Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel InP- and GaSb-based light sources for the near to far infrared

This topical review presents an overview on novel concepts for light emitting diodes (LEDs) and lasers for the near infrared to the THz regime. GaSb-based quantum well lasers are shown to be a promising concept for laser from the near to mid infrared. The GaSb-based edge-emitting lasers offer low thresholds for wavelengths ranging from about 2 to 3.7 μm. However, the development of vertical-cavity surface-emitting lasers and other advanced laser concepts is lagging behind due to material issues and complicated process technology. InP-based type-II quantum wells are an innovative concept for sources emitting in the wavelength range from 2 to 4 μm. This concept combines extended long wavelength emission with the reliable process technology of the already well-established InP-based lasers. Based on this, we present LEDs up to 3.5 μm wavelength, surface emitting lasers at 2.5 μm wavelength and edge emitting lasers up to 2.7 μm. For longer wavelengths, the so-called GaSb- and InAs-based interband cascade lasers can be used operating up to about 7 μm. The mid infrared range between 3 and 20 μm is also covered by quantum cascade lasers (QCL), which are dominating especially in the longer wavelength range above 7 μm. The far infrared reaching to the THz regime is exclusively covered by QCL. While for decades the only available semiconductor laser source for the far infrared and THz range was the direct THz QCL, recent progress demonstrated THz emission in nonlinear mid infrared QCLs. These devices are emitting THz by a nonlinear frequency conversion process, which allows operation at room temperature and beyond. Tunable THz lasers were demonstrated using both monolithic tuning mechanisms and an external cavity approach.
Association between osteoarthritis and urinary tract infection in older adults: A nationwide population-based cohort study

Osteoarthritis (OA) may increase urinary tract infection (UTI) in older adults. However, this issue remains unclear. We identified 8599 older patients (≥65 years) with OA, and an equal number of older patients without OA, matched by age, sex, and index date from the Taiwan National Health Insurance Research Database between 2001 and 2005. Past histories, including UTI and underlying comorbidities, were included in the analyses. Comparisons for any UTI, ≥1 hospitalization for UTI, and ≥3 hospitalizations for UTI between the 2 cohorts by following up until 2015 were performed. In both cohorts, the percentages of age subgroups were 65–74 years (65.7%), 75–84 years (30.1%), and ≥85 years (4.2%). The male sex was 42.4%. Patients with OA had an increased risk of any UTI compared with those without OA after adjusting for all past histories (adjusted hazard ratio [AHR]: 1.72; 95% confidence interval [CI]: 1.64–1.80). Compared with patients without OA, patients with OA also had an increased risk of ≥1 hospitalization for UTI and ≥3 hospitalizations for UTI (AHR: 1.13; 95% CI: 1.06–1.19 and AHR: 1.25; 95% CI: 1.13−1.38, respectively). In addition to OA, age 75–84 years, female sex, history of UTI, benign prostatic hyperplasia, indwelling urinary catheter, cerebrovascular disease, dementia, and urolithiasis were independent predictors for any UTI. This study showed that OA was associated with UTI in older adults. We suggest appropriately managing OA and controlling underlying comorbidities to prevent subsequent UTI.
Active Voltage Balancing Control of a Seven-Level Hybrid Multilevel Converter Topology

The series connection of a standard three-level active neutral-point-clamped (ANPC) inverter and two cascaded H-bridge (CHB) modules comprising the flying capacitors (FCs) instead of the isolated dc voltage sources forms a seven-level hybrid topology that is referred to as improved ANPC (I-ANPC) converter. This article proposes an active voltage balancing technique for control of this hybrid multilevel inverter. The suggested method is based on a set of effortless logic equations that aim to stabilize the FC voltages within the CHB modules at their requisite reference values as well as to control the output voltage of the overall hybrid converter based on the phase-shifted-carrier pulsewidth modulation strategy. The experimental results obtained from a single-phase I-ANPC inverter's prototype are presented to validate the proposed control technique and its simple logic equations.
Constraining the HBV model for robust water balance assessments in a cold climate

Robust projections of changes in the hydrological cycle in a non-stationary climate rely on trustworthy estimates of the water balance elements. Additional drivers than precipitation and temperature, namely wind, radiation, and humidity are known to have a significant influence on processes such as evaporation, snow accumulation, and snow-melt. A gridded version of the rainfallrunoff HBV model is run at a 1 × 1 km scale for mainland Norway for the period 1980–2014, with the following alterations: (i) the implementation of a physically based evaporation scheme; (ii) a net radiation-restricted degree-day factor for snow-melt, and (iii) a diagnostic precipitation phase threshold based on temperature and humidity. The combination of improved forcing data and model alterations allowed for a regional calibration with fewer calibrated parameters. Concurrently, modeled discharge showed equally good or better validation results than previous gridded model versions constructed for the same domain; and discharge trend patterns, snow water equivalent, and potential evaporation compared fairly to observations. Compared with previous studies, lower precipitation and evaporation values for mainland Norway were found. The results suggest that a more robust and more physically based model for climate change studies has been obtained, although additional studies will be needed to further constrain evaporation estimates.
Seasonal sea-ice variability and its trend in the Weddell Sea sector of West Antarctica

The Weddell Sea is susceptible to the ongoing climate change and experiences a reduction in an overall increase in the sea-ice extent (SIE). The nature of sea-ice in the Weddell Sea is largely associated with its geographical setup that determines the seasonal and decadal sea-ice variability. The study analysed long-term sea ice-ocean-atmosphere variability and trends (1979–2019) based on satellite and reanalysis measurements. The result shows the expansion of yearly SIE is 2.5 ± 3.5 × 103 km2 yr−1 with a significant increase in the austral summer (12.4 ± 4.6 × 103 km2 yr−1), whereas a decrease in the spring SIE (−4.8 ± 5.0 × 103 km2 yr−1) over the last four decades. Seasonal sea-ice concentration (SIC) variations in the Weddell Sea are associated with latitudinal thermal differences and westerlies intensification, culminating in the weakening or strengthening of the Weddell Gyre. The SIC recorded significant positive trends in the western and eastern parts of the Weddell Sea during the austral summer and autumn, respectively. These changes are consistent with the prevailing wind patterns and Weddell Gyre intensification in the respective seasons. During the austral winter and spring, significant negative SIC trends (north of 65° S) were recorded due to the easterlies intensification and weakening of the Weddell Gyre. While, the significant positive trends observed along the coast are linked with the easterlies intensification and sea-ice advection. Composite analysis reveals that the SIC variability is related to the sea surface temperature (SST) during austral summer and spring, whereas SST and net heat flux both regulate the SIC in the Weddell Sea during the austral winter and autumn. The positive Southern Annular Mode is associated with an increase in sea-ice during austral summer, while sea-ice decreases during the winter in the Weddell Sea. The present study reveals a strong relationship between the sea-ice variability and ocean-atmospheric forcings, but these relationships are not constant over time; therefore, continuous monitoring is required.
THE EFFECT OF NON-THERMAL PROCESSING OF HYSSOPUS OFFICINALIS ON ITS ANTIOXIDANT AND ANTIMICROBIAL ACTIVITIES

Hyssopus of­ficinalis L. is one of the most important medicinal plants. Medicinal herbs are contaminated by microorganisms. Conventional methods for reducing of microbial loads such as ethylene oxide, propylene oxide and also use of steam are hazardous and instead, non-thermal process such as microwave and gamma radiation are being used widely in order to eliminate the microbial contaminations with no or a little side effect. In the present study the effect of gamma and microwave irradiation on antioxidant and antimicrobial activities of Hyssopus of­ficinalis L. was investigated. Hyssopus samples were exposed to gamma irradiation at doses 10, 15, 20 and 25 kGy and microwave irradiationat power of 300, 450 and 600 W for 5 min. In order to undergo the sequence experiments, the hydroalcoholic (EtOH 50%) extracts of plant were prepared. The antioxidant activities of irradiated and control samples were evaluated by DPPH radical scavenging (RS), ferric reducing power (FRP), β-carotene bleaching (BCB) and total phenolic content (TPC) of sampels. In order to study the antimicrobial activity, for determination of minimal inhibitory concentration (MIC) on E. coli and S. aureus,broth diluting method was used. Results showed that gamma irradiation had no significant effect on antioxidant parameters, phenolic content and antimicrobial activities of sampels. Microwave treatment of Hyssopus at 300, 450 and 600 W for 5 min increased its antioxidant and antimicrobial activities. Results indicated that gamma and microwave irradiation do not have any negative effect on antioxidant and antimicrobial activities of Hyssopus.
Urban building detection using object-based image analysis (OBIA) and machine learning (ML) algorithms

The information on building features especially in the urban area is very important to support urban management and development. Nevertheless, the automated and transferable detection of building features is still challenging because of variations of the spatial and spectral characteristics to support urban building classification using remote sensing techniques. Most previous studies utilized high-resolution images to discriminate buildings from other land use in the urban area and indeed it involves a high cost to achieve that purpose. Consequently, this study utilized a medium resolution remote sensing image, Sentinel-2B with a 10-meter spatial resolution to classified the building in Selangor, Malaysia. In order to obtain a good classification accuracy, the suitable segmentation parameters (scale, shape and compactness) and features selection for building detection have been determined. Machine learning (ML) algorithms, namely Support Vector Machine (SVM) and Decision Tree (DT) classifiers have been applied to categorized five different classes which are water, forest, green area, building, and road. The result from these two classifiers was then have been compared and it is obviously showing that the SVM classifier is able to produce 20% better accuracy compared to the DT classifier, with 93% and kappa is 0.92. Thus, by enhancing the classification techniques in OBIA, building extraction accuracy using ML algorithms for medium resolution images can be improved and the expenses can be reduced as well.
‘Sharing the space’ in the agricultural knowledge and innovation system: multi-actor innovation partnerships with farmers and foresters in Europe

ABSTRACT Purpose Networks and partnerships are commonly-used tools to foster knowledge sharing between actors and organisations in the Agricultural Knowledge and Innovation System (AKIS), but in Europe the policy emphasis on including users, such as farmers and foresters, is relatively recent. This paper assesses user involvement in a diverse set of European Union (EU)-funded and non-EU (formal and informal) multi-actor partnerships. Design/methodology/approach A review of 200 projects and other partnerships including farmers and foresters, drawn from an integrated database of several thousand such examples, assessed the types of innovation challenges tackled, solutions proposed, innovation supported, parameters of actor participation and the expectations of success in each example. Findings All the reviewed policy instruments, not just those funded in the frame of the EIP-AGRI, can be used to involve users in co-innovation in agriculture, forestry and related value chains. Practical implications The EIP-AGRI is just one part of a complex matrix of multi-actor co-innovation activities involving farmers and foresters in Europe. Interlinking of innovation programmes and more effective targeting of these programmes at groups of actors, especially users, who are currently not sufficiently engaged are worthy aspirations. Theoretical implications Any analysis of co-innovation involving users must recognise the existence of a multiplicity (projects, non-project activities, formal and informal) of multi-actor approaches. Originality/value This research used a common methodology to review several forms of multi-actor partnerships involving users and other actors. Our data suggest that many of these are effective methods of supporting co-innovation and are, therefore, ‘sharing the space’ within the AKIS.
Application of machine learning methods for optimizing the technical and economic performance of generating systems

The paper is on the technical and economic performance optimization technique for thermal and power generating system using machine learning methods. The possibility of using regression analysis for parameter influence evaluation when calculating technical and economic performance in order to reach better generating unit efficiency is described. The approach to evaluate the parameter influence of a large distributed control system is presented.
One-step solid phase synthesis of a highly efficient and robust cobalt pentlandite electrocatalyst for the oxygen evolution reaction

Cobalt pentlandite (Co9S8) has recently emerged as an alternative non-noble metal based electrocatalyst for the oxygen evolution reaction (OER). Co9S8 is known for its intrinsic structural and electronic properties favorable for electrocatalytic applications, but the synthesis of stoichiometrically optimal Co9S8 electrocatalysts remains challenging. Herein, a facile one-step solid phase calcination approach is presented in which Co9S8 nanoparticles (NPs) were concurrently synthesised on carbon nanosheets (CNSs). The reaction mechanism for this synthesis was systematically investigated using TG/DSC-MS analysis. Relative to other cobalt chalcogenide electrocatalysts, the as-prepared thermally stable nanocomposite (Co9S8/CNS) has better electrocatalytic performance for the OER in alkaline electrolytes, exhibiting a smaller overpotential of 294 mV at a current density of 10 mA cm−2 with a Tafel slope of 50.7 mV dec−1. Furthermore, a minimum overpotential of 267 mV with a Tafel slope of 48.2 mV dec−1 could be achieved using highly conducting multi-walled carbon nanotubes (MWCNTs) as a conducting filler in the nanocomposites.
A Fast Community Detection Algorithm Based on Reconstructing Signed Networks

Signed networks depict the individual cooperative or hostile relationship in a population, which can help to deeply mine the characteristics of complex networks and predict the potential collaboration between individuals by analyzing their interaction within different groups or communities. In this article, first of all, an improved modularity function for signed networks is proposed on the basis of the existing modularity function. Then, a new community detection algorithm for signed networks has also been devised, and time complexity analysis shows that the time required for the algorithm has a linear relationship with the number of nodes in the sparse networks. Meanwhile, the affinity index that can be used to convert directed signed networks into the corresponding undirected signed networks is come up with. Finally, the current algorithm has been applied into several illustrative and realistic networks. The experimental results indicate that the number of communities given by the proposed algorithm is consistent with that of actual communities, and thus, it can be further conducive to identifying the community structure hidden within the real-world systems.
Fault-tolerant control approach based on constraint control allocation for 4WIS/4WID vehicles

The four-wheel independently driven and steered electric vehicle is a promising vehicle model having a strong potential for handling stability, flexibility, and consumption reduction. However, failure of the actuators of 4WIS/4WID vehicles could lead to performance reduction and dangerous accidents owing to their complex system. A fault-tolerant control approach is adopted in the integrated chassis controller such that the autonomously driven vehicle maintains its safety and stability while actuator failures occur. A linear quadratic regulator is utilized to track the reference path by adjusting the total forces and moment. To resolve any actuator failures, a control allocation method based on the pseudo-inverse matrix is introduced for decoupling the forces and moment based on the current state of the tires with cycle and correction. In the actuator control layer, the desired forces of the tires are achieved by regulating the steering angles and driving torques based on the inverse tire models of normal and flat tires. Three sets of experiments are used to test the efficiency of the proposed method when applied to a 4WIS/4WID vehicle. The results demonstrate that the proposed fault-tolerant control method can greatly improve the tracking performance and stability of 4WIS/4WID vehicles under conditions of actuator failures.
HAM: Hotspot-Aware Manager for Improving Communications With 3D-Stacked Memory

Emerging High-Performance Computing (HPC) workloads, such as graph analytics, machine learning, and big data science, are data-intensive. Data-intensive workloads usually present fine-grained memory accesses with limited or no data locality, and thus incur frequent cache misses and low utilization of memory bandwidth. 3D-stacked memory devices such as Hybrid Memory Cube (HMC) and High Bandwidth Memory (HBM) can provide significantly higher bandwidth than conventional memory modules. However, the traditional interfaces and optimization methods for JEDEC DDR devices do not allow to fully exploit the potential performance of 3D-stacked memory with the massive amount of irregular memory accesses of data-intensive applications. In this article, we propose a novel Hotspot-Aware Manager (HAM) infrastructure for 3D-stacked memory devices capable of optimizing memory access streams via request aggregation, hotspot detection, and in-memory prefetching. We present the HAM design and implementation, and simulate it on a system using RISC-V embedded cores with attached HMC devices. We extensively evaluate HAM with over 12 benchmarks and applications representing diverse irregular memory access patterns. The results show that, on average, HAM reduces redundant requests by 37.51 percent and increases the prefetch buffer hit rate by 4.2 times, compared to a baseline streaming prefetcher. On the selected benchmark set, HAM provides performance gains of 21.81 percent in average (up to 34.28 percent), and power savings of 35.07 percent over a standard 3D-stacked memory.
Assessing the genetic diversity of Myrsine umbellata (Primulaceae) in Brazilian Atlantic Forest remnants - an important step towards reforestation efforts

Abstract The investigation of genetic diversity in natural populations of species that show potential for use in reforestation programs is a key step in making management decisions. However, reforestation programs with native species in Brazil are still rarely based on a genetic understanding of the seed matrices used for seedling production. This is also the case for Myrsine umbellata, a dioecious shrub within the family Primulaceae that has been used in reforestation programs in Brazil, mainly due to its high production capacity of fruits attractive to the avifauna. The goal of this study was to measure intra- and interpopulational genetic diversity in natural populations of M. umbellata in six forest remnants of the Atlantic Forest using ISSR markers. The results revealed that the intrapopulational genetic diversity was greater than the genetic diversity among the studied populations. For this reason, the cultivation of seedlings from seeds obtained in more than one population seems the most appropriate strategy for reforestation purposes. Even though the most isolated populations are also the ones with highest genetic structure, all populations of M. umbellata included in this study revealed to be an important germplasm bank conserved in situ.
Empirical Analysis of Factors Contributing to Smartphone Addiction

Purpose: The purpose of this paper is to analyze the effects of loneliness, shyness, social anxiety, and external locus of control on the smartphone addiction among undergraduate, graduate, and postgraduate students in China. Methodology: Our research used questionnaires as a method of response generation. 240 returned questionnaires were useable from which results were derived. We used Pearson’s Correlation Analysis to analyze the relationships between the variables and their significance. The reliability of the study was assessed through Cronbach’s Alpha. Result and Findings: The analysis of the study revealed that the variables were significant and positively correlated with each other. Loneliness had a strong correlation to smartphone addiction, shyness and social anxiety were moderately correlated, whereas, external locus of control was weakly correlated to smartphone addiction. Research Implications: This study can be used by universities and educational ministries in China in order to understand the behaviors and addictions of students and can devise strategies on how to successfully tackle this situation in students. Limitations: The respondents’ pool of this study is constricted and only involves students in China. The analysis is purely quantitative and this study was conducted on a cross-sectional basis. Future Research: Further research can be conducted by diversifying the pool of respondents, conducting a longitudinal study, and employing qualitative methods to analyze the research.
Determination of the 1st Buckling and Collapse Loads for Integrally Stiffened Panels by Artificial Neural Network and Design of Experiment Methodology

Buckling is a structural instability that load carrying capacity of a structural element may suddenly decrease. This sudden change in the load carrying capacity may cause catastrophic failures. Therefore, determination of the first buckling and collapse loads of structural elements is essential. FE analyses and structural testing are used to determine buckling characteristics of a structural element. However, in early design stages, FE analyses are time consuming and structural testing is costly. In this paper, artificial neural network tool is used to reduce computational effort to determine buckling loads of integrally stiffened structural panels in early design stages. Moreover, Latin Hypercube Sampling (LHS) methodology is used to reduce the number of required FE analyses to generate database that artificial neural network is based on. Mean errors and fit performance model results are compared to determine accuracy of the neural network results.