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Globally, over three billion people rely on traditional solid fuels for cooking and heating, leading to significant household air pollution and critical public health concerns. While transitioning to clean energy carriers faces challenges of accessibility and affordability—especially among low-income, rural populations—alternative strategies like kitchen layout modifications and the use of ventilation fans may effectively reduce exposure to pollutants. Here, we analyze factors influencing the adoption of separated kitchens and mechanical ventilation and evaluate changes in human exposure to PM2.5 under different kitchen renovation scenarios by conducting a nationwide survey of household kitchen characteristics in rural China. We found that although 82% of rural households have kitchens separated from other rooms, only 34% use mechanical ventilation. The adoption of ventilation fans is significantly influenced by income and education levels. We estimate that widespread implementation of ventilation fans and separated kitchen designs could prevent approximately 67400 premature deaths annually, resulting in a health benefit of about USD 19 billion per year—substantially exceeding the costs involved. These findings suggest that cost-effective kitchen renovations offer enormous potential for substantial health benefits and present a practical solution compared to the challenges of clean energy transitions in rural areas.

Emerging environmental persistent free radicals (EPFRs), can generate reactive oxygen species (ROS), posing potential exposure risks to human health. Incomplete coal combustion is a major source of EPFRs. Organic carbonaceous fractions are essential and important players in the formation of EPFRs during coal combustion. However, relationship between individual organic carbonaceous and non-carbon fractions with EPFRs in such emissions are not well known. This paper investigated the characteristics of EPFRs discharged from simulated coal combustion. Our results showed that the concentration of EPFRs was major concentrated on PM1.1 (51.66–81.85 %), and more easily oxidized by oxygen resulting in producing more oxygen-centered radicals (semiquinone-type) in PM1.1. The mean of line width (ΔHp-p) was 5.87 ± 0.41G higher than that of biomass combustion, indicating more free radical species were emitted from coal combustion. Humic-like substances-carbon (HULIS-C) was the major contributor of the formation of EPFRs and facilitate the generated of EPFRs. Secondary processes have also contributed to the formation of EPFRs during the coal combustion. Our result also noted that there was no relationship between transition metals and EPFRs, may be due to the variability and complexity of the chemical properties and composition of PM. This is critical for the prediction of geochemical behavior and risk assessment of EPFRs, which can provide basic data to support policy development to address rural air pollutant emissions.

It is a fundamental problem to analyze the performance bound of multiple-input multiple-output dual-functional radar-communication systems. To this end, we derive a performance bound on the communication function under a constraint on radar performance. To facilitate the analysis, in this paper, we consider a simplified situation where there is only one downlink user and one radar target. We analyze the properties of the performance bound and the corresponding waveform design strategy to achieve the bound. When the downlink user and the radar target meet certain conditions, we obtain analytical expressions for the bound and the corresponding waveform design strategy. The results reveal a tradeoff between communication and radar performance, which is essentially caused by the energy sharing and allocation between radar and communication functions of the system.

Abstract Radar and communication are two key applications of radio technology, and they occupy a large portion of the frequency spectrum. Traditionally, radar and communication systems are operated at different frequencies, owing to their different functions and application areas. For instance, radar was mainly employed for sensing (target detection, localization, recognition, imaging, etc.) in the military field, while wireless communication was mainly for information delivery. However, along with the fast development of radio technologies and huge demand for information, the radio frequency (RF) spectrum is becoming increasingly congested, and the spectra of the radar system will be overlaid with those of wireless communication devices. Moreover, radar and communication are becoming increasingly merged in both technologies and applications. Besides the military field, radar has been widely employed in daily life including weather service, air traffic control, autonomous driving and security monitoring. Meanwhile, these applications rely Largely on information transmission through wireless communications. In this regard, integration of radar and communication (IRC) has proved to be a very promising development to address the spectrum congestion issue between radar and communications devices. This also brings us a number of key challenges in signal processing for both implementation of IRC and joint optimization between the two systems.

Abstract The problem of high‐resolution direction finding is considered using a distributed array network with array position errors, called partly calibrated array. These errors are common in practice, and significantly affect high angular resolution. Existing calibration methods rely on centralized data transmission, which requires all the measurements to be transmitted to a computation center. This centralized mode leads to communication bottlenecks and system inflexibility. Here, a decentralized mode is designed, where each sensor only communicates locally with their neighboring sensors. Based on this mode, a corresponding direction‐finding approach is proposed using sequential methods that process data one by one, which corresponds to the decentralized signal processing sensor by sensor. Simulations verify that the proposed method achieves decentralization with minor cost in estimation accuracy.

Abstract Infectious diarrhea in China showed a significant pattern. Many researchers have tried to reveal the drivers, yet usually only meteorological factors were taken into consideration. Furthermore, the diarrheal data they analyzed were incomplete and the algorithms they exploited were inefficient of adapting realistic relationships. Here, we investigate the impacts of meteorological and social factors on the number of infectious diarrhea cases in China. A machine learning algorithm called the Random Forest is utilized. Our results demonstrate that nearly half of infectious diarrhea occurred among children under 5 years old. Generally speaking, increasing temperature or relative humidity leads to increased cases of infectious diarrhea in China. Nevertheless, people from different age groups or different regions own different sensitivities to meteorological factors. The weight of feces that are harmfully treated could be a possible reason for infectious diarrhea of the elderly as well as children under 5 years old. These findings indicate that infectious diarrhea prevention for children under 5 years old remains a primary task in China. Personalized prevention countermeasures ought to be provided to different age groups and different regions. It is essential to bring the weight of feces that are harmfully treated to the forefront when considering infectious diarrhea prevention.

Abstract Block‐sparse signals, whose non‐zero entries appear in clusters, have received much attention recently. An unfolded network, named Ada‐BlockLISTA, was proposed to recover a block‐sparse signal at a small computational cost, which learns an individual weight matrix for each block. However, as the number of network parameters is increasingly associated with the number of blocks, the demand for parameter reduction becomes very significant, especially for large‐scale multidimensional harmonic retrieval (MHR) problems. Based on the dictionary characteristics in two‐dimensional (2D) harmonic retrieve problems, the authors introduce a weight coupling structure to shrink Ada‐BlockLISTA, which significantly reduces the number of weights without performance degradation. In simulations, the proposed block‐sparse reconstruction network, named AdaBLISTA‐CP, shows excellent recovery performance with a smaller number of learned parameters.

This paper introduces a W-band phased electromagnetic surface radar system. This phased electromagnetic surface works at 92～96 GHz and is manufactured with general PCB technology and processing accuracy requirements. The appropriate unit design makes the DC bias voltage of each PIN diode on the phased electromagnetic surface controlled for the current reversal purpose at a low cost and portability. A 180° phase shift of the unit cell can be provided as the current direction on the unit cell is reversed. Beam scanning in different directions can be formed when inputting the right spatial codes. This transmission type phased electromagnetic surface with the ability of beam scanning is used as the receiving antenna of the radar system. This paper presents a W-band phased electromagnetic surface radar system and its manufacture and measurement results, which are fundamental to further research of precision guidance, target identification, and imaging.

The random array subset selection (RASS) jammer, which randomly selects a subset out of the transmit array antenna from time to time, was verified to effectively counter against multistatic radar system (MSRS). However, the countermeasure ability of RASS is restricted, because the probabilities of activating antenna elements for transmission are constrained to be identical. To further enhance the countermeasure ability, we propose the resource allocation of distributed jammer under random selection (RADJRS) in this paper, where antenna elements of the jammer are activated randomly with different probabilities, abandoning the previous constraint and generalizing RASS. We establish an optimization model with respect to the selection probabilities, whose objective function is minimizing the output-to-input jamming energy ratio of the jamming suppression method used in MSRS. This non-convex optimization model is then relaxed into a convex problem, facilitating the solution of the problem. Numerical results verify the feasibility of the relaxation and demonstrate that RADJRS outperforms the RASS when fighting against MSRS.

This paper studies the problem surrounding distributed passive arrays (sensors) locating multiple emitters while performing self-calibration to correct possible errors in the assumed array directions. In our setting, only the angle-of-arrival (AoA) information is available for localization. However, such information may contain bias due to array directional errors. Hence, localization requires self-calibration. To achieve both, the key element behind our approach is that the received signals from the same emitter should be geometrically consistent if sensor arrays are successfully calibrated. This leads to our signal model, which is built on a mapping directly from emitter locations and array directional errors to received signals. Then we formulate an atomic norm minimization and use group sparsity to promote geometric consistency and align ‘ghost’ emitter locations from calibration errors. Simulations verify the effectiveness of the proposed scheme. We derive the Cramér Rao lower bound and numerically compare it to the simulations. Furthermore, we derive a necessary condition as a rule of thumb to decide the feasibility of joint localization and calibration.

Synthesising radar and communication waveforms in different directions using a common array is a hardware-saving solution for the coexistence of communication and radar in competitive spectrum environments. Here, the authors establish the problem of designing array probing signal to maximise power of the synthesised communication and radar waveforms, given array transmitting power, under no-waveform-error constraint and unit-modular-array-probing-signal constraint. Alternating direction method of multipliers (ADMM) is used to solve this problem. The results of numerical experiments show that, by solving this problem with ADMM, error of the synthesised waveforms can be completely eliminated, and the power of synthesised waveforms is almost optimal.

Distributed multiple input multiple output (MIMO) radar has attracted much attention for its improved detection and estimation performance as well as enhanced electronic counter-counter measures (ECCM) ability. To protect the target from being detected and tracked by such radar, we consider a barrage jamming strategy towards a distributed MIMO. We first derive the Cramer−Rao bound (CRB) of target parameters estimation using a distributed MIMO under barrage jamming environments. We then set maximizing the CRB as the criterion for jamming resource allocation, aiming at degrading the accuracy of target parameters estimation. Due to the non-convexity of the CRB maximizing problem, particle swarm optimization is used to solve the problem. Simulation results demonstrate the advantages of the proposed strategy over traditional jamming methods.