Search Results (75)

Clean and reliable integrated air refrigerators are essential for the cryogenic systems of Tokamak devices. However, the high-speed drive motors in these systems are subjected to severe axial asymmetric temperature span boundaries. The axial temperature difference between the compressor and expander ends can reach a maximum value of 180 °C. To mitigate the risk of localized thermal accumulation under such extreme conditions, this paper proposes a switchable cooperative thermal management strategy. The approach integrates internal cooling using self-produced cryogenic air from the expander with a dynamically switchable multi-channel water-cooling network. A segmented switching logic is implemented for the water-cooling channel and the cold-air channel at the bearing near the expander to adapt to variable thermal boundaries. A multi-source thermal network (MSTN) model was established, and the cooling parameters were optimized using a genetic algorithm (GA). Experimental results at 35,000 rpm under a −85.4 °C deep-cooling condition demonstrate that the strategy successfully maintains the peak motor temperature at 126.2 °C. The measured system cooling capacity of 17 kW surpasses the initial design target (15 kW), confirming the engineering feasibility and robustness of the proposed strategy for clean cryogenic cooling in fusion engineering applications. Full article

Reliable UAV command-and-control (C2) links require spectrum adaptation that responds to detected incumbent activity while keeping the ground station and aircraft synchronized. This study evaluates an incumbent-aware handoff workflow integrating FFT energy sensing, three-state temporal stabilization, pair-specific spectral admissibility filtering, bidirectional link-margin ranking, and a PROPOSE–ACK–EXECUTE transaction with retries and ordered backups. With a clean 32-window history, the corrected $K=768$ detector produced empirical $P_{\mathrm{fa}}=0.0997$ [0.0980, 0.1014] and $P_{\mathrm{d}}=0.9012$ [0.8995, 0.9029] at $-10$ dB. The point detection target was attained, while the 95% lower confidence bound was 0.8995. A representative logical detector-to-protocol composition at $-10$ dB and 10% independently imposed symmetric IID signaling loss gave a binary detection and recovery probability of 0.844 for the clean-history corrected $K=768$ profile, compared with 0.258 for corrected $K=51$; 95% occupied history reduced the $K=768$ value to 0.121. This composition does not exercise the mobility-coupled packet pathway or a common RF realization across sensing and signaling. In matched protocol trials, the full policy achieved $P_H=0.9321$ [0.9270, 0.9369] while removing EXECUTE; retries, backups, or margin-based ranking reduced reliability and changed the signaling–latency trade-off. Three idempotent EXECUTE transmissions reduced unresolved desynchronization to 0.008, and adding rendezvous recovery increased the eventual-recovery probability to 0.9582 at the cost of a 341-ms 95th-percentile recovery time. At 10% IID loss, the deadline-constrained success probability under the 250-ms internal interruption benchmark was 0.894. Bursty loss produced the largest degradation among the evaluated stress conditions. The results quantify how detector history quality, commit recovery, and protocol redundancy jointly affect reliability and latency. All reported quantitative validation is simulation-based or analytical/software verification; the fifteen-band by five-environment sweep is a static configuration-dependent operating envelope rather than field, hardware, or mobility-coupled validation. The reported detector–protocol integration is logical rather than fully RF-coupled, and no incumbent receiver or secondary-to-incumbent interference path is modeled. Full article

AI-assisted engineering workflows can assign stronger validity claims than their evidence supports. This study develops an executable evidence contract for preliminary power-electronics design by instantiating established multi-valued monitoring. A deterministic monitor checks input availability, numerical representation, admissible domains, model coverage, and semiconductor-loss allocation before assigning SATISFIED, VIOLATED, or UNRESOLVED to each constraint. A 2 kW, two-phase converter linking a 48–120 V battery to a 400 V bus provides the application. The monitor reproduces all 4096 local verdicts in 512 synthetic records and all 37 separate diagnostic controls. Under identical evidence rules, unresolved-as-pass produces 71.6% conditional unsupported acceptance, and unresolved-as-violation produces 89.4% conditional false violation. Global abstention preserves final decisions but retains only 35.8% local-state accuracy, showing why constraint-level localization matters. Seeded property checks, source-level mutation tests, and controlled revision replays corroborate the specified behavior. The revision workflow requires documented evidence updates through every supported route and invalidates stale evidence after design changes. Prior inputs and labels remain available through a versioned migration audit. The contribution is reproducible domain-specific decision assurance and traceability, not new three-valued logic. The results establish bounded software conformance, not evidence authenticity, independent physical-model validation, or hardware qualification. Full article

Restricting the $\left[[3L^3,2L^3+2,3]\right]$ FCC lattice code to a single triad sheet gives the $\left[[L^3,2L,L]\right]$ sheet code, with uniform weight-4 stabilizers and $K=4$ active connectivity. This code is isomorphic to L disjoint rotated 2D toric codes, so its memory is the toric code’s, and no density advantage is claimed. The contribution is a logical interconnect: native fault-tolerant joint Pauli measurement between selected logical qubits in independently encoded, co-located code blocks, with no dedicated routing region. Its natural use is entanglement generation and parity measurement between such blocks, since the $ZZ$ and $XX$ merges characterized here are exactly the two measurements a logical Bell measurement requires. Every FCC triangle has one edge in each triad sheet, so products of triangle measurements implement joint Pauli measurements across sheets while the merged code retains $d=L$. Finite-size crossing estimates under circuit-level depolarizing noise at $L\in \{4,6,8\}$ are $1.07\pm 0.05\%$ for the $ZZ$-merge, $0.76\pm 0.05\%$ with planar boundaries, and $0.91\pm 0.05\%$ for the $XX$-merge. The value of the primitive is that, for the pairs it reaches, it needs no routing region. Surface-code surgery merges only adjacent patches, so joining arbitrary pairs costs a reservation of roughly $1.5$ tiles per logical qubit; within the directly reachable set, the sheet architecture avoids that reservation, using $L^2$ physical qubits per logical, against $1.5L^2$ for a bussed toric array. That set is structured and limited: at the verified sizes of $L\in \{4,6,8\}$, each basis logical reaches L partners in one other sheet, giving $3L^2$ directly measurable pairs, corresponding to about $17\%$ of all logical pairs, and the same pattern is conjectured for general even L values. Three limits are stated rather than deferred: only the joint measurements are fault-tolerant primitives, the composed three-sheet CNOT being verified but not yet fault-tolerantly characterized; all thresholds are finite-size crossings at three lattice sizes, not asymptotic values; and pairs outside the reachable set would need routing as in any other architecture. Full article

Cardiovascular diseases remain one of the most critical medical conditions worldwide, often resulting in severe complications that demand early and precise diagnosis. While previous studies have predominantly addressed cardiovascular disease prediction using single-label classification models, such approaches are insufficient to capture the multi-label and interdependent nature of myocardial complications. Motivated by these limitations, this research proposes a novel framework that leverages a Hybrid Shallow Neural Network (HSNN) architecture, designed to balance model complexity and computational efficiency. Additionally, a new feature selection algorithm, termed Multi-label Gini Importance (MGI), is introduced, which thoroughly evaluates and selects the most relevant features across all labels by employing a Gini-impurity-based mechanism. To further enhance the integrity of the dataset, a K-nearest-neighbors-based imputation strategy is utilized for addressing missing values. Experimental evaluations show the superiority of the proposed methodology, demonstrating significant advancements over traditional machine learning algorithms and multi-label classification techniques. The proposed framework achieves an outstanding F1-score of $91.2\%$ and a Hamming loss of $0.052$. To further validate the practicality of the proposed approach, the HSNN model was implemented on an Altera Arria 10 GX FPGA platform, demonstrating its hardware efficiency and real-time processing capability. The hardware implementation achieves a maximum operating frequency of 210 MHz, a low inference latency of 1.85 $\mathrm{\mu }$s, and a high throughput of 540,000 inferences per second, while consuming only 4.2 W of power. Additionally, the design maintains low resource utilization across logic elements, DSP blocks, and memory, confirming its scalability and efficiency for embedded deployment. These findings affirm the robustness, interpretability, and predictive efficiency of the proposed system, offering a substantial contribution toward the development of intelligent clinical decision-support mechanisms for cardiovascular disease diagnosis and prognosis. Full article

Equipotential entry on ultrahigh voltage alternating current (UHV AC) transmission lines produces brief potential transfer current pulses with high amplitude. This study develops a coupled capacitance model that retains phase conductors A, B, and C, human body node M, and tower/ground reference G. Capacitances extracted with a three-dimensional quasi-electrostatic boundary element method (BEM) are incorporated into an electromagnetic transient (EMT) model with a cybernetic arc representation and explicit ignition and extinction logic. Under baseline conditions, the proposed model increased peak current by 2.281–2.985% and total specific energy by 10.189–12.868% relative to the traditional model. Coupling between the human body and the two nonworking phases contributed to the transient current balance at node M. Within the investigated configuration with an ideal source, phase-to-ground and phase-to-phase branch currents remained small relative to the peak transfer current. The calculated peak current and total specific energy were compared with reported 1000 kV tension tower measurements. Across the ranges examined in the sensitivity analysis, the values from the proposed model remained higher than the values from the traditional model, and total specific energy was most sensitive to the breakdown threshold and initial gap. Full article

Background and Objectives: Intraoperative sentinel lymph node (SLN) assessment is a pivotal step in early-stage breast cancer surgery. Frozen section (FS) analysis remains a widely utilised technique; however, its limitations, particularly in the detection of micrometastases, are well-documented. The process is both time-consuming and costly. The objective of this study was to investigate the potential of ex vivo shear-wave elastography (SWE) as a rapid and reproducible method for quantifying lymph node stiffness and predicting metastatic involvement. Materials and Methods: Between January 2022 and January 2024, 100 patients with biopsy-confirmed invasive breast cancer undergoing SLNB were enrolled in the study. A total of 384 excised axillary lymph nodes were subjected to ex vivo SWE immediately following excision, prior to the standard histopathological procedure. The mean stiffness (Emean) values were recorded, and the diagnostic performance was evaluated with the use of ROC analysis. Results: Of the 384 nodes examined, 45 (11.6%) were found to have metastases on final histopathology. It was demonstrated that Emean exhibited excellent discriminatory power, with an Area Under the Curve (AUC) value of 0.957. At the optimal cut-off of 28 kPa, the sensitivity and specificity levels were recorded as 91% and 87%, respectively. Conclusions: The findings of this study indicate that ex vivo SWE is a highly accurate and reproducible method for identifying metastatic sentinel lymph nodes. The technique has the potential to complement or selectively replace intraoperative FS, thereby reducing unnecessary tissue processing. The logical next step is multicentre validation. Full article

Bio-based phase change materials (PCMs) can increase transient heat storage in lightweight building envelopes, but their performance depends on the climate, transition properties, layer design, and assumptions used to translate thermal loads into carbon and cost indicators. Although PCM optimization, machine learning surrogates, and lifecycle assessment have each been studied extensively, comparatively few studies combine them while explicitly separating simulation-derived thermal outputs from scenario-dependent environmental and economic post-processing and benchmarking bio-based candidates against paraffin on a common wall area basis. This study develops a simulation-based screening framework for a south-facing office wall model using 18,000 EnergyPlus cases, climate-specific machine learning surrogates, TreeSHAP interpretation, NSGA-II optimization, and scenario-based lifecycle carbon and cost accounting. XGBoost achieved pooled held-out R² values of 0.974 for occupied discomfort degree-hours and 0.978 for total annual thermal demand. For Palermo, the directly re-simulated balanced configuration (T_m = 24.8 °C, L_h = 178 kJ/kg, 22 mm thickness, intermediate position) reduced occupant discomfort by 42.6% and the modeled single-zone total thermal demand by 6.0%. Under the central all-electric scenario (SCOP = SEER = 3.0, grid factor = 0.233 kg CO₂eq/kWh, 25 years), scenario-based net lifecycle carbon was −22.1 kg CO₂eq/m² for the analyzed south wall with an 8.0-year environmental payback, compared with −7.4 kg CO₂eq/m² and 19.2 years for RT28 paraffin. Energy savings did not recover the additional investment; the incremental lifecycle cost was +24.1 EUR/m². The theoretical contribution is a transparent, climate-dependent screening logic that couples surrogate interpretation with explicit evidence boundaries; the applied outcome is a palmitic–capric target-property region prioritized for laboratory validation rather than a deployment-ready product. Only directly re-simulated configurations are used for quantitative applied thermal claims; surrogate-only Pareto points are retained as exploratory screening candidates and are not interpreted as validated optima. The numerical results are specific to the modeled south-wall, single-zone boundary; whole-building and cross-regional application requires local recalibration and direct validation. By linking passive comfort, carbon accounting, material innovation, and responsible pre-experimental selection, the workflow is relevant to the decarbonization objectives represented by SDGs 7, 9, 11, 12, and 13. Full article

This paper organizes classical tools from complement topology into a local-to-global framework for primary meridian obstructions under admissible reconstruction. Let $D\subset M^d$ be a closed properly embedded submanifold of pure codimension c, and put $X=M\setminus D$. With coefficients for which the normal bundle is oriented, excision and the Thom isomorphism identify a relative fiber class ${\tau }_a\in H_c(M,X)$ for each component $D_a\subset D$. The connecting morphism sends this class to the global meridian ${\mu }_a=\partial {\tau }_a\in H_{c-1}\left(X\right)$. Thus, the familiar sphere or loop in a local normal slice survives globally precisely when ${\tau }_a$ is not supplied by an ambient c-cycle. Nonvanishing implies that the normal linking sphere is not null-homotopic and gives the primary degree–codimension relation $k=c-1$. Basepoint, orientation, disconnected-support, and nonorientable-normal-bundle issues are treated explicitly. A fixed-resolution tubular filling shows how a meridian can be killed when restoration of the defect center is admissible; under uniform tubular geometry, its support is controlled by the $(d-c)$-volume of the affected support. In codimension two, the surviving class lies in $H_1\left(X\right)$, the abelianization of the fundamental group, and is therefore the topological input available to a homotopy-invariant $\mathrm{U}\left(1\right)$ phase read-out. Compatible characters retain their meridian value under admissible continuation maps. The aim is not to introduce a new complement invariant or general classification theorem, but to provide a rigorous, reusable synthesis that keeps local detection, global survival, character evaluation, and continuation hypotheses logically distinct. Full article

This paper explores the problem of developing correcting functions that replace multiple unreliable (heuristic) recognition algorithms with a single, consistent decision function. The research concept stems from the fact that, in pattern recognition and collective expert decision-making problems, individual algorithms (heuristics) provide inconsistent or incomplete answers, requiring a mathematically sound method for their harmonization (correction) using test data. The methodological content of this paper consists of introducing classes of k-valued logic correcting functions that satisfy the constraints of preserving given sets (Constraint I) and monotonicity (Constraint II), and constructing algorithms for synthesizing a corrector that is optimal with respect to the linear performance functional for each combination of these constraints. The paper also provides a rationale for the optimality of the constructed algorithms (an optimality theorem) and an estimate of their time complexity. Full article

Voltage regulation of synchronous generators remains a significant and complex challenge in the field of engineering, particularly under varying load conditions. Although various control strategies have been applied to Automatic Voltage Regulator (AVR) systems for managing the terminal voltage of synchronous generators, Proportional–Integral–Derivative (PID) controllers continue to be one of the most fundamental and widely used approaches due to their simplicity, reliability, and robust structure. The tuning process, which involves determining the optimal values of the three fundamental parameters of a PID controller—namely the coefficients for the proportional, integral, and derivative terms (K_P, K_I, and K_D)—is essential to achieving the desired controller performance. While tuning can be performed through simple trial-and-error methods, such approaches often fail to yield satisfactory results. In this study, the tuning of a PID controller, which provides automatic voltage regulation for 1 kW stand-alone synchronous generator constructed as a real physical experimental setup, was performed using a novel hybrid method named ESOA-CDNN, which combines the Egret Swarm Optimization Algorithm (ESOA) and a Cascade Deep Neural Network (CDNN). The ESOA is utilized to optimize the number of hidden layer neurons, the weights, and the biases of the CDNN. Furthermore, since the PID controller can be readily implemented through a standard Programmable Logic Controller (PLC) within the proposed approach, there is no need for additional hardware in the control system. The PID controller tuning was conducted using four different methods: tuning via PLC, tuning using CDNN, tuning using CDNN optimized with Particle Swarm Optimization (PSO-CDNN), and the proposed ESOA-CDNN approach. Experimental results demonstrate that the PID controller tuned with the ESOA-DNN method significantly outperformed the others in terms of settling time and overshoot reduction. The experimental results under sudden load application (0–500 W, 0–1000 W, and 0–550 VA) and sudden load rejection (500–0 W, 1000–0 W, and 550–0 VA) demonstrate that the PID controller tuned using the ESOA-DNN method significantly outperformed the others in terms of settling time and overshoot reduction. Full article

This study presents and evaluates a system-level optimization of an electrical resistance tomography (ERT) measurement platform based on a ZYNQ-7020 all-programmable system-on-chip (APS-SoC). The design combines deterministic programmable-logic (PL) acquisition, processing-system (PS) configuration and communication scheduling, AXI/DMA data movement, Gigabit Ethernet transmission, and a seventh-order Butterworth excitation filter. FFT-based amplitude extraction and Tikhonov reconstruction remain on the host computer so that the reconstruction algorithm and regularization settings remain identical for the baseline and proposed systems; the present prototype is therefore not claimed as a fully standalone smart sensor. Under the same 16-electrode tap-water testing configuration, the average frame rate increased from 58.23 ± 1.88 FPS to 123.02 ± 1.62 FPS (mean ± sample standard deviation, n = 10), end-to-end latency decreased from 5.2 ms to 2.1 ms, SFDR increased from 68 dB to 95 dB, and SSIM increased from 0.72 to 0.94. In one representative static hardware record at 160 kHz, the calculated amplitude-stability SNR values were 65 dB and 100 dB for the baseline and proposed excitation paths, respectively, while total harmonic distortion decreased from 15.2% to 7.5%. These single-condition signal quality values are descriptive rather than uncertainty-bounded performance specifications. The image-quality differences are attributed primarily to cleaner boundary-voltage measurements with an unchanged reconstruction method, whereas the frame-rate gain reflects the combined PL/PS data path and Gigabit Ethernet upgrade. Full article

The startup process of gas turbines driven by the static frequency converter (SFC) exhibits complicated electromechanical coupling characteristics. Conventional simulation methods fail to integrate physical modeling with sequence of event (SOE) data and cannot support co-simulation of multiple startup schemes at the power station level. In this paper, a hierarchical digital twin architecture oriented to gas turbine SFC startup is established to realize intelligent deduction of sequential control and break through the technical limitations of traditional simulations. Relevant waveforms and data of the F-class heavy-duty gas turbine during startup are obtained via the digital twin. The maximum effective value of voltage is 12.07 kV, the maximum effective value of current is 1.6 kA, and the peak output power of the SFC reaches 15.67 MW. The system achieves the rated speed (3000 rpm) within an acceptable start-up duration, demonstrating satisfactory dynamic response. All the above data conform to the preset startup parameters and operation control logic of heavy-duty gas turbines. Full article

This study presents the design, development, and experimental validation of an automated flat blade grinding machine for the wood processing and plastic recycling industries in Ecuador. The machine was engineered following the VDI 2221/2222/2225 design methodology, integrating SolidWorks-based 3D modeling and ANSYS finite element analysis (FEA) to validate critical structural components. The selected configuration includes a Type 6 alumina grinding wheel (38A-60-K-VS), a mechanical clamping system, cutting fluid cooling, and a hardwired electromechanical control system that does not require a programmable logic controller (PLC). FEA results confirmed adequate safety factors (ηs > 16; ηf > 14) for the ACME 3/4–8 power screw under operational loads. Experimental testing on blade specimens (thickness: 3 mm; length: 70 mm; steel up to 60 HRC) demonstrated that four grinding passes at a 45° inclination angle reduced mean surface roughness (Ra) from 5.39 ± 1.83 µm (used blades) to 0.162 ± 0.092 µm, achieving values comparable to new blades (Ra = 0.601 ± 0.153 µm): a point-estimate reduction of 97% in mean Ra relative to the used-blade condition. The automated process reduced average grinding time by approximately 30% compared to manual methods, while maintaining noise levels within the 85 dB occupational exposure limit. Operator satisfaction surveys rated the system above 4.5/5.0 across all ergonomic and usability criteria. These results validate the proposed machine as a cost-effective, locally manufacturable solution to standardize blade maintenance in small and medium enterprises (SMEs) across Latin America. Full article

The global transition toward net-zero emissions has led to the restructuring of labor markets and an intensification of the demand for sustainability-competent business graduates. However, higher-education curricula lack an operationalized, job-competency-based framework, and this gap in knowledge is especially acute in emerging industrial economies that are facing pressures due to the ongoing decarbonization of the global supply chain. In this context, this study addresses two interrelated gaps in the relevant research: the lack of a structured system of criteria to assess competency in sustainability that is specifically geared toward business education, and the insufficient attention that has been paid to causal interdependencies among such criteria in previously developed frameworks. The authors apply a two-stage, hybrid multiple-criteria decision-making design based on the KSAO framework, which classifies professional competency into knowledge (K), skills (S), abilities (A), and other characteristics (O). A modified Delphi method that involved 12 academic and industry experts serving as surrogate assessors of competency requirements for business and management students was first used to consolidate 142 literature-derived items into 26 initial criteria, which were then refined into 12 core competencies in sustainability, identified through cross-domain expert consensus. Following this, fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) was applied to analyze the structure of causal influence among the retained criteria. The results identified interdisciplinary work as the primary driving competency and integrated problem-solving as the central hub with the highest prominence, with the two factors forming a bidirectional feedback dynamic that anchored the competency system. The retention of four “other” criteria (O-dimension)—ethical values, normative orientation, empathy, and adaptive resilience—confirmed that competency concerning sustainability in business education extends beyond technical knowledge into deeper dispositional attributes. These findings provide business schools in Taiwan with a structurally grounded logic of sequencing for their curricula, as well as a reference framework for curriculum design that is aligned with the Association to Advance Collegiate Schools of Business (AACSB) Societal Impact standards. While the findings are grounded in Taiwan’s specific ESG regulatory and industrial context, only the methodological approach is offered as a reference for comparable settings; the substantive findings require cross-national verification. Full article