Multidisciplinary Digital Publishing Institute (Switzerland)
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Research on TID Controller Design for Fractional-Order Time-Delay Systems
No full textFractional-order time-delay systems boast better dynamic performance than integer-order ones in optimally controlling industrial design objects. However, in lack of commendable methodologies, designing proper controllers for these systems confronts a plurality of challenges. This study puts forth an innovative design approach that merges frequency-domain analysis with time-domain optimization concepts, so that fractional-order Tilt-Integral-Derivative (TID) controllers can be acquired. To pursue a stable control system loop, the tilted and integral gains of fractional-order TID controllers are identified as per frequency-domain specifications, including gain crossover frequency and phase margin. In light of these specifications (e.g., the integral of time-weighted absolute error (ITAE)), the differential gain and fractional-order operator λ of the controller are determined, which accomplishes a desirable dynamic performance in the time domain. This article expounds on the procedure of how to develop the proposed fractional-order TID controller and furnishes illustrative examples for the research steps. As manifested by the simulation results, the proposed controller dramatically upgrades the control performance of the system in contrast to conventional PID, FOPI, and FOPID controllers. Moreover, it outperforms PID and fuzzy PID in terms of responding to the demand variations in step signals
Two-Stage Combining and Beamforming Scheme for Multi-Pair Users FDD Massive MIMO Relay Systems
No full textIn this study, we consider multi-pair user frequency division duplexing massive MIMO relay systems and design a two-stage combining and beamforming (TSCB) scheme based on statistical channel state information (S-CSI). By leveraging S-CSI to co-design the pre-combining matrix and the pre-beamforming matrix, the scheme reduces the equivalent channel matrix dimensions, thereby cutting the pilot overhead. In the first stage, the two matrices are constructed through a selection of beams from a discrete Fourier transform codebook and mathematically cast as a multivariate optimization problem. An alternative optimization algorithm is proposed by splitting it into three sub-problems. The first two are 0–1 integer programming problems solved by iterative beam selection, while the third is a convex problem that is solved using a convex optimization algorithm. In the second stage, the reduced-dimension equivalent matrices are then estimated with low overhead, and a digital precoding matrix is then designed using zero-forcing algorithms. Simulations confirm the TSCB scheme’s superior ESE performance over that of existing methods
Influence of Mo and Ni Alloying on Recrystallization Kinetics and Phase Transformation in Quenched and Tempered Thick Steel Plates
No full textThe production of heavy gauge quenched and tempered steel plates requires alloying strategies that ensure adequate hardenability and microstructural uniformity under limited cooling rates. Molybdenum (Mo) and nickel (Ni) are key elements in this context, as they influence both hot-working behavior and phase transformation kinetics. This study investigates the effect of Mo (0.25–0.50 wt%) and Ni (0–1.00 wt%) additions on static recrystallization and transformation behavior using laboratory thermomechanical simulations representative of thick plate rolling conditions. Multipass and double-hit torsion tests were performed to determine the non-recrystallization temperature (Tnr) and quantify softening kinetics, while dilatometry was employed to construct Continuous Cooling Transformation (CCT) diagrams and assess hardenability. Results indicate that Mo significantly increases Tnr and delays recrystallization through a solute drag mechanism, whereas Ni exerts a minor but measurable effect, likely associated with stacking fault energy rather than classical solute drag. Both elements reduce ferrite and bainite transformation temperatures, enhancing hardenability; however, Mo alone cannot suppress ferrite formation at practical cooling rates, requiring combined Mo–Ni additions to achieve fully martensitic microstructures. These findings provide insight into alloy design for thick plate applications and highlight the limitations of existing predictive models for Ni-containing steels
Mark for All Christians? The Theological Implications of Audience
No full textThis essay considers the theological implications of “audience criticism,” the widely held hypothesis that the Gospel of Mark was written to address the needs of a specific audience, and not, as Richard Bauckham has argued, with a general audience of all “Christians” in mind. The “all-Christians” hypothesis has generally been embraced by evangelical scholars, but less so by non-evangelicals. In agreement with Adele Reinhartz, I find an unstated theological premise underlying Bauckham’s argument, which is that the focus on Gospel audiences detracts from their witness to Christ. After consideration of references to the Marcan audience within the text and scholarly reconstructions of the Marcan community in space and time, I consider the theological implications of audience in light of the ancient doctrine of the communion of saints, with particular reference to the so-called “minor characters” of the gospel, some of whom, I cautiously suggest, were modeled on members of the Marcan community/communion
A Lightweight Dynamic Counting Algorithm for the Maize Seedling Population in Agricultural Fields for Embedded Applications
No full textIn the field management of maize, phenomena such as missed sowing and empty seedlings directly affect the final yield. By implementing seedling replenishment activities and promptly evaluating seedling growth, maize output can be increased by improving seedling survival rates. To address the challenges posed by complex field environments (including varying light conditions, weeds, and foreign objects), as well as the performance limitations of model deployment on resource-constrained devices, this study proposes a Lightweight Real-Time You Only Look Once (LRT-YOLO) model. This model builds upon the You Only Look Once version 11n (YOLOv11n) framework by designing a lightweight, optimized feature architecture (OF) that enables the model to focus on the characteristics of small to medium-sized maize seedlings. The feature fusion network incorporates two key modules: the Feature Complementary Mapping Module (FCM) and the Multi-Kernel Perception Module (MKP). The FCM captures global features of maize seedlings through multi-scale interactive learning, while the MKP enhances the network’s ability to learn multi-scale features by combining different convolution kernels with pointwise convolution. In the detection head component, the introduction of an NMS-free design philosophy has significantly enhanced the model’s detection performance while simultaneously reducing its inference time. The experiments show that the mAP50 and mAP50:95 of the LRT-YOLO model reached 95.9% and 63.6%, respectively. The model has only 0.86M parameters and a size of just 2.35 M, representing reductions of 66.67% and 54.89% in the number of parameters and model size compared to YOLOv11n. To enable mobile deployment in field environments, this study integrates the LRT-YOLO model with the ByteTrack multi-object tracking algorithm and deploys it on the NVIDIA Jetson AGX Orin platform, utilizing OpenCV tools to achieve real-time visualization of maize seedling tracking and counting. Experiments demonstrate that the frame rate (FPS) achieved with TensorRT acceleration reached 23.49, while the inference time decreased by 38.93%. Regarding counting performance, when tested using static image data, the coefficient of determination (R2) and root mean square error (RMSE) were 0.988 and 5.874, respectively. The cross-line counting method was applied to test the video data, resulting in an R2 of 0.971 and an RMSE of 16.912, respectively. Experimental results show that the proposed method demonstrates efficient performance on edge devices, providing robust technical support for the rapid, non-destructive counting of maize seedlings in field environments
Combining Statins with Radiotherapy for Prostate Cancer: From Photon Experience to Proton Potential
No full textStatins have shown promise as radiosensitizers in photon-based radiotherapy (RT), with studies demonstrating improved biochemical recurrence-free survival and reduced toxicity in prostate and other solid tumors. However, existing data derived entirely from photon-based RT and the potential synergy with proton therapy remain hypothetical at this stage. The current narrative review extrapolates the therapeutic benefits of statins observed in photon-based RT to proton therapy (PBT) to enhance therapeutic efficacy. The proposed combination of statins and PBT is a theoretical extension grounded in the mechanistic overlap between statin-induced radiosensitization and proton-specific advantages in dose conformity and linear energy transfer (LET). The hypothesis of enhanced synergy between statins and PBT warrants systematic preclinical testing and clinical trials before translation into standard practice
Synergistic Interactions Between Bacteria-Derived Metabolites and Emerging Technologies for Meat Preservation
No full textConsidering the challenges associated with implementing emerging technologies and bacterial-derived antimicrobial metabolites at an industrial scale in the meat industry, this comprehensive review investigates the interactions between lactic acid bacteria-producing antimicrobial metabolites and emerging food preservation technologies applied to meat systems. By integrating evidence from microbiology, food engineering, and molecular physiology, the review characterizes how metabolites-derived compounds exert inhibitory activity through pH modulation, membrane permeabilization, disruption of proton motive force, and interference with cell wall biosynthesis. These biochemical actions are evaluated in parallel with the mechanistic effects of high-pressure processing, pulsed electric fields, cold plasma, irradiation, pulsed light, ultrasound, ohmic heating and nanotechnology. Across the literature, consistent patterns of synergy emerge: many emerging technologies induce structural and metabolic vulnerabilities in microbial cells, thereby amplifying the efficacy of antimicrobial metabolites while enabling reductions in process intensity. The review consolidates these findings to elucidate multi-hurdle strategies capable of improving microbial safety, extending shelf life, and preserving the physicochemical integrity of meat products. Remaining challenges include optimizing combinational parameters, ensuring metabolite stability within complex matrices, and aligning integrated preservation strategies with regulatory and industrial constraints
A Physics-Informed Neural Network with Hybrid Architecture for Magnetic Core Loss Prediction Under Complex Conditions
No full textMagnetic core loss is an important indicator for describing the performance of magnetic elements. The traditional physical model has an insufficient performance for predicting the magnetic core loss of magnetic elements under complex conditions such as high temperature, non-sinusoidal waveform, and high frequency. To address this issue, this study proposes a physics-informed neural network (PINN)-based model for magnetic core loss prediction. In particular, this PINN-based model is constructed with a hybrid network architecture as a baseline algorithm, which combines a convolutional long short-term memory network (Conv-LSTM), power spectral density (PSD), and an ensemble learning method (including extreme gradient boosting (XGB), gradient boosting regression (GBR), and random forest (RF)). This design aims to address the complexity of magnetic core loss prediction. Moreover, the Steinmetz equation (SE) is improved to enhance the adaptability under complex conditions, and this improved Steinmetz equation (ISE) is integrated as physical constraints embedded in the neural network for magnetic core loss prediction. Based on the traditional data-driven loss term, the physical residual term is introduced as a regularization constraint to enable the prediction to satisfy both the observed data distribution and physical law. The experimental results show that the PINN-based model has a good prediction performance of magnetic core loss under complex conditions
Spatial Analysis of Rooftop Solar Energy Potential for Distributed Generation in an Andean City
No full textUrban energy systems in Andean cities face growing pressure to accommodate rising electricity demand while progressing toward decarbonization and grid modernization. Residential rooftop photovoltaic (PV) generation offers a promising pathway to enhance transformer utilization, reduce emissions, and improve distribution network performance. However, most GIS-based rooftop solar assessments remain disconnected from operational constraints of urban electrical networks, limiting their applicability for distribution planning. This study examines the technical and environmental feasibility of integrating residential PV distributed generation into the urban distribution network of an Andean city by coupling high-resolution geospatial solar potential analysis with monthly aggregated electricity consumption (MEC) and transformer loadability (LD) information. A GIS-driven framework identifies suitable rooftops based on solar irradiation, orientation, slope, shading, and three-dimensional urban geometry, while MEC data are used to perform energy-balance and planning-level transformer LD assessments. Results indicate that approximately 1.16 MW of rooftop PV capacity could be integrated, increasing average transformer LD from 21.5% to 45.8% and yielding an annual PV generation of about 1.9 GWh. This contribution corresponds to an estimated avoidance of 1143 metric tons of CO2 per year. At the same time, localized reverse power flow causes some transformers to reach or exceed nominal capacity, highlighting the need to explicitly consider network constraints when translating rooftop solar potential into deployable capacity. By explicitly linking rooftop solar resource availability with aggregated electricity consumption and transformer LD, the proposed framework provides a scalable and practical planning tool for distributed PV deployment in complex mountainous urban environments
Benzalkonium Chloride Tolerance Among Listeria innocua from Food and Food Processing Environments in Poland
No full textBenzalkonium chloride (BC) is widely used as a disinfectant in the food industry; however, increasing reports of Listeria innocua isolates exhibiting tolerance to this compound highlight the need to better understand their adaptive mechanisms. This study aimed to evaluate BC tolerance in 51 L. innocua isolates originating from raw and processed meat products (n = 32) and meat-processing environments in Poland (n = 19). Phenotypic tolerance was assessed using the agar diffusion method on two media: Brain Heart Infusion (BHI) agar and Mueller–Hinton (M-H) agar supplemented with 1.2% sheep blood, across BC concentrations of 0, 5, 10, 15, 20, 25, 30, 35, 40, and 45 µg/mL, allowing the determination of minimum inhibitory concentrations (MICs). Genotypic analysis of tolerance determinants (brcABC, ermC, qacE, qacF, qacG, qacH, and qacJ) was performed by PCR. On BHI agar, MIC values ranged from 15 to 30 µg/mL, with 15 µg/mL most frequently observed, whereas on blood-supplemented M-H agar, MICs were lower (5–20 µg/mL), most commonly 10 µg/mL. Among tolerance-associated genes, qacH was the most prevalent (29% of isolates), followed by brcABC (4%) and ermC (2%), while the remaining genes were absent. These findings suggest that food products may serve as a reservoir for L. innocua isolates harboring tolerance to BC and contribute to a deeper understanding of how this species adapts to quaternary ammonium compounds