Conferences Links
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Paper Review Claims |
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Paper ID |
Title |
Abstract |
Congress |
Claim |
| 1 |
79.Cnf-1225 |
Rule-Based Closed-Loop Service-Capacity Control
for Digital Queue Management in Government
Service Centers |
Abstract—Long and time-varying queues remain a practical
problem in government service centers, particularly when service
capacity is fixed while visitor demand changes during the day.
This paper presents a web-oriented digital queue architecture in
which ticket events provide feedback to a rule-based closed-loop
controller. The measured waiting time is compared with a desired
operating range, and the controller recommends increasing or
decreasing the number of active service counters. A time-stepped
evaluation compares a fixed two-counter policy with the proposed
policy over a 120-min demand profile. Under the same demand
and per-counter service-rate assumptions, the proposed controller
reduces the reported average waiting time from 5.91 to 1.69 min
and the maximum queue length from 76.32 to 47.19 visitors.
These changes correspond to reductions of 71.4% and 38.2%,
respectively, while the average number of active counters rises
from 2.00 to 2.25. The study therefore demonstrates a clear
waiting-time–capacity tradeoff and shows how ordinary digital
check-in records can support closed-loop operational decisions
without physical crowd sensors. The results are simulation-based
and are intended as a proof of concept rather than a field-validated
deployment.
Index Terms—digital queue management, feedback control,
government services, rule-based control, service capacity, waiting
time |
EngiTek 2026 |
Claim |
| 2 |
80.Cnf-1227 |
FED-Sentinel: Explainable Federated Multi-Modal
Learning for Evidence-Based Fault Detection in
Kubernetes Microservices |
—Kubernetes-orchestrated microservice deployments
fail in ways that outpace threshold-based monitoring: faults
cascade across loosely coupled, dynamically scheduled services
faster than a human operator, or a static alerting rule, can react.
This paper reports on FED-Sentinel, a multi-modal, federated,
explainable fault-detection pipeline that fuses container metrics,
Kubernetes events, distributed traces, and logs into a single
feature representation, scores that representation with a temporal
anomaly stage feeding an XGBoost–LightGBM stacking ensem
ble, aggregates model updates across simulated clusters under
differential privacy, and attaches SHAP-based explanations to
every prediction. Unlike prior work in this space, we separate
every reported number into two explicit classes: design targets
quoted against nine literature systems, and an independently
executed evaluation– five seeds, paired significance testing– against nine conventionally fitted detectors under identical
conditions. The executed pipeline reaches F1 = 0.9883 but is
not statistically distinguishable from a well-tuned Random Forest
(p = 0.11). An executed ablation shows the temporal anomaly
stage is not accuracy-additive and is the single most expensive
stage per inference; cross-workload zero-shot transfer collapses
(F1 = 0.1603); and differentially private federation is usable
only for privacy budgets ε ≥ 4. We report these negative and
boundary results alongside the positive ones and argue that an
honest accounting of which architectural components earn their
computational cost is itself a necessary contribution for systems
intended for production remediation. |
EngiTek 2026 |
Claim |
| 3 |
80.Cnf-1245 |
Stress-Testing an Evidence-First Bilingual Hiring Ranker: Keyword Stuffing and the Accuracy Trade-off in Arabic and English |
We study Muwafaq, a research prototype that ranks curriculum vitae (CV) documents for Arabic and English job ads by matching each job requirement to the best supporting work bullet or skills-list item, and that returns those evidence spans so a person can check each match. We stress-test it on a small synthetic benchmark of 250 CV and job pairs (25 jobs) with human grades. Its ranking quality, measured by normalised discounted cumulative gain at rank 10 (nDCG@10), is comparable to BM25 and higher than whole-CV embedding baselines. However, one visible keyword-stuffing line moves weak CVs up by about 4 to 5 places among ten, and every simple fix that cuts this gain also cuts accuracy. A copy-gate plus a gap penalty lowers the worst case over 20 attacks on 13 test jobs to about 2 places. A pool-level check, which compares each CV with the other CVs for the same job, lowers it to 1.1 (E5) and 0.7 (BGE-M3) places, at 0.035 to 0.047 lower nDCG@10 than the undefended score; this is close to, but not fully within, our 1-place and accuracy targets, and fake work bullets remain its worst case. Capacity-style and requirement-anchored fixes did not help, and a language-model judge loses accuracy and accepts fake bullets. We also report location, name and gender-wording checks and a small check on real CVs. We are not aware of a validated defence for visible keyword stuffing in CV ranking; ours is a partial mitigation. |
EngiTek 2026 |
Claim |
| 4 |
81.Cnf-1248 |
Comparative Assessment of Conventional and Microplane-Based Nonlinear Finite Element Modeling of Reinforced Concrete Beams in ANSYS |
Many civil engineering structures are constructed from Reinforced Concrete (RC). RC structures can be subjected to crushing, cracking, and reinforcement yielding, and hence their behavior is nonlinear. As a result, simplified linear analysis is inadequate in many cases. With the development of computers, nonlinear numerical analysis like the Finite Element (FE) Method became widely used. Furthermore, the FE method can be utilized in parametric studies with a lower cost compared to experimental testing. As a result, the FE method is an efficient tool for design decisions. ANSYS is a widely used FE analysis software in many applications. Modeling RC structures in ANSYS is possible using Solid 65 elements as it captures crushing and cracking behavior. However, solid 65 is a legacy element although it is still used in recent research works. In this study, solid 65-based modeling is compared with the new ANSYS technology the microplane model. A simply supported beam is considered with five modeling methods taking into account different rebar formulations. It is shown that the microplane model exhibits a smoother post-cracking response compared to the solid 65-based models. The effect of reinforcement modeling on results is minimal in terms of accuracy, but clear when it comes to numerical efficiency. |
EngiTek 2026 |
Claim |
| 5 |
46.Cnf-1228 |
Trends and technical prospects of hydrogen-battery hybrid energy storage systems: a bibliometric-driven analysis |
Hydrogen–battery hybrid energy storage systems (HESS) have emerged as a promising solution for integrating variable renewable energy sources by combining the rapid response of batteries with the long-duration capacity of hydrogen storage. However, no study has systematically mapped the bibliometric landscape of this interdisciplinary field to identify research trends, thematic clusters, and collaboration networks. This paper presents a bibliometric analysis of 366 Scopus-indexed documents published between 2003 and 2026, employing Biblioshiny for seven statistical analyses and VOSviewer for four network visualizations. Results reveal a compound annual growth rate exceeding 44% from 2019 to 2025, with China (133 articles) and India (124) leading global output. Keyword co-occurrence analysis identifies 11 thematic clusters centered on fuel cells, hydrogen storage, and battery systems, while bibliographic coupling reveals converging research fronts around optimization frameworks and techno-economic assessment. A focused technical analysis further examines the evolution of AI/ML-based energy management strategies, from classical pattern recognition to hierarchical deep reinforcement learning, and grid ancillary services from hybrid assets. The findings provide a structured roadmap for researchers and policymakers advancing hydrogen–battery integration for sustainable energy systems. |
GC-ElecEng 2026 |
Claim |
| 6 |
79.Cnf-1230 |
Volumetric reconstruction of lung cancer infection region based seed-guided fast marching method using computed tomography image processing |
Among all malignancies, lung cancer carries the heaviest mortality burden worldwide, and precise tumor-volume measurement on computed tomography (CT) scans underpins both treatment planning and longitudinal monitoring of the disease. The segmentation approaches dominating the field today are deep-learning-based, yet they demand extensive labeled training sets and offer limited insight into how a given tumor boundary was actually derived. Addressing this gap, the pipeline introduced here is a semi-automatic, seed-guided Fast Marching Method (FMM) that segments the tumor straight from CT images with no training data whatsoever, combining adaptive intensity-based thresholding, geodesic-distance region growing, and morphological refinement, driven by nothing more than a handful of user-selected seed points on each slice. The method was tested on 18 tumor cases from 10 patients, with each automatic result compared against an independently hand-drawn ground-truth mask. The proposed pipeline achieved a mean Dice coefficient of 0.811±0.100, an IoU of 0.692±0.128, a Sensitivity of 0.696±0.132, and a Precision of 0.994±0.010, with Precision exceeding 0.96 in nearly every case. Taken together, these figures demonstrate that a fully interpretable, training-free segmentation method can still deliver strong volumetric accuracy alongside a markedly low false-positive rate, making it a practical alternative to deep-learning-based tumor segmentation wherever annotated data or computing resources fall short. |
EngiTek 2026 |
Claim |
| 7 |
80.Cnf-1232 |
A comparative machine learning approach for predicting cardiovascular disease risk using clinical features |
Cardiovascular disease (CVD) remains a major global health challenge, highlighting the need for accurate and data-driven approaches for risk assessment. This study presents a comparative machine learning framework for predicting a continuous CVD risk score using clinical and demographic features. A dataset of 1,529 samples with 21 candidate features was preprocessed using appropriate imputation, categorical encoding, and feature scaling techniques. To identify the most informative predictors, a consensus-based feature selection framework was applied, resulting in eight key features, including body mass index, systolic blood pressure, total cholesterol, estimated low-density lipoprotein, diabetes status, waist-to-height ratio, fasting blood sugar, and high-density lipoprotein. Eight regression algorithms were evaluated, including Linear Regression, Ridge Regression, Decision Tree, Random Forest, Gradient Boosting, XGBoost, Gaussian Process Regression, and Support Vector Regression. Among the evaluated models, Support Vector Regression achieved the best performance, with a mean absolute error of 0.1328, root mean squared error of approximately 0.290, and an R² of 0.930 on the test set. Cross-validation also demonstrated consistent predictive performance. The findings highlight the potential of machine learning and data-driven feature selection for clinical risk prediction and support the development of reliable predictive tools for cardiovascular health assessment. |
EngiTek 2026 |
Claim |
| 8 |
55.Cnf-1233 |
Design Vision for Developing a Flying X-ray Fluorescence Spectroscopy System |
Traditional methods for geochemical exploration in rough terrain are restricted due to cost, accessibility, and inadequate spatial coverage. This paper presents a conceptual design roadmap for an integrated airborne system using x-ray fluorescence spectroscopy (XRF). This conceptual design depicts the relevance of this technology as an aerial platform capable of conducting in-situ elemental analyses. By creating this vision of how the various components and subsystems (ie. spectrometer, airborne systems, methods of data collection, etc.) could theoretically be integrated into a working airborne imaging hyper-spectral sensor, and creating an example of what a design like this could look like, demonstrates how these systems will operate as one functioning system. The proposed air payload system will also allow geochemists the capability to perform geochemical surveys with increased accuracy and effectiveness in challenging environments. This project is only conceptual and/or exhibits the theoretical limitations, issues, and potential possibilities of this type of system without the use of prototypes or performing real-world experiments. |
EngiTek 2026 |
Claim |
| 9 |
79.Cnf-1234 |
Role of hydrogen in achieving net-zero aviation goals: a review |
The current goals to achieve net-zero aviation face many challenges, including finding alternative fuels with high energy density and meeting strict safety standards. Hydrogen is a promising solution for reducing carbon emissions in aviation. This review discusses the potential of hydrogen to achieve net-zero aviation, focusing on combustion-based propulsion, electric fuel-cell propulsion, and onboard storage technologies. The key advantages and engineering barriers to aircraft integration with hydrogen are compared with jet fuel (kerosene). Combustion-based propulsion has led to innovations in lean-burn combustors, especially for larger aircraft (> 200 seats), while electric propulsion using fuel cells would be a viable option for UAVs and smaller regional aircraft due to its high efficiency and zero combustion emissions. Hybrid propulsion (combustion- and electric-based) is suggested for medium aircraft (~ 150 seats). Despite hydrogen's promise, challenges such as NOx emissions, cryogenic storage, thermal management, and the necessary infrastructure at airports remain to be addressed. Hydrogen may not be the complete short-term solution for low-carbon aviation—like sustainable aviation fuels—but it is expected to be vital in a broader strategy that includes both sustainable aviation fuels and hybrid technologies. |
EngiTek 2026 |
Claim |
| 10 |
79.Cnf-1236 |
Stage-Wise Probabilistic Risk Assessment of a Pressurized Water Reactor Coupled With a High-Temperature Electrolysis Hydrogen Facility |
The integration of light-water reactors with high-temperature steam electrolysis represents a viable approach to producing large commercial quantities of clean hydrogen in the near future, but it is necessary to demonstrate that the heat removal system, the direct electrical connection, and the hydrogen handling plant do not compromise reactor safety before the arrangement can be granted a license. This work carries out a stage-wise probabilistic risk assessment for a typical pressurized water reactor supplying 100, 500 and 1000 MW electrolysis plants, using the general data and fault-tree logic from the Idaho National Laboratory report INL/RPT-23-74319. The coupled plant is divided into three phases and 24 processing stages, the hazards of which correspond to steam-line breaks, transients, loss of off-site power at the switchyard and seismic events. The fault trees for the initiating events have been reconstructed and re-quantified using the smallest possible cut sets, then compared with the acceptance criteria set out in 10 CFR 50.59 and Regulatory Guide 1.174. The re-quantification agrees with three previously published result sets to within 0.3%. The greatest increase in the number of initiators is 7.69% (in the case of a steam-line break with two steam taps), which is below the 10 % limit, and the largest increase in core damage frequency is 1.9E-8 per reactor-year, occurring in Region III. Failure of the normally open isolation valve at each steam tap is responsible for more than 99% of this increase, while hydrogen detonation and generator overcurrent have a negligible effect. Safety margins depend on the number of steam taps, the valve rupture data, and the credited ventilation of the electrolysis enclosures; without that ventilation credit, the 1000 MW facility would increase the loss-of-off-site-power frequency by 10.8%. |
EngiTek 2026 |
Claim |
| 11 |
79.Cnf-1237 |
Structural integrity of spent nuclear fuel cladding under transportation loads: a thematic review and knowledge map |
The cladding of spent nuclear fuel must remain intact during handling and transport for retrieval. This article brings together eight previously published studies concerning the structural integrity of zirconium-alloy spent fuel rods under both normal and hypothetical accident conditions that could occur during transport, grouping the studies into five main themes: degradation-driven property characterization, multiscale structural modeling, uncertain rod-level parameters, failure criteria, and deterministic-probabilistic and margin-based assessments. The theories, methods, findings, and limitations are identified and summarized; a knowledge map is given as both a hierarchical outline and a mind map; and the collection of studies is compared with eleven recent external studies. The resistance of the rods is controlled by pellet-cladding bonding, spacer grid stiffness, and ductility that depends on the fuel cladding characteristics; under strain-based failure criteria, fuel with a high level of burnup has failure probabilities which are many orders of magnitude greater than those of fuel with lower burnup in the case of a 9-m side drop; and the normal transport loads measured are still well below the failure limits. Uniform elongation and hydrogen content are the most closely connected concepts, while the relationships among interfacial bonding, hydride-sensitive failure criteria, microstructural models, and probabilistic assessment remain underexplored. The paper also outlines future research priorities. |
EngiTek 2026 |
Claim |
| 12 |
79.Cnf-1238 |
An early fusion framework for monitoring multi-profile generating processes |
Modern manufacturing processes are monitored by several sensors that produce autocorrelated, multivariate profile signals, which violate the assumptions of classical multivariate control charts. Encoding time series as images and classifying them with Convolutional Neural Networks (CNNs) has proven effective for single-sensor profile monitoring, but its extension to multi-sensor processes remains limited. This paper proposes a multivariate image-based monitoring framework in which each sensor profile is encoded as a Gramian Angular Summation Field (GASF), and the images of all sensors are fused early by stacking them as channels of a single input to a compact convolutional network. The framework is evaluated on a six-sensor semiconductor wafer etching dataset of 1,194 production runs using stratified five-fold cross-validation with three input resolutions of 32, 64, and 100 pixels. The results show that the accuracy and fault detection improved steadily with resolution. At the full resolution of 100 pixels, the early fusion model achieved a mean accuracy of 98.66%, an F1 score of 0.993, and an area under the ROC curve of 0.976, detecting 93% of out-of-control runs while raising false alarms on fewer than 1% of in-control runs, with a training time of about 72 seconds per fold. These results compare favorably with a published Gaussian process benchmark on the same dataset. |
EngiTek 2026 |
Claim |
| 13 |
55.Cnf-1240 |
Improvement of aliasing rejection in a two-stage decimation filter using certain symmetrical polynomials |
This paper presents a novel two-stage multiplierless decimation filter with improved alias rejection. The first and second stages are corresponding comb filters. Additionally, the second stage cascades two modified comb filters of different lengths. The modified comb filter is obtained by converting to a zero coefficient at the comb's point of symmetry in the second stage. If the comb is odd, it has one point of symmetry; if it is even, it has two. As a result, the overall comb-folding bands gain new zeros, increasing aliasing rejection. We express complexity in the number of adders per output sample (APOS). We illustrate the method with two examples. Comparisons with recent methods demonstrate the benefits of this proposal. |
EngiTek 2026 |
Claim |
| 14 |
90.Cnf-1210 |
Supervised neural network based intrusion detection systems |
The magnificent problem for today’s computer networks is the internet attacks which is growing rapidly. Therefore, applying security methods to prevent such attacks on computer network is very important. Network attacks are challenging because they are continuously changing their patterns and techniques. Machine learning approaches play vital roles in detecting, and preventing attacks in different types of computer networks. The security is a critical issue whereas the security mechanism may affect the way of detecting, analysing, and preventing attacks. Therefore, security methods must modify their techniques to deal with. We have proposed Supervised Neural Network Based Intrusion Detection System (SNN-based IDS) to address a critical and timely issue in computer networking - the escalating threat of internet attacks. As these attacks continue to evolve, finding effective methods to detect and prevent them is of paramount importance. this paper leverages advanced technology to tackle the complex task of intrusion detection. This demonstrates the practical application of cutting-edge methods in cybersecurity. The paper suggests that the proposed model has the potential to predict and mitigate new types of attacks, even those not present in the training dataset. This feature is crucial in a rapidly evolving threat landscape. Our SNN is trained by KDDCUP’99 dataset which include 14 attack types in the test data, with an overall number of 24 training attack types. Experiments results show that the proposed system is capable to identifying the attacks and classifying them with high accuracy and reliability, regardless of the dataset's nonlinearity, size, or incompleteness. |
CompuSys 2027 |
Claim |
| 15 |
79.Cnf-1211 |
On the scalability of PM-EDS maglev systems:from laboratory prototypes to full-scale operation |
As a passive and energy-efficient alternative systemto the maglev technologies; Permanent Magnet ElectrodynamicSuspension (PM-EDS) systems was offered. However, scalabilityremains unclear. This paper present an analytical study of PM-EDS scalability using dimensionless parameters derived fromelectromagnetic relationships. Scaling conditions required topreserve system behavior across different sizes are established,showing that scalability depends on maintaining keydimensionless parameters. The results further reveal that systemperformance is constrained by the coupled effects of velocity,magnetic field strength, and system geometry, as well as regime-dependent behavior. |
EngiTek 2026 |
Claim |
| 16 |
79.Cnf-1212 |
NEUROFLOW: Early ICU delirium prediction via temporal convolutional networks with FHIR-native intervention orchestration |
Intensive care unit (ICU) delirium affects an estimated 7 million United States patients annually and is associated with 30-40% increases in one-year mortality, yet the evidence-based ABCDEF prevention bundle achieves only 0-1% worldwide compliance. Existing machine learning systems generate a risk score and stop, leaving the prediction-to-intervention gap unaddressed. This paper presents NEUROFLOW, a FHIR-native ICU delirium prediction and intervention orchestration system. A 56,065-parameter dilated causal Temporal Convolutional Network (TCN) with a learned pharmacodynamic embedding processes a 40-channel, 63-hour time-series tensor constructed directly from HL7 FHIR R4 resources. Trained on 74,829 MIMIC-IV ICU stays under strict temporal holdout validation, NEUROFLOW achieves AUROC 0.8125 and AUPRC 0.5638, warning 77.4% of eligible patients a median of 20 hours before clinical detection. When predicted risk exceeds a threshold, the system automatically writes a US Core CarePlan with six ABCDEF activities and scores bundle compliance hourly from existing FHIR Observations. Split conformal prediction intervals are encoded as structured FHIR extensions. Zero-shot external validation on 132,900 eICU admissions across 208 community hospitals yielded AUROC 0.6568; a label-noise analysis identified 11,405 clinically plausible undocumented cases, and an in silico sensitivity reclassification to the training prevalence recovered AUROC 0.8893, indicating the external gap reflects documentation incompleteness rather than model failure. |
EngiTek 2026 |
Claim |
| 17 |
79.Cnf-1253 |
Electric Vehicle Routing with Time-Dependent Waiting Time at Charging Stations Using Stochastic Optimization |
This paper addresses electric vehicle routing problems with deterministic and stochastic waiting times at charging stations. A new model is proposed to account for time-dependent queue delays to minimize total operating costs and penalties for late deliveries by optimizing routes and charging-station delays. The solution was proposed using a two-stage stochastic optimization methodology that combines adaptive large neighborhood search with a simulation-based approach to handle the complexity of the problem. Experimental results show that considering time-dependent stochastic waiting times in fleet planning results in more robust plans, lower total cost, and better customer experience due to fewer late deliveries. Real-time or dynamic routing could be a future extension in addition to non-homogeneous fleet considerations. |
EngiTek 2026 |
Claim |
| 18 |
55.Cnf-1213 |
Parametrically Efficient Conv‑TasNet for 44.1 kHz Speech Separation |
Speech separation is a fundamental task in audio processing, yet most state-of-the-art models are trained on low-frequency datasets (8–16
kHz) and are not readily adaptable to high-quality
audio (44.1 kHz). In this work, we propose
a modified Conv-TasNet architecture adapted
for high-fidelity speech separation at 44.1 kHz.
Our contributions include architectural modifications — replacing global layer normalization with
GroupNorm, introducing dynamic padding for
variable-length inputs, and systematically tuning
hyperparameters (kernel size, stride, TCN depth).
We generated a custom VCTK-2mix dataset comprising 30,000 training mixtures with controlled
SNR and implemented a fully reproducible pipeline
in PyTorch. Extensive experiments demonstrate
that our model achieves an SI-SNR of 16.88 dB,
SDR of 17.33 dB, SIR of 26.38 dB, and SAR of 17.92
dB, surpassing the original Conv-TasNet (15.3 dB
on WSJ0-2mix at 8 kHz). Our findings indicate
that increasing kernel size relative to the original
architecture is beneficial for 44.1 kHz processing,
while maintaining a compact model size suitable
for resource-constrained environments. The source
code is publicly available to ensure reproducibility. |
EngiTek 2026 |
Claim |
| 19 |
55.Cnf-1214 |
Application Of Rüppell's Fox Optimizer for Solving Economic Dispatch in Power System |
Economic Dispatch (ED) allocates power generation among thermal units to minimize total fuel cost subject to operational constraints. The resulting optimization is made non-linear and non-convex by real-world factors such as valve-point loading effects, transmission losses, and emission penalties. In this paper, the Rüppell's Fox Optimizer (RFO), a metaheuristic inspired by the foraging behaviour of Rüppell's foxes, is applied to constrained ED problems. RFO uses a multi-sensory search mechanism that combines eyesight, hearing, and scent-driven strategies to transition between exploration and exploitation. Two standard test systems are used for validation: a 6-unit IEEE 30-bus system with quadratic cost functions and B-coefficient transmission losses, and a 10-unit system with valve-point effects, losses, and emission penalty cast as a single-objective Combined Economic Emission Dispatch (CEED) problem. On the 6-unit system, a best fuel cost of 800.9034 $/hr was achieved by RFO, compared to PSO (806.1893 $/hr) and WOA (809.3234 $/hr), with statistical significance confirmed by the Wilcoxon rank-sum test (p < 0.001). On the 10-unit CEED, the lowest fuel cost of 112,886.6560 $/hr was recorded by RFO. Over 30 independent runs, RFO is shown to be a competitive solver for ED problems in power systems. |
EngiTek 2026 |
Claim |
| 20 |
55.Cnf-1216 |
Wavelet-Based Multiresolution Analysis for Fault Detection in Nonlinear PMSM Drives A Finite Element Study |
Wavelet-based multiresolution analysis is utilized
for fault diagnosis in permanent magnet synchronous motors
operating under direct torque control. The inherent challenges of this control method, namely variable switching frequency and hysteresis tolerance, are addressed by applying the maximum overlap discrete wavelet transform to the estimated electromagnetic torque signal. This noninvasive approach extracts time-frequency characteristics of the torque signal while isolating
the switching and hysteresis impact in separate bands to ensure clear identification of fault related components. The RMS values of the wavelet detail coefficients serve as inputs to a quadratic discriminant analysis classifier that not only detects and differentiates common faults including turn-turn short circuits, mixed eccentricity, and partial demagnetization but also estimates their severity levels. Validation is performed through high fidelity simulations using ANSYS-MAXWELL involving a finite element machine model integrated with the drive system implemented in ANSYS-SIMPLORER, under both normal and faulty conditions. This method also supports the creation of fault detection databases in practical settings, mitigating the scarcity
of experimental fault data. |
EngiTek 2026 |
Claim |
| 21 |
80.Cnf-1220 |
HAI-KDG: A Computable Framework for Measuring the Human–AI Knowledge Divergence |
Abstract— Generative artificial-intelligence (AI) systems now produce candidate knowledge faster than human experts can audit it, creating a widening and largely unmeasured asymmetry between what machines produce and what humans can comprehend, verify, and use. We argue that this asymmetry should be measured rather than described. Therefore, we introduce HAI-KDG, a framework which formalized as a five-component vector: scale, novelty, assimilation cost, verifiability, and rate of change. In this paper, three components of the framework are computed and validated, while the remaining two are formally specified with a calibration protocol fixed in advance. An explicit proximity criterion defines the machine-unique set, a two-level novelty measure combines distributional divergence with a cross-corpus neighbor-distance ratio (CNDR), and a five-layer architecture keeps its upper layers portable across domains. Directed single-stimulus tests on synthetic corpora with known ground truth show, over ten seeds, that each computed dimension responds selectively to its own stimulus and is robust to the framework's free parameters. A complete 3×3 grid of real-corpus studies exposes a dissociation that replicates across every condition: at lexical embeddings, fluent machine text diverges distributionally in seven of nine conditions (up to 9.2 control standard deviations, Holm-adjusted p < 0.05) while instance-level isolation stays near chance, a pattern that survives a change of lexical representation. Generated code inverts it, separating at both levels and reaching 0.986 ± 0.007 AUROC, where CNDR leads the density-based baselines but is indistinguishable from nearest-neighbor distance; its clear advantage over all baselines appears instead in the weak-signal text conditions. |
EngiTek 2026 |
Claim |
| 22 |
55.Cnf-1222 |
A Compact Tri-Band Printed Antenna for Future Wireless and 6G-Oriented Applications |
This paper presents a compact tri-band printed antenna based on a branched planar radiator, a tapered microstrip feeding structure, and a Partial Ground Plane. The antenna is designed on a 30 × 30 mm² Rogers RO4003C substrate with a thickness of 0.813 mm, relative permittivity of 3.38, dielectric loss tangent of 0.0027, and 0.035 mm copper thickness. The geometry was developed to produce three resonant operating points around 2.45, 3.5, and 5.8 GHz using different effective current paths in the branched radiator. The tapered feed provides a gradual transition from the wider 1.8 mm input section to the narrower 1.1 mm radiator-side section, reducing the impedance discontinuity and supporting the matching between the excitation line and the radiating structure. The three bands have VSWR of 2.55, 1.64 and 1.46 respectively, and realized gain of 1.12, 3.02 and 3.13 dB accordingly. The total efficiency of 82.1, 82.3 and 90.4 % are recorded at 2.45, 3.5 and 5.8 GHz respectively. Far-field results show frequency-dependent radiation characteristics, while the simulated current distributions demonstrate that field concentration changes with frequency and follows different portions of the branched radiator. The proposed structure provides a simple single-element approach for multi-band wireless. |
EngiTek 2026 |
Claim |
| 23 |
80.Cnf-1221 |
Using Hussein Search Algorithm for Optimizing Search in
Metadata of IDS |
Metadata analysis is the process of examining metadata
to discover patterns, relationships, trends, and useful information
about the underlying data. Query latency is
the total time from when a user submits a query until
the result is returned. It typically includes:Network Delay,
Query Parsing, Index Lookup, Search Time, Ranking,
Result Formatting,Transmission. Therefore, reducing
search time, or index lookup can reduce query Latency.
This paper explains how The Hussein Search Algorithm
is used for efficient search in Intrusion Detection Systems
(IDS) metadata. We evaluate its performance on synthetically
generated uniform and skewed metadata distributions
and compare it against classical algorithms including
binary search and interpolation search. Our results
demonstrate that the Hussein Search Algorithm achieves
expected constant-time average complexity with lower
memory overhead than hash-based methods while maintaining
competitive speed. The performance of Hussein
Search Algorithm (HSA) in term of search time per query
outperforms binary search algorithm, where it is 2.3-2.6
times faster than binary search algorithm for uniform data
distribution and highly faster than interpolation search for
skewed metadata. In addition, HSA maintains its advantage
in search time when the datasets size is increased,
whether, the searching time of the other algorithms increases
with the growth of the metadata size. |
EngiTek 2026 |
Claim |
| 24 |
79.Cnf-1223 |
Enhancement of the structural and optical properties of perovskite solar cells by the implementation of different nanostructured electron transmission layers |
The purpose of such research is to comprehensively
investigate the impact of various structures of Electron
Transmission Layer (ETL) on the optical and structural
properties of Perovskite solar cells. Four structures of electron
transmission layers were investigated with controlled
thicknesses. These structures were a single-layer of ⁓ 50 nm
ZnO, a single-layer of ⁓ 50 nm TiO2, Bi-layer of TiO2 / ZnO
(10/50 nm), and a Tri-layer of TiO2 / ZnO / TiO2 (10/50/10 nm).
UV-Vis spectroscopy revealed that the ZnO thin film of ⁓ 50
nm thickness transmits higher amount of visible light toward
Perovskite thin film than its counterparts, while TiO₂ ETLs is a
perfect UV light-blocker. XRD and SEM analyses showed that
a ZnO single layer enhanced perovskite crystallinity up to ~
89.5% and reduced strain by ~ 96%, making a smoother, more
uniform polycrystalline surface. These findings confirm ZnO as
the most efficient ETL for improving PSC performance, and
TiO₂ offering additional UV protection. This work is a
synergetic marriage between nanotechnology and the perfect
light-absorbing material – Perovskite, –where a very efficient
light-harvesting device with high crystallinity, higher stable and
less amount of defect is produced. |
EngiTek 2026 |
Claim |
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