Announcements

MDPI has just become the first open access (OA) publisher to reach the milestone of one million articles published. That is one million articles freely available to all, to circulate and build upon! We are proud to share this special moment with the global scientific community.

This landmark has been reached thanks to the immeasurable support of more than 600,000 expert reviewers, 66,000 editorial board members and 6700 hard-working colleagues across MDPI’s global offices.

Within more than 25 years of publishing, our journals received 2.1 million manuscripts and generated 4.6 million peer review reports to get to one million papers published.

Reaching the milestone of one million articles published reinforces our mission to remove any existing barriers and to make scientific research accessible to all. Since its inception, MDPI’s goal has been to create reliable processes to make science open. This is a path towards facilitating the dissemination of novel insights in scientific communities.

Regular feedback from authors and reviewers shows that our service is greatly appreciated and needed. At the same time, the feedback helps us identify areas for further improvement.

As it stands, a significant share of published research findings remain closed access. More than half of the content published with the most well-known legacy publishers stays behind a paywall, and that is not including articles published in hybrid OA journals, or made available months or years after publication.

A new policy announced by the US administration in August 2022 requires that, as of January 2026, all US federally funded research be made freely and immediately available after publication. While the new policy does not mandate articles be published under an open access license, it is aligned with the open access movement in removing all barriers to research. Similarly, some of the most advanced research institutions in the world intend to have all funded research articles published in open access by 2025.

MDPI is proud to be the leading agent of the transition to open access.

"Thanks a Million" to all the contributors!

We are pleased to announce that the website of the MDPI Sustainability Foundation has been revamped! For the past couple of months, our UX UI team and front-end developers have been working hard to launch the website in time for the opening of the Sustainability Awards nominations.

The website is not the only thing that has had a remodeling. Indeed, the format of the Emerging Sustainability Leader Award (ESLA) has been updated. ESLA is now a competition open to individual researchers or start-ups founded by researchers under the age of 35. Nominee applications will go through 2 rounds of selection until the final 3 are decided. The finalists will then be invited to give pitch presentations during the Award Ceremony to win either first place (10,000 USD) or runner-up (2 x 5000 USD).

The World Sustainability Award, on the other hand, remains the same: a total prize money of 100,000 USD is up for grabs by senior individual researchers or groups of researchers from the international research community.

Nominations for both the World Sustainability Award and the Emerging Sustainability Leader award are now open! Check out our new website for more information on how to nominate.

We are pleased to announce that the Best Paper Award sponsored by Entropy (ISSN: 1099-4300) for the 2022 International Conference on Communication, Image and Signal Processing was granted to Dr. Jiayu Zhang from Sichuan University. Congratulations!

Paper: “C110: CT-Guided Automatic Path Planning for Lung Puncture”
by Jianquan Zhong, Ling Tang, Ruizhi Hao, Jinyang Shen, Jiayu Zhang, Yuhang Gong, and Jing Zhang

An automatic CT image-based path planning method for lung puncture surgery is proposed due to the high failure rate, time consumption, and high radiation dose of the existing percutaneous lung puncture surgery. The method described in this paper implements automatic organ segmentation of chest CT images. It defines six constraining conditions combined with clinical a priori knowledge to find the optimal puncture path using a multi-objective Pareto optimization method. The rationality and validity of the method were validated based on 25 sets of clinical lung mass data. Experimental results show that the optimal paths found by this system all meet the clinician's surgical requirements.

Date: 30 November 2022
Time: 05:00 p.m. (CET)
Webinar ID: 858 2367 1531
Register here: https://us02web.zoom.us/webinar/register/3316663495003/WN_d9a8x1AITxepzbVMaZrB0A
Participation fee: none
Language: English
Webinar keywords: quantum computing; quantum algorithms; quantum machine learning; quantum simulations; quantum advantage; quantum hardware; NISQ devices
Related Special Issue: “Advances in Quantum Computing”

Chairs:

Speakers:

We are pleased to announce the winner of the Best Presentation Award that Entropy (ISSN: 1099-4300) sponsored at the 7th Workshop on Complexity in Economics and Finance, held on 19 October 2022 in Palma de Mallorca, Spain. Congratulations to Kiyoshi Kanazawa!

“Statistical analysis of a microscopic financial dataset for the long-range correlation of the order flow” by Kiyoshi Kanazawa

In the financial market, it is an established fact that order flow exhibits persistence: if you observe a buy (sell) order, you will likely observe a buy (sell) order even in the future. This phenomenon, called the long-range correlation (LRC), has been a topic under debate regarding its microscopic origin. One of the promising hypotheses in econophysics is the order-splitting hypothesis: in financial markets, many traders split their large metaorders into a series of small child orders. Because their order signs are kept the same during splitting, the market order sign has a (slight) predictability. This hypothesis was mathematically formulated by Lillo, Mike, and Farmer (LMF) in 2007. Interestingly, the LMF model predicts a quantitative relationship between a microscopic parameter (the power-law exponent of the metaorder-size distribution) and a macroscopic parameter (the power-law exponent for the order-sign autocorrelation function). In this talk, we present our statistical analysis to confirm the LMF prediction using a microscopic Tokyo Stock Exchange dataset. We develop a statistical method to measure the power-law exponents with less statistical bias, and we apply the method to confirm the LMF prediction. A long-standing problem in econophysics has been solved by our detailed data analysis of a microscopic financial dataset.

We are pleased to announce that the Best Poster Awards, sponsored by Entropy (ISSN: 1099-4300) for the 14th Italian Quantum Information Science Conference (IQIS 2022), were granted to Veronica Panizza (University of Trento) and Giovanni Di Fresco (University of Palermo). Congratulations!

The winning posters are as follows:

“Entanglement Witnessing for Lattice Gauge Theories”
by Veronica Panizza
Abstract: LGTs are at the core of fundamental physics and, recently, substantial theoretical and experimental efforts have gone into simulating LGTs using quantum technologies. In the quantum realm, entanglement plays a crucial role and its detection can be efficiently performed using entanglement witnesses. Yet, entanglement witnessing in LGTs is extremely challenging due to the gauge constraints, that severely limit the operators that can be employed to detect quantum correlations. In this work, we develop the theoretical framework of entanglement witnessing in lattice gauge theories and, by way of illustration, consider bipartite entanglement witnesses in a U(1) LGT (with and without fermionic matter). Our framework, which avoids the costly measurements required, e.g., by full-tomography, opens the way to future theoretical and experimental studies of entanglement in an important class of many-body models.

“Criticality and Compatibility in Multi-Parameter Quantum Metrology”
by Giovanni Di Fresco
Abstract: Many-body systems near a quantum phase transition (QPT) exhibit several properties which makes them appealing for metrological purposes. Indeed, it is now well established that the divergences of the quantum Fisher information (QFI) observed near a QPT can be used to increase the precision in the estimation of a parameter. Meanwhile, when it comes to the simultaneous estimation of multiple parameters, the benefits of criticality are much harder to analyze due to possible incompatibilities arising from the Heisenberg uncertainty. This involves the use of quite convoluted quantities, as the Holevo-Cramer-Rao bound, which are generally difficult to evaluate. Here we study the quantumness (R), a scalar index, which provides an asymptotic bound on the compatibility of a metrological scheme. The advantage of this approach is that R can be easily evaluated once the QFI and the mean Uhmlann curvature are known. Moreover, a scaling analysis of R reveals that many-body criticalities generally improve the compatibility in a multi-parameter framework. We also evaluate R in different representative systems, such as Ising chain and XY chain, in which we find this positive criticality effects.

Peer Review Week began 19 September 2022 under the theme of “Research Integrity: Creating and Supporting Trust in Research”. Through various blog articles, podcast, and webinar, we discussed this crucial subject throughout the week, celebrating the essential role peer review plays in maintaining research quality.

To begin, we held a Webinar on the topic. Professor Peter W. Choate and Dr. Emmanuel Obeng-Gyasi joined Dr. Ioana Craciun, one of MDPI’s scientific officers, for an in-depth discussion.

We invite you to view the event recording:

During the week, the MDPI Blog in a series articles highlighted how good Peer Review safeguards research integrity. The following topics were covered:

• Peer Review Week 2022
• Research Integrity
• What We’ve Learned About Peer Review Reports
• 4 Steps to the Perfect Peer Review Report
• How to Write the Perfect Peer Review Report: An Interview
• Inviting Great Peer Reviewers

In a new edition of Insight Faster, an MDPI podcast, we were delighted to talk to the co-chairs of the Peer Review Week committee, Jayashree Rajagopalan (Senior Manager of Global Community Engagement for CACTUS) and Danielle Padula (Head of Marketing and Community Development at Scholastica) to get their take on this year’s event and its related topics.

You can find the Podcast here.

We hope you enjoy the contents!

1. “Deep Ensemble of Weighted Viterbi Decoders for Tail-Biting Convolutional Codes”
by Tomer Raviv, Asaf Schwartz and Yair Be’ery
Entropy 2021, 23(1), 93; https://doi.org/10.3390/e23010093
Available online: https://www.mdpi.com/1099-4300/23/1/93

2. “Non Stationary Multi-Armed Bandit: Empirical Evaluation of a New Concept Drift-Aware Algorithm”
by Emanuele Cavenaghi, Gabriele Sottocornola, Fabio Stella and Markus Zanker
Entropy 2021, 23(3), 380; https://doi.org/10.3390/e23030380
Available online: https://www.mdpi.com/1099-4300/23/3/380

3. “Getting Ahead of the Arms Race: Hothousing the Coevolution of VirusTotal with a Packer”
by Héctor D. Menéndez, David Clark and Earl T. Barr
Entropy 2021, 23(4), 395; https://doi.org/10.3390/e23040395
Available online: https://www.mdpi.com/1099-4300/23/4/395

4. “Federated Quantum Machine Learning”
by Samuel Yen-Chi Chen and Shinjae Yoo
Entropy 2021, 23(4), 460; https://doi.org/10.3390/e23040460
Available online: https://www.mdpi.com/1099-4300/23/4/460

5. “A Review on Machine Learning Approaches for Network Malicious Behavior Detection in Emerging Technologies”
by Mahdi Rabbani, Yongli Wang, Reza Khoshkangini, Hamed Jelodar, Ruxin Zhao, Sajjad Bagheri Baba Ahmadi and Seyedvalyallah Ayobi
Entropy 2021, 23(5), 529; https://doi.org/10.3390/e23050529
Available online: https://www.mdpi.com/1099-4300/23/5/529

6. “A Hybrid Analysis-Based Approach to Android Malware Family Classification”
by Chao Ding, Nurbol Luktarhan, Bei Lu and Wenhui Zhang
Entropy 2021, 23(8), 1009; https://doi.org/10.3390/e23081009
Available online: https://www.mdpi.com/1099-4300/23/8/1009

7. “Solving Schrödinger Bridges via Maximum Likelihood”
by Francisco Vargas, Pierre Thodoroff, Austen Lamacraft and Neil Lawrence
Entropy 2021, 23(9), 1134; https://doi.org/10.3390/e23091134
Available online: https://www.mdpi.com/1099-4300/23/9/1134

8. “Deep Task-Based Quantization”
by Nir Shlezinger and Yonina C. Eldar
Entropy 2021, 23(1), 104; https://doi.org/10.3390/e23010104
Available online: https://www.mdpi.com/1099-4300/23/1/104

9. “Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network”
by Tao Wang, Changhua Lu, Yining Sun, Mei Yang, Chun Liu and Chunsheng Ou
Entropy 2021, 23(1), 119; https://doi.org/10.3390/e23010119
Available online: https://www.mdpi.com/1099-4300/23/1/119

10. “A New Deep Dual Temporal Domain Adaptation Method for Online Detection of Bearings Early Fault”
by Wentao Mao, Bin Sun and Liyun Wang
Entropy 2021, 23(2), 162; https://doi.org/10.3390/e23020162
Available online: https://www.mdpi.com/1099-4300/23/2/162

11. “An Efficient DenseNet-Based Deep Learning Model for Malware Detection”
by Jeyaprakash Hemalatha, S. Abijah Roseline, Subbiah Geetha, Seifedine Kadry and Robertas Damaševičius
Entropy 2021, 23(3), 344; https://doi.org/10.3390/e23030344
Available online: https://www.mdpi.com/1099-4300/23/3/344

12. “A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction”
by Dingming Wu, Xiaolong Wang and Shaocong Wu
Entropy 2021, 23(4), 440; https://doi.org/10.3390/e23040440
Available online: https://www.mdpi.com/1099-4300/23/4/440

13. “Deep Learning Methods for Heart Sounds Classification: A Systematic Review”
by Wei Chen, Qiang Sun, Xiaomin Chen, Gangcai Xie, Huiqun Wu and Chen Xu
Entropy 2021, 23(6), 667; https://doi.org/10.3390/e23060667
Available online: https://www.mdpi.com/1099-4300/23/6/667

14. “Deep Learning for Walking Behaviour Detection in Elderly People Using Smart Footwear”
by Rocío Aznar-Gimeno, Gorka Labata-Lezaun, Ana Adell-Lamora, David Abadía-Gallego, Rafael del-Hoyo-Alonso and Carlos González-Muñoz
Entropy 2021, 23(6), 777; https://doi.org/10.3390/e23060777
Available online: https://www.mdpi.com/1099-4300/23/6/777

15. “Compression Helps Deep Learning in Image Classification”
by En-Hui Yang, Hossam Amer and Yanbing Jiang
Entropy 2021, 23(7), 881; https://doi.org/10.3390/e23070881
Available online: https://www.mdpi.com/1099-4300/23/7/881

16. “Differentiable PAC–Bayes Objectives with Partially Aggregated Neural Networks”
by Felix Biggs and Benjamin Guedj
Entropy 2021, 23(10), 1280; https://doi.org/10.3390/e23101280
Available online: https://www.mdpi.com/1099-4300/23/10/1280

17. “A Lightweight YOLOv4-Based Forestry Pest Detection Method Using Coordinate Attention and Feature Fusion”
by Mingfeng Zha, Wenbin Qian, Wenlong Yi and Jing Hua
Entropy 2021, 23(12), 1587; https://doi.org/10.3390/e23121587
Available online: https://www.mdpi.com/1099-4300/23/12/1587

MDPI will be attending the Conference on Complex Systems (CCS2022) in Palma de Mallorca, Spain, which will take place from 17 to 21 October 2022. The CCS is the largest and most important annual meeting of the international complex systems community. It comes under the auspices of the Complex Systems Society. This edition, organized by IFISC, takes place after previous events held in Lyon, Singapore, Thessaloniki, and Cancun.

The following MDPI journals will be represented:

• Entropy;
• Fractal and Fractional;
• Symmetry;
• Mathematics;
• Dynamics;
• Algorithms;
• Systems;
• Informatics;
• MAKE;
• Information;
• Future Internet;
• Applied System Innovation;
• Data.

If you are attending this conference, please feel free to stop by our booth. Our delegates look forward to meeting you in person to answer any questions you may have. For more information about the conference, please visit the following link: https://www.ccs2022.org/.

We are pleased to announce the Best ECR (Early Career Researcher) Presentation Award, sponsored by Entropy (ISSN: 1099-4300) for CNS*2022 Workshop on Methods of Information Theory in Computational Neuroscience, which was granted to Dr. Demi Gao at the University of Melbourne. Congratulations!

“Towards Personalised Cochlear Implants: Quantifying Hearing Performance Using Information Theory”

Abstract:
Despite the development and success of cochlear implants over several decades, wide inter-subject variability in speech perception is reported. This suggests that cochlear implant user-dependent factors limit speech perception at the individual level. Clinical studies have demonstrated the importance of the number, placement, and insertion depths of electrodes on speech recognition abilities. However, these do not account for all inter-subject variability and to what extent these factors affect speech recognition abilities has not been studied. We unified information theoretic method and machine learning technique to quantitatively study the extent to which key factors limit the hearing performance with cochlear implants. The approach provides insights into personalised strategies for improving speech recognition outcomes.