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This paper aims to present a distributed Bayesian approach with connectivity maintenance to manage a multi-agent network search for a target on a two-dimensional plane.

The Bayesian framework is used to compute the local probability density functions (PDFs) of the target and obtain the global PDF with the consensus algorithm. An inverse power iteration algorithm is introduced to estimate the algebraic connectivity λ₂ of the network. Based on the estimated λ₂, the authors design a potential field for the connectivity maintenance. Then, based on the detection probability function, the authors design a potential field for the search target. The authors combine the two potential fields and design a distributed gradient-based control for the agents.

The inverse power iteration algorithm can distributed estimate the algebraic connectivity by the agents. The agents can efficient search the target with connectivity maintenance with the designed distributed gradient-based search algorithm.

Previous study has paid little attention to the multi-agent search problem with connectivity maintenance. Our algorithm guarantees that the strongly connected graph of the multi-agent communication topology is always established while performing the distributed target search problem.

The purpose of this paper is to design and implement a landmine detection robot (Venus) equipped with three electromagnetic sensors and controlled by ordered weighted averaging (OWA) sensor fusion approach. Higher numbers of detected mines in a fixed time interval and lower total power consumption are the achieved goals of this research.

OWA sensor fusion is exploited for data combination in this paper. Unlike most other landmine detection robots, Venus has three electromagnetic sensors, the positions of which can be adjusted according to the environmental conditions. Also, a novel approach for OWA weight dedication using Gaussian distribution function is applied and the whole idea is evaluated practically in several randomly mined fields. Finally, for better evaluation, performance of Venus is compared with the other two landmine detection robots.

The simulation and experimental results proved that in a predetermined interval of time, not only total energy consumption is reduced, but also by expanding the surface and the depth of influence of electromagnetic waves, the number of detected mines is considerably raised.

In contrast to the regular demining process, which is relatively expensive and complicated, the landmine detection method proposed in this research is surprisingly simple, cost effective, and efficient. Therefore, it may be attractive for every company or organization in this field of research.

The paper describes research which implements and evaluates a novel control approach based on OWA sensor fusion method, a new way of using Gaussian distribution function for determining OWA weights, and also an adaptive physical configuration for sensors based on environmental conditions.

The goal of this work is to evaluate how digital transformation disclosure in corporate news and press releases affects stock prices. We examine American and Chinese companies from the energy and industry sectors for two periods: pre-COVID-19 and during the COVID-19 pandemic.

To estimate the effects of disclosure of information related to digital transformation, we applied the bag-of-words (BOW) method. As the benchmark dictionary, we used Kindermann et al. (2021), with the addition of original dictionaries created via Latent Dirichlet allocation (LDA) analysis. We also employed panel regression analysis and random forest.

For USA energy sector, all aspects of digital transformation were insignificant in pre-COVID-19 period, while sustainability topics became significant during the pandemic. As for the Chinese energy sector, digital strategy implementation was significant in pre-pandemic period, while digital technologies adoption and business model innovation became relevant in COVID-19 period. The results show the greater significance of digital transformation aspects for industrials sectors compared to the energy sector. The result of random forest analysis proves the efficiency of the authors’ dictionary which could be applied in practice. The developed methodology can be considered relevant.

The research contributes to the existing literature in theoretical, empirical and methodological ways. It applies signaling and information asymmetry theories to the financial markets, digital transformation being used as an instrument. The methodological contribution of this article can be described in several ways. Firstly, our data collection process differs from that in previous papers, as the data are gathered “from investor’s point of view”, i.e. we use all public information published by the company. Secondly, in addition to the use of existing dictionaries based on Kindermann et al. (2021), with our own modifications, we apply the original methodology based on LDA analysis. The empirical contribution of this research is the following. Unlike past works, we do not focus on particular technologies (Hong et al., 2023) connected with digital transformation, but try to cover all multi-dimensional aspects of the transformational process and aim to discover the most significant one.

The purpose is to derive the most effective place in the air for an aerial robot, viz., drone to use as an alternative communication system during disasters.

In this technology-driven era, various concepts are becoming the area of interest for multiple researchers. Drone technology is also one of them. The researchers, with interest in drones, are therefore trying to understand the various uses of employing drones in diverse applications which are mind-boggling, starting from civil applications (viz., an inspection of power lines, counting wildlife, delivering medical supplies to inaccessible regions, forest fire detection, and landslide measurement) to military applications (viz., real-time monitoring, surveillance, patrolling, and demining). However, one area where its usage is still to be exploited in many countries is using drones as a relay when communication lines are disrupted due to natural calamities. This will be particularly helpful in rescuing the affected people as the aerial node will enable them to communicate to the rescue team using mobiles/ordinary landline telephones even when regular communication towers are destroyed due to disastrous natural calamities, for example, tsunamis, earthquakes, and floods. Various algorithms, namely, water filling algorithm, advanced water filling algorithm, equal power distribution algorithm, and particle swarm optimization, were therefore studied and analyzed using simulation in addition to various path loss models to realize the desired place for an aerial robot, viz., drone in the air, which will eventually be used as an alternative communication system for badly hit ground users due to any disaster.

It was found that the effective combination of the water filling algorithm and particle swarm optimization algorithm may be done to place the drone in the air to increase the overall throughput of the affected ground users.

The research is original. None of the parts of this research paper has been published anywhere.

Negative word-of-mouth has a variety of negative effects on companies. Thus, how consumers process and evaluate negative word-of-mouth is an important issue for companies. This research aims to investigate the effect of emotional intensity of negative word-of-mouth on consumers' perceived helpfulness.

The research model was developed based on attribution theory. A four-study approach involving two field experiments and two online experiments was employed to examine the proposed hypotheses.

The results show that the emotional intensity of negative word-of-mouth negatively affects altruistic motive attributions, while altruistic motive attributions positively affect perceived helpfulness and plays a mediating role in the relationship between the emotional intensity of negative word-of-mouth and perceived helpfulness. Consumers' self-construal moderates the effects of emotional intensity of negative word-of-mouth on altruistic motive attributions and perceived helpfulness, with the negative effects of emotional intensity of negative word-of-mouth on altruistic motive attributions and perceived helpfulness being weaker for consumers with high interdependent self-construal than for those with high independent self-construal.

The findings not only have a significant theoretical contribution, deepening the understanding of the effects of negative word-of-mouth but also have useful implications for practitioners to improve the management of negative word-of-mouth.

This systematic review, following preferred reporting items for systematic reviews and meta-analysis guidelines, rigorously investigates the emergent role of virtual reality (VR) technology in human movement training. The purpose of this study is to explore the effectiveness and evolution of VR in enhancing movement training experiences.

Acknowledging its pivotal role in diverse applications, such as sports and rehabilitation, human movement training is currently experiencing accelerated evolution, facilitated by the proliferation of wearable devices and mobile applications. This review conducted an exhaustive search across five different electronic databases, such as Web of Science, PubMed and ProQuest, resulting in the selection of 69 eligible articles published within the past five years. It also integrates 40 studies into a narrative summary, categorized based on the level of immersion offered by respective VR systems.

Enhanced immersion in VR potentially augments the effectiveness of movement training by engendering more realistic and captivating experiences for users. The immersive and interactive environments provided by VR technology enable tailored training experiences accompanied by precise, objective feedback. This review highlights the benefits of VR in human movement training and its potential to revolutionize the way training is conducted.

This systematic review contributes significantly to the existing literature by providing a comprehensive examination of the efficacy and evolution of VR in human movement training. By organizing the findings based on the level of immersion offered by VR systems, it provides valuable insights into the importance of immersion in enhancing training outcomes. In addition, this study identifies the need for future research focusing on the impacts of VR on learning and performance, as well as strategies to optimize its effectiveness and improve accessibility.

Over the past two decades, China’s outward foreign direct investment (OFDI) has risen remarkably. Whether such an increase affects the Chinese export diversification (ED) is a significant issue that has surprisingly remained unaddressed. This study aims to explain this issue that how OFDI plays a vital role in symmetric and asymmetric effects on its ED.

The authors introduce a robust nonlinear autoregressive distributed lag (NARDL) model. Ironically, the purpose of this study is to analyze the symmetric and asymmetric effect of OFDI on ED.

The authors propose that growing OFDI would be more advantageous to China, rather than the policies of contraction. Therefore, the study provides valuable policy insights to consider the long-run asymmetric momentum given to ED by China’s OFDI.

The results of this study may seem to be an important newsletter for further policy discussion on how China can catch up on the benefits of ED through OFDI.

The purpose of this work is to present an approach for autonomous detection of eye disease in fundus images. Furthermore, this work presents an improved variant of the Tiny YOLOv7 model developed specifically for eye disease detection. The model proposed in this work is a highly useful tool for the development of applications for autonomous detection of eye diseases in fundus images that can help and assist ophthalmologists.

The approach adopted to carry out this work is twofold. Firstly, a richly annotated dataset consisting of eye disease classes, namely, cataract, glaucoma, retinal disease and normal eye, was created. Secondly, an improved variant of the Tiny YOLOv7 model was developed and proposed as EYE-YOLO. The proposed EYE-YOLO model has been developed by integrating multi-spatial pyramid pooling in the feature extraction network and Focal-EIOU loss in the detection network of the Tiny YOLOv7 model. Moreover, at run time, the mosaic augmentation strategy has been utilized with the proposed model to achieve benchmark results. Further, evaluations have been carried out for performance metrics, namely, precision, recall, F1 Score, average precision (AP) and mean average precision (mAP).

The proposed EYE-YOLO achieved 28% higher precision, 18% higher recall, 24% higher F1 Score and 30.81% higher mAP than the Tiny YOLOv7 model. Moreover, in terms of AP for each class of the employed dataset, it achieved 9.74% higher AP for cataract, 27.73% higher AP for glaucoma, 72.50% higher AP for retina disease and 13.26% higher AP for normal eye. In comparison to the state-of-the-art Tiny YOLOv5, Tiny YOLOv6 and Tiny YOLOv8 models, the proposed EYE-YOLO achieved 6–23.32% higher mAP.

This work addresses the problem of eye disease recognition as a bounding box regression and detection problem. Whereas, the work in the related research is largely based on eye disease classification. The other highlight of this work is to propose a richly annotated dataset for different eye diseases useful for training deep learning-based object detectors. The major highlight of this work lies in the proposal of an improved variant of the Tiny YOLOv7 model focusing on eye disease detection. The proposed modifications in the Tiny YOLOv7 aided the proposed model in achieving better results as compared to the state-of-the-art Tiny YOLOv8 and YOLOv8 Nano.

This paper aims to provide details of a number of recent and significant agricultural robot research and development activities.

Following an introduction, this first provides a brief overview of agricultural robot research. It then discusses a number of specific activities involving robots for precision weed control and fertiliser application. A selection of harvesting robots and allied technological developments is then considered and is followed by concluding comments.

Agricultural robots are the topic of an extensive research and development effort. Several autonomous robots aimed at precision weed control and fertiliser application have reached the pre-production stage. Equally, harvesting robots are at an advanced stage of development. Both classes exploit state-of-the-art machine vision and image processing technologies which are the topic of a major research effort. These developments will contribute to the forecasted rapid growth in the agricultural robot markets during the next decade.

Robots are expected to play a significant role in meeting the ever increasing demand for food, and this paper provides details of some recent agricultural robot research and development activities.

The purpose of this study was to map the quantity (frequency), quality (impact) and structural indicators (correlations) of research produced on cloud computing in 48 countries and 3 territories in the Asia continent.

To achieve the objectives of the study and scientifically map the indicators, data were extracted from the Scopus database. The extracted bibliographic data was first cleaned properly using Endnote and then analyzed using Biblioshiny and VosViewer application software. In the software, calculations include citations count; h, g and m indexes; Bradford's and Lotka's laws; and other scientific mappings.

Results of the study indicate that China remained the most productive, impactful and collaborative country in Asia. All the top 20 impactful authors were also from China. The other most researched areas associated with cloud computing were revealed to be mobile cloud computing and data security in clouds. The most prominent journal currently publishing research studies on cloud computing was “Advances in Intelligent Systems and Computing.”

The study is the first of its kind which identified the quantity (frequencies), quality (impact) and structural indicators (correlations) of Asian (48 countries and 3 territories) research productivity on cloud computing. The results are of great importance for researchers and countries interested in further exploring, publishing and increasing cross country collaborations related to the phenomenon of cloud computing.