Miracle-dz/UniFAR-FTUs huggingface dataset:
Study on Ballistic Missile Infrared Hyperspectral Endmember Extraction Algorithm Based on Non-negative Matrix Factorization——Performance Analysis

According to the hyperspectral endmember extraction algorithm based on the non-negative matrix factorization, the performance of this method in various situation is researched. The emphases of this research are the number of the hybrid spectrums, the abundance coefficient setting methods of the hybrid spectrums and the noise in the hybrid spectrums. The simulation show that the algorithm has good performance in various situations and this method can be used in the endmember extraction of the ballistic missile hyperspectral data.
EAKOS - Energy Aware KMeans Offloading Scheme for Mobile Applications

Recently Mobile Cloud Computing has been considered an emerging trend to handle mobile applications that run on resource constraint mobile devices. Computation offloading is a major issue in mobile Cloud Computing where the performance can be enhanced by reducing the energy consumption of mobile applications. In this context, several solutions have been proposed to optimize computation offloading and the parameters to be considered while performing the operations. Identifying the modules of the mobile application for offloading to Cloud Infrastructure under different parametric conditions remains a challenge. Thus, this paper proposes Energy Aware KMeans Offloading Scheme (EAKOS) a dynamic computation offloading scheme with optimal partitioning using KMeans Clustering based on the frequency of use of the modules by the user, computation resource requirements, and the remaining battery power on the Mobile Device. The results show that this scheme can be beneficial for enhancing the performance of mobile applications with a 69% reduction in battery power consumption than the existing offloading schemes.
A HMSIW Dual-band Dual-polarization Cavity-backed Antenna

In this paper, a dual-band dual-polarization half -mode substrate integrated waveguide (HMSIW) cavity-backed antenna is presented. Loading folded slotline on the top surface of the antenna, dual-band characteristic can be achieved. Two radiation elements are orthogonal, so dual-polarization can be obtained in two operating bands. Two resonant modes are excited at 6. 76 GHz and 9.11 GHz, respectively. The peak gains are 3.9 dBi and 1.7 dBi respectively. The -10 dB impedance bandwidth of the two passbands are 6.71 - 6.8 GHz and 9.08 - 9.14 GHz, respectively.
Analysis of Shaft Voltage in Rotor Permanent Magnet Synchronous Motor System for Traction

Rotor permanent magnet motor is the most common permanent magnet motor. Permanent magnet motor is widely used in traction system because of its high efficiency, high power factor and small volume. The shaft voltage is one of the main reasons for bearing failure. Common mode voltage is one of the most important reasons for the generation of shaft voltage. In this paper, the influence of common mode voltage on the shaft voltage of IPM and SPM motors is studied, the shaft voltage analysis model is established, and the influence of coupling capacitance on the shaft voltage is discussed. The results provide reference for the design of motor and the suppression of shaft voltage.
The Meaning of Work and Self-Management Experiences among Elderly Workers with Multiple Chronic Diseases: A Qualitative Study

With population aging, increasingly many elderly individuals are expected to participate in economic activities. Elderly workers have a higher prevalence of multiple chronic diseases, making it necessary to examine elderly workers’ experiences of health-related self-management in work environments. This qualitative study investigated the meaning of work and health-related self-management experiences among elderly workers with multiple chronic diseases. The study participants were elderly workers residing in South Korea (65 years old or older) with at least two chronic diseases. Twelve participated in individual interviews, which were audio-recorded and transcribed. Qualitative content analysis was conducted with the transcribed data. Six themes, 21 sub-themes, and 40 codes were derived. The themes were “benefit of work on health and life”, “adaptation to a new work environment”, “endurance”, “continuous efforts to maintain health”, “difficulties in self-management”, and “requirements for health promotion”. The participants experienced difficulties in health-related self-management due to time constraints, poor work environment, and financial burdens. However, participants expressed their desire to invest effort into managing their physical and psychological health and to work as long as possible. Programs that consider the aging and health-related characteristics of elderly workers and their work environments should be developed and implemented.
A scanning tunnelling microscopy and spectroscopic study of bromine functionalised molecules on metal surfaces

This thesis discusses deposition, self-assembly, polymerisation and covalent bonding of bromine functionalised molecules on Au(111), Ag(111), Cu(111) and Cu(110) metal substrates are investigated. The debromination and subsequent covalent bonding is found to depend heavily on the reactivity of the substrate, the molecular coverage and anneal temperatures. Characterisation was performed using scanning tunnelling microscopy (STM), x-ray photoelectron spectroscopy (XPS) and synchrotron based photoelectron spectroscopy (PES) as well as x-ray absorption spectroscopy (XAS). Deposition of hexabromotriphenylene (HBTP) on the Au(111), Ag(111) and Cu(111) surfaces resulted in the formation of disordered, covalently bonded nano-networks which remained stable up to a temperature of 300˚C. C-Br bonds of HBTP can cleave at room temperature (RT) on Ag and Cu due to the substrate reactivity allowing new covalent C-C bonds to form. In the case of Cu, disordered networks are observed at RT while in contrast, more ordered networks develop on the Ag and Au substrates after annealing to 150˚C and 200˚C respectively. On the Au(111) surface bromine desorption begins at 250˚C whereas on the Ag(111) and Cu(111) surfaces the atomic bromine remains on the surface and forms a (√3×√3)R30˚ structure. The formation of atomically precise graphene nanoribbons (GNR) from a 10,10’-dibromo-9,9’-bianthryl (DBBA) precursor molecule was investigated on the Au(111), Ag(111), Cu(111) and Cu(110) surfaces. After deposition, the DBBA molecules undergo two thermally induced chemical changes; (i) a debromination reaction to produce covalently bonded polymerised chains and (ii) a cyclodehydrogenation reaction at higher temperatures to form GNRs. The threshold for the debromination reaction, polymerisation and final cyclodehydrogenation reaction decreases proportionally with the reactivity of the substrate. Ni(II) 5,15-dibromo-10,20-diphenyl (DBrDPP) was studied on the Cu(110) surface to investigate its suitability for integration into GNRs. Thermally induced covalent bonding of DBrDPP was observed via a C-Cu-C organo-metallic bond while a Ni-Cu ion exchange reaction occurs in the porphyrin macrocycle. At higher anneal temperatures domains of graphene emerge which exhibit a moire pattern with varying periodicities indicating a lattice mismatch between the graphene layer and the underlying copper substrate.
Mental health problems and correlates among 746 217 college students during the coronavirus disease 2019 outbreak in China

Abstract Aims Coronavirus disease 2019 (COVID-19) pandemic is a major public health concern all over the world. Little is known about the impact of COVID-19 pandemic on mental health in the general population. This study aimed to assess the mental health problems and associated factors among a large sample of college students during the COVID-19 outbreak in China. Methods This cross-sectional and nation-wide survey of college students was conducted in China from 3 to 10 February 2020. A self-administered questionnaire was used to assess psychosocial factors, COVID-19 epidemic related factors and mental health problems. Acute stress, depressive and anxiety symptoms were measured by the Chinese versions of the impact of event scale-6, Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7, respectively. Univariate and hierarchical logistic regression analyses were performed to examine factors associated with mental health problems. Results Among 821 218 students who participated in the survey, 746 217 (90.9%) were included for the analysis. In total, 414 604 (55.6%) of the students were female. About 45% of the participants had mental health problems. The prevalence rates of probable acute stress, depressive and anxiety symptoms were 34.9%, 21.1% and 11.0%, respectively. COVID-19 epidemic factors that were associated with increased risk of mental health problems were having relatives or friends being infected (adjusted odds ratio = 1.72–2.33). Students with exposure to media coverage of the COVID-19 ≥3 h/day were 2.13 times more likely than students with media exposure <1 h/day to have acute stress symptoms. Individuals with low perceived social support were 4.84–5.98 times more likely than individuals with high perceived social support to have anxiety and depressive symptoms. In addition, senior year and prior mental health problems were also significantly associated with anxiety or/and depressive symptoms. Conclusions In this large-scale survey of college students in China, acute stress, anxiety and depressive symptoms are prevalent during the COVID-19 pandemic. Multiple epidemic and psychosocial factors, such as family members being infected, massive media exposure, low social support, senior year and prior mental health problems were associated with increased risk of mental health problems. Psychosocial support and mental health services should be provided to those students at risk.
HIV-1 diversity and compartmentalization in urine, semen, and blood

Supplemental Digital Content is available in the text Abstract HIV-1 persists indefinitely in multiple cellular reservoirs despite antiretroviral therapy. We previously demonstrated HIV-1 compartmentalization in kidney and urine. Here, we further characterized viruses in urine and when available, compared them to those present in semen from HIV-1 positive participants with detectable plasma viremia to further understand the viral dynamics in the upper and lower genitourinary tract. Blood and urine samples were obtained from 19 HIV-1 positive participants. Simultaneous semen samples were obtained from 16 of the 19 participants. HIV-1 envelope (env) gene sequences were obtained by single-genome amplification (SGA) and neighbor-joining trees were constructed using the Kimura 2-parameter model. HIV-1 env gene sequences were amplified from blood in 19/19 (100%) participants, urine in 18/19 (95%) participants, and semen in 12/16 (75%). In individuals from which both urine and semen samples were obtained, differences in viral shedding between the 2 sources were observed, where HIV-1 env sequences could only be amplified from either urine or semen. Longitudinal phylogenetic analysis of urine-derived env sequences from 1 participant demonstrated that urine clusters distinct from blood are maintained over time (20 weeks), consistent with viral compartmentalization and local replication. Comparison of urine and semen derived sequences demonstrated either virus compartmentalization or equilibration. Our results demonstrate that when present, viral compartmentalization in urine persists over time. Comparison of timing of viral shedding in urine and semen samples from our cohort suggest different viral kinetics between the upper and lower genitourinary tract and sequence analysis suggests that HIV-1 populations in urine and semen can either be imported from blood or produced locally.
Discovering and Tracking-based Detection System for Optical Camera Communication

Detecting the transmitting source within a frame is fundamental in Optical Camera Communications (OCC). Until now, the detection techniques used today are discovery techniques, that is, in each frame or set of frames, the source is detected unequivocally. These techniques are expensive and may require learning (in case of using Deep Learning), which makes the implementation of receivers in users’ end devices, such as smartphones, a new challenge to consider. In this article, we propose a detection system oriented to devices with limited hardware making use of basic operations, and also proposes to use the tracking technique. Since the tracking does not detect whether the source is within the region being tracked, this combination allows to obtain higher camera capture rates (FPS). This system may allow limited hardware devices to implement OCC technology.
Evaluation of VCAM-1 Targeted Naringenin/Indocyanine Green-Loaded Lipid Nanoemulsions as Theranostic Nanoplatforms in Inflammation

Naringenin, an anti-inflammatory citrus flavonoid, is restrained from large-scale use by its reduced water solubility and bioavailability. To overcome these limitations, naringenin was loaded into lipid nanoemulsions directed towards vascular cell adhesion molecule (VCAM)-1, exposed by activated endothelium, and delivered intravenously in a murine model of lipopolysaccharide (LPS)-induced inflammation. To follow the in vivo bio-distribution, naringenin-loaded nanoemulsions were labeled with near-infrared probe Indocyanine Green (ICG). Based on ICG fluorescence, a VCAM-1-dependent retention of nanoemulsions was detected in the heart and aorta, while ultra-high-performance liquid chromatography (UHPLC) measurements showed a target-selective accumulation of naringenin in the heart and lungs. Correlated, fluorescence and UHPLC data indicated a mixed behavior of the VCAM-1 directed nanoparticles, which were driven not only by the targeting moiety but also by passive retention. The treatment with naringenin-loaded nanoemulsions reduced the mRNA levels of some inflammatory mediators in organs harvested from mice with acute inflammation, indicative of their anti-inflammatory potential. The data support a novel theranostic nanoplatform for inflammation, the naringenin/ICG-loaded nanoparticles that either by passive accumulation or effective targeting of the activated endothelium can be employed for imaging inflamed vascular areas and efficient delivery of the encapsulated therapeutic agent.
General, Organic, and Biological Chemistry

PART I: GENERAL CHEMISTRY. 1. Basic Concepts About Matter. 2. Measurements in Chemistry. 3. Atomic Structure and the Periodic Table. 4. Chemical Bonding: The Ionic Bond Model. 5. Chemical Bonding: The Covalent Bond Model. 6. Chemical Calculations: Formula Masses, Moles, and Chemical Equations. 7. Gases, Liquids, and Solids. 8. Solutions. 9. Chemical Reactions. 10. Acids, Bases, and Salts. 11. Nuclear Chemistry. PART II: ORGANIC CHEMISTRY. 12. Saturated Hydrocarbons. 13. Unsaturated Hydrocarbons. 14. Alcohols, Phenols, and Ethers. 15. Aldehydes and Ketones. 16. Carboxylic Acids, Esters, and Other Acid Derivatives. 17. Amines and Amides. PART III: BIOLOGICAL CHEMISTRY. 18. Carbohydrates. 19. Lipids. 20. Proteins. 21. Enzymes and Vitamins. 22. Nucleic Acids. 23. Biochemical Energy Production. 24. Carbohydrate Metabolism. 25. Lipid Metabolism. 26. Protein Metabolism. Answers to Selected Exercises Index Glossary
Optimal sizing and operation of isolated microgrids for developing countries: hedging uncertainties with Monte Carlo techniques

Achieving universal electrification is a challenging goal for governments in developing countries; the traditional approach of extending the national grid to rural areas has been usually pursued but since many communities are located far from the national grid, this approach may be sub-optimal. Hybrid microgrids, composed by renewables, batteries and a backup fuel-fired genset, are very promising solutions, but socio-political and geographical concerns undermine the optimal design of the system and the economic viability of projects. Recent advanced approaches use Monte Carlo techniques to size the system accounting for uncertainties regarding the load and renewable production; however, few papers have investigated the optimal number of Monte Carlo scenarios to evaluate for a proper sizing. In this study, we investigate the benefits and drawbacks of using Monte Carlo techniques to cope with uncertainties involved in the optimal design and operation of hybrid microgrids, proposing new sizing criteria and highlighting best practices. A case study using real data from a minigrid in Kenya is proposed.
Adaptive locomotion of lower limb exoskeleton based on oscillators and frequency adaptation

In this paper, we focus on the topic of adaptive control of the trajectory of the lower limb exoskeleton, and our ultimate goal is to establish a design principle of a controller in order to achieve natural human-like locomotion. We suggest dynamical movement primitives as a central pattern generator (CPG) of lower limb exoskeleton, this approach combines nonlinear oscillators, i.e. dynamical system that exhibit limit cycle behavior, this system can spontaneously generates trajectories for the robot. And by introducing adaptive factor, our control system is mathematically capable of learning the high level features (frequency, envelope, etc.) of a periodic input signal. Besides, we introduced foot pressure feedback into the system, and designed related hardware. Numerical simulations and experimental implementation on a physical robot demonstrate the effectiveness of the proposed locomotion controller.
An Artificial Intelligence-Driven Smart Home Towards Energy Efficiency: An Overview and Conceptual Model

Artificial intelligence (AI) has received immense attention from the research community and the industry, leading to AI being adopted in many real-world applications. The growing trend of deploying AI has dramatically changed the ergonomics of modern-day practices in many realms, including smart homes, healthcare, insurance, investment and banking, social services, infrastructure, and marketing. A smart home, often referred to as an intelligent home, comprises smart technologies supported by AI. In this context, this paper presents a comprehensive overview of the existing state of the art on AI applications in the context of energy-efficient smart homes. This paper also proposes a conceptual model for AI-driven smart home aiming to enhance the users’ comfort and energy efficiency phenomenon. Moreover, a brief overview of different AI techniques employed in energy efficiency applications is also part of this work. The presented conceptual model will significantly facilitate future research regarding smart homes in the context of energy efficiency.
Analytics-Aware Storage of Surveillance Videos: Implementation and Optimization

Increasingly more surveillance cameras in smart environments stream videos to storage servers for on-demand video analytics queries in the future. Unlike on-demand video services, in which maximizing the user-perceived video quality is the design objective, the considered storage servers aim to retain as much information as possible while offering enough space for incoming video clips. In this paper, we design, optimize, and implement an analytics-aware storage server on a smart campus testbed at NTHU, Taiwan, which consists of eight smart street lamps equipped with various sensors, network devices, analytics servers, and a storage server. We focus on the design and implementation of the storage server, and consider two key research problems: (i) how to efficiently determine the information amount of individual video clips and (ii) how to intelligently downsample individual video clips. More specifically, the first problem is to sample video frames from the stored video clips to analyze for approximations of the information amount without overloading the storage server. The resulting information amount is fed into the second problem to decide the video downsampling approaches for retaining as much information amount as possible without consuming excessive storage space. We propose two efficient algorithms to solve these two problems and compare their performance with the current practices via real experiments on our smart campus testbed. Our experiment results reveal the practicality and efficiency of our proposed design and algorithms, e.g., compared to the current practices, our storage server: (i) improves the per-request information amount by up to ~ 4 times, (ii) increases the total information amount by at most ~ 20%, (iii) boosts the number of saved video clips by up to ~ 35%, (iv) runs in real-time, and (v) scales well with larger storage space.
A Scalable Distributed System for Precision Irrigation

The level of adoption of Precision Agriculture (PA) technologies is still very different from one country to another and from one region to another in the same country. A major challenge is to develop PA from a best practice pursued by a minority of enlightened farmers to a widespread practice with sizeable impact on the use of environmental resources. One of the obstacles hindering PA is the lack of quantitative data readily accessible to farmers to guide their daily operations. In this paper, we present the information system developed within project POSITIVE to support and enhance precision irrigation across the whole Emilia-Romagna region. To this purpose, the POSITIVE information system establishes a service transforming satellite and sensor data into biophysical parameters and vegetation indices with full regional coverage. These data are automatically fed into a public irrigation advisory service (Irrinet+) thereby enabling precision irrigation and fertigation on a regional scale. Irrigation maps can be sent as advice to farmers or directly commanded to registered irrigation machines. The architecture of the distributed information system and the open protocols developed to achieve scalability and enable interaction of multiple heterogeneous components are reported in the paper.
Smart Sensor Node for Distributed Noise Monitoring

The exposure of millions of people to high noise levels in urban environments is a serious problem globally. The main source of noise is public transport, especially rail transport. The first step in determining the risk in this context is to conduct studies of the noise levels of major nodes of urban transport systems. This work presents the design and implementation of a smart sensor node for distributed noise pollution monitoring. The measurements with the sensor modules are mainly intended for estimating the noise load of the users of public transport and are made at a tram, subway and bus stops. The first measurement results show that the noise levels in the subway and bus stops may exceed the recommended exposure guidelines from the World Health Organization (WHO) and the European Environment Agency [1]. Based on the measurements, actions can be taken to reduce the noise exposure risk in subway and bus stations.