Miracle-dz/UniFAR-FTUs huggingface dataset:
A review on PV cells and nanocomposite‐coated PV systems

Solar radiation can be converted into electrical energy and generate electric power that can be utilized in multiple ways. The technological improvements have provided enormous solutions to the mankind for utilizing the solar energy although photovoltaic's (PV) by consuming sunlight. Photovoltaic is popularly known by the process of converting light to electricity. The current estimated growth by producing global power around 368 GW in 2017 and projecting 3000 to 10 000 GW by 2030. Looking at all the available solar cells, it has been observed that the dye‐sensitized solar cell (DSSC) when compared to mono‐Si or poly‐Si has been effective in its performance and also reduces production cost to a great extent. The power conversion efficiency (PCE) of DSSC has reached to a better extent and been discussed in the paper. There are other mechanisms through which the efficiency can be improved like applying the antireflection coating. Reflection is a usual phenomenon that happens when light incident from one medium to another varies in refractive index. This reflection is one of the important reasons for the loss of power in the PV Cell. So to improve the PCE, the Mono‐Si or DSSC PV Cells can be applied with a thin film antireflection coating by the nanocomposite film consisting of single‐ or multi‐wall carbon nanotubes with TiO2 and other efficient nanoparticles. This paper discusses on different kinds of nanocomposite materials, and their functionalities has been clearly given. Remarkable improvements have been recorded in the last 1 year by applying the antireflection coating; the PCE has further been increased enormously when compared to the uncoated solar cell for both DSSC and Mono‐Si PV cells.
Role of Cultural Metaphors in Teaching: A Survey

The present research explores through metaphors the mental images of secondary school students which they use while expressing feelings in Second language . The researcher asks each participant student to fill in the sentence “. (any feeling) is something like …… (metaphor) because . ” and to express his ideas by using this sentence and focusing on a single metaphor. Data were analyzed by qualitative (content analysis) as well as quantitative techniques. In the light of the data collected the researcher finds that the participants produced 63 valid metaphors for expressing feeling. These metaphors are collected under 5 different conceptual categories as to their common features. Conceptual categories showed significant difference in terms of gender and class level of the students. This situation put forward that metaphors can be used as a research tool for understanding, revealing and explaining the mental images of the students regarding the feeling expressions
ICE: a visual analytic tool for interactive clustering ensembles generation

Clustering methods are the most used algorithms for unsupervised learning. However, there is no unique optimal approach for all datasets since different clustering algorithms produce different partitions. To overcome this issue of selecting an appropriate technique and its corresponding parameters, cluster ensemble strategies are used for improving accuracy and robustness by a weighted combination of two or more approaches. However, this process is often carried out almost in a blind manner, testing different combinations of methods and assessing if its performance is beneficial for the defined purpose. Thus, the procedure for selecting the best combination tests many clustering ensembles until the desired result is achieved. This paper proposes a novel analytic tool for clustering ensemble generation, based on quantitative metrics and interactive visual resources. Our approach allows the analysts to display different results from state-of-the-art clustering methods and analyze their performance based on specific metrics and visual inspection. Based on their requirements/experience, the analysts can interactively assign weights to the different methods to set their contributions and manage (create, store, compare, and merge), such as for ensembles. Our approach's effectiveness is shown through a set of experiments and case studies, attesting to its usefulness in practical applications.
Advances in integrated photonic circuits for packet-switched interconnection

Sustained increases in capacity and connectivity are needed to overcome congestion in a range of broadband communication network nodes. Packet routing and switching in the electronic domain are leading to unsustainable energy- and bandwidth-densities, motivating research into hybrid solutions: optical switching engines are introduced for massive-bandwidth data transport while the electronic domain is clocked at more modest GHz rates to manage routing. Commercially-deployed optical switching engines using MEMS technologies are unwieldy and too slow to reconfigure for future packet-based networking. Optoelectronic packet-compliant switch technologies have been demonstrated as laboratory prototypes, but they have so far mostly used discretely pigtailed components, which are impractical for control plane development and product assembly. Integrated photonics has long held the promise of reduced hardware complexity and may be the critical step towards packet-compliant optical switching engines. Recently a number of laboratories world-wide have prototyped optical switching circuits using monolithic integration technology with up to several hundreds of integrated optical components per chip. Our own work has focused on multi-input to multi-output switching matrices. Recently we have demonstrated 8×8×8λ space and wavelength selective switches using gated cyclic routers and 16×16 broadband switching chips using monolithic multi-stage networks. We now operate these advanced circuits with custom control planes implemented with FPGAs to explore real time packet routing in multi-wavelength, multi-port test-beds. We review our contributions in the context of state of the art photonic integrated circuit technology and packet optical switching hardware demonstrations.
Simultaneous hydrogenation and UV-photolysis experiments of NO in CO-rich interstellar ice analogues; linking HNCO, OCN−, NH2CHO, and NH2OH

The laboratory work presented here, simulates the chemistry on icy dust grains as typical for the 'CO freeze-out stage' in dark molecular clouds. It differs from previous studies in that solid-state hydrogenation and vacuum UV-photoprocessing are applied simultaneously to co-depositing molecules. In parallel, the reactions at play are described for fully characterized laboratory conditions. The focus is on the formation of molecules containing both carbon and nitrogen atoms, starting with NO in CO-, H2CO-, and CH3OH-rich ices at 13 K. The experiments yield three important conclusions. 1. Without UV-processing hydroxylamine (NH2OH) is formed, as reported previously. 2. With UV-processing (energetic) NH2 is formed through photodissociation of NH2OH. This radical is key in the formation of species with an N-C bond. 3. The formation of three N-C bearing species, HNCO, OCN- and NH2CHO is observed. The experiments put a clear chemical link between these species; OCN- is found to be a direct derivative of HNCO and the latter is shown to have the same precursor as formamide (NH2CHO). Moreover, the addition of VUV competing channels decreases the amount of NO molecules converted into NH2OH by at least one order of magnitude. Consequently, this decrease in NH2OH formation yield directly influences the amount of NO molecules that can be converted into HNCO, OCN- and NH2CHO.
cotH Genes Are Necessary for Normal Spore Formation and Virulence in Mucor lusitanicus

Current treatment options for mucormycosis are inadequate, resulting in high mortality rates, especially among immunosuppressed patients. The development of novel therapies for mucormycosis has been hampered by lack of understanding of the pathogenetic mechanisms. ABSTRACT Mucormycosis is an invasive fungal infection caused by certain members of the fungal order of Mucorales. The species most frequently identified as the etiological agents of mucormycosis belong to the genera Rhizopus, Lichtheimia, and Mucor. The frequency of systemic mucormycosis has been increasing, mainly because of increasing numbers of susceptible patients. Furthermore, Mucorales display intrinsic resistance to the majority of routinely used antifungal agents (e.g., echinocandins and short-tailed azoles), which limits the number of possible therapeutic options. All the above-mentioned issues urge the improvement of molecular identification methods and the discovery of new antifungal targets and strategies. Spore coat proteins (CotH) constitute a kinase family present in many pathogenic bacteria and fungi and participate in the spore formation in these organisms. Moreover, some of them can act as virulence factors being receptors of the human GRP78 protein during Rhizopus delemar-induced mucormycosis. We identified 17 cotH-like genes in the Mucor lusitanicus genome database. Successful disruption of five cotH genes in Mucor was performed using the CRISPR-Cas9 system. The CotH3 and CotH4 proteins play a role in adaptation to different temperatures as well as in developing the cell wall structure. We also show CotH4 protein is involved in spore wall formation by affecting the total chitin content and, thus, the composition of the spore wall. The role of CotH3 and CotH4 proteins in virulence was confirmed in two invertebrate models and a diabetic ketoacidosis (DKA) mouse model. IMPORTANCE Current treatment options for mucormycosis are inadequate, resulting in high mortality rates, especially among immunosuppressed patients. The development of novel therapies for mucormycosis has been hampered by lack of understanding of the pathogenetic mechanisms. The importance of the cell surface CotH proteins in the pathogenesis of Rhizopus-mediated mucormycosis has been recently described. However, the contribution of this family of proteins to the virulence of other mucoralean fungi and their functionality in vital processes remain undefined. Through the use of the CRISPR-Case9 gene disruption system, we demonstrate the importance of several of the CotH proteins to the virulence of Mucor lusitanicus by using three infection models. We also report on the importance of one of these proteins, CotH4, to spore wall formation by affecting chitin content. Therefore, our studies extend the importance of CotH proteins to Mucor and identify the mechanism by which one of the CotH proteins contributes to the development of a normal fungal cell wall, thereby indicating that this family of proteins can be targeted for future development of novel therapeutic strategies of mucormycosis.
On the mixing properties of operations in a finite field

Abstract In the context of the approach given in Shannon's research [Bell Syst. Tech. J. 28: 656–715, 1949], of interest are the mixing properties of operations in a finite field, in particular, the action of each operation on cosets with respect to the action of the second operation. The good mixing properties give a possibility to construct on this base good cryptographic algorithms.
Transactional Services for Concurrent Mobile Agents over Edge/Cloud Computing-Assisted Social Internet of Things

The Web of Things (WoT) is a concept that aims to create a network of intelligent devices capable of remote monitoring, service provisioning, and control. Virtual and Physical Internet of Things (IoT) gateways facilitate communication, processing, and storage among social nodes that form the social Web of Things (SWoT). Peripheral IoT services commonly use device data. However, due to the limited bandwidth and processing power of edge devices in the IoT, they must dynamically alter the quality of service provided to their connected clients to meet each user's needs while also meeting the service quality requirements of other devices that may access the same data. Consequently, deciding which transactions get access to which Internet of Things data is a scheduling problem. Edge-cloud computing requires transaction management because several Internet of Things transactions may access shared data simultaneously. However, cloud transaction management methods cannot be employed in edge-cloud computing settings. Transaction management models must be consistent and consider ACIDity of transactions, especially consistency. This study compares three implementation strategies, Edge Host Strategy (EHS), Cloud Host Strategy (CHS), and Hybrid BHS (BHS), which execute all IoT transactions on the Edge host, the cloud, and both hosts, respectively. These transactions affect the Edge hosts as well. An IoTT framework is provided, viewing an Internet of Things transaction as a collection of fundamental and additional subtransactions that loosen atomicity. Execution strategy controls essential and additional subtransactions. The integration of edge and cloud computing demonstrates that the execution approach significantly affects system performance. EHS and CHS can waste wireless bandwidth, while BHS outperforms CHS and EHS in many scenarios. These solutions enable edge transactions to complete without restarting due to outdated IoT data or other edge or cloud transactions. The properties of these approaches have been detailed, showing that they often outperform concurrent protocols and can improve edge-cloud computing.
The Determinants of Implementation Behavior Questionnaire in multiprofessional rehabilitation context (DIBQ-mp): A mixed methods tailoring study with Finnish cross-cultural adaptation

Background: The Determinants of Implementation Behavior Questionnaire (DIBQ) identi�es factors that are facilitators of or barriers to professionals´ change of behavior after evidence-based training. The original English DIBQ consists of 93 items covering 18 domains. The DIBQ is built upon the Theoretical Domains Framework and the Behavioral Change Wheel. The purpose of the study is to tailor the DIBQ to the multiprofessional rehabilitation context as well as cross-culturally adapt it to the Finnish context. Methods: Cross-cultural translation followed by a two round, Delphi method involving experts in rehabilitation with diversity of professions (physicians, physiotherapists, occupational therapists, psychologists, nursing scientists, social scientists and health care policymakers) was conducted, 25 experts participated in Round 1, and 21 in Round 2. Participants evaluated the importance of each DIBQ item in changing professionals´ implementation behavior. Consensus to include items was de�ned as a mean score of ≥ 4 on a Likert scale (1 = Strongly Disagree, 2 = Disagree, 3 = Neither Agree nor Disagree, 4 = Agree, 5 = Strongly Agree) by ≥ 75% of Delphi participants. Items rated at agreement of ≤ 74% were either excluded or reviewed depending on qualitative judgements. The relevance of each item was indexed using content validity index (CVI). A second-round survey followed the same process. Comparison with tailored versions of Danish and Swedish (DIBQ-t) was also performed before reaching the consensus of the results. Results: The �nal validated multiprofessional DIBQ (DIBQ-mp) consists of 23 questions. Twenty-nine percent (n = 27) of the Round 1 questions did not reach acceptable agreement. Second-round survey was developed based on the results of the �rst round. In all, 4% (n=2) of the Round 2 questions did not reach acceptable agreement. Agreement was achieved on all items after Round 2 in comparison with DIBQ-t, and the Delphi process was concluded. The �nal DIBQ-mp has evidence of excellent content validity with 0.93 average item-level CVI. Conclusions: The study resulted in the Determinants of Implementation Behavior Questionnaire tailored to the multiprofessional rehabilitation context as well as a Finnish cross-culturally
Assortative Mating on Ancestry-Variant Traits in Admixed Latin American Populations

Background Assortative mating is a universal feature of human societies, and individuals from ethnically diverse populations are known to mate assortatively based on similarities in genetic ancestry. However, little is currently known regarding the exact phenotypic cues, or their underlying genetic architecture, which inform ancestry-based assortative mating. Results We developed a novel approach, using genome-wide analysis of ancestry-specific haplotypes, to evaluate ancestry-based assortative mating on traits whose expression varies among the three continental population groups – African, European, and Native American – that admixed to form modern Latin American populations. Application of this method to genome sequences sampled from Colombia, Mexico, Peru, and Puerto Rico revealed widespread ancestry-based assortative mating. We discovered a number of anthropometric traits (body mass, height, facial development and waist-hip ratio) and neurological attributes (educational attainment and schizophrenia) that serve as phenotypic cues for ancestry-based assortative mating. Major histocompatibility complex (MHC) loci show population-specific patterns of both assortative and disassortative mating in Latin America. Ancestry-based assortative mating in the populations analyzed here appears to be driven primarily by African ancestry. Conclusions This study serves as an example of how population genomic analyses can yield novel insights into human behavior.
LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models

The carbon footprint associated with large language models (LLMs) is a significant concern, encompassing emissions from their training, inference, experimentation, and storage processes, including operational and embodied carbon emissions. An essential aspect is accurately estimating the carbon impact of emerging LLMs even before their training, which heavily relies on GPU usage. Existing studies have reported the carbon footprint of LLM training, but only one tool, mlco2, can predict the carbon footprint of new neural networks prior to physical training. However, mlco2 has several serious limitations. It cannot extend its estimation to dense or mixture-of-experts (MoE) LLMs, disregards critical architectural parameters, focuses solely on GPUs, and cannot model embodied carbon footprints. Addressing these gaps, we introduce \textit{\carb}, an end-to-end carbon footprint projection model designed for both dense and MoE LLMs. Compared to mlco2, \carb~significantly enhances the accuracy of carbon footprint estimations for various LLMs. The source code is released at \url{https://github.com/SotaroKaneda/MLCarbon}.
Dual-path convolutional neural network using micro-FTIR imaging to predict breast cancer subtypes and biomarkers levels: estrogen receptor, progesterone receptor, HER2 and Ki67

Breast cancer molecular subtypes classification plays an import role to sort patients with divergent prognosis. The biomarkers used are Estrogen Receptor (ER), Progesterone Receptor (PR), HER2, and Ki67. Based on these biomarkers expression levels, subtypes are classified as Luminal A (LA), Luminal B (LB), HER2 subtype, and Triple-Negative Breast Cancer (TNBC). Immunohistochemistry is used to classify subtypes, although interlaboratory and interobserver variations can affect its accuracy, besides being a time-consuming technique. The Fourier transform infrared micro-spectroscopy may be coupled with deep learning for cancer evaluation, where there is still a lack of studies for subtypes and biomarker levels prediction. This study presents a novel 2D deep learning approach to achieve these predictions. Sixty micro-FTIR images of 320x320 pixels were collected from a human breast biopsies microarray. Data were clustered by K-means, preprocessed and 32x32 patches were generated using a fully automated approach. CaReNet-V2, a novel convolutional neural network, was developed to classify breast cancer (CA) vs adjacent tissue (AT) and molecular subtypes, and to predict biomarkers level. The clustering method enabled to remove non-tissue pixels. Test accuracies for CA vs AT and subtype were above 0.84. The model enabled the prediction of ER, PR, and HER2 levels, where borderline values showed lower performance (minimum accuracy of 0.54). Ki67 percentage regression demonstrated a mean error of 3.6%. Thus, CaReNet-V2 is a potential technique for breast cancer biopsies evaluation, standing out as a screening analysis technique and helping to prioritize patients.
Eviction strategies for semantic flow processing

: In order to cope with the ever-increasing data volume continuous processing of incoming data via Semantic Flow Processing systems have been proposed. These systems allow to answer queries on streams of RDF triples. To achieve this goal they match (triple) patterns against the incoming stream and generate/update variable bindings. Yet, given the continuous nature of the stream the number of bindings can explode and exceed memory; in particular when computing aggregates. To make the information processing practical Semantic Flow Processing systems, therefore, typically limit the considered data to a (moving) window. Whilst this technique is simple it may not be able to nd patterns spread further than the window or may still cause memory overruns when data is highly bursty. In this paper we propose to maintain bindings (and thus memory) not on recency (i.e., a window) but on the likelihood of contributing to a complete match. We propose to base the decision on the matching likelihood and not creation time (fo) or at random. Furthermore we propose to drop variable bindings instead of data as do load shedding approaches. Specically, we systematically investigate deterministic and the matching-likelihood based probabilistic eviction strategy for dropping variable bindings in terms of recall. We find that a matching likelihood based eviction can outperform fo and random eviction strategies on synthetic as well as real world data. Abstract. In order to cope with the ever-increasing data volume, distributed stream processing systems have been proposed. To ensure scalability most distributed systems partition the data and distribute the workload among multiple machines. This approach does, however, raise the question how the data and the workload should be partitioned and distributed. A uniform scheduling strategy—a uniform distribution of computation load among available machines—typically used by stream processing systems, disregards network-load as one of the major bot-tlenecks for throughput resulting in an immense load in terms of inter-machine communication. In this paper we propose a graph-partitioning based approach for workload scheduling within stream processing systems. We implemented a distributed triple-stream processing engine on top of the Storm realtime computation framework and evaluate its communication behavior using two real-world datasets. We show that the application of graph partitioning algorithms can decrease inter-machine communication substantially (by 40% to 99%) whilst maintaining an even workload distribution, even using very limited data statistics. We also ﬁnd that processing RDF data as single triples at a time rather than graph fragments (containing multiple triples), may decrease throughput indicating the usefulness of semantics.