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Dynamic intensification of distillation columns has shown significant promise in achieving energy savings with minimal investment in new equipment. Conceptually, it entails making a desired product as a blend of two auxiliary products (one with higher purity, the other with lower purity, but both having lower energy consumption). Practically, dynamic intensification means periodically switching between two operating states corresponding to the aforementioned products. Past work has relied on ad-hoc choices of auxiliary products. In this paper, we introduce a new optimization framework for selecting auxiliary products for dynamic intensification. An extensive case study concerning the separation of a methanol/propanol mixture is then presented. We show that optimizing the choice of auxiliary products can lead to significant energy savings (more than 3.6% compared to a column operated at steady state) derived from dynamic intensification.

High step-up DC–DC converters are essential in electric vehicle (EV) and photovoltaic (PV) applications, where low-voltage inputs must be efficiently boosted to higher levels. Conventional converters suffer from high switching losses, bulky components, and unstable regulation under dynamic conditions. To address these challenges, this paper proposes a compact transformer based high step-up boost (HGB) converter integrated with an SG3525 PWM controller and an analog proportional–integral (PI) compensation network. The novelty of the design lies in the jointly optimized transformer winding structure (20-gauge/8-turns primary, 32-gauge/176-turns secondary with center tap) and robust analog PI compensation, which together achieve high gain with reduced duty ratio stress and stable voltage regulation. A hardware prototype was developed and tested under both programmable DC source and real PV input conditions. Experimental results confirm that the converter reliably steps up 12 V to 200 V DC, with efficiency consistently close to 90% across load levels from 5.6 W to 40 W. Gate pulses and switching behavior were validated through simulation, showing correct complementary drive at 50 kHz and safe device stress margins. Ripple analysis further shows that inductor current ripple remains below 15%, and capacitor voltage ripple remains below 2%, ensuring smooth operation. The converter also demonstrated strong linear gain response, maintaining duty ratios between 60% (at 8.5 V input) and 40% (at 12 V input). Real-time PV tests confirmed regulated output under fluctuating irradiation, with voltages ranging from 140 V to 225 V. These results establish the proposed converter as an efficient, compact, and experimentally validated solution for renewable energy systems and EV powertrains.

This study develops a semi-empirical model of an alkaline water electrolyzer (AWE) based on thermodynamic and electrochemical principles to investigate cell voltage behavior during electrolysis. By importing polarization curve test data under specific operational conditions, eight undefined parameters are precisely fitted, demonstrating the model’s high accuracy in describing the voltage characteristics of alkaline electrolyzers. Additionally, an AWE system model is introduced to examine the influence of various operational parameters on system efficiency. This innovative approach not only provides detailed insights into the operational dynamics of AWE systems but also offers a valuable tool for optimizing performance and enhancing efficiency, advancing the understanding and optimization of AWE technologies.

In this paper, an optimal design of a high-efficiency DC-DC boost converter is proposed for RF energy harvesting Internet of Things (IoT) sensors. Since the output DC voltage of the RF-DC rectifier for RF energy harvesting varies considerably depending on the RF input power, the DC-DC boost converter following the RF-DC rectifier is required to achieve high power conversion efficiency (PCE) in a wide input voltage range. Therefore, based on the loss analysis and modeling of an inductor-based DC-DC boost converter, an optimal design method of design parameters, including inductance and peak inductor current, is proposed to obtain the maximum PCE by minimizing the total loss according to different input voltages in a wide input voltage range. A high-efficiency DC-DC boost converter for RF energy harvesting applications is designed using a 65 nm CMOS process. The modeled total losses agree well with the circuit simulation results and the proposed loss modeling results accurately predict the optimal design parameters to obtain the maximum PCE. Based on the proposed loss modeling, the optimally designed DC-DC boost converter achieves a power conversion efficiency of 96.5% at a low input voltage of 0.1 V and a peak efficiency of 98.4% at an input voltage of 0.4 V.

With the growing demand for high-efficiency DC-DC converters with a wide input voltage range for wireless power transmission, four-switch boost converters (FSBBs) are attracting attention due to their low current stress and flexible mode switching characteristics. However, their complex operating modes and nonlinear dynamic characteristics lead to high switching losses and limited efficiency of the system under conventional control. In this paper, an optimization algorithm is combined with the multi-mode control of an FSBB converter for the first time, and a combined optimization and voltage closed-loop control strategy based on the Whale Migration Algorithm (WMA) is proposed. Under the four-mode operation conditions of the FSBB converter, the duty cycle and phase shift parameters of the switching devices are dynamically adjusted by optimizing the values to maximize the efficiency under different operation conditions, with the premise of achieving zero-voltage switching (ZVS) and the optimization objective of minimizing the inductor current as much as possible. Simulation results show that the proposed FSBB switching control strategy combined with the WMA algorithm improves the efficiency significantly over a wide voltage range (120–480 V) and under variable load conditions, and the transfer efficiency is improved by about 1.19% compared with that of the traditional three-mode control, and the maximum transfer efficiency is 99.34%, which verifies the validity and feasibility of the proposed strategy and provides a new approach to the high-efficiency control and application of FSBB converters.

Underwater object detection is critical for marine resource utilization, ecological monitoring, and maritime security, yet it remains constrained by optical degradation, high energy consumption, and vulnerability to adversarial perturbations. To address these challenges, this study proposes an Adaptive Vision Transformer (A-ViT)-based detection framework. At the hardware level, a systematic power-modeling and endurance-estimation scheme ensures feasibility across shallow- and deep-water missions. Through the super-resolution reconstruction based on the Hybrid Attention Transformer (HAT) and the staged enhancement with the Deep Initialization and Deep Inception and Channel-wise Attention Module (DICAM), the image quality was significantly improved. Specifically, the Peak Signal-to-Noise Ratio (PSNR) increased by 74.8%, and the Structural Similarity Index (SSIM) improved by 375.8%. Furthermore, the Underwater Image Quality Measure (UIQM) rose from 3.00 to 3.85, while the Underwater Color Image Quality Evaluation (UCIQE) increased from 0.550 to 0.673, demonstrating substantial enhancement in both visual fidelity and color consistency. Detection accuracy is further enhanced by an improved YOLOv11-Coordinate Attention–High-order Spatial Feature Pyramid Network (YOLOv11-CA_HSFPN), which attains a mean Average Precision at Intersection over Union 0.5 (mAP@0.5) of 56.2%, exceeding the baseline YOLOv11 by 1.5 percentage points while maintaining 10.5 ms latency. The proposed A-ViT + ROI reduces inference latency by 27.3% and memory usage by 74.6% when integrated with YOLOv11-CA_HSFPN and achieves up to 48.9% latency reduction and 80.0% VRAM savings in other detectors. An additional Image-stage Attack QuickCheck (IAQ) defense module reduces adversarial-attack-induced latency growth by 33–40%, effectively preventing computational overload.

To address the insufficient energy efficiency prediction accuracy caused by topological coupling in the parallel operation of multiple chillers, this study proposes a physics-guided spatiotemporal fusion model combining Long Short-Term Memory (LSTM) and Graph Convolutional Network (GCN). The LSTM module extracts temporal features from single-unit energy consumption sequences; the GCN module captures spatial dependencies through constructed topological graphs representing cooling water networks and load distribution relationships. Based on the operational data from a large-scale data center cooling station, the model is trained and tested using 128 million raw records along with 8000 h of simulation data. Experimental results demonstrate that the GCN-LSTM model achieves a 19.4% reduction in Root Mean Square Error and a 2.06% decrease in Mean Absolute Percentage Error compared to standalone LSTM models at 30-min prediction intervals. The ablation experiment further verifies the necessity of GCN for spatial modeling, and its absence led to a significant increase in prediction error. The proposed GCN-LSTM architecture surpasses traditional single-model performance limitations and provides an adaptable solution for multi-equipment collaborative optimization. This approach establishes a new technical path for industrial energy conservation and intelligent control systems.

In order to expand the bandwidth of the power amplifier (PA), this paper proposes a PA optimization design method based on improved particle swarm optimization (PSO) with output power and efficiency as optimization objectives. Simulated annealing strategy and adaptive inertia weight are introduced to PSO to achieve the global optimum and accelerate the convergence speed. Considering performance metrics of PA, such as the efficiency and output power, a piecewise objective function is formulated for wideband PA optimization design. For validation, a wideband PA operating at 0.5–4.3 GHz (fractional bandwidth of 158.3%) was designed and fabricated. Measured results show a saturated output power ranging from 39.7 to 42.4 dBm and an efficiency between 60.5 and 68.8% within the operating bandwidth.

The spatial design of nursing units can significantly enhance nurses' work efficiency. However, existing studies primarily focus on optimizing space for individual department, often overlooking the diverse spatial needs of different departments due to variations in work content. In this study, questionnaires were collected from 456 nurses across five departments of nursing units in 14 general hospitals, and semi-structured interviews and behavioral observations were conducted in the same five departments of nursing units in one hospital to assess spatial design and work efficiency, thus evaluating the impact of specialized design on departmental performance. Additionally, agent-based modeling (ABM) was utilized to simulate nursing efficiency in each department, comparing spatial arrangements before and after optimization by simulating nurses' behaviors. Results indicated obvious differences in spatial needs among departments, suggesting that standardized nursing unit designs fail to meet specific departmental needs, resulting in low satisfaction of nurses. Adjusting the spatial environments of nursing units based on departmental differences can improve efficiency. This study proposes optimization strategies for the spatial environments of nursing units in different departments, aiming to enhance nursing efficiency, job satisfaction, and reduce occupational stress.

Wireless power transfer (WPT) for portable electronic applications has been gaining a lot of interest over the past few decades. This study provides a comprehensive review of the recent advancements in WPT technology, along with the challenges faced in its practical implementation. The modeling and design of WPT systems, including the effect of cross-coupling in multiple receivers, have been discussed and the techniques for efficiency improvement have been highlighted. The challenges of coil design, EMI shielding, and foreign object detection have been pointed out and various cutting-edge solutions have been presented. With improvements in wide bandgap technology, there is a push to operate WPT systems at mega-hertz frequencies. The reason for this is twofold: the miniaturization of the system and the ability to achieve a better magnetic link efficiency. However, with higher frequency comes the challenge of operating the power electronic components efficiently by using soft-switching techniques. Hence, an in-depth discussion on soft-switched topologies such as the Class D and Class E converters and their variations has been provided. Finally, the effects of magnetic field exposure on humans along with safety standards have been discussed.