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Shape memory alloy (SMA) actuators present opportunities for the development of novel actuating systems. High force-to-weight ratio, silent operation, muscle-like motion, biocompatibility, and simple design possibilities have attracted researchers to SMA actuators. Many SMA actuated systems in engineering and medical domains have been reported in the literature. Recently, SMAs have also being used to develop soft robotic systems. However, low absolute force and high cycle time have limited the widespread use of these actuators. Moreover, non-linear and unpredictable behaviour caused by hysteresis results in difficulties to accurately control it. Some work detailing the strategies to overcome these shortcomings has been reported in the literature, this paper presents an articulated brief review of the techniques to overcome low force, long cycle time, and material non-linearity issues.

Main conclusionIn the angiosperm root apical meristem, the holoploid DNA content is not directly related to cell cycle time. Instead, ploidy, chromosome number, and taxa emerge as key factors that influence this interaction.It is commonly considered that cell cycle time in the angiosperm root apical meristem is directly related to the holoploid DNA content, and this is one of the manifestations of the nucleotypic effect. In this paper, we significantly expanded the previously reported data on cell cycle time using the thymidine method and the rate-of-cell-production method and investigated the nucleotypic effect in different taxonomic groups, in diploids with varying numbers of chromosomes, and in species of different ploidy levels. The nucleotypic effect was most pronounced in diploids with a chromosome number of less than 17, especially in the orders Asparagales and Liliales. In diploids with 17 and a greater number of chromosomes, and in polyploids, the nucleotypic effect was only evident in these two orders and was practically absent in the rest of angiosperms, which have generally lower the holoploid DNA content and average DNA content per chromosome. Overall, our work demonstrates that in angiosperms the relationship between cell cycle time in the root apical meristem and the holoploid DNA content is complex and not homogeneous and it is not directly related to cell cycle time. Instead, ploidy, chromosome number, and taxa emerge as key factors that influence this interaction.

This study investigates production bottlenecks at the packing station of PT Anta Boga Pangan Nusantara, where manual operations resulted in prolonged cycle times and reduced efficiency. The research aims to design and implement a Karakuri Kaizen–based Lean Mover to minimize work time and enhance process performance. A quantitative approach was employed, involving direct observation, time measurement, and process analysis. The implementation of the Lean Mover successfully reduced the cycle time from 158.08 seconds to 24.74 seconds, outperforming the target takt time of 27 seconds. These findings demonstrate that low-cost, energy-efficient mechanical systems can effectively eliminate non-value-added activities, balance operator workloads, and improve productivity in food industry operations. The novelty of this study lies in the application of Karakuri Kaizen within the food manufacturing sector, a field rarely explored in previous research, thereby extending its applicability beyond conventional industrial settings. The study contributes theoretically by providing empirical evidence of Karakuri Kaizen’s effectiveness in optimizing manual operations in resource-constrained environments, and practically by offering an ergonomic, sustainable, and affordable alternative to high-cost automation systems.

Purpose: This study aimed at implementing lean six sigma to evaluate the productivity and manufacturing waste in the production line of a paper company Methodology/Approach: The study is a case study in nature. The method illustrates how lean six sigma (LSS) is used to evaluate the existing production process in a paper production company with focus on productivity and manufacturing waste. The study considered a real-time problem of customer's dissatisfaction. The gathered data is based on machine functionality (up time, down time and cycle time); materials and labour flow at every process stages of the production line. The optimization of the production process was based on lean tools like value stream mapping, process cycle efficiency, Kaizen, 5S and pareto chart Findings: Based on lean six sigma application, it was discovered that the present production performance was below standard and more manufacturing wastes were generated. The present productivity and manufacturing wastes are reported as low process cycle efficiency (23.4 %), low takt time (4.11 sec), high lead time (43200sec), high number of products not conforming to six sigma values, high down time (32.64 %) and excess labour flow (33). After the implementation of the lean six sigma tools for certain period of time, there are lots of improvements in the production line in terms of all the parameters considered. Research Limitation/ Implications: The study has demonstrated an application of lean six sigma in the case of solving real-time problems of productivity and manufacturing wastes which have a direct implication on customer's satisfaction. The lesson learned and implications presented can still be further modeled using some lean based software for validity Originality/Value: The study has contributed to the body of knowledge in the field of LSS with focus to process based manufacturing, unlike most literature in the field concentrate more on discrete based manufacturing.

This paper presents an accurate machining feedrate prediction technique by modelling the trajectory generation behaviour of modern CNC machine tools. Typically, CAM systems simulate machines’ motion based on the commanded feedrate and the path geometry. Such approach does not consider the feed planning and interpolation strategy of the machine’s numerical control (NC) system. In this study, trajectory generation behaviour of the NC system is modelled and accurate cycle time prediction for complex machining toolpaths is realised. NC system’s linear interpolation dynamics and commanded axis kinematic profiles are predicted by using finite impulse response (FIR)–based low-pass filters. The corner blending behaviour during non-stop interpolation of linear segments is modelled, and for the first time, the minimum cornering feedrate that satisfies both the tolerance and machining constraints has been calculated analytically for 3-axis toolpaths of any geometry. The proposed method is applied to 4 different case studies including complex machining toolpaths. Experimental validations show actual cycle times can be estimated with > 90% accuracy, greatly outperforming CAM-based predictions. It is expected that the proposed approach will help improve the accuracy of virtual machining models and support businesses’ decision-making when costing machining processes.

Robotic Compact Storage and Retrieval Systems (RCS/RS) offer numerous advantages, including high performance, scalability, and availability, which are essential for modern logistics and warehousing. However, information on the potential performance of RCS/RS is limited, primarily due to the diverse range of configurations available. This paper aims to address this gap by developing an analytical approach to predict the throughput of an RCS/RS with multiple robots serving several picking stations. The approach considers various parameters such as grid size, stack height, number of robots, and filling degree, alongside kinematic data. The cycle time for each robot is calculated assuming a uniform distribution of container stacks. Subsequently, a queueing system with limited capacity is constructed using performance data from a single robot. The analytical approach is validated using a discrete event simulation model of an RCS/RS. Following the validation, an extensive parameter variation and application example are conducted to demonstrate the versatility of the approach. This method offers a straightforward and efficient set of formulas for determining RCS/RS throughput, easily solvable using standard table or algebra programs.

Humans are said to have habitual and non-habitual chewing sides; however, the functional differences between the chewing sides of implant-supported denture wearers have not been sufficiently clarified. This study aimed to clarify the presence or absence of functional differences between the chewing sides in implant-supported denture wearers. Forty-five patients with bilateral posterior implants were included in this study. The participants were asked to chew a gummy jelly on one side, and the masticatory movement was recorded using a Motion Visi-trainer (MVT V1). For 10 cycles from the fifth cycle after the start of mastication, the pattern of the movement path, the opening distance, the masticatory width, and the cycle time were calculated as parameters of masticatory movement. The amount of glucose eluted during the chewing of gummy jelly was measured and used as a parameter of masticatory performance. Each parameter representing masticatory movement and masticatory performance was compared between the right and left chewing sides and between the habitual and non-habitual chewing sides using a chi-squared test or a paired t-test. There was no difference in the frequency of masticatory path patterns between the right and left chewing sides. Most participants had a normal pattern on the habitual chewing side; however, abnormal patterns were also observed on the non-habitual chewing side. When comparing right and left chewing, no significant difference was observed between chewing sides in terms of opening distance, masticatory width, cycle time, or amount of glucose eluted (p > 0.05). When comparing the habitual and non-habitual chewing sides, masticatory movement on the habitual chewing side showed a larger opening distance (p < 0.001) and masticatory width (p = 0.008), shorter cycle time (p = 0.004), and higher masticatory performance (p < 0.001). It was suggested that there is a functional difference between the habitual and non-habitual chewing sides in the masticatory movement and masticatory performance of implant-supported denture wearers.

Value steam mapping (VSM) is a lean manufacturing technique and it has emerged as the preferred way to support and implement the lean approach Grewal (Int J Manuf Technol Manag 15:3–4, 2008 ); Singh and Sharma (J Measuring Business Excellence 13:58–68, 2009 ). VSM is different than conventional recording techniques, as it captures the information at individual stations about station cycle time, up time or utilization of resources, set-up time or change over time, work in process inventory, man power requirement and the information flow from raw material to finish goods. It covers both value adding as well as non-value-adding activities. This paper covers the review and classification of literature on VSM, as there is hardly any paper on literature review of VSM, so it will be very beneficiary for both academician and industry people. Applications of VSM are also presented by a case study of a small manufacturing Indian industry and reduction in lead time, processing time, work in process inventory and manpower requirement at individual stations are noticed

This study proposes a hybrid big data analytics and Industry 4.0 (BD-I4) approach to enhancing the effectiveness of cycle time range projections for factory jobs. As a joint application of big data analytics and Industry 4.0, the BD-I4 approach is distinct from existing methods in this field. In this approach, each expert first constructs a fuzzy deep neural network to project the cycle time range of a job, an application of big data analytics (i.e., deep learning). Subsequently, the fuzzy weighted intersection operator is applied to aggregate the projected cycle times such that unequal authority levels can be considered, an application of Industry 4.0 (i.e., artificial intelligence). Applying the BD-I4 approach to a real case that the proposed methodology improved the projection precision by up to 72%, suggesting that instead of relying on a single expert, collaboration among multiple experts may be more effective and efficient.

Rock fragmentation is a key determinant of downstream excavation performance in surface mining operations. Among blasting design parameters, stemming depth governs explosive energy confinement; however, its field-scale influence on fragmentation quality and excavation efficiency remains insufficiently quantified. This study experimentally investigates the effect of two stemming depths (3 m and 4 m) on rock fragmentation characteristics and excavator cycle time through controlled field blasting trials conducted in a hard sandstone formation. All blasts were executed using identical drilling geometry, explosive type, and operational procedures, with stemming depth treated as the sole variable. Fragmentation outcomes were quantified using image-based analysis (Split Desktop), while excavator cycle time and its individual components were measured directly during loading operations. Statistical evaluation was performed using descriptive statistics, Mann–Whitney U tests, and one-way analysis of variance (ANOVA). The results show that a 3 m stemming depth produces consistently finer fragmentation, reflected by a lower median fragment size (D50 = 38.1 cm) compared to the 4 m configuration (D50 = 45.6 cm), along with a substantial reduction in oversize fragments (> 50 cm) from 27.9% to 12.4%. Improved fragmentation is associated with a statistically significant reduction in excavator digging time for 3 m stemming (14.2 ± 2.1 s) relative to 4 m stemming (18.6 ± 3.0 s; p  = 0.004). In contrast, no statistically significant difference is observed in the mean total excavator cycle time between the two configurations. These findings are limited to a single lithology and blast geometry and should therefore be regarded as conditionally applicable to comparable geological and operational settings. Nevertheless, the study demonstrates that appropriate stemming depth selection can reduce oversize generation and improve excavation smoothness, providing practical, field-based guidance for blast design optimization. Highlights Field-scale blasting experiments demonstrate that reducing stemming depth from 4 m to 3 m significantly improves rock fragmentation quality in hard sandstone. A 3 m stemming depth decreases median fragment size (D50) from 45.6 cm to 38.1 cm and reduces oversize fragments (> 50 cm) from 27.9% to 12.4%. Improved fragmentation results in a statistically significant reduction in excavator digging time (from 18.6 ± 3.0 s to 14.2 ± 2.1 s; Mann–Whitney p  = 0.004). No statistically significant difference is observed in mean total excavator cycle time, indicating that operational benefits are expressed through reduced variability and smoother excavation rather than faster average cycles. The findings provide site-specific, field-based evidence supporting stemming depth selection as a practical control parameter for improving excavation performance.