Search Results (50)

We propose and demonstrate a novel motion blur-free hybrid image sensing technique. Unlike the previous hybrid image sensors, we developed a homogeneous hybrid image sensing technique including 60 fps CMOS Image Sensor (CIS) and 1440 fps pseudo Dynamic Vision Sensor (DVS) image frames without any performance degradation caused by static bad pixels. To achieve the fast readout, we implemented two one-side ADCs on two photodiodes (PDs) and the pixel output settling time can be reduced significantly by using the column switch control. The high-speed pseudo DVS frame can be obtained by differentiating fast-readout CIS frames, by which, in turn, the world’s smallest pseudo DVS pixel (1.8 μm) can be achieved. In addition, we confirmed that CIS (50 Mp resolution) and DVS (0.78 Mp resolution) data obtained from the hybrid image sensor can be transmitted over the MIPI (4.5 Gb/s four-lane D-PHY) interface without signal loss. The results showed that the motion blur of a 60 fps CIS frame image can be compensated dramatically by using the proposed pseudo DVS frames together with a deblur algorithm. Finally, using the event simulation, we verified that a 1440 fps pseudo DVS frame can compensate the motion blur of the CIS image captured in the situation of jogging at a 3 m distance. Full article

With the wide application of battery energy storage systems (BESSs) in DC microgrids, BESSs are facing increasingly severe cyber threats, among which, false data injection attacks (FDIAs) seriously undermine the accuracy of battery state estimation by tampering with sensor measurement data. To address this problem, this paper proposes an improved generative adversarial network (WGAN-GP)-based detection and defence method for FDIAs in battery energy storage systems. Firstly, a more perfect FDIA model is constructed based on the comprehensive consideration of the dual objectives of circumventing the bad data detection (BDD) system of microgrid and triggering the effective deviation of the system operating state quantity; subsequently, the WGAN-GP network architecture introducing the gradient penalty term is designed to achieve the efficient detection of the attack based on the anomalous scores output from the discriminator, and the generator reconstructs the tampered measurement data. Finally, the state prediction after repair is completed based on Gaussian process regression. The experimental results show that the proposed method achieves more than 92.9% detection accuracy in multiple attack modes, and the maximum reconstruction error is only 0.13547 V. The overall performance is significantly better than that of the traditional detection and restoration methods, and it provides an effective technical guarantee for the safe and stable operation of the battery energy storage system. Full article

Environmental costs (carbon emissions) have come with China’s economic rise, and its forestry sector now faces difficulties in maintaining both its profit and the health of its ecosystems. This study assesses the impact of carbon emissions on forestry efficiency and total factor productivity (TFFP) in China’s 31 provinces between 2001 and 2021. Using the data envelopment analysis (DEA) model through the slack-based measure (SBM framework) and Malmquist–Luenberger index (MLI), we examine the efficiency and productivity growth of forestry, both with and without accounting for carbon emissions. The study reveals that when carbon emissions are not taken into account, traditional measures of productivity tend to overstate both efficiency and total factor forestry productivity (TFFP) growth, resulting in an average of 7.7 percent higher efficiency and 1.6 percent of additional TFFP growth per year. If we compare the regions, coast provinces with stricter technical regulations have improved efficiency in usage, but places like Tibet and Qinghai, with more vulnerable ecosystems, endure harsher consequences. Regardless of incorporating bad output into the TFFP estimation, China’s growth in forestry productivity primarily depends on efficiency change (EC) rather than technological change (TC). Full article

In the electric locomotive traction transmission system, a four-quadrant rectifier has a high fault rate owing to the complicated control and bad operating conditions, and the fault directly affects the system’s safety and stability. To address such an issue, a rectifier fault diagnosis approach regarding a local tangent space alignment (LTSA) dimensionality reduction to optimize the high-dimensional energy entropy feature is proposed. Firstly, the fault signal is analyzed by using different wavelet functions through wavelet packet multi-resolution decomposition technology so as to extract the frequency band information of the signal. Each wavelet function corresponds to a specific frequency band; the energy–information entropy ratio of each frequency band coefficient is calculated, and then, the wavelet function and optimal frequency band, which are appropriate for the fault signal, are determined. Secondly, the energy entropy of each coefficient in the optimal frequency band is calculated to form the high-dimensional energy entropy feature. The LTSA algorithm is adopted to optimize the high-dimensional feature, through the fault sample number and clustering results, solve the difficulty of selecting the inherent dimension and nearest neighbor number in high-dimensional data, and obtain the simple and effective low-dimensional feature vector to describe the fault features, which reduces the conflict and redundancy between features. Finally, the optimized fault features are used as an input to the classifier support vector machine (SVM), and the fault types are obtained through training and testing. To validate the efficacy of the presented approach, it is tested from the aspects of noise environment, sample proportion and algorithm complexity, and compared with advanced methods. The results indicate that the proposed technique attains an average accuracy of 99.0625% in four-quadrant rectifier fault diagnosis. Under a different signal-to-noise ratio (SNR) and different training and test ratios, the average value after 30 diagnoses is better. Compared with other methods, this method shows a high diagnostic rate and strong robustness in terms of output voltage, noise, training and test ratio. Full article

Virtual power plant (VPP) is a critical platform for modern distribution systems with distributed generators (DGs). However, its cybersecurity is susceptible to cyber-attacks such as false data injection attacks (FDIAs). The impacts of FDIAs on VPP-distribution cyber–physical power systems have not been thoroughly investigated in the literature. This study concentrates on the distribution–VPP joint system and designs a new FDIA framework, topology-distributed-generator attack (TDA), that manipulates power network topology and DG outputs. An attack vector is designed carrying incorrect topology, falsified DG outputs, and tampered power flow information that can bypass the existing bad data detection and topology error identification, misleading the decision-making in the control center. Additionally, TDA models are formulated to optimize attack vectors based on objectives of attack investment, VPP economic loss, and operational security. A hybrid solution framework is then proposed for the optimization problem above, where the corresponding submodules realize the bad data detection, topology error identification, and optimal dispatching in the optimal attack vector. The effectiveness and superiority of the proposal are numerically verified on a 62-node cyber–physical system. Key findings highlight that VPP-integrated distribution systems are more vulnerable under low-level renewable energy penetration and the urgent need for enhancing backup power supplies to mitigate such threats. Full article

The processes underlying human cognition are often divided into System 1, which involves fast, intuitive thinking, and System 2, which involves slow, deliberate reasoning. Previously, large language models were criticized for lacking the deeper, more analytical capabilities of System 2. In September 2024, OpenAI introduced the o1 model series, designed to handle System 2-like reasoning. While OpenAI’s benchmarks are promising, independent validation is still needed. In this study, we tested the o1-preview model twice on the Dutch ‘Mathematics B’ final exam. It scored a near-perfect 76 and 74 out of 76 points. For context, only 24 out of 16,414 students in the Netherlands achieved a perfect score. By comparison, the GPT-4o model scored 66 and 62 out of 76, well above the Dutch students’ average of 40.63 points. Neither model had access to the exam figures. Since there was a risk of model contamination (i.e., the knowledge cutoff for o1-preview and GPT-4o was after the exam was published online), we repeated the procedure with a new Mathematics B exam that was published after the cutoff date. The results again indicated that o1-preview performed strongly (97.8th percentile), which suggests that contamination was not a factor. We also show that there is some variability in the output of o1-preview, which means that sometimes there is ‘luck’ (the answer is correct) or ‘bad luck’ (the output has diverged into something that is incorrect). We demonstrate that the self-consistency approach, where repeated prompts are given and the most common answer is selected, is a useful strategy for identifying the correct answer. It is concluded that while OpenAI’s new model series holds great potential, certain risks must be considered. Full article

Apiculture has presented significant growth in the last decades in Europe and worldwide. According to the Food and Agriculture Organization (FAO), there were 25.1 million bee colonies in Europe in 2021, with most of them being located in the southeastern countries. Smart technologies have invaded almost every pillar of agriculture, including apiculture. Modern apiculture is rather more nomadic than sedentary. Nomadism in beekeeping requires monitoring the settlement of bee colonies, in more than one place per year, in order to select more honey and pollen and contribute to the overall growth of the bees. To this scope, it is efficient to monitor and have wide control of bees remotely, in parallel with other smart applications, in order to prevent crises that would affect bee survival and/or yield production. The objectives of this paper are to outline a series of automation systems in apiculture used as a means towards the optimization of bee apiary management processes. Four beekeepers’ case studies were used to demonstrate how sensors and communication means transfer multiple bee-related data from various bee apiary locations to a single control system. The methodology was based on input/output data evaluation, risk prioritization based on real data, and feedback to the beekeeper based on the potential risks. Based on the results, the most significant risks are related to bad weather conditions, varroa mites, and bee colony health. Furthermore, the beekeeper is able to optimize the whole management, operations, and strategic planning throughout the year. Last, it should be noted that the presented remote monitoring system will never substitute the necessity of traditional beekeeper visits, but it contributes to minimizing them based on the monitored daily data. Full article

Background and Objectives: Genitourinary syndrome, previously defined as vulvovaginal atrophy, manifests with signs and symptoms deriving from estrogen diminution in the female genitourinary tract. Stable ozonides are derivatives of artemisinin found to be stable against strong basic and acidic conditions. Vitamin E is an important antioxidant diminishing the output of reactive oxygen species in the oxidation of fats and the emanation of free radicals, reducing cellular injury and aging. The primary aim of the present study was to assess the positive effects of an ozonide plus a vitamin E acetate-based compound (Ozoile) on genitourinary syndrome symptom relief after a maximum of 20 days of treatment. Materials and Methods: The inclusion criteria for patients’ enrollment were women of child-bearing age or in menopause reporting genitourinary syndrome’s related symptoms, such as pain, burning, a bad smell, dyspareunia, dryness, itching, bleeding, and nervousness. The exclusion criteria were Sjogren’s syndrome and patients administered retinoic acid, an agent that causes mucosal dryness. Participants completed a questionnaire before and after 20 days of treatment. Results: The incidence of pain decreased from 16.7% to 11.8% (p-value < 0.0001). In addition, the mean symptom intensity decreased from 2.10 to 0.87 (p-value < 0.0001). Dryness was the most frequent pre-treatment symptom and decreased from 85.5% to 53.8% (p-value < 0.0001) (mean: 2.21 vs. 0.90; p-value < 0.0001). Conclusions: Ozoile was effective in reducing most gynecologic symptoms related to genitourinary syndrome. However, further studies are needed to compare its effect with other standards of care. Full article

Purpose: To investigate corneal densitometry artefacts found in Pentacam Scheimpflug scans and their potential effect on assessing keratoconic (KC) corneas compared to normal (N) corneas. Methods: The current study utilises Pentacam data of 458 N eyes, aged 35.6 ± 15.8 (range 10–87), referred to as the “N group”, and 314 KC eyes, aged 31.6 ± 10.8 (range 10–72), referred to as the “KC group”, where densitometry data were extracted and analysed via a custom-built MATLAB code. Radial summations of the densitometry were calculated at diameters ranging from 0.5 mm to 5.0 mm. The minimum normalised radial summation of densitometry (NRSD) value and angle were determined at each diameter and then linked. KC cone locations and areas of pathology were determined, and a comparison between N and KC groups was carried out both within the averaged area of pathology and over the corneal surface. Results: Joining minimum NRSD trajectory points marked a clear distortion line pointing to the nasal-superior direction at 65° from the nasal meridian. The findings were found to be independent of eye laterality or ocular condition. Consistency was detected in the right and left eyes among both the N and KC groups. The location of the KC cone centre and the area of pathology were determined, and the densitometry output was compared both within the area of pathology and over the whole cornea. When the average densitometry was compared between N and KC eyes within the KC area of pathology, the N group recorded a 16.37 ± 3.15 normalised grey-scale unit (NGSU), and the KC group recorded 17.74 ± 3.4 NGSU (p = 0.0001). However, when the whole cornea was considered, the N group recorded 16.71 ± 5.5 NGSU, and the KC group recorded 15.72 ± 3.98 NGSU (p = 0.0467). A weak correlation was found between the Bad D index and NGSU when the whole measured cornea was considered (R = −0.01); however, a better correlation was recorded within the KC area of pathology (R = 0.21). Conclusions: Nasal-superior artefacts are observed in the densitometry Pentacam maps, and analysis shows no significant differences in their appearance between N or KC corneas. When analysing KC corneas, it was found that the cone positions are mostly on the temporal-inferior side of the cornea, opposite to the densitometry artefact NRSD trajectory. The analysis suggests that the corneal densitometry artefacts do not interfere with the KC area of pathology as it reaches its extreme in the opposite direction; therefore, weighting the densitometry map to increase the contribution of the inferior-temporal cornea and decreasing that of the superior-nasal area would improve the classification or identification of KC if densitometry is to be used as a KC metric. Full article

The article describes machine learning using artificial neural networks (ANNs) to develop the parameters of the friction stir welding (FSW) process for three types of aluminum joints (EN AW 7075). The ANNs were built using a total of 608 experimental data. Two types of networks were built. The first one was used to classify good/bad joints with MLP 7-19-2 topology (one input layer with 7 neurons, one hidden layer with 19 neurons, and one output layer with 2 neurons), and the second one was used to regress the tensile load-bearing capacity with MLP 7-19-1 topology (one input layer with 7 neurons, one hidden layer with 19 neurons, and one output layer with 1 neuron). FSW parameters, such as rotational speed, welding speed, and joint and tool geometry, were used as input data for ANN training. The quality of the FSW joint was assessed in terms of microstructure and mechanical properties based on a case study. The usefulness of both trained neural networks has been demonstrated. The quality of the validation set for the regression network was approximately 93.6%, while the errors for the confusion matrix of the test set never exceeded 6%. Only 184 epochs were needed to train the regression network. The quality of the validation set was approximately 87.1%. Predictive maps were developed and presented in the work, allowing for the selection of optimal parameters of the FSW process for three types of joints. Full article

Kaplan turbines are generally used in working conditions with a high flow and low head. These are a type of axial-flow hydro turbine that can adjust the opening of the guide vanes and blades simultaneously in order to achieve higher efficiency under a wider range of loads. Different combinations of the opening of the guide vanes and blades (cam relationship) will lead to changes in the efficiency of the turbine unit as well as its vibration characteristics. A bad cam relationship will cause the low efficiency or unstable operation of the turbine. In this study, the relative efficiency and vibration of a large-scale Kaplan turbine with 200 MW output were tested with different guide vane and blade openings. The selection of the cam relationship curve for both optimal efficiency and optimal vibration is discussed. Compared with the cam relationship given by the model test, the prototype cam relationship improves the efficiency and reduces the vibration level. Compared to the optimal efficiency cam relationship, the optimal vibration cam relationship reduces the efficiency of the machine by 1% to 2%, while with the optimal efficiency cam relationship, the vibration of the unit increases significantly. This research provides guidance for the optimization of the regulation of a large adjustable-blade Kaplan turbine unit and improves the overall economic benefits and safety performance of the Kaplan turbine power station. Full article

This study validated muscle activation estimations generated by OpenSim during the gait of elderly fallers. Ten healthy elderly participants walked on an instrumented treadmill, monitored by motion capture, force platforms, and 12 surface EMG sensors. Static optimization was used to calculate muscle activations, evaluated through cosine similarity, comparing them with EMG signals from 12 muscles of the right leg. Findings revealed varied similarity levels across muscles and gait phases. During stance phase, tibialis anterior (TIBA), peroneus longus (PERL), soleus (SOL), gastrocnemius lateralis (GASL), semitendinosus (SEMI), tensor fasciae latae (TFL), and rectus femoris (RECF) demonstrated poor similarity (cosim < 0.6), while gluteus medius (GMED), biceps femoris long head (BFLH), and vastus lateralis (VL) exhibited moderate similarity (0.6 ≤ cosim ≤ 0.8), and gluteus maximus (GMAX) and vastus medialis (VASM) displayed high similarity (cosim > 0.8). During the swing phase, only SOL demonstrated inadequate similarity, while GASL, GMAX, GMED, BFLH, SEMI, TFL, RECF, and VASL exhibited moderate similarity, and TIBA, PERL, and VASM showed high similarity. Comparing the different 10% intervals of the gait cycle generally produced more favorable similarity results. For most of the muscles and intervals, good agreement was found. Moderate agreement was estimated in the cases of TIBA (0–10%), PERL (60–70%), GASL (60–70%), TFL (10–20%), RECF (0–10%, 80–100%), and GMED (50–60%). Bad agreement was found in the cases of SOL (60–70%), GASL (0–10%), and TFL (0–10%). In conclusion, the study’s validation outcomes were acceptable in most cases, underlining the potential for user-friendly musculoskeletal modeling routines to study muscle output during elderly gait. Full article

In the navigation process, monitoring the running state of ship power plant equipment is crucial. In bad weather, when the critical equipment is abnormal, it is especially necessary to find out the root cause of the failure as soon as possible. In this case, it is required to use rapid detection equipment to detect and judge the key parameters. This paper proposes a vibration sensor (VS-TENG) of triboelectric nanogenerators based on spring vibration. The sensor adopts the spring structure inside and vibrates with the ship power equipment to collect the low-frequency vibration energy. This paper uses the VS-TENG sensors of two different spring parameters to study the electrical signal output under the excitation conditions of varying vibration frequencies. The results show that in the frequency range of 3–500 Hz, the efficient processing of different vibration excitation frequency signals can be realized, and the vibration frequency can be accurately identified. The error of medium-high frequency identification in VS-TENG is less than 1%. Especially at the resonant frequency, the maximum voltage output value can be achieved. On the PT500 Mini test bench, VS-TENG can reasonably identify the motor frequency and shutdown state. Therefore, VS-TENG can be applied to the condition monitoring of the vibration of the ship’s power plant and has a broad application prospect. Full article

Purpose: Gene therapy actually seems to have promising results in the treatment of Leber Congenital Amaurosis and some different inherited retinal diseases (IRDs); the primary goal of this strategy is to change gene defects with a wild-type gene without defects in a DNA sequence to achieve partial recovery of the photoreceptor function and, consequently, partially restore lost retinal functions. This approach led to the introduction of a new drug (voretigene neparvovec-rzyl) for replacement of the RPE65 gene in patients affected by Leber Congenital Amaurosis (LCA); however, the treatment results are inconstant and with variable long-lasting effects due to a lack of correctly evaluating the anatomical and functional conditions of residual photoreceptors. These variabilities may also be related to host immunoreactive reactions towards the Adenovirus-associated vector. A broad spectrum of retinal dystrophies frequently generates doubt as to whether the disease or the patient is a good candidate for a successful gene treatment, because, very often, different diseases share similar genetic characteristics, causing an inconstant genotype/phenotype correlation between clinical characteristics also within the same family. For example, mutations on the RPE65 gene cause Leber Congenital Amaurosis (LCA) but also some forms of Retinitis Pigmentosa (RP), Bardet Biedl Syndrome (BBS), Congenital Stationary Night Blindness (CSNB) and Usher syndrome (USH), with a very wide spectrum of clinical manifestations. These confusing elements are due to the different pathways in which the product protein (retinoid isomer-hydrolase) is involved and, consequently, the overlapping metabolism in retinal function. Considering this point and the cost of the drug (over USD one hundred thousand), it would be mandatory to follow guidelines or algorithms to assess the best-fitting disease and candidate patients to maximize the output. Unfortunately, at the moment, there are no suggestions regarding who to treat with gene therapy. Moreover, gene therapy might be helpful in other forms of inherited retinal dystrophies, with more frequent incidence of the disease and better functional conditions (actually, gene therapy is proposed only for patients with poor vision, considering possible side effects due to the treatment procedures), in which this approach leads to better function and, hopefully, visual restoration. But, in this view, who might be a disease candidate or patient to undergo gene therapy, in relationship to the onset of clinical trials for several different forms of IRD? Further, what is the gold standard for tests able to correctly select the patient? Our work aims to evaluate clinical considerations on instrumental morphofunctional tests to assess candidate subjects for treatment and correlate them with clinical and genetic defect analysis that, often, is not correspondent. We try to define which parameters are an essential and indispensable part of the clinical rationale to select patients with IRDs for gene therapy. This review will describe a series of models used to characterize retinal morphology and function from tests, such as optical coherence tomography (OCT) and electrophysiological evaluation (ERG), and its evaluation as a primary outcome in clinical trials. A secondary aim is to propose an ancillary clinical classification of IRDs and their accessibility based on gene therapy’s current state of the art. Material and Methods: OCT, ERG, and visual field examinations were performed in different forms of IRDs, classified based on clinical and retinal conditions; compared to the gene defect classification, we utilized a diagnostic algorithm for the clinical classification based on morphofunctional information of the retina of patients, which could significantly improve diagnostic accuracy and, consequently, help the ophthalmologist to make a correct diagnosis to achieve optimal clinical results. These considerations are very helpful in selecting IRD patients who might respond to gene therapy with possible therapeutic success and filter out those in which treatment has a lower chance or no chance of positive results due to bad retinal conditions, avoiding time-consuming patient management with unsatisfactory results. Full article

To enhance national competitiveness, countries are committed to building a National Human Resource Development (NHRD) system to develop talents. However, studies have rarely investigated the internal development process of the NHRD system and the performance at the system and sub-system levels. Thus, this study constructed the performance evaluation model of the NHRD system from a two-stage process efficiency perspective that first cultivates talents and then uses the talents produced to create value. In addition, considering the problem of international talent flow and the time-lag effect, the bad output and the time-lag between inputs and outputs were incorporated into the model. The subjects included 60 countries, including Argentina, China, and OECD member countries. The results reveal that countries that excel at nurturing talents do not necessarily have the ability to effectively use talents to create value. Only having high-efficiency talent cultivation cannot strengthen competitiveness. Sensitivity analysis was also conducted to identify the input that affects talent cultivation and utilization efficiency, which could be used as a reference for competitiveness and NHRD performance improvement. Full article