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Many institutions face low deposit rates with their institutional repositories despite investing substantial resources in implementing and supporting these systems. Deposit rates are higher in IRs that offer mediated deposits; however, this can be a time and labor intensive process. This article describes a method for copying open access articles and corresponding descriptive metadata from open repositories for archiving in an institutional repository using Beautiful Soup and Selenium as web scraping tools. This method quickly added hundreds of articles to an IR without relying on faculty participation or consulting publisher policies, increasing repository downloads and usage. [ABSTRACT FROM AUTHOR]

The article, titled "On the Subject of WBS or BRM," is a letter to the editor written by Crispin Piney in response to Dr. Kenneth Smith's musings on work breakdown structures (WBS) and the value of assigning weights to milestones. Piney analyzes Smith's example of the Public Health Project and proposes a benefits map (BM) as an alternative approach to organizing components and determining their contributions to the target benefit. Piney argues that milestone weighting is important for tracking progress in programs, but it should be based on a valid representation of how each milestone contributes to the goal. The benefits map can serve as a planning tool and facilitate communication among team members and stakeholders. [Extracted from the article]

The pottery wheel machine is an essential piece of equipment in ceramics manufacturing. This paper presents the design of an intelligent pottery wheel machine aimed at addressing the issues of low clay material utilization and the challenges of pottery throwing operations. Traditional techniques require exceptional craftsmanship and proficient mechanical operation, and existing pottery wheel machines still demand attention to equipment usage and clay material knowledge. The intelligent pottery wheel machine integrates central positioning, automatic clay feeding, and clay storage. The pull mechanism stores and precisely feeds clay, regulating usage and minimizing excess. The automated settings of the clay storage and power push system replace the need for manual assessment of clay density and quality, ensuring balanced and high-quality clay extrusion. The continuous feeding setting allows for automatic addition of clay, saving replenishment time and reducing the labor intensity of adding clay. The lifting mechanism ensures that the clay body remains centered, avoiding repeated manual adjustments and allowing users to focus on creating pottery rather than spending excessive time mastering the centering technique. The machine's lightweight, modular design reduces clay waste, making the process more sustainable. By saving clay materials and improving throwing efficiency, it enhances the success rate of throwing. [ABSTRACT FROM AUTHOR]

Background and aim: Health anxiety is a mental disorder that characterized by an excessive fear about health and physical symptoms. High anxiety in pregnancy is associated with adverse outcomes. The aim of this study was to investigate the effect of prenatal education on health anxiety of primigravid women. Methods: The present study was quasi-experimental study. 122 primiparous pregnant women referred to comprehensive health services Shahrekord (A city in the southwest of Iran) clinics in 2019, after receiving consent to participate in the study, randomly divided into two intervention and control groups. The intervention group participated in 8 sessions (1.5-h), once every 2 weeks, from 20 to 37th weeks of gestation. The health anxiety questionnaire was completed on 20th (before the beginning of the courses), 28th and 37th weeks by two groups. Consequences of pregnancy included weight, Apgar score, delivery type, labor time and first breastfeeding time. SPSS version 16 software was used for data analysis. Results: No significant difference was found type of delivery, gestational age, height, weight, head length, Apgar score, duration of hospitalization and first breastfeeding time. The duration of the active and latent phase of labor was significantly lower and the weight of newborn was significantly higher in the intervention group than the control group (P < 0.05). At 37th week, the scores of illness concern, negative consequence and total health anxiety in the intervention group decreased by 3.42, 0.93 and 4.36 respectively and in control group increased by 2.82, 0.03 and 2.86. Conclusion: Pregnancy educational courses has positive effects on health anxiety, decrease duration of labor time and increased newborn weight. In order to improve the outcome of pregnancy, educational classes during pregnancy should be considered. [ABSTRACT FROM AUTHOR]

N-glycan, a critical quality attribute (CQA) of monoclonal antibodies, profoundly impacts potency, immunogenicity, safety, and clinical efficiency. Therefore, N-glycan should be monitored and controlled during development. The conventional 2-AB method is considered the gold standard for N-glycan analysis, which is labor intensive and time consuming. Moreover, its sample requirement is relatively high and cannot be met in early-stage development, including cell line development. In this study, we compared the performance of four rapid analytical methods in N-glycan analysis of mAbs, including the rapid 2-AB method, reduction method, off-line IdeS digestion method, and two-dimensional liquid chromatography-mass spectrometry (2D-LC-MS) method. Our results showed that these four rapid analytical methods could provide comparable N-glycan data. Moreover, these four rapid methods shortened the testing time for the conventional 2-AB method from days to just minutes. They also reduced the sample requirement for the conventional 2-AB method from milligrams to micrograms. Among these four rapid methods, the 2D-LC-MS method demonstrated great potential for applications in time-consuming cell line development because it required less testing time and a lower sample requirement. [ABSTRACT FROM AUTHOR]

Urinalysis is a heavily used diagnostic test in clinical laboratories; however, it is chronically held back by urine sediment microscopic examination. Current instruments are bulky and expensive to be widely adopted, making microscopic examination a procedure that still relies on manual operations and requires large time and labor costs. To improve the efficacy and automation of urinalysis, this study develops an acoustofluidic-based microscopic examination system. The system utilizes the combination of acoustofluidic manipulation and a passive hydrodynamic mechanism, and thus achieves a high throughput (1000 μL min−1) and a high concentration factor (95.2 ± 2.1 fold) simultaneously, fulfilling the demands for urine examination. The concentrated urine sample is automatically dispensed into a hemocytometer chamber and the images are then analyzed using a machine learning algorithm. The whole process is completed within 3 minutes with detection accuracies of erythrocytes and leukocytes of 94.6 ± 3.5% and 95.1 ± 1.8%, respectively. The examination outcome of urine samples from 50 volunteers by this device shows a correlation coefficient of 0.96 compared to manual microscopic examination. Our system offers a promising tool for automated urine microscopic examination, thus it has potential to save a large amount of time and labor in clinical laboratories, as well as to promote point-of-care urine testing applications in and beyond hospitals. [ABSTRACT FROM AUTHOR]

Connected vehicle fleets have formed significant component of industrial internet of things scenarios as part of Industry 4.0 worldwide. The number of vehicles in these fleets has grown at a steady pace. The vehicles monitoring with machine learning algorithms has significantly improved maintenance activities. Predictive maintenance potential has increased where machines are controlled through networked smart devices. Here, benefits are accrued considering uptimes optimization. This has resulted in reduction of associated time and labor costs. It has also provided significant increase in cost benefit ratios. Considering vehicle fault trends in this research predictive maintenance problem is addressed through hybrid deep learning-based ensemble method (HDLEM). The ensemble framework which acts as predictive analytics engine comprises of three deep learning algorithms viz modified cox proportional hazard deep learning (MCoxPHDL), modified deep learning embedded semi supervised learning (MDLeSSL) and merged LSTM (MLSTM) networks. Both sensor as well as historical maintenance data are collected and prepared using benchmarking methods for HDLEM training and testing. Here, times between failures (TBF) modeling and prediction on multi-source data are successfully achieved. The results obtained are compared with stated deep learning models. This ensemble framework offers great potential towards achieving more profitable, efficient and sustainable vehicle fleet management solutions. This helps better telematics data implementation which ensures preventative management towards desired solution. The ensemble method's superiority is highlighted through several experimental results. [ABSTRACT FROM AUTHOR]

Comparable data is essential to understand biodiversity patterns. While assemblage or community inventorying requires comprehensive sampling, monitoring focuses on as few components as possible to detect changes. Quantifying species, their evolutionary history, and the way they interact requires studying changes in taxonomic (TD), phylogenetic (PD) and functional diversity (FD). Here we propose a method for the optimization of sampling protocols for inventorying and monitoring assemblages or communities across these three diversity dimensions taking sampling costs into account. We used Iberian spiders and Amazonian bats as two case-studies. The optimal combination of methods for inventorying and monitoring required optimizing the accumulation curve of α-diversity and minimizing the difference between sampled and estimated β-diversity (bias), respectively. For Iberian spiders, the optimal combination for TD, PD and FD allowed sampling at least 50% of estimated diversity with 24 person-hours of fieldwork. The optimal combination of six person-hours allowed reaching a bias below 8% for all dimensions. For Amazonian bats, surveying all the 12 sites with mist-nets and 0 or 1 acoustic recorders was the optimal combination for almost all diversity types, resulting in >89% of the diversity and <10% bias with roughly a third of the cost. Only for phylogenetic α-diversity, the best solution was less clear and involved surveying both with mist nets and acoustic recorders. The widespread use of optimized and standardized sampling protocols and regular repetition in time will radically improve global inventory and monitoring of biodiversity. We strongly advocate for the global adoption of sampling protocols for both inventory and monitoring of taxonomic, phylogenetic and functional diversity. [ABSTRACT FROM AUTHOR]

Double poverty refers to the lack of both time and income. This study analyses precarious workers' double poverty, focusing on the case of South Korea, where the characteristics of its labour market perpetuate the risk of double poverty. This study set less than two-thirds of the median free time and less than two-thirds of the median income as poverty lines. Using Korean Labour & Income Panel Study data, this article identifies the double poor, experiencing both time and income poverty in the Korean labour market. It then examines the effects of occupational class, employment type, company size, social wage, trade union membership, and gender on double poverty. It is found that double poverty impacts women workers, low-skilled service workers, and non-regular workers. This study contributes to the discussion of precarious work by analysing the double poverty of time and income of precarious workers. Time poverty limits workers' capacity to escape income poverty as they are unable to work longer and invest in household production or human capital. Consequently, the workers' lack of time deepens their precarity. [ABSTRACT FROM AUTHOR]

My aim in this article is to contribute to defences of working time policies by attempting to meet an objection that comes from those who condemn these measures on the alleged grounds that they reduce economic output. What is more, as I emphasize throughout, it is possible to rebut such a concern in a fashion that is consistent with the demands of liberal anti-perfectionism. In itself, this is a philosophically striking and politically significant result. However, beyond this, much of the value of my discussion lies in the details of how to meet the relevant objection, and perhaps especially in my novel liberal defence of the ideal of the stationary state. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to examine the effect of birth ball exercise on labor pain, delivery duration, birth comfort, and birth satisfaction. Methods: The study used a randomized controlled trial design. All 120 primiparous pregnant women were randomly assigned to the intervention (IG) and control groups (CG). After the cervical dilatation reached 4 cm, the pregnant women in the IG performed birth ball exercises, adhering to the birth ball guide created by the researcher. No intervention was made in the control group other than standard midwifery care practices. Results: The labor pain level between the groups (VAS 1-when cervical dilatation was 4 cm) was similar to each other. The labor pain level (VAS 2- when cervical dilatation was 9 cm) scores of the women in the IG were significantly lower than those in the CG (p < 0.05). The time between the active phase of labor until dilatation is complete and the time until the baby's head comes out after full dilatation was found to be statistically significantly shorter in the IG compared to the CG (p < 0.05). Childbirth comfort and satisfaction mean scores between the groups were found to be statistically insignificant (p > 0.05). Conclusion: As a result of the study, it was determined that the birth ball exercise significantly reduced labor pain and labor time. We recommend that the birth ball exercise be applied to all low-risk pregnant women because it helps fetal descent and cervical dilatation, and shortens labor pain and delivery time. [ABSTRACT FROM AUTHOR]

This study aims to analyze the effect of work ability and work operational time on cadet productivity and analyze the role of the on-the-job training (OJT) variable in mediating the independent variable on the dependent variable in the Airport Electrical Engineering Occupation at Politeknik Matra Udara. Data were collected through questionnaires distributed to respondents. This research uses a quantitative approach, analyzes using Structural equation modeling (SEM), and uses the Smart-PLS statistical program. The research sample refers to the Isaac and Michael table using a 10% significance level. The sample of this study consisted of 115 cadets from various occupations in airport electrical engineering at the Air Matra Polytechnic who had participated in the on-the-job training (OJT) program. The results of the analysis show that work ability has a significant positive effect on cadet productivity. This shows that the higher a person's work ability, the higher his productivity. In addition, work-related operational time also has a positive effect on productivity. That is, the longer the working time owned by cadets, the higher the level of productivity. Furthermore, on-the-job training (OJT) proved to be a significant mediator in the relationship between work ability and work operational time and cadet productivity. This suggests that on-the-job training has an important role in improving productivity. [ABSTRACT FROM AUTHOR]

Female domestic workers (FDWs) experience space–time constraints and mobility challenges corresponding to their demanding daily work responsibilities. Studies have shown that FDWs' mobility and activities are primarily dictated by their employers through work tasks. It is unclear, though, how FDWs' activity and mobility patterns outside their employers' homes are shaped by individual and contextual factors, including government policies that mandate that FDWs be granted a rest day. Using an activity space approach combined with multilevel modeling, I evaluated mobility and activity patterns on work and rest days for a sample of Indonesian FDWs in Hong Kong. Data were collected using innovative online activity diaries and were triangulated with qualitative data from WhatsApp follow-up conversations. This research uncovers that FDWs' activity patterns differ significantly between work and rest days. FDWs rarely go outside on workdays except to perform work-related tasks. On the rest day, FDWs' activity spaces are much more expansive as they conduct social and personal activities that are essential for maintaining their well-being. By comparing work and rest days, this research deepens our understanding of geographical, social, and temporal aspects of FDWs' space–time constraints and (im)mobility experiences that encompass and go beyond employer-assigned tasks. Furthermore, the findings demonstrate the benefits of rest day policies for enhancing the visibility of marginalized women in public spaces, particularly in the context of FDWs' restrictive space–time constraints. [ABSTRACT FROM AUTHOR]

Copyright of Revista Ciência & Saúde Coletiva is the property of Associacao Brasileira de Pos-Graduacao em Saude Coletiva and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to its high quantum yield and unique spectral characteristics, quantum dots (QDs) color conversion materials have attracted great attention in the application of luminescent display products. However, there is a lack of effective optical model for QD color conversion materials, especially for the propagation and distribution of light, when the QD ink contains scattering particles. In this paper, a full‐view optical model of QD conversion and blue backlight device architecture is established. The model can not only output the spectral information in the front view, but also predict the spectral information of the QD color conversion device in other different views. At the same time, the effects of QD concentration, thickness, particle size and refractive index on the luminous efficiency of the device structure are also studied by using this optical model. On the other hand, the influence of conversion efficiency of QD and EL backlight on the power consumption and lifetime of QD products is also studied by using this optical model. The model can realize the output of the optical influence on the future products by the parameters related to QD color conversion materials and backlight without actual device fabrication, which can greatly reduce time and labor cost. [ABSTRACT FROM AUTHOR]

Longitudinal cutting is the most common process in steel structure manufacturing, and the man-hours of the process provide an important basis for enterprises to generate production schedules. However, currently, the man-hours in factories are mainly estimated by experts, and the accuracy of this method is relatively low. In this study, we propose a system that predicts man-hours with history data in the manufacturing process and that can be applied in practical structural steel fabrication. The system addresses the data inconsistency problem by one-hot encoding and data normalization techniques, Pearson correlation coefficient for feature selection, and the Random Forest Regression (RFR) for prediction. Compared with the other three Machine-Learning (ML) algorithms, the Random Forest algorithm has the best performance. The results demonstrate that the proposed system outperforms the conventional approach and has better forecast accuracy so it is suitable for man-hours prediction. [ABSTRACT FROM AUTHOR]

The possible production of fructo-oligosaccharides from sucrose was evaluated in the Base Business Unit (UEB) "Ignacio Agramonte'", which currently produces sorbitol, with low exploitation of the installed capacity, due to lack of glucose. There was an opportunity in infrastructure, time and labor to convert the entity into a multipurpose plant. A simulation model of the technology for the production of fructo-oligosaccharide syrup from sucrose was developed, based on the enzymatic transformation of sucrose into fructo-oligosaccharides, using the recombinant enzyme 1-SST (1-sucrose: sucrose fructosyltransferase) expressed in Pichia pastoris, obtained at the Center for Genetic Engineering and Biotechnology at Sancti Spíritus. Model validation yielded relative errors lesser than 5 %. Two improvement alternatives were evaluated based on increasing the production of FOS by using the actual installed capacity in the sorbitol factory (alternative 1) and also increasing the initial concentration of the sucrose solution (alternative 2). Feasibility of the alternatives was evaluated by comparing the annual net cash income (Anci) and expenses. Alternative 2 was the best, producing 841.5 t of fructo-oligosaccharides in 90 working days, with a benefit/cost ratio of 1,2. [ABSTRACT FROM AUTHOR]

Here, we present a method for concentrating radiocesium in seawater using ammonium molybdophosphate-polyacrylonitrile (AMP-PAN) resin using custom-made sample-loading equipment. It was designed to reduce analytical time and labor with eight peristaltic pumps connected in parallel with eight columns, each packed with AMP-PAN. Eighty liters of seawater containing radiocesium were concentrated to 0.01 L in 6 h with the proposed method and Cs recovery was > 85%. The minimum detectable activity was 0.2 mBq L–1 at 160,000-s measurement with a high-purity germanium detector. [ABSTRACT FROM AUTHOR]

Copyright of DYNA - Ingeniería e Industria is the property of Publicaciones Dyna SL and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To enhance the transplantation effectiveness of vegetables and promptly formulate subsequent work strategies, it is imperative to study the recognition approach for transplanted seedlings. In the natural and complex environment, factors like background and sunlight often hinder accurate target recognition. To overcome these challenges, this study explores a lightweight yet efficient algorithm for recognizing cabbage transplantation states in natural settings. Initially, FasterNet is integrated as the backbone network in the YOLOv5s model, aiming to expedite convergence speed and bolster feature extraction capabilities. Secondly, the introduction of the GAM attention mechanism enhances the algorithm's focus on cabbage seedlings. EIoU loss is incorporated to improve both network convergence speed and localization precision. Lastly, the model incorporates deformable convolution DCNV3, which further optimizes model parameters and attains a superior balance between accuracy and speed. Upon testing the refined YOLOv5s target detection algorithm, improvements were evident. When compared to the original model, the mean average precision (mAP) rose by 3.5 percentage points, recall increased by 1.7 percentage points, and detection speed witnessed an impressive boost of 52 FPS. This enhanced algorithm not only reduces model complexity but also elevates network performance. The method is expected to streamline transplantation quality measurements, minimize time and labor inputs, and elevate field transplantation quality surveys' automation levels. [ABSTRACT FROM AUTHOR]

The visual modality is central to both reception and expression of human creativity. Creativity assessment paradigms, such as structured drawing tasks Barbot (2018), seek to characterize this key modality of creative ideation. However, visual creativity assessment paradigms often rely on cohorts of expert or naïve raters to gauge the level of creativity of the outputs. This comes at the cost of substantial human investment in both time and labor. To address these issues, recent work has leveraged the power of machine learning techniques to automatically extract creativity scores in the verbal domain (e.g., SemDis; Beaty & Johnson 2021). Yet, a comparably well-vetted solution for the assessment of visual creativity is missing. Here, we introduce AuDrA – an Automated Drawing Assessment platform to extract visual creativity scores from simple drawing productions. Using a collection of line drawings and human creativity ratings, we trained AuDrA and tested its generalizability to untrained drawing sets, raters, and tasks. Across four datasets, nearly 60 raters, and over 13,000 drawings, we found AuDrA scores to be highly correlated with human creativity ratings for new drawings on the same drawing task (r =.65 to.81; mean =.76). Importantly, correlations between AuDrA scores and human raters surpassed those between drawings' elaboration (i.e., ink on the page) and human creativity raters, suggesting that AuDrA is sensitive to features of drawings beyond simple degree of complexity. We discuss future directions, limitations, and link the trained AuDrA model and a tutorial (https://osf.io/kqn9v/) to enable researchers to efficiently assess new drawings. [ABSTRACT FROM AUTHOR]

The division of labour remains persistently gendered, in particular among couples with children. Previous research shows that women's lower economic resources are an important factor driving these inequalities, but because gender and (relative) earnings are highly correlated in male–female couples, their relative importance is difficult to disentangle with observational data. Using a factorial survey conducted among approximately 700 employed men and women of childbearing age in Sweden, the United Kingdom, and the Netherlands, the contribution of relative earnings and gender in explaining work-care divisions in couples with children is disentangled. The results show that men and women do not differ in their preferences for their own work hours after childbirth, but both prefer the father to work more hours than the mother. Moreover, the combination of own and partners' preferred hours shows that men and women in all three countries prefer a modified male-breadwinner model after childbirth in scenarios where the male partner earns more or partners have equal earnings. Preferences for egalitarian divisions of labour appear to be slightly stronger in men compared to women and respondents with more egalitarian views on care tasks show less gender-specialization. [ABSTRACT FROM AUTHOR]

Experienced passage of time, the extent to which employees perceive the passage of work time as being fast or slow, is a fundamental aspect of work experience. We identify two novel temporal work design characteristics that can speed up employees' experienced passage of time: temporal predictability and task segmentation. Jobs with high temporal predictability do not make employees go through uncertain wait times before embarking on their next task. High task segmentation occurs when a large chunk of work time is segmented by categorically different temporal markers. We tested a model in which temporal predictability and task segmentation affect experienced passage of time, which in turn influences job performance, with five studies: two experiments that established the internal validity of temporal predictability and task segmentation (Studies 1a and 1b), a naturalistic field study in a factory that investigated the natural consequences of distinct temporal work design (Study 2), an organizational field study that constructively replicated the model using a sample of knowledge workers and their supervisors (Study 3), and an online survey in which we connected our model with the broader work design literature (Study 4). Altogether, the studies support a new temporal approach to work design. [ABSTRACT FROM AUTHOR]

In the field of welding inspection, radiographic non-destructive evaluation (NDE) is a widely used technique for detecting defects in welds. However, this technique requires professionally qualified workers to manually judge radiographs to determine the presence, type, and location of defects. Recently, deep learning techniques have been developed to automate this process by using image segmentation. Despite its effectiveness, small-size targets in segmentation can have blurred boundaries, making it difficult to accurately annotate them at the pixel level. In this study, we propose an automated approach using the Point-REND Res-UNet model to improve the accuracy of detecting welding defects. Our method uses the improved Point-Rend algorithm to iteratively refine coarse segmentation results, allowing for more accurate defect detection. We evaluate our approach on a set of X-ray data and demonstrate that it achieves an improvement in model dice of 6.22%. Our proposed approach can potentially save labor time and costs while enhancing the accuracy and efficiency of welding defect detection. [ABSTRACT FROM AUTHOR]

The labor force surveys (LFS) of all EU countries underwent a substantial redesign in January 2021. To ensure coherent labor market time series for the main indicators in the Norwegian LFS, we model the impact of the redesign. We use a state-space model that takes explicit account of the rotating pattern of the LFS. We also include auxiliary variables related to employment and unemployment that are highly correlated with the LFS variables we consider. The results of a parallel run are also included in the model. The purpose of the article is to quantify the structural breaks due to the redesign. This article makes two contributions to the literature on the effects of redesign in surveys with a rotating panel, such as the LFS. First, we suggest a symmetric specification of the process of the wave-specific effects. Second, we account for substantial fluctuations in the labor force estimates due to the COVID-19 pandemic in the time around the LFS redesign by applying time-varying hyperparameters for both the LFS variables and the auxiliary variables. The specification with time-varying hyperparameters shows a better fit compared to the specification with time-invariant hyperparameters. [ABSTRACT FROM AUTHOR]

Slow progression of labor is a common obstetrical problem with multiple associated complications. Tafoxiparin is a depolymerized form of heparin with a molecular structure that eliminates the anticoagulant effects of heparin. We report on 2 phase II clinical studies of tafoxiparin in primiparas. Study 1 was an exploratory, first-in-pregnant-women study and study 2 was a dose-finding study. Study 1 was performed to explore the effects on labor time of subcutaneous administration of tafoxiparin before onset of labor. Study 2 was performed to test the hypothesis that intravenous treatment with tafoxiparin reduces the risk for prolonged labor after spontaneous labor onset in situations requiring oxytocin stimulation because of dystocia. Both studies were randomized, double-blind, and placebo-controlled. Participants were healthy, nulliparous females aged 18 to 45 years with a normal singleton pregnancy and gestational age confirmed by ultrasound. The primary endpoints were time from onset of established labor (cervical dilation of 4 cm) until delivery (study 1) and time from start of study treatment infusion until delivery (study 2). In study 1, patients at 38 to 40 weeks of gestation received 60 mg tafoxiparin or placebo daily as 0.4 mL subcutaneous injections until labor onset (maximum 28 days). In study 2, patients experiencing slow progression of labor, a prolonged latent phase, or labor arrest received a placebo or 1 of 3 short-term tafoxiparin regimens (initial bolus 7, 21, or 35 mg followed by continuous infusion at 5, 15, or 25 mg/hour until delivery; maximum duration, 36 hours) in conjunction with oxytocin. The number of participants randomized in study 1 was 263, and 361 were randomized in study 2. There were no statistically significant differences in the primary endpoints between those receiving tafoxiparin and those receiving the placebo in both studies. However, in study 1, the risk for having a labor time exceeding 12 hours was significantly reduced by tafoxiparin (tafoxiparin 6/114 [5%] vs placebo 18/101 [18%]; P =.0045). Post hoc analyses showed that women who underwent labor induction had a median (range) labor time of 4.44 (1.2–8.5) hours with tafoxiparin and 7.03 (1.5–14.3) hours with the placebo (P =.0041) and that co-administration of tafoxiparin potentiates the effect of oxytocin and facilitates a shorter labor time among women with a labor time exceeding 6 to 8 hours (P =.016). Among women induced into labor, tafoxiparin had a positive effect on cervical ripening in 11 of 13 cases (85%) compared with 3 of 13 participants (23%) who received the placebo (P =.004). For women requiring oxytocin because of slow progression of labor, the corresponding results were 34 of 51 participants (66%) vs 16 of 40 participants (40%) (P =.004). In study 2, tafoxiparin had no positive effects on the secondary endpoints when compared with the placebo. Except for injection-site reactions in study 1, adverse events were no more common for tafoxiparin than for the placebo among either mothers or infants. There were few serious or treatment-related adverse events. Subcutaneous treatment with tafoxiparin before labor onset (study 1) may be effective in reducing the labor time among women undergoing labor induction and among those requiring oxytocin for slow progression of labor. Moreover, tafoxiparin may have a positive effect on cervical ripening. Short-term, intravenous treatment with tafoxiparin as an adjunct to oxytocin in patients with labor arrest (study 2) did not affect labor time or other endpoints. Both studies suggest that tafoxiparin has a favorable safety profile in mothers and their infants. [ABSTRACT FROM AUTHOR]

Manufacturing industries struggle to devise precise planning and scheduling solutions due to unpredictable business situations. Additionally, uncertainties in production such as machine breakdowns, labour absenteeism, cycle time deviations, etc., would further deteriorate production plans and lead to uncertainty in decision-making processes. Flow shops with bottlenecks are particularly susceptible to these disturbances. Moreover, the random variations in cycle time variations can cause the bottleneck to shift between different stages. Literature indicates that conventional job release methods are ineffective in addressing these difficulties. In contrast, workload control methods would provide better solutions. Hence, a flow shop model has been developed and simulated using the variables like process time variations and bottleneck shifting on the discrete-event simulation software. The flow shop model incorporates realistic shop characteristics which are subjected to random process time variations, so as to assess the performance. The outcomes of the experimentation demonstrate that order release methods play a pivotal role in improving the performance of flow shops in more volatile situations. [ABSTRACT FROM AUTHOR]

This study aimed to describe the work duration and sleep quality among young workers, conducted in coffee shops in the West Denpasar area in 2023. This study employed a quantitative descriptive method with an observational study design and a cross-sectional approach. The sample consisted of 56 coffee shop workers in the West Denpasar area. The variables in this study included the age of workers, work duration, and sleep quality. Data collection was done using primary data through a questionnaire instrument, and data analysis involved descriptive statistical tests. The research results showed that the average age of workers was 20.39 years, with the youngest worker being 18 years old and the oldest 24 years old. The majority of workers had poor sleep quality (80.4%) and worked with a duration of ≥40 hours per week (83.9%). This study concludes that young workers mostly work for ≥40 hours per week and have poor sleep quality. These findings indicate that the conditions of young workers have high-risk factors that could potentially trigger workplace accidents. [ABSTRACT FROM AUTHOR]

The extraction of entities and relationships is a crucial task in the field of natural language processing (NLP). However, existing models for this task often rely heavily on a substantial amount of labeled data, which not only consumes time and labor but also hinders the development of downstream tasks. Therefore, with a focus on enhancing the model's ability to learn from small samples, this paper proposes an entity and relationship extraction method based on the Universal Information Extraction (UIE) model. The core of the approach is the design of a specialized prompt template and schema on cotton pests and diseases as one of the main inputs to the UIE, which, under its guided fine-tuning, enables the model to subdivide the entity and relationship in the corpus. As a result, the UIE-base model achieves an accuracy of 86.5% with only 40 labeled training samples, which really solves the problem of the existing models that require a large amount of manually labeled training data for knowledge extraction. To verify the generalization ability of the model in this paper, experiments are designed to compare the model with four classical models, such as the Bert-BiLSTM-CRF. The experimental results show that the F1 value on the self-built cotton data set is 1.4% higher than that of the Bert-BiLSTM-CRF model, and the F1 value on the public data set is 2.5% higher than that of the Bert-BiLSTM-CRF model. Furthermore, experiments are designed to verify that the UIE-base model has the best small-sample learning performance when the number of samples is 40. This paper provides an effective method for small-sample knowledge extraction. [ABSTRACT FROM AUTHOR]

The precise prediction of painting man-hours is significant to ensure the efficient scheduling of shipyard production and maintain a stable production pace, which directly impacts shipbuilding cycles and costs. However, traditional forecasting methods suffer from issues such as low efficiency and poor accuracy. To solve this problem, this paper proposes a selective integrated learning model (ISA-SE) based on an improved simulated annealing algorithm to predict ship painting man-hours. Firstly, the improved particle swarm optimization (MPSO) algorithm and data grouping techniques are employed to achieve the optimal selection and hyperparameter optimization of base learners, constructing a candidate set of base learners. Subsequently, the simulated annealing algorithm is improved by adding random perturbations and using a parallel perturbation search mechanism to enhance the algorithm's global search capability. Finally, an optimal set of base learners is composed of the candidate set utilizing the ISA-SE model, and a heterogeneous ensemble learning model is constructed with the optimal set of base learners to achieve the precise prediction of ship painting man-hours. The results indicate that the proposed ISA-SE model demonstrates improvements in accuracy, mean absolute error, and root mean square error compared to other models, validating the effectiveness and robustness of ISA-SE in predicting ship painting man-hours. [ABSTRACT FROM AUTHOR]

The labor is a physiological event considered to have its own circadian (diurnal) rhythm, but some of the data remain conflicting, especially for preterm births. In this retrospective study, we analyzed the circadian trends of labor onset times in the Slovenian birth cohort from 1990 to 2018 with over 550,000 cases of singleton births. The number of term and preterm labor onsets was calculated for each hour in a day and circadian trends were evaluated for each of the study groups by modeling with a generalized Poisson distribution linked with the cosinor regression model using logarithmic link function. The induced labors were taken as the control group since the timing of labor depends mostly on the working schedule of personnel and not on the intrinsic rhythmic characteristics. For induced labors, the main peak in the number of labor cases was observed in the late morning hours (around 10 AM) for all gestational ages. The prominence of this peak becomes smaller in spontaneous premature labors with gradually disrupting rhythmicity in very preterm and extremely preterm cases. Labors starting with spontaneous contractions peak between 6 and 7 AM and lose the rhythmicity at 35 weeks of gestation while labors starting with a spontaneous rupture of membranes peak at 1 AM and lose the rhythmicity at 31 weeks of gestation, suggesting differences in underlying mechanisms. According to our knowledge, this is the first study that shows differences of circadian trends between different types of spontaneous labors, i.e., labors initiated with contraction and labors initiated with a spontaneous rupture of membranes. Moreover, the obtained results represent evidence of gradual disruption of rhythmicity from mild to extreme prematurity. [ABSTRACT FROM AUTHOR]

Type I hypersensitivity is triggered by mast cell degranulation, a stimulus-induced exocytosis of preformed secretory granules (SGs) containing various inflammatory mediators. The degree of degranulation is generally expressed as a percentage of secretory granule markers (such as β-hexosaminidase and histamine) released into the external solution, and considerable time and labor are required for the quantification of markers in both the supernatants and cell lysates. In this study, we developed a simple fluorimetry-based degranulation assay using rat basophilic leukemia (RBL-2H3) mast cells. During degranulation, the styryl dye FM1-43 in the external solution fluorescently labeled the newly exocytosed SGs, whose increase in intensity was successively measured using a fluorescence microplate reader. In addition to the rate of β-hexosaminidase secretion, the cellular FM1-43 intensity successfully represented the degree and kinetics of degranulation under various conditions, suggesting that this method facilitates multi-sample and/or multi-time-point analyses required for screening substances regulating mast cell degranulation. [ABSTRACT FROM AUTHOR]

-Nanocarriers (NCs) functionalized with chemical agents represent a novel approach for improved priming efficiency through targeted delivery. Multifunctional priming through the combined used of different agents in novel engineered nanocarriers (NENCs) has the potential to achieve multiple benefits in plants. The application of NENCs as seed coatings has the potential to improve crop yields, while achieving maximum cost-effectiveness compared with application at the plant level as it requires less time and labor. Gene-editing techniques can be used to modify the expression of targeted genes involved in plant priming as identified by transcriptomic approaches, and can enhance the ability of plants to respond to priming treatments and improve their overall performance. Plants are exposed to multiple threats linked to climate change which can cause critical yield losses. Therefore, designing novel crop management tools is crucial. Chemical priming has recently emerged as an effective technology for improving tolerance to stress factors. Several compounds such as phytohormones, reactive species, and synthetic chimeras have been identified as promising priming agents. Following remarkable developments in nanotechnology, several unique nanocarriers (NCs) have been engineered that can act as smart delivery systems. These provide an eco-friendly, next-generation method for chemical priming, leading to increased efficiency and reduced overall chemical usage. We review novel engineered NCs (NENCs) as vehicles for chemical agents in advanced priming strategies, and address challenges and opportunities to be met towards achieving sustainable agriculture. [ABSTRACT FROM AUTHOR]

In a streaming environment, the characteristics and labels of instances may change over time, forming concept drifts. Previous studies on data stream learning generally assume that the true label of each instance is available or easily obtained, which is impractical in many real-world applications due to expensive time and labor costs for labeling. To address the issue, an active broad learning based on multi-objective evolutionary optimization is presented to classify non-stationary data stream. The instance newly arrived at each time step is stored to a chunk in turn. Once the chunk is full, its data distribution is compared with previous ones by fast local drift detection to seek potential concept drift. Taking diversity of instances and their relevance to new concept into account, multi-objective evolutionary algorithm is introduced to find the most valuable candidate instances. Among them, representative ones are randomly selected to query their ground-truth labels, and then update broad learning model for drift adaption. More especially, the number of representative is determined by the stability of adjacent historical chunks. Experimental results for 7 synthetic and 5 real-world datasets show that the proposed method outperforms five state-of-the-art ones on classification accuracy and labeling cost due to drift regions accurately identified and the labeling budget adaptively adjusted. [ABSTRACT FROM AUTHOR]

A direct way to reduce scan time for chemical exchange saturation transfer (CEST)-magnetic resonance imaging (MRI) is to reduce the number of CEST images acquired in experiments. In some scenarios, a sufficient number of CEST images acquired in experiments was needed to estimate parameters for quantitative analysis, and this prolonged the scan time. For that, we aim to develop a general deep-learning framework to reconstruct dense CEST Z-spectra from experimentally acquired images at sparse frequency offsets so as to reduce the number of experimentally acquired CEST images and achieve scan time reduction. The main innovation works are outlined as follows: (1) a general sequence-to-sequence (seq2seq) framework is proposed to reconstruct dense CEST Z-spectra from experimentally acquired images at sparse frequency offsets; (2) we create a training set from wide-ranging simulated Z-spectra instead of experimentally acquired CEST data, overcoming the limitation of the time and labor consumption in manual annotation; (3) a new seq2seq network that is capable of utilizing information from both short-range and long-range is developed to improve reconstruction ability. One of our intentions is to establish a simple and efficient framework, i.e., traditional seq2seq can solve the reconstruction task and obtain satisfactory results. In addition, we propose a new seq2seq network that includes the short- and long-range ability to boost dense CEST Z-spectra reconstruction. The experimental results demonstrate that the considered seq2seq models can accurately reconstruct dense CEST images from experimentally acquired images at 11 frequency offsets so as to reduce the scan time by at least 2/3, and our new seq2seq network contributes to competitive advantage. [ABSTRACT FROM AUTHOR]

We examine the impact of unconditional cash transfers (UCTs) on fertility. We develop a theoretical model that demonstrates how UCTs affect fertility decisions, time allocations for leisure, labor and childrearing, and child health through health spending. We then empirically examine the impact of UCTs on fertility in Pakistan. Our theoretical model suggests that under certain conditions, UCTs are likely to increase fertility if UCTs increase child health regardless of how they affect parental leisure, labor and childrearing time. The empirical results suggest that UCTs have a positive effect on fertility. [ABSTRACT FROM AUTHOR]

Secondary metabolites extracted from plants have historically been critical for drug discovery, but their isolation involves expensive and complicated procedures in terms of time and labor resources. Thus, the biogenetic pathway offers the possibility of identifying specific compounds that have not yet been isolated and predicting their isolation from specific natural sources. In plants, biphenanthrenes represent a relatively small group of aromatic secondary metabolites that are considered as important taxonomic markers with promising biological activities. To date, 38 mixed phenanthrenoid dimers have been identified, the biosynthesis of which involves the radical coupling of the two subunits, namely, a phenanthrene and a dihydrophenanthrene. For each of the compounds, it is possible to identify the single phenanthrenic and dihydrophenanthrenic units constituting the considered dimer. Based on the biogenetic pathway, it is possible to identify 19 phenanthrenes and 17 dihydrophenanthrenes, and to distinguish those already known from those not yet isolated. By comparing the results of the possible biosynthetic pathway for each compound with the data in the literature, it is possible to identify three known phenanthrenes and seven known dihydrophenanthrenes, as well as eleven new phenanthrenes and five new dihydrophenanthrenes, and to identify from which plant it is possible to isolate them. This could direct the work of researchers seeking to identify known or new molecules useful for their possible biological properties, and ultimately, to confirm the veracity of the proposed and generally accepted biosynthetic pathway. [ABSTRACT FROM AUTHOR]

Abstract Background To evaluate the improvement of evaluation accuracy of cervical maturity for Chinese women with labor induction by adding objective ultrasound data and machine learning models to the existing traditional Bishop method. Methods The machine learning model was trained and tested using 101 sets of data from pregnant women who were examined and had their delivery in Peking University Third Hospital in between December 2019 and January 2021. The inputs of the model included cervical length, Bishop score, angle, age, induced labor time, measurement time (MT), measurement time to induced labor time (MTILT), method of induced labor, and primiparity/multiparity. The output of the model is the predicted time from induced labor to labor. Our experiments analyzed the effectiveness of three machine learning models: XGBoost, CatBoost and RF(Random forest). we consider the root-mean-squared error (RMSE) and the mean absolute error (MAE) as the criterion to evaluate the accuracy of the model. Difference was compared using t-test on RMSE between the machine learning model and the traditional Bishop score. Results The mean absolute error of the prediction result of Bishop scoring method was 19.45 h, and the RMSE was 24.56 h. The prediction error of machine learning model was lower than the Bishop score method. Among the three machine learning models, the MAE of the model with the best prediction effect was 13.49 h and the RMSE was 16.98 h. After selection of feature the prediction accuracy of the XGBoost and RF was slightly improved. After feature selection and artificially removing the Bishop score, the prediction accuracy of the three models decreased slightly. The best model was XGBoost (p = 0.0017). The p-value of the other two models was

Domain adaptation (DA) improves the generalization ability of models across source and target domains with different distributions. Current methods aim to reduce domain distribution divergence to learn transferable features. However, in most real cases, the number of available labeled samples is limited, and annotating labels requires significant time and labor costs, making it difficult to achieve high accuracy. To address this problem, we propose a novel method called Deep Geometric and Statistic Alignment for Fewer labeled Domain Adaptation (GSA4FDA). This method achieves the target task with fewer labeled source samples by combining manifold learning and leveraging the local geometric structure of sufficient unlabeled source samples. For domain alignment, we employ a joint geometric-statistical alignment and embed it into a specific layer of a deep convolutional neural network (CNN) to obtain high-level semantic information, taking into account the complementary nature of the two aspects. Specifically, we use the Nyström method and Maximum Mean Discrepancy (MMD) to compensate for the geometrical and statistical shift between domains. The experimental results on several datasets demonstrate the superiority of our method, particularly when there are limited labeled source samples available. [ABSTRACT FROM AUTHOR]

Implementing an efficient performance monitoring system is necessary to complete a facility building project on time, on budget, and by established standards and specifications. Inappropriate system monitoring process could lead to project failures such as delay which commonly occurs. If the delay happens, it is necessary to accelerate work productivity to overcome it. Earned value method (EVM) is one of the suggested global standards for measuring project performance. It effectively combines scope, cost, and schedule metrics and can provide a clear picture of the state of the project as of the control date. It also predicts the time prolong and completion budget. This paper proposes a guideline to implement EVM as a decision baseline on acceleration solutions for construction project scheduling by conducting an article review, since the EVM can predict the project’s time estimates at completion (TE). According to the guideline, when arranging network planning based on construction actual work performed, it is essential to use TE as the completion time. From the critical activities in network planning, contractors can conduct the acceleration process such as additional work hours (overtime) and additional manpower. This acceleration scenario later could be implemented in the project as the solution to overcome the delay. [ABSTRACT FROM AUTHOR]

The timely onset of female parturition is a critical determinant for pregnancy success. The highly heterogenous maternal decidua has been increasingly recognized as a vital factor in setting the timing of labor. Despite the cell type specific roles in parturition, the role of the uterine epithelium in the decidua remains poorly understood. This study uncovers the critical role of epithelial SHP2 in parturition initiation via COX1 and COX2 derived PGF2α leveraging epithelial specific Shp2 knockout mice, whose disruption contributes to delayed parturition initiation, dystocia and fetal deaths. Additionally, we also show that there are distinct types of epithelium in the decidua approaching parturition at single cell resolution accompanied with profound epithelium reformation via proliferation. Meanwhile, the epithelium maintains the microenvironment by communicating with stromal cells and macrophages. The epithelial microenvironment is maintained by a close interaction among epithelial, stromal and macrophage cells of uterine stromal cells. In brief, this study provides a previously unappreciated role of the epithelium in parturition preparation and sheds lights on the prevention of preterm birth. The mechanism of parturition initiation remains elusive. Here, authors show that epithelium is an underestimated site for parturition initiation via epithelial PGF2a regulated by SHP2-COX1/COX2. [ABSTRACT FROM AUTHOR]

In bone fracture preoperative surgical planning, 3D fracture reduction is of great significance. However, segmenting 3D fracture bones into fragments and assembling the bone fragments to restore their original morphology are labor intensive and time consuming. Current works, however, omit their synergistic effects between reduction and segmentation, which, we envision, can significantly benefit the fracture reduction. To this end, a method is proposed to alternatively segment and assemble the fragments by incorporating the contralateral bone as template and leveraging the synergistic effect on segmentation and reduction. The 3D fracture model is initially segmented into separated outer surface of fragments. The reduction is then conducted by template matching, specifically, a whole-to-whole matching strategy through deep learning of dense features is employed. Thereafter, the segmentation is further refined with the assistance of the template. The process of reduction and segmentation is iterated until the algorithm converges. Experiments were conducted on simulated data and clinical data, the mean segmentation accuracy is 95.26%, the assembling translation error is 3.28 ± 3.01 mm, and the assembling rotation error is 1.62 ± 1.71°. Results demonstrated the superiority of our method over state-of-the-art methods, and it can be used as a feasible scheme for fracture reduction planning. Moreover, it is also proved that segmentation and reduction can promote each other. [Display omitted] • Synergistic effect is exploited between segmentation and reduction. • A novel whole-to-whole matching tactics is proposed for template-matching. • The SSM is leveraged to generate and label the training data. [ABSTRACT FROM AUTHOR]

This article focuses on the introduction of crofting tenure in the Annexed Estates (1752–1784). In 1763, the Board of Commissioners in charge of managing the estates on behalf of the British Crown launched its most ambitious 'improvement' scheme. Parcels of three acres, usually detached from principal farms, were turned into new settlements for disbanded soldiers and sailors from the Seven Years War. The scheme, this article argues, was one of the earliest crofting schemes ever implemented in the Scottish Highlands. The plan hoped to put the Highlands' natural and human resources to the service of the state by ensuring that the Crown could tap into a future recruiting pool and that the Board would benefit from a cheap workforce to reclaim unproductive grounds and develop the linen manufacture in the estates. The settlements were also a way to solve 'improvement's contradictions via spatial planning by preventing depopulation caused by the creation of contiguous farmsteads. The failure of the experiment ultimately highlighted the entrenched contractions of 'improvement' ideology and led to the emergence of an alternative geography of 'improvement' by the turn of the century. [ABSTRACT FROM AUTHOR]

Exposure is an essential component of risk models and describes elements that are endangered by a hazard and susceptible to damage. The associated vulnerability characterizes the likelihood of experiencing damage (which can translate into losses) at a certain level of hazard intensity. Frequently, the compilation of exposure information is the costliest component (in terms of time and labor) of risk assessment procedures. Existing models often describe exposure in an aggregated manner, e.g., by relying on statistical/census data for given administrative entities. Nowadays, earth observation techniques allow the collection of spatially continuous information for large geographic areas while enabling a high geometric and temporal resolution. Consequently, we exploit measurements from the earth observation missions TanDEM-X and Sentinel-2, which collect data on a global scale, to characterize the built environment in terms of constituting morphologic properties, namely built-up density and height. Subsequently, we use this information to constrain existing exposure data in a spatial disaggregation approach. Thereby, we establish dasymetric methods for disaggregation. The results are presented for the city of Santiago de Chile, which is prone to natural hazards such as earthquakes. We present loss estimations due to seismic ground shaking and corresponding sensitivity as a function of the resolution properties of the exposure data used in the model. The experimental results underline the benefits of deploying modern earth observation technologies for refined exposure mapping and related earthquake loss estimation with enhanced accuracy properties. [ABSTRACT FROM AUTHOR]

In recent years, research reached a very high level of development and validation of augmented prototyping systems in support of collaborative design activities. However, there is still great scepticism in companies when it comes to integrating these new technologies within a consolidated working model. Among others, the main barrier to overcome concerns the lack of understanding of the impact of AR systems on the key objectives of a business, such as improving its efficiency and revenue. For this reason, this paper aims to quantify these indicators by observing the technological impact not on a single design session but on an entire product development process, during which the aspects related to its integration are also considered. Thanks to the collaboration with a design agency, it was possible to compare parameters such as the lead time, number of iterations, person-hours and costs between two similar and realistic projects in which only one was supported by projection-based AR technology. [ABSTRACT FROM AUTHOR]

Background: To evaluate the improvement of evaluation accuracy of cervical maturity for Chinese women with labor induction by adding objective ultrasound data and machine learning models to the existing traditional Bishop method. Methods: The machine learning model was trained and tested using 101 sets of data from pregnant women who were examined and had their delivery in Peking University Third Hospital in between December 2019 and January 2021. The inputs of the model included cervical length, Bishop score, angle, age, induced labor time, measurement time (MT), measurement time to induced labor time (MTILT), method of induced labor, and primiparity/multiparity. The output of the model is the predicted time from induced labor to labor. Our experiments analyzed the effectiveness of three machine learning models: XGBoost, CatBoost and RF(Random forest). we consider the root-mean-squared error (RMSE) and the mean absolute error (MAE) as the criterion to evaluate the accuracy of the model. Difference was compared using t-test on RMSE between the machine learning model and the traditional Bishop score. Results: The mean absolute error of the prediction result of Bishop scoring method was 19.45 h, and the RMSE was 24.56 h. The prediction error of machine learning model was lower than the Bishop score method. Among the three machine learning models, the MAE of the model with the best prediction effect was 13.49 h and the RMSE was 16.98 h. After selection of feature the prediction accuracy of the XGBoost and RF was slightly improved. After feature selection and artificially removing the Bishop score, the prediction accuracy of the three models decreased slightly. The best model was XGBoost (p = 0.0017). The p-value of the other two models was < 0.01. Conclusion: In the evaluation of cervical maturity, the results of machine learning method are more objective and significantly accurate compared with the traditional Bishop scoring method. The machine learning method is a better predictor of cervical maturity than the traditional Bishop method. [ABSTRACT FROM AUTHOR]

With the proliferation of e-commerce, the regional hub of a large-scale logistics company is required to sort and load a large number of packages into different delivery vehicles by dawn and deliver them to customers by noon on a daily basis. The efficiency of the sorting operation is thus a competitive advantage which directly impacts the company's service level. In this study, a data-driven business intelligence system for the semi-automated sorting facility is proposed for real-world implementation. To determine the cargo handling sequence, an information-based approach with a multi-criteria index function is developed. Then a simulation-based optimisation framework, which integrates a multi-objective search algorithm with a simulation model, is employed to fine-tune the parameters of the index function to perform optimally. The results of the numerical experiment show that the proposed technique is able to reduce 20% of the sorting operation duration, which equals a reduction of about 3600 man-hours per year. The study is a good example of applying emerging technologies in the logistics industry. [ABSTRACT FROM AUTHOR]

Within public health control strategies for SARS-CoV-2, whole genome sequencing (WGS) is essential for tracking viral spread and monitoring the emergence of variants which may impair the effectiveness of vaccines, diagnostic methods, and therapeutics. In this manuscript different strategies for SARS-CoV-2 WGS including metagenomic shotgun (SG), library enrichment by myBaits® Expert Virus-SARS-CoV-2 (Arbor Biosciences), nCoV-2019 sequencing protocol, ampliseq approach by Swift Amplicon® SARS-CoV-2 Panel kit (Swift Biosciences), and Illumina COVIDSeq Test (Illumina Inc.), were evaluated in order to identify the best approach in terms of results, labour, and costs. The analysis revealed that Illumina COVIDSeq Test (Illumina Inc.) is the best choice for a cost-effective, time-consuming production of consensus sequences.

An efficient part feeding is among the top challenges of many mass producers applying mixed-model assembly lines, for instance, in the automotive industry. This paper introduces a novel part feeding policy applied by a large German assembly plant for car engines: In-line kitting. Under this policy, the first stations of the line do not execute assembly operations, but are reserved for picking parts while passing containers of stock-keeping units (SKUs) arranged along the line. In this way, the parts are collected in traveling kits moving along with each workpiece on the conveyor, so that later assembly stations have the required parts directly available and do not lose precious labor time for unproductive parts handling. A major operational challenge when applying this part feeding policy is the walking effort for the human pickers while putting the SKUs of their respective stations into the traveling kits of the passing workpieces. Due to a high product variety, a large number of comparatively bulky SKU containers have to fit into each station, so that the walking distance to be covered by a worker during a work shift exceeds multiple kilometers. We show that this physical burden can be reduced significantly by balancing the workload among stations and optimizing the storage assignment of SKU containers within each in-line kitting station. We formulate the resulting optimization problem and provide suited solution procedures. Our computational study shows that the walking distance of pickers can be reduced significantly without producing any additional costs. [ABSTRACT FROM AUTHOR]