioreactor performance. Computational Fluid Dynamics (CFD) modelling combined with Lagrangian particle tracking was used to characterise the flow field inside the reactor; this information was combined with a Monte-Carlo model of light attenuation and a kinetic growth model to predict the performance of the system over the duration of the entire batch. The CFD model was validated against measurements of the overall hold-up, local hold-up and mixing time for superficial velocities between 0.6 and 6 cm s−1 in a pilot-scale bubble column photobioreactor, with the CFD predictions agreeing with the experimental data. Comparison was also made between the predicted biomass concentration and experimental measurements using the diatom Phaeodactylum tricornutum, with the model predictions being in good agreement with the experimental results. The model was used to investigate a range of operating conditions and reactor designs, with the most promising predicted to give a 40 % increase in the biomass productivity. Results from this work can be used for the in-silico design and optimisation of photobioreactor systems, thereby enabling their wider use as a sustainable production technology.