Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Output Optimization for Discrete-time Linear Multi-agent Systems

In this paper, a distributed output optimization problem is studied for discrete-time linear multi-agent systems. Different from most of existing distributed optimization literature, we assume the decision variables here are determined as outputs of linear agents. The design goal is to find distributed feedbacks for these high-order agents and collectively minimize the sum of individual objective functions. To overcome the difficulties brought by high-order dynamics, we first construct a dynamic estimator to generate the global optimal point, and then embed this estimator to some proper tracking controllers for these agents. The resultant control is shown to solve the formulated problem with some mild assumptions.
An Application of a Deep Learning Algorithm for Automatic Detection of Unexpected Accidents Under Bad CCTV Monitoring Conditions in Tunnels

In this paper, Object Detection and Tracking System (ODTS) in combination with a well-known deep learning network, Faster Regional Convolution Neural Network (Faster R-CNN), for Object Detection and Conventional Object Tracking algorithm will be introduced and applied for automatic detection and monitoring of unexpected events on CCTVs in tunnels, which are likely to (1) Wrong-Way Driving (WWD), (2) Stop, (3) Person out of vehicle in tunnel (4) Fire. ODTS accepts a video frame in time as an input to obtain Bounding Box (BBox) results by Object Detection and compares the BBoxs of the current and previous video frames to assign a unique ID number to each moving and detected object. This system makes it possible to track a moving object in time, which is not usual to be achieved in conventional object detection frameworks. A deep learning model in ODTS was trained with a dataset of event images in tunnels to Average Precision (AP) values of 0.8479, 0.7161 and 0.9085 for target objects: Car, Person, and Fire, respectively. Then, based on a trained deep learning model, the ODTS based Tunnel CCTV Accident Detection System was tested using four accident videos which including each accident. As a result, the system can detect all accidents within 10 seconds. The more important point is that the detection capacity of ODTS could be enhanced automatically without any changes in the program codes as the training dataset becomes rich.
Perception of health-care workers about hand hygiene and its resources at King Fahad Hospital of University

Background: Transmission of pathogens via health-care workersâ€™ hands is one of the most frequent means of spreading multiresistant organisms and the occurrence of health-careâ€“associated infections. Hand hygiene is the simplest, most effective method to prevent cross-infections. Hand hygiene perception plays an important role in determining hand hygiene compliance. Objective: To assess physiciansâ€™ perceptions and the available resources for hand hygiene at King Fahad Hospital of University. Materials and Method: A cross-sectional study was used on 209 physicians. Data were collected using a Word Health Organization Self-Report Questionnaire and a checklist for availability of hand hygiene resources. Result: More than half of the sample was male subjects (55.5%), with a mean age of (30 Â± 6.5 years). About 44% showed positive perception, and 38% were neutral about the importance of hand hygiene. The frequent use of alcohol-based handrub was significantly associated with positive perception of importance of hand hygiene (P = 0.031). Nonhuman resources of hand hygiene were available in all the departments, except for hand cream or lotion. Conclusion: Accordingly, we recommend raising the patientsâ€™ awareness about their right to remind health-care workers to clean their hands. Moreover, during training of health-care workers, the trainer should remind the health-care workers that the patient has the right to ask them to wash their hands.
Thermal Conductivity of Silicon Nanowire Using Landauer Approach for Thermoelectric Applications

The electronic and phononic band structure of silicon nanowires embedded in SiGeo.3 is calculated and used to investigate its effect on the thermoelectric properties by Landauer approach. The contribution from elec-tron/hole on power factor and electronic thermal con-ductance is less than that from phonons on lattice ther-mal conductance.
Velocidad de la marcha en adultos mayores de la comunidad en Lima, Perú.

Objetivo: Determinar la velocidad de la marcha en una poblacion adulta mayor en la comunidad de Lima Metropolitana y los factores asociados a una velocidad de la marcha disminuida. Material y metodos: Estudio transversal y descriptivo de una muestra probabilistica, polietapica estratificada por conglomerados de las seis zonas distritales de Lima Metropolitana de 246 personas mayores de 60 anos, no institucionalizadas. La velocidad de la marcha se calculo con el tiempo requerido para caminar ocho metros de diez en total y se utilizaron los criterios de Fried para determinar fragilidad. Resultados: La velocidad de la marcha promedio fue 0,92 ± 0,24 m/s con una distribucion por percentiles entre 0,77 m/s (p25) y 1,06 m/s (p75). Se encontro asociacion entre velocidad de la marcha disminuida y una mayor edad (0,95 ± 0,24 m/s, en personas de 60 a 69 anos vs. 0,84 ± 0,25 m/s, en mayores de 80 anos, p<0,05), el sexo femenino (0,86 ± 0,22 m/s, vs. 1,01 0,23 m/s, para el masculino, p<0,01), y la presencia de fragilidad (0,67 ± 0,24 m/s, vs. 1,04 ± 0,22 m/s, en no fragiles, p<0,01). Conclusiones: El promedio de la velocidad de la marcha fue de 0,92 ± 0,24 m/s. Se encontro una asociacion estadistica entre una velocidad de la marcha disminuida y la edad, el sexo femenino y la presencia de fragilidad.
Learning to see through haze: Radar-based Human Detection for Adverse Weather Conditions

In this paper, we present a lifelong-learning multisensor system for pedestrian detection in adverse weather conditions. The proposed method combines two people detection pipelines which process data provided by a lidar and an ultrawideband radar. The outputs of these pipelines are combined not only by means of adaptive sensor fusion, but they can also be used to help one another learn. In particular, the lidar-based detector provides labels to the incoming radar data, efficiently training the radar data classifier. In several experiments, we show that the proposed learning-fusion not only results in a gradual improvement of the system performance during routine operation, but also efficiently deals with lidar detection failures caused by thick fog conditions.
Weighted Consensus Problem for Multi-agent Systems

In this paper, the concept of consensus is generalized to weighted consensus, by which the bipartite consensus and cluster consensus problems can be unified in the proposed weighted consensus frame. The dynamics of agents are modeled by the general continuous-time linear systems. The interaction topology is modeled by edge- and node-weighted directed graphs. Under both state feedback and output feedback control strategies, the weighted consensus problems are transformed into the equivalent conventional consensus problems. Then, a distributed state feedback protocol and an output feedback protocol are proposed to solve the weighted consensus problems respectively. Finally, a simple example is given to illustrate the effectiveness of our proposed approaches.
An Ultra-High Resolution Resonant MEMS Accelerometer

This paper reports initial results on the implementation of a high-resolution resonant MEMS accelerometer with a noise floor of $17.8\text{ng}/\surd{}\text{Hz}$ and a bias stability of 17.5ng with a differential frequency readout configuration. The differential readout scheme provides for rejection of common mode environmental effects to first order. This prototype sensor demonstrates the best bias stability and noise floor of all MEMS resonant accelerometer configurations reported till date.
Limitations in the Measurement of the Shielding Effectiveness of Aeronautical Multi-ply CFC Laminates

Modern aircraft employ profusely carbon fiber composites (CFC) for their construction. Then, it is mandatory to characterize the behaviour of this kind of materials for its use under harsh electromagnetic environments. In that sense, this paper presents the application of two standards, the ASTM D4935 and the IEC 61000-4-21, to the measurement of the shielding effectiveness (SE) of aeronautical multi-ply CFC laminates frequently used for the construction of aircraft. Each technique is well suited for a specific material or realization and a specific frequency range. So, both techniques are complementary and should be developed in parallel. Taking into account the high SE expected for this kind materials, the set-ups imply the measurement of very low signals, that poses an experimental challenge that has to be faced. Then, in this study, different test samples and realizations of CFC materials have been fabricated and measured. Improvements on the traditional measurement procedures have been applied, obtaining promising results.
Italian multicenter study ⋆ on Epiquick®, Rapid Patch Test® and TRUE TestTM

Using 3 different ready‐to‐apply patch test systems (Epiquick®= EPI; Rapid Patch Test®= RPT; TRUE TestTM= TT), 316 patients sensitive to at least one allergen were studied in order to investigate the reproducibility of these tests in comparison with the Finn Chamber ®(FC) technique. Each patient was tested with a different ready‐to‐use system. Each test was compared to the same allergen applied with FC to either side of the upper back. Comparisons were made using EPI, RPT, and TT in 93 (183 tests), 119 (262 tests), and 104 (242 tests) patients respectively. When a positive test result occurred on only one side of the back, the concordance was defined as negative. Similarly, doubtful reactions on either side corresponding to positive or negative results were considered as non concordant. The concordance of positive reactions was 80.9% between the EPI and the FC technique, 77.5% between the RPT and the FC technique, and 72.3% between the TT and the FC technique. The frequency of questionable (+? ) and irritant reactions (IR) was about the same for the different tests. Nevertheless, when comparing the positive reactions vs +? , IR, or negative reactions in paired tests, positive results were more frequently obtained with the FC technique. Our data suggest that the standard method with FC allows a better detection of contact allergy than the new ready‐to‐use systems tested in this study.
Multi-Domain Time-Sensitive Networks - An East-Westbound Protocol for Dynamic TSN-Stream Configuration Across Domains

The requirements on industrial communication networks are changing. They need to offer end-to-end communication changeable on demand. Time-Sensitive Networks (TSN) adds real-time capabilities to Ethernet while providing mechanisms to centrally reconfigure network devices. Today, industrial networks are not connected to each other. Industry 4.0 will require communication across multiple industrial networks. With the help of an east-westbound communication protocol, control-plane instances negotiate the establishment of communication paths across multiple networks. This paper proposes a concept for an east-westbound protocol for TSN networks. It shows a negotiation process for provisioning real-time end-to-end connections over multiple TSN-domains. A prototype and a multi-domain TSN testbed has been implemented which shows the feasibility of the protocol. The performance of the proof-of-concept implementation has been analyzed.
A Multi-Objective Diagnosis Method for Gearbox: Multi Task Deep Learning Based on One-Dimensional Convolution

Deep learning has achieved fruitful results in the field of mechanical failure due to its powerful adaptive feature extraction and classification capabilities. However, many previous studies have been used in a single-label system to diagnose a single target failure, and the data set for verification is small, and it is not a good simulation of actual production. Under these circumstances of big data age, the single label system not only separates the connection between different target faults of mechanical equipment. , but also makes it difficult to completely describe the variety of health status information such as the location, type, and degree of equipment failure. Therefore, in this paper, we referred to the CWRU bearing data to collected more complex bearing and gear data. a diagnosis method based on multi task deep learning model is proposed, which can diagnose the faults of the bearing and gear in the gearbox at the same time. The advantage is that through separate task layers, it is possible to adaptively extract features of different targets from the same signal, and accelerate the convergence speed of the network by adding a BN (Batch Normalization) layer. The experimental results show that the method can accurately diagnose different faults of bearings and gears in the gear-box under various working conditions and a large number of samples.
Software-defined Flow Reservation: Configuring IEEE 802.1Q Time-Sensitive Networks by the Use of Software-Defined Networking

Communication systems are core elements of future cyber-physical systems (Industrial Internet, Industry 4.0, Internet of Things) which form the basis for applications such as smart production, smart grid, smart city, and the like. These applications will comprise data streams with very different communication requirements regarding data rate, latency, jitter, and reliability that may also vary over time. This calls for communication solutions that on one hand can adapt flexibly and on-demand to these needs and on the other hand can seamlessly support data streams with different quality-of-service requirements.Within this paper we present Software-defined Flow Reservation (SDFR), an approach that combines Software-defined Networking (SDN) technologies with Time-Sensitive Networking. Special focus is on the configuration of the time-sensitive network. SDFR implements the IEEE 802.1Qcc standard in the logically centralized SDN controller and, thus, allows for a flexible software-driven configuration of the underlying network.This flexibility enables operators to support multiple configuration interfaces simultaneously and to easily modify traffic classes, scheduling approaches, and network behavior. SDFR allows to re-use existing solutions for well-understood SDN use cases while simultaneously supporting novel TSN-specific solutions.
Young people's access to tobacco : a mixed-method systematic review

This report describes the findings and methods of a systematic review of research exploring how young people aged 11-18 years access tobacco in the UK. The review was commissioned to support the development of policies to reduce rates of smoking among young people; it sought to understand the relative importance of both retail and social sources of tobacco for young people.
Electronic and structural properties of interstitial titanium in crystalline silicon from first-principles simulations

We demonstrate the presence of small Jahn-Teller distortions for interstitial titanium in silicon at different charge states by performing ground state DFT calculations. We prove the existence of three charged transition levels within the band gap by using a non-empirical parameter-free approach, based on the GW approximation, in agreement with DLTS measurements.
Prediction of Bus Arrival Time based on GPS Data: Taking No. 6 Bus in Huangdao District of Qingdao City as an Example

The development of bus priority strategy is of great significance to solve urban traffic problems, and one of the issues that passengers are most concerned about in bus travel is bus arrival time. In this paper, firstly, the related technologies of bus GPS data processing and bus arrival time prediction are studied, and then the running time of historical section is analyzed by K-Means clustering method, and the rolling prediction model of bus arrival time is put forward; finally, taking No. 6 bus in Huangdao District of Qingdao as an example, the prediction results of bus arrival time between the proposed model and other comparative models are discussed. The results show that the model can accurately predict the bus arrival time and meet the travel demand of passengers.
Partitioning Manual Assembly Workstations for Flexible Manufacturing

Many production processes are still realized with manual assembly, due to its flexibility and efficiency. The individual needs of the customers lead to a bigger variety of products and components in manual assembly workstations. With the variety of the components and products, the scope of the manual assembly workstation as well as its overall complexity increases. In this paper, we present a method to counter this effect by dividing the manual workstation into multiple subsystems. Our method minimizes the number of times a worker has to switch between the individual subsystems in order to search for components. Experimental results indicate that our method performs better on this task compared to other approaches and can this way be used to increase the productivity of a manual assembly workstation.
Inertial Sensor-Based Gait Analysis for Evaluating the Effects of Acupuncture Treatment in Parkinson’s Disease

Acupuncture is a newly trend of complementary treatment for Parkinson’s disease. Although its effectiveness on improving motor function has been evidenced in many clinical studies, a thorough analysis on these improvements is still needed for figuring out the mechanism of acupuncture. In this study, two shank-mounted inertial measurement units were used to analyze the effects of acupuncture on improving gait in Parkinson’s disease. Four subjects with Parkinson’s disease were recruited and walking trials were performed before and immediately after their acupuncture treatment. Eleven gait parameters were calculated from the measurements of the IMUs and the changes before and after acupuncture were analyzed by several statistics methods. The results showed that the subjects had obvious improvements on gait normalcy and certain gait parameters after acupuncture treatment.
Speech Information Hiding Algorithm Based on Complete Binary Tree Dynamic Codebook Grouping

In order to protect the data security and privacy of Voice over Internet Protocol (VoIP), a new algorithm for speech information hiding is proposed based on complete binary tree dynamic codebook grouping (CBTDCG). The algorithm uses the quantization index modulation (QIM) method to divide the dynamic codebook into two parts (corresponding to the embedded secret information bits ‘0’ and ‘1’ respectively), and adopts the complete binary tree to achieve the optimal grouping. The utility of complete binary tree makes the algorithm have the characteristics of fast search and high accuracy. The sender selects the corresponding code word according to the secret information bits for embedding the secret information into carrier speech. At the receiving end, the secret information bits are extracted according to the features of the code word vectors by the receiver. The experimental results show that the hidden capacity of the algorithm is larger than 450bps and the loss of the PESQ is less than 4%. Verification shows that the algorithm possesses a good robustness with a low bit error rate after being interfered by noise and the speech stream carrying secret information has better imperceptibility.
Mathematics and programming: marriage or divorce?

The literature about teaching programming usually reputes its learning as a difficult task. Some of them studied different aspects aiming to identify the reasons behind the high failure rates, such as students' mathematics background and text comprehension. This study is focused on identifying which mathematical knowledge positively correlates with programming skills acquisition. We claim that mathematical concepts related to induction, inference and logical reasoning are among such knowledge. To confirm it we conducted a quasi-experiment with students who were enrolled at a first programming course (CS1). We analysed their mathematical knowledge through a test and gathered information on their difficulties and strategies used to overcome their programming difficulties. The data sample was too small to allow for strong assertions. However, the results indicates a possible correlation between mathematical abilities and programming potential. Also, students repute the logic of programming and teaching methodology as the main sources of difficulties. Furthermore, many of them prefer to ask their teachers or look at websites to tackle their difficulties.
Ellipsoid multi-axial sensor calibration with temperature compensation

Multi-axial sensors are commonly used in the vehicle motion measurement, like in the attitude measurement and inertial navigation. An ellipsoid fitting is a method useful for calibration of this kind of sensor. In the paper extension of this method by direct temperature compensation is proposed. The experiment with simultaneous testing of eight sensors gives data for calibration and also makes possible analysis of different forms of sensor response approximation. New finding is that, beside possibility of increase accuracy by temperature compensation, a temperature gradient in the sensor model additionally reduces sensor error.
Event-triggered based scaled consensus for multi-agent systems

This note aims to solve scaled consensus problem for multi-agent systems by event triggered techniques in a unified framework. As a more general class of consensus problems, scaled consensus can possess the same behaviors of flocking, bipartite and cluster consensus by using proper scales. Event-triggered protocols are considered to avoid unnecessary continuous controllers updating as well as reduce the computation burden and energy consumption. A centralized event-triggered scheme is firstly proposed and then its distributed counterpart, with only locally exchanging information between individuals are available. It is proved that under such event-triggered protocols, scaled consensus can be achieved and Zeno behavior is excluded. Finally, simulations are given to illustrate the effectiveness of our results.
Contusion index: its importance in management of traumatic brain contusions.

Objective: To describe the changes of Glasgow Outcome Scale (GOS) and Glasgow Coma Scale (GCS) aftersurgical and / or conservative management of patients with contusions based on Contusion Index (C. I.). Study Design: Descriptive StudySetting: Department ofNeurosurgery, Dow University ofHealth Sciences/ Civil Hospital, Karachi. Duration of Study: Two years and five months between August 2006 and January 2009.Subjects and Methods: 50 patients of ages between 6 to 75 years, including both sexes were evaluated on thebasis of contusion index as determined by computerized tomographic scan (C.T. Scan) findings and theirGlasgow outcome scores were established. Results: Patients with contusion index 0 - 3 should be conservatively managed. Patients with contusion index6 should be given the benefit of some sort of surgical procedure first, which showed best results with surgicalmanagement and poor results with conservative management. Patients with contusion index 9, no matter what,always show a poor outcome. Conclusion: We recommend that a future study should be done to arrive at a decision regarding conservativeversus surgical management of patients with cerebral contusions based on Contusion Index which will help usavoid unnecessary surgeries and vice versa.