Miracle-dz/UniFAR-FTUs huggingface dataset:
Single Incision Tubular Decompression to Treat Multi-level Lumbar Spinal Stenosis: A Retrospective Review

Objective: To evaluate the technical feasibility and assess the clinical outcomes of tubular decompression (TD) in cases of multilevel lumbar canal stenosis operated through a single incision. TD has established itself in the surgical management of single level lumbar stenosis. Literature on performance of TD for multilevel stenosis through a single incision are non-existent. Methods: All patients undergoing TD for multilevel lumbar stenosis through a single incision from January 2007 to January 2018 were included. Patient demographics, operative and peri-operative details were documented. Patient based clinical outcomes, namely Visual Ana-logue Scale (VAS) scale for back and leg pain and Oswestry Disability Index (ODI) were assessed. Results: Favorable tube trajectory and adequate decompression could be achieved through a single incision to decompress multiple levels. The VAS improved from mean 3±1.5 (2–5) to 2±0.8 (1–4) and 7±1.4 (4–9) to 2±1 (1–5) for back and leg pain respectively; while the ODI improved from a mean 44.6±8.6 (32–68) to 20.2±5.3 (16–42) at 3 months post-op and was maintained at 1±0.8 (1–4), 1.6±0.67 (1–3) and 19±2.9 (16–26) respectively at 2 years fol-low-up. Conclusion: TD for multilevel stenosis done through a single incision is a feasible option with good to excellent results.
Study of Forming Behaviour of Square Hole Mild Steel Perforated Sheet Metal

As the study of formability of perforated sheet metals using conventional approach is exhaustive and time consuming, Finite Element Analysis is used to carry out the same. This paper attempts to study the effects of perforation parameters (viz. hole size, open area and thickness) on the formability of square hole Perforated Mild Steel Sheet Metal. Finite element analysis is done using commercial Finite Element Analysis software LS-DYNA. Parametric analysis is carried out to optimize process parameters using Taguchi’s L9 orthogonal array. From the results obtained through simulation, the analysis of variance (ANOVA) is carried out to determine which group has best condition for drawing and regression equation is obtained to know, how a single response variable (Major or Minor strain) is related to a variety of predictor variables (percentage open area, hole size and thickness) and the graphs are plotted between them using MINITAB software.
Trends in the Prevalence of Cardiometabolic Multimorbidity in the United States, 1999–2018

Cardiometabolic multimorbidity (co-existence of ≥1 cardiometabolic diseases) is increasingly common, while its prevalence in the U.S. is unknown. We utilized data from 10 National Health and Nutrition Examination Survey (NHANES) two-year cycles in U.S. adults from 1999 to 2018. We reported the age-standardized prevalence of cardiometabolic multimorbidity in 2017–2018 and analyzed their trends during 1999–2018 with joinpoint regression models. Stratified analyses were performed according to gender, age, and race/ethnicity. In 2017–2018, the prevalence of cardiometabolic multimorbidity was 14.4% in the U.S., and it was higher among male, older, and non-Hispanic Black people. The three most common patterns were hypertension and diabetes (7.5%); hypertension, diabetes, and CHD (2.2%); and hypertension and CHD (1.8%). During 1999–2018, the prevalence of cardiometabolic multimorbidity in U.S. adults increased significantly, with an averaged two-year cycle percentage change (AAPC) of 3.6 (95% CI: 2.1 to 5.3). The increasing trend was significant for both genders, most age groups except for 60–79 years, and non-Hispanic White people. For common patterns, the trend was increasing for hypertension and diabetes and hypertension, diabetes, and CHD, while it was decreasing for hypertension and CHD. Our findings provide evidence that cardiometabolic multimorbidity has risen as an austere issue of public health in the U.S.
Deep learning-based safety behavior identification of operations and maintenance personnel on a substation

With the growth of the economy, electricity consumption is also increasing. To ensure the regular running of substations, operation and maintenance personnel need to inspect the equipment on-site, but they are also exposed to a hazardous environment. Since the source of danger cannot be eliminated entirely, the safety behavior of the operation and maintenance personnel in the substation needs to be identified. For this purpose, we propose an automated approach based on deep learning. We first collected images of operation and maintenance personnel on a substation as a dataset. Then, an object detection model based on YOLO v5 is proposed, which can accurately detect the substation's operation and maintenance personnel, and the personal protective equipment (PPE). Combining with individual posture estimation, using a one-dimensional convolutional neural network (1D-CNN), it is able to determine whether the PPEs are being worn correctly. Finally, the safety behavior identification is verified by an experiment, and satisfactory results are obtained. With the method in this paper, it is possible to automatically identify whether substation operations and maintenance personnel are wearing PPE correctly according to the regulations, which has a positive impact on improving the efficiency of safety management of substations.
Efficacy of Combination Therapies for the Treatment of Multi-Drug Resistant Gram-Negative Bacterial Infections Based on Meta-Analyses

There is increasing evidence regarding the optimal therapeutic strategies for multidrug-resistant (MDR) bacteria that cause common infections and are resistant to existing antibiotics. Combination therapies, such as β-lactam combined with β-lactamase inhibitors or combination antibiotics, is a therapeutic strategy to overcome MDR bacteria. In recent years, the therapeutic options have expanded as certain combination drugs have been approved in more countries. However, only a handful of guidelines support these options, and the recommendations are based on low-quality evidence. This review describes the significance and efficacy of combination therapy as a therapeutic strategy against Gram-negative MDR pathogens based on previously reported meta-analyses.
A Review of: "Transcending New Public Management: The Transformation of Public Sector Reforms edited by

Universitat Aut noma De Barcelona,Online Publication Date: 01 April 2008To cite this Article: Ballart, Xavier (2008) 'A Review of: “Transcending New PublicManagement: The Transformation of Public Sector Reforms edited by TomChristensen and Per Laegreid”', International Public Management Journal, 11:2,244 — 246To link to this article: DOI: 10.1080/10967490802095748URL: http://dx.doi. org/10.1080/10967490802095748PLEASE SCROLL DOWN FOR ARTICLEFull terms and conditions of use: http://www.informaworld. com/terms-and-conditions-of-access.pdfThis article maybe used for research, teaching and private study purposes. Any substantial or systematic reproduction,re-distribution, re-selling, loan or sub-licensing, systematic supply or distribution in any form to anyone is expresslyforbidden.The publisher does not give any warranty express or implied or make any representation that the contents will becomplete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should beindependently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings,demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with orarising out of the use of this material.
Creating Honeypots to Prevent Online Child Exploitation

Honeypots have been a key tool in controlling and understanding digital crime for several decades. The tool has traditionally been deployed against actors who are attempting to hack into systems or as a discovery mechanism for new forms of malware. This paper presents a novel approach to using a honeypot architecture in conjunction with social networks to respond to non-technical digital crimes. The tool is presented within the context of Child Exploitation Material (CEM), and to support the goal of taking an educative approach to Internet users who are developing an interest in this material. The architecture that is presented in the paper includes multiple layers, including recruitment, obfuscation, and education. The approach does not aim to collect data to support punitive action, but to educate users, increasing their knowledge and awareness of the negative impacts of such material.
QUALITY IMPROVEMENT OF POSTGRADUATE MEDICAL PROTOCOL (PMP) SUBMITTED TO NATIONAL MEDICAL RESEARCH REGISTRY (NMRR), MALAYSIA

Postgraduate medical protocols (PMPs) collectively represent approximately 20% of the total number of medical research protocols approved by Medical Research & Ethics Committee (MREC) in 2012. Previous studies found most of these PMPs required further amendment upon an initial MREC review, which led to a delay in obtaining ethical approval. Therefore, this cross-sectional study aimed to identify the weaknesses of PMPs submitted for ethical approval. Present findings demonstrated the three main weaknesses identified in research protocols are the lack of description for (i) study design, (ii) total number of subjects planned to be enrolled and (iii) subject’s exclusion criteria; each with error rates of 21%, 20% and 13%, respectively. In addition, the two main weaknesses found in patient information sheets/ informed consent forms (PIS/ICFs) are the lack of description for (i) compensation in the event of trial related injury and (ii) anticipated trial expenses; each with error rates of 16% and 15%, respectively. Hence, this paper provides an in-depth discussion of how to improve the development of both research protocols and PIS/ICFs based on the five common weaknesses identified in this study. It also aims to serve as a useful guide for medical students to develop quality PMPs which are both ethical and scientifically-sound by adhering to all the applicable regulatory requirements.
Guidelines for Internet resource selection

Compte tenu de la rapide evolution du reseau Internet, les utilisateurs de bibliotheques ont maintenant acces a un grand nombre d'informations differentes dans le monde entier. Cet article propose donc un guide mis en place par l'Internet Working Group de la Houston Academy of Medecine-Texas Medical Center Library, ce guide donne des criteres de selection et d'evaluation des ressources d'Internet aux utilisateurs potentiels des bibliotheques
Multimodal Medical Image Fusion of Positron Emission Tomography and Magnetic Resonance Imaging Using Generative Adversarial Networks

Multimodal medical image fusion is a current technique applied in the applications related to medical field to combine images from the same modality or different modalities to improve the visual content of the image to perform further operations like image segmentation. Biomedical research and medical image analysis highly demand medical image fusion to perform higher level of medical analysis. Multimodal medical fusion assists medical practitioners to visualize the internal organs and tissues. Multimodal medical fusion of brain image helps to medical practitioners to simultaneously visualize hard portion like skull and soft portion like tissue. Brain tumor segmentation can be accurately performed by utilizing the image obtained after multimodal medical image fusion. The area of the tumor can be accurately located with the information obtained from both Positron Emission Tomography and Magnetic Resonance Image in a single fused image. This approach increases the accuracy in diagnosing the tumor and reduces the time consumed in diagnosing and locating the tumor. The functional information of the brain is available in the Positron Emission Tomography while the anatomy of the brain tissue is available in the Magnetic Resonance Image. Thus, the spatial characteristics and functional information can be obtained from a single image using a robust multimodal medical image fusion model. The proposed approach uses a generative adversarial network to fuse Positron Emission Tomography and Magnetic Resonance Image into a single image. The results obtained from the proposed approach can be used for further medical analysis to locate the tumor and plan for further surgical procedures. The performance of the GAN based model is evaluated using two metrics, namely, structural similarity index and mutual information. The proposed approach achieved a structural similarity index of 0.8551 and a mutual information of 2.8059.
phenofit: An R package for extracting vegetation phenology from time series remote sensing

Satellite‐derived vegetation indices (VIs) provide a way to analyse vegetation phenology over decades globally. However, these data are often contaminated by different kinds of optical noise (e.g. cloud, cloud shadow, snow, aerosol), making accurate phenology extraction challenging. We present an open‐source state‐of‐the‐art R package called phenofit$$ phenofit $$ to extract vegetation phenological information from satellite‐derived VIs. phenofit$$ phenofit $$ adopts state‐of‐the‐art phenology extraction methods, such as a weight updating function for reducing optical noise contamination, a growing season division function for separating the VI time series into different vegetation cycles, and rough and fine fitting functions for reconstructing VI time series. They work together to make phenology extraction from frequently contaminated VIs easier and more accurate. Compared against other widely used phenology extraction tools, for example, TIMESAT$$ \mathrm{TIMESAT} $$ and phenopix$$ phenopix $$ , phenofit$$ phenofit $$ provides flexible input and output options, a practical growing season division function, rich curve fitting and phenology extraction functions, and robust performance under different kinds of optical noise. In addition to working with VIs from mesoscale satellites (e.g. MODIS and AVHRR), phenofit$$ phenofit $$ can also reconstruct vegetation time series and extract phenology using other sources, such as VIs from high‐resolution optical satellites (e.g. Sentinel‐2 and Landsat) and radar satellites (e.g. Sentinel‐1), vegetation greenness indices from digital cameras and gross primary production estimations from eddy‐covariance sites. As such, phenofit$$ phenofit $$ can contribute to the study of ecological process dynamics and assist in effective modelling of global change impacts on vegetation, as caused by climate variability and human intervention. Code and data of case studies are available at https://zenodo.org/record/6425745 (Kong, 2022a).
WSN and its Application

The area of wireless sensor network is one of the emerging and fast growing field in the world but a key issue in WSN is maximizing the network lifetime. Network lifetime is important in sensor node when the sensor node, distributed typically in remote area is powered by finite energy batteries. This brought about low cost, low power and multifunctional sensor nodes. However, the major fact that sensor node run out of energy quickly has been an issue and many energy routing protocols have been proposed to sort out the problem and increase the longetivity of the network. This is the only reason why routing protocol in WSN mainly focus on the power conservation. Most of the protocol is designed to minimize the energy conservation in sensor network. This thesis work proposed a hierarchical routing technique which shows energy.
An Optimization-Based Diabetes Prediction Model Using CNN and Bi-Directional LSTM in Real-Time Environment

Diabetes is a long-term illness caused by the inefficient use of insulin generated by the pancreas. If diabetes is detected at an early stage, patients can live their lives healthier. Unlike previously used analytical approaches, deep learning does not need feature extraction. In order to support this viewpoint, we developed a real-time monitoring hybrid deep learning-based model to detect and predict Type 2 diabetes mellitus using the publicly available PIMA Indian diabetes database. This study contributes in four ways. First, we perform a comparative study of different deep learning models. Based on experimental findings, we next suggested merging two models, CNN-Bi-LSTM, to detect (and predict) Type 2 diabetes. These findings demonstrate that CNN-Bi-LSTM surpasses the other deep learning methods in terms of accuracy (98%), sensitivity (97%), and specificity (98%), and it is 1.1% better compared to other existing state-of-the-art algorithms. Hence, our proposed model helps clinicians obtain complete information about their patients using real-time monitoring and can check real-time statistics about their vitals.
Multi‐Stroke Positive Cloud‐To‐Ground Lightning Sharing the Same Channel Observed With a VHF Broadband Interferometer

This work presents the first observation of a multi‐stroke positive cloud‐to‐ground lightning flash sharing the same channel to ground mapped with a very high frequency broadband interferometer and a Lightning Mapping Array. This type of lightning flash is very rarely observed, and it is currently unclear how frequent it is and even under what conditions it occurs. Our observations indicate a scenario where the first downward positive leader initiates from a decayed negative channel. After the first return stroke, some of the main negative channel branches stop propagating and are likely cut off. A fast recoil leader and/or a fast breakdown play a crucial role in reconnecting these previously decayed leader channels and initiating the subsequent positive stroke. The mechanism we propose to describe the phenomenon allows us to explain its rarity and the discrete positive charge transfer to the ground.
Optimized hybrid RNN model for human activity recognition in untrimmed video

Abstract. Human activity recognition is a field of video processing that requires restricted temporal analysis of video sequences for estimating the existence of different human actions. Designing an efficient human activity model requires credible implementations of keyframe extraction, preprocessing, feature extraction and selection, classification, and pattern recognition methods. In the real-time video, sequences are untrimmed and do not have any activity endpoints for effective recognition. Thus, we propose a hybrid gated recurrent unit and long short-term memory-based recurrent neural network model for high-efficiency human action recognition in untrimmed video datasets. The proposed model is tested on the TRECVID dataset, along with other online datasets, and is observed to have an accuracy of over 91% for untrimmed video-based activity recognition. This accuracy is compared with various state-of-the-art models and is found to be higher when evaluated on multiple datasets.
TRAPUM discovery of 13 new pulsars in NGC 1851 using MeerKAT

We report the discovery of 13 new pulsars in the globular cluster NGC 1851 by the TRAPUM Large Survey Project using the MeerKAT radio telescope. The discoveries consist of six isolated millisecond pulsars (MSPs) and seven binary pulsars, of which six are MSPs and one is mildly recycled. For all the pulsars, we present the basic kinematic, astrometric, and orbital parameters, where applicable, as well as their polarimetric properties, when these are measurable. Two of the binary MSPs (PSR J0514-4002D and PSR J0514-4002E) are in wide and extremely eccentric (e>0.7) orbits with a heavy white dwarf and a neutron star as their companion, respectively. With these discoveries, NGC 1851 is now tied with M28 as the cluster with the third largest number of known pulsars (14). Its pulsar population shows remarkable similarities with that of M28, Terzan 5 and other clusters with comparable structural parameters. The newly-found pulsars are all located in the innermost regions of NGC 1851 and will likely enable, among other things, detailed studies of the cluster structure and dynamics.
Development of an Independent Front Suspension for Truck Tractors

Design and experimental validation stages of an independent front suspension (IFS) that is designed for truck tractors of articulated commercial vehicles are summarised. Firstly, the suspension geometry, which satisfies the required conditions of minimum deviation of camber angle and track width during wheel travel, is obtained within the given design volume by using Multibody Systems (MBS) and Design of Experiments (DOE) approaches. Subsequently, the kinetic analysis is carried out for the suspension system and the critical design loads that may act on the structural elements are determined. Taking these loads into account, the mechanical design of the suspension system elements is performed. The Finite Element Analysis (FEA) is applied to the complete suspension system for the chosen critical load conditions. Stress concentrated regions on the crucial system elements are determined and improvements are indicated, which result in the reduction of stress concentrations. In the last part of the study, prototyping and fatigue tests are carried out. Throughout bench tests, in which real service conditions are simulated, no failure of any sort is encountered. The final suspension system (pair) is 31% lighter than an equivalent rigid front axle in terms of load capacity.
Association between Dietary Theobromine and Cognitive Function in a Representative American Population: A Cross-Sectional Study

Despite reports on neuroprotective effects of dietary theobromine intake, whether dietary theobromine has beneficial effects on cognitive function is unclear. To investigate the association between dietary theobromine and cognitive function. A cross-sectional study. Data were collected from the 2011–2014 cycles of the National Health and Nutrition Examination Survey conducted by the Centers for Disease Control and Prevention of the USA. A representative American population aged ≥60 years. L-theobromine was treated as a log transform and dichotomous form (the highest quantile vs. others). Cognitive function was measured using four tests Consortium to Establish a Registry for Alzheimer’s Disease Word Learning tests, Consortium to Establish a Registry for Alzheimer’s Disease delayed recall test, animal fluency test, and digit symbol substitution test. We conducted multiple regression analyses and subgroup analyses to study the association between theobromine and cognitive performance. Basic characteristics, lifestyle factors, disease history, and nutritional intake were adjusted for in these models. A total of 2,845 participants were included in the study. The highest quantile of L-theobromine intake was positively associated with sores of delayed recall, animal fluency, and digit symbol substitution tests (β, 95% confidence interval, P 0.11, −0.00–0.30, 0.049; 0.50, 0.02–0.99, 0.043; 1.55, 0.33–2.77, 0.015; respectively) in the fully adjusted model, but not with immediate recall score (β=0.13, 95% confidence interval −0.16–0.43, P=0.361). Subgroup analyses showed that L-theobromine intake was associated with cognitive performance in the highest quantile of caffeine intake. Daily theobromine intake was associated with cognitive performance in a large nationally representative population. However, further research is needed to corroborate our findings.