Search Results (2,726)

Remote supervision of agricultural robots depends on continuous interpretation of robot status and wireless link quality. In smart greenhouses, crop canopies, metallic frames, cultivation rows, and non-line-of-sight propagation can cause intermittent packet loss and RSSI attenuation. Treating such transient degradation as immediate communication failure can interrupt robot operation unnecessarily, whereas delayed recognition of persistent loss can compromise safety. This study proposes a probabilistic communication-state inference method for remotely supervised agricultural robots. The robot-to-gateway wireless link is represented by three states: normal, degraded, and failure. The degraded state acts as an uncertainty buffer that preserves recoverable degradation before failure escalation. Packet reception ratio, received signal strength, and trajectory-derived context are used to update state probabilities through a bounded transition mechanism. Field experiments with a mobile agricultural robot in a smart greenhouse showed an accuracy of $0.915\pm 0.007$ and a macro F1-score of $0.907\pm 0.008$, while reducing the premature failure rate to $18.0\pm 1.4\%$. Comparisons with threshold-based, moving-average, and adapted WSN fault-detection baselines, including a FedLSTM-inspired baseline, showed that binary fault-detection logic cannot explicitly preserve recoverable degraded communication intervals. The results indicate that probabilistic degradation modeling supports communication-aware remote supervision by distinguishing transient degradation from failure-level communication loss. Full article

This paper investigates backscatter-aided relaying for interactive dual-HAP wireless-powered sensor networks (WPSNs), in which two cooperative sensor groups transmit sensed data to opposite hybrid access points (HAPs) using harvested radio-frequency energy. Each group consists of multiple source sensor nodes (SNs) and one relay SN selected according to its proximity to the target HAP. To reduce local cooperation overhead, source SNs reuse the wireless power transfer (WPT) signal as a controllable carrier and convey their information to the relay SN through passive backscatter communication. The collected information is then delivered to the target HAPs through direct source transmission and relay forwarding. A source common-throughput maximization problem is formulated by jointly optimizing time allocation, transmit energy allocation, and dual-HAP energy beamforming, subject to energy-causality and relay minimum-rate constraints. To address the resulting non-convexity, an alternating optimization algorithm is developed, where the time-and-energy allocation subproblem is transformed into a convex form and the energy beamforming matrices are updated through energy-feasibility margin maximization. Numerical results show that the proposed scheme outperforms active cooperation without backscatter and direct transmission, demonstrating the effectiveness of integrating passive local information collection, relay-assisted uplink transmission, and optimized dual-HAP WPT. Full article

Background: Postpartum hemorrhage (PPH) remains a leading cause of maternal morbidity and mortality worldwide, accounting for approximately 27% of maternal deaths. In Spain, its incidence ranges from 2.5% to 5.2%. Clinical management has evolved toward a stepwise approach integrating pharmacological, mechanical, and surgical interventions. This study aims to analyze the evolution of these techniques during the 2020–2024 period to optimize decision-making and maternal outcomes. Methods: A systematic review was conducted following the PRISMA 2020 guidelines. Comprehensive searches were performed in PubMed, Scopus, and the Cochrane Library for studies published between 2020 and 2024 in English and Spanish. The PICO framework was utilized to evaluate interventions including intrauterine balloon tamponade (UBT), compression sutures, and arterial embolization, prioritizing outcomes such as bleeding control and fertility preservation. Out of 34 identified records, 13 studies met the final inclusion criteria. Results: The findings demonstrate a clear trend toward conservative management. Intrauterine balloon tamponade reported success rates of 80–90% in controlling refractory bleeding and significantly reduced the hysterectomy rates. B-Lynch compression sutures showed success rates between 68.4% and 100%, with generally favorable fertility outcomes. However, combining these sutures with devascularization increased the risk of uterine necrosis. Additionally, the early administration of tranexamic acid (TXA) within 3 h of birth was confirmed as a critical factor in reducing mortality. Conclusions: Acute PPH management has shifted toward protocol-based, sequential, and less invasive strategies. The implementation of standardized algorithms, care bundles, and simulation-based training is essential to reduce decision inertia and improve obstetric safety. While conservative mechanical and surgical techniques are effective, institutional protocols must be regularly updated to consolidate these technological and organizational advances. Full article

Mosquito control personnel work within health departments, public works, private companies, and other agencies. These essential outdoor workers have highly specialized training and are faced with a variety of potential health and safety hazards (e.g., arthropod bites and stings, exposure to insecticides and other chemicals, working with heavy equipment, noise, heat, solar ultraviolet radiation, slips, trips, and/or falls). Mosquito control personnel undergo employer-provided and other types of training on a variety of topics from regulatory updates to new surveillance and control techniques that are required for safety purposes and to maintain their applicator license. Here, an exploratory baseline survey was conducted among members of the North Carolina Mosquito and Vector Control Association (NCMVCA) and the Virginia Mosquito Control Association (VMCA). There was a 28% response rate so results should be interpreted with caution in this pilot study. Most respondents reported utilizing ultra-low volume insecticide application equipment for controlling adult mosquitoes. Backpack sprayers were utilized by less than half of respondents. Those who reported using respirators showed higher concern about insecticide-related health effects than those who did not use respirators. Outdoor workers encounter various potential hazards and utilize several forms of personal protective equipment to reduce risks. This baseline work can be considered a starting point for implementing and strengthening occupational safety and health awareness and preventive measures for mosquito control workers. Knowledge of health and safety hazards can reduce workplace risk. Full article

Traditional Network Intrusion Detection Systems (NIDSs) face persistent challenges in detecting zero-day attacks due to concept drift, high false-positive rates, and limited adaptability. This research introduces a Cognitive Network Intrusion Detection System (CNIDS) whose central novelty is that effective zero-day handling does not arise from any single mechanism but from the interaction between continual representation learning, persistent vector memory, and human-aligned feedback. By reframing zero-day resilience as a continuous learning process rather than a static detection task, CNIDS emphasizes adaptive operational behavior over raw automated accuracy. The proposed framework integrates Continual Pre-Training (CPT) to align representations with evolving traffic, Supervised Fine-Tuning (SFT) to preserve precision on known attacks, and a Human-in-the-Loop Reinforcement Signal (HRS) that converts low-confidence alerts into structured learning updates. These components are unified through a vector database that functions as long-term episodic memory, enabling similarity-based reasoning and cross-dataset generalization. Ablation results show that disabling any component degrades zero-day adaptation: removing CPT increases drift sensitivity, removing vector memory prevents knowledge retention, and removing human feedback collapses learning to static inference. Using a class-exclusion zero-day protocol on NSL-KDD, UNSW-NB15, and CICIDS2017, CNIDS raises zero-day detection from 0% to 18.2% while maintaining precision above 80% and stabilizing false positives. Full article

Artificial intelligence (AI) is now applied across oncology, including imaging, pathology, therapy selection, toxicity assessment, and survival modeling, and large language models (LLMs) have been adopted particularly quickly. Reported physician use of AI roughly doubled between 2023 and 2026. The frameworks used to evaluate these tools have not advanced at the same rate. Many are available, but each was designed for a single stage of the AI lifecycle, and none directly answers the question most relevant to a treating clinician: whether a given tool is appropriate for the individual patient under care. We reviewed the frameworks in current use, including TRIPOD+AI, PROBAST+AI, CLAIM, SPIRIT-AI, CONSORT-AI, DECIDE-AI, MINIMAR, and CREMLS, together with the oncology-specific ESMO EBAI and the newer LLM-specific guidance (TRIPOD-LLM, MI-CLEAR-LLM, CHART, and ESMO ELCAP). Each addresses part of the evaluation problem, but none is sufficient on its own for oncology, where the standard of care changes rapidly, assays drift, biomarker-defined subgroups are small, and a model validated in one period may perform poorly in the next. LLMs introduce additional challenges, including sensitivity to prompting, undisclosed vendor updates, behavioral drift, and hallucination rates approaching 50% on clinical quality-assurance tasks. The predictive-model frameworks were not designed to capture these failure modes. The central argument of this review is that the frameworks the field requires already exist; what is missing is their mandatory adoption. We propose that journals and regulators move from recommending these frameworks to requiring their use, and we outline a cancer-aware evaluation pathway together with the specific responsibilities of authors, reviewers, journals, and regulators in implementing it. Full article

Background/Objectives: This updated systematic review and meta-analysis provide a focused synthesis of contemporary evidence on clinical outcomes reported after the Latarjet procedure and Free Bone Block (FBB) techniques for anterior shoulder instability, focusing on recurrence, patient-reported outcome measures (PROMs), return to sport, complications, and osteoarthritis progression. Given that most available studies report single-procedure cohorts, between-technique comparisons were interpreted in the context of indirect evidence and study-level heterogeneity. Methods: A systematic review and meta-analysis were conducted according to PRISMA 2020 guidelines. The updated search included studies published from 2019 to May 2024 and was integrated with 70 studies from the previous review that were re-screened according to the same eligibility criteria. Eligible studies reported outcomes for the Latarjet or FBB procedure, with a minimum 2-year follow-up and at least five patients in the relevant treatment cohort. Risk of bias was assessed using the RoB 2 and MINORS tools. Outcomes were synthesized using random-effects meta-analysis. Pooled estimates were calculated separately for each procedure, and between-technique contrasts were treated as exploratory and descriptive when appropriate. Heterogeneity was assessed using I². Results: Ninety-eight studies with 6043 patients and 6071 shoulders were included: 72 on Latarjet, 23 on FBB, and 3 direct comparative studies. Both procedures were associated with low recurrence rates, improved PROMs, and comparable return-to-sport rates. Recurrence was 7% for Latarjet and 5% for FBB. Return to sport was 66% after Latarjet and 65% after FBB. Complication rates were 5% for Latarjet and 8% for FBB, while osteoarthritis progression was 12% and 9%, respectively. PROMs improved after both techniques, although differences between procedures should be interpreted cautiously because of the indirect nature of most comparisons and substantial heterogeneity across studies. Conclusions: Both Latarjet and FBB procedures were associated with generally favorable outcomes for anterior shoulder instability with bone loss in the included studies. The available evidence suggests broadly comparable clinical outcomes, with possible differences in complication profile, recurrence pattern, and osteoarthritis progression. However, these findings should be interpreted considering differences in patient characteristics, follow-up duration, surgical technique, graft type, and outcome definitions across studies. Current evidence does not allow definitive conclusions regarding the superiority of one technique over the other, but it provides useful descriptive outcome profiles to inform clinical decision-making and guide future direct comparative research. Full article

This paper presents a computational model of delirium in the Intensive Care Unit (ICU), in which delirium is defined as the endpoint of a self-reinforcing cycle of predictive failure between two bidirectionally coupled agents: the patient and the ICU room environment. Drawing on the active inference framework and the free energy principle, the paper proposes that delirium is not a property of the patient in isolation but a relational phenomenon that emerges when the environment persistently fails to predict the patient’s internal state. This failure triggers a causal feedback mechanism in which desynchronization pressure progressively sharpens the patient’s prior beliefs—implementing precision rigidity in the correct active inference sense: not a brain overwhelmed by noise but a brain locked into a state that incoming observations can no longer update. The model is implemented as a two-agent POMDP in which both agents maintain generative models and continuously attempt to predict each other’s states. The room agent (R)—understood as the environment-side sensing–inference–actuation loop, whether instantiated by clinical staff or by an automated monitoring system—infers the patient (P)’s latent parameters $({\theta }_{\mathrm{cog}},{\theta }_{\mathrm{emo}})$ over time and builds a progressively personalized generative model of the patient. Synchronization is operationalized via two commensurable directional surprisal metrics: $S_{R\to P}=-ln{Q}_R\left(s^{*}\right)$, the room’s surprisal at the patient’s true state, and $S_{P\to R}=-lnP(o_R\mid Q_P)$, the patient’s surprisal at the room’s observations. A systematic ablation study across four model variants shows that room inference is the architectural component necessary to reproduce the synchronization–delirium relationship: when the room infers, the association between synchronization and declared delirium is strong and stable, whereas a non-inferring room collapses to ceiling delirium rates and a weak association. $\theta$ learning and the prior-sharpening feedback do not increase the strength of this association; instead they shape the phenotypic gradient, reducing ceiling effects in vulnerable phenotypes and amplifying the separation between them. The model is presented as a computational hypothesis generator rather than a calibrated clinical predictor, and its implications for ICU design are discussed. Full article

High-Frequency Trading (HFT) dashboards require rapid reception, aggregation, and visualization of order book and trade update streams that may arrive at multi-million message rates. Conventional CPU-based and CPU-GPU hybrid visualization pipelines can suffer from significant delays during periods of burst due to CPU-mediated rendering, synchronization, kernel launch overhead, and copies on the host. This paper presents a visualization pipeline that is entirely resident on the graphics processor with zero-copy access to NIC accessible pinned buffers, persistent CUDA processing, fused stage execution of the parse-aggregate pipeline, and persistent CUDA OpenGL buffer interoperation. The goal is not to reach production status but rather to see whether host-to-host data movement can be decreased and whether the stages of GPU processing can be consolidated to improve latency, throughput and frame cadence in controlled HFT-style workloads. The evaluated workstation achieved a mean ingest-to-pixel latency of 6.3 ms using the proposed design compared to 29.4 ms for the current design, with sustained throughput of 10.2 million messages per second, which is 20 times greater than the current design, and a steady-state range of 185 to 192 frames per second with a burst floor of 178 frames per second for the proposed design. The improvement observed can be attributed to both the zero-copy ingestion and fused persistent kernel execution. Based on the obtained results, the proposed method of use of this technique in the implementation of real-time financial visualization under the proposed conditions is possible. More general testing is still required on other NICs, other generations of GPUs and PCIe configurations, workload traces, and actual exchange feeds. Full article

Control subarea division is essential for coordinated signal control, but methods based mainly on local correlation or static topology may not adequately capture traffic-state propagation under dynamic traffic loading. This study proposes a control subarea division method that explicitly models traffic-state propagation by integrating state-guided colored random walk and path entropy analysis. Intersection correlation degree and traffic state are used to construct a state-guided colored random walk process, in which transition probabilities are updated according to network connectivity and traffic-state consistency. Path entropy characterizes propagation uncertainty, and control subareas are identified by minimizing the distribution discrepancy between node-level and subarea-level path responses. To compare partitioning schemes, five complementary metrics were adopted: variance reduction rate of spatial delay, delay reduction rate, congestion mitigation index, stop reduction rate, and queue reduction rate. A VISSIM microsimulation model with dynamic traffic loading was developed to compare the proposed method with the Whitson and Fast Newman methods. The proposed method achieved the best performance across all five metrics, with values of 41.47%, 23.77%, 25.96%, 23.59%, and 15.08%, respectively. These results indicate that the proposed method improves spatial balance and network efficiency while mitigating bottlenecks, reducing stops, and suppressing queue accumulation. Full article

Memory-augmented language agents are increasingly used for long-horizon personalized interaction, but they still struggle to adapt to user-specific evidence without gradually drifting away from a stable and reliable persona. In this paper, we study how to maintain long-term persona invariance while preserving the utility of memory-based personalization. We propose a verification-aware memory framework that separates fast-changing episodic memory from a slow persona state and treats persona evolution as a controlled state transition rather than an unconstrained memory write. At a high level, retrieved evidence can only induce a bounded tentative persona update, and the update is committed only if an external verifier confirms that the resulting symbolic trace satisfies explicit persona specifications. Across long-term memory, personalized dialogue, persona consistency, and over-personalization benchmarks, our method achieves the best overall performance, improving utility metrics on LongMemEval, LoCoMo, and PersonaMem-v2 while reducing hard violation rate from 6.9 to 3.2 and increasing repair success rate from 67.5 to 74.9 on reliability benchmarks. These results show that explicit verification can make memory-augmented agents both more adaptive and more dependable, offering a practical path toward safer long-term personalization. Full article

Croatia’s accession to the European Union in 2013, followed by the abolition of the EU milk quota regime in 2015, exposed a structurally vulnerable dairy and beef sector to the dual pressure of single-market price convergence and post-quota production reallocation. While prior partial equilibrium modelling work on Croatian agriculture has assessed broad accession impacts and CAP reform scenario comparisons, no study has yet quantified the post-quota structural transition trajectory of the Croatian dairy and beef sectors with mechanism-level interpretation under the actually implemented CAP framework. This study simulates and analytically interprets the medium-term (to 2030) trajectories of the Croatian dairy and beef sectors using the AGMEMOD partial equilibrium framework, with an updated Croatian country-level model incorporating CAP 2023–2027 parameters, the post-quota dairy module specification, and historical data extending to 2024. Results indicate continued structural contraction: dairy cow numbers decline from 70.9 thousand head (2024) to 66.0 thousand head (2030); milk production falls 8.6% to 399.6 million litres; total cattle population declines by 4.0% and beef and veal meat production falls 11.0% to 33.1 thousand tonnes. Self-sufficiency rates deteriorate to 41% for milk and 51% for beef by 2030. The study contributes empirical evidence on small Member State post-quota adjustment dynamics, demonstrates that the current CAP 2023–2027 instruments are unlikely to reverse the contraction trajectory under baseline assumptions, and raises explicit food security implications for national agri-food policy. Full article

Tomato harvesting in protected horticulture is a critical task that faces challenges due to labor shortages, high environmental stress in greenhouses, and the complex nature of clustered fruit arrangements. This study proposes the global–local sequence ranking network (GL-SeqNet), a novel model designed to optimize intra-cluster tomato harvesting sequences by integrating global and local features using a deep learning approach. GL-SeqNet fuses the global structural information of tomato clusters with local fruit attributes to dynamically update the harvesting sequence, ensuring the optimal target is selected at each step. The model features a dual-stream architecture, comprising separate global and local backbones, and utilizes a ranking head for prioritizing intra-cluster targets. An AI-based image object-removal tool was used to simulate the dynamic structural changes within a cluster during harvesting, facilitating the creation of a state evolution dataset. The experimental results showed that the best overall performance was achieved with a global resolution of 112 × 112 and a local resolution of 56 × 56, yielding a Top-1 accuracy of 0.950, a position match rate (PMR) of 0.970, and an inference time of only 22.6 ms, along with faster convergence. The results underscore the potential of global and local fusion strategies and ranking-based learning for effective harvesting sequence optimization. Full article

This study assessed the level of service quality satisfaction among customers of carpentry technicians in Cabadbaran City, with emphasis on ten service dimensions: craftsmanship, timeliness, communication, durability, price transparency, customer service, design collaboration, reputation, and after-sales service. Employing a descriptive research design, data were collected through a self-constructed questionnaire administered to customers who had previously availed of carpentry services in the city. Results indicated that overall satisfaction across all dimensions was generally positive, as reflected in customers’ agreement ratings. However, notable gaps were identified that suggest areas for improvement. Craftsmanship and timeliness emerged as the most critical concerns, with respondents citing inconsistent job quality and delays in project completion. Issues of customization and durability were also highlighted, as some customers reported limited flexibility in design options and doubts about the long-term sturdiness of products. Communication shortcomings, particularly in providing project updates and explaining processes, further affected satisfaction. Design collaboration raised concerns as several customers felt excluded from important design decisions. After-sales service received mixed evaluations, with limited follow-up once projects were completed. Price transparency was another issue, with participants expressing the need for clearer cost breakdowns and greater value for money. Reputation was considered moderately satisfactory but inconsistent due to varying customer experiences. Overall, the study emphasizes the need for carpentry technicians to strengthen critical service quality dimensions to better meet customer expectations, improve satisfaction, and enhance the sustainability of their services. Full article

Siziwang Banner in Inner Mongolia is a typical arid and semi-arid grassland region where ecological environmental quality is highly sensitive to climate variability and land use and land cover change (LULCC). Clarifying the long-term coupling relationship between LULCC and ecological environmental quality is essential for regional ecological protection and sustainable land management. Based on the Google Earth Engine (GEE) platform, this study integrated multi-temporal Landsat imagery and CLCD-based land use datasets, including an updated 2024 land use layer, to construct a Remote Sensing Ecological Index (RSEI) using standardized and direction-corrected principal component analysis. land use transition matrix analysis, spatial autocorrelation analysis, ecological contribution rate calculation, and GeoDetector were further applied to reveal the spatiotemporal evolution patterns, ecological effects, and driving mechanisms of LULCC in Siziwang Banner from 2000 to 2024. The results showed that: (1) grassland was consistently the dominant land use type, accounting for more than 90% of the total area. The overall land use pattern was characterized by stable grassland dominance, decreasing farmland and unused land, and slight increases in grassland and construction land; forestland showed a high relative growth rate but remained very small in absolute area. (2) The regional ecological environmental quality remained at a lower-to-medium level, with mean RSEI values ranging from 0.27 to 0.47. RSEI showed a phased pattern of initial improvement, subsequent decline, and partial recovery; the marked decline around 2015 was associated with the combined effects of drought stress and land use degradation rather than a single driving factor. RSEI exhibited significant positive spatial autocorrelation, with Moran’s I values ranging from 0.898 to 0.993. High-value clusters were mainly distributed in the southern region, whereas low-value clusters were concentrated in the central and northern regions. (3) Different land use transitions produced differentiated ecological effects. The conversion of unused land to grassland contributed positively to ecological restoration, while grassland degradation and construction land expansion exerted negative effects. The positive RSEI response of some grassland-to-farmland transitions should be interpreted cautiously in relation to local irrigation and intensive farmland management. (4) GeoDetector results indicated that land use type and DEM were the dominant factors controlling the spatial differentiation of RSEI, with average q values of 0.7188 and 0.6178, respectively. The interaction between DEM and land use type showed the strongest explanatory power, indicating that ecological quality was jointly shaped by land use structure and natural background conditions. This study provides a scientific basis for grassland protection, unused-land restoration, farmland management, and spatially differentiated ecological restoration in Siziwang Banner and similar ecologically fragile arid and semi-arid grassland regions. Full article