Search Results (18,257)

Dynamic Spectrum Sharing (DSS) enables spectrum sharing between Long-Term Evolution (LTE) and New Radio (NR) systems, addressing spectrum scarcity in NR. To avoid interference when supporting NR traffic within LTE spectrum, key factors must be compatible. Effective DSS techniques are essential for coexistence. This paper discusses these issues in two parts. Part I covers LTE and NR coexistence using DSS, introducing resource grids, control signals, and channels, and explores DSS approaches for NR data traffic, including NR Synchronization Signal/Physical Broadcast Channels (SSB) transmission via LTE Multicast-Broadcast Single-Frequency Network (MBSFN) and non-MBSFN subframes with associated challenges and standardization efforts for DSS improvement. Part II presents a DSS technique using MBSFN subframes in a heterogeneous network with a macrocell and picocells running on LTE, and in-building small cells running on NR, sharing LTE spectrum via DSS. An optimization problem is formulated to manage traffic through MBSFN allocation, determining the optimal number of MBSFN subframes per LTE frame. System simulations indicate DSS improves Spectral and Energy Efficiency in small cells. The paper concludes with key lessons for LTE and NR coexistence. Full article

Large Language Models (LLMs) offer new opportunities to devise automated implementation generation methods that can tackle problem solving beyond traditional methods, which usually require algorithmic specifications and use only static domain knowledge. LLMs can support devising new methods to support activities in tackling open-ended problems, like problem framing, exploring possible solving approaches, feature elaboration and combination, advanced implementation assessment, and handling unexpected situations. This paper presents a detailed overview of the current work on LLMs, including model prompting, retrieval-augmented generation (RAG), and reinforcement learning. It then proposes a novel, LLM-based Cognitive Architecture (CA) to generate programming code starting from verbal discussions in natural language, a particular kind of problem-solving activity. The CA uses four strategies, three top-down and one bottom-up, to elaborate, adaptively process, memorize, and learn. Experiments are devised to study the CA performance, e.g., convergence rate, semantic fidelity, and code correctness. Full article

This paper presents the design and testing of a lightweight, low-cost robotic arm with an extended vertical range. The 9-degree-of-freedom (DOF) system comprises a 6-DOF arm and a 3-DOF gripper. To minimize weight, the six wrist and gripper joints are cable-driven, with all actuators relocated to the shoulder assembly. As a result, the wrist and gripper only weigh 222 g and 113 g, respectively, significantly reducing the inertia on the end effector. The arm utilizes a SCARA-configuration that slides along a tower for extended vertical reach. A key innovation is a closed-section frame that attaches the arm to the tower, in which the bending and torsional loads from the payload can be directly transferred onto the static structure. In contrast to conventional design, this design does not require the shoulder motor to take the bending load directly. Instead, the motor only needs to overcome the rolling friction of the reaction load. Experimental results demonstrate that this approach reduces the required motor torque by a factor of 30. Consequently, the prototype can manipulate a 3 kg payload at a 0.5 m lateral reach while weighing only 4.5 kg, costing USD 1200, and consuming a maximum of 11.1 W of power. Full article

With the rapid growth of wireless devices, security has become a key research concern in beyond-5G (B5G) and sixth-generation (6G) networks. Non-orthogonal multiple access (NOMA), one of the supporting technologies, is a strong contender to enable massive connectivity, increase spectrum efficiency, and guarantee high-quality access for a sizable user base. Furthermore, the scientific community has recently paid close attention to the effects of hardware impairments (HIs). The safe transmission of NOMA in a two-user uplink relay network is examined in this paper, taking into account both hardware limitations and the existence of listening devices. Each time frame in a mobile network environment comprises two phases in which users use a relay (R) to interact with the base station ($BS$). The research focuses on scenarios where a malicious device attempts to intercept the uplink signals transmitted by users through the R. Using important performance and security metrics, such as connection outage probability (COP), secrecy outage probability (SOP), and intercept probability (IP), system behavior is evaluated. To assess the system’s security and reliability under the proposed framework, closed-form analytical expressions are derived for SOP, IP, and COP. The simulation results provide the following insights: (i) they validate the accuracy of the derived analytical expressions; (ii) the study significantly deepens the understanding of secure NOMA uplink transmission under the influence of HIs across all the network entities, paving the way for future practical implementations; and (iii) the results highlight the superior performance of secure and reliable NOMA uplink systems compared to benchmark orthogonal multiple access (OMA) counterparts when both operate under the same HI conditions. Furthermore, an extended model without a relay is considered for comparison with the proposed relay-assisted scheme. Moreover, the numerical results indicate that the proposed communication model achieves over 90% reliability (with a COP below 0.1) and provides approximately a 30% improvement in SOP compared to conventional OMA-based systems under the same HI conditions. Full article

This paper presents the design and implementation of a vision-based score detection system tailored for smart IoT basketball applications. The proposed architecture leverages a compact, low-cost device comprising a high-resolution overhead camera and a Raspberry Pi 5 microprocessor equipped with a hardware accelerator for real-time object detection. The detection pipeline integrates convolutional neural networks (YOLO-based) and custom preprocessing techniques to localize the basketball hoop and track the ball trajectory. A scoring event is confirmed when the ball enters the defined scoring zone with downward motion over multiple frames, effectively reducing false positives caused by occlusions, multiple balls, or irregular shot directions. The system is part of a scalable IoT analytics platform known as Koško, which provides real-time statistics, leaderboards, and user engagement tools through a web-based interface. Field tests were conducted using data collected from various public and school courts across Niš, Serbia, resulting in a robust and adaptable solution for automated basketball score monitoring in both indoor and outdoor environments. The methodology supports edge computing, multilingual deployment, and integration with smart coaching and analytics systems. Full article

Optical tracking of unknown space objects requires both spatial accuracy and temporal stability to enable high-resolution identification through narrow field-of-view sensors. Traditional performance indices such as RMS error and persistence time (PT) have been used for controller tuning, but they each capture only a subset of the requirements for successful optical identification. This paper proposes a new composite metric, the Persistence-Weighted Tracking Index (PWTI), which combines spatial precision and segment-level continuity into a single measure. The metric assigns a frame-level score based on positional error and accumulates weighted scores over the longest continuous in-threshold segment. Using PWTI as the optimization objective, a genetic algorithm (GA) is employed to tune the PID gains of a frame-by-frame offset correction controller. Comparative simulations under various observation scenarios demonstrate that the PWTI-based approach outperforms RMS- and PT-based tuning methods in both alignment accuracy and consistency. The results validate the proposed metric as a more suitable performance indicator for optical identification tasks involving unknown or uncataloged targets. Full article

Backgrounds: Accurate annotation of open reading frames (ORFs) is fundamental for understanding gene function and post-transcriptional regulation. A critical but often overlooked aspect of transcriptome annotation is the selection of authentic translation start sites. Many genome annotation pipelines identify the longest possible ORF in alternatively spliced transcripts, using internal methionine codons as putative start sites. However, this computational approach ignores the biological reality that ribosomes select start codons based on sequence context, not ORF length. Methods: Here, we demonstrate that this practice leads to systematic misannotation of nonsense-mediated decay (NMD) targets in the Arabidopsis thaliana Araport11 reference transcriptome. Using TranSuite software to identify authentic start codons, we reanalyzed transcriptomic data from an NMD-deficient mutant. Results: We found that correct ORF annotation more than doubles the number of identifiable NMD targets with premature termination codons followed by downstream exon junctions, from 203 to 426 transcripts. Furthermore, we show that incorrect ORF annotations can lead to erroneous protein structure predictions, potentially introducing computational artefacts into protein databases. Conclusions: Our findings underscore the importance of biologically informed ORF annotation for accurate assessment of post-transcriptional regulation and proteome prediction, with implications for all eukaryotic genome annotation projects. Full article

Invasive alien species (IASs) pose escalating threats to global ecosystems, biodiversity, and human well-being. Public participation in IAS monitoring is often limited by taxonomic expertise gaps. To address this, we established a multi-taxa image dataset covering 54 key IAS in China, benchmarked nine deep learning models, and quantified impacts of varying scenarios and target scales. EfficientNetV2 achieved superior accuracy, with F1-scores of 83.66% (original dataset) and 93.32% (hybrid dataset). Recognition accuracy peaked when targets occupied 60% of the frame against simple backgrounds. Leveraging these findings, we developed EyeInvaS, an AI-powered system integrating image acquisition, recognition, geotagging, and data sharing to democratize IAS surveillance. Crucially, in a large-scale public deployment in Huai’an, China, 1683 user submissions via EyeInvaS enabled mapping of Solidago canadensis, revealing strong associations with riverbanks and roads. Our results validate the feasibility of deep learning in empowering citizens in IAS surveillance and biodiversity governance. Full article

Offshore wind turbines are rapidly scaling in size, which amplifies the need for credible integrated load analyses that consistently resolve the coupled dynamics among rotor–nacelle–tower systems and their support substructures. This study presents a comprehensive ultimate limit state (ULS) load assessment for a fixed jack-up-type substructure (hereafter referred to as K-wind) coupled with the IEA 15 MW reference wind turbine. Unlike conventional monopile or jacket configurations, the K-wind concept adopts a self-installable triangular jack-up foundation with spudcan anchorage, enabling efficient transport, rapid deployment, and structural reusability. Yet such a configuration has never been systematically analyzed through full aero-hydro-servo-elastic coupling before. Hence, this work represents the first integrated load analysis ever reported for a jack-up-type offshore wind substructure, addressing both its unique load-transfer behavior and its viability for multi-MW-class turbines. To ensure numerical robustness and cross-code reproducibility, steady-state verifications were performed under constant-wind benchmarks, followed by time-domain simulations of standard prescribed Design Load Case (DLC), encompassing power-producing extreme turbulence, coherent gusts with directional change, and parked/idling directional sweeps. The analyses were independently executed using two industry-validated solvers (Deeplines Wind v5.8.5 and OrcaFlex v11.5e), allowing direct solver-to-solver comparison and establishing confidence in the obtained dynamic responses. Loads were extracted at the transition-piece reference point in a global coordinate frame, and six key components (Fx, Fy, Fz, Mx, My, and Mz) were processed into seed-averaged signed envelopes for systematic ULS evaluation. Beyond its methodological completeness, the present study introduces a validated framework for analyzing next-generation jack-up-type foundations for offshore wind turbines, establishing a new reference point for integrated load assessments that can accelerate the industrial adoption of modular and re-deployable support structures such as K-wind. Full article

Diverse learning algorithms, optimization methods, and natural selection share a common mathematical structure despite their apparent differences. Here, I show that a simple notational partitioning of change by the Price equation reveals a universal force–metric–bias (FMB) law : $\Delta$$\mathbf{\theta }$= $\mathbf{M}\mathbf{f}+\mathbf{b}$ + $\mathbf{\xi }$. The force $\mathbf{f}$ drives improvement in parameters, $\Delta \mathbf{\theta }$, in proportion to the slope of performance with respect to the parameters. The metric $\mathbf{M}$ rescales movement by inverse curvature. The bias $\mathbf{b}$ adds momentum or changes in the frame of reference. The noise $\mathbf{\xi }$ enables exploration. This framework unifies natural selection, Bayesian updating, Newton’s method, stochastic gradient descent, stochastic Langevin dynamics, Adam optimization, and most other algorithms as special cases of the same underlying process. The Price equation also reveals why Fisher information, Kullback–Leibler divergence, and d’Alembert’s principle arise naturally in learning dynamics. By exposing this common structure, the FMB law provides a principled foundation for understanding, comparing, and designing learning algorithms across disciplines. Full article

This paper examines how sustainable leadership and strategic sustainability integration are framed and supported for SMEs in the EU. We apply comparative document analysis (CDA) to 35 policy, industry, and NGO reports published in 2020–2025 for Germany, Sweden, Poland, and Spain. Multi-level materials (EU, national, industry/NGO) were thematically coded, and the synthesis is presented in a multi-level conceptual framework linking policies, leadership, strategy, barriers, and transferable practices. The analysis indicates systematic differences in institutional maturity: Sweden and Germany display denser, more navigable support ecosystems and clearer leadership narratives, whereas Poland and Spain exhibit greater fragmentation and a more compliance-oriented framing. Instrument menus are broadly similar (grants/co-funding, concessional finance, advisory vouchers, training, standards/toolkits, green public procurement), yet accessibility and measurement strength diverge; outcome tracking (e.g., energy savings, CO₂e avoided) is more consistent in Sweden/Germany than in Poland/Spain. Green–digital coupling is pivotal: sequencing “on-ramps” (advisory/vouchers) into innovation finance accelerates adoption; where such on-ramps are thin, uptake concentrates among already prepared firms. Implications follow for policy design and practice: prioritize simple entry points for micro- and small enterprises, strengthen monitoring with meaningful KPIs, and ensure regional parity in access to finance and advisory. For SME leaders, role-modeling, employee development, and experimentation help embed sustainability when formal structures are lean. Beyond mapping patterns, this study provides an auditable operationalization of sustainable leadership for document analysis and a transferable framework to compare policy mixes and ecosystem readiness across countries. Full article

A hybrid energy system (HES) integrates various energy resources to attain synchronized energy output. However, HES faces significant challenges due to rising energy consumption, the expenses of using multiple sources, increased emissions due to non-renewable energy resources, etc. This study aims to develop an energy management strategy for distribution grids (DGs) by incorporating a hydrogen storage system (HSS) and demand-side management strategy (DSM), through the design of a multi-objective optimization technique. The primary focus is on optimizing operational costs and reducing pollution. These are approached as minimization problems, while also addressing the challenge of achieving a high penetration of renewable energy resources, framed as a maximization problem. The third objective function is introduced through the implementation of the demand-side management strategy, aiming to minimize the energy gap between initial demand and consumption. This DSM strategy is designed around consumers with three types of loads: sheddable loads, non-sheddable loads, and shiftable loads. To establish a bidirectional communication link between the grid and consumers by utilizing a distribution grid operator (DGO). Additionally, the uncertain behavior of wind, solar, and demand is modeled using probability distribution functions: Weibull for wind, PDF beta for solar, and Gaussian PDF for demand. To tackle this tri-objective optimization problem, this work proposes a hybrid approach that combines well-known techniques, namely, the non-dominated sorting genetic algorithm II and multi-objective particle swarm optimization (Hybrid-NSGA-II-MOPSO). Simulation results demonstrate the effectiveness of the proposed model in optimizing the tri-objective problem while considering various constraints. Full article

Artificial intelligence (AI) is rapidly transforming rheumatology, particularly in imaging and laboratory diagnostics where data complexity challenges traditional interpretation. This narrative review summarizes current evidence on AI-driven tools across musculoskeletal ultrasound, radiography, MRI, CT, capillaroscopy, and laboratory analytics. A structured literature search (PubMed, Scopus, Web of Science; 2020–2025) identified 90 relevant publications addressing AI applications in diagnostic imaging and biomarker analysis in rheumatic diseases, while twelve supplementary articles were incorporated to provide contextual depth and support conceptual framing. Deep learning models, notably convolutional neural networks and vision transformers, have demonstrated expert-level accuracy in detecting synovitis, bone marrow edema, erosions, and interstitial lung disease, as well as in quantifying microvascular and structural damage. In laboratory diagnostics, AI enhances the integration of traditional biomarkers with high-throughput omics, automates serologic interpretation, and supports molecular and proteomic biomarker discovery. Multi-omics and explainable AI platforms increasingly enable precision diagnostics and personalized risk stratification. Despite promising performance, widespread implementation is constrained by significant domain-specific validation gaps, data heterogeneity, lack of external validation, ethical concerns, and limited workflow integration. Clinically meaningful progress will depend on transparent, validated, and interoperable AI systems supported by robust data governance and clinician education. The transition from concept to clinic is under way—AI will likely serve as an augmenting rather than replacing partner, standardizing interpretation, accelerating decision-making, and ultimately facilitating precision, data-driven rheumatologic care. Full article

In this paper, the torsional stiffness simulation and validation process for a fully electric Formula Student car are reported. The optimization of the performance and efficiency of the cars affects various aspects of both the powertrain and the car body. Three crucial themes can be identified for the development of the cars: the power maps the inverter uses to manage the electric motor, the aerodynamic kit installed onboard, and the overall weight of the car. In this regard, in fact, it is not obvious that a higher value of chassis torsional stiffness leads to better performance in terms of speed or energy consumption. To achieve the best balance between torsional stiffness and weight, different simulations are needed. In this paper, we report a way to validate the simulation of the torsional stiffness value, reproducing the forces exchanged between the chassis and the suspension system. The forces used to simulate the torsion are obtained from track tests. To achieve the goal, the analysis is conducted with several experimental tests on two different chassis: the 2021 steel frame tube and the 2023 carbon fiber monocoque of the “Sapienza Fast Charge” Formula Student Electric team. The main result of the research presented here has been achieved; the numerical calculation procedure for the stiffness of Formula Student-type frames has been experimentally validated, allowing design modifications and developments to be studied by quickly verifying their influence on the stiffness of the new frame. A realistic comparison was also made between the two frames, the 2021 frame with space-frame technology and the 2023 frame with a carbon fiber monocoque. The results obtained, both in simulations and experimentally, clearly show that the monocoque frame has 350% greater torsional stiffness than the space-frame type. This result was obtained with the two bare chassis having the same weight. Full article

Previous studies have demonstrated that high-yielding dairy cows experience endoplasmic reticulum (ER) stress in the liver during early lactation. To date, most insights into the role of ER stress in metabolism and disease pathophysiology have been derived from rodent and human models. In dairy cattle, however, the specific impact of ER stress on metabolic pathways and its contribution to disease development remain insufficiently characterized. The objective of this study was therefore to investigate the molecular effects of ER stress using a bovine liver cell model (BFH12 cells). ER stress was induced by incubation with Tunicamycin (TM) and Thapsigargin (TG). Molecular responses to ER stress were assessed via a whole-genome array analysis and PCR targeting genes involved in selected metabolic pathways. Incubation with both ER stress inducers resulted in a marked upregulation of genes associated with the unfolded protein response (UPR) within a 4 to 24-h time frame, indicative of the production of robust ER stress in these cells. Unexpectedly, treatment with TM led to a downregulation of numerous genes involved in lipid biosynthesis, including those related to lipogenesis and cholesterol synthesis. Furthermore, incubation with TM and TG induced upregulation of genes involved in fatty acid oxidation and was accompanied by a reduction in intracellular triglyceride concentrations. Genes associated with inflammatory responses were upregulated by both TM and TG, whereas genes encoding antioxidant enzymes were downregulated. Genes involved in ketogenesis did not exhibit a consistent pattern of regulation. Overall, several effects of ER stress previously described in rodent models could not be replicated in this bovine liver cell system. Extrapolating these findings to dairy cows suggests that while ER stress may contribute to hepatic inflammation, it is unlikely to play a significant role in the development of hepatic lipidosis or ketosis. Full article