Reviewerly/exHarmony huggingface dataset:
Vigilance in infectious disease emergencies: Expanding the concept

In their 2010 book, Lorna Weir and Eric Mykhalovskiy conceptualised the role of vigilance in unknown and emerging infectious disease threats. Theirs is a macro-level account which draws on empirical data to describe vigilance as a set of technical and political arrangements that govern collection, analysis, interpretation and communication of data as it pertains to unknown threats. In this paper we expand their work to detail a conceptual analysis of the role of vigilance at the micro-level during periods of high infectious disease threat. Our data are daily press conferences and associated non-discursive tools in New South Wales (NSW), Australia during times of heightened COVID-19 risk. This paper is a conceptual analysis that draws on theories of vigilance and related concepts to show how a key aspect of vigilance is making previously unseen threats visible or present. Communications formulated and encouraged three types of vigilance as a set of governing relations: institutional or authority-based; individual outward-facing; and individual inward-facing. We also describe the relationship between vigilance and related concepts that are used in response to anticipated public threats. Authority based vigilance involved contact tracing and policing of movement and behaviours. In individual outward facing vigilance people were asked to be alert to, analyse, and react to risk in their immediate environment. Inward facing vigilance required people to gather and react to information about their own behaviours and within their own bodies. There was a relationship between different types of vigilance; as risk increased and authority-based vigilance was less successful in containing the spread of infection, individual vigilance had a stronger role to play. This extension of vigilance at the micro-level sees some of the same unintended consequences as Weir and Mykhalovskiy describe at the global level, particularly in how burdens are inequitably distributed and experienced.
Immersive Realities: A Comprehensive Guide from Virtual Reality to Metaverse

This comprehensive literature review explores the diverse landscape of immersive realities, offering a thorough examination of key technologies from Virtual Reality (VR) to the emerging and innovative concept of the Metaverse. The article concentrates the nuances of Extended Reality (XR), Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), providing an in-depth understanding of their individual functionalities, applications, and evolving interplay. The paper also mentions about wearable technologies which play key role in the realization of these immersive realities. The article begins by explaining the fundamental principles of each immersive reality, tracing their technological evolution and highlighting distinctive features that define their user experiences. It then navigates through the complex intersections and synergies between these immersive technologies, shedding light on how they collectively contribute to shaping the future of digital interaction. The paper unfolds how the journey of immersive realities lead to the Metaverse which is relatively a novel paradigm that transcends individual immersive realities, creating a unified, interconnected digital space. The exploration of the Metaverse encompasses its conceptual foundations, potential societal impacts, and the fusion of various immersive technologies within this evolving ecosystem. With an inclusive approach, this paper caters to both novices and seasoned professionals in the field, offering insights into the practical applications associated with different immersive realities. By fostering a comprehensive understanding of these technologies, the paper aims to empower readers to navigate and contribute to the dynamic landscape of immersive realities.
Workshop Strategi Revolusioner Pembelajaran Modern Melalui Integrasi Teknologi Cerdas dan Platform Media Sosial bagi Pendidikan Generasi Digital

Education in the current digital era demands the use of smart technology and social media platforms in the learning process to suit the character of the digital generation. However, the competence of lecturers in Indonesia in this regard is still limited. Therefore, the workshop "Revolutionary Strategies for Modern Learning Through the Integration of Smart Technology and Social Media Platforms for Digital Generation Education" is important to implement. This workshop aims to provide understanding and skills to 187 lecturers in implementing technology and social media for learning innovation for digital generation students. Workshop material includes smart technology trends, use of social media platforms, digital evaluation and assessment strategies, as well as discussion of challenges and opportunities for integrating technology and social media in digital era learning. Participants' enthusiasm was quite high and the evaluation results showed that 91% were interested in implementing modern learning strategies after the workshop.
Synergizing cybersecurity in healthcare with novel bioprocessing for sustainable energy-centric water remediation

Abstract The introduction of several novel chemicals, materials, and processes with varied levels of complexity in recent decades has been a result of the incredibly rapid technological advancement that has characterised those decades. This in turn has led to an increase in the number of pollutants released into the environment, necessitating their effective removal. The results of environmental monitoring reveal that several pollutants are exceeding the limit in ground water, which raises questions about the efficacy of the wastewater treatment methods now in use. This research proposes a novel method for sustainable smart grid (SG)-based energy analysis in water remediation and network cybersecurity analysis for healthcare application. Here the water-caused damages have been analysed based on a healthcare application using SG energy analysis and the network cyber security analysis is carried out using the federated blockchain model (SGEA_FB). Experimental analysis is carried out in terms of network integrity, throughput, scalability, and training accuracy. According to an analysis of the exergy destruction rates in every method component, power generation subsystem has the greatest exergy destruction rate at 15.4 MW. A training accuracy of 96%, throughput of 86%, network integrity of 76%, and scalability of 73% were all achieved by the suggested method.
The influence of leadership style, business ethics, and environmental performance toward corporate social performance and its impact on corporate financial performance

The study presents research clarifying the influence of leadership style, business ethics, and environmental performance on corporate social performance and its implications for corporate financial performance. A survey was done in manufacturing companies of Indonesia’s State Own Enterprise (SOE) and affiliates. Corporate performance is not solely directed towards shareholders or exclusively defined by financial measures; rather, it encompasses a focus on the impact of the organization on the society in which it operates. The style of leadership plays a crucial role in influencing the strategic management process, enabling the organization to implement successful strategies to realize its vision. Business ethics provide advantages to companies by fostering a favorable influence on their competitive edge in the corporate landscape. Environmental concerns are gaining growing significance among various corporate stakeholders, encompassing consumers, shareholders, prospective investors, creditors, regulatory bodies, employees, and the wider public. The finding of the study was that a leadership style had a positive influence on corporate social performance but a negative influence on corporate financial performance. Business ethics had a negative influence on financial performance but a positive influence on corporate social performance and environmental performance. Environmental performance had a negative influence on corporate social performance but positive influence on financial performance. Corporate social performance has a positive influence on corporate financial performance.
Automation of autonomous mobile robot docking based on the counter growth rapidly exploring random tree method

Objectives. The article substantiates the relevance of automatic docking of autonomous mobile robots. Specific examples show that the implementation of the automatic docking functions of autonomous robots reveals the potential for creating multi-agent systems with a transformable structure. The aim of the work is to develop means for automatic docking of autonomous mobile robots in complex scenarios and an uncertain environment. Methods. The proposed approach to automating autonomous mobile robot docking is reduced to a modification of the counter-growth rapidly-exploring random tree (RRT) method. It is based on the parallel execution of a decentralized route planning algorithm with mutual coordination of distributed computing processes. The effectiveness of the complex of algorithmic and software tools developed was evaluated using computer and natural simulation methods. The final series of full-scale experiments was carried out on the example of JetBot AI kit Nvidia platforms for automatic docking of autonomous robots. This was performed using the means and methods of intelligent control, visual navigation, technical vision and wireless network communication. Results. The study analyzed the features of automatic docking as one of the tasks of group control of autonomous robots. This is part of multi-agent systems, capable of reconfiguring structures for purposeful changes to the existing set of functional properties and application possibilities. The study also proposes a decentralized modification of the counter-growth RRT method. This allows the movements of autonomous mobile robots in the course of their mutual approach and subsequent docking to be planned. A set of software-algorithmic tools was developed to automate the docking of autonomous robots. A series of model and full-scale experiments were carried out to confirm the effectiveness of the approach developed herein. Conclusions. The modification presented herein of the counter-growth RRT method, traditionally used for planning the movements of manipulators and mobile platforms, is complementary to the tasks it resolves. This enables the docking of autonomous robots to be automated. The results obtained open up the potential for universal schedulers with extended functionality for autonomous robot control systems to be designed.
Teachers’ Perspectives on the Development of 21st Century Skills in Early Childhood Development

The study focusses on teachers` perspectives on the development of 21st century skills in Early Childhood Development (ECD). The study was motivated by mixed reactions by various teachers on the competency-based curriculum. A qualitative approach was adopted in order to get in- depth information from the teachers in their natural setting. A case study design was adopted drawing on five schools which were purposively sampled to obtain rich data. Semi- structured interviews were used to generate data. The study comprised ten Early Childhood Development teachers and five school heads who were purposively sampled. Data were analysed using thematic content analysis. The findings reveal that teachers have a good understanding of the 21st century skills. A few teachers, however, conflated the 21st century skills with learning areas. Findings further reveal challenges such as misconceptions on play pedagogies and lack of resources. The study recommends, strategies such as engaging in income generating ECD projects will capacitate schools to buy teaching and learning materials for teachers necessary in developing the 21st century skills. Another recommendation is the implementation of staff development for teachers and administrators on ECD curriculum.
What lies on top lies also beneath? Connecting crop surface modelling to buried archaeology mapping.

A recommendation of: Sosic Klindzic Rajna; Vuković Miroslav; Kalafatić Hrvoje; Šiljeg Bartul Digital surface models of crops used in archaeological feature detection – a case study of Late Neolithic site Tomašanci-Dubrava in Eastern Croatia https://doi.org/10.5281/zenodo.7970703
Integration of Augmented Reality Into Glioma Resection Surgery: A Case Report

Augmented reality (AR) is an exciting technology that has garnered considerable attention in the field of neurosurgery. Despite this, clinical use of this technology is still in its infancy. An area of great potential for this technology is the ability to display 3D anatomy overlaid with the patient to assist with presurgical and intraoperative decision-making. A 39-year-old woman presented with headaches and was experiencing what was described as a whooshing sound. MRI revealed the presence of a large left frontal mass involving the genu of the corpus callosum, with heterogeneous enhancement and central hemorrhagic necrosis, confirmed to be a glioma. She underwent a craniotomy with intraoperative MRI for resection. An augmented reality system was used to superimpose 3D holographic anatomy onto the patient's head for surgical planning. This report highlights a new AR technology and its immediate application to cranial neurosurgery. It is critical to document new uses of this technology as the field continues to integrate AR as well as other next-generation technologies into practice.
Deep learning forecasting of large induced earthquakes via precursory signals

Abstract Precursory phenomena to earthquakes have always attracted researchers’ attention. Among the most investigated precursors, foreshocks play a key role. However, their prompt identification with respect to background seismicity still remains an issue. The task is worsened when dealing with low-magnitude earthquakes. Despite that, seismology and, in particular real-time seismology, can nowadays benefit from the use of Artificial Intelligence (AI) to face the challenge of effective precursory signals discrimination. Here, we propose a deep learning method named PreD-Net (precursor detection network) to address precursory signal identification of induced earthquakes. PreD-Net has been trained on data related to three different induced seismicity areas, namely The Geysers, located in California, USA, Cooper Basin, Australia, Hengill in Iceland. The network shows a suitable model generalization, providing considerable results on samples that were not used during the network training phase of all the sites. Tests on related samples of induced large events, with the addition of data collected from the Basel catalogue, Switzerland, assess the possibility of building a real-time warning strategy to be used to avoid adverse consequences during field operations.
Using design research and human-centered design to address growing pains in a busy, urban emergency department: a faculty, clinician, and student collaboration between nursing, design, and medicine

Abstract In 2021, a large urban university-based hospital transitioned to a new two-floor emergency department. Despite the new environment, there were usability and workflow challenges with the space. The authors of this paper created a multidisciplinary, human-centered design collaborative of clinicians, university faculty, and students in an effort to increase emergency department efficiency. After thorough design-research and clinician-focused collaboration, the authors and design team identified the need to improve medical supply retrieval time, which directly impacts patient care and clinician satisfaction. The primary interventions consisted of a redesign that is as follows: (a) created standardized icons related to organ system, (b) increased visibility of supply labels, and (c) reorganized supplies based on usage data. Although a successful project, it was not without several barriers discussed in this article, including design researcher and clinician-level setting and engagement, academic/institutional policies, and conflicting schedules. In addition, the lessons learned from implementing human-centered design concepts into clinical workflow sets forth future research opportunities and inspiration for other institutions to collaborate.
High-efficiency reinforcement learning with hybrid architecture photonic integrated circuit

Abstract Reinforcement learning (RL) stands as one of the three fundamental paradigms within machine learning and has made a substantial leap to build general-purpose learning systems. However, using traditional electrical computers to simulate agent-environment interactions in RL models consumes tremendous computing resources, posing a significant challenge to the efficiency of RL. Here, we propose a universal framework that utilizes a photonic integrated circuit (PIC) to simulate the interactions in RL for improving the algorithm efficiency. High parallelism and precision on-chip optical interaction calculations are implemented with the assistance of link calibration in the hybrid architecture PIC. By introducing similarity information into the reward function of the RL model, PIC-RL successfully accomplishes perovskite materials synthesis task within a 3472-dimensional state space, resulting in a notable 56% improvement in efficiency. Our results validate the effectiveness of simulating RL algorithm interactions on the PIC platform, highlighting its potential to boost computing power in large-scale and sophisticated RL tasks.
Determining Intrusion Attacks Against Online Applications Using Cloud-Based Data Security

Cloud technology makes it possible for users to access information from anywhere, all the time, on any device, and that is the major cause of the many different types of assaults. In principle, multiple dangers, including data leakage, information leakage, and unauthorized information accessibility, are active in cloud environment layering. Modern technological advancements are made accessible on a daily basis through cloud technology. In the cloud, access control and encryption solutions are more complicated. Because of this greater level, security flaws in online applications and systems are more likely to occur. Somewhere at the ends of the end nodes, a malignant insider can carry out protection assaults. Nevertheless, problems with user privacy and data protection on cloud-based social networking sites continue to exist. Such problems are not known to users. On that social networking site, they post a variety of images, videos, and private information that endures even after eradication. However, some of the data that has been made public was intended to be kept private; as a result, online social information has significantly increased the risk of personally identifiable information leaking. The context of cloud technology depends on the customer capabilities such as quick storing and retrieving offered through cloud computing environments. Dependable cloud providers use a number of methodologies to deliver various digital services, creating a variety of security risks. In this paper, the study of determining intrusive cyber-attacks over the online applications using the cloud data security. Restricting access to shared resources is essential to prevent hackers from stealing vulnerabilities in cloud computing to get unauthorised access to a user's activities as well as information. Gaining access to customer information and obstructing the use of cloud computing are the primary objectives of intrusions on cloud services.
Divergent evolutionary rates of primate brain regions as revealed by genomics and transcriptomics

Although the primate brain contains numerous functionally distinct structures that have experienced diverse genetic changes during the course of evolution and development, these changes remain to be explored in detail. Here we utilize two classic metrics from evolutionary biology, the evolutionary rate index (ERI) and the transcriptome age index (TAI), to investigate the evolutionary alterations that have occurred in each area and developmental stage of the primate brain. We observed a higher evolutionary rate for those genes expressed in the non-cortical areas during primate evolution, particularly in human, with the highest rate of evolution being exhibited at brain developmental stages between late infancy and early childhood. Further, the transcriptome age of the non-cortical areas was lower than that of the cerebral cortex, with the youngest age apparent at brain developmental stages between late infancy and early childhood. Our exploration of the evolutionary patterns manifest in each brain area and developmental stage provides important reference points for further research into primate brain evolution.
Recovery of polarization entanglement in partially coherent photonic qubits

Partially coherent photonic qubits, owing to their robustness in propagation through random media compared to fully coherent qubits, find applications in free-space communication, quantum imaging, and quantum sensing. However, the reduction of spatial coherence degrades entanglement in qubits, adversely affecting entanglement-based applications. We report the recovery of entanglement in the partially coherent photonic qubits generated using a spontaneous parametric downconversion process despite retaining their multimode nature. This study utilizes an electron multiplying charge-coupled device (EMCCD) to perform coincidence measurements, eliminating the need for raster scanning of single-pixel detectors, which simplifies optical alignment, enhances precision, and reduces time consumption. We demonstrate that the size of apertures used to select biphotons substantially impacts the visibility and S-parameter of polarization-entangled partially coherent qubits. The entanglement is recovered with partial spatial coherence properties by choosing small sizes of the apertures in the captured image plane. This study could help in the advancement of free-space quantum communication, quantum imaging, and quantum metrology.
PMONN: an optical neural network for photonic integrated circuits based on micro-resonator

We propose an improved optical neural network (ONN) circuit architecture based on conventional micro-resonator ONNs, called the Phase-based Micro-resonator Optical Neural Network (PMONN). PMONN's core architecture features a Convolutions and Batch Normalization (CB) unit, comprising a phase-based (PB) convolutional layer, a Depth-Point-Wise (DPW) convolutional layer, and a reconstructed Batch Normalization (RBN) layer. The PB convolution kernel uses modulable phase shifts of Add-drop MRRs as learnable parameters and their optical transfer function as convolution weights. The DPW convolution kernel amplifies PB convolution weights by learning the amplification factors. To address the internal covariate shift during training, the RBN layer normalizes DPW outputs by reconstructing the BN layer of the electronic neural network, which is then merged with the DPW layer in the test stage. We employ the tunable DAs in the architecture to implement the merged layer. PMONN achieves 99.15% and 91.83% accuracy on MNIST and Fashion-MNIST datasets, respectively. This work presents a method for implementing an optical neural network on the improved architecture based on MRRs and increases the flexibility and reusability of the architecture. PMONN has potential applications as the backbone for future optical object detection neural networks.