Facts reveal that numerous road accidents worldwide occur due to fatigue, drowsiness, and distraction while driving. Few works on the automated drowsiness detection problem, propose to extract physiological signals of the driver including ECG, EEG, heart variability rate, blood pressure, etc. which make those solutions non-ideal. While recent ones propose computer vision-based solutions but show limited performances as either they use hand-crafted features with conventional techniques like Naïve Bayes and SVM or use excessively bulky deep learning models which are still low on performances. Hence in this work, we propose an ensemble deep learning architecture that operates over incorporated features of eyes and mouth subsamples along with a decision structure to determine the fitness of the driver. The proposed ensemble model consists of only two InceptionV3 modules that help in containing the parameter space of the network. These two modules respectively and exclusively perform feature extraction of eyes and mouth subsamples extracted using the MTCNN from the face images. Their respective output is passed to the ensemble boundary using the weighted average method whose weights are tuned using the ensemble algorithm. The output of this system determines whether the driver is drowsy or non-drowsy. The benchmark NTHU-DDD video dataset is used for effective training and evaluation of the proposed model. The model established a train and validation accuracy of 99.65% and 98.5% respectively with an accuracy of 97.1% on the evaluation dataset which is significantly higher than those achieved by models proposed in recent works on this dataset.

Climate stress poses a threat to the agricultural sector, which is vital for both the economy and livelihoods in general. Quantifying its risk to food security, livelihoods, and sustainability is crucial. This study proposes a framework to estimate the impact climate stress on agriculture in terms of three objectives: assessing the regional vulnerability (exposure, sensitivity, and adaptive capacity), analysing the climate variability, and measuring agricultural performance under climatic stress. The vulnerability of twenty-two sub-regions in Jammu, Kashmir, and Ladakh is assessed using indicators to determine the collective susceptibility of the agricultural framework to climate change. An index-based approach with min–max normalization is employed, ranking the districts based on their relative performances across vulnerability indicators. This work assesses the impact of socio-economic and climatic indicators on the performance of agricultural growth using the benchmark Ricardian approach. The parameters of the agricultural growth function are estimated using a linear combination of socio-economic and exposure variables. Lastly, the forecasted trends of climatic variables are examined using a long short-term memory (LSTM)-based recurrent neural network, providing an annual estimate of climate variability. The results indicate a negative impact of annual minimum temperature and decreasing land holdings on agricultural GDP, while cropping intensity, rural literacy, and credit facilities have positive effects. Budgam, Ganderbal, and Bandipora districts exhibit higher vulnerability due to factors such as low literacy rates, high population density, and extensive rice cultivation. Conversely, Kargil, Rajouri, and Poonch districts show lower vulnerability due to the low population density and lower level of institutional development. We observe an increasing trend of minimum temperature across the region. The proposed LSTM synthesizes a predictive estimate across five essential climate variables with an average overall root mean squared error (RMSE) of 0.91, outperforming the benchmark ARIMA and exponential-smoothing models by 32–48%. These findings can guide policymakers and stakeholders in developing strategies to mitigate climate stress on agriculture and enhance resilience.

OBJECTIVE. The purpose of this article is to discuss the use, comparative efficacy, and general technical considerations of percutaneous ablation, alone or in combination with other therapies, for the treatment of hepatocellular carcinoma (HCC). CONCLUSION. Percutaneous ablation is a mainstay treatment for early-stage HCC, offering survival comparable to that of surgical resection for small lesions. It can act as a primary curative therapy or bridge therapy for patients waiting to undergo liver transplant. New ablation modalities and combining tumor ablation with other therapies, such as transarterial chemoembolization, can improve clinical outcomes and allow treatment of larger lesions. Combining thermal ablation with systemic chemotherapy, including immunotherapy, is an area of future development.

This study determined the effects of thermal conductivity on RF ablation tissue heating using mathematical modelling and computer simulations of RF heating coupled to thermal transport. Computer simulation of the Bio-Heat equation coupled with temperature-dependent solutions for RF electric fields (ETherm) was used to generate temperature profiles 2 cm away from a 3 cm internally-cooled electrode. Multiple conditions of clinically relevant electrical conductivities (0.07–12 S m⁻¹) and ‘tumour’ radius (5–30 mm) at a given background electrical conductivity (0.12 S m⁻¹) were studied. Temperature response surfaces were plotted for six thermal conductivities, ranging from 0.3–2 W m⁻¹ °C (the range of anticipated clinical and experimental systems). A temperature response surface was obtained for each thermal conductivity at 25 electrical conductivities and 17 radii (n = 425 temperature data points). The simulated temperature response was fit to a mathematical model derived from prior phantom data. This mathematical model is of the form (T = a + bR^c exp^dR σ^f exp^gσ) for RF generator-energy dependent situations and (T = h + k exp^mR + n exp^pσ) for RF generator-current limited situations, where T is the temperature (°C) 2 cm from the electrode and a, b, c, d, f, g, h, k, m, n and p are fitting parameters. For each of the thermal conductivity temperature profiles generated, the mathematical model fit the response surface to an r² of 0.97–0.99. Parameters a, b, c, d, f, k and m were highly correlated to thermal conductivity (r² = 0.96–0.99). The monotonic progression of fitting parameters permitted their mathematical expression using simple functions. Additionally, the effect of thermal conductivity simplified the above equation to the extent that g, h, n and p were found to be invariant. Thus, representation of the temperature response surface could be accurately expressed as a function of electrical conductivity, radius and thermal conductivity. As a result, the non-linear temperature response of RF induced heating can be adequately expressed mathematically as a function of electrical conductivity, radius and thermal conductivity. Hence, thermal conductivity accounts for some of the previously unexplained variance. Furthermore, the addition of this variable into the mathematical model substantially simplifies the equations and, as such, it is expected that this will permit improved prediction of RF ablation induced temperatures in clinical practice.

Fibrolamellar hepatocellular carcinoma (FL-HCC) is a rare primary liver tumor that typically presents at an advanced stage in early adolescents and adults with no underlying liver disease. Surgical resection is the first-line treatment, and patients who are not surgical candidates face limited treatment options with poor long-term outcomes. Herein we report the first documented, technically successful treatment of FL-HCC with extrahepatic spread using transarterial radioembolization (TARE) in a 16-year-old male patient with surgically unresectable disease. Subsequent imaging revealed tumor necrosis and a 20% reduction in size, and the patient survived 20 months post-treatment, a marked improvement relative to historical data in the literature. Further research should examine the potential role of yttrium-90 TARE in the treatment of FL-HCC patients with metastatic disease.