Miracle-dz/UniFAR-FTUs huggingface dataset:
Climate change effects on precipitation organization : a summertime case study in the southeast United States

The delivery of precipitation through large and small scale precipitation features plays a key role in the hydrological cycle. Therefore, it is important to understand how the organization of precipitation will change as the earth warms. The organization of precipitation can be characterized into either widespread, mesoscale precipitation features (MPF) or short-lived, isolated precipitation features (IPF). The Weather Research and Forecasting (WRF) model was used to simulate precipitation features at a 3 km resolution during the 17-22 June 2010 period in the Southeast US under present and future climate conditions. In this methodology, the model is first run in present climate mode and then rerun with an adjusted initial state that adds projected temperature anomalies for the 2090s based on Representative Concentration Pathways (RCPs) 4.5 and 8.5 from the Coupled Model Intercomparison Project Phase 5 (CMIP5) in the IPCC Fifth Assessment Report (AR5). In the future climate simulations, small changes in precipitation occurred under RCP4.5 warming, but many significant changes were noted under RCP8.5 warming. Domain-averaged precipitation increased in the future climate simulations, with the largest changes over the ocean relative to the continent. In the future climates, IPF grew larger in length and eventually coalesced into MPF, reducing the total number of IPF and increasing the number of MPF. IPF and MPF also extended deeper into the troposphere and produced more precipitation overall.
Peripheral immune responses to filoviruses in a reservoir versus spillover hosts reveal transcriptional correlates of disease

Several filoviruses, including Marburg virus (MARV), cause severe disease in humans and nonhuman primates (NHPs). However, the Egyptian rousette bat (ERB, Rousettus aegyptiacus), the only known MARV reservoir, shows no overt illness upon natural or experimental infection, which, like other bat hosts of zoonoses, is due to well-adapted, likely species-specific immune features. Despite advances in understanding reservoir immune responses to filoviruses, ERB peripheral blood responses to MARV and how they compare to those of diseased filovirus-infected spillover hosts remain ill-defined. We thus conducted a longitudinal analysis of ERB blood gene responses during acute MARV infection. These data were then contrasted with a compilation of published primate blood response studies to elucidate gene correlates of filovirus protection versus disease. Our work expands on previous findings in MARV-infected ERBs by supporting both host resistance and disease tolerance mechanisms, offers insight into the peripheral immunocellular repertoire during infection, and provides the most direct known cross-examination between reservoir and spillover hosts of the most prevalently-regulated response genes, pathways and activities associated with differences in filovirus pathogenesis and pathogenicity.
Comparisons, and applications of SHELOCK, DETECTR, CRISPR typing PCR

Existing viruses such as bird flu, Zika virus, Ebola virus, SARS virus and the newly popular virus SARS-CoV-2 in recent years have caused great threats and death to humans. Rapid nucleic acid tests play an important role in clinical diagnosis. Bacteria are the genetic material of the adaptive immune defense system used to resist foreign invasion, and the RNAguided adaptive immune system is composed of CRISPR and Cas proteins, which are present in many kinds of bacteria and most bacteria. The CRISPR-Cas system can edit almost any DNA or RNA precisely. Kellner and colleagues developed a diagnostic tool that utilizes CRISPR-Cas enzymes and nucleic acid pre-amplification to detect specific RNA or DNA sequences. The SHERLOCK platform is a highly precise tool for recognizing specific nucleic acid sequences. Developed by Chen and colleagues, this innovative approach employs endonuclease-targeted CRISPR trans-reporter (DETECTR) technology that combines Cas12a ssDNase activation with isothermal amplification, enabling homosensitivity in DNA detection. Through the utilization of this advanced method, highly accurate detection of nucleic acid targets can be achieved. Qiao Wang and co-authors have introduced a novel approach for identifying and characterizing target DNA using the Cas9 nuclease, which they termed ctPCR (Cas9-sgRNA- or CRISPR typing PCR). This method enables accurate detection and typing of specific DNA sequences by leveraging the precise targeting capabilities of Cas9 and sgRNA. The technology allows easy, rapid, specific and sensitive detection and typing of target DNA. In this article, we review these three techniques and describe their rationale, comparisons, and applications.
Semantic Maps for Knowledge Graphs: A Semantic-Based Summarization Approach

Knowledge Graphs (KGs) have emerged as a powerful tool for representing semantic structured information and enabling the development of intelligent systems. This paper focuses on the generation of semantic maps as summarization method for KGs. We propose a strategy that utilizes centroid-based clustering algorithms, namely Affinity Propagation and Partitioning Around Medoids (PAM), to capture the semantic distance between nodes in the KG and generate meaningful clusters. Our experiments demonstrate divergent results between the two clustering algorithms, with Affinity Propagation showing qualitative coherence and meaningfulness, while PAM performs well in terms of internal validation metrics. We leverage the computed centroids to infer a main term of the semantic map, which contributes to the visually appealing and informative representation of the KG. The combination of semantic distance capture, clustering algorithms, and centroid-based inference facilitates a comprehensive understanding of the KG. Our findings highlight the importance of considering both qualitative and quantitative evaluation measures in assessing clustering results. The effectiveness of semantic maps is showcased in visualizing KGs and advancing the field of knowledge graph visualization. The integration of centroid-based clustering algorithms, qualitative evaluation, and inference methods offers improved clarity and interpretability for complex KG analysis.
Analysis of Adaptive Beamforming Techniques for Photonics-based Coherent MIMO Radars

In this paper, adaptive beamforming techniques are applied to photonics-based coherent multiple-input multiple-output (MIMO) radars with widely separated antennas for obtaining high target resolution and sidelobe suppression capabilities in the MIMO ambiguity function (MIMO-AF). Among the available MIMO beamforming techniques, data-independentand data-dependentapproaches do exist. In this paper, one estimation technique per family is analyzed (i.e., data-independent, data-dependent without array steering vector errors and data-dependent with array steering vector errors). More specifically, the least-squares estimator, the Capon and the robust Capon beamformers are investigated. Key performance indicators (KPIs) are identified for describing the resolution and sidelobe suppression capabilities of the proposed techniques. Simulations are performed to evaluate the KPIs from the MIMO-AF under non-ideal conditions, such as amplitude mismatches among the radar channels and antenna positions affected by random errors. The advantages and disadvantages of the proposed adaptive beamforming techniques are illustrated.
State-of-the-art review on energy management systems, challenges and top trends of renewable energy based microgrids

Due to the increasing demand for electrical energy worldwide and environmental concerns, modern power systems are looking for a fundamental change. These changes include reducing dependence on the primary electricity grid and using renewable energy sources on a large scale. The emergence of microgrids in electrical energy systems will improve the level of these systems due to technical, economic, and environmental benefits. In this research work, the authors have conducted extensive studies on control methods, types of power sources, and the size of microgrids and analyzed them in tabular form. In addition, the review of communication technologies and standards in microgrids, as well as the review of microgrid energy management systems to optimize the efficiency of microgrids, is one of the main goals of the authors in this article. Also, in this article, the top 10 trends of microgrids in 2023 have been examined to increase the flexibility of network infrastructure, which helps readers to improve their strategic decisions by providing an overview of emerging technologies in the energy industry.
MRIMA: An MRAM-Based In-Memory Accelerator

In this paper, we propose MRIMA, as a novel magnetic RAM (MRAM)-based in-memory accelerator for nonvolatile, flexible, and efficient in-memory computing. MRIMA transforms current spin transfer torque magnetic random access memory (STT-MRAM) arrays to massively parallel computational units capable of working as both nonvolatile memory and in-memory logic. Instead of integrating complex logic units in cost-sensitive memory, MRIMA exploits hardware-friendly bit-line computing methods to implement complete Boolean logic functions between operands within a memory array in a single clock cycle, overcoming the multicycle logic issue in contemporary processing-in-memory (PIM) platforms. We present practical case studies to demonstrate MRIMA’s acceleration for binary-weight and low bit-width convolutional neural networks (CNNs) as well as data encryption. Our device-to-architecture co-simulation results on CNN acceleration demonstrate that MRIMA can obtain <inline-formula> <tex-math notation="LaTeX">$1.7 {\times }$ </tex-math></inline-formula> better energy-efficiency and <inline-formula> <tex-math notation="LaTeX">$11.2{\times }$ </tex-math></inline-formula> speed-up compared to ASICs, and <inline-formula> <tex-math notation="LaTeX">$1.8 {\times }$ </tex-math></inline-formula> better energy-efficiency and <inline-formula> <tex-math notation="LaTeX">$2.4 {\times }$ </tex-math></inline-formula> speed-up over the best DRAM-based PIM solutions. As an advanced encryption standard (AES) in-memory encryption engine, MRIMA shows ~77% and 21% lower energy consumption compared to CMOS-ASIC and recent domain-wall-based design, respectively.
Synergistic antibacterial mechanism of silver-copper bimetallic nanoparticles

The excessive use of antibiotics in clinical settings has resulted in the rapid expansion, evolution, and development of bacterial and microorganism resistance. It causes a significant challenge to the medical community. Therefore, it is important to develop new antibacterial materials that could replace traditional antibiotics. With the advancements in nanotechnology, it has become evident that metallic and metal oxide nanoparticles (MeO NPs) exhibit stronger antibacterial properties than their bulk and micron-sized counterparts. The antibacterial properties of silver nanoparticles (Ag NPs) and copper nanoparticles (Cu NPs) have been extensively studied, including the release of metal ions, oxidative stress responses, damages to cell integrity, and immunostimulatory effects. However, it is crucial to consider the potential cytotoxicity and genotoxicity of Ag NPs and Cu NPs. Numerous experimental studies have demonstrated that bimetallic nanoparticles (BNPs) composed of Ag NPs and Cu NPs exhibit strong antibacterial effects while maintaining low cytotoxicity. Bimetallic nanoparticles offer an effective means to mitigate the genotoxicity associated with individual nanoparticles while considerably enhancing their antibacterial efficacy. In this paper, we presented on various synthesis methods for Ag-Cu NPs, emphasizing their synergistic effects, processes of reactive oxygen species (ROS) generation, photocatalytic properties, antibacterial mechanisms, and the factors influencing their performance. These materials have the potential to enhance efficacy, reduce toxicity, and find broader applications in combating antibiotic resistance while promoting public health.
The Gender Aspects of Agroforestry Practices in Maytemeko Watershed, Northwestern Ethiopia

This study was conducted to assess the gender aspect of agroforestry practices (AFPs) in terms of participation and benefit in northwest Ethiopia. An explanatory sequential research design of mixed method was employed to obtain and analyze the data required for the study. Household questionnaire survey which was collected from 138 randomly selected households, ethnographic observation and interview were used to collect data. The research result showed that though most women are implementing AFPs compared to their male counterparts, the number of trees and shrubs on farm plots are lower for women. At household level, women are taking highest responsibility to manage AFPs. It was also found that 40.3% (166.5 tons/year) of energy for cooking and heating is obtained from trees and shrubs grown in AFPs followed by natural forest wood sources (32.2% or 134 tons/year). The majority of the respondents (56%) confirmed that women and girls are responsible for fuel wood collection from the natural forest areas. In contrast, for those households who obtained the fuel wood from AFPs, men are most responsible to prepare the fuel wood. An average time which was needed to collect a bundle of fuel wood from natural forest area was four times more than the time needed to collecting the same amount of fuel wood from AFPs. This indicated that AFP is playing a crucial role in reducing the burden of women and girls by reducing the time and labor spent for fuel collection. Generally, implementing AFP is not only used to save the natural forest cover, but it also has an implication in gender balance for fuel wood collection. Thus, stressing the multiple benefits of AFP, agricultural extension workers should encourage farmers to plant trees on their farm lands by recognzing the gender aspects as well.
A Model Predictive Control Scheme for a Single-Phase Integrated Battery Charger With Active Power Decoupling for EV Application

Integrated battery chargers (IBCs) are one of the best alternatives among the numerous types of onboard electric vehicle (EV) chargers available, considering the size and weight constraints of EVs. In this article, a model predictive control (MPC) scheme for a single-phase IBC is proposed for the first time. The IBC repurposes the motor drive, which consists of a three-phase induction motor and a three-phase inverter, for charging purposes. The proposed MPC scheme is implemented with the objective of minimization of switching frequency using double prediction algorithm, along with maintaining a sinusoidal grid current profile at unity power factor (UPF) during charging mode. Furthermore, the proposed scheme addresses the issue of ripple power at the dc link with twice the grid frequency, which is inherently present in a single-phase IBCs. This ripple component is effectively suppressed by using active power decoupling by reutilizing one of the stator windings of the induction motor along with the inverter legs without any additional hardware requirement. Additionally, the power reversal capability of IBC is also demonstrated. The control scheme was implemented on hardware by using Texas instruments$^{\prime }$ DSP TMS320F28335.
Inertia-Based Indices to Determine the Number of Clusters in K-Means: An Experimental Evaluation

This paper gives an experimentally supported review and comparison of several indices based on the conventional K-means inertia criterion for determining the number of clusters, <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>, in datasets, using the popular Silhouette width index as a benchmark. Our experiments involve a novel version of the Elbow index, defined using values of <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> two or three steps apart. We also discuss alternative ways of computing the inertia and summarizing its values. Even though there are no overall winners in our experiments, some of our results are very conclusive and can be used as a guide for indices determining the number of clusters in K-means.
Automatic analysis of X (Twitter) data for supporting depression diagnosis

Depression is an increasingly common problem that often goes undiagnosed. The aim of this paper was to determine whether an analysis of tweets can serve as a proxy for assessing depression levels in the society. The work considered keyword-based sentiment analysis, which was enhanced to exclude informational tweets about depression or about recovery. The results demonstrated the words used in the posts most often and the emotional polarity of the tweets. A schedule of user activity was mapped out and trends related to daily activity of users were analyzed. It was observed that the identified X (Twitter) activity related to depression corresponded well with reports on persons with depression and statistics related to suicidal deaths. Therefore, it could be construed that people with undiagnosed depression express their feelings in social media more often, looking, in this way, for help with their emotional problems.
Current balancing control for an interleaved boost power converter

ABSTRACT The interleaved boost power converter has the advantages of ripple cancellation and better efficiency. The major problem of the interleaved boost power converter is the current balancing among different phases of the boost power converters. In this paper, a current balancing control method for equalizing the currents of two-phase interleaved boost power converter is proposed. The output current can effectively detect the mismatch between the boost power converters for the interleaved boost power converter. The output current is used to perform both the current balance and the current-mode control. The salient feature is that only one current sensor is used in the proposed current balancing control method. A hardware prototype is developed, and the experimental results verify the performance of the proposed current balancing control method is as expected.
Stability Analysis of Nonlinear Dynamic Systems Using the Lyapunov Method Approach

A new approach to the stability analysis of homogeneous nonlinear systems is proposed, based on the concept of candidate Lyapunovfunctions, where the focus is not on the positive definiteness of the candidate Lyapunovfunctions, but on the negative definiteness of their derivatives. After having ensured the negative definiteness of the derivative function, on the basis of the sign assignment of the primitive function, the stability of the equilibrium is analyzed, where the necessary and sufficient conditions are declared at the same time. The selection of the tendency of the Lyapunov candidate function is primarily performed in the form of a linear combination of some simple functions. The unknown coefficients in the structure of the candidate function are computed based on the negative definiteness of the derivative function. Then, using these coefficients in Lyapunov function, sign of primitive function in state space is argued. Thus, the stability/instability of the equilibrium point can be deduced from the triple sign settings of the candidate function. Furthermore, in the process of negative definiteness of the derivative function, the coefficients are obtained using two independent methods. The proposed theoretical results are supported and their effectiveness is demonstrated by numerical simulations.
Enhanced alkaline stability and performance of alkali‐doped quaternized poly(vinyl alcohol) membranes for passive direct ethanol fuel cell

Direct ethanol fuel cells (DEFCs) emerge as the new research energy field since fast production of electricity, high efficiency conversion, and simple fabrication process. The production cost, conductivity properties, and ethanol permeability of membrane were the main problem that limited the DEFC performance and commercialization. In this study, a low cost, good ionic conductivity and low ethanol permeability of an anion exchange membrane based on incorporation KOH‐doped quaternized poly(vinyl alcohol) (QPVA) membrane (designed as QPVA/KOH) is synthesized and cross‐linked with glutaraldehyde solution. The membrane is expected to cut the production cost and enhance the performance. In this work, an optimum of alkali‐doped concentration has influence the membrane performance. The membrane has reveal high chemical stability even doped with 8‐M KOH solution in 100°C. The morphology of membranes remained unbreakable and achieved high range of ionic conductivity (~10−2 S cm−1). The membranes present maximum ionic conductivity 1.29 × 10−2 S cm−1 at 30°C and 3.07 × 10−2 S cm−1 at 70°C. The ethanol permeability of membrane is lower compared with the commercial membranes. Power density of alkaline DEFCs with platinum‐based catalyst by using cross‐linked QPVA/KOH membrane is 5.88 mW cm−2, which is higher than commercial membranes at 30°C temperature. At 70°C, power density has increased up to 11.28 mW cm−2 and significantly increased up to 22.82 mW cm−2 via the nonplatinum‐based catalyst. Moreover, according to the durability test, the performance of passive alkaline DEFC by using cross‐linked QPVA/KOH membrane has maintained at 36.2% level. With such efficiency, the stack current density has been able to stay above 120 mA cm−2 for over 1000 hours, at 70°C.
Development of a Convolutional Neural Network Architecture for Classifying Foliar Diseases in Plants

This study aims to build and test a CNN architecture on a database extracted from the PlantVillage platform. The database contains 54,305 images, divided into 39 classes, with 38 classes related to foliar diseases and one class for background images. Due to the small amount of images in each class, Data Augmentation techniques were used to generate additional synthetic images during the network training process. The results achieved were an accuracy of 99.55%, precision of 94.25%, AUC of 99.81% and F1-Score of 90.86%.
Advancements and Challenges in Handwritten Text Recognition: A Comprehensive Survey

Handwritten Text Recognition (HTR) is essential for digitizing historical documents in different kinds of archives. In this study, we introduce a hybrid form archive written in French: the Belfort civil registers of births. The digitization of these historical documents is challenging due to their unique characteristics such as writing style variations, overlapped characters and words, and marginal annotations. The objective of this survey paper is to summarize research on handwritten text documents and provide research directions toward effectively transcribing this French dataset. To achieve this goal, we presented a brief survey of several modern and historical HTR offline systems of different international languages, and the top state-of-the-art contributions reported of the French language specifically. The survey classifies the HTR systems based on techniques employed, datasets used, publication years, and the level of recognition. Furthermore, an analysis of the systems’ accuracies is presented, highlighting the best-performing approach. We have also showcased the performance of some HTR commercial systems. In addition, this paper presents a summarization of the HTR datasets that publicly available, especially those identified as benchmark datasets in the International Conference on Document Analysis and Recognition (ICDAR) and the International Conference on Frontiers in Handwriting Recognition (ICFHR) competitions. This paper, therefore, presents updated state-of-the-art research in HTR and highlights new directions in the research field.