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This study aims to show the inconsistency of the approach to the development of artificial intelligence as an independent tool (just one more tool that humans have developed); to describe the logic and concept of intelligence development regardless of its substrate: a human or a machine and to prove that the co-evolutionary hybridization of the machine and human intelligence will make it possible to reach a solution for the problems inaccessible to humanity so far (global climate monitoring and control, pandemics, etc.).

The global trend for artificial intelligence development (has been) was set during the Dartmouth seminar in 1956. The main goal was to define characteristics and research directions for artificial intelligence comparable to or even outperforming human intelligence. It should be able to acquire and create new knowledge in a highly uncertain dynamic environment (the real-world environment is an example) and apply that knowledge to solving practical problems. Nowadays artificial intelligence overperforms human abilities (playing games, speech recognition, search, art generation, extracting patterns from data etc.), but all these examples show that developers have come to a dead end. Narrow artificial intelligence has no connection to real human intelligence and even cannot be successfully used in many cases due to lack of transparency, explainability, computational ineffectiveness and many other limits. A strong artificial intelligence development model can be discussed unrelated to the substrate development of intelligence and its general properties that are inherent in this development. Only then it is to be clarified which part of cognitive functions can be transferred to an artificial medium. The process of development of intelligence (as mutual development (co-development) of human and artificial intelligence) should correspond to the property of increasing cognitive interoperability. The degree of cognitive interoperability is arranged in the same way as the method of measuring the strength of intelligence. It is stronger if knowledge can be transferred between different domains on a higher level of abstraction (Chollet, 2018).

The key factors behind the development of hybrid intelligence are interoperability – the ability to create a common ontology in the context of the problem being solved, plan and carry out joint activities; co-evolution – ensuring the growth of aggregate intellectual ability without the loss of subjectness by each of the substrates (human, machine). The rate of co-evolution depends on the rate of knowledge interchange and the manufacturability of this process.

Resistance to the idea of developing co-evolutionary hybrid intelligence can be expected from agents and developers who have bet on and invested in data-driven artificial intelligence and machine learning.

Revision of the approach to intellectualization through the development of hybrid intelligence methods will help bridge the gap between the developers of specific solutions and those who apply them. Co-evolution of machine intelligence and human intelligence will ensure seamless integration of smart new solutions into the global division of labor and social institutions.

The novelty of the research is connected with a new look at the principles of the development of machine and human intelligence in the co-evolution style. Also new is the statement that the development of intelligence should take place within the framework of integration of the following four domains: global challenges and tasks, concepts (general hybrid intelligence), technologies and products (specific applications that satisfy the needs of the market).

This study aims to find out if organizations are still practicing a hybrid workplace arrangement after COVID-19 ease of restrictions, determine the positive and negative sides of a hybrid workplace, ascertain the challenges organizations are currently facing in implementing a hybrid workplace and examine how successful team learning has been in hybrid workplaces.

This study adopted a mixed approach. Two sets of data (quantitative and qualitative) were used to answer the research questions.

This study found that most organizations within professional service firms are still implementing hybrid workplaces even though COVID-19 restrictions have been eased. This study also found that one of the advantages of implementing a hybrid work arrangement includes employees’ opportunity to spend more time with their families. On the other hand, the disadvantages discovered were gradual loss of corporate identity, a feeling of loneliness and others. One of the challenges organizations are facing in implementing this working system is the lack of an ergonomic workplace and appropriate technology for remote working. With evidence, this study ends with finding out that companies are not successful as expected in terms of team learning in a hybrid workplace.

To the best of the author’s knowledge, this is among the first to look at hybrid workplace in the African setting where COVID restrictions which highlights the practice has not really been an issue. This study also combines its findings with those done on the subject before to firmly clarify attributes as they exist.

The purpose of this paper is to explore the various forms of organizational and team-level actions that were perceived to be helpful or not helpful by employees as they navigate the hybrid work arrangements and how these had an impact on their work behaviors and experiences. This research utilized Bronfenbrenner's ecological systems theory as framework.

The exploratory study used a qualitative approach in gathering data via online survey from a total of 45 Filipino employees working in a hybrid work arrangement for at least three months. The analysis utilized both inductive and deductive methodologies in examining the data. Inductive thematic analysis was used in coding the data based on the participants' responses, while the deductive approach ensured that the themes are aligned with the research questions and reflect the different systems within Bronfenbrenner's EST (1986).

Results surfaced helpful organizational (e.g. provision of work tools, financial assistance, supportive policies and engagement and wellness initiatives) and team level actions (i.e. use of technology-based communication tools, open virtual door policy, effective performance management system, employee care practices and team engagement activities). Actions that were perceived as not helpful include inadequate technological infrastructure, poor communication, insufficient training, punitive policies/practices and leadership issues at the organizational level as well as unresponsive colleagues and ineffective implementation of policies/processes at the level of teams. Employees reported being able to build on savings, becoming more productive and having greater work–life balance amid hybrid work. However, they continue to be challenged by blurred boundaries and inability to disconnect from work similar to when work was done remotely and now with sustaining momentum given the shifts on where they do their work.

The findings of this study may guide programs and initiatives of human resource management practitioners and organizational leaders as they support employees in navigating through hybrid work.

The research expands extant knowledge on practices and experiences in hybrid work (Gifford, 2022). It also contributes to studies on human resource management that are nuanced based on where work is performed (Ng and Stanton, 2023) or with emerging work arrangements.

Taking into consideration the current human need for agricultural produce such as rice that requires water for growth, the optimal consumption of this valuable liquid is important. Unfortunately, the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption. Therefore, designing and implementing a mechanized irrigation system is of the highest importance. This system includes hardware equipment such as liquid altimeter sensors, valves and pumps which have a failure phenomenon as an integral part, causing faults in the system. Naturally, these faults occur at probable time intervals, and the probability function with exponential distribution is used to simulate this interval. Thus, before the implementation of such high-cost systems, its evaluation is essential during the design phase.

The proposed approach included two main steps: offline and online. The offline phase included the simulation of the studied system (i.e. the irrigation system of paddy fields) and the acquisition of a data set for training machine learning algorithms such as decision trees to detect, locate (classification) and evaluate faults. In the online phase, C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.

The proposed approach is a comprehensive online component-oriented method, which is a combination of supervised machine learning methods to investigate system faults. Each of these methods is considered a component determined by the dimensions and complexity of the case study (to discover, classify and evaluate fault tolerance). These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods. As a result, depending on the conditions under study, the most efficient method is selected in the components. Before the system implementation phase, its reliability is checked by evaluating the predicted faults (in the system design phase). Therefore, this approach avoids the construction of a high-risk system. Compared to existing methods, the proposed approach is more comprehensive and has greater flexibility.

By expanding the dimensions of the problem, the model verification space grows exponentially using automata.

Unlike the existing methods that only examine one or two aspects of fault analysis such as fault detection, classification and fault-tolerance evaluation, this paper proposes a comprehensive process-oriented approach that investigates all three aspects of fault analysis concurrently.

A zero-day vulnerability is a complimentary ticket to the attackers for gaining entry into the network. Thus, there is necessity to device appropriate threat detection systems and establish an innovative and safe solution that prevents unauthorised intrusions for defending various components of cybersecurity. We present a survey of recent Intrusion Detection Systems (IDS) in detecting zero-day vulnerabilities based on the following dimensions: types of cyber-attacks, datasets used and kinds of network detection systems.

Purpose: The study focuses on presenting an exhaustive review on the effectiveness of the recent IDS with respect to zero-day vulnerabilities.

Methodology: Systematic exploration was done at the IEEE, Elsevier, Springer, RAID, ESCORICS, Google Scholar, and other relevant platforms of studies published in English between 2015 and 2021 using keywords and combinations of relevant terms.

Findings: It is possible to train IDS for zero-day attacks. The existing IDS have strengths that make them capable of effective detection against zero-day attacks. However, they display certain limitations that reduce their credibility. Novel strategies like deep learning, machine learning, fuzzing technique, runtime verification technique, and Hidden Markov Models can be used to design IDS to detect malicious traffic.

Implication: This paper explored and highlighted the advantages and limitations of existing IDS enabling the selection of best possible IDS to protect the system. Moreover, the comparison between signature-based and anomaly-based IDS exemplifies that one viable approach to accurately detect the zero-day vulnerabilities would be the integration of hybrid mechanism.

The purpose of this review paper is to address the substantial challenges of the outdated exoskeletons used for rehabilitation and further study the current advancements in this field. The shortcomings and technological developments in sensing the input signals to enable the desired motions, actuation, control and training methods are explained for further improvements in exoskeleton research.

Search platforms such as Web of Science, IEEE, Scopus and PubMed were used to collect the literature. The total number of recent articles referred to in this review paper with relevant keywords is filtered to 143.

Exoskeletons are getting smarter often with the integration of various modern tools to enhance the effectiveness of rehabilitation. The recent applications of bio signal sensing for rehabilitation to perform user-desired actions promote the development of independent exoskeleton systems. The modern concepts of artificial intelligence and machine learning enable the implementation of brain–computer interfacing (BCI) and hybrid BCIs in exoskeletons. Likewise, novel actuation techniques are necessary to overcome the significant challenges seen in conventional exoskeletons, such as the high-power requirements, poor back drivability, bulkiness and low energy efficiency. Implementation of suitable controller algorithms facilitates the instantaneous correction of actuation signals for all joints to obtain the desired motion. Furthermore, applying the traditional rehabilitation training methods is monotonous and exhausting for the user and the trainer. The incorporation of games, virtual reality (VR) and augmented reality (AR) technologies in exoskeletons has made rehabilitation training far more effective in recent times. The combination of electroencephalogram and electromyography-based hybrid BCI is desirable for signal sensing and controlling the exoskeletons based on user intentions. The challenges faced with actuation can be resolved by developing advanced power sources with minimal size and weight, easy portability, lower cost and good energy storage capacity. Implementation of novel smart materials enables a colossal scope for actuation in future exoskeleton developments. Improved versions of sliding mode control reported in the literature are suitable for robust control of nonlinear exoskeleton models. Optimizing the controller parameters with the help of evolutionary algorithms is also an effective method for exoskeleton control. The experiments using VR/AR and games for rehabilitation training yielded promising results as the performance of patients improved substantially.

Robotic exoskeleton-based rehabilitation will help to reduce the fatigue of physiotherapists. Repeated and intention-based exercise will improve the recovery of the affected part at a faster pace. Improved rehabilitation training methods like VR/AR-based technologies help in motivating the subject.

The paper describes the recent methods for signal sensing, actuation, control and rehabilitation training approaches used in developing exoskeletons. All these areas are key elements in an exoskeleton where the review papers are published very limitedly. Therefore, this paper will stand as a guide for the researchers working in this domain.

Heat exchangers (HEs) which provide heat transfer and transfer energy through direct or indirect contact between fluids have an essential role in many processes as a part of various industries from pharmaceutical production to electronic devices. Using nanofluid as working fluid and integrating different types of turbulators could be used to upgrade the thermal effectiveness of HEs. Recently, to obtain more increment in thermal effectiveness, hybrid nanofluids are used that are prepared by mixing two or more various nanoparticles. The purpose of this experimental and numerical study is investigating different scenarios for improving the effectiveness of a concentric U-tube type HE.

In the numerical section of this study, different turbulator modifications, including circular and quarter circular rings, were modeled to determine the effect of adding turbulator on thermal performance. In addition, Al₂O₃/water and SiO₂/water single and Al₂O₃–SiO₂/water hybrid nanofluids were experimentally tested in an unmodified concentric U-tube HE in two different modes, including counter flow and parallel flow. Al₂O₃–SiO₂/water hybrid nanofluid was prepared at 2% (wt./wt.) particle ratio and compared with Al₂O₃/water and SiO₂/water single type nanofluids at same particle ratios and with distilled water.

Numerical modeling findings exhibited that integrating turbulators to the concentric tube type HE caused to raise in the effectiveness by improving heat transfer area. Also, experimental results indicated that using both hybrid and single type nanofluids notably upgraded the thermal performance of the concentric U-tube HE. Integrating turbulators cannot be an effective alternative in a concentric U-tube type HE with lower diameter because of raise in pressure drop. Numerically achieved findings exhibited that using quarter circular turbulators decreased pressure drop in comparison with circular turbulators. According to the experimental outcomes, using hybrid Al₂O₃–SiO₂/water nanofluid leads to obtain more thermal performance in comparison with single type nanofluids. The highest increment in overall heat transfer coefficient of HE by using Al₂O₃–SiO₂/water nanofluid achieved as 58.97% experimentally.

The overall outcomes of the current research exhibited the positive impacts of using hybrid nanofluid and integrating turbulators. In this empirical and numerical survey, numerical simulations were performed to specify the impact of applying different turbulators and hybrid nanofluid on the flow and thermal characteristics in a concentric U-tube HE. The achieved outcomes exhibited that using hybrid nanofluid can notably increase the thermal performance with negligible pressure drop in comparison with two different turbulator modifications.

This study aims to offer a hybrid stand-alone system for electric vehicle (EV) charging stations (CS), an emerging power scheme due to the availability of renewable and environment-friendly energy sources. This paper presents the analysis of a photovoltaic (PV) with an adaptive neuro-fuzzy inference system (ANFIS) algorithm, solid oxide fuel cell (SOFC) and a battery storage scheme incorporated for EV CS in a stand-alone mode. In previous studies, either the hydrogen fuel of SOFC or the irradiance is controlled using artificial neural network. These parameters are not controlled simultaneously using an ANFIS-based approach. The ANFIS-based stand-alone hybrid system controlling both the fuel flow of SOFC and the irradiance of PV is discussed in this paper.

The ANFIS algorithm provides an efficient estimation of maximum power (MP) to the nonlinear voltage–current characteristics of a PV, integrated with a direct current–direct current (DC–DC) converter to boost output voltage up to 400 V. The issue of fuel starvation in SOFC due to load transients is also mitigated using an ANFIS-based fuel flow regulator, which robustly provides fuel, i.e. hydrogen per necessity. Furthermore, to ensure uninterrupted power to the CS, PV is integrated with a SOFC array, and a battery storage bank is used as a backup in the current scenario. A power management system efficiently shares power among the aforesaid sources.

A comprehensive simulation test bed for a stand-alone power system (PV cells and SOFC) is developed in MATLAB/Simulink. The adaptability and robustness of the proposed control paradigm are investigated through simulation results in a stand-alone hybrid power system test bed.

The simulation results confirm the effectiveness of the ANFIS algorithm in a stand-alone hybrid power system scheme.

Dual-thrust hybrid unmanned aerial vehicle (UAV) technology offers a highly robust and efficient system that incorporates the take-off and landing capabilities of rotary-wing aircraft with the endurance capacities of fixed-wing aircraft. The purpose of this study is to model and control a hybrid UAV in three distinct flight modes: rotary-wing, fixed-wing and over-actuated model.

Model predictive control (MPC) along with linear models are applied to design controllers for the rotary-wing or vertical take-off and transition to the fixed-wing flight. The MPC algorithm is implemented with two approaches, first in its usual form and then in a new form with the help of tracking error variables as state variables.

Because the tracking error variables are more compatible with the cost function used in MPC, the results improve significantly. This is especially important for a safe and stable transition from rotary-wing to fixed-wing flight, which should be done quickly. The authors also propose a control allocation strategy with MPC algorithm to exploit the thrust and control inputs of both rotary-wing and fixed-wing systems for the transition phase. As the control system is over-actuated, the proposed algorithm distributes the control signal among the actuators better than the MPC alone. The numerical results show that the flight trajectory is also improved.

The research background is reviewed in the introduction section. The other sections are originally developed in this paper to the best of the authors’ knowledge.

This study aims at investigating how hybrid intelligence might enhance employee engagement in breakthrough innovation. Specifically, it empirically examines the mediating role of self-extinction and moderating role of social intelligence.

This study, using the lens of socio-technical system (STS) theory, collected data from 317 employees through cross-sectional survey. The hypotheses were tested using MPlus 8.3 by applying Structural Equation Modelling (SEM).

The results support the proposed model, suggesting that hybrid intelligence fosters employees' breakthrough innovation engagement and such a relationship is fully mediated by self-extinction. Besides, the findings provide support for the positive moderating impact of social intelligence on such indirect relationships in a way that high social intelligence will further strengthen the relationship.

As a pioneering contribution, the study uncovers the social mechanism that underlies hybrid intelligence–breakthrough innovation engagement relationship via self-extinction. The research suggests managers leveraging employees' social intelligence for playing a critical role in countering the negative impact of self-extinction by enhancing the employees' engagement in the breakthrough innovation process.