Miracle-dz/UniFAR-FTUs huggingface dataset:
Recognizing the Types of Beans Using Artificial Intelligence

Many studies have previously addressed the recognition of plant leaf types. The process of identifying these leaf types involves a crucial feature extraction stage. Image feature extraction is pivotal for distinguishing the types of objects, thus demanding optimal feature analysis for accurate leaf type determination. Prior research, which employed the CNN method, faced challenges in effectively distinguishing between long bean and green bean leaves when identifying bean leaves. Therefore, there is a need to conduct optimal feature analysis to correctly classify bean leaves. In our research, we analyzed 69 features and explored their correlations within various image types, including RGB, L*a*b, HSV, grayscale, and binary images. The primary objective of this study is to pinpoint the features most strongly correlated with the recognition of bean leaf types, specifically green bean, soybeans, long beans, and peanuts. Our dataset, sourced from farmers' fields and verified by experienced senior farmers, consists of 456 images. The most highly correlated feature within the bean leaf image category is STD b in the L*a*b image. Furthermore, the most effective method for leaf type recognition is Neural Network Backpropagation, achieving an accuracy rate of 82.28% when applied to HSV images.
A Resource Aware Memory Requirement Calculation Model for Memory Constrained Context-Aware Systems

Smart spaces are physical environments equipped with sensors, actuators, and other computing devices to gather data and provide intelligent services to users. These spaces are made possible by ubiquitous computing, particularly context-aware computing. Although these systems are mainly implemented on mobile and other resource-constrained wearable devices, different techniques have been adopted for their implementation. Rule-based reasoning is a relatively easy-to-implement approach that can solve real-world problems. Rule-based systems rely on a set of assertions that constitute the working memory and a set of rules that govern what should be done with the set of assertions. Despite its relative simplicity, the working memory size is a critical factor in developing these systems, particularly for resource-constrained devices. In this paper, we propose techniques for efficiently calculating the working memory size. Our results show that all three techniques, DWM, APS, and SAPS, performed well in different ways. However, APS and SAPS consumed from 25% to 100% less memory than existing techniques.
Comparative performance of femur and humerus epiphysis for sex diagnosis

La determination du sexe des restes humains endommages et incomplets demeure toujours un des problemes les plus compliques de l'analyse anthropologique des squelettes en raison de la faible fiabilite des methodologies existantes. Dans ce travail, nous verifierons la fiabilite de ces dernieres en les appliquant cependant a un materiel qui se rencontre assez frequemment dans un etat de conservation acceptable - les epiphyses proches du femur et de l'humerus ainsi que celles eloignees de l'humerus. Nous utiliserons un echantillon de 200 squelettes adultes provenant de la Collection des Squelettes Adultes Identifies du Musee Anthropologique de l'Universite de Coimbra, et nous realiserons cinq mesures anthropometriques: les diametres verticaux et transversaux de la tete de l'humerus et du femur et la largeur biepicondylienne de l'humerus. Les points de scission pour chacune de ces dimensions ont ete mesures; et les taux de classement correct obtenus ont ete analyses. En plus, les resultats trouves ont ete compares avec ceux obtenus par l'application des points de scission rapportes par d'autres chercheurs (Stewart, 1979 in Krogman and Iscan, 1986; Pearson et Bell, 1917-1919 in Krogman and Iscan, 1986), sur la base d'autres series de reference.
STATE-OF-THE-ART REPORT ABOUT DURABILITY OF POST-TENSIONED BRIDGE SUBSTRUCTURES

Durability design requires an understanding of the factors influencing durability and the measures necessary to improve durability of concrete structures. The objectives of this report are to: (1) survey the condition of bridge substructures in Texas; (2) provide background material on bridge substructure durability; and (3) review durability research and field experience for post-tensioned bridges. A condition survey of existing bridges in Texas was used to identify trends in exposure conditions and common durability problems. The forms of attack on durability for bridge substructures in Texas are reviewed. Basic theory for corrosion of steel in concrete is presented, including the effect of cracking. Corrosion protection measures for post-tensioned concrete are presented. Literature on sulfate attack, freeze-thaw damage, and alkali-aggregate reaction is summarized. Literature on the field performance of prestressed concrete bridges and relevant experimental studies of corrosion in prestressed concrete is included. Crack prediction methods for prestressed concrete members are presented.
Metals and nutrient seasonal variations in the Namphong River NE Thailand and land use practices.

The Namphong river catchment in NE Thailand, part of the Mekong River catchment, has agro-industrial activities, dry land farming, rice cultivation and river cage aquaculture. Most utilize irrigation while larger industrial activities extract groundwater. The tropical wet and dry seasons cause variation in river flow. Potential effects of water quality may arise annually from constituents introduced by agricultural and other practices. Nutrients are added to soil to improve agricultural production and directly to the river in cage aquaculture from fish feed. The seasonal variations were considered from heavy metals (cadmium, copper, iron, manganese, lead and zinc) and nutrients in the Namphong River during one annual cycle and their relationships to water quality. Water samples were collected from 10 sites along the Namphong River in January 2012 (dry season mean flow 8 sec/m) and 9 sites in September 2012 (wet season mean flow 28 sec/m). Labile metals estimated by the Diffusive Gradient in Thin Films (DGT) technique showed no toxicity to aquatic biota. Most metals showed concentration and location consistency in both seasons. Iron and manganese in pulp mill discharge and vegetable cultivation downstream showed high concentrations in September indicating a groundwater or saturated soil seepage source from reducing conditions. Nutrients decreased by half from January to September; total N was higher at upstream sites in January while nitrate and orthophosphate were not significant and pH and electrical conductivity were consistent for both seasons. Hardness and alkalinity increased in September and were highest in pulp mill discharge indicating a groundwater source. Total and suspended solids were affected by wet season runoff. Variations in metals and nutrients in January and September were attributed to differences in Namphong River flow and from seasonality and agricultural activities. Large agroindustries, particularly pulp, contributed the highest concentrations of nutrients and metals to the river.
Process Parameter Optimization for CO2 Laser Polishing of Fused Silica Using the Taguchi Method

Fused silica was polished to a high quality by a CO2 laser beam with a rapid scanning rate. The rapid scanning rate produced a line laser heat source, resulting in a “polishing line” during the polishing process. The Taguchi method was used to evaluate the comprehensive influence of polishing process parameters on the polishing qualities. Four factors, namely the length of laser reciprocating scanning (A), laser beam scanning speed (B), feed speed (C), and defocusing amount (D), were investigated in this study. The optimal process parameter combination (A1B1C1D1) was obtained. The surface roughness of fused silica was reduced from Ra = 0.157 μm to 0.005 μm. Through analysis of variance (ANOVA), it was found that laser beam scanning speed (B) had a significant influence on the polishing quality. The interaction of the two factors plays a decisive role in the determination of the best process parameters, and the influence of other multi-factor interaction can be ignored; the interaction between A × B is the largest, with a contribution of 42.69%.
Endotyping Eosinophilic Inflammation in COPD with ELAVL1, ZfP36 and HNRNPD mRNA Genes

Background: Chronic obstructive pulmonary disease (COPD) is a common disease characterized by progressive airflow obstruction, influenced by genetic and environmental factors. Eosinophils have been implicated in COPD pathogenesis, prompting the categorization into eosinophilic and non-eosinophilic endotypes. This study explores the association between eosinophilic inflammation and mRNA expression of ELAVL1, ZfP36, and HNRNPD genes, which encode HuR, TTP and AUF-1 proteins, respectively. Additionally, it investigates the expression of IL-9 and IL-33 in COPD patients with distinct eosinophilic profiles. Understanding these molecular associations could offer insights into COPD heterogeneity and provide potential therapeutic targets. Methods: We investigated 50 COPD patients, of whom 21 had eosinophilic inflammation and 29 had non-eosinophilic inflammation. Epidemiological data, comorbidities, and pulmonary function tests were recorded. Peripheral blood mononuclear cells were isolated for mRNA analysis of ELAVL1, ZfP36, and HNRNPD genes and serum cytokines (IL-9, IL-33) were measured using ELISA kits. Results: The study comprised 50 participants, with 66% being male and a mean age of 68 years (SD: 8.9 years). Analysis of ELAVL1 gene expression revealed a 0.45-fold increase in non-eosinophilic and a 3.93-fold increase in eosinophilic inflammation (p = 0.11). For the ZfP36 gene, expression was 6.19-fold higher in non-eosinophilic and 119.4-fold higher in eosinophilic groups (p = 0.07). Similarly, HNRNPD gene expression was 0.23-fold higher in non-eosinophilic and 0.72-fold higher in eosinophilic inflammation (p = 0.06). Furthermore, serum levels of IL-9 showed no statistically significant difference between the eosinophilic and non-eosinophilic group (58.03 pg/mL vs. 52.55 pg/mL, p = 0.98). Additionally, there was no significant difference in IL-33 serum levels between COPD patients with eosinophilic inflammation and those with non-eosinophilic inflammation (39.61 pg/mL vs. 37.94 pg/mL, p = 0.72). Conclusions: The data suggest a notable trend, lacking statistical significance, towards higher mRNA expression for the ZfP36 and HNRNPD genes for COPD patients with eosinophilic inflammation compared to those with non-eosinophilic inflammation.
Employee participation in corporate volunteering as the moderator of links between relationships at work, work meaningfulness and affective commitment

PurposeThe aim of this study is to test for a moderating role of employee participation in volunteering in links between employees' relationships with peers and supervisors, work meaningfulness (WM) and affective commitment (AC). Design/methodology/approachThe study is based on a survey conducted on a sample of 711 employees, both those involved and those non-involved in corporate volunteering (CV). FindingsThe results suggest that employee participation in CV strengthens the effects that employees' perceptions of positive relationships with peers and perceived supervisor support (PSS) have on employees' AC. Contrary to expectations, although participation in CV strengthens employees' sense of WM, it does not affect its links with other phenomena analyzed in this study.Originality/valueCV is a fast-growing practice in corporate social responsibility. The reasons companies implement CV include the benefits they gain from it, such as positive effects on employee attitudes and behaviors. The paper contributes to the understanding of CV effects on employee attitudes and behaviors and builds a better business case for this CSR practice.
Detection of miR-155 Using Peptide Nucleic Acid at Physiological-like Conditions by Surface Plasmon Resonance and Bio-Field Effect Transistor

MicroRNAs are small ribonucleotides that act as key gene regulators. Their altered expression is often associated with the onset and progression of several human diseases, including cancer. Given their potential use as biomarkers, there is a need to find detection methods for microRNAs suitable for use in clinical setting. Field-effect-transistor-based biosensors (bioFETs) appear to be valid tools to detect microRNAs, since they may reliably quantitate the specific binding between the immobilized probe and free target in solution through an easily detectable electrical signal. We have investigated the detection of human microRNA 155 (miR-155) using an innovative capturing probe constituted by a synthetic peptide nucleic acid (PNA), which has the advantage to form a duplex even at ionic strengths approaching the physiological conditions. With the aim to develop an optimized BioFET setup, the interaction kinetics between miR-155 and the chosen PNA was preliminarily investigated by using surface plasmon resonance (SPR). By exploiting both these results and our custom-made bioFET system, we were able to attain a low-cost, real-time, label-free and highly specific detection of miR-155 in the nano-molar range.
A Machine Learning-Based Digital Twin for Anti-Counterfeiting Applications With Copy Detection Patterns

In this paper, we present a new approach to model a printing-imaging channel using a machine learning-based “digital twin” for copy detection patterns (CDP). The CDP are considered as modern anti-counterfeiting features in multiple applications. Our digital twin is formulated within the information-theoretic framework of TURBO initially developed for high energy physics simulations, using variational approximations of mutual information for both encoder and decoder in the bidirectional exchange of information. This model extends various architectural designs, including paired pix2pix and unpaired CycleGAN, for image-to-image translation. Applicable to any type of printing and imaging devices, the model needs only training data comprising digital templates sent to a printing device and data acquired by an imaging device. The data can be paired, unpaired, or hybrid, ensuring architectural flexibility and scalability for multiple practical setups. We explore the influence of various architectural factors, metrics, and discriminators on the overall system’s performance in generating and predicting printed CDP from their digital versions and vice versa. We also performed a comparison with several state-of-the-art methods for image-to-image translation applications. The simulation code and extended results are publicly available at https://gitlab.unige. ch/sip-group/digital-twin.
Serotype, antibiotic susceptibility and whole-genome characterization of Streptococcus pneumoniae in all age groups living in Southwest China during 2018–2022

Background Streptococcus pneumoniae is a common pathogen that colonizes the human upper respiratory tract, causing high morbidity and mortality worldwide. This study aimed to investigate the prevalence status of S. pneumoniae isolated from patients of all ages in Southwest China, including serotype, antibiotic susceptibility and other molecular characteristics, to provide a basis for clinical antibiotic usage and vaccine development. Methods This study was conducted from January 2018 to March 2022 at West China Hospital, West China Second University Hospital, First People’s Hospital of Longquanyi District (West China Longquan Hospital), Meishan Women and Children’s Hospital (Alliance Hospital of West China Second University Hospital) and Chengdu Jinjiang Hospital for Women and Children Health. Demographic and clinical characteristics of 263 pneumococcal disease (PD) all-age patients were collected and analyzed. The serotypes, sequence types (STs), and antibiotic resistance of the strains were determined by next-generation sequencing, sequence analysis and the microdilution broth method. Results The most common pneumococcal serotypes were 19F (17.87%), 19A (11.41%), 3 (8.75%), 23F (6.46%) and 6A (5.70%). Coverage rates for PCV10, PCV13, PCV15, PCV20 and PCV24 were 36.12, 61.98, 61.98, 63.12 and 64.26%, respectively. Prevalent STs were ST271 (12.55%), ST320 (11.79%), ST90 (4.18%), ST876 (4.18%) and ST11972 (3.42%). Penicillin-resistant S. pneumoniae (PRSP) accounted for 82.35 and 1.22% of meningitis and nonmeningitis PD cases, respectively. Resistance genes msrD (32.7%), mefA (32.7%), ermB (95.8%), tetM (97.3%) and catTC (7.6%) were found among 263 isolates. Most isolates showed high resistance to erythromycin (96.96%) and tetracycline (79.85%), with more than half being resistant to SXT (58.94%). A few isolates were resistant to AMX (9.89%), CTX (11.03%), MEN (9.13%), OFX (1.14%), LVX (1.14%) and MXF (0.38%). All isolates were susceptible to vancomycin and linezolid. Conclusion Our study provides reliable information, including the prevalence, molecular characterization and antimicrobial resistance of S. pneumoniae isolates causing pneumococcal diseases in Southwest China. The findings contribute to informed and clinical policy decisions for prevention and treatment.
Wide beds with conservation tillage

CONSERVATION tillage systems have tremendous potential for controlling erosion, but crop yields generally decline when the systems are used on poorly drained soils ( 5 ). Lower soil temperatures caused by high soil moisture reduce crop germination, emergence, and early growth ( 6 ). The problem is confined pretty much to the surface soil. Established plants will root deeper in response to interdependent soil warming and moisture losses later in the season. Crop waterlogging is another problem in areas with poor surface drainage. Such cold, wet soil problems occur throughout the major silt and clay soil cropping areas of the United States. Conservation tillage systems have enough potential to make even skeptics take a second look. Such systems can reduce labor, machinery investment, and field power requirements. They can improve timeliness of operations, reduce seedling diseases, and control runoff and erosion. But the systems will be successful only if all their components are mutually beneficial. Apparently, something is missing in current conservation tillage technology that limits the adaptation of systems to poorly drained soils. We believe that conservation tillage systems built around the use of permanent wide beds will accomplish what other systems have failed to accomplish and thereby answer the objections of skeptics …
Input/output‐to‐state stability for switched nonlinear systems with unstable subsystems

In this paper, a couple of sufficient conditions for input/output‐to‐state stability (IOSS) of switched nonlinear systems with non‐IOSS subsystems are derived by exploiting the multiple Lyapunov functions (MLFs) method. A state‐norm estimator–based small‐gain theorem is also established for switched interconnected nonlinear systems under some proper switching laws, where the small‐gain property of individual connected subsystems is not required in the whole state space instead only in some subregions of the state space. The state‐norm estimator for the switched system under study is explicitly designed via a constructive procedure by exploiting the MLFs method and the classical small‐gain technique. The presented results permit removal of a technical condition in existing literature, where all subsystems in switched systems are IOSS or some are IOSS. An illustrative example is also provided to illustrate the effectiveness of the theoretical results.
Multivariate Analysis of the Main Operational Variables Involved in Steel Producing on BOF Using Time Series Tools

There is significant interest in accurately modeling the operational variables of the steel-making process in LD converters. Despite this, the task is challenging due to the complex interactions between process variables, which are not entirely comprehended. Often, decisions in the industry are grounded in experience. This study aims to introduce a robust model that can effectively guide engineers and technicians by forecasting the future behavior of steelmaking variables in the BOF furnace. We employed multivariate time series analysis to reach this goal, utilizing tools like Vector Autoregression models, ElasticNet, K-Nearest-Neighbors, Multiple Linear Regression, and Long Short-Term Memory Neural networks. These models were tested on data from three distinct steel production campaigns. A successful model was identified, predicting 35 out of the 42 chosen variables, demonstrating the potential to correlate a majority of the selected parameters.
Discovering optimal features for neuron-type identification from extracellular recordings

Advancements in multichannel recordings of single-unit activity (SUA) in vivo present an opportunity to discover novel features of spatially-varying extracellularly-recorded action potentials (EAPs) that are useful for identifying neuron-types. Traditional approaches to classifying neuron-types often rely on computing EAP waveform features based on conventions of single-channel recordings and thus inherit their limitations. However, spatiotemporal EAP waveforms are the product of signals from underlying current sources being mixed within the extracellular space. We introduce a machine learning approach to demix the underlying sources of spatiotemporal EAP waveforms. Using biophysically realistic computational models, we simulate EAP waveforms and characterize them by the relative prevalence of these sources, which we use as features for identifying the neuron-types corresponding to recorded single units. These EAP sources have distinct spatial and multi-resolution temporal patterns that are robust to various sampling biases. EAP sources also are shared across many neuron-types, are predictive of gross morphological features, and expose underlying morphological domains. We then organize known neuron-types into a hierarchy of latent morpho-electrophysiological types based on differences in the source prevalences, which provides a multi-level classification scheme. We validate the robustness, accuracy, and interpretations of our demixing approach by analyzing simulated EAPs from morphologically detailed models with classification and clustering methods. This simulation-based approach provides a machine learning strategy for neuron-type identification.
Three Decades of Low Power: From Watts to Wisdom

Low power technologies have been critical to the advancement of many fields of research including computer architecture, analog, digital, and mixed signal circuit design, semiconductor technologies, wireless technologies, networks-on-chip, distributed computing, Internet-of-Things, and machine learning on mobile devices. Many of the research advancements in each of these low power fields seem independent of the other fields, but are actually inter-related through the flow of ideas between fields of research. Many ideas used in research seem novel within one field, but in actuality they are often borrowed from other fields showing the interdisciplinary nature of research with ideas transferring from applied physics and chemistry to areas in semiconductor technologies and computer engineering. In this paper, we first examine a few significant research ideas in the area of low power technologies and then explore the interdisciplinary nature, growth, and impact of low power technologies through a network science based analysis of research publications over the past 30 years.