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With the escalating integration of renewable energy sources (RESs) within the manufacturing sector, the synergy between energy and production management has gained paramount importance. To harmonise energy efficiency with production effectiveness, the concept of energy-aware scheduling, comprising off-line, on-line, and hybrid scheduling, has been introduced. While each approach has merits and drawbacks in handling unexpected disturbances, recovering optimal performance, and managing computational costs, their designs often tend to be ad hoc. Currently, there is a lack of a systematic design that effectively balances computational costs, robustness to unexpected disturbances, and optimal performance. Moreover, existing results do not fully leverage the flexibility in factories integrated with Renewable Energy Sources (RESs). In response, this study introduces the paradigm of event-triggered hybrid scheduling (ETHS) to achieve a balance among optimality, robustness, and computational cost by fully exploring the system's flexibility. The efficacy of ETHS is empirically demonstrated through a comprehensive case study involving a real-world manufacturing facility. Diverse scenarios are scrutinised, accompanied by sensitivity analyses pertaining to parameter choices. The outcomes conspicuously underscore the potency of ETHS in facilitating the harmonious alignment of energy and production management within the ambit of manufacturing industries. [ABSTRACT FROM AUTHOR]

[This corrects the article DOI: 10.3389/fnhum.2021.644593.].

We estimate that 208,000 deep brain stimulation (DBS) devices have been implanted to address neurological and neuropsychiatric disorders worldwide. DBS Think Tank presenters pooled data and determined that DBS expanded in its scope and has been applied to multiple brain disorders in an effort to modulate neural circuitry. The DBS Think Tank was founded in 2012 providing a space where clinicians, engineers, researchers from industry and academia discuss current and emerging DBS technologies and logistical and ethical issues facing the field. The emphasis is on cutting edge research and collaboration aimed to advance the DBS field. The Eighth Annual DBS Think Tank was held virtually on September 1 and 2, 2020 (Zoom Video Communications) due to restrictions related to the COVID-19 pandemic. The meeting focused on advances in: (1) optogenetics as a tool for comprehending neurobiology of diseases and on optogenetically-inspired DBS, (2) cutting edge of emerging DBS technologies, (3) ethical issues affecting DBS research and access to care, (4) neuromodulatory approaches for depression, (5) advancing novel hardware, software and imaging methodologies, (6) use of neurophysiological signals in adaptive neurostimulation, and (7) use of more advanced technologies to improve DBS clinical outcomes. There were 178 attendees who participated in a DBS Think Tank survey, which revealed the expansion of DBS into several indications such as obesity, post-traumatic stress disorder, addiction and Alzheimer's disease. This proceedings summarizes the advances discussed at the Eighth Annual DBS Think Tank.

Transformative technologies are enabling the construction of threedimensional (3D) maps of tissues with unprecedented spatial and molecularresolution. Over the next seven years, the NIH Common Fund Human BiomolecularAtlas Program (HuBMAP) intends to develop a widely accessible framework forcomprehensively mapping the human body at single-cell resolution by supportingtechnology development, data acquisition, and detailed spatial mapping. HuBMAPwill integrate its efforts with other funding agencies, programs, consortia,and the biomedical research community at large towards the shared vision of acomprehensive, accessible 3D molecular and cellular atlas of the human body, inhealth and various disease settings.

ObjectiveRecent advances in neural engineering have restored mobility to people with paralysis, relieved symptoms of movement disorders, reduced chronic pain, restored the sense of hearing, and provided sensory perception to individuals with sensory deficits.ApproachThis progress was enabled by the team-based, interdisciplinary approaches used by neural engineers. Neural engineers have advanced clinical frontiers by leveraging tools and discoveries in quantitative and biological sciences and through collaborations between engineering, science, and medicine. The movement toward bioelectronic medicines, where neuromodulation aims to supplement or replace pharmaceuticals to treat chronic medical conditions such as high blood pressure, diabetes and psychiatric disorders is a prime example of a new frontier made possible by neural engineering. Although one of the major goals in neural engineering is to develop technology for clinical applications, this technology may also offer unique opportunities to gain insight into how biological systems operate.Main resultsDespite significant technological progress, a number of ethical and strategic questions remain unexplored. Addressing these questions will accelerate technology development to address unmet needs. The future of these devices extends far beyond treatment of neurological impairments, including potential human augmentation applications. Our task, as neural engineers, is to push technology forward at the intersection of disciplines, while responsibly considering the readiness to transition this technology outside of the laboratory to consumer products.SignificanceThis article aims to highlight the current state of the neural engineering field, its links with other engineering and science disciplines, and the challenges and opportunities ahead. The goal of this article is to foster new ideas for innovative applications in neurotechnology.

This study aimed at identifying applications of learning curve (LC) modelling at individual, group, and organisational levels in Industry 4.0 (I4.0) environments. For that, a scoping review on four databases was conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. Our results indicated that LCs are more prominently adopted in I4.0 to model learning at the individual level using technologies oriented to sensing and communication (e.g. big data, IoT, wireless sensors, cloud computing, remote control, or monitoring). However, the effect of a few processing and actuation technologies, such as augmented/virtual reality, collaborative robots, and machine learning/AI, on learning seems promising. Further, despite the number of studies investigated, few explicitly described the LC model used to represent the impact of I4.0 technologies on learning. Our findings allowed the proposition of five research directions. Literature on both LC and I4.0 is still fragmented, poorly addressing their relationship. As I4.0 is an innovative approach that allows more extensive information exchange and processing, new ways of using I4.0 technologies to expedite data collection, which has always constrained LC practical applications, should be devised to close the gap between I4.0 and learning. [ABSTRACT FROM AUTHOR]

Invasive intracranial electrodes are used in both clinical and research applications for recording and stimulation of brain tissue, providing essential data in acute and chronic contexts. The impedance characteristics of the electrode–tissue interface (ETI) evolve over time and can change dramatically relative to pre-implantation baseline. Understanding how ETI properties contribute to the recording and stimulation characteristics of an electrode can provide valuable insights for users who often do not have access to complex impedance characterizations of their devices. In contrast to the typical method of characterizing electrical impedance at a single frequency, we demonstrate a method for using electrochemical impedance spectroscopy (EIS) to investigate complex characteristics of the ETI of several commonly used acute and chronic electrodes. We also describe precise modeling strategies for verifying the accuracy of our instrumentation and understanding device–solution interactions, both in vivo and in vitro. Included with this publication is a dataset containing both in vitro and in vivo device characterizations, as well as some examples of modeling and error structure analysis results. These data can be used for more detailed interpretation of neural recordings performed on common electrode types, providing a more complete picture of their properties than is often available to users. Dataset: Data discussed here can be found on the Dryad repository at the following link: https://doi.org/10.5061/dryad.8931zcrvw , accessed on 9 April 2024. Dataset License: The dataset is published under a CC0 license. [ABSTRACT FROM AUTHOR]

The proceedings of the 3rd Annual Deep Brain Stimulation Think Tank summarize the most contemporary clinical, electrophysiological, imaging, and computational work on DBS for the treatment of neurological and neuropsychiatric disease. Significant innovations of the past year are emphasized. The Think Tank's contributors represent a unique multidisciplinary ensemble of expert neurologists, neurosurgeons, neuropsychologists, psychiatrists, scientists, engineers, and members of industry. Presentations and discussions covered a broad range of topics, including policy and advocacy considerations for the future of DBS, connectomic approaches to DBS targeting, developments in electrophysiology and related strides toward responsive DBS systems, and recent developments in sensor and device technologies.

Neural prosthetic interfaces based upon penetrating microelectrode devices have broadened our understanding of the brain and have shown promise for restoring neurological functions lost to disease, stroke, or injury. However, the eventual viability of such devices for use in the treatment of neurological dysfunction may be ultimately constrained by the intrinsic brittleness of silicon, the material most commonly used for manufacture of penetrating microelectrodes. This brittleness creates predisposition for catastrophic fracture, which may adversely affect the reliability and safety of such devices, due to potential for fragmentation within the brain. Herein, we report the development of titanium-based penetrating microelectrodes that seek to address this potential future limitation. Titanium provides advantage relative to silicon due to its superior fracture toughness, which affords potential for creation of robust devices that are resistant to catastrophic failure. Realization of these devices is enabled by recently developed techniques which provide opportunity for fabrication of high-aspect-ratio micromechanical structures in bulk titanium substrates. Details are presented regarding the design, fabrication, mechanical testing, in vitro functional characterization, and preliminary in vivo testing of devices intended for acute recording in rat auditory cortex and thalamus, both independently and simultaneously.