Search Results (638)

Quantitative operational data mining and control strategies are essential for energy-saving regulation in intelligent District Heating Systems (DHSs). However, due to complex industrial environments and heterogeneous sensor networks, operational heating data often face a “zero ground-truth labels” bottleneck, rendering conventional residual-based unsupervised quality control algorithms ineffective without calibration baselines. To resolve this, this paper proposes an unsupervised multivariate data quality assessment framework constrained jointly by physical and statistical topologies. The framework evaluates time-series data streams across five dimensions: completeness, typicality, consistency, uniqueness, and timeliness. At the statistical topology level, the Mahalanobis distance identifies the spatiotemporal distribution center of multivariate variables, replacing traditional accuracy metrics with statistical typicality to enable self-consistent quantification without ground truth. At the physical topology level, coupled logical relations between primary and secondary heating networks are extracted as rigid first-principles constraints. An information entropy weight method then adaptively determines indicator weights to eliminate subjective biases. Full-sample validation was conducted using real-world SCADA data across a complete heating season from a regional network zone (46 heat exchange stations). The network-wide average data quality score reached 0.905, confirming overall control-loop input readiness, though specific stations exhibited cascading degradation from localized physical faults. This framework systematically reveals data quality heterogeneity in complex DHS and provides a generalizable theoretical baseline for Industrial Internet of Things (IIoT) data cleansing under zero-ground-truth conditions. Full article

With the continuous digital transformation of the construction industry, differences in the depth and effectiveness of BIM application during the construction phase of building projects have become increasingly evident. To systematically evaluate BIM application maturity during this stage, this study develops a construction phase-specific maturity assessment framework. First, based on the characteristics of BIM application during the construction phase and the logic of maturity assessment, the concept of BIM application maturity is defined. An evaluation indicator system is then constructed using the Balanced Scorecard as an organising framework, covering four dimensions: financial and cost-effectiveness, customer and delivery value, internal processes and efficiency, and learning and innovation capability. Second, grey relational analysis is used to screen and optimise the initial indicators, while the AHP-CRITIC combined weighting method is adopted to integrate subjective expert judgement and objective data characteristics. On this basis, a cloud model-based evaluation method is introduced to transform qualitative maturity assessment into quantitative evaluation while considering fuzziness and randomness. Finally, the proposed framework is applied to Project C as an empirical case application. The results show that the overall BIM application maturity of Project C during the construction phase is classified as the Integration Level. Among the four first-level dimensions, the customer and delivery value dimension performs relatively strongly, while the learning and innovation capability dimension shows the lowest expectation value. The proposed framework can help identify the maturity level and relative weaknesses of BIM applications during the construction phase, and can provide a reference for targeted BIM improvement and construction management decision-making. Full article

Many politicians heavily leverage social media platforms to further their interests and professional goals. TikTok, the widely popular social network established in 2016, has amassed a vast user following, especially among the younger demographic known as Generation Z. This study conceptualizes it as part of a platformized political communication environment that enables direct-to-audience communication beyond traditional journalistic gatekeeping. The current study aims to analyze high-visibility TikTok content produced by active Israeli politicians by scrutinizing the best-performing videos created by politicians representing diverse political ideologies. High-visibility content refers to videos that generated relatively high engagement, exposure, and platform presence, and therefore had greater potential to influence or impact audiences compared with less visible posts. The goal is to characterize and understand the content patterns and visibility dynamics of these successful TikTok videos posted by politicians. The findings suggest that the popularity of TikTok videos posted by politicians is associated with gender, age, and political affiliation. Men, older individuals, and those with right-wing affiliations tend to create content that garners the highest levels of popularity. Moreover, a considerable proportion of the sampled high-visibility videos do not engage directly with political subject matters. This suggests that, among best-performing political TikTok posts, visibility and engagement may sometimes outweigh explicit political or ideological discourse. This implies that politicians feel compelled to participate on the platform, even when their most visible content does not specifically revolve around promoting political messages. Consequently, the findings lend cautious support to the view that platform logic shapes political visibility and communication practices, even in politicians’ use of TikTok. In doing so, this study contributes to understanding how commercial social media platforms such as TikTok shape political visibility, engagement, and direct-to-audience communication. Full article

This applied systems-integration study addresses the “Impedance Mismatch” inherent in multi-energy microgrids—namely, the conflict where disparate physical domains traditionally force the use of isolated or highly rigid Supervisory Control and Data Acquisition (SCADA) data silos. We present a scalable, open-source edge-to-cloud telemetry pipeline orchestrated entirely through a custom MING+React stack (Mosquitto, InfluxDB, Node-RED, Grafana). The core architectural contribution is a category-specific Canonical Data Model (CDM) that functions as a translation layer, effectively decoupling sensor hardware from ingestion logic. Coupled with an automated Null-Pruning middleware loop and Metadata Separation, this architecture maintains high input purity prior to time-series persistence. To validate the system, the middleware was subjected to a high-fidelity stochastic simulation utilizing a Strict Corridor Algorithm to mimic physical inertia across 12 distinct energy vectors. Simulation-based validation demonstrates that under simulated conditions, the pipeline maintains data integrity for valid telemetry packets, achieves high accuracy in pruning malformed data, and operates with low latency under concurrency. These findings demonstrate the feasibility of this applied architecture as a resilient, cross-domain research environment for modern Digital Learning Factories. Full article

This study introduces the Painted Wolf Decision Optimizer (PWO), the first deterministic, bio-inspired decision framework for discrete multi-criteria decision making (MCDM) derived from specific observed decision behaviors of African wild dogs, including quorum sensing, dominance hierarchy, collective voting, and experience-based learning. Unlike conventional nature-inspired metaheuristics that rely on stochastic search across continuous domains, PWO defines a new class of Discrete Bio-Inspired Decision Operators. It formalizes key ethological mechanisms of Lycaon pictus: quorum sensing, hierarchical dominance, and reinforcement-based learning. Additionally, it encodes the principle of survival-through-precision, demonstrating how coordinated strategic alignment can outperform structural dominance under resource constraints, inspired by the high hunting efficiency of African wild dogs. PWO integrates three cognitive weighting components: subjective collective preferences (sneeze-based voting), objective data variability (entropy weighting), and experiential reinforcement (pack memory). These are fused via the Mathematical Compromiser, a convex operator that assigns internal trust based on signal stability rather than fixed weighting rules. Applied to European EV gigafactory location selection, PWO reconciled tensions between cost-driven executive preferences and sustainability-based performance indicators, identifying Spain as the most robust alternative. Sensitivity analysis across the dominance spectrum ($D=0\to 1$) and multiple episodes confirmed ranking stability without rank reversal. The Markovian update formalizes longitudinal learning for future multi-episode applications. Beyond discrete selection, PWO functions as a diagnostic and competitive resilience mechanism, revealing whether decisions are shaped by leadership authority, structural necessity, historical trends, or precision-based survival logic. It provides a transparent and strategically adaptive architecture for sustainable governance and high-stakes competitive decision environments. Full article

Given the current global trend of actively exploring the integration of AI into dispute resolution, we contend that, under existing theoretical and normative frameworks, there is as yet no sound basis for incorporating AI—least of all for replacing judges—into these processes. This is not to dismiss AI’s contribution to dispute resolution; rather, we seek to clarify how AI can be responsibly strengthened in this field. Rather than adopting simplistic technological skepticism, we develop a comprehensive theoretical framework that integrates perspectives from computer science and jurisprudence. By tracing the logical sequence of dispute resolution—goal setting, data input, subject identification, algorithmic processing, and the output and attribution of responsibility—we identify five fundamental issues. First, at the goal-setting stage, a fundamental conflict arises between algorithms, which aim to optimize efficiency, and the judiciary, which pursues multiple values (such as a just resolution and a swift one). Second, at the data-input stage, the principle of “garbage in, garbage out” manifests as the intractable and dynamically interactive problem of “algorithmic bias.” Third, at the subject-identification stage, allowing AI to replace human adjudicators—whether fully or partially—alters the judicial proceedings and undermines procedural justice. Fourth, at the algorithmic-processing stage, the black box and the hallucinations of deep learning are in sharp tension with the judiciary’s exacting demands for certainty and reasoned explanation. Finally, at the accountability stage, outsourcing judicial authority to private developers creates a supervisory vacuum and weakens the state’s liability for compensation. We therefore propose that the legal responses and rule-making needed to address these foundational issues be put in place before, not after, technological implementation. Full article

Contrasting mainstream operational definitions of mindfulness, this article introduces the Dialectical Mandala Model of Mindfulness (DMMM), a dialectically articulated framework integrating Daoist internal alchemy and Buddhist contemplative theory. It reconceptualizes mindfulness as a multilayered developmental architecture rather than a set of techniques or cognitive skills, extending beyond cognitive adjustment to encompass the psycho-physical dynamics of qi, shen (spirit), hun (cloud-soul), and po (white-soul). Grounded in the catuṣkoṭi framework, the DMMM proposes a reconstruction of the dialectical logic underlying certain contemplative traditions through a systematic integration of the subject–object polarity, and aligns with the second stage of Kwang-Kuo Hwang’s three-step epistemological strategy for developing indigenous psychology, contributing to theory-building within cross-cultural and indigenous psychological discourse. The study combines close textual analysis with a reflexive use of autoethnographic vignettes from long-term practice, not as reports of experience themselves but as an epistemically situated resource for model construction. The DMMM formalizes a recursively unfolding developmental trajectory of mindfulness cultivation into a system of four interrelated cycles, each comprising four distinct phases. Across these phases, the developmental qualities of faith, understanding, practice, and realization function as recurrent structural principles rather than merely experiential descriptors. By articulating the internal dynamics and causal coherence of these phases, the model offers a systematic theoretical account of how spontaneous or non-discursive states may be understood as structured by a deeper dialectical logic. These four cycles provide a framework for analyzing transformations in mindfulness across stages and traditions, thereby contributing to religious studies, contemplative studies, and cross-cultural psychological theory-building. Full article

E-commerce platforms increasingly rely on recommendation systems whose internal logic is often opaque, making explanation design important for consumer evaluation. Drawing on procedural justice theory, this study examines whether process-based explanations function as procedural justice cues in e-commerce recommendations and how they relate to algorithmic trust and continuance intention. In a between-subjects online experiment with 394 Chinese consumers (197 per condition), participants received either an outcome-based recommendation or a process-disclosure package that disclosed data inputs and reasoning and therefore bundled procedural content with greater specificity and informational richness. Relative to outcome-based explanations, this package increased perceived procedural justice and was associated with higher trust in the algorithm and greater continuance intention. Perceived procedural justice and trust formed a theoretically ordered indirect pathway, but this ordering should be read as theory-grounded rather than causally established because the mediators and outcome were measured contemporaneously. Exploratory moderation analyses suggested that responsiveness to process-based explanations reflected broader self-reported digital interpretive capacity rather than algorithm-specific literacy alone. Robustness checks further indicated that the procedural justice pathway was not eliminated by explanation clarity, cognitive load, scenario realism, product attractiveness, or privacy intrusiveness. The findings position process-disclosure packages as practical transparency tools while cautioning that their benefits depend on consumers’ interpretive capacity and processing costs. Full article

We present an interpretable evolutionary-fuzzy feature extraction framework for high-dimensional electroencephalography (EEG) classification. The proposed method combines an evolution strategy (ES) optimizer with fuzzy membership encoding to automatically discover compact, nonlinear feature representations from raw EEG signals. Applied to a chemosensory experiment distinguishing nasal breathing conditions during taste perception (N = 10 between-subjects participants, 1600 trials, 612 raw features), the framework achieves 89.50% cross-validated accuracy, equivalent to or exceeding all 25-feature baselines, while reducing dimensionality by 95.9% (from 612 to 25 features). The method produces fully interpretable fuzzy rules, enabling neuroscientists to inspect the decision logic rather than relying on nontransparent classifiers. A comprehensive validation including noise robustness analysis (0–30% Gaussian noise) and between-subjects generalization assessment is provided. Due to the between-subjects design, this study focuses on demonstrating the within-dataset discriminative capacity and the interpretability of the feature extraction pipeline, rather than claiming true subject-independent generalization. Full article

Battery locomotives used in underground coal mining operations require continuous maintenance, and failures occurring during these operations pose significant occupational safety and health (OSH) risks. Traditional Risk Assessment Methods (TRAMs), particularly the Risk Matrix Method (RMM), often fail to capture the uncertainty and subjectivity inherent in complex mining environments. This study develops a fuzzy logic-based risk assessment framework to improve the evaluation of accident risks associated with maintenance and repair activities in battery locomotive workshops of an underground coal mine in Turkey. Two fuzzy inference models (FL-Basic and FL-Advanced) based on expert knowledge and linguistic variables were designed using Mamdani-type inference with centroid defuzzification. The mathematical formulation of the fuzzy inference and defuzzification steps is presented explicitly, and a six-step algorithm formalises the proposed framework. The rule base of FL-Advanced systematically upweights the severity dimension relative to RMM through reassignment of 16 of the 25 consequent categories. The outputs of these models were compared with RMM to analyse their effectiveness in identifying critical hazards. Application results from Karadon Hard Coal Company show that the proposed FL-Advanced model significantly reduces ambiguity, prioritises high-severity risks more realistically, and provides a more consistent decision-making structure for OSH specialists. The study highlights the advantages of fuzzy logic for modelling uncertain, incomplete, and human-dependent data in hazardous underground mining conditions. Full article

Offshore drilling operations face the critical challenge of maintaining precise weight-on-bit (WOB) control during automatic drilling feed while subjected to vessel heave disturbances. This study investigates an integrated closed-circuit hydraulic cylinder lifting system that combines full-stroke drill string compensation with potential energy recovery capabilities, addressing the control coupling problem inherent in traditional split-design systems. A longitudinal vibration model of the drill string is established using lumped mass, stiffness, and damping principles incorporating the Rayleigh method. A co-simulation model implementing nested PID control logic is developed on the AMESim platform to evaluate automatic drilling feed performance under both passive and semi-active compensation modes. Simulation results demonstrate that the proposed integrated control strategy effectively mitigates bottom-hole WOB fluctuations, with top drive velocity accurately tracking set drilling feed rates (0.01–0.02 m/s) within a response time of approximately 10 s. The system maintains operational stability under sea conditions up to Grade 6 (heave wave height ≤ 4.578 m, period 14 s), beyond which accumulator piston limit-stroke collision risks emerge. These findings validate the feasibility of integrated hoisting-compensation design and establish quantitative operational limits, providing theoretical foundations for next-generation marine drilling systems targeting ultra-deepwater and natural gas hydrate exploitation. Full article

Objective Image Quality Assessment (IQA) is a fundamental pillar of computer vision, essential for optimizing tasks ranging from supervised machine learning to real-time video streaming. While IQA aims to quantify image degradation caused by noise and artifacts, a persistent gap remains between technical objective measurements and subjective human perception. Objective IQA has advanced significantly through full-reference (FR) metrics designed to approximate human judgment. Standard measures such as the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and root mean square error (RMSE) provide established benchmarks; however, they frequently fail to capture nuanced human visual preferences, often penalizing perceptually insignificant shifts or favoring overly smoothed images. Conversely, modern deep-learning metrics like LPIPS offer better perceptual alignment but remain computationally prohibitive for real-time, resource-constrained environments. This paper addresses these challenges through a dual-purpose approach. First, it provides a comprehensive survey and tutorial of the IQA landscape, offering self-contained mathematical derivations of classical error sensitivity measures, including MSE, RMSE, MAE, Euclidean distance, RMSLE, and Huber loss, as well as artificial neural network (ANN) approaches. This foundational review ensures a rigorous understanding of the field’s mathematical evolution. We introduce the Adaptive Contrast-Weighted Structural Similarity (ACSSIM) framework. ACSSIM is a lightweight hybrid metric that enhances classical FR-IQA by incorporating local weighting derived from human visual system (HVS) properties. Specifically, it targets Weber’s Law-based contrast and entropy, which are key elements of our hybrid quality assessment logic and key components of non-reference image quality metrics. Extensive numerical experiments on the TID2013 and KADID-10k benchmark show that ACSSIM improves correlation with human subjective judgments compared with the baseline PSNR and SSIM. Our results confirm that ACSSIM maintains low computational overhead, bridging the gap between efficiency and accuracy for practical deployment. We made our code publicly available to facilitate future research in efficient perceptual modeling. Full article

Reliable indoor localization is required for automated guided vehicles (AGVs), robot validation, and industrial digital-twin applications, but ultrasonic positioning can degrade sharply when acoustic visibility changes. This paper evaluates Marvelmind Super-Beacon localization in controlled laboratory experiments involving both AGV tracking and UR10 robot-arm positioning. The non-inverse architecture (NIA) and inverse architecture (IA) configurations are included as parallel validation scenarios to assess the robustness of the proposed mitigation framework across different Marvelmind deployment modes. The baseline analysis identifies the dominant acoustic failure modes, including multipath-induced scatter, crossover-zone handover jumps, update-rate degradation, complete non-line-of-sight (NLoS) outages, and height-dependent 3D jitter. To mitigate these effects, an embedded ultrasonic–inertial pipeline is implemented on an ESP32-S3-WROOM-1 module. The system combines UART packet validation, interrupt-driven ICM-20948 inertial acquisition at 500 Hz, sliding-window kinematic outlier rejection, and a 15-state error-state Kalman filter (ESKF). The embedded estimator logic is designed to maintain motion continuity during intermittent or corrupted acoustic positioning while reintroducing validated ultrasonic absolute corrections. Using recorded AGV and UR10 datasets, mitigation performance was quantitatively assessed through a firmware-consistent replay of the recorded measurements, using the same gating, inertial propagation, and measurement-update logic as the real-time ESP32-S3 implementation. Across ten trials per configuration, the replay-based trial-mean RMSE in the 2D AGV scenarios decreased from 101.2–104.1 mm for raw ultrasonic data to 47.2–48.7 mm after fusion, while peak failure-interval errors were reduced by 64.2–65.7%. In the 3D UR10 scenarios, replay-based trial-mean RMSE decreased from 157.6–158.4 mm to 80.2–80.5 mm, and peak height-sensitive 3D errors were reduced by 58.8–60.0%. The results demonstrate the feasibility of embedded ultrasonic–inertial robustness enhancement for localization in controlled laboratory AGV and robot-arm scenarios. While the proposed approach shows promising performance under the investigated conditions, further validation is required before extending the conclusions to larger-scale and dynamically changing industrial environments. Full closed-loop online robot localization and control based directly on the fused localization output remain subjects for future investigation. Full article

Databases serve as critical repositories of digital evidence in criminal investigations, and the recoverability of deleted data is a key determinant of forensic success. Microsoft SQL Server, one of the most widely deployed relational database management systems, has been the subject of multiple forensic studies examining how deleted records persist in physical database files across different acquisition methods. A previous study established a reference baseline using SQL Server 2008 and 2017, demonstrating that the Database Shrink operation causes version-specific and method-specific behavior: under logical collection with Shrink applied in SQL Server 2017, unallocated deleted data becomes fully initialized, rendering recovery impossible—a pattern not observed in SQL Server 2008 or under physical collection in either version. With the release of SQL Server 2025, the most significant architectural update to the platform in a decade, it remained unknown whether these forensic behaviors persist in the latest version. This study replicates the experimental design of in a controlled SQL Server 2025 environment, applying the same deletion scenario (DELETE command without conditions), the same two acquisition methods (logical and physical collection), and the same Shrink condition. The results demonstrate that SQL Server 2025 does not reproduce the version-specific initialization behavior observed in SQL Server 2017: across all four experimental conditions, deleted data residue in unallocated page space remains recoverable, indicating a fundamental change in the interaction between the Shrink operation and the logical collection mechanism. This recoverability is a double-edged property: while it benefits forensic investigators by preserving deleted evidence, it simultaneously represents a data-sanitization risk from a security and privacy standpoint, as deleted records are not reliably erased. These findings provide updated forensic guidance for digital investigators operating in contemporary SQL Server environments. Specifically, the results inform acquisition-method selection in real-world investigations where a suspect may have deleted records and where only a logical backup (.bak) is available to investigators. Full article

The Chinese state-owned enterprise (SOE) reform at the end of the twentieth century displaced millions of workers into a rapidly shifting market. How do displaced workers navigate the coexistence of historical structures, of relations of production and social reproduction, both old and new? This article takes up these questions through two films by director Zhang Meng—Piano in a Factory and Lucky Dog—that center on former SOE workers amidst postsocialist China’s new economic order. In both films, characters navigate life outside the SOE structure by improvising with limited resources and repurposing what remains available, by way of both creativity and constraint. Reading these acts, this article proposes the term labor of making-do to name a practical, creative activity through which subjects navigate limited material conditions, competing subjectivities, and disrupted social relations in the shifting reality. Drawing on embodied socialist skills and memory, making-do as a particular form of labor reproduces subjects in a new historical structure. In the face of the market, it sometimes functions outside market logics. While informed by the historical legacy of the socialist ideology of labor, the labor of making-do is not primarily a nostalgic attempt to restore socialist ideals; nor is it a deliberate resistance to capitalism. Rather, it demonstrates a lived, spontaneous and often unglamorous process by which precarious subjects negotiate sustenance and survival, and through which revised subjectivities are produced. It is through such contradictory and quotidian processes that social reproduction unfolds across coexisting historical structures. Full article