Miracle-dz/UniFAR-FTUs huggingface dataset:
Randomized Comparison Of Tramadol, Nalbuphine And Fentanyl Used As Premedication In Attenuation Of Hemodynamic Response To Laryngoscopy And Postoperative Pain In Laparoscopic Cholecystecyomy

Background: Stress response to laryngoscopy during endotracheal intubation and pain after surgeries under General Anesthesia are one of the major concern and challenge for anesthetists. A number of methods have been used to modify the laryngoscopy and endotracheal intubation response, including different intubation devices, beta blockers, vasodilators and opioids. Opioids are good analgesics and have role in attenuation of stress response to laryngoscopy when used in premedication prior to laryngoscopy. Our aim of study is to compare the efficacy and safety of three opioids ie; Tramadol, Nalbuphine and Fentanyl as stress buster and analgesic when used in single dose as premedication in General Anaesthesia for Laparoscopic Cholecystectomy. Methods: One twenty patients of ASA grade I&II were randomly selected with forty patients in each group. Drug was given 10 min before starting the anaesthesia. Patients were assessed for change in hemodynamic parameter from baseline in Immediate post intubation (IPI), 1, 2 and 5min post intubation to note the stress response to laryngoscopy. Intra operative vitals were recorded for every 15min till end of surgery and in immediate and 5min post extubation period. VAS was recorded after awakening from anaesthesia. Any adverse reaction like nausea vomiting, respiratory depression and sedation were also recorded. Results: Nalbuphine and Fentanyl were found to be better drug compared to Tramadol for attenuation of hemodynamic response to Laryngoscopy (p<0.0001). No significant difference in hemodynamics was found intraoperatively in all the three drugs. Nalbuphine and Tramadol were having good analgesia in immediate post operative period. Mild respiratory depression and sedation was reported with Nalbuphine. Nausea vomiting was significantly high with Tramadol (p<0.001). Conclusion: Nalbuphine is one of the better choices for relieving stress to laryngoscopy and intubation, maintaining good hemodynamics intraoperatively and providing pain relief in immediate postoperative period.
Optimal Design of Wireless-Powered Hierarchical Fog-Cloud Computing Networks

This paper investigates the optimal design of wireless- powered hierarchical fog-cloud computing networks, where energy-constrained users first harvest energy from a hybrid access point (HAP) and then offload their computation tasks to fog/cloud servers via the HAP or compute the tasks locally by us- ing the harvested energy. An optimization problem is formulated to maximize the minimal energy balance among multiple users by jointly optimizing offloading decisions, communication and computation resource allocations in the system, where computational capacity, processing delay, energy harvesting (EH) and energy consumption constraints are considered. To efficiently solve such a mixed-integer combinatorial non-convex problem, a penalized successive convex approximation (P-SCA)- based algorithm is designed, which is able to converge to a suboptimal solution with the polynomial time computational complexity. Numerical results show that compared to communication and computation resource allocation, the offloading decision is the dominant factor on affecting the system performance. It is also found that local computing is a better choice for users with relatively poor channel gains while fog/cloud computing is a better choice for users with relatively good channel gains. Specifically, cloud computing is preferred if the cloud computational capacity is strong enough and the wired-link data rate is high enough; Otherwise, fog computing is preferred. Besides, more users are served, less max-min energy balance can be obtained.
Heating Load Forecasting for Combined Heat and Power Plants Via Strand-Based LSTM

Heating load forecasting is the premise for guiding heating operation management and dispatching. Heating load forecasting is a time series prediction problem which requires us to predict the real-time heating loads in the next 24 hours using available historical records and weather information. In this paper, we propose a model for short-term heating load forecasting based on a properly designed strand-based long short term memory (LSTM) recurrent neural network. We present how the data are pre-processed, and the loss function is designed to improve the model’s performance. Furthermore, an ensemble strategy is incorporated with the LSTM model to enhance its generalization and robustness. On offline (historical) testing data, the proposed model performs satisfactory predictions which meet the requirements of the local power plant. In addition to offline tests, we also implement the model to an online system of a power plant in Shandong province, China. The model made continuous forecasting without human interference for four months during the heating season of 2018. The model reported satisfactory online testing results that were comparable with the offline experiments using historical data.
A Three-Stage Algorithm Based on a Semi-Implicit Approach for Solving the Power-Flow in Realistic Large-Scale ill-Conditioned Systems

Solving the Power-Flow in realistic large-scale ill-conditioned systems supposes a challenging task for most of available solution methodologies. This paper tackles this issue by developing a novel efficient and robust Power-Flow method. It is mainly based on a Semi-Implicit approach but incorporates other numerical arrangements for enhancing its features. The resulting three-stage algorithm is validated using several realistic ill-conditioned systems ranging from 3012 to 70000-buses. Results show that the developed methodology constitutes an efficient and robust Power-Flow solution technique, outperforming the results obtained with other available approaches.
Energy Optimization Analysis of Turbofan Engine for More-Electric Civil Aircraft

The civil aviation industry is moving into the more-electric environment where the civil aircraft uses electricity to meet the multiple energy demands of the associated aircraft subsystems. The civil aircraft’s turbofan engine, which is the largest energy supply system of civil aircraft, will thus utilize more fuel resource to provide increased electric energy besides of its conventional responsibility of maintaining the fundamental thrust requirements by aircraft. This will introduce new challenge for the energy optimization analysis of aircraft turbofan engine: it was nearly a simple optimal setting of engine’s component parameters for keeping required thrust, but under the more-electric environment it will become an optimization problem in order to minimize the fuel consumption while obeying the multiple constraints by thermodynamic limits of turbofan engine and by varying electric power demands associated with the flight profile of aircraft. We present a complete modeling in this paper for the energy optimization analysis of turbofan engine for more-electric civil aircraft and formulate it as a nonlinear programming form. We propose an algorithm based on Benders decomposition method to solve this problem; the numerical results demonstrate the economic effectiveness of the proposed modeling and algorithm.
Development of 2T-SONOS Cell Using a Contamination-Free Process Integration for a Highly Reliable Code Storage eNVM

We report a low-cost and highly reliable 2T-SONOS embedded nonvolatile memory (eNVM) using a novel contamination-free process integration. The proposed 2T-SONOS cell is fabricated on a 90-nm high-voltage CMOS process for a touch screen controller embedded display driver IC applications. For a code storage purpose, eNVM with moderate memory density, simple integration, platform logic and process compatibility, and sufficient retention lifetime are critically important. Thanks to the contamination-free charge trap layer and optimized junction, we have achieved enough retention lifetime without using any special materials for the charge trap layer. The 120 °C data retention lifetime of over 10 years is verified using 77 $\times \,\,{3}$ samples of 32-kB memory density intellectual property. By correlating high-temperature retention bake results with technology computer-aided design simulations, we can understand long-term charge redistribution behavior inside trap nitride, which is significantly different from floating gate type NVM.
An Efficient Data Scheduling Scheme for Cloud- Based Big Data Framework for Smart City

Continuous increase in urban population causes enormous pressure on the limited resources of cities including transport, energy, water, housing, public services, and others. Hence, the need to plan and develop smart cities-based solutions for enhanced urban governance is becoming more evident. The technological framework for smart cities services connect hundreds of data collecting device networks (e.g., sensor networks) with central server or cloud through Internet. Scheduling of these enormous data (or big data) both at the device networks and central server/cloud is significantly important to facilitate timely and priority-based smart city services. This paper introduces a cloud- based big data framework and Priority-based, Dynamic and Time sensitive data processing and Scheduling (PDTS) approach that works both for the device networks and cloud-based big data framework. Simulation results demonstrate that the proposed PDTS approach reduces the number of data transmission and data processing time as opposed to data scheduling only in cloud-based big data framework.
Deep Regression Neural Network for Industrial Surface Defect Detection

Industrial product surface defect detection is very important to guarantee high product quality and production efficiency. In this work, we propose a regression and classification based framework for generic industrial defect detection. Specifically, the framework consists of four modules: deep regression based detection model, pixel-level false positive reduction, connected component analysis and deep network for defect type classification. To train the detection model, we propose a high performance deep network structure and an algorithm to generate label data to capture the defect severity information from data annotation. We have tested the method on two public benchmark datasets, AigleRN and DAGM2007, and an in-house capacitor image dataset. The results have shown that our method can achieve the state-of-the-art performance in terms of detection accuracy and efficiency.
Performance of One-Bit Massive MIMO with Oversampling under Adjacent Channel Interference

Massive multiple-input multiple-output (MIMO) systems have attracted much attention lately due to the many advantages they provide over single- antenna systems. Owing to the many antennas, low- cost implementation and low power consumption per antenna are desired. To that end, massive MIMO structures with one-bit analog-to-digital converters (ADC) have been investigated in many studies. However, the effect of a strong interferer in the adjacent band on one-bit quantized massive MIMO systems have not been examined yet. In this study, we analyze the performance of uplink massive MIMO with one-bit ADCs under frequency selective fading with orthogonal frequency division multiplexing (OFDM) in the perfect and imperfect receiver channel state information cases. We derive analytical expressions for the bit error rate and ergodic rate. We show that the interfering band can be suppressed by increasing the number of antennas or the oversampling rate when a zero-forcing receiver is employed.
RLS-VNE: Repeatable Large-Scale Virtual Network Embedding over Substrate Nodes

Embedding multiple virtual networks (VNs) on a shared substrate network (SN), known as virtual network embedding (VNE), is a challenging problem in cloud platforms. VNE methods can provide strategies to deploy VNs onto SN resources. However, as the scale of VN greatly increases, traditional VNE methods are time-consuming and waste link resource. Meanwhile, traditional VNE methods assign each virtual node of the same VN to different substrate nodes, whereas it is hard to provide larger scale SN to provision the VN. In order to efficiently embed large-scale VNs, multiple virtual nodes from the same VN need to share the same substrate node. We therefore model a repeatable large-scale virtual network embedding (RLSVNE) problem in this study, provisioning large-scale VNs, and propose a heuristic method (Rlsvne) to handle RLS-VNE. Rlsvne pre-processes the VN topology before embedding. In the pre-processing stage, the VN topology is processed through graph coarsening, partitioning, and uncoarsening. After the pre-processing, Rlsvne accomplishes an embedding stage with a topology- aware repeatable embedding solution. 1,000 and 10,000-scale VNE experiments are conducted to demonstrate our Rlsvne. The evaluation results demonstrate that our Rlsvne outperforms three modified heuristics. Rlsvne shows improved performance in reducing substrate cost and fully utilizing substrate resources, achieving high acceptance ratio and revenue values.
Grid-based Multi-clustering Protocol for Wireless Sensor Network using Sugeno Fuzzy Inference System

In wireless sensor network, clustering-based data gathering is an important approach for achieving energy efficiency. In the existing clustering algorithm, either cluster heads are selected in each round or after a fixed number of rounds of data collection. Selection of cluster head in each round causes more communication overhead in the network. However, selection of cluster head after a fixed number of rounds causes premature death of overloaded cluster head and unbalanced energy consumption among the nodes. In order to solve premature death of cluster head as well as unbalanced energy consumption, this paper proposes a Grid-based multi-clustering protocol using sugeno fuzzy inference system. Performance evaluation of the proposed protocol and its comparison with the three well-known clustering protocols are presented in detail.
Dynamic Control of a Rigid Pneumatic Gripper

Pneumatic grippers are hugely employed in robotic applications. Nonetheless, their control is not easy due to difficulty in managing the pressure inside their air chambers. Pneumatic grippers have often simple structure though the lack of affordable control algorithms complicates their usage. Motivated by these reasons, we wish to deliver a new control architecture for the closed-loop control of pneumatic grippers actuated by pressure regulators. The proposed architecture is composed of a main controller resorting to an optimization algorithm and of a state observer that estimates pressures in both gripper chambers, along with the exerted force. Instead, measured quantities (i.e. physical pressure in the gripper chambers and force recorded by a load cell between the gripper fingers) are used as inputs for the state observer to improve its output. The pneumatic gripper performance benefits from the joint action of the controller and of the state observer, as experimentally demonstrated. The gripper response will be shown for different types of inputs and on different setups.
Automatic Fusion of Satellite Imagery and AIS data for Vessel Detection

Being able to fuse information coming from different sources, such as AIS and satellite images is of major importance for maritime domain awareness, for example, to locate and identify vessels that may have purposely turned off their AIS transponder, preventing illegal activities. This paper presents a fully-automatic method for fusing AIS data and SAR satellite images for vessel detection. The proposed framework is based on the automatic annotation of satellite images by correlating them with AIS data producing train and test datasets which are provided as input to a convolutional neural network (CNN). The CNN was trained to detect the presence of ships in sectors of the image. Our automatic process allows the neural network to learn on a large amount of data, without the need for hand-labelled datasets. Our neural network, trained on our automatically-generated test set of images, achieved an accuracy of 88% at ship detection, and an area under ROC curve of 94,6%. Our estimate of real world accuracy is about 86-90%.
Understanding the Structural Characteristics of Data Platforms Using Metadata and a Network Approach

With the emergence of global platforms for trading and buying/selling data, data have become a profitable commodity. The growth of such platforms has necessitated the further expansion of the scope of data in digital economies. To this end, understanding the nature of available data and their relationships between them has become an important challenge for expanding their use. Thus, in this study, we assumed data on the platforms as a population and metadata as the samples. Thus, we quantitatively investigated the structural characteristics of data platforms, while avoiding the risk of lost business opportunities and privacy issues by not sharing the data themselves. By observing the characteristics of data and variables, we found that the data network had a structure that was locally dense and globally sparse, which is quite similar to networks of human relationships. Moreover, we found that data play different roles on the platforms when divided into sharing conditions, namely, shareable data and sensitive data. Finally, we discussed the potential tactics for individuals who create/use data platforms based on our findings. The contributions of this study include a new framework for data platform observation and a method that uses metadata and a network approach to analyze structural characteristics of data.
Sound Active Attention Framework for Remote Sensing Image Captioning

Attention mechanism-based image captioning methods have achieved good results in the remote sensing field, but are driven by tagged sentences, which is called passive attention. However, different observers may give different levels of attention to the same image. The attention of observers during testing, then, may not be consistent with the attention during training. As a direct and natural human–machine interaction, speech is much faster than typing sentences. Sound can represent the attention of different observers. This is called active attention. Active attention can be more targeted to describe the image; for example, in disaster assessments, the situation can be obtained quickly and the corresponding disaster areas can be located related to the specific disaster. A novel sound active attention framework is proposed for more specific caption generation according to the interest of the observer. First, sound is modeled by mel-frequency cepstral coefficients (MFCCs) and the image is encoded by convolutional neural networks (CNNs). Then, to handle the continuity characteristic of sound, a sound module and an attention module are designed based on the gated recurrent units (GRUs). Finally, the sound-guided image feature processed by the attention module is imported into the output module to generate descriptive sentence. Experiments based on both fake and real sound data sets show that the proposed method can generate sentences that can capture the focus of human.
Synchronization in Multiplex Networks of Chua’s Circuits: Theory and Experiments

In many natural and man-made networks, nodes communicate with each other through different types of interconnections giving rise to so-called multiplex networks. In this paper, we study the interplay between synchronous steady state solutions and the topology of multiplex networks of Chua’s circuits. In particular, we consider all circuits to be simultaneously interconnected through a network of resistors (static diffusive coupling) and a network of capacitors (dynamic diffusive coupling) each with its own structure. We find that the presence of dynamic links and the structure of the multiplex network affect the stability of the emerging synchronous solutions. Theoretical results are discussed and validated using a dedicated experimental set-up.
Total Variation Distance and Improved Random Coding Union Bounds

This paper presents two new upper bounds on the probability of error for maximum likelihood decoding in the finite blocklength regime. The first bound applies to fixed channel codes and uses the well-known union bound to express the probability of decoding error in terms of the total variation distance between the conditional transition probabilities. It is shown that this bound is tighter than the Bhattacharyya union bound and its variants. The second bound is an improvement on the Random Coding Union bound. In fact, the only decoding events that render it an upper bound, rather than an equality, are the ties a maximum likelihood decoder resolves in favour of the correct codeword. These bounds are then used to give new results for random spherical codes on the Gaussian channel.
Comparative Study of the Availability and Use of Information Technology in the Subject of Education in Public and Private Universities of Islamabad and Rawalpindi

The study was designed to compare availability and use of information technology in the subject of education in public and private universities of Islamabad and Rawalpindi. The objectives of the study were, first, to highlight the status of information technology in public and private universities of Islamabad and Rawalpindi; second, to compare the availability and utilization of resources for information technology in public and private universities of Islamabad and Rawalpindi; third, to find out the problems in the use of information technology in public and private universities of Islamabad and Rawalpindi and to recommend strategies for better use of information technology in public and private universities of Islamabad and Rawalpindi. The design of the study was descriptive, and it was a survey study. Two questionnaires were used for data collection: one for teachers and one for students. A stratified random sampling technique was used. Two groups of teachers were selected from public and private universities, and 50% teacher and 10% students were selected from the population. The data were analyzed in terms of percentage, and t test was also applied. A significant difference was found between the availability and usage of equipment in education departments of public and private universities of Islamabad and Rawalpindi. Difference between the students learning and teachers training skills was not significant.