Plans to expand African vaccine production face steep hurdles.

Moderna's pause on Kenya project highlights difficulties in creating a competitive vaccine sector on the continent.

[Analysis of symptoms and influencing factors of low back work-related musculoskeletal disorders among workers in a container manufacturing factory].

Objective: To investigate the occurrence of low back work-related musculoskeletal disorders (WMSDs) among workers in a container manufacturing factory, and to explore the influencing factors. Methods: In June 2022, 952 workers from a container factory were selected as the research objects by cluster random sampling. Through questionnaire survey, the incidence of low back WMSDs symptoms among workers in the past one year was collected, and the influencing factors of low back WMSDs were analyzed by logistic regression. Results: The incidence rate of low back WMSDs was 46.7% (445/952). The factors with higher exposure at work were frequent slight bending (77.0%, 733/952), frequent overtime (74.1%, 705/952), and the need to turn around while working (62.3%, 593/952). Multivariate logistic regression analysis showed that age over 40 years old, smoking, drinking, often bending over slightly, sitting for a long time, maintaining a large bending posture for a long time, often working overtime, limited operating space, and there was always a need to complete conflicting things in the container manufacturing factory workers were the risks of increasing the low back WMSDs (OR=1.68, 1.96, 2.47, 1.49, 1.84, 2.11, 1.90, 1.82, 2.00, P<0.05). Standing at work, always friendly colleagues, and always supportive and helpful leaders were protective factors for low back WMSDs (OR=0.60, 0.32, 0.40, P<0.05) . Conclusion: The incidence of low back WMSDs symptoms in container manufacturing workers is high, and work-related factors such as frequent slight bending, long time holding large bending posture and limited operating space are the focus of ergonomic intervention in container manufacturing enterprises.

Technical, Environmental, and Economic Analysis Comparing Low-Carbon Industrial Process Heat Options in U.S. Poly(vinyl chloride) and Ethylene Manufacturing Facilities.

Electrification and clean hydrogen are promising low-carbon options for decarbonizing industrial process heat, which is an essential target for reducing sector-wide emissions. However, industrial processes with heat demand vary significantly across industries in terms of temperature requirements, capacities, and equipment, making it challenging to determine applications for low-carbon technologies that are technically and economically feasible. In this analysis, we develop a framework for evaluating life cycle emissions, water use, and cost impacts of electric and clean hydrogen process heat technologies and apply it in several case studies for plastics and petrochemical manufacturing industries in the United States. Our results show that industrial heat pumps could reduce emissions by 12-17% in a typical poly(vinyl chloride) (PVC) facility in certain locations currently, compared to conventional natural gas combustion, and that other electric technologies in PVC and ethylene production could reduce emissions by nearly 90% with a sufficiently decarbonized electric grid. Life cycle water use increases significantly in all low-carbon technology cases. The levelized cost of heat of viable low-carbon technologies ranges from 15 to 100% higher than conventional heating systems, primarily due to energy costs. We discuss results in the context of relevant policies that could be useful to manufacturing facilities and policymakers for aiding the transition to low-carbon process heat technologies.

Case study of chemical and enzymatic degumming processes in soybean oil production at an industrial plant.

The vegetable oil degumming process plays a critical role in refining edible oil. Phospholipids (PL) removal from crude extracted soybean oil (SBO) by the enzymatic degumming process has been investigated in this work. Enzymatic degumming of extracted SBO with microbial phospholipase A1 PLA-1 Quara LowP and Lecitase Ultra enzymes have also been studied comparatively. The main novelty of our work is the use of the enzymatic degumming process on an industrial scale (600 tons a day). Many parameters have been discussed to understand in detail the factors affecting oil losses during the degumming process. The factors such as chemical conditioning (CC) by phosphoric acid 85%, the enzyme dosage mg/kg (feedstock dependent), the enzymatic degumming reaction time, and the characteristics of the plant-processed SBO have been discussed in detail. As a main point, the degummed oil with a phosphorus content of < 10 mg/kg increases yield. Quara LowP and Lecitase Ultra enzymes are not specific for certain phospholipids PL; however, the conversion rate depends on the SBO phospholipid composition. After 4 h, over 99% of Phospholipids were degraded to their lysophospholipid LPL (lysolecithin). The results showed a significant effect of operating parameters and characteristics of different origins of SBO, fatty acids FFA content, Phosphorus content and total divalent metals (Calcium Ca, Magnesium Mg and Iron Fe mg/kg) content on the oil loss. The benefit of using enzymatic degumming of vegetable oils rather than traditional chemical refining is that the enzymatic degumming process reduces total oil loss. This decrease is known as enzymatic yield. The enzymatic degumming also decreases wastewater and used chemicals and running costs; moreover, it enables physical refining by lowering the residue phosphorus to < 10 mg/kg.

Bacterial community and filamentous population of industrial wastewater treatment plants in Belgium.

The discharge of industrial water requires the removal of its pollutants, where biological wastewater treatment plants (WWTPs) are the most used systems. Biological WWTPs make use of activated sludge (AS), where bacteria are responsible for the removal of pollutants. However, our knowledge of the microbial communities of industrial plants is limited. Understanding the microbial population is essential to provide solutions to industrial problems such as bulking. The aim of this study was to identify at a high taxonomic resolution the bacterial population of 29 industrial WWTPs using 16S rRNA amplicon sequencing. Our results revealed that the main functional groups were dominated by Thauera and Zoogloea within denitrifiers, Dechloromonas in phosphate-accumulating organisms, and Defluviicoccus in glycogen-accumulating organisms. The activated sludge characterization indicated that 59% of the industrial plants suffered from bulking sludge, with DSVI values of up to 448 mL g-1. From the bulking cases, 72% corresponded to filamentous bulking with Thiothrix as the most abundant filament; meanwhile, the other 28% corresponded to viscous bulking sludge in which Zoogloea was the most abundant genus. Furthermore, the bacterial population did not share a core of taxa across all industrial plants. However, 20 genera were present in at least 50% of the plants comprising the general core, including Thauera, Ca. Competibacter, and several undescribed microorganisms. Moreover, statistical analysis revealed that wastewater salinity strongly affected the microbial richness of the industrial plants. The bacterial population across industrial plants differed considerably from each other, resulting in unique microbial communities that are attributed to the specificity of their wastewaters. KEY POINTS: • The general core taxa of industrial plants were mostly made up of undescribed bacterial genera. • Filamentous bacteria constituted on average 4.1% read abundance of the industrial WWTPs. • Viscous bulking remains a significant type of bulking within industrial WWTPs.

A novel process for the complete recycling of exhausted coffee capsules with a fully circular approach: Design of the industrial plant and Techno-Economic analysis of the process.

The objective of this paper is to present the technical and feasibility analysis of an innovative mechanical recycling system for exhausted coffee capsules. This recycling process involved the sorting of spent coffee grounds (SGC) and the subsequent drying and melt-mixing of a portion of these grounds with the remaining capsule components, mainly composed of polypropylene (PP), along with optional virgin PP. These newly developed composite materials exhibited mechanical and rheological properties comparable or even surpassing those of virgin PP. They were also successfully utilized for the injection of new capsule shells, thus alignin with the principles of the circular economy. In addition to the technical aspects, this paper present a comprehensive Techno-Economic Analysis (TEA) of the proposed recycling processes, considering the inclusion of virgin PP (0-20 %) and the initial moisture content (MSGC) of SGC(5-55 %) as varying factors. An industrial plant, designed to handle up to 190 million exhausted coffee capsules and produce up to 1500 tons of recycled compund was appropriately sized. The analysis revealed that processes are profitable across all examined scenarios and that the Net Present Value ranged between 800 k€ (for vPP = 20 % and MSGC = 55 %) and 2000 k€ (for vPP = 0 % and MSGC = 5 %).

Quantitative microbial spoilage risk assessment caused by fungi in sports drinks through multilevel modelling.

The risk of fungal spoilage of sports drinks produced in the beverage industry was assessed using quantitative microbial spoilage risk assessment (QMSRA). The most relevant pathway was the contamination of the bottles during packaging by mould spores in the air. Mould spores' concentration was estimated by longitudinal sampling for 6 years (936 samples) in different production areas and seasons. This data was analysed using a multilevel model that separates the natural variability in spore concentration (as a function of sampling year, season, and area) and the uncertainty of the sampling method. Then, the expected fungal contamination per bottle was estimated by Monte Carlo simulation, considering their settling velocity and the time and exposure area. The product's shelf life was estimated through the inoculation of bottles with mould spores, following the determination of the probability of visual spoilage as a function of storage time at 20 and 30 °C using logistic regression. The Monte Carlo model estimated low expected spore contamination in the product (1.7 × 10-6 CFU/bottle). Nonetheless, the risk of spoilage is still relevant due to the large production volume and because, as observed experimentally, even a single spore has a high spoilage potential. The applicability of the QMSRA during daily production was made possible through the simplification of the model under the hypothesis that no bottle will be contaminated by more than one spore. This simplification allows the calculation of a two-dimensional performance objective that combines the spore concentration in the air and the exposure time, defining "acceptable combinations" according to an acceptable level of spoilage (ALOS; the proportion of spoiled bottles). The implementation of the model at the operational level was done through the representation of the simplified model as a two-dimensional diagram that defines acceptable and unacceptable areas. The innovative methodology employed here for defining and simplifying QMSRA models can be a blueprint for future studies aiming to quantify the risk of spoilage of other beverages with a similar scope.

Noise-induced hearing loss among manufacturing factory workers in Kuching, Sarawak: Prevalence and associated risk factors.

INTRODUCTION: Noise-induced hearing loss (NIHL) is a common problem worldwide. Increased globalisation, as well as industrialisation, gives rise to an increase in the incidence of NIHL worldwide. Malaysia is not spared from this problem, either. The objectives were to determine the prevalence of NIHL and its associated factors among manufacturing factory workers. MATERIAL AND METHODS: A cross-sectional study was done in Kuching, Sarawak, involving 173 randomly selected respondents among manufacturing factory workers. Data collected were respondents' workplace monitoring data and their audiometry records obtained from the factory record, and the otoscopy examinations performed. In addition, respondents were required to fill up an interviewer-guided questionnaire. RESULTS: The prevalence of NIHL was high (49.7%). The factors which were found to have a significant association with NIHL in bivariate analysis were age (p < 0.05, 95% CI), male gender (p < 0.05; OR - 7.60; CI 3.34 -18.38), duration of employment (p <0.05), knowledge of noise level (p < 0.05; OR - 4.11; CI 1.10 - 15.28), working at polishing department (p < 0.05; OR - 4.23; CI 2.13 - 8.43), and smoking (p < 0.05; OR - 39.6; CI 16.5 - 94.8). Pack-years of smoking were also found to have a significant association with p < 0.05. However, only smoking was statistically significant in multivariate analysis, where the risk of developing NIHL was 27.55 (p < 0.005; CI 10.74 - 70.64) among smokers. CONCLUSION: The high prevalence of NIHL despite the existing Hearing Conservation Program (HCP) may indicate that there may be some elements in HCP that require close monitoring by the factory management, and the importance of smoking cessation among the workers exposed to noise at the workplace should be highlighted.

Environmental Monitoring of Food Manufacturing Facilities for Listeria: A Case Study.

Environmental monitoring programs (EMPs) for food production facilities are useful for verifying general sanitation controls and are recommended as verification measures to ensure that the Hazard Analysis Critical Control Point plan is working effectively. In this study, EMPs for Listeria were conducted at three food production facilities to assess the efficacy of sanitation control and establish effective sanitation control methods. In Facility A, L. monocytogenes was detected in the clean area although in Zone 3, non-food-contact surfaces. To prevent contamination from dirty areas, the cleaning practices in the preparation room were investigated. Normal cleaning combined with disinfection with carbonated hypochlorite water (chlorine concentration, 150 ppm) proved effective. At Facility B, a salad product and its ingredients (pastrami and salami) were positive for L. monocytogenes serotype 3b. The bacterial count was <10/g in all samples. However, when inoculated with L. monocytogenes isolates, the growth of approximately 2 log cfu/g was observed on pastrami after 48 h of incubation at 10°C. The ingredients were commercially purchased blocks that were sliced in a slicer at Facility B and used as salad toppings. Because both unopened blocks were negative for L. monocytogenes, contamination of the slicer was suspected. Sampling of the slicer revealed that contamination by L. monocytogenes serotype 3b was more extensive after use than before use. Therefore, the slicer was disassembled, cleaned, and disinfected thoroughly. In Facility C, L. monocytogenes serotype 4b (4e) was detected in all the dirty, semiclean, and clean areas. The strain was also isolated from the wheels of a smoking cart transported across the zones. Therefore, efforts were made to frequently clean and disinfect the cart. EMPs revealed the presence of Listeria in each facility and allowed remedial measures to be undertaken. Continued monitoring and Plan-Do-Check-Act cycles were considered desirable.

Application of machine learning for risky sexual behavior interventions among factory workers in China.

Introduction: Assessing the likelihood of engaging in high-risk sexual behavior can assist in delivering tailored educational interventions. The objective of this study was to identify the most effective algorithm and assess high-risk sexual behaviors within the last six months through the utilization of machine-learning models. Methods: The survey conducted in the Longhua District CDC, Shenzhen, involved 2023 participants who were employees of 16 different factories. The data was collected through questionnaires administered between October 2019 and November 2019. We evaluated the model's overall predictive classification performance using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. All analyses were performed using the open-source Python version 3.9.12. Results: About a quarter of the factory workers had engaged in risky sexual behavior in the past 6 months. Most of them were Han Chinese (84.53%), hukou in foreign provinces (85.12%), or rural areas (83.19%), with junior high school education (55.37%), personal monthly income between RMB3,000 (US$417.54) and RMB4,999 (US$695.76; 64.71%), and were workers (80.67%). The random forest model (RF) outperformed all other models in assessing risky sexual behavior in the past 6 months and provided acceptable performance (accuracy 78%; sensitivity 11%; specificity 98%; PPV 63%; ROC 84%). Discussion: Machine learning has aided in evaluating risky sexual behavior within the last six months. Our assessment models can be integrated into government or public health departments to guide sexual health promotion and follow-up services.

Regional impacts on air quality and health of changing a manufacturing facility's grid-boiler to a combined heat and power system.

Poor air quality is linked to numerous adverse health effects including strokes, heart attacks, and premature death. Improving energy efficiency in the industrial sector reduces air emissions and yields health benefits. One of these strategies, replacing an existing grid boiler (GB) with a combined heat and power (CHP) system, can improve a facility's energy efficiency but can also increase local air emissions, which in turn can affect health outcomes. Previous studies have considered air-emissions and health outcomes of CHP system installation at a single location, but few studies have investigated the regional air quality and health impacts of replacing an existing GB with new CHP system. This study estimates the emission changes and associated health impacts of this shift in 14 regions in the US, representing different electricity generation profiles. It assumes that one manufacturing facility in each region switches from an existing GB to a CHP system. The monetized annual US health benefits of shifting a single GB to a CHP in each of the 14 regions range from $-5.3 to 0.55 million (2022 USD), while including CHP emission control increases the benefits by 100-170% ($9,000 to 1.15 million (2022 USD)). This study also includes a sensitivity analysis, which suggests that the facility location (region, state, and county), boiler efficiency, and emission control of the CHP are key factors that would determine whether shifting from a GB to CHP system would result in health benefits or burdens.Implications: Combined heat and power (CHP) systems offer industrial facilities the opportunity to improve their energy efficiency and reduce greenhouse gas emissions. However, CHP systems also combust more fuel on site and can also increase local air emissions. This study evaluates how converting an existing grid boiler (GB) system to a CHP system (with or without emission control) affects local (from combustion) and regional emissions (from electricity consumption) and the associated health burdens in different US regions. A facility can use this study's analysis as an example for estimating the tradeoffs between local emission changes, regional emission changes, and health effects. It also provides a comparison between the incremental cost of adding SCR (compared to uncontrolled CHPs) and the NPV of the monetized health benefits associated with adding the SCR.

Automated Micro-Crack Detection within Photovoltaic Manufacturing Facility via Ground Modelling for a Regularized Convolutional Network.

The manufacturing of photovoltaic cells is a complex and intensive process involving the exposure of the cell surface to high temperature differentials and external pressure, which can lead to the development of surface defects, such as micro-cracks. Currently, domain experts manually inspect the cell surface to detect micro-cracks, a process that is subject to human bias, high error rates, fatigue, and labor costs. To overcome the need for domain experts, this research proposes modelling cell surfaces via representative augmentations grounded in production floor conditions. The modelled dataset is then used as input for a custom 'lightweight' convolutional neural network architecture for training a robust, noninvasive classifier, essentially presenting an automated micro-crack detector. In addition to data modelling, the proposed architecture is further regularized using several regularization strategies to enhance performance, achieving an overall F1-score of 85%.