Search Results (103)

In this paper, a reconfigurable multi-form radar compound coherent jamming signal generator is proposed based on a dual-polarization quadrature phase shift keying (DP-QPSK) modulator cascaded with an intensity modulator (IM). The radar signal and jamming seed signal are loaded on the upper path and the lower path of the DP-QPSK modulator to achieve carrier-suppressed single-sideband (CS-SSB) modulation and phase modulation, respectively. The periodic rectangular pulse (PRP) signal is fed into the IM to achieve interrupted-sampling repeater jamming in the optical domain. In our proposed scheme, cosine phase modulation and interrupted-sampling repeater jamming (CPMJ-ISRJ) and frequency shift and interrupted-sampling repeater jamming (FSJ-ISRJ) are obtained only by changing the form of the jamming seed signal, without changing the overall structure of the scheme. The jamming effectiveness of the above schemes is evaluated through simulation. Multiple false targets are obtained after cross-correlation with the original radar signal. The number of generated false targets can reach 18. We also conducted a detailed simulation to analyze the impact of different parameters on the jamming effect. Because the scheme is filter-free, it has a large frequency tuning range. Moreover, due to the special CS-SSB modulation, the modulated signals are immune to the chromatic dispersion-induced power fading effect. The proposed scheme has potential application prospects in future electronic countermeasure systems. Full article

Intercellular communication is crucial for the coordination of skeletal muscle development. However, the intricate signaling networks that regulate chicken myogenesis are not yet fully elucidated. In this study, we utilized CellChat analysis on single-cell and single-nucleus RNA sequencing data to systematically delineate cell–cell communication patterns across five critical developmental stages of chicken skeletal muscle: embryonic day 4 (E4), day 6 (E6), day 12 (E12), day 18 (E18), and post-hatch day 30 (P30). Our findings indicate that communication architectures are highly stage-specific, with mesenchymal cells acting as the predominant signaling hub during the early embryonic stages (E4–E6), whereas fibro-adipogenic progenitors become the principal communicators during mid-to-late embryogenesis (E12–E18). At E4, the communication network was relatively simple, comprising 51 ligand–receptor pairs primarily involving the neural cell adhesion molecule, slit guidance ligand, and midkine (MK) signaling pathways between myogenic progenitors and mesenchymal cells. By E6, the network had expanded significantly, encompassing 6237 ligand–receptor pairs across 51 signaling pathways, which coincided with the emergence of multiple myogenic lineages. Peak communication complexity was observed at E12, characterized by 11,675 ligand–receptor pairs and 61 signaling pathways, reflecting the secondary wave of myogenesis. Comparative analysis across developmental stages revealed key signaling transitions: the pleiotrophin and MK pathways were predominantly active during the early phase of myogenic commitment (E4–E6), whereas the collagen, laminin, and adhesion G protein-coupled receptor L pathways were more prominent during the secondary myogenesis phase (E6–E12). Notably, a significant shift in communication patterns was observed from E12 to E18, marked by a reduction in developmental pathway signaling and an increase in immune-related communications. By P30, the communication network had stabilized into a homeostatic state, centered on interactions among myofibers, stromal cells, and the vascular system. This comprehensive atlas of intercellular communication offers novel insights into the signaling dynamics underpinning chicken skeletal muscle development. Full article

Introduction: Long-term ecological monitoring is essential to understand the responses of fish communities to global change in transitional ecosystems. Coastal lagoons are particularly vulnerable to eutrophication, which can trigger abrupt regime shifts, mass mortality events, and loss of ecological functions. The Mar Menor coastal lagoon (SE Spain) represents one of the most impacted Mediterranean systems, providing a unique opportunity to assess long-term ecological responses of fish assemblages to sustained anthropogenic pressure. Objetives: This study aims to synthesize long-term monitoring data to evaluate structural, functional, and population-level responses of fish assemblages to eutrophication processes, and to identify critical habitats and mechanisms supporting resilience. Methodology: We integrated multiple datasets derived from long-term monitoring programs (2002–2004 as and 2018–2025), including community structure, functional diversity, population dynamics of resident species, and habitat-based indicators. Analyses encompassed pre-impact, eutrophication, and post-disturbance phases, allowing for a multi-scale assessment of ecological responses. Results: Eutrophication-driven disturbances caused major shifts in fish assemblages, including declines in biomass and abundance, species-specific responses, and increased dominance of opportunistic trophic groups. Functional diversity analyses revealed strong homogenization processes and loss of specialist traits, indicating reduced ecosystem functionality. Population dynamics of resident species reflected habitat degradation, highlighting their value as ecological indicators. Despite these impacts, shallow coastal habitats acted as critical refuges, buffering hypoxic conditions and enabling partial persistence and recovery of fish communities. Conclusions: Our results demonstrate that long-term monitoring provides essential insights into the mechanisms driving fish community responses to eutrophication. The identification of functional changes and refuge habitats is key for adaptive management. Protecting and restoring critical habitats, particularly shallow areas, is crucial to enhance resilience and guide conservation strategies in Mediterranean coastal lagoons under global change. Full article

Background/Objectives: Despite ongoing innovation, few interventions—including Clinical Decision Support Systems (CDSS)—are successfully integrated into routine care. Understanding the process through which innovations are implemented is therefore essential for advancing practice and research. In perinatal settings, evidence on how CDSS implementation unfolds and which strategies support adoption, scale-up, and sustainment remains limited. This study aimed to understand the implementation process, key determinants and implementation strategies of a shared antenatal psychosocial CDSS (i.e., the Born in Belgium Professionals [BIB-Pro]) implemented in a real-world, cross-sectoral perinatal care setting. Methods: A qualitative exploratory case study was conducted between January and March 2025. Data included semi-structured interviews with all seven implementation agents, document analysis of the implementation plan. Directed content analysis was applied using the Exploration, Preparation, Implementation, Sustainment (EPIS) framework to categorise contextual determinants and the ERIC taxonomy to classify implementation strategies. Data were synthesised across the four EPIS phases. Results: The implementation process unfolded across all EPIS phases, showing a shift in responsibility from the policy level to the implementation team and healthcare organisations. Implementation was shaped by key determinants across multiple levels: (1) the bridging functions by the BIB-Pro implementation agents connecting policy, innovation, and organisational practice; (2) the system-level leadership and funding by the National Institute for Health and Disability Insurance that enabled initiation and sustainability; and (3) the multilevel stakeholder involvement and inter-organisational collaboration across care settings. In addition, the personal attributes of implementation agents—accessibility, active listening, adaptability, and persistent follow-up—were also identified as relevant factors in the implementation process. Across the implementation process, a broad range of implementation strategies was identified. The most prominent ERIC strategies were developing stakeholder interrelationships, evaluative and iterative strategies, engaging stakeholders, training and educating stakeholders, and providing interactive assistance. Barriers encountered during the implementation process included fragmented care networks, inconsistent regional referral structures, legal uncertainties, and variable digital readiness. In response to these challenges, implementation strategies were applied to support collaboration, clarify procedures and provide targeted support. Conclusions: This study provides insight into how a CDSS was introduced, scaled, and sustained across complex multiple Belgian perinatal care settings. Strong bridging functions, stakeholder interrelationships, iterative evaluation, and system-level support were key factors throughout the implementation process. Across all phases, stakeholder interrelationship strategies and evaluative and iterative strategies were the most prominent and consistently applied, supporting stakeholder engagement and sustained use of the platform. These findings offer actionable guidance for implementing digital tools in multi-organisational and multi-level contexts within perinatal care and other healthcare settings. Full article

With the wide application of artificial intelligence (AI) in the Internet of Vehicles (IoV), IoV is under pressure for data transmission and real-time sensing. Integrated sensing and communication (ISAC) is one of the key technologies to alleviate that pressure. Obstacles can cause communication disruptions and increased delays, hindering autonomous driving information acquisition and causing traffic hazards. The application of Reconfigurable Intelligent Surfaces (RISs) aims to solve this problem. This study focuses on RIS-assisted multi-base station (MBS) scenarios in the presence of obstacles. This study aims to maximize the communication rate, minimize the sensing error, and reduce the switching frequency by optimizing the RIS phase shift and beamforming. The problem is modeled as mixed integer nonlinear programming (MINLP) and further described as a Markov Decision Process (MDP). We use Long Short-Term Memory (LSTM) to predict the environmental state and propose two optimization algorithms, Multi-Factor Decision Deep Deterministic Policy Gradient (MFD-DDPG) and Mixed Discrete and Continuous Action DDPG (MDCA-DDPG). In the first algorithm, we consider multiple factors to make a switching decision and use DDPG to yield the optimal action. The second algorithm improves DDPG by outputting a discrete switching decision and a continuous optimized action simultaneously. Simulations show that the proposed algorithms significantly improve the system performance, and the communication rate is increased by more than 40% in specific multi-vehicle scenarios compared to the benchmark. Full article

Automatic modulation classification (AMC) is a key component of spectrum awareness, cognitive radio, and signal intelligence, enabling receivers to identify modulation schemes from noisy in-phase and quadrature (IQ) observations. Traditional approaches rely on likelihood-based methods or handcrafted feature extraction, which often struggle under channel impairments and real-world variability. Recent advances in deep learning enable models to learn directly from multiple signal representations, including raw IQ samples, engineered features, and time–frequency or constellation-based encodings, improving adaptability across diverse signal conditions. This paper presents a structured review of deep learning approaches for AMC, including CNNs, RNN/LSTM models, and transformer-based architectures, with a focus on performance, robustness, and system-level trade-offs. We analyze how representation choices, dataset design, and evaluation protocols influence reported results, and highlight key challenges such as domain shift, low-SNR environments, and multi-signal interference. Finally, we outline future directions focused on improving generalization, integrating classical signal processing with learning-based methods, and enabling efficient deployment in real-world and resource-constrained systems. Full article

The large-scale integration of distributed energy resources poses numerous challenges to distribution networks. At present, multi-terminal flexible interconnection has become a key development trend for active distribution networks integrated with high-penetration distributed energy resources. Conventional unified power flow controllers (UPFCs) are mainly designed for high-voltage transmission networks and lack distribution-adapted control strategies, making it difficult for them to meet the networking requirements for multi-terminal interconnection. Moreover, most existing studies still focus on two-terminal devices, soft open points and improved UPFC topologies for transmission networks. Existing multi-port schemes mostly adopt only shunt-side structures without series compensation branches, which fail to regulate voltage magnitude and phase difference, thus failing to suppress closing inrush currents and mitigate busbar voltage sags. Meanwhile, such schemes struggle with three-phase imbalance, feeder load imbalance and bidirectional power flow fluctuations in distribution networks, and lack adaptive power allocation capability among multiple ports. To solve the above problems, this paper proposes a VSC-based series–shunt hybrid multi-terminal flexible interconnection converter. The proposed topology consists of one series-side VSC and n − 1 shunt-side VSCs connected through a common DC capacitor; it removes the shunt-side transformer, and effectively reduces cost and volume, while achieving phase shifting, voltage regulation and power flow control. Meanwhile, dual closed-loop PI cross-decoupling control and a flexible closing strategy are adopted to independently regulate the active and reactive power of each feeder, adapt to three-phase imbalance and load imbalance conditions, suppress inrush currents, and realize flexible power mutual support among multiple ports, thereby significantly enhancing adaptability to distribution networks. Full article

Long-term sports assessment is a challenging task in video understanding, since it requires judging subtle movement variations over minutes and evaluating action–music coordination. However, in many sporting events the background music is only weakly related to the performed movements, and the cues that matter for synchrony are often temporal and structural, such as small phase or tempo deviations that occur around decisive moments, rather than semantic correspondences between audio content and action categories. Prior approaches typically rely on implicit cross-modal fusion over dense sequences to learn such weak associations, which can smooth out near-miss misalignment and become brittle under tempo or phase shifts. To address this issue, we propose BEATSCORE, a beat-guided audio–visual learning framework that explicitly models action–music alignment at the beat level and performs event-centric sparse grading for long videos. In our framework, we first convert audio and motion into beat-synchronous tokens, enabling direct comparison on a unified rhythmic timeline. We then introduce a beat-level contrastive objective with near-offset hard negatives to sharpen sensitivity to misalignment. To handle the sparsity of decisive moments, we further design an event proposal and grading module that scores a small set of key segments and aggregates them via learnable multiple-instance pooling into a final assessment score. We evaluate BEATSCORE on public long-term sports benchmarks to demonstrate improved accuracy with competitive efficiency. Full article

The reconstruction of detrital flux, paleoclimate, paleosalinity, paleo-primary productivity, paleohydrodynamic conditions, and paleo-water depth enhances understanding of sedimentary processes and their drivers during deep-time greenhouse-icehouse transitions, such as the Eocene–Oligocene transition. This study uses detailed geochemical analyses of major oxides and trace elements in sediment samples collected from the Beni Suef Formation (Bartonian–Priabonian) and the Maadi Formation (Priabonian) in the southern Tethys shelf (Egypt, northeastern Desert). Detrital proxies, including Si/Al, Ti/Al, and Zr/Al, indicate an enhanced influx of terrigenous sediments in the middle portion of the Qurn Member of the Beni Suef Formation, as further supported by noticeable facies variations, particularly the transition from shale to coarser silt- and sand-sized fractions. Paleoclimate indicators (Sr/Ba, Rb/Sr, K₂O/Al₂O₃, and Sr/Cu) point to a climatic shift from humid to arid conditions, consistent with the regional Late Eocene aridification across the Tethyan realm. Paleosalinity proxies (Sr/Ba, Ca/Al, and Mg/Al×100) suggest episodic intensification of open-marine influence and a reduction in freshwater input, with an upsection increase in Sr/Ba ratios, reflecting phases of enhanced marine water settings or decreased terrestrial runoff. Primary productivity was evaluated using multiple geochemical proxies, including P, Ni/Al, Cu/Al, P/Al, P/Ti, and Babio ratios. These collectively indicate generally low primary productivity interrupted by intervals of enhanced paleoproductivity or increased organic matter export to the sediments. This interpretation is further supported by the low total organic carbon (TOC) values. These results highlight the sensitivity of the southern Tethys shelf to Middle–Late Eocene climatic variability and the key role of prevailing paleoenvironmental conditions in controlling sediment supply, water chemistry, and biological productivity. Full article

The primary objective of this research is to identify, analyse, and compare the development of risk management approaches adopted by Slovak industrial enterprises in two distinct economic periods: during a phase of economic growth (2019) and during a period of global crises and regional crises with significant global implications, which have had substantial global economic, energy, and security impacts, as well as the increasing intensity of cyber threats affecting enterprises in Slovakia (2022–2023). Emphasis is placed on identifying key factors influencing the effectiveness of risk management implementation, as well as on assessing the use of individual stages of the risk management process in business practice. The research has a quantitative character and consists of two empirical surveys conducted through questionnaire-based data collection. The first survey was carried out in 2019 under conditions of economic growth, while the second was conducted in 2022–2023 in the context of multiple global crises and regional crises, particularly the impacts of the COVID-19 pandemic, the global energy crisis, the military conflict in Ukraine, and increasing cyber threats. The first study obtained 450 valid responses, and the second obtained 390 responses from enterprises operating across various sectors of the private economy in Slovakia. The results of the study confirmed the existence of significant differences in companies’ approaches to risk management depending on the economic context. During the period of economic growth, the main reason for insufficient attention to risks was low staff motivation, with enterprises focusing primarily on risk identification, analysis, and assessment, and less on designing specific mitigation measures. In contrast, during the period of global crises and regional crises, companies’ attitudes shifted, with stronger resistance to implemented measures but, at the same time, increased attention to the development of risk-reduction actions. Neglecting systematic preventive steps increases companies’ vulnerability to crises, which may result in operational, financial, and reputational losses, delayed responses, and a decline in competitiveness. The two-phase nature of the research made it possible to capture the dynamics of managerial behaviour under different economic conditions and to formulate practical recommendations for integrating risk management into both strategic and operational levels of management. Full article

To effectively manage and conserve ecosystems, it is crucial to understand how vegetation changes over time and space and what drives these changes. The Normalized Difference Vegetation Index (NDVI) is a key measure of plant growth that is highly sensitive to climate variations. Despite its importance, there has been limited research on vegetation changes in the upper sections of the Irtysh River. In our study, we combined various datasets, including NDVI, temperature, precipitation, soil moisture, elevation, and land cover. We conducted several analyses, such as Theil–Sen median trend analysis, Mann–Kendall trend and mutation tests, partial correlation analysis, the geographical detector model, and wavelet analysis, to reveal the region’s pronounced warming and moistening trend in recent years, the response relationship between NDVI and the climate, and the primary drivers influencing NDVI variations. We also delved into the spatiotemporal evolution of NDVI and identified key factors driving these changes by analyzing atmospheric circulation patterns. Our main findings are as follows: (1) Between 1901 and 2022, the area’s temperature rose by 0.018 °C/a, with a noticeable increase in the rate of warming around 1990; precipitation increased by 0.292 mm/a. From 1950 to 2022, soil moisture exhibited a steady increase of 0.0002 m³ m⁻³/a. Spatial trend distributions indicated that increasing trends in temperature and precipitation were evident across the entire region, while trends in soil moisture showed significant spatial variation. (2) During 1982 to 2022, the vegetation greening trend was 0.002/10a, indicating a gradual improvement in vegetation growth in the study area. The spatial distribution of monthly average NDVI values revealed that the main growing season of vegetation spanned April to November, with peak NDVI values occurring in June–August. Combined with serial partial correlation and spatial partial correlation analysis, temperatures during April to May effectively promoted the germination and growth of vegetation, while soil moisture accumulation from June to August (or January to August) effectively met the water demand of vegetation during its growth process, with a significant promoting effect. Geographical detector results demonstrate that temperature exhibits the strongest explanatory power for NDVI variation, whereas land cover has the weakest. The synergistic promotional effect of multiple climatic factors is highly pronounced. (3) Wavelet analysis revealed that the periodic characteristics of NDVI and climate variables over a 2–15-year timescale may have been associated with the impacts of atmospheric circulation. Taking NDVI and climatic factors from June to August as an example, before 2000, temperature was the dominant influencing factor, followed by precipitation and soil moisture; after 2000, precipitation and soil moisture became the primary drivers. The North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) were the primary atmospheric circulation patterns influencing vegetation variability in the region. Their effects were reflected in the inverse relationship observed between NAO/AO indices and NDVI, with typical phases of high and low NDVI closely corresponding to shifts in NAO and AO activity. This study helps us to understand how plants have been changing in the upper parts of the Irtysh River. These insights are critical for guiding efforts to develop the area in a way that is sustainable and beneficial for the environment. Full article

Background: The year 2025 witnessed paradigm-shifting advances in gynecologic oncology, with pivotal clinical trial results redefining therapeutic standards across cervical, ovarian, endometrial, and vulvar cancers. Objectives: This systematic review aimed to comprehensively identify, synthesize, and critically evaluate pivotal phase II and III randomized controlled trials and major studies presented at the major annual meetings, alongside significant peer-reviewed publications from 2025 that introduce innovative therapeutic strategies across gynecologic malignancies. Methods: Conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, this review involved exhaustive searches of electronic databases (PubMed/MEDLINE, Embase), conference proceedings (ASCO 2025, ESMO 2025), and major oncology journals for records from January to December 2025. Inclusion criteria encompassed: (1) Phase II or III randomized controlled trials (RCTs) and (2) Non-randomized studies (including phase I and II trials), reporting on novel therapeutic approaches in gynecologic oncology. All studies were required to report primary survival endpoints (overall survival or progression-free survival) or key efficacy outcomes. Study selection, data extraction, and methodological quality assessment were performed independently by two reviewers, with disagreements resolved through consensus or third-party adjudication. Results: From 1842 records, 23 studies met inclusion criteria (17 phase-III RCTs and 6 non-phase III RCTs/early-phase studies), distributed as follows: cervical cancer (9 studies, 39%), ovarian cancer (9 studies, 39%), endometrial cancer (4 studies, 17.5%), and vulvar cancer (1 study, 4.5%). The major advances identified include: (1) In cervical cancer, the KEYNOTE-A18 trial established pembrolizumab combined with chemoradiotherapy as a new standard for high-risk locally advanced disease, while the PHENIX trial validated sentinel lymph node biopsy as a safe surgical de-escalation strategy. (2) In ovarian cancer, the ENGOT-ov65/KEYNOTE-B96 trial demonstrated the first statistically significant overall survival improvement with an immune checkpoint inhibitor in platinum-resistant recurrent disease, establishing pembrolizumab plus weekly paclitaxel as a new standard of care. Novel therapeutic mechanisms, including glucocorticoid receptor modulation (ROSELLA trial) and cadherin-6-targeted antibody-drug conjugates (REJOICE-Ovarian01), showed remarkable efficacy. (3) In endometrial cancer, updated analyses from NRG GY018 and RUBY trials solidified the role of first-line immuno-chemotherapy, with differential benefits according to mismatch repair status. (4) In vulvar cancer, a pivotal phase II study demonstrated meaningful clinical activity of anti-PD-1 therapy in advanced disease. (5) The extensive circulating tumor DNA analysis from the CALLA trial provided crucial insights into biomarker dynamics in cervical cancer. Conclusions: The convergence of high-impact data from 2025 established multiple new standards of care, emphasizing biomarker-driven approaches, immunotherapy integration across disease stages, and novel mechanisms to overcome resistance, while highlighting challenges in treatment sequencing and global access. Full article

The integration of reconfigurable intelligent surfaces (RISs) with flying ad hoc networks (FANETs) offers new opportunities to enhance performance in aerial communications. This paper proposes a novel FANET architecture in which each unmanned aerial vehicle (UAV) or drone is equipped with an RIS comprising M passive elements, enabling dynamic manipulation of the wireless propagation environment. We address the joint power allocation and RIS configuration problem to maximize the sum spectral efficiency, subject to constraints on maximum transmit power and unit-modulus phase shifts. The formulated optimization problem is non-convex due to coupled variables and interference. We develop an alternating optimization-based joint power and phase shift (AO-JPPS) algorithm that decomposes the problem into two subproblems: power allocation via successive convex approximation and phase optimization via Riemannian manifold optimization. A key contribution is addressing the RIS coupling effect, where the configuration of each RIS simultaneously influences multiple communication links. Complexity analysis reveals polynomial-time scalability, while derived performance bounds provide theoretical insights. Numerical simulations demonstrate that our approach achieves significant spectral efficiency gains over conventional FANETs, establishing the effectiveness of RIS-assisted drone networks for future wireless applications. Full article

The rapid growth of wireless devices, the expansion of the Internet of Things, and the aggregate demand for Ultra-Reliable Low-Latency communications (URLLC) are driving the improvement of next-generation wireless systems. One promising emerging technology in this area is cell-free massive Multiple Input Multiple Output (maMIMO) networks. The distributed nature of Access Points presents unique security challenges that must be addressed to unlock their full potential. This paper studies the key security concerns in Cell Free Massive MIMO (CFMM) networks, including eavesdropping, Denial-of-Service attacks, jamming, pilot contamination, and methods for enhancing Physical Layer Security (PLS). We also provide an overview of security solutions specifically designed for CFMM networks and introduce a case study of a Reconfigurable Intelligent Surface (RIS)-aided secure scheme that jointly optimizes the RIS phase shifts with the artificial noise (AN) covariance under power constraints. The non-convex optimization problem is solved via the block coordinate descent (BCD) alternating optimization scheme. The combined RIS, AN, and beamforming configuration achieves a balanced trade-off between security and energy performance, resulting in moderate improvements over the individual schemes. Full article

This study investigates bit error rate (BER)-constrained power allocation for uplink non-orthogonal multiple access (NOMA) systems in which a base station employs one-bit analog-to-digital converters. Although one-bit quantization significantly reduces hardware costs and receiver power consumption, it also introduces severe nonlinear distortions that degrade detection performance. To address this challenge, a pairwise error probability expression is first derived for the one-bit quantized uplink NOMA model, from which an analytical upper bound on the BER is obtained. Based on this characterization, a fairness-driven max–min power allocation strategy is formulated to minimize the BER of the worst-performing user. A closed-form solution for the optimal power allocation is obtained under binary phase-shift keying (BPSK) signaling. Simulation results verify the tightness of the analytical BER bound and demonstrate that the proposed power allocation scheme provides noticeable BER improvements that compensate for the performance degradation caused by one-bit quantization. Full article