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Gesture recognition plays an important role in many fields such as human–computer interaction, medical rehabilitation, virtual and augmented reality. Gesture recognition using wearable devices is a common and effective recognition method. This study aims to combine the inverse magnetostrictive effect and tunneling magnetoresistance effect and proposes a novel wearable sensing glove applied in the field of gesture recognition.

A magnetostrictive sensing glove with function of gesture recognition is proposed based on Fe-Ni alloy, tunneling magnetoresistive elements, Agilus30 base and square permanent magnets. The sensing glove consists of five sensing units to measure the bending angle of each finger joint. The optimal structure of the sensing units is determined through experimentation and simulation. The output voltage model of the sensing units is established, and the output characteristics of the sensing units are tested by the experimental platform. Fifteen gestures are selected for recognition, and the corresponding output voltages are collected to construct the data set and the data is processed using Back Propagation Neural Network.

The sensing units can detect the change in the bending angle of finger joints from 0 to 105 degrees and a maximum error of 4.69% between the experimental and theoretical values. The average recognition accuracy of Back Propagation Neural Network is 97.53% for 15 gestures.

The sensing glove can only recognize static gestures at present, and further research is still needed to recognize dynamic gestures.

A new approach to gesture recognition using wearable devices.

This study has a broad application prospect in the field of human–computer interaction.

The sensing glove can collect voltage signals under different gestures to realize the recognition of different gestures with good repeatability, which has a broad application prospect in the field of human–computer interaction.

Employee recognition systems are ubiquitous in organizations (WorldatWork, 2019) and have positive effects on work outcomes (e.g. Stajkovic and Luthans, 2001). However, psychologically meaningful recognition relies on the recognition giver being motivated to observe and recognize coworkers. Crises such as the COVID-19 pandemic may impact recognition giving in varying ways, yet little research considers this possibility.

This longitudinal field study examined the impact of the COVID-19 crisis on recognition and acknowledgment giving among frontline and nonfrontline healthcare workers at daily and aggregated levels. We tested the relationships between publicly available daily indicators of COVID-19 and objectively measured daily recognition and acknowledgment giving within a web-based platform.

We found that the amount of daily recognition giving was no different during the crisis compared to the year before, but fewer employees gave recognition, and significantly more recognition was given on days when COVID-19 indicators were relatively high. In contrast, the amount of acknowledgment giving was significantly lower in frontline staff and significantly higher in nonfrontline staff during the pandemic than before, but on a daily-level, acknowledgment was unrelated to COVID-19 indicators.

Our results suggest that organizational crises may at once inhibit and stimulate employee recognition and acknowledgment.

Our research is the first to empirically demonstrate that situational factors associated with a crisis can impact recognition giving behavior, and they do so in ways consistent with ostensibly contradictory theories.

The paper explores the notion of worker voice in terms of its implications for supply chain justice. The paper proposes the value of the recognition perspective on social justice for framing workers’ experiences in global supply chains and identifies opportunities for the advancement of the worker voice agenda with recognition justice in mind.

The paper adopts a conceptual approach to explore the notion of worker voice in supply chains in terms of the recognition perspective on social justice.

Sustainable supply chain management (SSCM) scholarship has considered worker voice in terms of two key paradigms, which we term communication and representation. To address recognition justice for workers in global supply chains, the worker voice agenda must consider designing worker voice mechanisms to close recognition gaps for workers with marginalised identities; the shared responsibilities of supply chain actors to listen alongside the expectation of workers to use their voice; and the expansion of the concept of worker voice to cut across home-work boundaries.

The paper offers conceptual clarity on the emerging notion of worker voice in SSCM and is the first to interrogate the implications of recognition justice for the emergent worker voice agenda. It articulates key opportunities for future research to further operationalise worker voice upon a recognition foundation.

This study aims to develop and validate a multidimensional scale to measure the motivating factors that lead to opportunity recognition in social entrepreneurship among higher education institute (HEI) students.

The scale was developed through two phases; in phase 1, semi-structured interviews with social entrepreneurs and aspiring students were conducted to explore themes for item generation. Phase 2 included developing and validating the scale using exploratory (EFA) and confirmatory factor analysis (CFA). The sample included HEI students (n = 300 for EFA, n = 300 for CFA) with either academic background or volunteering experiences in social entrepreneurship.

A 24-item scale is developed in the study, with six factors measuring the motivating factors influencing opportunity recognition in social entrepreneurship: life experiences, social awareness, social inclination, community development, institutional voids and natural option for a meaningful career.

The scale facilitates the development of theories and models in social entrepreneurship. The scale also enables policymakers and social entrepreneurship educators to understand the motivating factors that lead to opportunity recognition among students. It would help them to provide target-specific support to students.

To the best of the authors’ knowledge, this study is the first attempt to develop a scale that measures opportunity recognition in social entrepreneurship based on specific motivating factors. The study used the model by Yitshaki and Kropp (2016) as the conceptual framework. This study is the first attempt to triangulate the model’s findings using a quantitative methodology and through the development of a measurement scale. Besides, the scale adds value to social entrepreneurship research, which lacks empirical research on HEI students.

The purpose of this paper is to present a system for recognition of location names in ancient books written in languages, such as Chinese, in which proper names are not signaled by an initial capital letter.

Rule-based and statistical methods were combined to develop a set of rules for identification of product-related location names in the local chronicles of Guangdong. A name recognition system, with functions of document management, information extraction and storage, rule management, location name recognition, and inquiry and statistics, was developed using Microsoft's .NET framework, SQL Server 2005, ADO.NET and XML. The system was evaluated with precision ratio, recall ratio and the comprehensive index, F.

The system was quite successful at recognizing product-related location names (F was 71.8 percent), demonstrating the potential for application of automatic named entity recognition techniques in digital collation of ancient books such as local chronicles.

Results suffered from limitations in initial digitization of the text. Statistical methods, such as the hidden Markov model, should be combined with an extended set of recognition rules to improve recognition scores and system efficiency.

Electronic access to local chronicles by location name saves time for chorographers and provides researchers with new opportunities.

Named entity recognition brings previously isolated ancient documents together in a knowledge base of scholarly and cultural value.

Automatic name recognition can be implemented in information extraction from ancient books in languages other than English. The system described here can also be adapted to modern texts and other named entities.

We present evidence regarding how a card check recognition process affects the labor relations climate during the period preceding recognition and that which immediately follows. Interviews with managers, interviews with union representatives, and surveys of workers indicate that card check typically results in a less prolonged, costly, and stressful recognition and negotiations process. Although the resulting contracts are often similar to those in other parts of a heavily unionized corporation, sometimes they reflect a different business context – and hence are somewhat more favorable to employers without being substantially less favorable to employees. This reality is reflected in the positive reaction of the U.S. stock markets to union recognition by an employer through a card check process. Employers make card check agreements primarily for business reasons, and investors respect their judgment as to the impact of such agreements on the bottom line.

This chapter uncovers the destabilizing and transformative dimensions of a legal process commonly described as assimilation. Lawyers working on behalf of a marginalized group often argue that the group merits inclusion in dominant institutions, and they do so by casting the group as like the majority. Scholars have criticized claims of this kind for affirming the status quo and muting significant differences of the excluded group. Yet, this chapter shows how these claims may also disrupt the status quo, transform dominant institutions, and convert distinctive features of the excluded group into more widely shared legal norms. This dynamic is observed in the context of lesbian, gay, bisexual, and transgender (LGBT) rights, and specifically through attention to three phases of LGBT advocacy: (1) claims to parental recognition of unmarried same-sex parents, (2) claims to marriage, and (3) claims regarding the consequences of marriage for same-sex parents. The analysis shows how claims that appeared assimilationist – demanding inclusion in marriage and parenthood by arguing that same-sex couples are similarly situated to their different-sex counterparts – subtly challenged and reshaped legal norms governing parenthood, including marital parenthood. While this chapter focuses on LGBT claims, it uncovers a dynamic that may exist in other settings.

Single-shot multi-category clothing recognition and retrieval play a crucial role in online searching and offline settlement scenarios. Existing clothing recognition methods based on RGBD clothing images often suffer from high-dimensional feature representations, leading to compromised performance and efficiency.

To address this issue, this paper proposes a novel method called Manifold Embedded Discriminative Feature Selection (MEDFS) to select global and local features, thereby reducing the dimensionality of the feature representation and improving performance. Specifically, by combining three global features and three local features, a low-dimensional embedding is constructed to capture the correlations between features and categories. The MEDFS method designs an optimization framework utilizing manifold mapping and sparse regularization to achieve feature selection. The optimization objective is solved using an alternating iterative strategy, ensuring convergence.

Empirical studies conducted on a publicly available RGBD clothing image dataset demonstrate that the proposed MEDFS method achieves highly competitive clothing classification performance while maintaining efficiency in clothing recognition and retrieval.

This paper introduces a novel approach for multi-category clothing recognition and retrieval, incorporating the selection of global and local features. The proposed method holds potential for practical applications in real-world clothing scenarios.