Miracle-dz/UniFAR-FTUs huggingface dataset:
Advanced Detection Method for Dengue NS1 Protein Using Ultrasensitive ELISA with Thio-NAD Cycling

Dengue fever, a mosquito-borne disease in tropical and subtropical climates caused by the dengue virus (DENV), has become a major social and economic burden in recent years. However, current primary detection methods are inadequate for early diagnosis of DENV because they are either time-consuming, expensive, or require training. Non-structural protein 1 (NS1) is secreted during DENV infection and is thus considered a suitable biomarker for the development of an early detection method. In the present study, we developed a detection method for the NS1 protein based on a previously reported thio-NAD cycling ELISA (i.e., ultrasensitive ELISA) and successfully achieved a LOD of 1.152 pg/mL. The clinical diagnosis potential of the detection system was also evaluated by using 85 patient specimens, inclusive of 60 DENV-positive and 25 DENV-negative specimens confirmed by the NAAT method. The results revealed 98.3% (59/60) sensitivity and 100% (25/25) specificity, which was in almost perfect agreement with the NAAT data with a kappa coefficient of 0.972. The present study demonstrates the diagnostic potential of using an ultrasensitive ELISA as a low-cost, easy-to-use method for the detection of DENV compared with NAAT and could be of great benefit in low-income countries.
Diversity of Fungal Colonization in Respiratory Tract of Naïve Lung Cancer and The Emergence of Voriconazole Resistant Aspergillus

Fungal spores in the air can be inhaled and enter the human respiratory tract. The entry of fungi into the respiratory tract can cause colonization or infection depending on the host immune response. Fungal colonization is the first step into debilitating fungal disease in humans, especially in immunocompromised groups. The increased rate of drug-resistant fungi has been reported in human disease and the environment. This study aims to examine the diversity of fungal colonization in humans and the rate of fungal resistance to voriconazole. This cross-sectional study was done in patients with naïve lung cancer who had not been previously treated with any cancer therapy nor given antifungal agent. Induced sputum from 70 subjects was collected and inoculated in the Sabouraud Dextrose Agar medium. Macroscopic and microscopic examinations were performed to identify fungal species. Voriconazole susceptibility tests were done using the disc diffusion method. This study found Candida albicans, Aspergillus niger, Aspergillus fumigatus, and Penicillium sp. among the most common lower respiratory tract colonies. This study also found the colonization of up to 5 species in a single subject. A high rate of voriconazole-resistant Aspergillus sp. was found (42.4%) among 59 isolates tested. Given that these subjects had never taken antifungal agents previously, the high rate of voriconazole resistance might be attributed to the environment, such as community and agriculture. Mitigation of antifungal use in the agricultural sector, fungal diversity in the environment, and clinical study of fungal colonization/ infection in other high-risk groups are needed.
Bitcoin Price Prediction Based on CNN-Bi-LSTM-Attention Model

Due to many factors, Bitcoin has experienced huge price fluctuations since its emergence, and it has received extensive attention. Forecasting the price of bitcoin is of great significance for investors and for the country's future development. This paper collects the data of bitcoin price and indicator that may affect the price, and then use random forest algorithm for feature selection to remove all nonessential indicators. Then, CNN-Bi-LSTM-Attention model is built to train the data and predict the price of bitcoin. Finally, this model is compared with other models. It can be found that this model has higher prediction accuracy and better prediction effect than traditional models such as LSTM and CNN-LSTM.
Ag-Doped MoSe2/ZnO Heterojunctions: A Highly Responsive Gas-Sensitive Material for Selective Detection of NO Based on DFT Study

In this work, the adsorption and sensing behavior of Ag-doped MoSe2/ZnO heterojunctions for H2, CH4, CO2, NO, CO, and C2H4 have been studied based on density functional theory (DFT). In gas adsorption analysis, the adsorption energy, adsorption distance, transfer charge, total electron density, density of states (DOS), energy band structure, frontier molecular orbital, and work function (WF) of each gas has been calculated. Furthermore, the reusability and stability of the Ag-doped MoSe2/ZnO heterojunctions have also been studied. The results showed that Ag-doped MoSe2/ZnO heterojunctions have great potential to be a candidate of highly selective and responsive gas sensors for NO detection with excellent reusability and stability.
A Hypertuned Lightweight and Scalable LSTM Model for Hybrid Network Intrusion Detection

Given the increasing frequency of network attacks, there is an urgent need for more effective network security measures. While traditional approaches such as firewalls and data encryption have been implemented, there is still room for improvement in their effectiveness. To effectively address this concern, it is essential to integrate Artificial Intelligence (AI)-based solutions into historical methods. However, AI-driven approaches often encounter challenges, including lower detection rates and the complexity of feature engineering requirements. Finding solutions to overcome these hurdles is critical for enhancing the effectiveness of intrusion detection systems. This research paper introduces a deep learning-based approach for network intrusion detection to overcome these challenges. The proposed approach utilizes various classification algorithms, including the AutoEncoder (AE), Long-short-term-memory (LSTM), Multi-Layer Perceptron (MLP), Linear Support Vector Machine (L-SVM), Quantum Support Vector Machine (Q-SVM), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA). To validate the effectiveness of the proposed approach, three datasets, namely IOT23, CICIDS2017, and NSL KDD, are used for experimentation. The results demonstrate impressive accuracy, particularly with the LSTM algorithm, achieving a 97.7% accuracy rate on the NSL KDD dataset, 99% accuracy rate on the CICIDS2017 dataset, and 98.7% accuracy on the IOT23 dataset. These findings highlight the potential of deep learning algorithms in enhancing network intrusion detection. By providing network administrators with robust security measures for accurate and timely intrusion detection, the proposed approach contributes to network safety and helps mitigate the impact of network attacks.
A Review of Shrimp Cellular Receptors for WSSV: Potential Targets for Antiviral Strategies in Shrimp Aquaculture

Abstract The white spot syndrome virus (WSSV) is a highly lethal pathogen for several crustacean species, which implies that this virus has caused high mortalities and significant economic losses for the global shrimp aquaculture industry. Viruses’ entry into the host cell depends on the interaction between those proteins located on the surface of the virus and its attachment to specific surface-exposed cell receptors. In this review, the findings in the interaction of the receptors recognized by WSSV proteins are summarized and discussed, providing thus a comprehensive and finer understanding of the importance of these molecules for WSSV pathogenesis, as well as suggesting strategies targeting critical components for the design and development of therapeutic antivirals against WSSV that could be used in shrimp farming.
Long-Range Retarded Elastic Metamaterials: Wave-Stopping, Negative, and Hypersonic or Superluminal Group Velocity

This paper investigates new phenomena in elastic wave propagation in metamaterials, characterised by long-range interactions. The kind of waves borne in this context unveil wave-stopping, negative group velocity, instability and hypersonic or superluminal effects, both for instantaneous and for nonlocal retarded actions. Closed form results are presented and a universal propagation map synthesizes the expected properties of these materials. Perspectives in physics, engineering and social dynamics are discussed.
Hormonal and proteomic analyses of southern blight disease caused by Athelia rolfsii and root chitosan priming on Cannabis sativa in an in vitro hydroponic system

Abstract Southern blight disease, caused by the fungal pathogen Athelia rolfsii, suppresses plant growth and reduces product yield in Cannabis sativa agriculture. Mechanisms of pathology of this soil‐borne disease remain poorly understood, with disease management strategies reliant upon broad‐spectrum antifungal use. Exposure to chitosan, a natural elicitor, has been proposed as an alternative method to control diverse fungal diseases in an eco‐friendly manner. In this study, C. sativa plants were grown in the Root‐TRAPR system, a transparent hydroponic growth device, where plant roots were primed with . 2% colloidal chitosan prior to A. rolfsii inoculation. Both chitosan‐primed and unprimed inoculated plants displayed classical symptoms of wilting and yellowish leaves, indicating successful infection. Non‐primed infected plants showed increased shoot defense responses with doubling of peroxidase and chitinase activities. The levels of growth and defense hormones including auxin, cytokinin, and jasmonic acid were increased 2–5‐fold. In chitosan‐primed infected plants, shoot peroxidase activity and phytohormone levels were decreased 1.5–4‐fold relative to the unprimed infected plants. When compared with shoots, roots were less impacted by A. rolfsii infection, but the pathogen secreted cell wall‐degrading enzymes into the root‐growth solution. Chitosan priming inhibited root growth, with root lengths of chitosan‐primed plants approximately 65% shorter than the control, but activated root defense responses, with root peroxidase activity increased 2.7‐fold along with increased secretion of defense proteins. The results suggest that chitosan could be an alternative platform to manage southern blight disease in C. sativa cultivation; however, further optimization is required to maximize effectiveness of chitosan.
A Biphasic Glucose Response during an Oral Glucose Tolerance Test Is Associated with Greater Plasma Insulin and GLP-1 Responses and a Reduction in 1-Hour Glucose but Does Not Relate to the Rate of Gastric Emptying in Healthy, Older Adults

Background: The pattern of the plasma glucose response curve during an oral glucose tolerance test (OGTT) is of prognostic significance with “biphasic” when compared with “monophasic” patterns being associated with greater insulin sensitivity/secretion and a reduced risk of progression to diabetes. The relationships of the glucose response curves with gastric emptying and incretin hormone secretion are not known. Methods: Thirty-six adults (age > 65 years) without known diabetes consumed a 300 mL drink containing 75 g glucose and 150 mg C13-acetate at baseline and follow-up after 5.8 ± 0.1 years. Plasma glucose, glucagon-like peptide-1 (GLP-1), glucose independent insulinotropic polypeptide (GIP) and insulin were measured, and participants classified according to the pattern of their glucose response. Gastric emptying was measured on breath samples (stable isotope breath test). Results: At baseline, 22 participants had a “monophasic” and 14 a “biphasic” glucose response. The 1 h plasma glucose response curve was greater and the GLP-1 AUC0–120 min and insulin secretion lower in the monophasic group. There were no differences in gastric emptying, GIP or insulin sensitivity. At the follow-up, the 1 h glucose response curve was greater again, while GLP-1 AUC0–120 min was lower in the monophasic group. Conclusions: A biphasic curve is associated with a higher 60 min glucose response curve and increases in GLP-1, but no difference in either GIP or gastric emptying.
Regret-Optimal Cross-Layer Co-Design in Networked Control Systems—Part II: Gauss-Markov Case

In the first part of this two-letter series, we proposed a cross-layer framework for joint optimal Quality-of-Control (QoC) and Quality-of-Service (QoS) co-design for networked control systems. In this second part, we employ this framework to perform optimal co-design for networked control systems comprising multiple Gauss-Markov systems. We analytically derive the joint optimal policies based on the information couplings between the physical and the network layers. A numerical case study illustrates the framework.
Comparative Study of Leak Detection in PVC Water Pipes Using Ceramic, Polymer, and Surface Acoustic Wave Sensors

The detection and location of pipeline leakage can be deduced from the time arrival leak signals measured by acoustic sensors placed at the pipe. Ongoing research in this field is primarily focused on refining techniques for accurately estimating the time delays. This enhancement predominantly revolves around the application of advanced signal processing methods. Additionally, researchers are actively immersed in the utilization of machine learning approaches on vibro-acoustic data files, to determine the presence or absence of leaks. Less attention has been given to evaluating the sensitivity, performance, and overall effectiveness of these sensors in leak detection; although acoustic methods have been successfully used for leak detection in metallic pipes, they are less effective in plastic pipes due to the high attenuation of leak noise signals. The primary thrust of this research centers on identifying sensors that not only possess sensitivity but also exhibit high efficiency. To accomplish this goal, we conducted an exhaustive evaluation of the performance of three distinct categories of acoustic sensors employed for detecting water leaks in plastic pipes: specifically, lead zirconate titanate (PZT) sensors, polyvinylidene fluoride (PVDF) sensors, and surface acoustic wave (SAW) sensors. Our evaluation encompassed the performance of PVDF and SAW sensors in leak detection, comparing them to PZT sensors under a variety of conditions, including different leak sizes, flow rates, and distances from the leak. The results showed that all three sensors, when they were placed in the same position, were able to detect water leaks in plastic pipes with different sensitivities. For small leaks (1 mm, 2 mm), the PVDF sensor showed the greatest sensitivity (0.4 dB/L/h, 0.33 dB/L/h), followed by the SAW sensor (0.16 dB/L/h, 0.14 dB/L/h), and finally the PZT (0.13 dB/L/h, 0.12 dB/L/h). Similarly, for larger leaks (4 mm, 10 mm), the PVDF sensor continued to show superior sensitivity (0.2 dB/L/h, 0.17 dB/L/h), followed by the SAW sensor (0.13 dB/L/h, 0.11), and finally the PZT sensor (0.12 dB/L/h, 0.1 dB/L/h), outperforming the PZT sensor. This suggests that SAW and PVDF sensors, have the potential to serve as valuable, cost-effective alternatives to traditional commercial leak noise transducers. The outcomes of this comparative study involving three acoustic sensors hold the potential to advance the development of robust and dependable systems for the detection of water leaks in plastic pipelines.
Pengaruh Sikap Guru pada Keberhasilan Belajar Siswa di Sekolah Dasar 55/Sridadi

Education has an important role in improving quality human resources with intelligence and skills. Education must be carried out consciously and the learning process is planned so that everything that is done by teachers and students is a learning process to achieve an educational goal. This study aims to determine the effect of teacher attitudes on student learning success. In research using quantitative methods with survey analysis. The results that can be obtained from this study are that there is a positive and significant influence between teacher attitudes on student learning success at SDN 55/1 Sridadi as evidenced by the results of a simple regression analysis conducted
Particle phenomenology from M theory inspired models

This thesis focuses on low-energy particle phenomenology arising from G2 compactifications of M theory. We construct a supersymmetric SO(10) model that can be naturally realised in this framework. An appropriate discrete symmetry combined with a symmetry breaking Wilson line suppresses the μ-term and dangerous triplet–matter interactions at the compactification scale. Stabilised moduli introduce back the forbidden terms providing the μ-term with the phenomenologically expected value of O(TeV). In our model triplets are light and regenerated triplet interactions induce proton decay but safely within experimental constraints. In order to restore gauge unification we introduce extra, light, vector-like matter multiplets that together with the (unstable) lightest supersymmetric particle (LSP) can provide interesting experimental signatures. We also present a mechanism that generates high scale vacuum expectation values (VEV)s for the scalar components of right-handed neutrinos N of the vector-like pair that further break the gauge symmetry into the Standard Model SU(3)C × SU(2)Y ×U(1)Y as well as can induce the correct neutrino masses. The other significant part of the thesis is focused on collider phenomenology of string/M theory inspired models. In particular we study a prospect for electroweakino discovery at a proposed 100 TeV collider with three leptons plus missing transverse energy signature. We design simple but effective signal regions for this case and using simplified detector-level analysis we evaluate discovery reach and exclusion limits. Assuming 3000 fb−1 of integrated luminosity, W-inos could be discovered (excluded) up to 1.1 (1.8) TeV if the spectrum is not compressed.
Marine Wildlife and Tourism Management: Insights from the Natural and Social Sciences

Marine environments have long been places of exploration, subsistence, transport and trade, but it is only recently that marine tourism has extended beyond coastal resorts and beaches. Demand for marine wildlife experiences has grown considerably in recent decades but a corresponding increase in the conservation of these environments as well as adequate legislative and management responses aimed at sustainability has not yet followed. This book demonstrates that through scientific approaches to understanding and managing tourist interactions with marine wildlife, sustainable marine tourism can be achieved.Drawing from disciplines such as marine and conservation biology and behavioural ecology, the effects of human disturbance on marine wildlife as well as management approaches to moderate these impacts are explored. Social science perspectives are also used to understand consumer demand and the ethical and legislative problems that this demand creates.
The clay mineralogy, weathering and mudslide behaviour of coastal cliffs

Reviews of geoinorphological and geotechnical literature suggest that the physico-chemical properties of clay are important in processes of soil deformation but in practice such properties are seldom considered in explanations of slope instability. This thesis establishes the importance of clay mineralogy and solute geochemistry upon the seasonal behaviour of coastal mudsildes. Analyses of the 'static' and 'dynamic' properties of mudsildes include a regional survey of landslide-prone geological strata and on-site analyses of two mudslides at Warden Point, north Kent, and Worbarrow Bay, Dorset. Twenty soil parameters were determined f or 95 samples, and the results correlated to establish the degree of association between variables and residual shear strength. Laboratory simulation tests established the importance of clay mineralogy and chemistry upon shear strength. The seasonal behaviour of inudslidirig was monitored at one site over 20 months. Automated techniques recorded movement, pore water pressure and weather conditions, along with periodic assessments of soil moisture, atmospheric and pore water chemistry. Data were collated at several time-scales and correlated to identify relationships between parameters; antecedent time-lags were established between the variables. Seasonal fluctuations in pore water chemistry were related to simulated results which suggested that residual strength was naturally sensitive to change. The nature and chemistry of clays were found to have important effects upon the location and behaviour of landslides. Such effects were exacerbated by weathering reactions; these were modelled using WATEQF and P}{REEQE and applied to a process-response flow diagram. The temporal and spatial controls of residual shear strength were fundamentally related to the physico-chemical properties. Fluctuations in shear strength were not only found to be a major cause of the seasonal reactivation of mudslides but also the primary control of the underlying mechanisms of behaviour, Infinite slope models revealed that the pore water pressures and undrained loading were less sensitive to change than the residual strength of mudslides: realistic factors of safety were only obtained when the seasonal changes in strength were considered by the model.
Hcci and Cai Engines for the Automotive Industry

Part 1 Overview: Motivation, definition, and history of HCCI/CAI engines. Part 2 Gasoline HCCI/CAI combustion engines: Overview of CAI/HCCI gasoline engines Two-stroke CAI engines Four-stroke gasoline HCCI engines with thermal management Four-stroke CAI engines with residual gas trapping Four-stroke CAI engines with internal exhaust gas recirculation (EGR) HCCI control CAI control and CAI/SI switching Fuel effects in CAI gasoline engines. Part 3 Diesel HCCI combustion engines: Overview of HCCI diesel engines HCCI combustion with early and multiple injection in the heavy-duty diesel engine Narrow angle direct injection (NADITM) concept for HCCI diesel combustion Low-temperature and premixed combustion concept with late injection HCCI fuel requirements. Part 4 HCCI/CAI combustion engines with alternative fuels: Natural gas HCCI engines HCCI engines with other fuels. Part 5 Advances modeling and experimental techniques: Auto-ignition and chemical kinetic mechanisms of HCCI combustion Overview of modeling techniques and their application to HCCI/CAI engines Overview of advanced optical techniques and their applications to HCCI/CAI engines. Part 6 Future directions for CAI/HCCI engines: Outlook and future directions in HCCI/CAI engines.
Research on digital knowledge management model within enterprises

In the era of digital economy, the digital transformation of enterprise management mode has greatly promoted the efficiency of internal knowledge utilization and enterprise innovation process, while the construction mechanism of enterprise digital management still needs to be theoretically explored. This paper adopts literature research method to define the concept of knowledge digitization and discusses the motives and mechanisms of the operation mechanism of the internal knowledge digitization management mode of enterprises based on the perspective of the whole life cycle management of data. Digital technology is incorporated into the enterprise internal knowledge management system, and digital knowledge management organization makes the knowledge in the enterprise knowledge management system more interrelated and more intelligent and convenient in solving business. It promotes the in-depth and universal development of the theory of intra-enterprise knowledge management mode and provides practical reference for improving the level of enterprise knowledge management.