InPhase: Phase-based Ranging and Localization

Ranging and subsequent localization have become more and more critical in today’s factories and logistics. Tracking goods precisely enables just-in-time manufacturing processes. We present the InPhase system for ranging and localization applications. It employs narrowband 2.4 GHz IEEE 802.15.4 radio transceivers to acquire the radio channel’s phase response. In comparison, most other systems employ time-of-flight schemes with Ultra Wideband transceivers. Our software can be used with existing wireless sensor network hardware, providing ranging and localization for existing devices at no extra cost. The introduced Complex-valued Distance Estimation algorithm evaluates the phase response to compute the distance between two radio devices. We achieve high ranging accuracy and precision with a mean absolute error of 0.149 m and a standard deviation of 0.104 m. We show that our algorithm is resilient against noise and burst errors from the phase-data acquisition. Further, we present a localization algorithm based on a particle filter implementation. It achieves a mean absolute error of 0.95 m in a realistic 3D live tracking scenario.

Methods of Comparison of the Economic Order Quantity and Just-In-Time Restocking Technologies. The Case Study

Estimation of the optimal size of order is one of the key tasks in determining the parameters of the urban freight restocking system. The existing analytical models and methods are considering each technology separately and they do not compare the Economic Order Quantity (EOQ) and Justin-tme (JIT) restocking technologies. The purpose of this research was to evaluate efficiency of the JIT and EOQ restocking technologies. The research would help in selecting the delivery model, analyzing functioning of existing JIT and EOQ models. The article presents an approach to determining the comparison in organizing supplies to the retailer. For this, the two supply models were compared. The Just-in-Time model is characterised by costs that are spend on transportation. The Economic Order Quantity model includes costs of transportation and storage in a warehouse. After calculations, application of the Just-in-Time model in the given conditions was determined.

Recruitment, Readiness, and Retention of Providers at a Field Hospital during the Pandemic

In response to the coronavirus disease 2019 (COVID-19) pandemic, the State of Maryland established a 250-bed emergency response field hospital at the Baltimore Convention Center to support the existing healthcare infrastructure. To operationalize this hospital with 65 full-time equivalent (FTE) clinicians in less than four weeks, more than 300 applications were reviewed, 186 candidates were interviewed, and 159 clinicians were credentialed and onboarded. The key steps to achieve this undertaking involved employing multidisciplinary teams with experienced personnel, mass outreach, streamlined candidate tracking, pre-interview screening, utilizing all available expertise, expedited credentialing, and focused onboarding. To ensure staff preparedness, the leadership developed innovative team models, applied principles of effective team building, and provided ‘just in time’ training on COVID-19 and non-COVID-19 related topics to the staff. The leadership focused on staff safety and well-being, offered appropriate financial remuneration and provided leadership opportunities that allowed retention of staff.

The paradoxes of just-in-time system: an abductive analysis of a public food manufacturing and exporting company in Thailand

Purpose study aims to investigate the implementation and impact of a just-in-time (JIT) system in a food manufacturing and exporting company in Thailand. Design/methodology/approach At the company, the authors used an anomaly case study. The authors performed content analysis on the data collected through semi-structured interviews and direct observations to determine operational flows through customer order, production and delivery. The authors constructed a framework that helped in mapping current operations and subsequently assessing JIT’s impacts; the authors reported the best practices to the company’s owner. Based on the follow-up after a year, the authors used an abductive approach to refine the JIT theory using data from case organizations and relevant studies. Findings The company encountered errors and delays in international delivery owing to inadequate inputs resulting from uncertain agricultural production, delayed contact with freight forwarders, improper documentation and insufficient staffing. Besides the highly centralized system, the limitations of the JIT philosophy contributed to the issues, thereby increasing mental and physical health problems and turnover rate. Owing to these paradoxical effects, the authors extended the JIT theory. Of the study’s several recommendations, the company observed only the following: contacting the freight forwarder after the purchase order confirmation, not production completion. The authors observed increased customer satisfaction, despite the additional cost of booking containers early. Originality/value This research presents a balanced JIT that can minimize JIT’s impacts and resource shortage, owing to demand-supply uncertainties and sustain competitiveness.

Transforming Technology Purpose and Integration

While the shift to emergency remote teaching was sudden and caught many off-guard, the reality exists that we need to better prepare faculty to utilize technology in a meaningful way and integrate it into lessons. This chapter provides an overview of two aspects: 1) preparing faculty for use of technology through a modified transitional learning model so that they are supported with just-in-time professional learning and 2) introducing them to the PICRAT technology framework to assist them in the design of their lessons. Both the model and the framework are constructivist in nature and align with transformative learning theory. Examples of what each of these structures look like are provided within the narrative.

Role of Technological Innovations in Improving Manufacturing Resilience in the Post-Covid Era

Technological progress is critical for improving manufacturing resilience and achieving self-sufficiency in any nation. The COVID-19 crisis has shown the importance of, as well as the threats associated with, our new industrial processes. Manufacturing's critical position in both industrialized and emerging economies has been shown by shortages of medical supplies and disruptions in basic goods. These delays and market fluctuations have also highlighted the pitfalls of focusing solely on a globalized distribution system characterized by fragmented supply chains, the separation of engineering and manufacturing, and just-in-time production. Although globalisation was the trend before the COVID-19 pandemic, several countries realized the dangers and began to look inward, focusing on developing local manufacturing capacities. In addition, the automotive sector has established itself as a cornerstone of post-COVID-19 recovery strategies.

Poincaré maps for detecting chaos in fractional-order systems with hidden attractors for its Kaplan-Yorke dimension optimization

<abstract><p>The optimization of fractional-order (FO) chaotic systems is challenging when simulating a considerable number of cases for long times, where the primary problem is verifying if the given parameter values will generate chaotic behavior. In this manner, we introduce a methodology for detecting chaotic behavior in FO systems through the analysis of Poincaré maps. The optimization process is performed applying differential evolution (DE) and accelerated particle swarm optimization (APSO) algorithms for maximizing the Kaplan-Yorke dimension ($ D_{KY} $) of two case studies: a 3D and a 4D FO chaotic systems with hidden attractors. These FO chaotic systems are solved applying the Grünwald-Letnikov method, and the Numba just-in-time (jit) compiler is used to improve the optimization process's time execution in Python programming language. The optimization results show that the proposed method efficiently optimizes FO chaotic systems with hidden attractors while saving execution time.</p></abstract>