Labor in the textile and garment industry is at the heart of a series of recent books on South Asia. Together these books document the different scales at which textile and garment work has been structured and restructured over the last century, and its implications for workers, their health as well as collective solidarity. Across the countries of Bangladesh, India, Nepal, and Sri Lanka, the industry developed and declined in vastly different temporalities and rhythms. Yet, as these works reveal, workers have often been confronted with similar challenges brought on by the boom-and-bust cycles of industrial development. In each case, textile and garment workers have been forced to navigate transitions to premature deindustrialization, closure, or national/transnational industrial policy changes. The books center workers and their long “post”-industrial or industrial “afterlives,” as they cope with the dramatic changes in the global manufacturing of textile and garment.

This article presents a detailed examination of circular target localization techniques for measuring robot pose and performing online pose correction. The investigated target localization methods include centroiding, ellipse fitting with point data and gradient information, and ellipse fitting methods with augmented and corrected input data. The performance of each method is evaluated in terms of accuracy and precision of measurements through experimental comparison with a laser tracker. This study provides technical and practical insights for selecting an appropriate target localization method in robotic applications. It also introduces a vision-based solution for robot relative error correction, comprising the calibration procedure and a closed-loop control with a proportional–integral-derivative controller for pose correction. Results show enhanced accuracy in robot positioning relative to workpiece, highlighting the effectiveness of the proposed solution in robotic drilling applications.

Soft robotics is rapidly advancing, particularly in medical device applications. A particular miniaturized manipulator design that offers high dexterity, multiple degrees-of-freedom, and better lateral force rendering than competing designs, has great potential for minimally invasive surgery. However, it faces challenges such as the tendency to suddenly and unpredictably deviate in bending plane orientation at higher pressures. In this work, we identified the cause of this deviation as the buckling of the partition wall and proposed design alternatives along with their manufacturing process to address the problem without compromising the original design features. In both simulation and experiment, the novel design managed to achieve a better bending performance in terms of stiffness and reduced deviation of the bending plane. We also developed an artificial neural network-based inverse kinematics model to further improve the performance of the prototype during vectorization. This approach yielded mean absolute errors in orientation of the bending plane below $5^{\circ }$.

In this study, we present a hybrid kinematic modeling approach for serial robotic manipulators, which offers improved accuracy compared to conventional methods. Our method integrates the geometric properties of the robot with ground truth data, resulting in enhanced modeling precision. The proposed forward kinematic model combines classical kinematic modeling techniques with neural networks trained on accurate ground truth data. This fusion enables us to minimize modeling errors effectively. In order to address the inverse kinematic problem, we utilize the forward hybrid model as feedback within a non-linear optimization process. Unlike previous works, our formulation incorporates the rotational component of the end effector, which is beneficial for applications involving orientation, such as inspection tasks. Furthermore, our inverse kinematic strategy can handle multiple possible solutions. Through our research, we demonstrate the effectiveness of the hybrid models as a high-accuracy kinematic modeling strategy, surpassing the performance of traditional physical models in terms of positioning accuracy.

From the 1940s to the 1970s, British governments steered manufacturing businesses to peripheral regions designated as needing more employment. This approach was delivered through a Regional Policy that deployed industrial location controls and financial incentives. Effectiveness varied over time but was dramatic in the mid-1940s, when it boosted the regional stock of secondary manufacturing to the extent that its legacy remains visible today. The literature describes how this Regional Policy was a peacetime policy, albeit one formulated during the war. This article, however, proposes that the most successful phase of Regional Policy was an extension of wartime policies governing regional manufacturing businesses producing munitions. It uses a case study of Wales to make two arguments. One is that the Regional Policy associated with the postwar period began to be implemented before the war had ended. The other is that the Board of Trade pursued the policy through repurposed wartime governance mechanisms within an economy that remained subject to onerous state controls. The case outlines a short but consequential burst of assertive state involvement that shaped business activity throughout much of regional Britain, echoing Philip Scranton and Patrick Fridenson’s arguments concerning “the state always being in” given its role in shaping markets, business behavior, and regulations.

In recent years, deep learning-based robotic grasping methods have surpassed analytical methods in grasping performance. Despite the results obtained, most of these methods use only planar grasps due to the high computational cost found in 6D grasps. However, planar grasps have spatial limitations that prevent their applicability in complex environments, such as grasping manufactured objects inside 3D printers. Furthermore, some robotic grasping techniques only generate one feasible grasp per object. However, it is necessary to obtain multiple possible grasps per object because not every grasp generated is kinematically feasible for the robot manipulator or does not collide with other close obstacles. Therefore, a new grasping pipeline is proposed to yield 6D grasps and select a specific object in the environment, preventing collisions with obstacles nearby. The grasping trials are performed in an additive manufacturing unit that has a considerable level of complexity due to the high chance of collision. The experimental results prove that it is possible to achieve a considerable success rate in grasping additive manufactured objects. The UR5 robot arm, Intel Realsense D435 camera, and Robotiq 2F-140 gripper are used to validate the proposed method in real experiments.

The Belt and Road Initiative (BRI) mobilizes Chinese construction and investment in developing countries. Ethiopia is Africa's “model” BRI country, due to China's elaborate infrastructure financing and building and its many manufacturing enterprises. Based on field and documentary research, we examine the BRI's meaning, as understood from the perspective of Ethiopia, in comparison to many China-oriented studies. We find that it is an informal Chinese state promise that even when capital flows from China to non-BRI states are curbed, flows to BRI states will be encouraged, and that Ethiopia exercises agency in leveraging the BRI for its development agenda. Using a comprehensive data set, we show that Chinese investment has become even more important in Ethiopia with the BRI and that neither COVID-19 nor Ethiopia's civil war has reversed that trend. We also discuss local criticisms of Chinese activities, which challenge the wholly positive view of the BRI, but do not affirm the US-generated negative narrative.

The transition towards circular economy means a radical systemic shift that requires re-design and innovation of business models. However, this radical systemic shift also creates high levels of uncertainty, which pose challenges to the circular business model innovation (CBMI) process. Using the transition towards circular plastics as a case context, this study aims to conceptualize different forms of uncertainty affecting CBMI, and to link them with approaches for managing these uncertainties. Based on interviews with incumbent manufacturing firms that have transitioned to circular plastics, or are in the process of doing so, we identified three domains of uncertainty: goal uncertainty, development uncertainty, and outcome uncertainty. We discuss the nature and sources of these uncertainties, and present different approaches chosen by manufacturers to manage these uncertainties in the context of their business. Our findings highlight the complex nature of uncertainty, and the importance of a nuanced consideration of uncertainty as a factor in the CBMI process. Moreover, our mapping of core uncertainties for CBMI and approaches to manage these uncertainties can guide practitioners in the innovation process.

Determining accurate standard time using direct measurement techniques is especially challenging in companies that do not have a proper environment for time measurement studies or that manufacture items requiring complex production schedules. New and specific time measurement techniques are required for such companies. This research developed a novel time estimation approach based on several machine learning methods. The set of collected inputs in the manufacturing environment, including a number of products, the number of welding operations, product's surface area factor, difficulty/working environment factors, and the number of metal forming processes. The data were collected from one of the largest bus manufacturing companies in Turkey. Experimental results demonstrate that when model accuracy was measured using performance measures, k-nearest neighbors outperformed other machine learning techniques in terms of prediction accuracy. “The number of welding operations” and “the number of pieces” were found to be the most effective parameters. The findings show that machine learning algorithms can estimate standard time, and the findings can be used for several purposes, including lowering production costs, increasing productivity, and ensuring efficiency in the execution of their operating processes by other companies that manufacture similar products.