Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward all‐inorganic perovskite solar cells: Materials, performance, and stability

Organic‐inorganic perovskites have proven to be outstanding solar cell light‐absorbing materials due to their spectacular properties such as high tolerance to defects, bandgap tunability, high carrier mobility, long diffusion length, and high absorption coefficient. However, the associated stability issues in harsh conditions hinder their commercial prospects. In addition to the instability of the absorbing perovskite layer, the usage of organic transport layers and its vulnerable interface with the absorbing layer also contributes to the overall instability of the device. Taking a cue from inorganic solar cell counterparts, researchers have now turned their attention to the development of inorganic perovskites to improve the stability of perovskite solar cells (PSCs). Work in this direction has led to power conversion efficiency (PCE) beyond 19% with improved stability. Here, we review the progress made toward the development of all‐inorganic perovskite materials and their integration into PSCs as stable absorbers. We have discussed in extensive detail the work done so far for different inorganic perovskite materials, their associated device performance, and stability. Besides this, we have also discussed the integration of inorganic transport layers in PSCs replacing the organic counterparts used traditionally and their effect on the stability of the device. The stability of all‐inorganic PSCs under different harsh environmental conditions has also been discussed in detail. Finally, we have presented a summary and outlook on the realization of all‐inorganic efficient and stable PSCs toward their commercial adaptability.
ACTIVE BEHAVIOURAL BIOMETRIC AUTHENTICATION USING CAT SWARM OPTIMIZATION VARIANTS WITH DEEP LEARNING

Security issues have only been compounded by the advent of distributed networks and global internet availability. Combating these security issues depends on being able to correctly authenticate a valid user. This paper presents variants of our CRNM framework which is an efficient cat swarm optimized deep learning model to accurately authenticate a valid user through signature behavioral patterns and biometric information of the user. The behavioral patterns considered here are keystroke and mouse dynamics. Face recognition has been included as a means to decrease the false rejection rate of the system. The major contributions of this work include comparison of various cat optimization variants for active authentication, performance analysis of our model with different state of the art systems. The fitness functions tested include Rosenbrock, Rastrigin and Griewank while CSO variants studied are ADCSO, AICSO and PCSO. Results of our experiments indicate that the proposed authentication system is faster and more efficient than existing frameworks. We achieve accuracy 98.29%, FAR 0.01 and FRR 1.02.
Linking Bacterial Communities to Optical-Derived Properties of Porewater DOM in Sediments in the Coastal East China Sea

Bacterial communities and porewater dissolved organic matter (DOM) pool are intimately interactive in sedimentary environments. Estuarine coastal regions are an interactive area between terrestrial and marine influences in terms of DOM origins and freshness. Yet, we know little about the relationships between the bacterial communities and DOM in those regions. In this study, porewater DOM samples were collected from 42 sites in the coastal East China Sea. The porewater DOM optical properties were determined by fluorescence and absorption spectra, while the corresponding bacterial community compositions of those sediments were examined by 16S rRNA gene high-throughput sequencing. The results showed that bacterial species richness was positively correlated with multiple terrestrial indicators based on the optical properties of DOM, which implied that heterogeneous DOM from terrestrial origins might harbor a wider spectrum of bacterial taxa in marine sediments. The analysis of the co-occurrence network of the bacterial communities showed that the edges and density for samples with low DOM freshness were 3.4 times and 3 times those for the samples with high DOM freshness, respectively. This suggested that the connection among the bacterial taxa under the lower DOM freshness condition were enhanced and that reduced freshness of DOM may encourage more complimentary utilization of resources. The findings provide a new insight into such interactive processes of heterogeneous organic matter utilization meditated by microorganisms in coastal sediments.
Effect of charging protocol and carbon electrode selection in Na–O_2 batteries

Nonaqueous sodium- and lithium-oxygen batteries are of interest because of their high theoretical specific energies relative to state-of-the-art Li-ion batteries. However, several challenges limit rechargeability, including instability of the carbon electrode and electrolyte with reactive oxygen species formed during cycling. This work investigates strategies to improve the cycling efficiency of the Na–O_2 system and minimize irreversible degradation of electrolyte and electrode materials. We show that charging cells with a constant current/constant voltage (CCCV) protocol is a promising technique made possible by the slight solubility of sodium superoxide in nonaqueous electrolytes. In addition, the type of carbon electrode has a significant impact on cell performance and efficacy of the cycling protocol. Graphitic carbon electrodes coupled with CCCV charging demonstrate higher reversibility, more efficient oxygen evolution, and less outgassing than conventional cells using a porous carbon paper electrode and only a constant current charge. Graphical abstract
Integrasi Rencana Strategis dan Pengukuran Kinerja Menggunakan Kerangka Teori Strategic Performance Management dengan Pendekatan Soft Systems Methodology

Tulisan ini mengkaji proses integrasi rencana strategis dan pengukuran kinerja di Badan Litbang dan Diklat Kementerian Agama menggunakan kerangka teori Strategic Performance Management dengan Soft Systems Methodology. Berdasarkan perbandingan antara model konseptual dengan situasi permasalahan di real world, untuk problem solving interest dihasilkan beberapa saran perubahan atau rekomendasi terkait struktur, proses dan sikap. Sedangkan untuk research interest disimpulkan bahwa kerangka teori Strategic Performance Management dapat digunakan untuk melakukan integrasi rencana strategis dan pengukuran kinerja di sektor publik, karena mampu menjawab kompleksitas permasalahan yang ada, mendukung proses integrasi strategi jangka panjang dan tujuan operasional, memastikan pengukuran kinerja mendukung pencapaian strategis dan pembelajaran organisasi, serta meningkatkan integrasi dan analisis data serta memfasilitasi kerjasama dan kolaborasi.
Data analysis results from the GOLD experiments

Analyses of uplink and downlink data from recent free-space optical communications experiments carried out between Table Mountain Facility and the Japanese ETS-VI satellite are presented. Fluctuations in signal power collected by the satellite's laser communication experiment due to atmospheric scintillation and its amelioration using multiple uplink beams are analyzed and compared to experimental data. Downlink data was analyzed to determine the cause of a larger than expected variation in signal strength. In spite of the difficulty in deconvolving atmospheric effects from pointing errors and spacecraft vibration, experimental data clearly indicate significant improvement in signal reception on the uplink with multiple beams, and the need for stable pointing to establish high data rate optical communications.
Artificial Neural Network Analysis for Cost Estimation of Building Projects in India

In Construction Management, it is difficult to predict the cost estimate during the preliminary stage of the project because of limited information and unknown factors. Artificial Neural Networks can help in the prediction of estimate because of their simplicity and adaptability to non-linear problems. Due to their self-organizing nature they can be used to solve the problems even with low level programming. This makes them useful in interpreting and generalizing inadequate input information. ANN’s are crud e derivatives of the biological neural network with single layered or multi-layered neuron in the form of input layer, hidden layer and output layer. The neural network first has to undergo training from historical data in order to make predictions or show results. The size of the data set, number of hidden neurons and the neural network architecture determines the success of the results. Selecting the right data set becomes imperative in this case. For the purpose of cost estimation, the cost drivers were taken as inputs and their estimated costs were taken as the target value. The cost drivers were selected carefully through literature review and survey to provide more accurate results for the estimate. The main drivers identified were: type of building, location, seismic zone, project complexity, ground condition, soil condition, plot area, plinth area, built-up area, number of stories, number of basement, principal structural material, type of foundation, level of design complexity, modular design, market conditions, construction conditions, risk factor, impact of risk, estimated duration of work, specification, quality of work and detailed cost estimate of project. A Pareto analysis performed on the significant drivers showed that the Duration of Work, Complexity of the Building, Plinth Area and Built-up Area, Height and Specifications were the most important cost drivers in a construction project. A problem was formulated based on these drivers with numerical and categorical data. The data set was trained with a neural network using the MATLAB software using feed forward backpropagation. Training was carried out till the greatest correlation and least Mean Squared Error was obtained after multiple iterations. This trained data was used to predict the cost for a new project. The output of the testing was 87% accurate despite the small data set used.
Reliability and radiation tolerance of robots for nuclear applications

The reliability of a robot for nuclear applications will be affected by environmental factors such as dust, water, vibrations, heat, and, in particular, ionising radiation. The present report describes the work carried out in a project addressing the reliability and radiation tolerance of such robots. A widely representative range of components and materials have been radiation tested and the test results has been collated in a database along with data provided by the participants from earlier work and data acquired from other sources. A radiation effects guide has been written for the use by designers of electronic equipment for robots. A generic reliability model has been set up together with generic failure strategies, forming the basis for specific reliability modelling carried out in other projects. Modelling tools have been examined and developed for the prediction of the performance of electronic circuits subjected to radiation. Reports have been produced dealing with the prediction and detection of upcoming failures in electronic systems. Operational experience from the use of robots in radiation work in various contexts has been compiled in a report, and another report has been written on cost/benefit considerations about the use of robots. Also the possible impact of robots on the safety of the surrounding plant has been considered and reported. The present report is an edited version of the final report for the CEC/TELEMAN project No. FI2T-CT90-0011, ENTOREL. ISBN 87-550-2219-7 ISSN 0106-2840 Grafisk Service, Ris
Community Detection in Multiplex Networks Based on Evolutionary Multitask Optimization and Evolutionary Clustering Ensemble

Community detection in multiplex networks is an emerging research topic in the field of network science. Existing methods usually ignore the similarities among component layers of a multiplex network when detecting its community structures, which decreases the detection efficiency. In this article, we decompose the community detection in multiplex networks into two problems and propose a novel algorithm that can detect both the specific community partition for each component layer (layer-level community structure) and the composite community structure shared by all layers. First, by specifying the modularity optimization on a network layer as an optimization task, we model the layer-level community detection as a multitask optimization (MTO) problem and employ an evolutionary MTO algorithm to solve it. In this way, the topology correlations among different layers can be utilized to facilitate the community detection. Second, we propose an evolutionary clustering ensemble method to find the composite community structure based on the layer-level community partitions and the multiplex network. The proposed method is tested on both synthetic and real-world benchmark networks and compared with classical and state-of-the-art algorithms. Experimental results show that the proposed algorithm has superior community detection performances on multiplex networks.
Role of Interleukin-6 in the Antigen-Specific Mucosal Immunoglobulin A Responses Induced by CpG Oligodeoxynucleotide-Loaded Cationic Liposomes

An advantage of mucosal vaccines over conventional parenteral vaccines is that they can induce protective immune responses not only at mucosal surfaces but also in systemic compartments. Despite this advantage, few live attenuated or inactivated mucosal vaccines have been developed and applied clinically. We recently showed that the intranasal immunization of ovalbumin (OVA) with class B synthetic oligodeoxynucleotides (ODNs) containing immunostimulatory CpG motif (CpG ODN)-loaded cationic liposomes synergistically exerted both antigen-specific mucosal immunoglobulin A (IgA) and systemic immunoglobulin G (IgG) responses in mice. However, the mechanism underlying the mucosal adjuvant activity of CpG ODN-loaded liposomes remains unknown. In the present study, we showed that the intranasal administration of CpG ODN-loaded cationic liposomes elicited interleukin (IL)-6 release in nasal tissues. Additionally, pre-treatment with an anti-IL-6 receptor (IL-6R) antibody attenuated antigen-specific nasal IgA production but not serum IgG responses. Furthermore, the intranasal administration of OVA and CpG ODN-loaded cationic liposomes increased the number of IgA+/CD138+ plasma cells and IgA+/B220+ B cells in the nasal passages. This increase was markedly suppressed by pre-treatment with anti-IL-6R blocking antibody. In conclusion, IL-6 released by CpG ODN-loaded cationic liposomes at the site of administration may play a role in the induction of antigen-specific IgA responses by promoting differentiation into IgA+ plasma cells for IgA secretion from B cells.
ReActLab: A Custom Framework for Sensorimotor Experiments “in-the-wild”

Over the last few years online platforms for running psychology experiments beyond simple questionnaires and surveys have become increasingly popular. This trend has especially increased after many laboratory facilities had to temporarily avoid in-person data collection following COVID-19-related lockdown regulations. Yet, while offering a valid alternative to in-person experiments in many cases, platforms for online experiments are still not a viable solution for a large part of human-based behavioral research. Two situations in particular pose challenges: First, when the research question requires design features or participant interaction which exceed the customization capability provided by the online platform; and second, when variation among hardware characteristics between participants results in an inadmissible confounding factor. To mitigate the effects of these limitations, we developed ReActLab (Remote Action Laboratory), a framework for programming remote, browser-based experiments using freely available and open-source JavaScript libraries. Since the experiment is run entirely within the browser, our framework allows for portability to any operating system and many devices. In our case, we tested our approach by running experiments using only a specific model of Android tablet. Using ReActLab with this standardized hardware allowed us to optimize our experimental design for our research questions, as well as collect data outside of laboratory facilities without introducing setup variation among participants. In this paper, we describe our framework and show examples of two different experiments carried out with it: one consisting of a visuomotor adaptation task, the other of a visual localization task. Through comparison with results obtained from similar tasks in in-person laboratory settings, we discuss the advantages and limitations for developing browser-based experiments using our framework.
Comparative Transcriptome Analysis Provides Insights Into the Mechanism by Which 2,4-Dichlorophenoxyacetic Acid Improves Thermotolerance in Lentinula edodes

As the widest cultivated edible mushroom worldwide, Lentinula edodes suffers serious yield and quality losses from heat stress during growth and development, and in our previous study, exogenous 2,4-Dichlorophenoxyacetic acid (2,4-D) was found to improve the thermotolerance of L. edodes strain YS3357, but the molecular mechanism remains unclear. Here, we explored the potential protective mechanism of exogenous 2,4-D against heat stress by transcriptome analysis. 2,4-D possible improve the thermotolerance of L. edodes through regulating antioxidant genes, transcription factors, energy-provision system, membrane fluidity, and cell wall remodeling. Furthermore, 2,4-D was also found to regulate the saturation levels of fatty acids and ATP content in L. edodes mycelium under heat stress. This study proposed a regulatory network of 2,4-D in regulating L. edodes response to heat stress, providing a theoretical basis for improving L. edodes thermotolerance, and facilitating the understanding of the molecular mechanism of exogenous hormones in alleviating abiotic stress damage to macrofungi.
HOTRUNZ: an open-access 1 km resolution monthly 1910–2019 time series of interpolated temperature and rainfall grids with associated uncertainty for New Zealand

Abstract. Long time series of temperature and rainfall grids are fundamental to understanding how these environmental variables affect environmental or ecological patterns and processes such as plant distributions, plant and animal phenology, wildfires, and hydrology. Ideally such temperature and rainfall grids are openly available and associated with uncertainties so that data-quality issues are transparent to users. We present a History of Open Temperature and Rainfall with Uncertainty in New Zealand (HOTRUNZ) that uses climatological aided natural neighbour interpolation to provide monthly 1 km resolution grids of total rainfall, mean air temperature, mean daily maximum air temperature, and mean daily minimum air temperature across New Zealand from 1910 to 2019. HOTRUNZ matches the best available temporal extent and spatial resolution of any open-access temperature and rainfall grids that include New Zealand and is unique in providing associated spatial uncertainty in the variables' units. The HOTRUNZ grids capture the dynamic spatial and temporal nature of monthly temperature and rainfall and the uncertainties associated with the interpolation. We also demonstrate how to quantify and visualise temporal trends across New Zealand that recognise the temporal and spatial variation in uncertainties in the HOTRUNZ data. The HOTRUNZ data are openly available at https://doi.org/10.7931/zmvz-xf30 (Etherington et al., 2021).
Cardiac Rehabilitation and Mortality Risk Reduction in Peripheral Artery Disease at 6-Month Outcome

The management of patients with peripheral artery disease (PAD) is integrative and multidisciplinary, in which cardiac rehabilitation (CR) plays a prognostic role in terms of functional status, quality of life, and long-term impact on morbidity and mortality. We conducted a prospective cohort study on 97 patients with PAD admitted to a single tertiary referral center. Based on a prognostic index developed to stratify long-term mortality risk in PAD patients, we divided the cohort into two groups: low and low-intermediate risk group (45 cases) and high-intermediate and high risk group (52 cases). We analyzed demographics, clinical parameters, and paraclinical parameters in the two groups, as well as factors associated with cardiological reassessment prior to the established deadline of 6 months. Obesity (p = 0.048), renal dysfunction (p < 0.001), dyslipidemia (p < 0.001), tobacco use (p = 0.048), and diabetes mellitus (p < 0.001) are comorbidities with long-term prognostic value. Low-density lipoprotein cholesterol (p = 0.002), triglycerides (p = 0.032), fasting glucose (p = 0.011), peak oxygen uptake (p = 0.005), pain-free walking distance (p = 0.011), maximum walking time (p < 0.001), and maximum walking distance (p = 0.002) influence the outcome of PAD patients by being factors associated with clinical improvement at the 6-month follow-up. PAD patients benefit from enrollment in CR programs, improvement of clinical signs, lipid and carbohydrate profile, and weight loss and maintenance of blood pressure profile within normal limits, as well as increased exercise capacity being therapeutic targets.
Lietuvos politinių partijų ir nepriklausomų politinių pilietinių judėjimų sekėjų „Facebooke“ polinkis į politinę poliarizaciją ir informacinio šulinio formavimąsi

Tiek užsienyje, tiek Lietuvoje vis dažniau dalinamasi politinėmis naujienomis socialinėse medijose. Tačiau ligi šiol nebuvo atlikta mokslinių tyrimų, padedančių įvertinti šios platformos svarbą politinio turinio kūrimui ir vartojimui. Socialinių tinklų kontekstas bei vyraujantis selektyvus turinys ypač veikia labiausiai pažeidžiamas, aiškių politinių pažiūrų neturinčias žmonių grupes. Dėl to didėja politinė poliarizacija, formuojasi informaciniai šuliniai. Straipsnyje, remiantis tyrimais, apibrėžiami politinių naujienų vartojimo pokyčiai, tiesiogiai nulemti socialinių medijų platformų aplinkos. Įvardijamos priežastys bei sąlygos, kurioms esant tikėtinas informacinių šulinių formavimasis socialinėse medijose. Šio proceso apraiškoms identifikuoti pasitelktas aštuonių Lietuvos politinių partijų ir politinių pilietinių judėjimų „Facebooko“ paskyrų auditorijos informacinės elgsenos empirinis tyrimas. Jis atliktas 2021 m. gegužės 1 d. – birželio 30 d. , kai buvo akivaizdūs skirtingų politinių pažiūrų auditorijų susidūrimai ir iniciatyvos. Tyrimo rezultatai atskleidė, kad auditorijos linkusios į politinę poliarizaciją, joms būdingi informacinio šulinio bruožai. Politiniai pilietiniai judėjimai, skirtingai nei politinės partijos, labiau linkę į politinę poliarizaciją bei pasižymi ryškesniais informacinio šulinio bruožais. Ši auditorija labiau linkusi laikytis išankstinių nuostatų, o tai neišvengiamai veda link kraštutinių nuomonių.