Search Results (13)

High-fidelity imaging in extreme low light is challenged by ultra-low signal-to-noise ratios. We propose a hybrid noise modeling framework integrating physical priors with generative adversarial networks (GANs). The method simulates photon shot noise via Poisson distribution and incorporates readout, row, and quantization noise. A multi-layer perceptron (MLP) dynamically maps ISO levels to noise intensities in logarithmic space, followed by a residual U-Net for non-linear refinement. Results on the SID datasets show that our method outperforms state-of-the-art approaches in terms of Average Kullback–Leibler Divergence (AKLD). Denoising networks trained on our synthetic noise achieve performances comparable to those trained on real-world paired datasets. Full article

This paper presents the basis for LoGlo PDT, a new treatment for glioblastoma. Glioblastoma is currently treated with maximal safe resection, temozolomide, and ionizing irradiation. Mortality in 2024 remains over 80% within several years from diagnosis. Oral 5-aminolevulinic acid (5-ALA) is an FDA/EMA approved drug that is selectively taken up by malignant cells, including by glioblastoma. In photodynamic treatment of glioblastoma, intense intraoperative light causes glioblastoma tissue that has taken up 5-ALA to generate cytotoxic reactive oxygen species. The requirement for intense light flux has restricted photodynamic treatment to a single one-hour intraoperative session. We analyze here published data showing that external light, illuminating the entire intact scalp, can attain low μW/cm² flux several cm into intact brain that would be sufficient to mediate 5-ALA photodynamic treatment of glioblastoma if the light and 5-ALA are delivered continuously over 24 h. At the core of LoGlo PDT regimen is the dataset showing that, for a given fluence, as the duration of PDT light delivery goes down, light intensity (flux) delivered must go up to achieve the same glioblastoma cell cytotoxicity as would a weaker light (lower flux) delivered over a longer time. Thus, a repetitive, noninvasive PDT of glioblastoma using an external light source may be possible. We analyze 5-ALA cellular physiology to show that three non-oncology drugs, ciprofloxacin, deferiprone, and telmisartan, can be repurposed to increase light energy capture after 5-ALA, thereby increasing photodynamic treatment’s glioblastoma cell cytotoxicity. The LoGlo PDT approach uses both drug augmentation and prolonged ultra-low noninvasive transcranial light delivery for a repetitive, noninvasive 5-ALA photodynamic treatment of glioblastoma. Full article

Non-terrestrial network (NTN) is a trending topic in the field of communication, as it shows promise for scenarios in which terrestrial infrastructure is unavailable. Unmanned autonomous intelligent systems (UAISs), as a physical form of artificial intelligence (AI), have gained significant attention from academia and industry. These systems have various applications in autonomous driving, logistics, area surveillance, and medical services. With the rapid evolution of information and communication technology (ICT), 5G and beyond-5G communication have enabled numerous intelligent applications through the comprehensive utilization of advanced NTN communication technology and artificial intelligence. To meet the demands of complex tasks in remote or communication-challenged areas, there is an urgent need for reliable, ultra-low latency communication networks to enable unmanned autonomous intelligent systems for applications such as localization, navigation, perception, decision-making, and motion planning. However, in remote areas, reliable communication coverage is not available, which poses a significant challenge for intelligent systems applications. The rapid development of non-terrestrial networks (NTNs) communication has shed new light on intelligent applications that require ubiquitous network connections in space, air, ground, and sea. However, challenges arise when using NTN technology in unmanned autonomous intelligent systems. Our research examines the advancements and obstacles in academic research and industry applications of NTN technology concerning UAIS, which is supported by unmanned aerial vehicles (UAV) and other low-altitude platforms. Nevertheless, edge computing and cloud computing are crucial for unmanned autonomous intelligent systems, which also necessitate distributed computation architectures for computationally intensive tasks and massive data offloading. This paper presents a comprehensive analysis of the opportunities and challenges of unmanned autonomous intelligent systems in UAV NTN, along with NTN-based unmanned autonomous intelligent systems and their applications. A field trial case study is presented to demonstrate the application of NTN in UAIS. Full article

The recent trend in hyper-scale computing has shown that applications are getting hungrier for low, consistent latency for predictably fast service. In accordance with this change, storage vendors have innovated their SSDs to keep ultra-low latencies while delivering high IOPS. Even though these SSDs can provide significant value to a wide variety of workloads, their practical impact on performance has not been discussed much. In this context, our study aims to empirically examine the impact of emerging ultra-low latency SSDs on workload performance, particularly in comparison to the latest conventional SSDs. We conduct benchmarking using a diverse set of data-intensive workloads to comprehensively assess the performance of both ultra-low latency SSDs and conventional SSDs. Our research provides an in-depth performance analysis of the examined SSDs, shedding light on their comparative strengths and weaknesses. The results of our study contribute valuable insights into the practical implications of adopting ultra-low latency SSDs across various workloads, guiding future decisions in storage technology. Full article

Phthalocyanines are potentially promising photosensitizers (PSs) for photodynamic therapy (PDT), but the inherent defects such as aggregation-caused quenching effects and non-specific toxicity severely hinder their further application in PDT. Herein, we synthesized two zinc(II) phthalocyanines (PcSA and PcOA) monosubstituted with a sulphonate group in the alpha position with “O bridge” and “S bridge” as bonds and prepared a liposomal nanophotosensitizer (PcSA@Lip) by thin-film hydration method to regulate the aggregation of PcSA in the aqueous solution and enhance its tumor targeting ability. PcSA@Lip exhibited highly efficient production of superoxide radical (O₂^∙−) and singlet oxygen (¹O₂) in water under light irradiation, which were 2.6-fold and 15.4-fold higher than those of free PcSA, respectively. Furthermore, PcSA@Lip was able to accumulate selectively in tumors after intravenous injection with the fluorescence intensity ratio of tumors to livers was 4.1:1. The significant tumor inhibition effects resulted in a 98% tumor inhibition rate after PcSA@Lip was injected intravenously at an ultra-low PcSA@Lip dose (0.8 nmol g⁻¹ PcSA) and light dose (30 J cm⁻²). Therefore, the liposomal PcSA@Lip is a prospective nanophotosensitizer possessing hybrid type I and type II photoreactions with efficient photodynamic anticancer effects. Full article

Currently, the challenges that analytical chemistry has to face are ever greater and more complex both from the point of view of the selectivity of analytical methods and their sensitivity. This is especially true in quantitative analysis, where various methods must include the development and validation of new materials, strategies, and procedures to meet the growing need for rapid, sensitive, selective, and green methods. In this context, given the International Guidelines, which over time, are updated and which set up increasingly stringent “limits”, constant innovation is required both in the pre-treatment procedures and in the instrumental configurations to obtain reliable, accurate, and reproducible information. In addition, the environmental field certainly represents the greatest challenge, as analytes are often present at trace and ultra-trace levels. These samples containing analytes at ultra-low concentration levels, therefore, require very labor-intensive sample preparation procedures and involve the high consumption of organic solvents that may not be considered “green”. In the literature, in recent years, there has been a strong development of increasingly high-performing sample preparation techniques, often “solvent-free”, as well as the development of hyphenated instrumental configurations that allow for reaching previously unimaginable levels of sensitivity. This review aims to provide an update of the most recent developments currently in use in sample pre-treatment and instrument configurations in the environmental field, also evaluating the role and future developments of analytical chemistry in light of upcoming challenges and new goals yet to be achieved. Full article

To utilize continuous ultralow intensity signals from oxide synaptic transistors as artificial synapses that mimic human visual perception, we propose strategic oxide channels that optimally utilize their advantageous functions by stacking two oxide semiconductors with different conductivities. The bottom amorphous indium–gallium–zinc oxide (a-IGZO) layer with a relatively low conductivity was designed for an extremely low initial postsynaptic current (PSC_i) by achieving full depletion at a low negative gate voltage, and the stacked top amorphous indium–zinc oxide (a-IZO) layer improved the amplitude of the synaptic current and memory retention owing to the enhancement in the persistent photoconductivity characteristics. We demonstrated an excellent photonic synapse thin-film transistor (TFT) with a precise synaptic weight change even in the range of ultralow light intensity by adapting this stacking IGZO/IZO channel. The proposed device exhibited distinct ∆PSC values of 3.1 and 18.1 nA under ultralow ultraviolet light (350 nm, 50 ms) of 1.6 and 8.0 μW/cm². In addition, while the lowest light input exhibited short-term plasticity characteristics similar to the “volatile-like” behavior of the human brain with a current recovery close to the initial value, the increase in light intensity caused long-term plasticity characteristics, thus achieving synaptic memory transition in the IGZO/IZO TFTs. Full article

An artificial retina neuron is proposed and implemented by CMOS technology. It can be used as an image sensor in the Artificial Intelligence (AI) field with the benefit of ultra-low power consumption. The artificial neuron can generate signals in spike shape with pre-designed frequencies under different light intensities. The power consumption is reduced by removing the film capacitor. The comparator is adopted to improve the stability of the circuit, and the power consumption of the comparator is optimized. The power consumption of the proposed CMOS neuron circuit is suppressed. The ultra-low-power artificial neuron with variable threshold shows a frequency range of 0.8–80 kHz when the input current is varied from 1 pA to 150 pA. The minimum DC power is 35 pW when the input current is 5 pA. The minimum energy of the neuron is 3 fJ. The proposed ultra-low-power artificial retina neuron has wide potential applications in the field of AI. Full article

Dynamic light scattering (DLS) is a popular method of particle size measurement, but at ultra-low particle concentrations, the occurrence of number concentration fluctuations limits the use of the technique. Number fluctuations add a non-Gaussian term to the scattered light intensity autocorrelation function (ACF). This leads to an inaccurate particle size distribution (PSD) being recovered if the normal DLS analysis model is used. We propose two methods for inverting the DLS data and recovering the PSDs when number fluctuations are apparent. One is to directly establish the relationship between the non-Gaussian ACF and the PSD by the kernel function reconstruction (KFR) method while including the non-Gaussian term to recover the PSD. The other is to remove the effect of the non-Gaussian term in the ACF by the baseline reset (BR) method. By including the number fluctuation term, the ideal recovered PSD can be obtained from the simulated data, but this will not happen in the experimental measurement data. This is because the measured intensity ACF contains more noise than the simulated ACF at ultra-low concentration. In particular, the baseline noise at the tail of long delay time of ACF overwhelms the number fluctuation term, making it difficult to recover reliable PSD data. Resetting the baseline can effectively remove the digital fluctuation term in ACF, which is also a feasible method to improve PSD recovery under ultra-low concentration. However, increasing noise at ultra-low concentrations can lead to errors in determining an effective baseline. This greatly reduces the accuracy of inversion results. Results from simulated and measured ACF data show that, for both methods, noise on the ACF limits reliable PSD recovery. Full article

Controlling the motion of macroscopic oscillators in the quantum regime has been the subject of intense research in recent decades. In this direction, opto-mechanical systems, where the motion of micro-objects is strongly coupled with laser light radiation pressure, have had tremendous success. In particular, the motion of levitating objects can be manipulated at the quantum level thanks to their very high isolation from the environment under ultra-low vacuum conditions. To enter the quantum regime, schemes using single long-lived atomic spins, such as the electronic spin of nitrogen-vacancy (NV) centers in diamond, coupled with levitating mechanical oscillators have been proposed. At the single spin level, they offer the formidable prospect of transferring the spins’ inherent quantum nature to the oscillators, with foreseeable far-reaching implications in quantum sensing and tests of quantum mechanics. Adding the spin degrees of freedom to the experimentalists’ toolbox would enable access to a very rich playground at the crossroads between condensed matter and atomic physics. We review recent experimental work in the field of spin-mechanics that employ the interaction between trapped particles and electronic spins in the solid state and discuss the challenges ahead. Our focus is on the theoretical background close to the current experiments, as well as on the experimental limits, that, once overcome, will enable these systems to unleash their full potential. Full article

Formaldehyde (HCHO), a chemical compound used in the fabrication process of a broad range of household products, is present indoors as an airborne pollutant due to its high volatility caused by its low boiling point ( $T=-19$ °C). Miniaturization of analytical systems towards palm-held devices has the potential to provide more efficient and more sensitive tools for real-time monitoring of this hazardous air pollutant. This work presents the initial steps and results of the prototyping process towards on-chip integration of HCHO sensing, based on the Hantzsch reaction coupled to the fluorescence optical sensing methodology. This challenge was divided into two individually addressed problems: (1) efficient airborne HCHO trapping into a microfluidic context and (2) 3,5–diacetyl-1,4-dihydrolutidine (DDL) molecular sensing in low interrogation volumes. Part (2) was addressed in this paper by proposing, fabricating, and testing a fluorescence detection system based on an ultra-low light Complementary metal-oxide-semiconductor (CMOS) image sensor. Two three-layer fluidic cell configurations (quartz–SU-8–quartz and silicon–SU-8–quartz) were tested, with both possessing a 3.5 µL interrogation volume. Finally, the CMOS-based fluorescence system proved the capability to detect an initial 10 µg/L formaldehyde concentration fully derivatized into DDL for both the quartz and silicon fluidic cells, but with a higher signal-to-noise ratio (SNR) for the silicon fluidic cell ( $SN{R}_{silicon}=6.1$ ) when compared to the quartz fluidic cell ( $SN{R}_{quartz}=4.9$ ). The signal intensity enhancement in the silicon fluidic cell was mainly due to the silicon absorption coefficient at the excitation wavelength, $a\left({\lambda }_{abs}=420 \mathrm{nm}\right)=5\times {10}^4{\mathrm{cm}}^{-1}$ , which is approximately five times higher than the absorption coefficient at the fluorescence emission wavelength, $a\left({\lambda }_{em}=515 \mathrm{nm}\right)=9.25\times {10}^3{\mathrm{cm}}^{-1}$ . Full article

Under the condition of ultra-low emission for power plants, the particulate matter concentration is significantly lower than that of typical power plants a decade ago, which posed new challenges for the particulate matter monitoring of stationary emission. The monitoring of particulate matter mass concentration based on ensemble light scattering has been found affected by particle size. Thus, this study develops a method of using the scattering angular distribution to obtain the real-time particle size, and then correct the particulate matter concentration with the real-time measured particle size. In this study, a real-time aerosol concentration and particle size measurement setup is constructed with a fixed detector at the forward direction and a rotating detector. The mass concentration is measured by the fixed detector, and the particle size is measured from the intensity ratio of the two detectors. The simulations show that the particle size has power law functionality with the angular spacing of the ripple structure according to Mie theory. Four quartz aerosols with different particle size are tested during the experiment, and the particle size measured from the ripple width is compared with the mass median size measured by an electrical low pressure impactor (ELPI). Both techniques have the same measurement tendency, and the measurement deviation by the ripple width method compared with ELPI is less than 15%. Finally, the measurement error of the real-time mass concentration is reduced from 38% to 18% with correction of the simultaneously measured particle size when particle size has changed. Full article

This paper provides an overview of the main manufacturing technologies of displays, focusing on those with low and ultra-low levels of power consumption, which make them suitable for current societal needs. Considering the typified value obtained from the manufacturer’s specifications, four technologies—Liquid Crystal Displays, electronic paper, Organic Light-Emitting Display and Electroluminescent Displays—were selected in a first iteration. For each of them, several features, including size and brightness, were assessed in order to ascertain possible proportional relationships with the rate of consumption. To normalize the comparison between different display types, relative units such as the surface power density and the display frontal intensity efficiency were proposed. Organic light-emitting display had the best results in terms of power density for small display sizes. For larger sizes, it performs less satisfactorily than Liquid Crystal Displays in terms of energy efficiency. Full article