Miracle-dz/UniFAR-FTUs huggingface dataset:
Nutritional and Metabolic Profile in Diabetic Patients and Relationship with Metabolic Syndrome

Background & Objectives: Diabetes and metabolic syndrome spread alarmingly throughout the whole world including Algeria, so our study makes the links closer between these two entities and that through studying nutritional, metabolic and physical profiles. Study Design: 204 patients were recruited and interviewed (anthropometric measurements, blood pressure, age, sex, personal and family history, the practice of physical activity and evaluation of food consumption). Patients and Methods: A cross-sectional survey was conducted in the western Algerian region (Sidi Bel Abbes). Patients with MetS are defined according to the criteria of the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III). Results: The results show that the frequency of the metabolic syndrome is 86.27% with predominance of women (92.15% women vs 80.39% men). The age group most affected by the MetS among women and men is [54 - 79] years. We also note that 51.28% of men present three criteria of MetS, while 65.21% of women have four and five criteria. The results show that the criteria most dominant in men are hypertension and type 2 diabetes, whereas in women, the high waist is the most abundant criterion. Moreover, the whole is underlined by a low physical activity with only 21.56% who practice it regularly (15.95% women vs 28.04% men). The estimate of food intake shows a qualitative imbalance: protein intake is 19.65% in women vs. 19.43% in men represented mainly by vegetable protein (83.72% for women vs. 72.85% for men); lipids intake is characterized by a lower consumption than the recommendation of the Mediterranean diet concerning the mono-unsaturated fatty acids (MUFA) (39.44% among women vs 40.24% at the men), as in poly unsaturated fatty-acids including (PUFA) (23.30% among women vs 23.64% at the men) whereas an increase in the consumption of the saturated fatty-acids (SFA) is observed in the whole population including (37.24% among women vs 36.10% at the men); lower concentration in calcium, magnesium and en fibers; important concentration of sodium and an insufficient contribution of water. Conclusion: We should be aware of the importance of the modulation of these risk factors through harmonization of “lifestyle” to prevent the occurrence of metabolic syndrome.
A low-noise 71-dBΩ transimpedance 31-GHz bandwidth optical receiver with automatic gain control in 0.13-µm SiGe BiCMOS

A low-noise high-gain high-speed optical receiver is fabricated in 0.13- (cid:22) m SiGe BiCMOS. The transimpedance ampliﬁer incorporating a diﬀerential common-base shunt-feedback topology features isolation to input capacitance and high transimpedance limit. Speciﬁcally, a comprehensive analytical expression of the input-referred noise current power spectral density of the transimpedance ampliﬁer is derived and investigated, which provides insights for noise optimization. Additionally, the linearity of intermediate stages is improved by a modiﬁed variable-gain ampliﬁeroperatingwithautomaticgaincontrol. Themeasuredresultsshowalownoiselevelof14.5pA p Hz , 31-GHz bandwidth, and the maximum 71-dB Ω transimpedance.
An Adaptive Encryption-as-a-Service Architecture Based on Fog Computing for Real-Time Substation Communications

The recent outbreak of industrial cyberattacks indicates that the current industrial network security architecture is under serious challenges. As one of the critical industrial networks, the heterogeneous and real-time substation network lacks compatibility with the conventional cryptography architecture represented by secure sockets layer/transport layer security (SSL/TLS) and public key infrastructure (PKI). To enhance the security of smart substations under the premise of low latency, in this article, we present a novel encryption-as-a-service architecture based on fog computing in this article. The architecture offloads encryption to dedicated devices and makes certificate and key management available through unified web services on the fog and cloud layers. Based on this architecture, we propose MX-SORTS, maximizing security on real-time communication of different services, an algorithm for adaptive configuration of encrypting and signing substation network traffic. By the contrast experiments with the conventional cryptography architecture, we prove that the encryption-as-a-service architecture can significantly improve the real-time and security performance of substation networks.
Scheduling of Flowshop Type Production with Dannenbring Method to Obtain Optimal Results with Observing Waiting Time and Weight of Jobs

Linear programming is a step or process to get maximum results with existing resources or it can be said the process of looking for optimal value. One part of scheduling is production scheduling. production scheduling is the use of resources that are few but make the most profit. This paper discusses production scheduling using flowshop type production scheduling by taking into account transportation times and work weights. This paper uses the Dannenbring method with the aim of finding the optimal sequence of problems by considering the total production time (makespan) and the average Weigted Mean Flow Time (WMFT). Based on the results of the Dannenbring method, the results of flowshop scheduling are obtained with a total makespan of 75 hours and the WMFT value is 28, 87 hours.
Crop Disease Detection And Control Robot

In the proposed system robotics model provides a facility to control the movement of agriculture vehicle. The quality and quantity of agricultural products can reduce by Plant diseases which have produced an enormous post effect scenario. Early pest detection is a major issue dealt with the plantation crops. First step involves in keen and regular observation of plants. Then the diseased plants will be classified and the affected part of the plants images will be acquired using camera.
Application Research of a Practical and New Intelligent Question Answering System

Intelligent question-answering system (IQAS) can help users quickly retrieve the answers to the required questions which is currently a popular research direction of NLP. This paper combines the research of relevant scholars and the analysis of their advantages and disadvantages, and an intelligent question-and-answer system is designed, which can automatically generate a question-and-answer knowledge base based on a given document. It can retrieve the knowledge base and recommend answers automatically, quickly and accurately according to the keywords provided by users. The system realized text semantic understanding and analysis, and its MRR value reached a satisfactory 0.7381. At the same time, the paper extends an automatic answer text generation technology based on Seq2Seq model, which can be a useful supplement to the traditional question-and-answer recommendation generation strategy.
Screening for Hepatocellular Carcinoma in Chronic Carriers of Hepatitis B and C in Markazi Province, Iran

Background and Aims: Chronic HBV and HCV infections are known as important underlying causes of hepatocellular carcinoma (HCC). Their prevalence in Iran are estimated to be 1.7-5% and 0.5-1%, respectively. We investigated the prevalence of HCC in chronic carriers of HBV and HCV via alpha-feto protein (AFP) measurement and ultrasonography as well as pathological assessments. Methods: In this cross-sectional study, HBV and HCV positive cases were determined from documents in blood banks since 1985. From 1064 aviable cases, 514 were randomly selected and followed up for two consecutive years. For all patients, AFP testing and sonography were done after reconfirmation of chronicity. Cases with nodules or mass in sonography and AFP levels above 20 ng/ml were considered suspicious for HCC and after rechecking were referred for liver biopsy. Finally, HCC was confirmed pathologically. Results: The mean subjects' age was 35.19±13.16 years and hepatitis duration was 6.12±5.66 years. Twenty-nine (5.7%) had elevated AFP and 18 (3.5%) had nodules in sonogram. In 16 (3.13%) cases, HCC was detected pathologically. Risk of HCC was correlated with age >40 years, cirrhosis and hepatitis duration (P<0.001). There was no correlation between the risk of HCC and the type of hepatitis, HBeAg positivity, sex and ALT levels. Conclusions: Early screening for HCC using AFP and sonography can be useful for HCC detection in HCV and HBV chronic carriers.
Effect of Information Architecture on the Usability of a University Website: A Comparative Study of Selected Websites of Punjab (India)

A well-planned information architecture (IA) of a website can enhance the end users' efficiency, learnability, controllability and intention to revisit the site. Its significance is even more in the context of academic websites where the generation, management, and distribution of information are among the major activities. However, it remains a neglected issue as designers of academic websites have overlooked the important aspect of ‘intuitive user navigation' and focused primarily on its ‘look and feel. ' Thus, the current study aims to analyze and compare the effectiveness of information architectural designs of some randomly selected university websites of Punjab (India) through a usability testing technique. For this purpose, the performance metric measured was the information seeking time. The usability session of each subject was captured through Camtasia Studio software. The findings of this study highlight the shortcomings of presently designed academic websites which adversely affect the usability of a website.
Toward Dynamic HART-Centric Task Offloading over FiWi Infrastructures in the Tactile Internet Era

Contemporary resource constraint mobile devices face great challenges to execute computationally expensive tasks of low-latency Tactile Internet applications in a time-efficient manner. The so-called HART has emerged as a promising platform to address the burden of resource-limited mobile devices by allowing collaboration among humans, cloud agents, and robots for task execution. To facilitate a better task execution performance, dynamic task offloading onto suitable actors represents an important research problem, taking time-varying task, robot, and cloud agent properties into account. To tackle this open problem, this article first discusses characteristics, applications, and challenges of HART tasks. Next, this article investigates the problem of whether and where a HART task should be offloaded and presents a COTA strategy by taking task offloading gain and communication overhead into account. For HART-centric task offloading, this article leverages central cloud, mobile edge computing (MEC) servers, and multi-robot FiWi networks. Finally, numerical results are presented to examine the performance of the proposed COTA scheme over FiWi based Tactile Internet infrastructures.
Automatic segmentation of kidney and kidney tumor based on 3D convolutional neural networks

Kidney cancer is a huge threat to humans, and the surgery is the most common treatment. For clinicians, knowing the morphology of the kidney and kidney tumor in advance may be helpful for surgery. Automatic segmentation of kidney and kidney tumor is a promising approach for these efforts. In this paper, we proposed a based 3D convolutional neural network to segment kidney and kidney tumor using the data from the 2019 Kidney Tumor Segmentation Challenge.
Range sidelobe suppression for OFDM‐integrated radar and communication signal

To improve the RadCom system of performing both data transmission and radar-sensing functions, here a waveform design method for orthogonal frequency division multiplexing (OFDM) signal is proposed to solve the problem of high range sidelobe level in radar detection process while maintaining a reasonable channel capacity. The method only transmits data in the sub-channels with preferable channel quality. Then, calculate the weight of the chosen subcarriers through the convex optimisation method to traditionally minimise the peak-to-sidelobe (PSL) ratio. Simulation results prove that, compared with traditional range sidelobe suppression method, this algorithm is more suitable for RadCom system.
Reliable chip design from low powered unreliable components

The pace of technological improvement of the semiconductor market is driven by Moore’s Law, enabling chip transistor density to double every two years. The transistors would continue to decline in cost and size but increase in power. The continuous transistor scaling and extremely lower power constraints in modern Very Large Scale Integrated(VLSI) chips can potentially supersede the benefits of the technology shrinking due to reliability issues. As VLSI technology scales into nanoscale regime, fundamental physical limits are approached, and higher levels of variability, performance degradation, and higher rates of manufacturing defects are experienced. Soft errors, which traditionally affected only the memories, are now also resulting in logic circuit reliability degradation. A solution to these limitations is to integrate reliability assessment techniques into the Integrated Circuit(IC) design flow. This thesis investigates four aspects of reliability driven circuit design: a)Reliability estimation; b) Reliability optimization; c) Fault-tolerant techniques, and d) Delay degradation analysis. To guide the reliability driven synthesis and optimization of combinational circuits, highly accurate probability based reliability estimation methodology christened Conditional Probabilistic Error Propagation(CPEP) algorithm is developed to compute the impact of gate failures on the circuit output. CPEP guides the proposed rewriting based logic optimization algorithm employing local transformations. The main idea behind this methodology is to replace parts of the circuit with functionally equivalent but more reliable counterparts chosen from a precomputed subset of Negation-Permutation-Negation(NPN) classes of 4-variable functions. Cut enumeration and Boolean matching driven by reliability-aware optimization algorithm are used to identify the best possible replacement candidates. Experiments on a set of MCNC benchmark circuits and 8051 functional microcontroller units indicate that the proposed framework can achieve up to 75% reduction of output error probability. On average, about 14% SER reduction is obtained at the expense of very low area overhead of 6.57% that results in 13.52% higher power consumption. The next contribution of the research describes a novel methodology to design fault tolerant circuitry by employing the error correction codes known as Codeword Prediction Encoder(CPE). Traditional fault tolerant techniques analyze the circuit reliability issue from a static point of view neglecting the dynamic errors. In the context of communication and
On Fault-Tolerant Bin Packing for Online Resource Allocation

We study an online fault-tolerant bin packing problem that models reliable resource allocation. In this problem, each item is replicated and has <inline-formula><tex-math notation="LaTeX">$f+1$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>f</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="li-ieq1-2948327.gif"/></alternatives></inline-formula> replicas including one primary and <inline-formula><tex-math notation="LaTeX">$f$</tex-math><alternatives><mml:math><mml:mi>f</mml:mi></mml:math><inline-graphic xlink:href="li-ieq2-2948327.gif"/></alternatives></inline-formula> standbys. The packing of items is required to tolerate up to <inline-formula><tex-math notation="LaTeX">$f$</tex-math><alternatives><mml:math><mml:mi>f</mml:mi></mml:math><inline-graphic xlink:href="li-ieq3-2948327.gif"/></alternatives></inline-formula> faulty bins, <italic>i.e.</italic>, to guarantee that at least one correct replica of each item is available regardless of which <inline-formula><tex-math notation="LaTeX">$f$</tex-math><alternatives><mml:math><mml:mi>f</mml:mi></mml:math><inline-graphic xlink:href="li-ieq4-2948327.gif"/></alternatives></inline-formula> bins turn to be faulty. Any feasible packing algorithm must satisfy an exclusion constraint and a space constraint. The exclusion constraint is generalized from the fault-tolerance requirement and the space constraint comes from the capacity planning. The target of bin packing is to minimize the number of bins used. We first derive a lower bound on the number of bins needed by any feasible packing algorithm. We then study three heuristic algorithms named mirroring, shifting and mixing under a particular setting where all items have the same size. The mirroring algorithm has a low utilization of the bin capacity. Compared with the mirroring algorithm, the shifting algorithm requires fewer bins. However, in online packing, the process of opening bins by the shifting algorithm is not smooth. It turns out that even for packing a few items, the shifting algorithm needs to quickly open a large number of bins. The mixing algorithm adopts a dual average strategy to gradually open new bins for incoming items. We prove that the mixing algorithm is feasible and show that it balances the number of bins used and the process of opening bins. Finally, to pack items with different sizes, we extend the mirroring algorithm by adopting the First-Fit strategy and extend both the shifting and mixing algorithms by involving the harmonic strategy. The asymptotic competitive ratios of the three extended algorithms are analyzed respectively.
Real-time surgical instrument tracking in robot-assisted surgery using multi-domain convolutional neural network

Image-based surgical instrument tracking in robot-assisted surgery is an active and challenging research area. Having a real-time knowledge of surgical instrument location is an essential part of a computer-assisted intervention system. Tracking can be used as visual feedback for servo control of a surgical robot or transformed as haptic feedback for surgeon–robot interaction. In this Letter, the authors apply a multi-domain convolutional neural network for fast 2D surgical instrument tracking considering the application for multiple surgical tools and use a focal loss to decrease the effect of easy negative examples. They further introduce a new dataset based on m2cai16-tool and their cadaver experiments due to the lack of established public surgical tool tracking dataset despite significant progress in this field. Their method is evaluated on the introduced dataset and outperforms the state-of-the-art real-time trackers.
Determinación de anticuerpos séricos contra proteínas de choque térmico en pacientes con glaucoma

SUMMARY Purpose: To determine serum antibody levels for bacterial heat shock proteins 60 kd in patients with glaucoma and healthy subjects. Methods: We performed comparative analysis of serum antibody levels for two different bacterial heat shock proteins 60k in 22 patients with chronic open angle glaucoma, 21 in normal-tension glaucoma and 22 healthy subjects using an indirect enzyme-linked immunosorbent assay. Results: Patients with glaucoma had higher levels of antibodies against bacterial heat shock proteins compared with healthy subjects, this being statistically significant. Conclusions: Mexican patients with glaucoma present higher levels of bacterial heat shock proteins. We require additional research in order to evaluate their relationship with the pathophysiologic mechanisms of glaucoma.
Four-Dimensional Modeling of fMRI Data via Spatio–Temporal Convolutional Neural Networks (ST-CNNs)

Since the human brain functional mechanism has been enabled for investigation by the functional magnetic resonance imaging (fMRI) technology, simultaneous modeling of both the spatial and temporal patterns of brain functional networks from 4-D fMRI data has been a fundamental but still challenging research topic for neuroimaging and medical image analysis fields. Currently, general linear model (GLM), independent component analysis (ICA), sparse dictionary learning, and recently deep learning models, are major methods for fMRI data analysis in either spatial or temporal domains, but there are few joint spatial–temporal methods proposed, as far as we know. As a result, the 4-D nature of fMRI data has not been effectively investigated due to this methodological gap. The recent success of deep learning applications for functional brain decoding and encoding greatly inspired us in this paper to propose a novel framework called spatio–temporal convolutional neural network (ST-CNN) to extract both spatial and temporal characteristics from targeted networks jointly and automatically identify of functional networks. The identification of default mode network (DMN) from fMRI data was used for evaluation of the proposed framework. Results show that only training the framework on one fMRI data set is sufficiently generalizable to identify the DMN from different data sets of different cognitive tasks and resting state. Further investigation of the results shows that the joint-learning scheme can capture the intrinsic relationship between the spatial and temporal characteristics of DMN and thus it ensures the accurate identification of DMN from independent data sets. The ST-CNN model brings new tools and insights for fMRI analysis in cognitive and clinical neuroscience studies.
Prevalência de cárie e fluorose dentária em escolares do município de Paracambi-RJ

The objective of this study was to evaluate the prevalence of dental caries and fluorosis in schoolchildren of Paracambi, RJ. The sample was composed by 263 12-year-old schoolchildren and it was utilized the DMFT and Dean index for evaluation of caries and fluorosis, respectively. It was verified that the DMFT was 3.2 and the decayed component was the most frequently observed. The prevalence of dental fluorosis was 40.6% and the very mild degree was the most prevalent (31.2%). It was concluded that caries prevalence in Paracambi can be considered moderate, although bellow the established goals of WHO for the year of 2000, while the prevalence of fluorosis was high, although without aesthetic impact on the population.
Effect of DC Filters on Selection of Fault Discriminator for Unit
 Protection of Monopolar HVDC Transmission System

Various research works reported on the unit protection of monopolar high-voltage direct-current transmission system assumed either no dc filter or a certain type of dc filter in the system. From the survey of literature, it appears that no study has been made to evaluate the effect of the type of dc filter on the system performance and consequent effect on the selection of fault discriminator for development of protection schemes. The work reported in this paper aims at selection of suitable fault discriminators for unit protection of HVDC transmission system in the presence of three different types of dc filter. The study has been carried out on a 500-kV line- commutated-converter-based monopolar HVDC system equipped with any of the three filters, namely, cascaded double-tuned, parallel double-tuned or triple-tuned filter. Simulation has been carried out with PSCAD/EMTDC. Critical analysis of the simulation results shows how the type of the dc filter influences the choice of fault discriminator