Maria Lamarca, Maria Lamarca, Maria Lamarca
et al.
BackgroundWhile psychological interventions are effective at improving symptoms of psychosis, accessible, cost- and time-efficient treatments remain limited. Personalized medicine has emerged as a promising approach, tailoring interventions to individual needs. Metacognitive Training (MCT), with its established efficacy and adaptable format, is well-suited for personalization. The PERMEPSY project (Towards a Personalized Medicine Approach to Psychological Treatment for Psychosis) aims to deliver tailored MCT intervention for individuals with psychosis.MethodsPERMEPSY is an international study funded by ERAPerMed (JTC2022) involving five clinical partners (Spain, Chile, France, Germany, Poland) and one technological partner (Spain). The project involves a proof-of-concept clinical trial recruiting 51 participants from each center for a total of 255 adult participants with psychosis in a prospective study (Registration: NCT06603922, 19-09-2024). The trial will test the efficacy of a Machine Learning (ML)-derived platform at predicting clinical and functional outcomes from baseline scores and compare a personalized MCT (P-MCT) to a classical MCT based on the platform’s predictions.AimsPERMEPSY seeks to (1) develop and test the predictive power of an algorithm that could support decision-making, and (2) ascertain whether P-MCT is more effective than MCT at improving key symptoms and cognitive impairments associated to psychosis.ResultsA harmonized retrospective database enabled the development of a predictive ML algorithm, integrated into an innovative platform. This platform provides clinicians with the information needed to deliver P-MCT. Predictions include changes in positive symptoms (e.g., delusions), insight, self-esteem, and treatment adherence.DiscussionBy integrating diverse data types and innovative technology, PERMEPSY addresses the need for personalized, effective treatment in psychosis, aiming to reduce individual and systemic burdens while supporting clinicians in their decision-making.
Privacy-preserving state estimation for linear time-invariant dynamical systems with crowd sensors is considered. At any time step, the estimator has access to measurements from a randomly selected sensor from a pool of sensors with pre-specified models and noise profiles. A Luenberger-like observer is used to fuse the measurements with the underlying model of the system to recursively generate the state estimates. An additive privacy-preserving noise is used to constrain information leakage. Information leakage is measured via mutual information between the identity of the sensors and the state estimate conditioned on the actual state of the system. This captures an omnipotent adversary that not only can access state estimates but can also gather direct high-quality state measurements. Any prescribed level of information leakage is shown to be achievable by appropriately selecting the variance of the privacy-preserving noise. Therefore, privacy-utility trade-off can be fine-tuned.
Mohammad Aldossary, Ibrahim Alzamil, Jaber Almutairi
Due to Internet of Drones (IoD) technology, drone networks have proliferated, transforming surveillance, logistics, and disaster management. Distributed Denial of Service (DDoS) attacks, malware infections, and communication abnormalities increase cybersecurity dangers to these networks, threatening operational safety and efficiency. Current Intrusion Detection Systems (IDSs) fail to handle drone transmission data’s dynamic, high-dimensional nature, resulting in inadequate real-time anomaly identification and mitigation. This study presents the Cross-Layer Convolutional Attention Network (CLCAN), a new IDS architecture for IoD networks. CLCAN accurately detects complex cyber threats using multi-scale convolutional processing, hierarchical contextual attention, and dynamic feature fusion. Preprocessing methods like weighted differential scaling and gradient-based adaptive resampling improve data quality and reduce class imbalances. Contextual attribute transformation captures the nuanced network behaviors needed for anomaly identification. The proposed technique is shown to be necessary and effective by real-world drone communication dataset evaluations. CLCAN outperforms CNN, LSTM, and XGBoost with 98.4% accuracy, 98.7% recall, and 98.1% F1-score. The model has a remarkable AUC of 0.991. CLCAN can handle datasets of over 118,000 balanced data records in 85 s, compared to 180 s for comparable frameworks. This study pioneers a unified security solution for Drone-to-Drone (D2D) and Drone-to-Base Station (D2BS) communications, filling a crucial IoD security gap. It protects mission-critical drone operations with a strong, efficient, and scalable IDS from emerging cyber threats.
Дмитро АНРДЄЄВ, Олексій ЛИГУН, Андрій ДРОЗД
et al.
Critical infrastructures are fundamental to the seamless operation of modern societies, encompassing sectors such as energy, healthcare, transportation, and communications. Ensuring their reliability, performance, continuous operation, safety, maintenance, and protection is a national priority for countries worldwide.
The digital twins play a crucial role in critical infrastructure, as they enhance security, resilience, reliability, maintenance, continuity, and operational efficiency across all sectors. Among the benefits offered by digital twins are intelligent and autonomous decision-making, process optimization, improved traceability, interactive visualization, and real-time monitoring, analysis, and prediction. Furthermore, the study revealed that digital twins have the capability to bridge the gap between physical and virtual environments, can be used in combination with other technologies, and can be integrated into various contexts and industries.
The use of digital twins was explored as the foundation for developing a modern monitoring system for critical infrastructure facilities enables multi-level assessment of asset conditions in real time, ensuring precise threat detection, anomaly identification, and timely decision-making. Integration with artificial intelligence and big data technologies allows not only the collection and analysis of large volumes of information but also the creation of adaptive behavioral models for systems in emergency situations.
Special attention was given to the method of optimizing critical IT infrastructure using digital twins, which combines virtual modeling, predictive algorithms, and automated management. The proposed approach enhances the reliability of digital systems, minimizes downtime, optimizes maintenance costs, and strengthens cybersecurity. This system is especially relevant in the context of growing risks and increasing demands for the stability of strategically important infrastructure assets.
The application of digital twins for monitoring and optimizing critical infrastructure demonstrates considerable potential for improving its resilience, safety, and operational efficiency. The approaches discussed in the study confirm the relevance of implementing digital models as tools for timely risk identification, failure prediction, and informed decision-making. By integrating such technologies, organizations can reduce operational costs, minimize downtime, and improve the overall stability of infrastructure operations. Therefore, digital twins represent a vital step toward the digital transformation and modernization of mission-critical systems across various sectors.
Mochammad Hannats Hanafi Ichsan, Cecilia Sik-Lanyi, Tibor Guzsvinecz
Abstract New technologies, such as Virtual Reality (VR) / Virtual Environment (VE), which focus on User Experience (UX) to provide more engaging and immersive experiences, can help people grow their skills. Technology advancement is also an essential component of VR development. However, the literature needs to contain more studies on using VR as an assistive tool for skill development. This study aims to explore the impact of VR technological advancements on skill development through UX design taxonomies using a Systematic Literature Review (SLR). Skill development classification was conducted based on social, emotional, and behavioral (SEB) aspects. The selected studies that met the eligibility selection criteria were examined and synthesized. The study’s findings highlight the necessity of technology development for VR technology to accomplish UX for skill development, allowing them to become more self-sufficient. This research can enrich researchers and VR developers, particularly software, hardware, and artificial intelligence (AI) experts. More research should be conducted on the long-term use of VR as an assistive device, particularly for those seeking skill improvement to improve their quality of life.
In this study, we explored the developmental characteristics of Pinellia ternate bulbils as well as the key gene networks driving the development of bulbils. Based on physiological and biochemical reactions as well as transcriptome technology, this study determined the content of endogenous metabolites and related enzyme activities during the five growth stages of the bulbils, obtained the transcriptome information of all samples. The results showed that the contents of sucrose and starch increased significantly in the ZY_2 and ZY_4 stages, and the changes in the activities of SPS, SuSy, and SS were basically consistent with the changing characteristics of sucrose and starch content. The contents of ABA and JA generally showed an increasing trend from ZY_1 to ZY_4, while the content of IAA was significantly higher only in ZY_1 and ZY_4 stages compared to other stages. In order to get more bioinformatic support for these results, RNA-Seq analysis was performed. There were 12 key enzyme genes differentially expressed in the sucrose-starch metabolic pathway, and 14 enzyme genes differentially expressed in the above-mentioned endogenous hormone metabolic pathway. Their expression characteristics well supported the measurement results of physiological and biochemical substances. Our results showed that ZY_2 and ZY_4 stages are the critical periods for the accumulation of sucrose and starch in the bulbils. JA has an important role in the whole development process of bulbils, which may enhance the adaptability of the bulbils to the environment in the transition process from the tender to the mature tissues. The low concentration of GA was beneficial to the normal development of the bulbils. IAA may have a strong regulatory role in the initial formation stage of the bulbils, which is beneficial to their tissue differentiation. In addition, four core transcripts involved in the bulbils development process were screened using WGCNA. This study provides an information source for analyzing the molecular mechanism of bulbils growth and development, and also helps to address the lack of genetic information in non-model plant species.
At its core, information access and seeking is an interactive process. In existing search engines, interactions are limited to a few pre-defined actions, such as "requery", "click on a document", "scrolling up/down", "going to the next result page", "leaving the search engine", etc. A major benefit of moving towards generative IR systems is enabling users with a richer expression of information need and feedback and free-form interactions in natural language and beyond. In other words, the actions users take are no longer limited by the clickable links and buttons available on the search engine result page and users can express themselves freely through natural language. This can go even beyond natural language, through images, videos, gestures, and sensors using multi-modal generative IR systems. This chapter briefly discusses the role of interaction in generative IR systems. We will first discuss different ways users can express their information needs by interacting with generative IR systems. We then explain how users can provide explicit or implicit feedback to generative IR systems and how they can consume such feedback. Next, we will cover how users interactively can refine retrieval results. We will expand upon mixed-initiative interactions and discuss clarification and preference elicitation in more detail. We then discuss proactive generative IR systems, including context-aware recommendation, following up past conversations, contributing to multi-party conversations, and feedback requests. Providing explanation is another interaction type that we briefly discuss in this chapter. We will also briefly describe multi-modal interactions in generative information retrieval. Finally, we describe emerging frameworks and solutions for user interfaces with generative AI systems.
This article stands as a tribute to the enduring legacy of Jacob Ziv and his landmark contributions to information theory. Specifically, it delves into the groundbreaking individual-sequence approach -- a cornerstone of Ziv's academic pursuits. Together with Abraham Lempel, Ziv pioneered the renowned Lempel-Ziv (LZ) algorithm, a beacon of innovation in various versions. Beyond its original domain of universal data compression, this article underscores the broad utility of the individual-sequence approach and the LZ algorithm across a wide spectrum of problem areas. As we traverse through the forthcoming pages, it will also become evident how Ziv's visionary approach has left an indelible mark on my own research journey, as well as on those of numerous colleagues and former students. We shall explore, not only the technical power of the LZ algorithm, but also its profound impact on shaping the landscape of information theory and its applications.
Abstract While many organizations have successfully leveraged big data analytics capabilities to improve their performance, our understanding is limited on whether and how big data analytics capabilities affect social innovation in organizations. Based on the organizational information processing theory and the organizational learning theory, this study aims to investigate how big data analytics capabilities support social innovation, and how knowledge ambidexterity mediates this relationship. A total of 354 high-tech companies in China, this study shows that big data analytics management, big data analytics technology, and big data analytics personnel capabilities all have positive effects on social innovation. In addition, both knowledge exploration and knowledge exploitation play a mediating role in this process. Furthermore, a polynomial regression and response surface analysis shows that social innovation increases when knowledge exploration and knowledge exploitation are highly consistent but declines when knowledge exploration and knowledge exploitation are inconsistent. This study not only provides new perspectives for understanding how big data analytics capabilities contribute to social innovation, complementing the existing literature on big data analytics capabilities and social innovation, but also provides important practical guidance on how organizations can develop big data analytics capabilities to improve social innovation and solve social problems in the digital age.
History of scholarship and learning. The humanities, Social Sciences
It has been proposed that information sharing, which is a ubiquitous and consequential behavior, plays a critical role in cultivating and maintaining a sense of shared reality. Across three studies, we tested this theory by investigating whether or not people are especially likely to share information that they believe will be interpreted similarly by others in their social circles. Using neuroimaging while members of the same community viewed brief film clips, we found that more similar neural responding of participants was associated with a greater likelihood to share content. We then tested this relationship using two behavioral studies and found (1) that people were particularly likely to share content that they believed others in their social circles would interpret similarly and (2) that perceived similarity with others leads to increased sharing likelihood. In concert, our findings support the idea that people are driven to share information to create and reinforce shared understanding, which is critical to social connection.
The strength of the phonon anharmonic effect of the pristine FeSi2 and Co-doped Fe0.94Co0.06Si2 is investigated by a Raman scattering study on the vibrational properties of those materials in the temperature range of 300–1523 K. All the vibrational modes exhibit significant redshifts with increasing temperature, and their spectral widths increase simultaneously. The structure transition from the semiconducting β phase to the metallic α phase is evidenced by the sudden disappearance of the vibrational modes. The extended Klemens model is applied to study the anharmonic effect on the phonon frequency shift and damping constant, and the four-phonon decaying process is expected to be the dominant one after doping the metal Co. Such an enhancement is also suggested contributing to the reduction of the thermal conductivity in Fe0.94Co0.06Si2. In addition, the vibrational properties of the mode at 250 cm−1 are more sensitive to the anharmonicity effect than that of the mode at 195 cm−1. This work provides valuable insights for understanding the high-order anharmonic effects in thermoelectric materials, especially in chemically doped materials.
Lestari Juniawati Ani, Edi Nugroho Lukito, Insap Santosa Paulus
The implementation of the smart city concept in Indonesia has become a necessity and is no longer an option, but a necessity. Indeed, the complexity of the problems facing the government is very high and requires smart solutions. As a form of supporting local governments in Indonesia in developing smart city master plans, the Ministry of Communication and Informatics of the Republic of Indonesia in 2017 launched the "Movement Towards 100 Smart Cities" program. During the implementation of the program, the Ministry of Communication and Informatics has compiled a guidebook that was used by local governments. However, the guidebook is considered unable to accommodate all the needs of local governments to develop smart city master plans. This research aims to identify the synchronization between RPJMD and smart city master plans in Indonesia by using literature analysis and document analysis methods that aim to facilitate local governments in preparing smart city master plans. The analysis results show a link between the RPJMD document and the smart city master plan based on the mapping carried out on the RPJMD document which has previously been prepared as a regional development planning document.
Accurate fault detection and classification help to analyze fault causes and quickly restore faulty phases. Deep learning can automatically extract fault features and identify fault types from the original three-phase voltage and current signals. However, this still imposes challenges such as recognition accuracy and computational complexity. More importantly, high level fault features cannot be extracted in the one-dimensional time series. This paper presents a robust fault classification method based on SA-MobileNetV3 for transmission systems. Considering that the SE (Squeeze-and-Excitation) attention module cannot aggregate the spatial dimension information on the channel, SA (shuffle attention) module is introduced into MobileNetV3, which can effectively fuse the importance of pixels in different channels and in different locations at the same channel. Also, transforming the time series three-phase voltage and current signals into two-dimensional images based on CWT (continuous wavelet transform) makes the proposed method be similar to image recognition, which can mine high level fault features and classify the faults visually. To verify the effectiveness of the method, a 735kV transmission line model is built for data generation through Simulink. Various kinds of fault conditions and factors are considered to verify the adaptability and generalizability. Simulation results show that the method can quickly and accurately identify 11 types of faults, and the accuracy rate is as high as 99.90%. A comparison between the proposed method and other existing techniques shows the superiority of the proposed SA- MobileNetV3, and better anti-noise performance makes it more suitable for real fault signals taken on-site.
The visualization of workshop information can affect production management and efficiency. Information can be presented both graphically and non-graphically (for example, in the form of data lists or tables). Graphical representations are intuitive and clear, but currently, most of them are based on statistical data, which makes it difficult to convey logical linkages between information and cannot help managers make decisions effectively. With the aim of designing the workshop production system with visual processes in small-sized enterprises, the key visualization technologies of the process flow chart, including the visual design of process flow chart, process card management, process flow chart release, process control, and production schedule monitoring, were all addressed in detail. On this basis, the mechanical manufacturing workshop production management system was created using C#.NET as the programming language. The main contribution of the research is that the system designed used the process flow chart as the main line through all functional modules and integrated all process data on the process nodes of the process flow chart to realize the graphical monitoring of workshop production schedule. The visualization technology of the process flow chart makes the system simple to use and easy to understand, which significantly improves information management and work efficiency in the workshop. Additionally, it provides the technical foundation for flow-driven production information transfer in the workshop and can serve as a universal standard for the process module in workshop production management systems.
Neural networks are known to exploit spurious artifacts (or shortcuts) that co-occur with a target label, exhibiting heuristic memorization. On the other hand, networks have been shown to memorize training examples, resulting in example-level memorization. These kinds of memorization impede generalization of networks beyond their training distributions. Detecting such memorization could be challenging, often requiring researchers to curate tailored test sets. In this work, we hypothesize -- and subsequently show -- that the diversity in the activation patterns of different neurons is reflective of model generalization and memorization. We quantify the diversity in the neural activations through information-theoretic measures and find support for our hypothesis on experiments spanning several natural language and vision tasks. Importantly, we discover that information organization points to the two forms of memorization, even for neural activations computed on unlabelled in-distribution examples. Lastly, we demonstrate the utility of our findings for the problem of model selection. The associated code and other resources for this work are available at https://rachitbansal.github.io/information-measures.