Search Results (389)

Supplemental light-emitting diode (LED) lighting is essential for greenhouse crop production when solar radiation is insufficient, but it also contributes substantially to operating costs. Conventional strategies based on fixed photosynthetic photon flux density (PPFD) do not accurately reflect plant photosynthetic status, often leading to inefficient use of light energy. A chlorophyll fluorescence (CF)-based biofeedback system offers a plant-driven approach that dynamically adjusts light output to maintain target photosynthetic parameters. This system has been successfully tested in growth chambers with controlled environmental conditions, but no research has been conducted in greenhouses yet. This study developed and tested a greenhouse-compatible biofeedback lighting system using ‘Casey’ lettuce (Lactuca sativa) to evaluate its performance compared with conventional light controls. Two biofeedback control logics were applied: electron transport rate (ETR)-based (target ETR of 85 or 120 µmol·m⁻²·s⁻¹) and quantum yield of photosystem II (Φ_PSII)-based control (target Φ_PSII of 0.735), with constant PPFD- and timer-based lighting as reference treatments. Both biofeedback logics maintained their target values, confirming stable performance under dynamic greenhouse conditions. Despite successful real-time light regulation in greenhouse conditions, shoot biomass and energy-use efficiency did not differ among treatments under moderate greenhouse conditions (p > 0.05). This study establishes a functional prototype of a real-time physiological biofeedback system for greenhouse supplemental lighting control. Full article

Calving in cattle is divided into two main stages: dilation and expulsion, during which timely assistance can reduce reproductive losses. This study presents a smart ear-mounted device as a proof-of-concept heart-rate monitoring platform for calving-stage assessment in dairy cows. The prototype preserves the form factor of a conventional ear tag and integrates a MAX30105 optical sensor, an Arduino Nano microcontroller, local micro-SD storage, and an autonomous power supply. Field tests were conducted in Holstein cows at Rancho El Pinar, Trancoso, Zacatecas, Mexico. Heart rate was recorded every 10 min and grouped according to physiological stages around calving. The results showed distinctive heart rate patterns, with higher values during dilation and lower values after delivery, supporting the use of ear-mounted heart rate monitoring as a non-invasive descriptive marker of stage-related physiological variation around labor. An average temperature profile from 70 h before to 50 h after calving was also incorporated as complementary descriptive evidence of peripartum physiological variation. Because heart rate is a non-specific physiological variable affected by stress, movement, ambient temperature, feeding, health status, and sensor contact, the present study does not propose HR as a stand-alone or definitive predictor of calving or dystocia. Instead, the device is presented as a proof-of-concept platform for future multi-indicator monitoring and validation studies. The proposed system is presented as a proof-of-concept invention that combines a practical wearable format with physiological monitoring and a conceptual decision-support logic that remains to be validated and integrated with additional indicators before any field implementation. Full article

The European Common Agricultural Policy (CAP) commits, in its Treaty foundation, to a fair standard of living for the agricultural community and, in its post-2014 architecture, to enhanced territorial cohesion. Yet repeated reform cycles have left the regional concentration of payments in many Member States visibly untouched. This paper asks why. We document the persistence of the territorial concentration of CAP transfers across the 13 Greek NUTS-2 regions over the 2010–2025 period (€47.65 bn cumulative), identify the CAP design mechanisms that mechanically reproduce it, and quantify how much of the observed aggregate stationarity is the artefact of compositional shifts versus genuinely offsetting forces. Using the universe of payment disbursements aggregated to 13 NUTS-2 regions and 51 NUTS-3 prefectures, we (i) test for σ- and β-convergence and Lorenz dominance, (ii) decompose Theil-T between and within regions and across Pillar I/Pillar II, and (iii) run four counterfactual simulations: Pillar II share held at its 2010 level, Article: 17-style capping at a 12–15% NUTS-2 ceiling, an Article: 29-style lower-tail floor, and a concentration-elasticity perturbation of the top region. The territorial distribution of support proves strikingly stable: standard inequality measures stay within a narrow band for sixteen consecutive years, and the ranking of regions barely changes, so formal convergence tests detect no narrowing over time. Three messages follow. First, this persistence is not accidental but built into the architecture of the CAP—through historical-reference entitlement values, the per-hectare logic of the Basic Payment Scheme, the geographic concentration of coupled support in cotton and livestock, and the cadastral fragmentation of the island prefectures. Second, the apparent stability conceals two large and opposing forces: the post-2014 expansion of Pillar II has reduced regional disparities, while a widening of the Pillar I distribution has increased them by almost the same amount, so aggregate stationarity reflects policy effort cancelling out, not the absence of it. Third, the instruments already in the CAP toolbox have real redistributive power: capping the largest region’s envelope and redistributing the surplus to lagging regions, or introducing a lower-tail floor, would roughly halve measured inequality. Therefore, the spatial concentration of CAP transfers in Greece is a designed equilibrium rather than an unsolved residual, and reducing it requires instruments that act asymmetrically on the top of the distribution. Full article

Deep neural networks have been widely applied in architectural analysis and design research, supporting tasks such as facade recognition, floor-plan analysis, and architectural visual classification. However, although existing models possess strong predictive capabilities, their decision-making processes remain characterized by a pronounced black-box nature, making it difficult to provide architects with understandable and traceable grounds for judgment. This limits their practical value in the architectural field, as designers require not only accurate outputs but also interpretable explanatory evidence regarding the basis of decision-making. This issue is particularly critical in architectural interpretation, where judgments are rarely made solely on the basis of isolated visual features, but are instead often formed through comparison and negotiation with precedents, spatial logic, and domain knowledge. To address this challenge, this paper proposes ArchiExplain, a multi-level interpretability framework for architectural image understanding, aiming to enable a deeper understanding of architectural images. The main contributions of this study are threefold: (1) We construct two architectural datasets for interpretability evaluation: a facade dataset composed of streetscape images from Harbin, China, and Greece, and a floor-plan dataset consisting of Real-plan drawings from real design cases and standardized generated R-plan drawings. Unlike existing datasets that primarily serve style recognition, semantic parsing, or image generation tasks, the datasets in this paper focus on evaluating the correspondence among model explanations, precedent associations, visual evidence, and predictive judgments. (2) Based on the above datasets, we propose the ArchiExplain framework. Unlike attribution methods such as Grad-CAM, Saliency Maps, and Integrated Gradients, which mainly reveal local discriminative regions, or influence-based methods that only trace the influence of training samples, this framework integrates training-sample influence tracing, Saliency Maps, and Integrated Gradients. It establishes a unified evidential chain among precedent samples, discriminative image regions, and final predictions, thereby transforming neural network decisions into an interpretable reasoning process with architectural significance. (3) Experimental results show that ArchiExplain performs stably on 100 randomly selected test samples, achieving an accuracy of 98.41% in the facade classification task and 98.34% in the floor-plan classification task. Further deletion/occlusion faithfulness analysis shows that the main attribution methods outperform the random baseline. Meanwhile, a questionnaire study involving 28 architects further verifies the consistency between model explanations and human architectural cognition. These findings indicate that ArchiExplain can enhance the transparency of architectural deep learning models and has practical application potential in architectural design analysis, model diagnosis, and precedent-based learning. Full article

Most UAV missions currently require visiting multiple checkpoints to perform field tasks in environments with varying levels of obstacle complexity. These missions become more challenging because UAVs have limited onboard resources, particularly in terms of energy, making it necessary to determine a safe and efficient path that enables all required visits to be completed while minimizing both travel distance and energy consumption. To address these challenges, this study proposes a hybrid fuzzy metaheuristic approach that integrates Cuckoo Search and Tabu Search for multi-objective UAV path planning. The proposed approach generates collision-free paths in environments with static obstacles and employs fuzzy logic to construct a unified evaluation function, in which distance and energy values are mapped to membership functions and combined into a single fitness score to guide the optimization process. Cuckoo Search drives global exploration of the solution space, while Tabu Search refines solutions locally. Together, they improve path quality and avoid premature convergence. Experimental results across two scenarios with varying obstacle densities and checkpoint counts demonstrate the efficacy of the proposed hybrid approach. Compared with two baseline algorithms, the hybrid approach achieves reductions in path length ranging from 0.01% to 42.11% and in energy consumption ranging from 0.08% to 27.91%, depending on scenario complexity. Moreover, it maintains a high success rate of 96–100% as both checkpoint counts and obstacle density increase, whereas the baseline algorithms drop to 3–13% in more complex environments. These results highlight the effectiveness and scalability of the approach for multi-checkpoint UAV path planning in obstacle-rich environments. Full article

Operational energy-forecasting pipelines require traceable execution from data ingestion to monitoring, yet few studies evaluate whether such pipelines continue to enforce quality controls when inputs or configurations are degraded. This study implements a previously proposed seven-phase forecasting lifecycle as a configuration-driven system on a self-hosted ClearML platform. The implementation is organised into five architectural domains: data and configuration, lifecycle phases and gates, orchestration, document artifact governance, and human-in-the-loop oversight. The pipeline is evaluated through six runs on four years of hourly electricity-consumption data from a Norwegian kindergarten building. Two baseline runs, in automatic and human-in-the-loop modes, demonstrate end-to-end execution and produce an XGBoost champion model with a 24-h-ahead test RMSE of 1.19 kW. Four controlled variants then test the validation-route logic by injecting missing data, shuffled consumption values, restrictive feature selection, and missing foundation-document sections. The first three variants are detected by phase-level sub-checkpoints, while the fourth is detected by Gate 0 through document-structure validation. The runs exercise revise-and-recover, override-then-terminate, and immediate-abort response pathways. The evaluation therefore demonstrates lifecycle execution, validation-route behaviour, and artifact traceability under controlled conditions; claims about live-deployment performance and multi-building generalisation are out of scope and identified as next steps. Full article

Existing accounts of platform-mediated complementor growth rest on two limiting assumptions: that platform enablement constitutes a homogeneous environmental input and that firm growth is a unitary outcome. This double simplification obscures how distinct platform provisions generate qualitatively different forms of firm transformation. This study asks which combinations of mechanistically distinct platform enablement types and internal strategic response capabilities activate which forms of leapfrog growth among complementor firms operating under dual institutional governance. We employ fuzzy-set Qualitative Comparative Analysis (fsQCA) on survey data from 374 complementor firms in China’s autonomous driving platform ecosystem. Five antecedent conditions are examined across two dimensions: platform enablement, comprising rule-based enablement (RE) and business platform enablement (BPE); and strategic response capabilities, comprising network linkage capability (NLC), organizational ambidexterity (OA), and policy responsiveness (PR). Three outcome variables capture three non-reducible leapfrog dimensions: technology-chain (TL), value-chain (VL), and institutional-chain (IL) transitions. A reverse-causality robustness check and a common-method-bias assessment corroborate the validity of findings. The analysis identifies equifinal configurational pathways with distinct dominant logics across the three chains. Technology-chain transitions are predominantly network-linkage-driven; value-chain transitions are policy-responsiveness-anchored; institutional-chain transitions exhibit genuine equifinality between network-linkage and policy-responsiveness pathways, both requiring dual-platform enablement as a universal structural precondition. No single enabling condition or capability suffices; leapfrog growth is irreducibly configurational and causally asymmetric. The study offers a dual-enablement, three-chain configurational framework for understanding platform-mediated firm growth under dual institutional governance. For complementor firms, findings support dimension-selective capability investment over uniform accumulation strategies. For platform orchestrators, differentiated governance design calibrated to specific complementor upgrading trajectories outperforms homogeneous resource provisioning. For policymakers, institutionalized consultative channels linking private platform governance with public regulatory processes are recommended to facilitate coordinated digital industrial transformation. Full article

The aim of this paper is to study the impact of the variability of inertia of interconnected electrical systems on transient stability. In the first part of the paper, after formulating the transient stability problem as a boundary value problem, we demonstrate how to evaluate the transient stability margin, considering the impact of the randomness of inertia, with reference to a single area connected to an infinite power grid. In the second part of the paper, we determine the distributional properties of the transient stability margin for two interconnected areas, considering the correlation of the area’s inertias, described as random variables. To demonstrate the robustness of the procedure, two case studies are analyzed. In the first case, the random variables are described as correlated lognormal random variables, while in the second they are considered as correlated gamma random variables. The numerical analyses reported in the final part of the paper show an almost linear dependence of transient stability margin from inertia while correlation coefficient affects mainly the transient stability random variable’s dispersion rather than its magnitude. Some useful considerations are performed regarding the applicability and validity of the linearized probabilistic method instead of the Monte Carlo method. The Monte Carlo method allows considering the non-linearity of the model, which is more pronounced in the tails. This could affect, in some critical cases, the priority of access to production in the logic of the free market. In such a case, greater accuracy allows for a more transparent mechanism of access to production. Full article

Background: Long-read, single-CpG-resolution sequencing is redefining the information-to-depth ratio in epigenomics. While conventional methylome analysis often requires high coverage, we propose a scalable pipeline designed to extract high-density regulatory logic from shallow sequencing data. Methods: By utilizing the progenitor-like HepaRG cell line as a model for liver plasticity, we validated this framework across two divergent developmental trajectories: hepatic maturation and sphere-induced retrodifferentiation. Our technical approach combines CpG-centric enrichment and regional methylation aggregation to reconstruct regulatory landscapes from sparse data. Using long-read Nanopore sequencing, we mapped the dynamics of 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC). Results: Our pipeline revealed that these trajectories are not inverse processes but engage distinct epigenetic strategies. Hepatic maturation is characterized by the accumulation of 5hmC that partially targets repressive heterochromatin (H3K9me3, H4K20me3) and pioneer factors such as FOXA2. In contrast, retrodifferentiation increases 5mC, potentially silencing adult regulators such as HNF1A via Polycomb-associated networks. In addition, aggregation-based analysis can distinguish widespread focal perturbations from a restricted subset of transcription factors that translate epigenetic changes into regional accessibility. Conclusions: This study provides a scalable computational framework for investigating cellular fate transitions, proving that high-value epigenetic insights are attainable even at reduced sequencing depths. Full article

Metric-based software security plays a crucial role in allowing software developers to make informed decisions about their development practices, while also allowing software users to evaluate the security risks associated with the software they use. Metrics are increasingly used to ensure code security, but there has been little formal evaluation of their broader applicability to date, and interpretation of their results remains a qualitative task. To address this gap, we introduce DevSecTrust, a standardised evaluation framework for measuring and comparing software development security metrics. DevSecTrust provides: (i) a unified control-mapped metric schema, (ii) outcome-based calibration and validation against real vulnerability and maintenance data, and (iii) robustness and manipulability testing to assess metric reliability. This paper analyses two software development security tools, MITRE’s Hipcheck and OpenSSF’s Scorecard, to evaluate and contrast the metrics they produce against widely used open-source software projects. Our quantitative comparison identified low correlation and inconsistent distributions between the tools’ outputs, and our qualitative analysis of feature weighting and scoring logic revealed foundational differences in how each tool conceptualises “secure development”. These inconsistencies complicate trust in development security metrics and hinder their interpretability and operational value. This contributes to a path toward standardised measurement of software development security. Full article

Industrial heritage adaptive reuse occupies a structurally privileged position for degrowth: heritage listing already institutionalises material sufficiency as a regulatory obligation, mandating low intervention and resisting the demolish-and-replace logic of resource-intensive development. Yet this regulatory floor imposes no ceiling on how protected structures are programmed or who benefits; the same statutory instrument can produce different schemes depending entirely on governance. This paper demonstrates that gap through two contrasting UK gasholder adaptive reuse projects: King’s Cross Gasholders in London (private-led, luxury residential) and Granton Gasholder in Edinburgh (council-led community park). Applying De Castro Mazarro et al.’s multi-scalar degrowth framework across building, neighbourhood, and city scales through document analysis and site observations, we identify structural mechanisms explaining why building-scale alignment fails to propagate upward. The findings indicate three governance conditions are necessary to convert the structural degrowth potential of industrial heritage into substantive outcomes: public control over development decisions, community participation extended to strategic priorities rather than design preferences, and explicit integration of degrowth values into upstream planning frameworks. Industrial heritage adaptive reuse is not inherently a degrowth practice, but it is one of the few urban development contexts where the regulatory preconditions for degrowth alignment are already in place. Realising that potential requires governance structures that treat sufficiency and collective wellbeing as binding objectives, not rhetorical claims. Full article

The accelerating convergence of geopolitical volatility, technological disruption, environmental stress, and societal transformation has rendered traditional strategic management frameworks insufficient. Organizations now operate in environments defined not only by disruptions with existential implications but by wickedness—conditions in which problems are ambiguous, stakeholders disagree, and solutions reshape the challenge itself. Building on the premise that strategy itself is a wicked problem, this article advances a central claim: organizational resilience is best understood as an architectural capability largely grounded in humanity-based identity. Unlike organizational structure, mission, or even current strategy, each of which may be transient in turbulent environments, organizational identity, which is a construct that derives from individuals and humanity, provides an enduring basis for harmonizing the organization and its environment. Utilizing the lens of “humanity”—in its two dimensions of humankind and humaneness—we synthesize research on wicked problems, organizational identity, dynamic capabilities, modular design, alliances and smart power, and hybrid intelligence. We then propose an integrative model linking humanity-driven identity to resilience through three vectors—Inspirational Transformative Ambition, Innovative Value Networks, and Hybrid Intelligence Ecosystems—operationalized via a recently developed diagnostic tool. Finally, we offer corroborative evidence for the “Business of Humanity” logic, arguing that aligning humankind (opportunity across the full market spectrum) with humaneness (values-based evaluation) strengthens resilience by expanding opportunity sets while enhancing legitimacy, trust, and stakeholder alignment. Full article

To address the challenges of difficult value quantification, lack of market benchmarks, and scarcity of historical data for embedded software amidst the intelligent transformation of equipment systems, this study develops a scientific price estimation method based on functional capability contribution. A nonlinear pricing model is constructed to accurately characterize the two-stage evolution of software price: diminishing marginal utility during the mature technology accumulation stage and exponential growth during the technical bottleneck breakthrough stage. To ensure the consistency of pricing logic between hardware and software, a penalty function is innovatively designed to modify the standard likelihood function, effectively transforming practical business logic into a model regularization term. Parameter estimation is achieved by employing a Bayesian inference framework integrated with operational constraints, utilizing Markov Chain Monte Carlo (MCMC) sampling to realize robust posterior inference under small-sample constraints. Empirical analysis demonstrates that the proposed method achieves superior cross-domain data transfer performance compared to traditional baseline models, with a Leave-One-Out Cross-Validation (LOOCV) Mean Absolute Percentage Error (MAPE) of 21.2%. This research provides a practical value-oriented price estimation method for embedded equipment software pricing. Full article

Lean Six Sigma (LSS) improvement work increasingly depends on information-intensive activities such as document handling, data interpretation, reporting, and communication, yet current discussions of Artificial Intelligence in LSS remain largely technology-centric. This paper proposes a task-first, process-centric framework to support the governed application of Large Language Model (LLM)-enabled tools in such environments. The study makes three contributions: (i) a set of cross-functional organizational process types relevant to LSS practice, (ii) a functional classification of recurring tasks and LLM-enabled tool categories, and (iii) a dual-encoded task–tool matching matrix that separates alignment strength from interaction mode, distinguishing capability fit from governance logic. The framework is empirically anchored through two real-world industrial applications: customs document processing and shop-floor data digitalization and reporting. The results show that (i) stronger outcomes emerge when LLM-enabled support is matched to bounded, repetitive, and structured work, or when analytical support is built on stable and traceable data layers; (ii) operational value depends not only on technical capability, but on workflow embeddedness, data readiness, and human validation checkpoints. The framework also clarifies where support, augmentation, and partial automation are appropriate for different task classes and under explicit accountability constraints in information-intensive administrative work connected to improvement practice and governance. Full article

Reliable tabular data correction is a prerequisite for trustworthy analytics in enterprise information systems. Tabular data in such environments frequently contain formatting errors, semantic conflicts, missing values, and cross-field inconsistencies that degrade downstream analytics and machine learning performance. Rule-based methods efficiently handle structural violations but miss context-dependent errors, whereas large language models (LLMs) offer strong semantic-correction capability at inference costs prohibitive for enterprise-scale deployment. This paper formulates data error correction as a progressive decision process and proposes a complexity-aware framework with three processing stages. The first stage applies deterministic rules for low-complexity structural errors. The second stage employs a task-specialized distilled language model for medium-complexity semantic correction. The third stage performs neural probabilistic–logical reasoning on a factor graph for high-complexity cross-field errors. A learnable routing mechanism assigns each record to the appropriate stage based on a lightweight complexity score. Layer-wise conformal prediction is further introduced to construct calibrated prediction sets with coverage guarantees at each stage, together with a rejection mechanism for low-confidence corrections. The framework is evaluated on one enterprise dataset and two public benchmarks (Hospital and Flights). It improves the record-level complete repair rate by 2.1 to 3.1 percentage points over the strongest baseline (GPT-4o-Direct) and by up to 16.8 points over purely rule-based repair, while reducing average inference latency by approximately 80% relative to direct GPT-4o invocation. Ablation studies confirm the critical role of complexity-aware routing and rule-trigger features, and reliability analyses show that hierarchical conformal calibration maintains tighter coverage than single-level alternatives across varying confidence requirements. These results indicate that complexity-aware progressive routing coupled with hierarchical conformal calibration provides a practical path toward high-throughput, auditable, and reliability-controlled data cleaning suitable for enterprise deployment. Full article