Engineering

Abstract: The memristor, theorized as the fourth fundamental passive circuit element, has attracted intense interest for next-generation memory and neuromorphic computing. Gel-based memristors exploit ionic migration within hydrated polymer matrices to produce history-dependent conductance, offering mechanical flexibility, biocompatibility, and simple solution processing. Although magnetically assisted switching via the Lorentz force remains conceptually appealing, prior demonstrations required lithographically patterned electrodes and purified ionic liquids, which precluded low-cost, informal experimentation. The ORLOK (Organic Response-Linked Output Key) project addressed this limitation by realizing a volatile gel-electrolyte memristor fabricated entirely from commercial-grade materials. The active layer utilized a sodium-alginate gel doped with dissolved iron ions and sodium chloride, housed within a polymeric straw bounded by aluminum foil and carbon-felt electrodes. Hand-wound solenoid coils were used to generate perpendicular magnetic fields of approximately 5.3 mT (copper, 20 turns) and 1.7 mT (steel, 10 turns) to deflect mobile ionic trajectories. Current-voltage characterization using Arduino Due, the baseline device exhibited a quantifiable hysteresis loop with an integrated area of 0.000158 mA·V and a resistance coefficient of variation of 0.125, confirming genuine memristive behavior. Volatile memory testing achieved 99.07 +/- 0.2% read/write accuracy at 1.82 kHz, demonstrating memristive effects. Although the Lorentz force exerted on individual ions was negligible, collective perturbation from thermal and electrochemical factors within the mobile ionic population were sufficient to modulate conductivity. These results establish a proof-of-concept for neuromorphic, low-cost logic elements fabricated from accessible precursors.

Abstract: A bidirectional DC-DC converter is the part that lets an electric vehicle both draw a fast charge and push power back during vehicle-to-grid (V2G) operation, so its control decides how well the DC link holds up when the load jumps or the power reverses. This paper sets out a full switching-level model of a synchronous half-bridge converter that sits between a 400 V battery pack and a 750 V DC link at 50 kW, and it controls that converter with a two-loop sliding-mode scheme: an inner sliding-mode current loop with a boundary layer to limit chatter, and an outer loop that holds the link voltage. The controller is written out term by term and compared against a conventional PI cascade on the same plant. Under a 40 to 100 percent load step the sliding-mode controller settles the link in 0.72 ms with a 2.66 percent dip, against 1.75 ms and 4.20 percent for the PI cascade, and its response barely changes when the inductor is 40 percent larger and its resistance 50 percent higher than the controller assumes, which is the invariance property sliding-mode control is meant to give. Power reverses from full discharge to full charge in 0.255 ms. A converter loss model puts peak efficiency at 98.44 percent near 19 kW and 97.79 percent at the rated 50 kW. The converter is then driven by a real-world charging-demand profile taken from 41,213 charging sessions recorded at the Newcastle Helix site, and it holds the DC link within 9.53 V across the day. Every figure comes from the accompanying code and can be regenerated.

Abstract: Dual-parameter photonic sensors often report a single detection limit without specifying its statistical definition or calibration transferability. This fully computational study evaluates these issues in a coupled interface-mode multilayer for simultaneous refractive-index (RI) and temperature sensing. The two resonances occur at 1517 and 1651 nm, with loaded Q factors of 232 and 208 and RI sensitivities of 90.71 and 329.41 nm/RIU. Modal-overlap analysis links the sensitivity matrix to distinct thermal-to-index response ratios. Bounded nonlinear calibration yields held-out errors of 2.43 × 10⁻⁵ RIU and 0.155°C. At 1% false alarm and 95% detection, device-specific probability-of-detection limits are 4.36 × 10⁻⁵ RIU and 0.123°C; transferring one nominal calibration across devices worsens them by factors of 81 and 203 under the linewidth-limited repeatability model. A sensitivity-matched trivial control gives statistically equivalent yield within the declared margin, bounding the topological claim to the studied operating point. Structural audits further show that the sensing modes are cavity-selected, the nanolaminate provides no net measured performance benefit, and hyperbolicity is unobservable at normal incidence. The results support bias-aware detection and calibration-transfer analysis as more informative benchmarks than nominal sensitivity alone.

Abstract: Improving the performance and lifespan of lithium-ion batteries is a key challenge for the development of electric vehicles. However, accurately estimating the state of charge (SoC), state of health (SoH), and lifecycle remains complex due to the electrical, thermal, and aging phenomena associated with these energy storage systems. Against this backdrop, this study proposes a hybrid approach combining an electro-thermal model with a feedforward neural network (FNN) to improve the estimation of key lithium-ion battery performance indicators within a temperature range of 0 °C et 40 °C. The developed methodology was implemented in MATLAB/Simulink and applied to the analysis of the vehicle's power profile, as well as the evolution of SoC, SoH, and battery lifecycle. The results demonstrate an accuracy of 95.3% for State of Charge (SoC) estimation, with a mean absolute error of 4.7%. For State of Health (SoH) estimation, the accuracy is 95.8% accompanied by a mean absolute error of 4.2%. Lastly, for lifecycle prediction, the accuracy is 92.5% with a mean absolute error of 7.5%. The performance results demonstrate the robustness of the proposed approach and its ability to replicate battery dynamic behavior under climatic conditions representative of the African context. This contribution opens up promising avenues for optimizing battery management systems and advancing the sustainable development of electric mobility.

Abstract: Ordinary differential equations (ODEs) are essential for modeling and governing physical phenomena and engineering systems across a wide range of scientific and engineering disciplines. Conventional photonic ODE solving systems exhibit a limited coefficient tunability, where the ODE coefficients are constrained by the device architecture. Here, an order- and coefficient-tunable microwave photonic (MWP) ODE solver based on an integrated microcomb source is demonstrated. A transversal filter structure is employed to directly synthesize the desired transfer function through convolution operations, enabling independently tuned ODE coefficients. We experimentally demonstrated simplified first-order, general first-order, and second-order MWP ODE solvers with different coefficients. For the input Gaussian pulse with a pulse width of ~0.1 ns, the measured output waveforms of the ODE solvers agree well with the calculated results, confirming the effectiveness of our approach. A highly reconfigurable MWP ODE solver with high processing accuracy has been achieved by using our approach, which offers a solution for applications in modern control systems, thermal diffusion, and biochemical reactions modelling.

Abstract: This paper presents the redesign and metrological validation of a primary microwave microcalorimeter for RF power measurements from 10 MHz to 40 GHz. The proposed developments include a compact modular microcalorimeter head and double-sided thin-film thermopiles designed to improve thermoelectric sensitivity, measurement repeatability and operational flexibility. The redesigned architecture supports APC-7 coaxial (10 MHz–18 GHz), WR42 waveguide (18–26.5 GHz) and WR28 waveguide (26.5–40 GHz) configurations while preserving the conventional RF/DC substitution principle. Experimental results demonstrate an approximately threefold increase in thermopile output voltage together with improved measurement repeatability. Primary calibration results confirm that the redesigned microcalorimeter preserves the corrected effective efficiency while significantly reducing the expanded calibration uncertainty over the complete operating frequency range.

Abstract: A non-sinusoidal regime is commonly found in power grids and affects the nominal operation and performance of industrial equipment supplied by the electrical network. The non-sinusoidal regime is mainly caused by nonlinear circuit elements, especially power-electronic devices. In real industrial cases, the harmonic content of voltage and current varies; consequently, the RMS values can change significantly, influencing both power flow and power quality. To determine these variations, this article proposes a method for analyzing the dependence between the harmonic weights of voltage and current and the sensitivities of reactive and apparent powers. After introducing dependency relationships between real, reactive, and apparent powers and harmonic weights, the method uses these relationships to calculate sensitivities when one or more parameters are modified. A numerical algorithm is implemented in MATLAB/Simulink to determine the sensitivities in a real case. The values obtained are compared with those directly calculated from measured data, and the small errors demonstrate the accuracy of the proposed method.

Abstract: The increasing integration of renewable energy sources, distributed generation, and smart grid technologies has significantly increased the dynamic complexity of modern electric power systems. Under such operating conditions, synchronous generators are required to maintain stable voltage, enhance transient performance, and suppress electromechanical oscillations despite continuous variations in load demand and network disturbances. Conventional excitation control systems based on proportional–integral (PI) and proportional–integral–derivative (PID) regulators exhibit limited adaptability to nonlinear operating conditions because of their fixed controller parameters and simplified control structures. Consequently, developing advanced excitation control strategies capable of improving the dynamic stability and operational reliability of synchronous generators has become an important research challenge. This paper proposes an Adaptive Hybrid Excitation Control (AHEC) strategy for longitudinal–transverse excited synchronous generators to improve dynamic stability under variable operating conditions. The proposed approach integrates coordinated longitudinal and transverse excitation control with adaptive parameter tuning and nonlinear feedback compensation into a unified control framework. A comprehensive nonlinear mathematical model of the generator is developed in the synchronous dq-reference frame by considering stator electrical dynamics, dual excitation winding dynamics, electromagnetic cross-coupling, magnetic saturation, and rotor mechanical motion. Based on the developed model, an adaptive hybrid excitation controller is synthesized to coordinate excitation currents in real time, ensuring optimal magnetic flux distribution, enhanced damping characteristics, and improved transient performance. The effectiveness of the proposed control strategy is evaluated through detailed MATLAB/Simulink simulations under various operating scenarios, including sudden load changes, voltage sags, reactive power fluctuations, parameter uncertainties, and three-phase short-circuit faults. The obtained results are compared with those of conventional Automatic Voltage Regulator (AVR), PI, PID, and Adaptive PID excitation controllers using key dynamic performance indicators such as voltage overshoot, settling time, steady-state error, damping ratio, rotor-angle deviation, and transient stability margin. Simulation results demonstrate that the proposed Adaptive Hybrid Excitation Control strategy substantially improves voltage regulation accuracy, suppresses electromechanical oscillations, accelerates transient recovery, and enhances the overall dynamic stability of the longitudinal–transverse excited synchronous generator. Compared with conventional excitation control methods, the proposed controller provides superior robustness against nonlinear disturbances and parameter variations while maintaining stable operation over a wide range of operating conditions. The proposed methodology offers an effective solution for next-generation synchronous generators employed in renewable energy systems, autonomous micro grids, and intelligent power networks.

Abstract: Electric motorcycles are central to SubSaharan Africa’s transition to electric mobility, yet manufacturers in the region typically rely on costly and time-consuming physical prototyping to optimise powertrains built from imported components. This paper presents a validated full-powertrain digital twin of the Roam Air, an electric motorcycle assembled in Nairobi, Kenya, developed in MATLAB/Simulink as four interconnected subsystems: the battery, the controller, the motor, and the vehicle dynamics. The battery is modelled as a Thévenin equivalent circuit whose parameters were experimentally derived at the pack level through Hybrid Pulse Power Characterisation tests, and the controller replicates the motorcycle’s field-oriented control with a maximum torque per ampere strategy, including its battery current and voltage limiting behaviour. The motorcycle’s regenerative braking characteristics, drag coefficient, and rolling resistance coefficient were experimentally obtained through braking, coasting, and coast-down tests. The digital twin ingests rider inputs and GPS information, and predicts the motor’s speed and the battery’s power. Validation against six measured drive cycles in Stellenbosch, South Africa, demonstrates strong agreement between predicted and measured profiles, with Pearson’s r values of 0.905 0.981 for battery power and 0.912–0.996 for motor speed, and energy consumption predicted to within 2.71% for five of the six trips. The presented modelling and characterisation framework offers manufacturers a transferable, computationally efficient alternative to iterative physical prototyping for powertrain optimisation under Sub-Saharan African operating conditions.

Abstract: Polarization control is essential for modern optical systems. Recently, two-dimensional (2D) materials featuring highly anisotropic absorption across broad wavelength regions have emerged as a promising solution for realizing optical polarizers with operation bandwidths far exceeding those achievable using bulk materials. Despite this, existing 2D-material-based optical polarizers face critical limitations in simultaneously delivering high polarizer figures of merit (FOMs), robust power endurance, and scalable fabrication, hindering their practical deployment beyond laboratory. Here, we overcome this bottleneck by demonstrating optical polarizers through integrating 2D MXene films onto silicon photonic devices and subsequently converting them in-situ into 2D titanium dioxide (TiO₂). The 2D TiO₂ exhibits significantly improved anisotropic absorption and thermal / chemical stability compared with MXene, enabling the hybrid polarizers to achieve a record high polarizer FOM of ~17.5 (among 2D-material-coated devices) and excellent power tolerance (stable under >2 W coupled average power), together with a broad operation bandwidth (>140 nm) and a high fabrication yield (~98%). We also demonstrate microring resonator polarizers achieving high polarization extinction ratios up to ~12 dB. With outstanding overall performance and strong capability for large-scale manufacturing, these polarizers represent a solid step towards industry-ready implementations of 2D-material-based optical polarizers.

Abstract: This paper investigates stable and compact diagnostic feature signatures for faults related to demagnetization in BLDC/PMSM drives. Discovery analysis on the public DUDU-BLDC benchmark compares current, speed, and combined representations under explicit top-k budgets using ReliefF, mRMR, LASSO, and Bayesian automatic relevance determination logistic ranking. A second March 2026 experimental campaign, released as DUDU-BLDC 1.5, comprises 50 recordings from five physical motors under altered acquisition conditions. On this second campaign, five-fold recording-grouped validation with three deterministic repeats reached balanced accuracies of 0.806 and 0.779 for the two confirmatory within-motor tasks. Motor-held-out balanced accuracies fell to 0.512 and 0.604, with most physical-motor estimates near chance, exposing substantial between-motor heterogeneity. A paired experiment that quantised the DUDU-BLDC raw current signals to the approximately 0.08 A resolution of DUDU-BLDC 1.5 produced a mean absolute balanced-accuracy change of 0.0046, although the maximum change was 0.0725 and individual ranking-stability changes were larger. The results support compact signatures for repeated monitoring within an established or calibrated motor population and show aggregate robustness to reduced current resolution. They do not establish universal transfer to unseen motor instances; broader deployment requires motor-specific calibration or more diverse multi-motor training data.

Abstract: This work systematically evaluates the capability of generative large language models (LLMs), specifically GPT-4o, to support the full design, simulation, and optimization workflow of low-dropout (LDO) linear voltage regulators. The study covers four core design phases: pre-design specification mapping, transistor-level circuit topology generation, SPICE simulation guidance, and post-simulation performance fine-tuning, with an extended investigation into the integration of magnetic inductive components within the LDO signal path. GPT-4o autonomously proposes a single-stage differential-pair error amplifier architecture with thin-oxide MOS transistors, provides sizing guidance for the PMOS pass element, and recommends passive compensation networks to secure closed-loop stability. The LDO testbench adheres to low-voltage portable electronics specifications: an input range of 0.8-1.2 V, tunable 0.7-1.1 V output, maximum 250 mA load current, and integrable output capacitance below 10 nF. Transient and small-signal AC SPICE simulations validate LLM-assisted circuit implementations, quantifying settling time reduction via compensation capacitors and verifying adequate phase margin across operating bandwidth. A key novel extension explores three distinct inductor placement schemes, input-side supply filtering, out-of-loop LC output filtering, and inductive loading embedded within the feedback divider, rooted in Maxwell's electrodynamic principles and MOSFET small-signal device physics. Comparative Bode and output impedance analysis reveals that inductors inserted inside the feedback path introduce resonant complex poles, severe gain peaking, and degraded phase margin, while inductors placed external to the feedback sensing tap preserve regulator stability while suppressing high-frequency electromagnetic interference. Despite robust performance in topology drafting and simulation instruction, GPT-4o exhibits notable limitations: it occasionally omits critical passive components matching design constraints, generates syntactically flawed SPICE netlists for custom transistor subcircuits, and fails self-correction for unconventional magnetic load configurations. Overall, this work demonstrates that LLMs function as powerful auxiliary engineering assistants to accelerate iterative analog design, though rigorous human cross-verification of component sizing, loop stability, and passive network topology remains mandatory for reliable LDO implementation, particularly when integrating inductive magnetic elements.

Abstract: This study presents a spatiotemporal analysis and predictive modeling approach for assessing distributed photovoltaic potential in La Maná, Ecuador, using hourly NASA POWER satellite data from 2014 to 2025. A total of 105,168 hourly records of global horizontal irradiance, air temperature, relative humidity, and wind speed were processed. The methodology included data cleaning, temporal feature generation, hourly, monthly, and interannual solar resource analysis, and the comparison of machine learning models for predicting global horizontal irradiance as an indicator of photovoltaic potential. The results showed a relatively stable solar resource throughout the study period, with hourly maximum values occurring around midday and low season-al variability. Among the evaluated models, Gradient Boosting achieved the best predictive performance, with an MAE of 35.14 W/m², an RMSE of 68.44 W/m², and a coefficient of determination of 0.8875. The feature importance analysis revealed that the hourly component dominated solar resource prediction; however, when this effect was excluded, air temperature emerged as the most influential meteorological variable. These findings demonstrate that integrating hourly satellite data with machine learning provides a robust alternative for evaluating photovoltaic potential in tropical regions with limited ground-based meteorological monitoring infrastructure.

Abstract: Electrical machines, including motors, generators, and transformers, are fundamental components of modern industrial systems, power networks, transportation infrastructure, and renewable energy installations. Their reliable operation is essential for maintaining system efficiency, operational safety, and economic performance. However, these machines are continuously exposed to electrical, mechanical, thermal, and environmental stresses that can lead to performance degradation and unexpected failures. Conventional condition monitoring and maintenance approaches, such as vibration analysis, Motor Current Signature Analysis (MCSA), infrared thermography, acoustic emission monitoring, partial discharge testing, and Dissolved Gas Analysis (DGA), have been widely used for machine health assessment. Despite their effectiveness, these methods often require expert interpretation, extensive manual analysis, and periodic inspections, limiting their ability to detect incipient faults and support predictive maintenance. Recent advances in sensing technologies, the Industrial Internet of Things (IIoT), edge computing, and cloud analytics have enabled the collection of large volumes of operational data, creating new opportunities for Artificial Intelligence (AI)-based health management systems. This review presents a comprehensive overview of AI applications in electrical machine health monitoring, fault diagnosis, and predictive maintenance. The paper discusses electrical machine classifications, common fault mechanisms, condition monitoring data sources, maintenance strategies, and the limitations of conventional diagnostic approaches. Furthermore, the review examines machine learning (ML), deep learning (DL), hybrid AI approaches, and Explainable Artificial Intelligence (XAI) techniques for motors, generators, and transformers. Finally, key challenges related to data quality, model interpretability, computational complexity, cybersecurity, and standardization are discussed, together with future research directions involving Digital Twins, multimodal data fusion, federated learning, and edge intelligence.

Abstract: This article presents the results of measuring the permittivity of a powder material. The measurements were performed using a resonance method. The method is based on a volumetric strip-slot. A computer model was developed to demonstrate the fundamental feasibility of using such a device for permittivity measurements. Calculations were made to determine the optimal number of measurements to achieve the specified accuracy. Confidence intervals for the measured parameters were obtained.

Abstract: Loudness discrepancies between Internet advertisements and the program material they accompany affect user comfort, yet, unlike broadcast television, streaming services are subject to few binding loudness regulations. This article presents a combined objective and subjective study of the loudness of Internet advertisements. Objective loudness was measured in LUFS units according to Recommendation ITU-R BS.1770-4 using an application built on the pyloudnorm package and validated against the ITU-R BS.2217-2 compliance material. Fifteen advertisement–program pairs collected from popular Polish streaming and video-on-demand services were analyzed. In the subjective experiment, thirty listeners compared the loudness of each advertisement with that of the accompanying program material using a bounded, bipolar category-rating scale administered through the webMUSHRA framework; the ordinal character of such ratings is explicitly acknowledged in the analysis. The ratings were examined with one-way analysis of variance and Tukey post hoc tests within listener groups, and the per-sample mean ratings were correlated with the objective loudness differences. Subjective ratings correlated strongly with objective LUFS differences, and rating consistency decreased with lower listener expertise in audio processing and with self-reported hearing problems. The results document the extent to which loudness recommendations are exceeded in Internet advertising and provide a reusable methodology for monitoring compliance. Although the problem is illustrated with examples drawn from Polish services, the authors' exploratory checks of foreign platforms indicate that a similar situation prevails across many Internet services worldwide.

Abstract: This study examines how the location of reinforcement-learning (RL) compensation affects the control of a low-voltage dual-active-bridge converter using triple-phase-shift modulation. A proportional–integral triple-phase-shift controller is used as the baseline, and two deep deterministic policy gradient schemes are compared. The first directly corrects the three modulation variables, whereas the second adjusts the phase-shift command before it is mapped to those variables. The three control structures are tested at the rated operating point, during fixed and stepwise input-voltage changes from 80 to 120 V, and under fixed and stepwise load changes at an input voltage of 100 V. Direct correction of the modulation variables gives no consistent improvement, mainly because the variables are strongly coupled. The phase-shift-level scheme avoids this difficulty by leaving the modulation mapping to the existing controller. At the rated point, it reduces the steady-state voltage error by 29.34% and the full-transient peak inductor current from 69.82 to 36.84 A. During the load tests, it also gives lower voltage ripple and peak inductor current and performs better during load transitions. For this converter, placing the RL action at the higher phase-shift level is more effective than directly modifying the coupled modulation variables.

Abstract: Vehicle-to-grid (V2G) technology lets a parked electric vehicle push power back to the network, so a fleet of cars can act as distributed storage. Many studies report large benefits from this, such as peak shaving and better use of renewable energy, but most describe their simulation only in words, name no test network, and compare a single charging behavior against a do-nothing case. It is then hard to separate what V2G delivers from what the control strategy delivers. This paper builds and compares two clearly defined strategies on one fully specified system. The first is a price-following rule: each vehicle charges when energy is cheap and discharges when it is dear, with no knowledge of the network. The second is a receding-horizon model predictive control (MPC) strategy that re-plans every hour and lowers the system peak, the energy cost, and the battery wear together. Both run on the IEEE 33-bus distribution feeder at low (10%), medium (30%), and high (50%) EV shares, and every hour is checked with a full AC power flow rather than an assumption. The central result is not a simple win for the smart controller. At a 50% share the price rule makes the system peak 23.7% worse than having no V2G at all, because the whole fleet reacts to one price signal at once, while the MPC cuts the peak by 18 to 28% in every case. The MPC also shows higher worst-case loading on some individual lines, and that is reported rather than hidden. Results are given as they came out of the model.

Abstract: Automatic modulation recognition (AMR) is a key enabling technique for intelligent spectrum sensing, non-cooperative wireless signal analysis, and communication monitoring. However, its reliability degrades significantly under low signal-to-noise ratio (SNR) conditions, where noise weakens local I/Q waveform patterns, blurs time-frequency structures, and limits the robustness of single-domain representations. Although multimodal AMR methods have improved recognition by combining I/Q and time-frequency representations, many of them still use fixed fusion rules, which may not reflect the changing reliability of different modalities across SNR levels. In addition, most existing backbones model signal sequences in a Euclidean manner and lack an explicit mechanism to capture adaptive relational structures among signal tokens under strong noise. To address these limitations, this paper proposes MPCG-Net, a modulation-prior-enhanced cross-modal graph learning framework for low-SNR AMR. MPCG-Net first performs modulation-prior-enhanced cross-modal encoding, where learnable multi-resolution spectro-temporal representation learning enhances the input representation and training-stage SNR-aware cross-modal consistency alignment improves the complementary interaction between I/Q and spectro-temporal tokens. A modulation context memory module then refines the fused tokens before graph construction. In the graph-based relational learning stage, a temporal similarity graph with self-loops, local temporal-chain edges, and top-k feature-similarity edges is built to learn more discriminative graph-level signal representations. Experiments on RadioML2016.10A, RadioML2016.10B, and RML22 validate the effectiveness of the proposed design. Compared with representative baseline models, MPCG-Net improves the average accuracy by 1.66–11.01 percentage points in the -20 to 0 dB low-SNR range while maintaining a lightweight model scale, demonstrating robust recognition performance without relying on excessive network capacity.

Abstract: Integrated nested microring resonators (NMRRs) with multiple split resonances are theoretically investigated for advanced spectral engineering. First, the spectral response of NMRRs of different orders is systematically analyzed, revealing the evolution of extinction ratio (ER), quality (Q) factor, and mode-splitting degree with device structural parameters. Next, the split resonances of NMRRs are engineered to realize different filter functions with outstanding performance, including dense wavelength division multiplexing (DWDM) and Fano filtering. The former enables reduced channel spacing by a factor of ~18 without increasing the device footprint, whereas the latter achieves high slope rates (SRs) of up to ~1337 and ~1616 dB/nm while maintaining low insertion losses (ILs) of 3 and 5 dB, respectively. Finally, fabrication tolerances and practical limitations are analyzed to guide the design and optimization of NMRRs, together with discussion on their potential for other applications. These results highlight the strong potential of NMRRs as highly versatile integrated photonic filters with flexible spectral engineering capabilities.