Search Results (833)

In Norway, agroclimatic zones (ACZs) are a valuable tool for national analyses in subject areas concerning the optimized management of agricultural land resources. However, current Norwegian ACZs have been criticized for having an outdated standard climate normal (1931–1960), a limited representation of the local climatic variation, a lack of important model parameters, and weak methodological documentation. Therefore, this paper presents new ACZs for Norway that address these weaknesses. The most significant methodological updates are the use of the standard climate normal of 1991–2020, additional weather data variables, the downscaling of weather data to 250 m hexagons, and the incorporation of phenological crop models for spring wheat, spring barley, and forage grass. The grass model was calibrated with the number of grass harvests at research stations, while the grain models were calibrated with subsidy claim data. The modeled zones for the three crops were combined into the general ACZs. Example maps of the crop zones and new ACZs for the selected regions and the whole country are presented. The new ACZs are more robust, agronomically relevant, and better aligned with the current climatic conditions in Norway. The deliberate exclusion of factors other than climate ensures the new ACZs’ national comparability and their applicability in policy development, land-use planning, climate adaptation, and agronomic assessments at the national scale. Full article

Sewage sludge management is increasingly shifting from a liability-focused “treat-and-dispose” approach toward resource recovery, where digestion residues and their liquid fractions are treated as secondary feedstocks for nutrient, water, and energy recovery. In Europe, the recast Urban Wastewater Treatment Directive strengthens performance and monitoring requirements and reinforces the need for efficient sludge treatment and downstream valorization routes. This review synthesizes evidence on how pretreatment-induced changes in digestate properties translate into membrane performance outcomes and maps practical design implications for selecting pretreatment-membrane trains for nutrient recovery and reclaimed water production. Pressure-driven membrane methods (MF/UF/NF/RO), together with membrane distillation and electrodialysis, are central candidates for producing clarified water streams and concentrating nutrients; however, their performance is governed by digestate rheology, colloidal stability, and the composition of soluble microbial products and inorganic ions, which collectively shape fouling and scaling risks. Pretreatments such as thermal hydrolysis and microwave conditioning can modify floc structure and solubilize organics, with potential benefits for dewaterability and mass transfer, but can also shift particle size distributions toward fines and increase fouling propensity if not coupled with appropriate solid–liquid separation and conservative flux control. Emphasis is placed on mechanisms and operational trade-offs rather than single-point performance claims, highlighting where evidence is robust and where further comparability and full-scale validation remain necessary. Full article

The proliferation of Unmanned Aerial Vehicles (UAVs) in various applications has created a pressing need for robust and efficient communication systems. Fifth-generation (5G) networks can support UAV connectivity through high bandwidth and low-latency communication; however, rapid three-dimensional UAV mobility creates handover-management challenges that can increase signalling overhead, service interruption, and Quality of Service (QoS) degradation. This paper presents an integrated framework that combines LSTM-based multi-UAV trajectory prediction with proactive handover optimization using an Advantage Actor–Critic (A2C) Deep Reinforcement Learning (DRL) agent. The LSTM predictor is evaluated on a real-world UAV trajectory dataset and reports a root mean square error (RMSE) of 4.37 m over a 5 s prediction horizon after conversion to a local East–North–Up coordinate frame. A lightweight simulation-level coordination mechanism is included to reduce simultaneous target-cell contention among multiple UAVs; it is not claimed as a new standardized 3GPP signalling procedure. Handover performance is evaluated by replaying 180 held-out flight trajectories in a controlled 5G simulation across ten independent random seeds. Under these stated assumptions, the proposed framework achieves a handover success rate of $94.2\pm 0.8$%, an average SINR of $15.8\pm 0.2$ dB, a handover delay of $45.2\pm 1.1$ ms, and a handover frequency of $0.85\pm 0.05$ HOs/min, outperforming the tuned 3GPP A3, reactive SINR, and CASH baselines in the reported simulation results (Wilcoxon signed-rank test, $p<0.01$, Bonferroni-corrected). The experimental setup is described in detail to support methodological transparency and facilitate future replication, but the handover results should be interpreted as simulation-based evidence rather than live-network validation. Full article

Contemporary Blue Planetary Health frameworks frequently approach marine degradation primarily as a technical management problem while insufficiently addressing the relational, ethical, and political–economic conditions driving ocean collapse. The framework proposes that dominant marine governance paradigms continue to reproduce anthropocentric and extractivist assumptions that reduce oceans to economic assets rather than recognizing them as living multispecies relational systems. In response, the study develops the Blue Stratified Relational Responsibility Framework (BSRRF), an interdisciplinary model integrating multispecies ethics, marine psychophysiology, environmental humanities, political ecology, Indigenous relational ontologies, and ocean governance. The framework advances three central claims: marine sustainability requires relational rather than purely instrumental governance; humans possess asymmetrical ecological responsibility due to their technological and institutional power; and meaningful Blue Planetary Health transformation requires simultaneous shifts in moral imagination, affective perception, governance systems, and political economy. The study further critiques dominant Blue Economy paradigms for reproducing extractivist and colonial dynamics under narratives of sustainability and innovation. Ultimately, the framework argues that although the ocean crisis manifests ecologically, its underlying drivers are simultaneously epistemological, political, economic, and civilizational. Consequently, advancing Blue Planetary Health requires integrated transformations in education, governance, public policy, and multispecies ethical responsibility. Full article

Background/Objectives: Penile rehabilitation (PR) techniques are claimed to counteract chronic degenerative processes of cavernous tissue, such as penile hypoxia, neurovascular damage, and cavernous fibrosis. The objective of this umbrella review is to synthesize findings from existing meta-analyses to evaluate the efficacy of traditional and emerging PR strategies, providing an evidence-based roadmap for clinical management after surgery for prostate cancer. Methods: Conducted in accordance with PRIOR guidelines, a comprehensive literature search of PubMed, the Cochrane Library, and Scopus was performed through April 2026. The review included primarily systematic reviews and meta-analyses investigating pharmacological, physical, surgical, and regenerative interventions for post-prostatectomy erectile dysfunction (ED). Methodological quality was independently assessed using standardized tools. Results: PDE5 inhibitors (PDE5-is) significantly improve erectile function during active treatment, yet evidence supporting their role in promoting spontaneous, “unassisted” recovery remains limited. Vacuum erectile devices demonstrate high efficacy for assisted intercourse but show minimal impact on returning to baseline function compared to placebo. Penile prosthesis (PP) implantation maintains robust efficacy with exceptionally high satisfaction rates (83–85%), proving independent of prior pelvic surgery. Although early-phase trials suggest clinical potential for regenerative therapies like low-intensity extracorporeal shockwave therapy, platelet-rich plasma, and stem cell interventions, the evidence is currently undermined by substantial heterogeneity in study protocols and concerns regarding methodological quality. Conclusions: PR following radical prostatectomy remains a complex challenge characterized by poor evidence. While PDE5-Is are established first-line therapy for assisted function, PP remains the most reliable definitive treatment for refractory ED cases. Regenerative approaches show promise but remain investigational until standardized protocols and large-scale trials are established. Full article

The emergence and increasing use of artificial intelligence (AI) in healthcare have paved the way for highly personalized and time-saving approaches in the field of precision medicine. It can be applied to determine a prognosis, diagnosis, and recommended treatment, and may also be used for patient monitoring. As AI applications become more widely available, reliable and easy to use, they are rapidly reshaping the traditional roles of professionals and patients in the therapeutic relationship. On the positive side, professionals may have more time to communicate with patients and provide individualized care, whereas patients may become more empowered and autonomous due to AI-facilitated personalized information and monitoring. On the negative side, AI applications threaten to reduce the role of professionals to a mediating one in clinical decision-making, provide patients with misinformation, and lead to misunderstandings that hinder patients’ autonomy. In this narrative review, we examine the main ethical issues related to the AI-induced shift in roles in the therapeutic relationship, within four inter-related themes: the validity of claims that algorithms outperform humans in certain tasks; the ways in which AI saves time for health professionals but also takes time to properly explain and implement; the issues of trust and accountability, especially if AI suggestions lead to patient harm; and what AI’s alleged cost-effectiveness means for professionals’ employment and remuneration. Across the three roles, we find a common pattern: AI tends to absorb the technical and data-processing parts of clinical work while leaving its relational core to humans. Physicians move toward oversight and interpretation, nurses retain the attentiveness and responsiveness that define care, and patients gain tools for self-management that can widen autonomy or, left unguided, erode it. Whether the overall effect is benign depends less on the technology than on how outperformance is evidenced, how the freed time is used, how trust and accountability are anchored in people, and how cost pressures are managed. The article concludes with some suggestions for prudent use of AI in healthcare, indicating the appropriate measures that can be used to harness the power of AI without damaging the traditional cornerstones of the therapeutic relationship. Full article

Small- and medium-sized enterprises (SMEs) operate under conditions of rapid change, competitive pressure and growing informational complexity, while their owner-managers often have limited time and cognitive bandwidth to interpret emerging strategic possibilities. Artificial intelligence (AI) is beginning to change this by extending how firms detect signals, interpret shifting environments and evaluate possible strategic responses. However, existing work in dynamic capabilities, sensemaking and microfoundations does not fully explain how AI-augmented cognitive systems shape organisational interpretive capacity, strategic adaptability and sustainable competitive positioning. This article addresses that gap by developing Strategic Edge Architecture (SEA), a sociotechnical microfoundational theory of how AI-augmented cognitive infrastructure enhances environmental sensing, prospective sensemaking, adaptive strategic response and sustainability integration in SMEs. Drawing on a multiparadigm theoretical synthesis, this article integrates insights from strategic management, organisational cognition, microfoundations, AI governance and sustainability strategy. SEA conceptualises strategic capability as an emergent property of cognitive infrastructure within which human and AI systems interact to support environmental interpretation, strategic adaptation and sustainable growth. The framework proposes a causal pathway through which AI augmentation strengthens sensing and sensemaking, with human-in-the-loop governance acting as a key moderating condition. The article concludes with formal propositions to guide future empirical research on AI-augmented organisational cognition, whilst recognising that the framework’s claims remain inferential and require empirical examination before SEA’s explanatory power can be assessed. Full article

Feline Infectious Peritonitis (FIP) has been transformed from a fatal disease to a treatable condition following the introduction of GS-441524, a nucleoside analogue targeting feline coronavirus replication. However, the widespread use of unregulated compounded formulations and the absence of validated analytical tools for therapeutic drug monitoring (TDM) represent critical gaps in clinical FIP management. This study describes the development and full ICH M10-compliant validation of a high-throughput LC-MS/MS method for the quantification of GS-441524 in feline serum, incorporating an automated protein precipitation protocol and a PBS-BSA surrogate matrix in accordance with 3Rs principles. The method met all acceptance criteria across validated parameters, including linearity (0.1–50 µg/mL), accuracy (bias within ±12.5%), precision (CV ≤ 10.9%), selectivity, extraction recovery (87.5–107.9%), and stability under clinically relevant storage conditions. Matrix equivalence between PBS-BSA and authentic feline serum was confirmed, enabling routine calibration without animal-derived materials. The validated method was applied to clinical TDM in cats undergoing GS-441524 treatment for FIP, providing preliminary evidence of inter-individual pharmacokinetic variability. The compounded formulations administered to the TDM cohort were independently verified by LC-MS/MS, confirming drug content within ±15% of labelled claims and excluding pharmaceutical quality as a confounding factor in the interpretation of serum drug concentrations. Full article

Background/Objectives: Despite well-documented chemical and physical hazards in healthcare settings, existing reviews of occupational reproductive risks have largely focused on single-agent risk estimation and have rarely translated occupational hygiene evidence into clinical decision-making frameworks for reproductive counseling and surveillance. This narrative review synthesizes evidence across multiple occupational exposure categories—antineoplastic agents, high-level disinfectants (HLDs), sterilants, and work-organization factors—and proposes an integrated, clinically operational framework for preconception counseling, pregnancy-sensitive risk stratification, exposure-control verification, and reproductive health surveillance among women healthcare workers. Methods: A structured narrative literature search was conducted across PubMed/MEDLINE, Scopus, Web of Science, and Embase from database inception through January 2025 and updated in March 2026. The review was guided by a Population–Exposure–Comparison–Outcome (PECO) framework and structured using Search–Appraisal–Synthesis–Analysis (SALSA) principles and the Scale for the Assessment of Narrative Review Articles (SANRA). Evidence quality was summarized using a modified hierarchy-of-evidence classification provided as a reader aid. This narrative review employed structured transparency tools but does not claim the methodological status of a systematic review. Quantitative meta-analytic pooling was not performed owing to substantial heterogeneity across study designs, exposure assessment methods, and outcome definitions; findings were synthesized narratively by exposure category. Results: The strongest and most consistent evidence was identified for occupational exposure to antineoplastic agents, which has been associated with spontaneous abortion, stillbirth, congenital abnormalities, impaired fecundability, and selected cancer-related concerns. HLDs and sterilants represent exposure categories warranting precautionary attention, with some evidence suggesting possible adverse effects on fecundability and early pregnancy maintenance; however, findings are considerably more heterogeneous, context-dependent, and reliant on self-reported exposure assessment than those for antineoplastic agents. Broader workplace factors, including shift work, prolonged working hours, physical workload, and mixed exposures, may further contribute to reproductive risk. The synthesis supports task-specific occupational history taking, exposure-control verification, and pregnancy-sensitive risk stratification. Conclusions: This review provides a multi-exposure, clinically operational framework that bridges occupational hygiene evidence with reproductive healthcare delivery, offering practical decision-support tools for clinicians managing women healthcare workers during preconception, pregnancy, and lactation. The framework includes structured occupational history-taking questions, a clinical decision pathway with evidence-tier classification, and a prevention matrix linking exposure sources to workplace controls and clinical actions. Integrating task-specific occupational history taking into routine reproductive care may improve detection of preventable workplace risks and support timely accommodation, while clinicians should calibrate recommendation strength to the underlying evidence quality for each exposure category. Full article

AI-enabled cyber–physical systems (CPSs) are increasingly deployed in public governance contexts where they sense human populations, infer classifications or risks, and trigger interventions that can shape liberty, equality, and access to essential services. In these deployments, governance failures often arise not only from model error but from systems-level interactions across data generation, model updates, organizational practices, and downstream actuation. This paper introduces a Risk–Rights–Rules (3R) architecture that treats fundamental rights and legal rules as enforceable constraints on the sensing–inference–actuation loop, rather than as external ethical aspirations. Building on established risk-management baselines and safety engineering practice, we specify a testable assurance object, a structured 3R assurance case, that links rights claims to explicit assumptions, measurable evidence, and accountable control points across the lifecycle. The approach is designed to reduce “legitimacy drift” in stochastic decision pipelines by making uncertainty, demographic error, contestability, and procurement leverage auditable at the system level. The result is a governance blueprint for high-consequence public-sector AI deployments for governance failures, which is both technically robust and institutionally defensible. Full article

The proliferation of large language model (LLM) agents has enabled increasingly complex multi-step automation; however, composing multiple agents into coherent systems introduces significant orchestration challenges that remain poorly documented. This survey examines LLM-based multi-agent orchestration from 2023 through early 2026 (literature cutoff: March 2026), with explicit attention to the evidence hierarchy used to interpret deployment claims. We propose a three-topology, one-adaptivity taxonomy—centralized, decentralized, and hierarchical coordination topologies, each optionally augmented with a dynamic–adaptive control axis—grounded in classical multi-agent systems theory and recent empirical evidence. We compare six leading frameworks (LangGraph, CrewAI, AutoGen/Microsoft Agent Framework, OpenAI Agents SDK, MetaGPT, and DSPy) along axes directly relevant to practitioners: state-management granularity, token-cost structure, failure-recovery options, and design philosophy. The emerging protocol stack is examined in terms of why MCP (agent-to-tool) and A2A (agent-to-agent) occupy complementary layers, how the ACP–A2A merger signals protocol convergence, and where ANP’s decentralized-discovery design fits. Production design considerations—state management, task planning, error handling, scalability, and security—are evaluated with reference to published benchmarks. Vendor-reported figures are marked † throughout and held to a documented evidence hierarchy, which separates them from peer-reviewed and government-evaluator measurements. We close by identifying eight open challenges and proposing a six-dimension evaluation framework for multi-agent coordination quality. This paper offers practitioners a decision framework covering taxonomy, framework selection, protocol adoption, and early operational pilots. Full article

The energy recovery of biomass is frequently implemented through single-output systems or passive management schemes, resulting in underutilization of its thermodynamic potential and losses in economic value, climate benefits, and useful co-products. This study formalizes the concept of biopolygeneration as a diagnostic principle aimed at maximizing biomass utilization through the simultaneous production of multiple energy services and the valorization of secondary streams. A dimensionless metric, the Biopolygeneration Diagnostic Index (BDI), is proposed to quantify this concept. The index is bounded within $[0,1]$ and integrates five sub-indices: energy efficiency ($I_E$), thermal integration ($I_T$), energy self-sufficiency ($I_A$), exergetic quality of outputs ($I_Q$), and co-product valorization ($I_V$). Weights were determined using the Analytic Hierarchy Process ($w_1=0.40$, $w_2=0.24$, $w_3=w_4=0.14$, $w_5=0.08$; $\mathrm{CR}=0.007$). The BDI was evaluated using six cases, including five operating plants and one validated computational model representing five biomass conversion technologies in four countries. Results ranged from 0.453 for an engine without combined heat and power (CHP) to 0.733 for a cascade trigeneration system. Under identical feed conditions, the incorporation of CHP ($C1\to C2$) increased the BDI from 0.453 to 0.715, representing a 57.7% improvement attributable solely to heat recovery. Current limitations include the small validation sample ($n=6$) and the reconstruction of $I_A$ and $I_V$ from technological characteristics due to the absence of standardized reporting in the literature. Although these sub-indices account for only 22% of the total weighting ($w_{I_A}+w_{I_V}=0.22$), the present results should be considered a proof of concept rather than a fully empirical validation. The BDI provides a thermodynamically consistent framework for comparing bioenergy systems across technologies and supports technical, regulatory, and investment decision making. Broader validation using larger measurement-based datasets is required before claims of universality can be established. Full article

Background: Hyperprolactinemia is a common endocrine disorder with significant reproductive and systemic implications. This study aimed to investigate the nationwide epidemiological trends, longitudinal shifts in pharmacological treatment, and the temporal associations of concurrent conditions and long-term sequelae in Korean women with claims-based hyperprolactinemia. Methods: A nationwide, population-based retrospective cohort study was conducted using data from the Health Insurance Review & Assessment Service (HIRA) of South Korea from 2009 to 2021. Female patients aged 10–59 years with hyperprolactinemia diagnostic claims were evaluated. We analyzed annual prevalence, incidence, diagnostic procedures, and dopamine agonist prescription patterns. Associated clinical conditions were classified into two categories based on the timing of their diagnosis relative to hyperprolactinemia: concurrent or underlying conditions present at baseline, and long-term complications that developed during the follow-up period. Results: A total of 95,616 female patients were identified after applying the selection criteria. The prevalence and incidence of hyperprolactinemia peaked among women in their early thirties, with an absolute peak at age 32. A significant pharmacological paradigm shift was observed: bromocriptine was the predominant therapy during the early study period, but cabergoline prescriptions surpassed bromocriptine in 2017. Regarding clinical work-ups, only 5.3% of the entire cohort underwent a sella magnetic resonance imaging (MRI). Regarding associated clinical conditions, reproductive disorders such as infertility (28.0%) and polycystic ovary syndrome (24.8%) showed high overall prevalence but low incidence of new diagnoses during the follow-up period. Conversely, among the patients affected by bone disorders, more than 60% of the total osteoporosis and osteopenia cases were diagnosed subsequent to the initial hyperprolactinemia diagnosis. Significant post-diagnosis incidence was also observed for metabolic disorders, including dyslipidemia and diabetes mellitus. Conclusions: Hyperprolactinemia in Korean women is highly concentrated in the peak reproductive years. The shift toward cabergoline reflects evolving clinical guidelines and improved drug accessibility. Our findings highlight that while reproductive issues often present concurrently, bone loss and metabolic complications frequently emerge as post-diagnosis sequelae. Therefore, clinical management should extend beyond prolactin normalization to include proactive, multidisciplinary screening for skeletal and metabolic health. Full article

Sustainable aviation supply chains (SCs) are increasingly exposed to risks arising from environmental regulations, social responsibility pressures, and economic uncertainties. These risks are associated with different SC members and may propagate through operational dependencies among suppliers, maintenance service providers, and airline operators. To support systematic risk assessment, this study proposes a hybrid Analytical Hierarchy Process-Bayesian network (AHP-BN) framework for sustainable aviation SC risk management. The intended contribution is a contextual and structural extension of existing AHP-BN logic to member-level sustainability risk propagation in aviation SCs, rather than a claim that AHP-BN integration itself is fundamentally new. The proposed framework first classifies sustainability risks into environmental, social, and economic dimensions and identifies the risk exposure relationship between SC members and risk factors. For the weighting component, Analytical Hierarchy Process (AHP) is used to derive relative importance weights from specified illustrative pairwise comparison matrices in the numerical experiment. Bayesian network (BN) is employed to model probabilistic dependencies among nodes defined by SC members and risk factors. The two methods are coupled through a weighted expected risk index, which integrates AHP-derived weights, member-specific exposure intensities, probabilities inferred by BN, and losses associated with different risk states. A numerical illustration based on a synthetic aviation SC with suppliers, maintenance service providers, and airline operators is conducted to demonstrate the computational procedure and diagnostic use of the proposed framework rather than to validate an empirical risk profile of the aviation industry. Within this illustrative setting, cost volatility, supplier reliability, emissions regulation, and sustainable aviation fuel availability emerge as the major contributors to the overall risk index under the assumed inputs. The analysis further indicates that the proposed framework can identify critical active pairs of SC members and risk factors, reveal vulnerabilities at the levels of SC members and sustainability dimensions, and provide a transparent decision-support tool for sustainable aviation SC risk assessment, while the resulting rankings should be interpreted as conditional outputs under the assumed input parameters. Full article

Large language model (LLM)-driven computer use agents (CUAs) automate graphical user interface (GUI) tasks but often re-solve previously encountered subtasks, increasing token use and latency. We address this limitation with a directed graph-based persistent memory in which nodes represent observable GUI states and edges encode executable action sequences. We formalize the memory-augmented agent as $\mathcal{S}=⟨A,\mathrm{\Sigma },\mathcal{G},\delta ,\pi ,\mathrm{\Phi }⟩$, define task reachability and memory-coverage conditions inspired by functional stability theory, and derive token-cost efficiency bounds. In control-theoretic terms, the Manager–Worker architecture can be interpreted as a closed-loop system where memory provides experience-based feedback; this interpretation is used as an analogy rather than a full model-reference adaptive control proof. Experiments on OSWorld show that the proposed agent cuts both the LLM token consumption and execution time by about 50% versus a memoryless baseline while preserving comparable success rates (≈36.9% on 15-step and ≈46.9% on 50-step tasks). The demonstrated contribution is therefore operational efficiency through reusable graph memory, not a claim of improved task success or classical Lyapunov stability. Full article