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Organizational scholars studying compensation often place an emphasis on certain employee groups (e.g., executives). Missing from this discussion is research on the compensation systems for low-wage jobs. In this review, the authors argue that workers in low-wage jobs represent a unique employment group in their understanding of rent allocation in organizations. The authors address the design of compensation strategies in organizations that lead to different outcomes for workers in low-wage jobs versus other workers. Drawing on and integrating human resource management (HRM), inequality, and worker literatures with compensation literature, the authors describe and explain compensation systems for low-wage work. The authors start by examining workers in low-wage work to identify aspects of these workers’ jobs and lives that can influence their health, performance, and other organizationally relevant outcomes. Next, the authors explore the compensation systems common for this type of work, building on the compensation literature, by identifying the low-wage work compensation designs, proposing the likely explanations for why organizations craft these designs, and describing the worker and organizational outcomes of these designs. The authors conclude with suggestions for future research in this growing field and explore how organizations may benefit by rethinking their approach to compensation for low-wage work. In sum, the authors hope that this review will be a foundational work for those interested in investigating organizational compensation issues at the intersection of inequality and worker and organizational outcomes.

This paper aims to reduce waste management and generate wealth by investigating the novelty of combining chicken feather fiber and bamboo particles to produce hybrid biocomposites. This is part of responsible production and sustainability techniques for sustainable development goals. This study aims to broaden animal and plant fiber utilization in the sustainable production of epoxy resins for engineering applications.

This research used two reinforcing materials [chicken feather fiber (CFF) and bamboo particles (BP)] to reinforce epoxy resin. The BPs were kept constant at 6 Wt.%, while the CFF was varied within 3–15 Wt.% in the composites to make CFF-BP polymer-reinforced composite (CFF-BP PRC). The mechanical experiment showed a 21% reduction in densities, making the CFF-BP PRC an excellent choice for lightweight applications.

It was discovered that fabricated composites with 10 mm CFF length had improved properties compared with the 15 mm CFF length and pristine samples, which confirmed that short fibers are better at enhancing randomly dispersed fibers in the epoxy matrix. However, the ballistic properties of both samples matched. There is a 40% increase in tensile strength and a 54% increase in flexural strength of the CFF-BP PRC compared to the pristine sample.

According to the literature review, to the best of the authors’ knowledge, this is a novel study of chicken fiber and bamboo particles in reinforcing epoxy composite.

The purpose of this study is to forecast the mechanical properties of ternary blended concrete (TBC) modified with oyster shell powder (OSP) and shea nutshell ash (SNA) using deep neural network (DNN) models.

DNN models with three hidden layers, each layer containing 5–30 nodes, were used to predict the target variables (compressive strength [CS], flexural strength [FS] and split tensile strength [STS]) for the eight input variables of concrete classes 25 and 30 MPa. The concrete samples were cured for 3–120 days. Levenberg−Marquardt's backpropagation learning technique trained the networks, and the model's precision was confirmed using the experimental data set.

The DNN model with a 25-node structure yielded a strong relation for training, validating and testing the input and output variables with the lowest mean squared error (MSE) and the highest correlation coefficient (R) values of 0.0099 and 99.91% for CS and 0.010 and 98.42% for FS compared to other architectures. However, the DNN model with a 20-node architecture yielded a strong correlation for STS, with the lowest MSE and the highest R values of 0.013 and 97.26%. Strong relationships were found between the developed models and raw experimental data sets, with R² values of 99.58%, 97.85% and 97.58% for CS, FS and STS, respectively.

To the best of the authors’ knowledge, this novel research establishes the prospects of replacing SNA and OSP with Portland limestone cement (PLC) to produce TBC. In addition, predicting the CS, FS and STS of TBC modified with OSP and SNA using DNN models is original, optimizing the time, cost and quality of concrete.

Visual feedback control is a promising solution for robots work in unstructured environments, and this is accomplished by estimation of the time derivative relationship between the image features and the robot moving. While some of the drawbacks associated with most visual servoing (VS) approaches include the vision–motor mapping computation and the robots’ dynamic performance, the problem of designing optimal and more effective VS systems still remains challenging. Thus, the purpose of this paper is to propose and evaluate the VS method for robots in an unstructured environment.

This paper presents a new model-free VS control of a robotic manipulator, for which an adaptive estimator aid by network learning is proposed using online estimation of the vision–motor mapping relationship in an environment without the knowledge of statistical noise. Based on the adaptive estimator, a model-free VS schema was constructed by introducing an active disturbance rejection control (ADRC). In our schema, the VS system was designed independently of the robot kinematic model.

The various simulations and experiments were conducted to verify the proposed approach by using an eye-in-hand robot manipulator without calibration and vision depth information, which can improve the autonomous maneuverability of the robot and also allow the robot to adapt its motion according to the image feature changes in real time. In the current method, the image feature trajectory was stable in the camera field range, and the robot’s end motion trajectory did not exhibit shock retreat. The results showed that the steady-state errors of image features was within 19.74 pixels, the robot positioning was stable within 1.53 mm and 0.0373 rad and the convergence rate of the control system was less than 7.21 s in real grasping tasks.

Compared with traditional Kalman filtering for image-based VS and position-based VS methods, this paper adopts the model-free VS method based on the adaptive mapping estimator combination with the ADRC controller, which is effective for improving the dynamic performance of robot systems. The proposed model-free VS schema is suitable for robots’ grasping manipulation in unstructured environments.

Social media platforms that disseminate scientific information to the public during the COVID-19 pandemic highlighted the importance of the topic of scientific communication. Content creators in the field, as well as researchers who study the impact of scientific information online, are interested in how people react to these information resources and how they judge them. This study aims to devise a framework for extracting large social media datasets and find specific feedback to content delivery, enabling scientific content creators to gain insights into how the public perceives scientific information.

To collect public reactions to scientific information, the study focused on Twitter users who are doctors, researchers, science communicators or representatives of research institutes, and processed their replies for two years from the start of the pandemic. The study aimed in developing a solution powered by topic modeling enhanced by manual validation and other machine learning techniques, such as word embeddings, that is capable of filtering massive social media datasets in search of documents related to reactions to scientific communication. The architecture developed in this paper can be replicated for finding any documents related to niche topics in social media data. As a final step of our framework, we also fine-tuned a large language model to be able to perform the classification task with even more accuracy, forgoing the need of more human validation after the first step.

We provided a framework capable of receiving a large document dataset, and, with the help of with a small degree of human validation at different stages, is able to filter out documents within the corpus that are relevant to a very underrepresented niche theme inside the database, with much higher precision than traditional state-of-the-art machine learning algorithms. Performance was improved even further by the fine-tuning of a large language model based on BERT, which would allow for the use of such model to classify even larger unseen datasets in search of reactions to scientific communication without the need for further manual validation or topic modeling.

The challenges of scientific communication are even higher with the rampant increase of misinformation in social media, and the difficulty of competing in a saturated attention economy of the social media landscape. Our study aimed at creating a solution that could be used by scientific content creators to better locate and understand constructive feedback toward their content and how it is received, which can be hidden as a minor subject between hundreds of thousands of comments. By leveraging an ensemble of techniques ranging from heuristics to state-of-the-art machine learning algorithms, we created a framework that is able to detect texts related to very niche subjects in very large datasets, with just a small amount of examples of texts related to the subject being given as input.

With this tool, scientific content creators can sift through their social media following and quickly understand how to adapt their content to their current user’s needs and standards of content consumption.

This study aimed to find reactions to scientific communication in social media. We applied three methods with human intervention and compared their performance. This study shows for the first time, the topics of interest which were discussed in Brazil during the COVID-19 pandemic.

The purpose of this paper is to develop a familiarity analysis of resistive-capacitive (RC) network active circuit operating with unfamiliar low-pass (LP) type negative group delay (NGD) behavior. The design method of NGD circuit is validated by simulation with commercial tool and experimental measurement.

The present research work methodology is structured in three main parts. The familiarity theory of RC-network LP-NGD circuit is developed. The LP-NGD circuit parameters are expressed in function of the targeted time-advance. Then, the feasibility study is based on the theory, simulation and measurement result comparisons.

The RC-network based LP-NGD proof of concept is validated with −1 and −0.5 ms targeted time-advances after design, simulation, test and characterized. The LP-NGD circuit unity gain prototype presents NGD cut-off frequencies of about 269 and 569 Hz for the targeted time-advances, −1 and −0.5 ms, respectively. Bi-exponential and arbitrary waveform signals were tested to verify the targeted time-advance.

The performance of the unfamiliar LP-NGD topology developed in the present study is limited by the parasitic elements of constituting lumped components.

The NGD circuit enables to naturally reduce the undesired delay effect from the electronic and communication systems. The NGD circuit can be exploited to reduce the delay induced by electronic devices and system.

As social impacts of the NGD circuit application, the NGD function is one of prominent solutions to improve the technology performances of future electronic device in term of communication aspect and the transportation system.

The originality of the paper concerns the theoretical approach of the RC-network parameters in function of the targeted time-advance and the input signal bandwidth. In addition, the experimental results are also particularly original.

This study aims to assess the effect of entrepreneurial orientation on supply chain performance both directly and indirectly through entrepreneurial competence, as well as the moderating role of local community networking in these relationships, within the context of institutional voids in Ghana.

The study utilized a cross-sectional survey data from 225 small and medium sized enterprises (SMEs) in order to test the hypotheses. The data analysis was conducted using partial least squares structural equation modelling techniques.

The results revealed that entrepreneurial orientation is a significant positive predictor of both entrepreneurial competence and supply chain performance. Similarly, entrepreneurial competence was shown to positively predict supply chain performance, both directly and as a mediator between entrepreneurial orientation and supply chain performance. Local community networking, however, positively moderated the relationship between entrepreneurial orientation and entrepreneurial competence but not the relationship between entrepreneurial orientation and supply chain performance.

This study contributes to literature by looking at the relationship between entrepreneurial orientation, entrepreneurial competence, local community networking and supply chain performance within the context of an emerging economy with institutional voids. The study shows the importance of an entrepreneurial mindset in developing the necessary skills, competences and abilities needed to survive in the turbulent business environment.

Small retail stores in the luxury market face significant challenges due to fluctuations in market demand. This task turns challenging as it requires effectively coordinating and translating customer needs into specific requirements that align with retail goals and available resources. However, limited empirical research exists investigating how managers can address service value and quality attributes in small retail stores. This article aims to bridge this gap by investigating the role of quality function deployment (QFD) in improving market and quality requirements management in small retail stores.

Based on the case study, a customer survey was initially conducted to gather information on critical characteristics valued in the luxury retail segment. QFD was used to assist the company in identifying and prioritizing key quality attributes to meet customer requirements effectively.

The findings demonstrate that implementing QFD in small luxury retail stores empowers managers to identify previously neglected product and service quality aspects. The article shows that QFD informs organizational adaptations that align with the demands of the retail market, leading to an improved ability to meet customer expectations and enhance customer value through the development of enhanced products and services. The study showcases the efficacy of the tested methodology in effectively capturing and prioritizing both tangible and intangible customer needs in retail.

Findings offer valuable insights to retail managers of small luxury stores, providing actionable market-oriented strategies. By implementing the recommended practices, managers can improve the store’s competitiveness and better cater to the customer base.

This study contributes to bridging persistent knowledge gaps by addressing the unique context of small luxury retail stores and introducing the application of QFD in this setting. The insights gained from this research are relevant to both retailing and quality management literature. Considering the growing prevalence of transformations in the retail industry, the study provides practical implications for retail managers in effectively navigating these changes.