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Information and communication technologies have opened the way to guide recent developments in the field of parking. In this paper these technologies are applied to model a decision support system that gives insight into 6-months ahead parking occupancy forecasts for 57 off-street parking locations in Amsterdam. An effect analysis was conducted into the influence of weather-, event-, parking tariff-, and public transport attributes on parking occupancy. The most influential factors on the parking occupancy were the scheduling of artistic and sports events, the addition of a public transport line, and the weather variables thunderstorm, average wind speed, temperature, precipitation, and sunshine duration. Parking tariffs did not significantly contribute to model performance, which could have been because of the lack of data and time variability in the parking tariffs of the examined parking locations. The forecasting algorithms compared were the seasonal naive model as a benchmark approach, the Box–Jenkins seasonal autoregressive integrated moving average with and without exogenous regressors (SARIMAX and SARIMA, respectively), exponential smoothing models, and the long short-term memory neural network. The SARIMAX model outperformed the other algorithms for the 6-months ahead forecasts according to the lowest root mean square error (RMSE). By including the event factor, the model improved by 24% based on the RMSE. Weather variables improved the predictive performance by 8%. Future studies could focus on the addition of more event variables, extension into an online model, and the impact of spatial–temporal features on parking occupancy.

How can Community Managed Water Supply (CMWS) become more sustainable? Recent studies in several countries indicate that the sustainability of many CMWS is poor. As a result, their long term impact on village lives is limited. This paper presents the findings of research on the sustainability and long-term impact of a group of CMWS created by the Rural Water Supply and Sanitation Project (RWSSP) in Kyrgyzstan, Central Asia. This project adopted a Community-Based Approach to maximize the sustainability and long-term impact of its CMWS. The research assessed the sustainability of these CMWS a decade after their completion. It used six measures to assess sustainability and examined four long-term impacts. The results are compared with studies from other countries. The influence of Community-Based Approach (CBA) on the results is discussed. Recommendations are made for changes in policies and strategies to improve the sustainability and long term impact of future CMWS in Central Asia and elsewhere.

Considering multidimensional structure of the multi-exposure images,a new Tensor product and Tensorsingular value decomposition based Multi-Exposure image Fusion (TT-MEF) method is proposed. The main innovation of this work is to explore a new feature representation of multi-exposure images in the new tensor domain and design the fusion strategy on this basis. Specifically,the luminance and the chrominance channels are fused separately to maintain color consistency. For the luminance fusion,the luminance channel of multi-exposure images is divided into two parts,that is,de-mean term and mean term. The de-mean term is represented as a tensor to extract the feature. Then,the tensor product and tensor-singular value decomposition (T-SVD) are used to design a tensor feature extractor. Furthermore,a fusion strategy of the de-mean term is presented according to the visual saliency model,and a fusion strategy of the mean term is defined by the local and the global visual weights to control counterpoise between the local and global luminance. For the chrominance fusion,a new fusion strategy is also designed by the tensor product and T-SVD,similar to the luminance fusion. Finally,the fused image is obtained by combining the luminance and chrominance fusion. Experimental results show that the proposed TT-MEF method generally outperforms the existing state-of-the-art in terms of subjective visual quality and objective evaluation.

A single infrared image or visible image for the same scene is usually insufficient to simultaneously reveal the infrared objects and the scene details. Thus, image fusion techniques play an important role in producing a single image from the images captured by infrared and visible sensors. In this paper, we propose a novel total variation (TV)-based fusion for infrared and visible images. In our model, a weighted fidelity term is employed to fuse both the infrared objects in the infrared image and the salient scenes in the visible image. To this end, a weight estimation method is developed based on the global luminance contrast-based saliency. Also, to overcome the over-fitting, two constraints are further introduced to merge more details from the visible image and prevent the luminance degradation for the fused result, respectively. Moreover, joint norms are exploited to produce a better result. $L_{2,1,rc}$ provides the structural group sparseness for the fidelity term, whereas $L_{1/2}$ presents the better gradient sparse for the detail preserving term and $L_{2}$ is utilized for the luminance degradation preventing term. Experimental results indicate that the proposed method can give state-of-the-art performances both in visual perception and quantitative scores than other methods.

We tackle the problem of few-shot image classification in the context of remote sensing hyperspectral images (HSIs). Due to the difficulties in collecting a large number of labeled training samples, the few-shot classification techniques hold much prominence in remote sensing in general. One of the bottlenecks in designing few-shot learning (FSL) systems arises from the fact that the model is likely to overfit in the presence of few training samples and the complex spectral feature distributions of the land-cover classes. To this end, we introduce a stable prototypical network (SPN) for FSL by judiciously incorporating dropout and DropBlock-based regularizers jointly within the framework and averaging model parameters using the Monte Carlo approximation. Besides, a novel variance loss term to reduce the uncertainty of the network is considered together with the cross-entropy-based classification loss to train the model in an end-to-end manner. The experimental analysis on three benchmark HSI datasets confirms the SPN's superior performance.

In this letter, using the sparse unmixing framework, a weighted collaborative sparse and L1/2 low-rank regularization with superpixel segmentation method is proposed for hyperspectral unmixing. The method outlined here first uses superpixel segmentation to obtain local homogeneous regions. The reason for this approach is that the shape and size of superpixels are adaptive, which are better for obtaining homogeneous regions than square patches. Next, the weighted collaborative sparse term and L1/2 low-rank regularization were utilized to exploit the spatial and spectral correlation of each superpixel. In addition, the smoothness between adjacent pixels is enforced by total variation regularization. Finally, the proposed method and several state-of-the-art methods were tested on two simulated data sets and two real data sets. The results demonstrate the superiority of the method proposed here.

International audience; In recent years, deep learning (DL) has attracted increasing attention in hyperspectral unmixing (HU) applications due to its powerful learning and data fitting ability. The autoencoder (AE) framework, as an unmixing baseline network, achieves good performance in HU by automatically learning low-dimensional embeddings and reconstructing data. Nevertheless, the conventional AE-based architecture, which focuses more on the pixel-level reconstruction loss, tends to lose some significant detailed information of certain materials (e.g., material-related properties) in the reconstruction process. Therefore, inspired by the perception mechanism, we propose a cycle-consistency unmixing network, called CyCU-Net, by learning two cascaded AEs in an end-to-end fashion, to enhance the unmixing performance more effectively. CyCU-Net is capable of reducing the detailed and material-related information loss in the process of reconstruction by relaxing the original pixel-level reconstruction assumption to cycle consistency dominated by the cascaded AEs. More specifically, cycle consistency can be achieved by a newly proposed self-perception loss, which consists of two spectral reconstruction terms and one abundance reconstruction term. By taking advantage of the self-perception loss in the network, the high-level semantic information can be well preserved in the unmixing process. Moreover, we investigate the performance gain of CyCU-Net with extensive ablation studies. Experimental results on one synthetic and three real hyperspectral data sets demonstrate the effectiveness and competitiveness of the proposed CyCU-Net in comparison with several state-of-the-art unmixing algorithms.

Rationale Infants born prematurely have impaired capacity to deal with oxidative stress shortly after birth. Objectives We hypothesize that the relative impact of exposure to air pollution on lung function is higher in preterm than in term infants. Methods In the prospective BILD-birth-cohort of 254 preterm and 517 term infants, we investigated associations of particulate matter (PM10) and nitrogen dioxide with lung function at 44 weeks postconceptional age and exhaled markers of inflammation and oxidative stress response (fraction of exhaled nitric oxide (FeNO)) in an explorative hypothesis-driven study design. Multilevel mixed-effects models were used and adjusted for known confounders. Measurements and Main Results Significant associations of PM10 during the second trimester of pregnancy with lung function and FeNO were found in term and preterm infants. Importantly, we observed stronger positive associations in preterm infants (born 32 - 36 weeks), with an increase of [184.9 (79.1, 290.7) mL/min] minute ventilation per 10 ��g/m3 increase in PM10 than in term infants [75.3 (19.7, 130.8) mL/min] (pprematurity �� PM10 interaction = 0.04, after multiple comparison adjustment padj = 0.09). Associations of PM10 and FeNO differed between moderate to late preterm [3.4 (-0.1, 6.8) ppb] and term [-0.3 (-1.5, 0.9) ppb] infants, the interaction with prematurity was significant (pprematurity �� PM10 interaction = 0.006, padj = 0.036). Conclusion Preterm infants showed significant higher susceptibility even to low-to-moderate prenatal air pollution exposure than term infants, leading to increased impairment of postnatal lung function. FeNO results further elucidate differences in inflammatory/oxidative stress response comparing preterms to terms.

Generative Adversarial Networks (GAN) has become more and more popular these years. However, it is difficult to train and suffers from the training instability problem. To tackle this difficulty, this paper proposes a novel approach. Our idea is intuitive but proven to be very useful. In essence, it combines saturating loss and non-saturating loss into the loss function. Thus it will exploit the complementary statistical properties from two kinds of loss functions to effectively improve the training stability. We term our method twin discriminator Generative Adversarial Networks (TWGAN) which, unlike GAN, has a generator and a twin discriminator. The twin discriminator consists of two discriminators with identical architecture and both of them aim to distinguish whether the samples are from real data or fake data. We develop theoretical analysis to show that, given the optimal discriminators, optimizing the generator of TWGAN reduces to minimizing the Kullback-Leibler (KL) divergence between the distribution of generated data (Pg) and the distribution of real data (Pdata), hence effectively addressing the training instability problem. Extensive experiments on MNIST, Fashion MNIST, CIFAR-10/100 and STL-10 datasets demonstrate that the competitive performance of our TWGAN in generating good quality and diverse samples over baselines. The obtained highest inception score (IS) and lowest Frechet Inception Distance (FID), compared with other state-of-the-art GANs, show the superiority of our TWGAN.

The convolutional model, which describes the relation among poststack seismic data, wavelet, and reflectivity, is the foundation of seismic deconvolution (SD). However, this model is only an approximation of the seismic wave equation, and it may not work in complex cases especially when the medium is anelastic, heterogeneous, and anisotropic. In this article, we propose a generalized convolutional model for poststack seismic data. A deep-learning-based data correction term is added to characterize the data ingredients that cannot be characterized by the convolutional model. The data correction term of the new model is realized using the long-short term memory (LSTM)-based deep learning architecture, of which parameters are learned based on the dataset from several well logs. Based on the new model, we propose an SD method and investigate its performance in building reflectivity models using complex numerical examples. The results verified that the new model can accurately characterize complex seismic data, which cannot be characterized by a convolutional model. In addition, the proposed SD method has significant advantages over traditional methods in building high-fidelity reflectivity models in complex cases.

In this letter, a novel spatial peak-aware collaborative representation (SPaCR) method is proposed for hyperspectral imagery (HSI) classification, which introduces spectral-spatial information among superpixel clusters into regularization terms to construct a new collaborative representation (CR)-based closed-form solution. The proposed method is composed of the following key steps. First, the raw HSI is clustered into many superpixels according to an oversegmentation strategy. Then, cluster pixels are determined based on spectral-spatial correlation between pixels within each superpixel. Next, spectral distance and spatial coherence of superpixel clusters corresponding to training samples and testing pixels are fused to define differences between pixels. Finally, the difference information between clusters as a spectral-spatial feature-induced regularization term is incorporated into the objective function. Experimental results on the Indian Pines and the University of Pavia HSIs indicated that the proposed SPaCR method, without any preprocessing and postprocessing, outperforms well-known and state-of-the-art classifiers on the limited labeled samples.

Despite the increasing need to address long-term challenges, public sector organizations are incentivized to focus on short-term results. This article uses an ethnographic approach to analyse how members of a regional water authority understand and deal with long-term policy problems as part of their everyday practices. It reveals three specific dilemmas: investing in the realization of objects or objectives, adopting a stable or responsive approach, and taking a proactive or reactive stance towards the external environment. The concept of strategic agility enables organizations to respond proactively to unexpected developments by devising strategies to steer as well as to accommodate change.

In this study, we propose an interpretable deep network for variational pansharpening (VP), named VP-Net. Different from traditional priors using linear operators, such as the gradient, we construct a prior based on the similarity between panchromatic (PAN) and high-resolution multispectral (HRMS) images by a nonlinear operator that can be learned through a deep network. Considering the spectral difference of various satellite multispectral (MS) imaging platforms, we specifically seek the aforementioned similarity from the PAN image and the intensity of the HRMS image to reduce the spectral distortion. Based on this prior, we propose a novel VP model by further incorporating a data fidelity term from the low-resolution MS image. Specifically, we build the VP-Net by unrolling the variable splitting method for an optimal solution to this model. Consequently, all modules in VP-Net have clear physical meanings and strong generalization capabilities. Meanwhile, all parameters and the aforementioned nonlinear operator are learned in VP-Net, avoiding the difficulty of selecting optimal handcrafted parameters in traditional methods. Therefore, VP-Net not only achieves an optimal balance between spatial and spectral qualities but also has a strong generalization capability across different types of training and testing data. In the experiment, we first demonstrate the superiority of the proposed method over the current state of the arts in terms of both visual effect and quantitative analysis on different satellite datasets. Moreover, we carry out an normalized difference vegetation index (NDVI) experiment to demonstrate its potential in remote sensing.

Accurate wind power forecasting is a vital factor in day-ahead dispatch and increasing the level of penetration of renewable energy. The feature extraction of meteorological factors related to wind power output is a big challenge to improve forecasting accuracy, and selecting key meteorological factors based on experience will decrease the prediction accuracy. Therefore, a day-ahead wind power combined forecasting approach is innovatively proposed through key meteorological factors selection, data decomposition and reconstruction, combined forecasting model generation, and optimization strategy. Correlated variables namely variational mode decomposition and weighted permutation entropy (VMD-WPE) decomposed historical wind power and key meteorological factors are used as the inputs. A forecasting model based on convolutional neural network (CNN) and long short-term memory network (LSTM) is used to forecast future wind power. Four optimizers with different optimization performances are used to find the best parameters of the forecasting model to obtain accurate prediction results. Multiple comparative experiments from regional wind farms in Ningxia and Jilin of China are utilized as case studies to evaluate the effectiveness of the proposed model. Results show that the proposed approach outperforms other benchmark prediction models, taking into account multiple-error metrics including error metrics, accuracy rate, and improvement percentages.

This paper aims to present solutions based on inherently safer design (ISD) principles to reduce the risks related to interventions in confined spaces and discuss their general applicability in a design situation or installations retrofit by exploring the client-designer relationship. A literature review on ISD solutions for confined spaces was followed by semi-structured interviews with 15 experts on confined space risk management in Quebec, Canada. The solutions based on the literature were confronted with experts' knowledge on the real-life conditions in confined space interventions. Our findings suggest that considering the entire confined space's life cycle in design is still very marginal (e.g., questioning the necessity to enter). As designers tend to replicate non intrinsically safe designs (e.g., inadequate entrances dimensions), end-users will then require retrofitting the structures over time, which usually costs more in the long term than implementing ISD solutions earlier. The notion of declassification of the confined space to eliminate confined spaces can give a false sense of safety. Its contribution to accident prevention depends on how end-users reduce inherent or task-induced risks as low as reasonably practicable. This paper categorized declassification into three types: total elimination, hazard-oriented declassification, and organizational declassification. Since the implementation guidelines of these risk reduction strategies are absent in the literature, this paper provides a risk reduction model that shows how end-users can identify application phases, declassify a confined space, and manage residual risks at the design phase in real-life environments.

Using temporal and spatial correlations to predict wind speed is still one of the most challenging and least-researched fields in wind speed prediction. How to make full use of the spatial correlation of wind speed between adjacent wind turbines to improve the accuracy of wind speed prediction is significant. Therefore, to address the challenges of making multi-location, short-term wind speed predictions, we propose a multi-location wind speed prediction model based on spatiotemporal joint learning, which called Mixed-SqueezeNet-BiGRU. By designing the SqueezeNet model with a deformable convolutional network, the spatial correlation of a multi-location time series is learned by using the spatial wind speed matrix; by introducing the Channel Shuffle operation of ShuffleNet, the characteristics of multi-location and multi-channel are output to ensure the orderly flow of information among the channels. To describe the dynamic information of the time series, a time series dynamic network model based on the bi-directional gating cycle unit, BiGRU, is introduced to model the long-term and nonlinear dependence of the time series. Experiments were carried out on three wind farm's wind speed dataset to verify that the Mixed-SqueezeNet-BiGRU short-term wind speed prediction model has a better prediction performance than some advanced methods.

The growing number of COVID-19 cases puts pressure on healthcare services and public institutions worldwide. The pandemic has brought much uncertainty to the global economy and the situation in general. Forecasting methods and modeling techniques are important tools for governments to manage critical situations caused by pandemics, which have negative impact on public health. The main purpose of this study is to obtain short-term forecasts of disease epidemiology that could be useful for policymakers and public institutions to make necessary short-term decisions. To evaluate the effectiveness of the proposed attention-based method combining certain data mining algorithms and the classical ARIMA model for short-term forecasts, data on the spread of the COVID-19 virus in Lithuania is used, the forecasts of epidemic dynamics were examined, and the results were presented in the study. Nevertheless, the approach presented might be applied to any country and other pandemic situations. The COVID-19 outbreak started at different times in different countries, hence some countries have a longer history of the disease with more historical data than others. The paper proposes a novel approach to data registration and machine learning-based analysis using data from attention-based countries for forecast validation to predict trends of the spread of COVID-19 and assess risks. © 2021 Tech Science Press. All rights reserved.

This article surveys the Indian (Sanskrit and Prakrit) loanwords used in the Christian literature in Sogdian, including some which have not been noticed previously. In particular, it discusses a possible borrowing of the Buddhist Sanskrit technical term citta-saṃtāna-, used in a Christian Sogdian text in the sense train of thought

Acute Kidney Injury (AKI) is a common problem in hospitalized patients and is associated with poor clinical outcome. We studied the incidence, severity, and short-term outcome of AKI amongst patients admitted to a general hospital in Hong Kong.

Purpose: The purpose of this study was to inform school-based services for children with acquired brain injury (ABI) by describing and analyzing functional student-outcome data from a state-wide, school-based, school re-entry consultation program, BrainSTEPS (Strategies Teaching Educators, Parents, and Students), in Pennsylvania. Method: A nonexperimental, retrospective analysis was conducted with data collected during a pilot follow-up survey for BrainSTEPS. Caregivers reported on 337 students with ABI participating in BrainSTEPS. Results: Most students post-ABI who were participating in BrainSTEPS were enrolled in regular education and reported no ongoing symptoms, as well as no parent perception of need for additional BrainSTEPS consultation during the time period of the follow-up survey. Current receipt of therapy was significantly associated with injury type, χ 2 (1, n = 329) = 16.72, p < .001. A multiple logistic regression was significant ( p < .001) in predicting the need for additional BrainSTEPS consultation. More severe injuries, educational placement postinjury of regular education with a 504 plan (compared to regular education), and current receipt of therapy significantly increased the odds of need for consultation. Conclusions: Due to the wide range of experiences of students in this sample, hospital-to-school transition services, educational supports and services, and long-term follow-up must be individualized for children with ABI. Speech-language pathologists are critical members of the school-based academic team for students with ABI to decrease long-term unmet needs.

Purpose: In this work, we have determined the long-term average speech spectra (LTASS) and dynamic ranges (DR) of 17 Indian languages. This work is important because LTASS and DR are language-dependent functions used to fit hearing aids, calculate the Speech Intelligibility Index, and recognize speech automatically. Currently, LTASS and DR functions for English are used to fit hearing aids in India. Our work may help improve the performance of hearing aids in the Indian context. Method: Speech samples from native talkers were used as stimuli in this study. Each speech sample was initially cleaned for extraneous sounds and excessively long pauses. Next, LTASS and DR functions for each language were calculated for different frequency bands. Similar analysis was also performed for English for reference purposes. Two-way analysis of variance was also conducted to understand the effects of important parameters on LTASS and DR. Finally, a one-sample t test was conducted to assess the significance of important statistical attributes of our data. Results: We showed that LTASS and DR for Indian languages are 5–10 dB and 11 dB less than those for English. These differences may be due to lesser use rate of high-frequency dominant phonemes and preponderance of vowel-ending words in Indian languages. We also showed that LTASS and DR do not differ significantly across Indian languages. Hence, we propose a common LTASS and DR for Indian languages. Conclusions: We showed that differences in LTASS and DR for Indian languages vis-à-vis English are large and significant. Such differences may be attributed to phonetic and linguistic characteristics of Indian languages.

Objective. To evaluate the clinical effects and survival prognosis of radical gastrectomy assisted by external vision in gastric cancer. Methods. A total of 60 hospitalized gastric cancer patients from June 2016 to December 2018 were selected and divided into the observation group and control group according to different surgical procedures. The control group was treated with traditional open radical gastrectomy, while the observation group was treated with radical gastrectomy assisted by an external vision microscope. Relevant surgical indicators, visual analogue scale (VAS), postoperative complications, and life quality assessment were analyzed and compared between the two groups. Results. The incision length and intraoperative blood loss in the observation group were smaller than those in the control group, and the difference was statistically significant ( P < 0.05 ); compared with the control group, the observation group had significantly shorter hospital stay, earlier postoperative first exhaust time, and lower gastric fluid volume at the 3rd day after surgery ( P < 0.05 ). The pain scores of the observation group at 1, 4, and 12 weeks after surgery were lower than those of the control group ( P < 0.05 ), and the difference was significant. The quality of life scores at the 1st week and 12th week after surgery showed that the dysphagia symptom scores of the observation group and the control group were significantly reduced but the two groups had significant differences in fatigue, physical function, pain score, postoperative pain, and overall quality of life. The observation group was significantly better than the control group ( P < 0.05 ). Follow-up studies showed no significant difference between mortality and cancer recurrence ( P > 0.05 ); the patients recovered well at postoperation, and the diet of the observation group was better than that of the control group ( P < 0.05 ); gastric reflux and knife pain were less than those of the control group ( P < 0.05 ). Conclusion. Radical gastrectomy assisted by external vision for gastric cancer yields clinical benefits for gastric cancer patients, which not only dramatically shortened the length of hospital stay but also effectively ameliorated the quality of life of patients, all indicating that external vision-assisted surgery was significantly better than traditional gastrectomy in improving the postoperative quality of life of gastric cancer patients in the absence of increasing the risk of adverse events.

To determine the association of anti-obesity medications (AOMs) with weight loss maintenance over 2 years.This is a retrospective observational cohort study of adults treated for obesity between 1 April 2014 and 1 April 2016 at a tertiary academic weight management center and who completed 2 years of follow-up. Main outcome measures were mean percent weight loss, percent of individuals who achieved clinically significant long-term weight loss (≥5% weight loss over 2 years), and long-term weight loss maintenance (achievement of ≥5% weight loss at 1 year and maintenance of the ≥5% reduction for the second year).Of the 1566 new patients, 421 completed 1- and 2-year follow-up appointments. Patients were mostly female and on average 51 years old; they weighed 100.1 kg and had a BMI of 35.8 kg/mLong-term weight loss maintenance was observed among adults with medically managed obesity who completed 2 years of follow-up.

In clinical, the center of pressure (CoP) is commonly used for accessing the stability of a person’s postural control, which is highly associated with various neurological diseases and movement disorders such as Alzheimer’s disease, Parkinson’s disease, chronic ankle instability. Such a disease usually has a long development or rehabilitation process which requires long-term CoP monitoring. The current CoP evaluation process does not meet the requirement, as it is often complicated and expensive through either the lab-based equipment or the clinical evaluation procedure. Different wearable sensor-based systems with less cost and restrictions have emerged, but their way of CoP calculation requires deliberate calibration of the positions of their sensors, which are not feasible in daily CoP monitoring. In this study, we developed a long-term CoP monitoring system in a smart-shoe form. First, a thin and flexible smart insole with optimal sensor locations was designed to be compact and energy sufficient for a whole-day usage. Then, a user-friendly app on the smartphone with a cloud-based data managing system was developed for applications in both clinical and home environments. Additionally, a simplified CoP estimation model was created without the need for calibration. Lastly, a machine learning-based human activity recognition method was incorporated to make the CoP detection process more automatic. Through a thorough validation test with the clinical level lab equipment, our system can generate the CoP measurements with high accuracy.

In this paper the benchmarking functions are used to evaluate and check the particle swarm optimization (PSO) algorithm. However, the functions utilized have two dimension but they selected with different difficulty and with different models. In order to prove capability of PSO, it is compared with genetic algorithm (GA). Hence, the two algorithms are compared in terms of objective functions and the standard deviation. Different runs have been taken to get convincing results and the parameters are chosen properly where the Matlab software is used. Where the suggested algorithm can solve different engineering problems with different dimension and outperform the others in term of accuracy and speed of convergence.

PURPOSE This real-life cohort of patients describes the treatment patterns and compares the overall survival (OS) and hazard risk of utilization of multiple therapies. MATERIALS AND METHODS Electronic medical registries of patients with stage III non–small-cell lung cancer (NSCLC) regularly attended in 72 hospitals were included. Univariate and multivariate analyses were conducted to evaluate the primary patients' characteristics leading to better OS and cancer-specific survival. RESULTS A total of 3,363 patients with stage III NSCLC followed over 19 years were included in this study. The median age was 66.00 (58.00-72.00) years, 65% male, and 41.2% with squamous cell carcinoma followed by adenocarcinoma (34.6%) and undifferentiated carcinoma (13.1%) in clinical stage T3 (50.3%), T2 (29.3%), and T4 (12.3%). The median survival (in months) was 18.4 (95% CI, 16.9 to 19.5) in patients submitted to radiotherapy plus chemotherapy, 11.2 (95% CI, 10.5 to 12.1) to chemotherapy, 31.5 (95% CI, 25.9 to 37.7) to surgery plus chemotherapy, and 33.8 (95% CI, 28.3 to 47.8) to chemotherapy plus radiotherapy plus surgery. The median cancer-specific survival (in months) was 19.3 (95% CI, 17.9 to 20.9) in patients submitted to radiotherapy plus chemotherapy, 12.1 (95% CI, 11.1 to 12.9) to chemotherapy, 36.9 (95% CI, 29.6 to 43.2) to surgery plus chemotherapy, and 41.3 (95% CI, 32.1 to 61.3) to chemotherapy plus radiotherapy plus surgery. The patients treated with multiple chemotherapy plus radiotherapy followed by surgery had significantly better OS and lower mortality rates than those treated with other treatments (adjusted hazard ratio, 0.55; 95% CI, 0.45 to 0.66; P < .001). At the end of the study, 11.2% and 10.7% of the patients were living with and without cancer, respectively. CONCLUSION Our real-life 19-year cohort study has shown that only 30.3% of the total patients with stage III NSCLC have been submitted to standard chemotherapy and radiotherapy treatment. This may show a substantial difference between the recruited clinical trials' patients and the real-life patients' characteristics in daily routine treatment.

Background There is currently no existing consensus regarding timing of knee arthroplasty. This study aimed to develop a weighted scoring system from patient-reported outcome measures (PROMs) to guide timing of knee arthroplasty based on preoperative severity and long-term effectiveness. Methods Prospectively collected data of 766 total knee arthroplasties (TKAs) and 382 unicompartmental knee arthroplasties (UKAs) at a single institution were analyzed. PROMs were assessed preoperatively and at 10 years using the Knee Society Score (KSS), Oxford Knee Score (OKS), and Short Form-36 physical component score (SF-36 PCS). Receiver operating characteristic analysis identified thresholds where preoperative PROMs predicted 10-year clinically meaningful improvements (minimal clinically important difference [MCID]). Threshold weights were assigned to PROMs based on their ability to predict MCID in isolation or in combination. Results Poorer baseline PROMs predicted 10-year MCID attainments. The threshold of 49.5 points for the KSS, 30.5 points for the OKS, and 40.7 points for the SF-36 PCS and 55.5 points for the KSS, 33.5 points for the OKS, and 40.5 points for the SF-36 PCS was weighted 1 point for predicting MCID in 1 PROM for TKA and UKA, respectively. The threshold of 33.4 and 33.9 points for the SF-36 PCS was weighted 2 points for predicting MCID in 2 PROMs, whereas 29.3 and 31.3 points for the SF-36 PCS were weighted 3 points for predicting MCID in 3 PROMs for TKA and UKA, respectively. The sum of weighted components from 0 (lowest) to 5 (highest) represented likelihood for long-term benefits at 10 years. Conclusion This scoring system is a useful clinical adjunct for deciding timing of knee arthroplasty and prioritizing patients in institutions with long waitlists. Level of Evidence II.

An emerging literature has shown concerns about the impact of the pandemic on the proliferation of digital surveillance. Contributing to these debates, in this paper we demonstrate how the pandemic facilitates digital surveillance in three ways: (1) By shifting everyday communication to digital means it contributes to the generation of extensive amounts of data susceptible to surveillance. (2) It motivates the development of new digital surveillance tools. (3) The pandemic serves as a perfect justification for governments to prolong digital surveillance. We provide empirical anecdotes for these three effects by examining reports by the Global Digital Policy Incubator at Stanford University. Building on our argument, we conclude that we might be on the verge of a dangerous normalization of digital surveillance. Thus, we call on scholars to consider the full effects of public health crises on politics and suggest scrutinizing sources of digital data and the complex relationships between the state, corporate actors, and the sub-contractors behind digital surveillance.

Violent injury is known to be a chronic, recurrent issue, with high rates of recidivism in the 5 years following initial injury. Much of the strategy behind violence intervention programs, who are ...

Several energy management strategies, of increasing complexity, are compared in term of cost benefit for a Photovoltaic /battery system providing electricity to an accommodation building with an electric vehicle. Rule based control methods with or without production forecasting are implemented and compared to a linear programming strategy used as a reference. The improvement in term of gain between simplest and reference methods is about 27%. It appears that the battery cycles number differs greatly (up to 55%), leading to a more or less rapid ageing. Battery degradation models are thus added and a corresponding cost is introduced in the strategy benefit. The results, depending on the initial battery cost, are significantly impacted, changing the relevance of the control strategies.

One of the main factors in achieving effective and efficient performance isattention to human resources. Having reliable and truly appropriate human resourceswith needs is the hope of every manager. The company needs employees with the appropriate abilities with job roles. As for the position of PT. Bogowonto Primalaras still uses a work period limit where employees cannot fill vacant positions at any time, based on the criteria of tenure an employee fulfills the term of service that is in accordance with his expectations. where on assessment in absenteeism is carried out by collecting daily report data that makes HRD send one by one who has sent the daily report. Where is the report related to the productivity criteria of an employee who has reached the target or not.

The term ‘responsible AI’ has been coined to denote AI that is fair and non-biased, transparent and explainable, secure and safe, privacy-proof, accountable, and to the benefit of mankind. Since 2016, a great many organizations have pledged allegiance to such principles. Amongst them are 24 AI companies that did so by posting a commitment of the kind on their website and/or by joining the ‘Partnership on AI’. By means of a comprehensive web search, two questions are addressed by this study: (1) Did the signatory companies actually try to implement these principles in practice, and if so, how? (2) What are their views on the role of other societal actors in steering AI towards the stated principles (the issue of regulation)? It is concluded that some three of the largest amongst them have carried out valuable steps towards implementation, in particular by developing and open sourcing new software tools. To them, charges of mere ‘ethics washing’ do not apply. Moreover, some 10 companies from both the USA and Europe have publicly endorsed the position that apart from self-regulation, AI is in urgent need of governmental regulation. They mostly advocate focussing regulation on high-risk applications of AI, a policy which to them represents the sensible middle course between laissez-faire on the one hand and outright bans on technologies on the other. The future shaping of standards, ethical codes, and laws as a result of these regulatory efforts remains, of course, to be determined.

We aimed (1) to estimate the short-term effect (postintervention period) of neurodynamic techniques on pain, symptom severity, functional status, electrophysiological status, grip strength, and pinch strength in people with carpal tunnel syndrome (CTS); and (2) to estimate the effect of neurodynamic techniques compared to other physical therapy modalities and surgical interventions.Intervention systematic review with meta-analysis.We searched the MEDLINE, Cochrane Database of Systematic Reviews, Web of Science, Physiotherapy Evidence Database, and Scopus databases from their inception to September 2020.We included randomized controlled trials reporting the effect of neurodynamic techniques on pain, symptom severity, function, distal motor latency, grip strength, and pinch strength in people with CTS.Using the DerSimonian-Laird method, we estimated pooled standardized mean differences (SMDs) and 95% confidence intervals (CIs). We used the Grading of Recommendations Assessment, Development and Evaluation approach to judge the certainty of the evidence of each pairwise comparison.There were 22 trials included (n = 1203 people with CTS; mean age, 26.0 to 57.9 years; mean symptom duration, 4.1 to 62.8 months). There was very low-certainty evidence of neurodynamic techniques improving pain (SMD, -0.54; 95% CI: -0.95, -0.13) and function (SMD, -0.35; 95% CI: -0.61, -0.09). There was no significant effect on symptom severity (very low certainty), distal motor latency (very low certainty), and grip and pinch strength (low certainty).Neurodynamic techniques were effective for improving pain and function in people with CTS, albeit with very low-certainty evidence.

Objective:Few studies have evaluated patients with spontaneous subarachnoid haemorrhage (sSAH) from an intensivist perspective. This study aimed to report the results of patients with sSAH in a high-volume centre monitored by a team experienced in the fields of brain surgery, interventional radiology and intensive care.Materials and Methods:Data of patients with sSAH followed up between January 2014 and July 2018 in the intensive care unit (ICU) were retrieved from ICU patient observation charts, file records and hospital automated information system.Results:This study enrolled 150 patients, of which 61 (40.7%) patients died despite receiving intensive care. Mortality rates between patients with (42.8%) and without (40%) vasospasm were comparable (p=0.917). Vasospasm developed in 37.8% of the 45 patients who underwent endovascular coiling and in 19.2% of those who underwent neurosurgical clipping (p=0.044). The median times that elapsed before endovascular or surgical procedures were 2.5 [interquartile range (IQR): 2-5] days in the surviving group and 2 (IQR: 1-5) days in the deceased group (p=0.164). Blood sodium and blood chloride levels were significantly higher in the deceased group from the third day onward. The median blood sodium level exceeded 142 mEq/L in the deceased group, but was lower than 142 mEq/L on the same day in the surviving group.Conclusion:The results of this study suggest that Glasgow coma scale (GCS) at admission to the ICU is one of the important factors that affect treatment success. GCS is an important independent factor in selecting the timing or type of treatment (surgical clipping/endovascular coiling) and medical treatments such as nimodipine in patients with sSAH requiring intensive care. In addition, the incidence of vasospasm was higher in patients who underwent endovascular coiling. Increased sodium and chloride values during follow-up are the only parameters significantly associated with mortality.

Urban noise is a common environmental exposure that may increase the burden of hypertension in communities, yet it is largely unstudied in the United States, and it has not been studied in relation to blood pressure (BP) control. We investigated associations of urban noise with BP levels and control in the United States. We used repeated BP and medication data from Chicago-based participants of the Chicago Health and Aging Project (≥65 years) and MESA (Multi-Ethnic Study of Atherosclerosis; ≥45 years). Using a spatial prediction model with project-specific measurements, we estimated noise at participant homes. We imputed BP levels for those on medication and used mixed-effects models to evaluate associations with noise. Logistic regression was used for uncontrolled and apparent treatment-resistant hypertension. Models were run separately by cohort and altogether, all with adjustment for age, sex, sociodemographic factors, and other plausible sources of confounding. We evaluated 16 462 BP measurements from 6764 participants (6073 Chicago Health and Aging Project and 691 MESA) over an average of 4 years. For both cohorts, we found that greater levels of noise were associated with higher BP levels and greater risk of apparent treatment-resistant hypertension. In our pooled models, 10-dBA higher residential noise levels corresponded to 1.2 (95% CI, 0.1–2.2) and 1.1 mm Hg greater (95% CI, 0.6–1.7) systolic and diastolic BPs as well as a 20% increased odds of apparent treatment-resistant hypertension (odds ratio per 10 dBA: 1.2 [95% CI, 1.0–1.4], P =0.04). Urban noise may increase BP levels and complicate hypertension treatment in the United States.

Didik Librianto,1 Ismail Hadisoebroto Dilogo,2 Achmad Fauzi Kamal,2 Ifran Saleh,2 Fachrisal Ipang,1 Dina Aprilya2 1Orthopedic Spine Surgeon, Fatmawati General Hospital, Jakarta, Indonesia; 2Department of Orthopedic and Traumatology, Faculty of Medicine Universitas Indonesia, Jakarta, IndonesiaCorrespondence: Dina AprilyaDepartment of Orthopedic and Traumatology, Faculty of Medicine Universitas Indonesia, Prof. Soelarto building, 1st floor, RS Fatmawati Street, Jakarta, 12430, IndonesiaTel +6289655106136Fax +6221-7660616Email dina.cia.aprilya@gmail.comBackground: Kyphosis in spondylitis tuberculosis (STb) is more than just a cosmetic issue. It has a potentially detrimental effect on both spine-associated structures and cardiopulmonary function. It can be corrected in any stage of STb; however, the corrective surgery is challenging, especially in the late case, in which the additional stiffness of the spine can come into consideration. To date, the cantilever technique is still a gold standard for sagittal plane deformity correction. However, no study to date has explored its effectiveness for thoracolumbar kyphotic deformity, especially that caused by spondylitis tuberculosis.Methods: This is a retrospective study of 16 consecutive cases of spondylitis tuberculosis with thoracolumbar kyphosis that underwent corrective surgery in our center in the period of 2020– 2021. We aim to evaluate the effectiveness of the cantilever technique that we use for kyphotic correction in thoracolumbar STb patients.Results: At the 3-months follow-up, the mean Cobb angle was 14.6°± 10.27°, with the mean gain of 20.90°± 12.00° and positively correlate with the thoracolumbar kyphosis (TLK) correction (68.69%, r = 0.654, p = 0.001). The mean thoracic kyphosis, lumbar lordosis, and sagittal vertebral axis were 30.6°± 13.08°, 39.4°± 16.02°, and 1.4± 4.09 cm, respectively, with sagittal Cobb difference of 12.70± 9.85.Conclusion: The kyphotic Cobb angle reduction by cantilever technique in the thoracolumbar area significantly improved the thoracolumbar kyphosis and realign the spinal sagittal axis. Thus, the cantilever technique remains the gold standard for sagittal plane deformity correction which can be applied for kyphotic deformity correction in thoracolumbar STb cases.Keywords: thoracolumbar spine, kyphotic deformity, spondylitis tuberculosis, deformity correction, cantilever technique

Introduction: Laparoscopic one-anastomosis gastric bypass (OAGB) is a bariatric surgery that can be used to treat obesity. It is considered a new procedure in the Gulf Region. The purpose of this s...

Background Ovarian cancer (OC) is a common malignancy in the gynecological tumor. Standard treatment for ovarian cancer is surgery and chemotherapy based on paclitaxel and platinum. However, traditional chemotherapy for ovarian cancer is limited by drug resistance and systemic side effects. It is imperative to explore effective treatment options for refractory ovarian cancer. Case Presentation A 52-year-old female initially presented with lower abdominal distension and migratory pain. After the laparoscopic exploration and biopsy, immunohistochemistry showed poorly differentiated adenocarcinoma originated from ovarian (cT3NxM1, stage IV, peritoneal and abdominal wall metastasis). The next generation sequence detected ERRFI1 (T187A, exon4) mutation. Results The patient received first-line chemotherapy (paclitaxel, nedaplatin plus avastin), followed by maintenance therapy with gefitinib, achieving a 15-month progression-free survival (PFS). After disease progression and second-line treatment failure, endostar plus apatinib was administered for 14 cycles and she obtained a PFS of 14 months without long-term adverse events. Conclusion We believe that the ERRFI1 gene may be a potential target of gefitinib. Importantly, endostar combined with apatinib is worth recommending for maintenance treatment in refractory ovarian cancer.

BACKGROUND AND OBJECTIVES Australian information on the physical health of general practice patients with a mental illness is limited. The aim of this study was to explore the physical health of patients with a severe and/or long-term mental illness (SMI). METHOD Analysis was performed of routinely collected data from patients visiting one of 452 general practice sites participating in the national MedicineInsight program during 2017-18. RESULTS Of the 173,861 participants, 9.1% had recorded SMI. Almost three-quarters had a record of the selected long-term physical health conditions, compared with half of patients without recorded SMI (adjusted odds ratio: 2.2, 95% confidence interval: 2.1, 2.3). Patients with SMI were also more likely to have a history of smoking or moderate-to-heavy drinking. DISCUSSION More patients with SMI had records of the investigated health conditions than those without SMI. They also had higher rates of modifiable risk factors. As patients with SMI are likely to visit their general practitioners often, this presents an opportunity for intervention that may improve patient outcomes.

Rationale: Chronically critically ill patients admitted to a long-term acute care hospital (LTACH) setting are a vulnerable population of intensive care unit (ICU) survivors. Little is known of the goals and functional outcomes achieved by patients after rehabilitation in the LTACH setting. Objectives: We sought to examine patient goals and functional outcomes, including swallowing function, among ICU survivors admitted to an LTACH with a tracheostomy. Methods: This was a prospective observational cohort study of chronic critically ill LTACH patients. Results: Fifty older subjects with a median duration of intubation before tracheostomy of 13 days were enrolled. ICU-acquired weakness and cognitive impairment were present in 40 (80%) and 36 (72%) patients, as measured by the Medical Research Council scale and Montreal Cognitive Assessment, respectively. Mental health problems were also common, with 16 (32%) patients experiencing moderate to severe anxiety, 9 (18%) experiencing moderate to severe depression, and 11 (22%) reporting symptoms consistent with post-traumatic stress disorder, according to the Hospital Anxiety and Depression Scale and Post-Traumatic Stress Syndrome 10-Questions Inventory, respectively. Pharyngeal dysfunction, as measured by Fiberoptic Endoscopic Evaluation of Swallow examination, was present in 37 (74%) patients. Patient goals, in decreasing order of frequency, included eating and drinking, speaking, walking, returning home, and toileting. By LTACH discharge, goal attainment was variable, with 97% of those who ranked speaking as important being able to speak and 88% of those who ranked eating and drinking as important being able to eat and drink but with only 21% being able to walk and only 18% being able to self-toilet. Discharge to the home or acute rehabilitation setting, achieved in 52% of the population, was associated with greater strength, as measured by the total Medical Research Council score (P = 0.002) as well as by the European 5-dimension, 5-level health-related quality of life instrument domains of mobility (P = 0.008) and self-care (P = 0.04). Conclusions: Goal attainment during this period of recovery was variable. The ability to speak, eat, and drink, frequently identified as goals by these patients, were achieved, whereas functional goals such as walking were rarely achieved. These findings highlight the importance of identifying patient goals and setting realistic expectations informed by functional assessments when rehabilitating this vulnerable patient population in the LTACH and subsequent post–acute care settings.

Objectives: This study explores the effects of the Active Plus intervention aiming to decrease loneliness among older adults (>65 years) with chronic diseases. Methods: A randomized controlled trial (RCT) was performed (N = 585; age: M = 74.5 years, SD = 6.4), assessing loneliness at baseline, 6 months and 12 months. Outcome measures in the multilevel linear regression analyses were total, social and emotional loneliness. Results: At 12 months, significant decreases in total ( B = −.37, p = .01) and social loneliness ( B = −.24, p = .02) were found. Age was a significant moderator for total and social loneliness; however, the intervention was effective only for participants aged 80 years and older. Discussion: The Active Plus intervention showed a significant decrease in total and social loneliness and was especially beneficial for the vulnerable age group of 80 years and older. A more comprehensive tool for measuring social activity and mobility impairments, and using a longer time frame to detect loneliness changes, may form interesting future research.

Background and Aim Following an index episode of acute pancreatitis, sometimes the inflammation subsides completely, but sometimes inflammation persists and progresses to chronic pancreatitis, which may be or may not be preceded by recurrent acute pancreatitis. Some patients may also develop diabetes mellitus. There is only limited information on the longer‐term outcome of patients with acute pancreatitis. The aim of this study was to evaluate the longer‐term consequences of acute pancreatitis in the form of the development of recurrent attacks of acute pancreatitis, chronic pancreatitis, diabetes, or pancreatic carcinoma. Methods The index study included 122 patients who presented with their first episode of acute pancreatitis. This retrospective, cross‐sectional survey was performed 5 years after the index episode. Results Of the 122 patients, 96 were available for follow‐up while 4 were known to have died (one from pancreatic cancer). On reassessment after 5 years, 28 of 96 patients had further episodes of pancreatitis. Fifteen patients were diagnosed as having recurrent acute pancreatitis, 13 patients were diagnosed as having chronic pancreatitis, while 17 developed new‐onset diabetes. Recurrent acute pancreatitis was more common in younger patients, while chronic pancreatitis was associated with alcohol abuse and a more severe index episode. The development of diabetes was more common with advanced age. Conclusions In this study, a good proportion of patients progressed to chronic pancreatitis and diabetes within 5 years after surviving acute pancreatitis., Cross‐sectional review was done after 5 years of follow‐up of acute pancreatitis. Out of 100 patients, 15% patients developed recurrent acute pancreatitis, 13% patients developed chronic pancreatitis, and 17% patients developed diabetes.