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In the United States, the \"right to choose\" an abortion is the law of the land. But what if a woman continues her pregnancy because she didn't really have a choice? What if state laws, federal policies, stigma, and a host of other obstacles push that choice out of her reach?     Based on candid, in-depth interviews with women who considered but did not obtain an abortion, No Real Choice punctures the myth that American women have full autonomy over their reproductive choices. Focusing on the experiences of a predominantly Black and low-income group of women, sociologist Katrina Kimport finds that structural, cultural, and experiential factors can make choosing abortion impossible-especially for those who experience racism and class discrimination. From these conversations, we see the obstacles to \"choice\" these women face, such as bans on public insurance coverage of abortion and rampant antiabortion claims that abortion is harmful. Kimport's interviews reveal that even as activists fight to preserve Roe v. Wade, class and racial disparities have already curtailed many women's freedom of choice.   No Real Choice analyzes both the structural obstacles to abortion and the cultural ideologies that try to persuade women not to choose abortion. Told with care and sensitivity, No Real Choice gives voice to women whose experiences are often overlooked in debates on abortion, illustrating how real reproductive choice is denied, for whom, and at what cost. 

This introduction presents an overview of the key concepts discussed in the subsequent chapters of this book. The book offers valuable contributions for academic researchers, professional audiences, and managers. It brings together a variety of perspectives to create a model for the development of sustainable speak‐up systems. The book helps the readers to identify the major organizational, structural, and cultural obstacles to speaking up through speak‐up arrangements, in their own organizations. It also helps the readers to understand the specific policy and legislation requirements that can promote or impede the effective implementation of speak‐up arrangements, and how these can be translated into commercial and public organizations across sectors and cultures. The book concludes with recommendations on how to use the speak‐up data that is captured when arrangements are in place to help assess the receptiveness of an organization towards employee voice.

Nanoparticle interactions in solution affect their binding to biomolecules, their electronic properties, and their packing into larger crystals. However, the theories that describe larger colloidal particles fail for nanoparticles, because the interactions do not add together linearly. Nanoparticles also have complex shapes and are closer in size to the solvent molecules. Silvera Batista et al. review approaches that can treat the nonadditive nature of nanoparticle interactions, resulting in a more complete understanding of nanoparticles in solution. Science , this issue p. 10.1126/science.1242477 Understanding interactions between inorganic nanoparticles (NPs) is central to comprehension of self-organization processes and a wide spectrum of physical, chemical, and biological phenomena. However, quantitative description of the interparticle forces is complicated by many obstacles that are not present, or not as severe, for microsize particles (μPs). Here we analyze the sources of these difficulties and chart a course for future research. Such difficulties can be traced to the increased importance of discreteness and fluctuations around NPs (relative to μPs) and to multiscale collective effects. Although these problems can be partially overcome by modifying classical theories for colloidal interactions, such an approach fails to manage the nonadditivity of electrostatic, van der Waals, hydrophobic, and other interactions at the nanoscale. Several heuristic rules identified here can be helpful for discriminating between additive and nonadditive nanoscale systems. Further work on NP interactions would benefit from embracing NPs as strongly correlated reconfigurable systems with diverse physical elements and multiscale coupling processes, which will require new experimental and theoretical tools. Meanwhile, the similarity between the size of medium constituents and NPs makes atomic simulations of their interactions increasingly practical. Evolving experimental tools can stimulate improvement of existing force fields. New scientific opportunities for a better understanding of the electronic origin of classical interactions are converging at the scale of NPs.

We consider scalar lattice differential equations posed on square lattices in two space dimensions. Under certain natural conditions we show that wave-like solutions exist when obstacles (characterized by “holes”) are present in the lattice. Our work generalizes to the discrete spatial setting the results obtained in Berestycki, Hamel, and Matuno (2009) for the propagation of waves around obstacles in continuous spatial domains. The analysis hinges upon the development of sub and super-solutions for a class of discrete bistable reaction-diffusion problems and on a generalization of a classical result due to Aronson and Weinberger that concerns the spreading of localized disturbances.

As technology continues to develop, computer vision (CV) applications are becoming increasingly widespread in the intelligent transportation systems (ITS) context. These applications are developed to improve the efficiency of transportation systems, increase their level of intelligence, and enhance traffic safety. Advances in CV play an important role in solving problems in the fields of traffic monitoring and control, incident detection and management, road usage pricing, and road condition monitoring, among many others, by providing more effective methods. This survey examines CV applications in the literature, the machine learning and deep learning methods used in ITS applications, the applicability of computer vision applications in ITS contexts, the advantages these technologies offer and the difficulties they present, and future research areas and trends, with the goal of increasing the effectiveness, efficiency, and safety level of ITS. The present review, which brings together research from various sources, aims to show how computer vision techniques can help transportation systems to become smarter by presenting a holistic picture of the literature on different CV applications in the ITS context.

Cellulose nanocrystals (CNCs), produced by the acid hydrolysis of wood, cotton or other cellulose-rich sources, constitute a renewable nanosized raw material with a broad range of envisaged uses: for example, in composites, cosmetics and medical devices. The intriguing ability of CNCs to self-organize into a chiral nematic (cholesteric) liquid crystal phase with a helical arrangement has attracted significant interest, resulting in much research effort, as this arrangement gives dried CNC films a photonic band gap. The films thus acquire attractive optical properties, creating possibilities for use in applications such as security papers and mirrorless lasing. In this critical review, we discuss the sensitive balance between glass formation and liquid crystal self-assembly that governs the formation of the desired helical structure. We show that several as yet unclarified observations—some constituting severe obstacles for applications of CNCs—may result from competition between the two phenomena. Moreover, by comparison with the corresponding self-assembly processes of other rod-like nanoparticles, for example, carbon nanotubes and fd virus particles, we outline how further liquid crystal ordering phenomena may be expected from CNCs if the suspension parameters can be better controlled. Alternative interpretations of some unexpected phenomena are provided, and topics for future research are identified, as are new potential application strategies. Biomaterials: Nanocellulose in order Cellulose, a renewable biopolymer used throughout history, in particular to make clothing and paper, has recently attracted the interest of materials scientists in its nanocrystalline form. These nanofibers — produced by the acid hydrolysis of for instance cotton or wood — show promise for use in composites, cosmetics and medical devices. A Sweden-South Korea-based team led by Jan Lagerwall and Lennart Bergström now review the self-assembly of cellulose nanocrystals into a “chiral nematic” liquid-crystalline phase, which exhibits long-range ordering and adopts a helical superstructure. They compare the behavior of nanocellulose to other rod-like nanoparticles, such as nanotubes, and discuss the competitive gelation that can occur, which yields a glassy — rather than liquid-crystalline — phase. Through its chiral nematic arrangement, nanocellulose is endowed with interesting mechanical and optical properties. Furthermore, its liquid-crystalline suspensions can be processed into thin films, whose development and potential applications are discussed. The chiral liquid crystalline self-organization of cellulose nanocrystals into helical arrangements, giving the resulting materials photonic crystal properties and enhanced mechanical behavior, are comprehensively summarized and compared with other rod-like nanoparticles, for example, carbon nanotubes and fd virus. The consequences of the sensitive balance between liquid crystal formation and glass/gel formation are discussed in detail, in particular regarding the development toward control of helix pitch and orientation. Important topics for future studies are identified and suggestions for novel applications are made.

This review analyzes the current practices in the data-driven characterization, design and optimization of disordered nanoporous materials with pore sizes ranging from angstroms (active carbon and polymer membranes for gas separation) to tens of nm (aerogels). While the machine learning (ML)-based prediction and screening of crystalline, ordered porous materials are conducted frequently, materials with disordered porosity receive much less attention, although ML is expected to excel in the field, which is rich with ill-posed problems, non-linear correlations and a large volume of experimental results. For micro- and mesoporous solids (active carbons, mesoporous silica, aerogels, etc.), the obstacles are mostly related to the navigation of the available data with transferrable and easily interpreted features. The majority of published efforts are based on the experimental data obtained in the same work, and the datasets are often very small. Even with limited data, machine learning helps discover non-evident correlations and serves in material design and production optimization. The development of comprehensive databases for micro- and mesoporous materials with low-level structural and sorption characteristics, as well as automated synthesis/characterization protocols, is seen as the direction of efforts for the immediate future. This paper is written in a language readable by a chemist unfamiliar with the data science specifics.

The extensive literature on innovation management lacks a holistic theory, yet offers valuable frameworks, concepts, and tools for analyzing and managing the innovation process. Research gaps are evident in understanding the impact of specific innovation management techniques or tools (IMT) on innovation performance. Notably, limited studies demonstrate the influence of IMT on performance, primarily through qualitative case studies, and there is a notable shortage of diverse methodologies examining the interaction and collective impact of these tools alongside other innovation drivers. This paper investigates the significance of IMT in relation to other factors contributing to Innovation Market Success (IMS). Using Bootstrapped Structural Equation Modelling and Necessary Conditions Analysis on a dataset of 354 medium-sized enterprises in Germany and Austria, the study examines the interconnectedness and significance of IMT with other innovation performance determinants. Findings suggest a need to reassess the perceived importance of innovation management tools, highlighting an overemphasis in current research, while overlooking other crucial success factors. This study enhances understanding of IMT's role and impact, advocating for their strategic use in harnessing a firm's resources and capabilities to generate new competitive advantages, aligning with the Resource-based View of the Firm.

With the popularity of carbon-free vehicle obstacle avoidance races, the requirements for the accuracy and reliability of vehicle motion control are getting higher and higher. Aiming at the problems of trajectory deviation and debugging difficulties of the carbon-free vehicle during the movement process, the Revolute-Slider-Slider-Revolute (RSSR) mechanism is adopted as the steering device, which is aimed at improving the motion control precision and obstacle avoidance ability of the vehicle. Firstly, from the kinematics point of view, a kinematics model based on the spatial four-link mechanism is established, and the influence of each parameter change on the trajectory of the vehicle is analyzed. On this basis, the influence of each key parameter on the motion process is further explored through practical debugging, so as to derive a general law of the vehicle’s motion under the drive of the RSSR mechanism. Through numerical simulation analysis, the accuracy of the theoretical model is verified, and the structural design of the vehicle is optimized accordingly. The actual debugging results show that the vehicle can realize smooth operation, which fully proves the practicality and effectiveness of the established mathematical model and the research of the RSSR mechanism.

In order to enhance the passability and motion flexibility of a mobile robot operating on unstructured terrain, a centipede style multi-drive module articulated mobile robot has been developed. This robot combines passive deformation wheels with flexible articulation devices, enabling it to passively adapt to complex and variable obstacle terrains. Additionally, the passive deformation wheels have been optimized. Based on the study of the passive deformation mechanism of the wheels, a mechanical model of the robot’s obstacle-crossing capability has been established, and the robot’s passability across different terrains has been analyzed. Experimental results under various terrains demonstrate that the robot features a rational structural design, excellent obstacle-crossing performance, and high motion flexibility, allowing it to passively adapt to complex and variable obstacle terrains.