Search Results (931)

This paper proposes an intelligent power adaptation framework for Free-Space Optics (FSO) communication systems operating under different weather conditions exploiting a deep learning (DL) analysis of received eye diagram images. The system incorporates two Convolutional Neural Network (CNN) architectures, LeNet and Wide Residual Network (Wide ResNet) algorithms to perform regression tasks that predict received signal quality metrics such as the Quality Factor (Q-factor) and Bit Error Rate (BER) from the received eye diagram. These models are evaluated using Mean Squared Error (MSE) and the coefficient of determination (${\mathrm{R}}^2$ score) to assess prediction accuracy. Additionally, a custom CNN-based classifier is trained to determine whether the BER reading from the eye diagram exceeds a critical threshold of ${10}^{-4}$; this classifier achieves an overall accuracy of 99%, correctly detecting 194/195 “acceptable” and 4/5 “unacceptable” instances. Based on the predicted signal quality, the framework activates a dual-amplifier configuration comprising a pre-channel amplifier with a maximum gain of 25 dB and a post-channel amplifier with a maximum gain of 10 dB. The total gain of the amplifiers is adjusted to support the operation of the FSO system under all-weather conditions. The FSO system uses a 15 dBm laser source at 1550 nm. The DL models are tested on both internal and external datasets to validate their generalization capability. The results show that the regression models achieve strong predictive performance, and the classifier reliably detects degraded signal conditions, enabling the real-time gain control of the amplifiers to achieve the quality of transmission. The proposed solution supports robust FSO communication under challenging atmospheric conditions including dry snow, making it suitable for deployment in regions like Northern Europe, Canada, and Northern Japan. Full article

The Karp and Rabin (KR) fingerprint is a special hash-like function widely utilized for efficient string matching. Recently, Sharma et al.leveraged its linear and symmetric properties to facilitate private database queries. However, their approach mainly protects encrypted or secret-shared databases rather than public databases, where only the query privacy is required. In this paper, we focus explicitly on privacy-preserving queries over public read-only databases. We propose a novel fingerprint-based keyword query scheme using the distributed point function (DPF), which effectively hides users’ data access patterns across two symmetric mirror servers. Moreover, we provide a rigorous analysis of the false positive probability inherent in fingerprinting and discuss strategies for its minimization. Our scheme achieves efficiency close to plaintext methods, significantly reducing deployment complexity. Full article

Accurate micro gas flow measurement is critical for medical ventilator calibration, environmental gas monitoring, and semiconductor manufacturing. Laminar flowmeters are widely employed in micro gas flow measurement applications owing to their inherent advantages of high linearity, the absence of moving components, and a broad measurement range. Nevertheless, due to the low measurement accuracy under micro gas flow caused by nonlinear errors and a relatively complex structure, traditional laminar flow measurement devices exhibit limitations in micro gas flow measurement scenarios. This study proposes a novel micro gas flow measurement method based on a single capillary laminar flow element, which simplifies the structure and enhances applicability in the field of micro gas flow. Through structural optimization with precise control of the capillary length–diameter ratios and theoretical error correction based on computational analysis, nonlinear errors were effectively reduced while improving the measurement accuracy in the field of micro gas flow. The proposed methodology was systematically validated through computational fluid dynamics simulations (ANSYS Fluent 2021 R1) and experimental investigations using a dedicated test platform. The experimental results show that the relative error of the measurement system within the full measurement range is less than ±0.6% (1–10 cm³/min; cm³/min means cubic centimeter per minute), and its accuracy is superior to 1% of reading (1% Rd) or 1.5% of reading (1.5% Rd) of conventional laminar flowmeters. The fitting curve of the flow rate versus the pressure difference derived from the measurement results maintains an excellent linear correlation (R² > 0.99), thus confirming that this method has practical application value in the field of micro gas flow measurement. Full article

A virome survey of banana plantations and their surrounding plants was carried out at nation-wide level in Malawi using virion associated nucleic acids (VANA) high throughput sequencing (HTS) on pooled samples and appropriate alien controls. In total, 366 plants were sequenced, and 23 plant virus species were detected, three species on banana (275 plants) and 20 species in surrounding plants (91 plants). Two putative novel virus species; ginger tymo-like virus and pepper derived totivirus were detected and confirmed by RT-PCR on ginger and pepper. Nine known virus species and detected a host plant was identified for two of them. No viral exchange between banana and surrounding plants was observed. Results from the VANA protocol, applied to pooled banana samples, were compared with previous targeted PCR results obtained from individual banana samples. HTS test detected better BanMMV than IC-(RT)-PCR on individual samples (better inclusivity) but detected with much lower sensitivity BBTV and BSV species, often with less than 10 reads per sample. Detection of novel and known viruses and new host plants calls for strengthened sanitory and phytosanitory measures within and beyond banana production systems. Our research confirms that HTS sensitivity depends on sampling, pooling protocol and targeted virus species. Full article

The standard genetic code (SGC) plays a fundamental role in encoding biological information, but its evolutionary origins remain unresolved and widely debated. Thus, we used a methodology based on the evolutionary algorithm to investigate the emergence of stable coding systems. The simulation began with a population of varied primitive genetic codes that ambiguously encoded only a limited set of amino acids (labels). These codes underwent mutation, modeled by dynamic reassignment of labels to codons, gradual incorporation of new amino acids, and information exchange between themselves. Then, the best codes were selected using a specific fitness function F that measured the accuracy of reading genetic information and coding potential. The evolution converged towards stable and unambiguous coding systems with a higher coding capacity facilitating the production of more diversified proteins. A crucial factor in this process was the exchange of encoded information among evolving codes, which significantly accelerated the emergence of genetic systems capable of encoding 21 labels. The findings shed light on key factors that may have influenced the development of the current genetic code structure. Full article

In the past hundred years, three pontiffs have written apostolic letters to commemorate anniversaries relating to Dante: in 1921, Benedict XV marked the sixth centenary of the death of the great poet; in 1965, Paul VI judged it opportune to write on the occasion of the seventh centenary of his birth; and in 2021, Pope Francis added his voice to the numerous others wishing to honour the memory of the supreme Florentine poet on the seventh centenary of his death. Each letter is a product of its time: one hundred years ago, the Pope—still confined within the Vatican and refusing to recognise the Kingdom of Italy due to the Roman Question—addressed his text “to the beloved sons, professors and pupils of literary institutes and centres of higher learning within the Catholic world”; Paul VI, in full accord with the spirit of the Second Vatican Council and its vision of a Church seeking collaboration with the world, addressed his writing to Dante scholars more broadly, and within the same letter, together with other academic authorities, established the Chair of Dante Studies at the Catholic University of the Sacred Heart in Milan; Pope Francis today, in his outward-facing style of evangelisation, challenges everyone to (re)read Dante, whose teaching remains relevant seven hundred years after his death. Despite the differing political contexts and ecclesial agendas, Benedict XV, Paul VI, and Pope Francis are united on one point: Dante is a Christian poet—critical of the Church, certainly, but loyal to his faith and desirous of a religious institution that is more serious and less corrupt. This brief study presents the homage which the Church, today, seven centuries later, renders to this Poet—now widely recognised as a passionate witness of an arduous and active faith, in pursuit of justice and freedom. Full article

Background/Objectives: Evidence-based practice (EBP) has been widely adopted in clinical nursing practice, with nursing education efforts consistently emphasizing its importance in strengthening implementation efforts. Despite these efforts to promote translational research, the level of implementation of evidence-based practice (EBP) in clinical nursing practice remains unsatisfactory. This study aimed to identify specific organizational, individual, and research-related barriers to the utilization of research in clinical practice among nurses in Sabah, Malaysia, to determine factors associated with these perceived barriers and to assess nurses’ awareness and understanding of the implementation of evidence-based practice. Methods: A cross-sectional study was conducted in 2019 using the BARRIERS scale, a validated tool that measures perceived barriers to the utilization of research across four domains: organizational barriers, nurses’ research awareness and values, quality of research, and research communication. This study involved nurses from five tertiary hospitals in Sabah, Malaysia. Results: A total of 562 nurses participated in the study, with a mean age of 34.3 years (SD = 7.96) and mean duration of clinical practice of 10.0 years (SD = 7.58). While 66.5% of the nurses had heard of EBP, only 7.3% reported understanding it very well. The top three barriers to the utilization of research were ‘the nurse does not feel she/he has enough authority to change patient care procedures’ (35.9%), ‘the nurse does not have time to read research’ (27.8%), and ‘research reports/articles are not published fast enough’ (25.8%). Among the four domains, organizational barriers scored highest (mean = 2.7, SD = 0.72), followed by research communication (mean = 2.6, SD = 0.73). Conclusions: The study findings emphasize the challenges nurses encounter in integrating research into clinical practice and highlight the need for ongoing efforts to promote the utilization of evidence-based practice and research among nurses in Sabah, while addressing the identified gaps. Full article

Rouxiella badensis DSM 100043^T had been previously proven to produce a novel glucoselipid biosurfactant which has a very low critical micelle concentration (CMC) as well as very good stability against a wide range of pH, temperature, and salinity. In this study, we performed a function-based library screening from a R. badensis DSM 100043^T genome library to identify responsible genes for biosynthesis of this glucoselipid. The identified open reading frames (ORFs) were cloned into several constructs in Escherichia coli for gene permutation analysis and the individual products were analyzed using high-performance thin-layer chromatography (HPTLC). Products of interest from positive expression strains were purified and analyzed by liquid chromatography/electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) and nuclear magnetic resonance (NMR) for further structure elucidation. Function-based screening of 5400 clones led to the identification of an operon containing three ORFs encoding acetyltransferase GlcA (ORF1), acyltransferase GlcB (ORF2), and phosphatase/HAD GlcC (ORF3). E. coli pCAT2, with all three ORFs, resulted in the production of identical R. badensis DSM 100043^T glucosedilipid with Glu-C_10:0-C_12:1 as the main congener. ORF2-deletion strain E. coli pAFP1 primarily produced glucosemonolipids, with Glu-C_10:0,3OH and Glu-C_12:0 as the major congeners, predominantly esterified at the C-2 position of the glucose moiety. Furthermore, fed-batch bioreactor cultivation of E. coli pCAT2 using glucose as the carbon source yielded a maximum glucosedilipid titer of 2.34 g/L after 25 h of fermentation, which is 55-fold higher than that produced by batch cultivation of R. badensis DSM 100043^T in the previous study. Full article

Translation initiation site (TIS) prediction in mRNA sequences constitutes an essential component of transcriptome annotation, playing a crucial role in deciphering gene expression and regulation mechanisms. Numerous computational methods have been proposed and achieved acceptable prediction accuracy. In our previous work, we developed NeuroTIS, a novel method for TIS prediction based on a hybrid dependency network combined with a deep learning framework that explicitly models label dependencies both within coding sequences (CDSs) and between CDSs and TISs. However, this method has limitations in fully exploiting the primary structural information within mRNA sequences. First, it only captures label dependency within three neighboring codon labels. Second, it neglects the heterogeneity of negative TISs originating from different reading frames, which exhibit distinct coding features in their vicinity. In this paper, under the framework of NeuroTIS, we propose its enhanced version, NeuroTIS+, which allows for more sophisticated codon label dependency modeling via temporal convolution and homogenous feature building through an adaptive grouping strategy. Tests on transcriptome-wide human and mouse datasets demonstrate that the proposed method yields excellent prediction performance, significantly surpassing the existing state-of-the-art methods. Full article

The summarized content of one or more extensive text documents helps users extract only the most important key information, instead of reviewing and reading hundreds of pages of text. This study uses extractive and abstractive mechanisms to automatically extract and summarize information retrieved from various web documents on the same topic. The research aims to develop a methodology for designing and developing an information system for pre- and post-processing natural language obtained through web content search and web scraping, and for the automatic generation of a summary of the retrieved text. The research outlines two subtasks. As a first step, the system is designed to collect and process up-to-date information based on specific criteria from diverse web resources related to project funding, initiated by various organizations such as startups, sustainable companies, municipalities, government bodies, schools, the NGO sector, and others. As a second step, the collected extensive textual information about current projects and programs, which is typically intended for financial professionals, is to be summarized into a shorter version and transformed into a suitable format for a wide range of non-specialist users. The automated AI software tool, which will be developed using the proposed methodology, will be able to crawl and read project funding information from various web documents, select, process, and prepare a shortened version containing only the most important key information for its clients. Full article

The Fabaceae family, the third-largest among flowering plants, is nutritionally vital, providing rich sources of protein, dietary fiber, vitamins, and minerals. Leguminous plants, such as soybeans, peas, and chickpeas, typically contain two to three times more protein than cereals like wheat and rice, with low fat content (primarily unsaturated fats) and no cholesterol, making them essential for cardiovascular health and blood sugar management. Since the release of the soybean genome in 2010, genomic research in Fabaceae has advanced dramatically. High-quality reference genomes have been assembled for key species, including soybeans (Glycine max), common beans (Phaseolus vulgaris), chickpeas (Cicer arietinum), and model legumes like Medicago truncatula and Lotus japonicus, leveraging long-read sequencing, single-cell technologies, and improved assembly algorithms. These advancements have enabled telomere-to-telomere (T2T) assemblies, pan-genome constructions, and the identification of structural variants (SVs) and presence/absence variations (PAVs), enriching our understanding of genetic diversity and domestication history. Functional genomic tools, such as CRISPR-Cas9 gene editing, mutagenesis, and high-throughput omics (transcriptomics, metabolomics), have elucidated regulatory networks controlling critical traits like photoperiod sensitivity (e.g., E1 and Tof16 genes in soybeans), seed development (GmSWEET39 for oil/protein transport), nitrogen fixation efficiency, and stress resilience (e.g., Rpp3 for rust resistance). Genome-wide association studies (GWAS) and comparative genomics have further linked genetic variants to agronomic traits, such as pod size in peanuts (PSW1) and flowering time in common beans (COL2). This review synthesizes recent breakthroughs in legume genomics, highlighting the integration of multi-omic approaches to accelerate gene cloning and functional confirmation of the genes cloned. Full article

FOXP3+ regulatory T cells (Tregs) play a central role in the regulation of the immune system. Human Tregs preferentially express a FOXP3 isoform known as delta 2, which lacks exon 2. In addition to FOXP3, Tregs also express isoforms of other Treg-related molecules, such as CTLA-4 and IKZF-2. It is hypothesized that Tregs possess a unique isoform repertoire based on their unique gene and isoform expression profiles, which include FOXP3. Here, we identified a Treg-specific unique isoform repertoire confirmed by long-read high-throughput isoform sequencing called Iso-seq, which is uniquely capable of providing data on genome-wide isoform usage. Notably, while conventional T cells (Tconvs) do not exhibit this pattern, Tregs preferentially express the full-length FOXP3 isoform. Interestingly, the preferential expression of ICOS and PD-L1 upon T-cell receptor (TCR) stimulation was noted in activated Tregs but not in Tconvs or non-activated Tregs. Moreover, using a PD-L1 antibody blockade on Tregs did not diminish FOXP3 expression; however, it significantly reduced the suppressive function. Therefore, Tregs may have a unique isoform repertoire, which becomes pronounced upon polyclonal TCR stimulation. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder leading to movement impairment, cognitive decline, and psychiatric symptoms. Key manifestations of PD include bradykinesia (the slowness of movement), changes in voice or speech, and gait disturbances. The quantification of neurological disorders through voice analysis has emerged as a rapidly expanding research domain, offering the potential for non-invasive and large-scale monitoring. This review explores existing research on the application of machine learning (ML) in speech, voice, and language processing for the diagnosis of PD. It comprehensively analyzes current methodologies, highlights key findings and their associated limitations, and proposes strategies to address existing challenges. A systematic review was conducted following PRISMA guidelines. We searched four databases: PubMed, Web of Science, Scopus, and IEEE Xplore. The primary focus was on the diagnosis, detection, or identification of PD through voice, speech, and language characteristics. We included 34 studies that used ML techniques to detect or classify PD based on vocal features. The most used approaches involved free speech and reading-speech tasks. In addition to widely used feature extraction toolkits, several studies implemented custom-built feature sets. Although nearly all studies reported high classification performance, significant limitations were identified, including challenges in comparability and incomplete integration with clinical applications. Emerging trends in this field include the collection of real-world, everyday speech data to facilitate longitudinal tracking and capture participants’ natural behaviors. Another promising direction involves the incorporation of additional modalities alongside voice analysis, which may enhance both analytical performance and clinical applicability. Further research is required to determine optimal methodologies for leveraging speech and voice changes as early biomarkers of PD, thereby enhancing early detection and informing clinical intervention strategies. Full article

Mechanical defects and sensor failures can substantially undermine the reliability of low-cost sensors, especially in applications where measurement inaccuracies or malfunctions may lead to critical outcomes, including system control disruptions, emergency scenarios, or safety hazards. To overcome these challenges, this paper presents a novel Self-X architecture with sensor redundancy, which incorporates dynamic calibration based on multidimensional mapping. By extracting reliable sensor readings from imperfect or defective sensors, the system utilizes Self-X principles to dynamically adapt and optimize performance. The approach is initially validated on synthetic data from tunnel magnetoresistance (TMR) sensors to facilitate method analysis and comparison. Additionally, a physical measurement setup capable of controlled fault injection is described, highlighting practical validation scenarios and ensuring the realism of synthesized fault conditions. The study highlights a wide range of potential TMR sensor failures that compromise long-term system reliability and demonstrates how multidimensional mapping effectively mitigates both static and dynamic errors, including offset, amplitude imbalance, phase shift, mechanical misalignments, and other issues. Initially, four individual TMR sensors exhibited mean absolute error (MAE) of 4.709°, 5.632°, 2.956°, and 1.749°, respectively. To rigorously evaluate various dimensionality reduction (DR) methods, benchmark criteria were introduced, offering insights into the relative improvements in sensor array accuracy. On average, MAE was reduced by more than 80% across sensor combinations. A clear quantitative trend was observed: for instance, the MAE decreases from 4.7°–5.6° for single sensors to 0.111° when the factor analysis method was applied to four sensors. This demonstrates the concrete benefit of sensor redundancy and DR algorithms for creating robust, fault-tolerant measurement systems. Full article

Flow cytometry is a powerful and widely used tool for the analysis of various cell populations, but its capabilities are severely limited by the need to apply correction of fluorescent signals from near or similar fluorochromes when analyzing multicolor panels. Spectral flow cytometry extends the capabilities of classical cytometry by reading the full fluorescence spectrum of fluorophores and their subsequent spectral separation. This significantly increases the number of markers analyzed in a single panel and thus allows for more in-depth studies of cell populations. In the age of big data analysis, this represents a serious advantage of spectral cytometry and can significantly increase its use in scientific and clinical practice. This review describes the principle of spectral cytometry, advantages and limitations of the method, and summarizes the newest deep immunophenotyping panels developed and validated for spectral cytometry. Full article