Miracle-dz/UniFAR-FTUs huggingface dataset:
Vehicle with Learning Capabilities: A Study on Advancement in Urban Intelligent Transport Systems

Vehicular Ad-hoc Network is subclass of Mobile Ad-hoc networks and plays a vital role in Intelligent Transportation System implementation for smart cities. In the transportation scenario finding out an optimized path and locating the malicious vehicle is a big challenge. Routing is challenging in this technology because of reasons like high mobility of the node, rapid change in topology, interruption in connectivity, etc. The mobility of nodes affects the link stability and congestion is the effect of limited node resources. It also degrades the Quality-of-Service performance. Unmanned Ariel Vehicle helps to mitigate such conditions with the combination of learning capability. In this paper, the contribution of the Unmanned Ariel Vehicle to a smart transportation system has been studied and the working of different protocols is also analyzed. This paper gives a complete overview of the advancement of the system and implementation concept of different smart traffic management systems in urban areas.
The Biosafety Research Road Map: The Search for Evidence to Support Practices in the Laboratory—Zoonotic Avian Influenza and Mycobacterium tuberculosis

Introduction: The Biosafety Research Road Map reviewed the scientific literature on a viral respiratory pathogen, avian influenza virus, and a bacterial respiratory pathogen, Mycobacterium tuberculosis. This project aims at identifying gaps in the data required to conduct evidence-based biorisk assessments, as described in Blacksell et al. One significant gap is the need for definitive data on M. tuberculosis sample aerosolization to guide the selection of engineering controls for diagnostic procedures. Methods: The literature search focused on five areas: routes of inoculation/modes of transmission, infectious dose, laboratory-acquired infections, containment releases, and disinfection and decontamination methods. Results: The available data regarding biosafety knowledge gaps and existing evidence have been collated and presented in Tables 1 and 2. The guidance sources on the appropriate use of biosafety cabinets for specific procedures with M. tuberculosis require clarification. Detecting vulnerabilities in the biorisk assessment for respiratory pathogens is essential to improve and develop laboratory biosafety in local and national systems.
Derivation methods for hybrid knowledge bases with rules and ontologies

The raising popularity of the Semantic Web to describe concepts of the applications domains has revealed some aspects that could not be dealt by the Web Ontology Language (OWL) but are easily expressible in rule languages. These limitations of OWL made clear the need of integrating nonmonotonic rule-based sys- tems with monotonic ontologies in order to achieve the complete realisation of Semantic Web. However, this integration cannot be done straightforwardly as it faces many semantic and com- putational problems. These factors have sparked a heated debate which led to a research in different directions. Moreover, the combination of such two prominent subjects is not only useful in the Semantic Web context. In fact, there are many use case applications, particularly applied to a biomedical domain, where such interoperation between rules and ontologies is desirable. In the first part of this thesis we present a structured overview of the current state of the art on the subject of combining rules and ontologies. The objective is to describe several approaches that have appeared on the last years, explaining how they achieve their different degrees of integration. We then focus on one specific proposal for the combination of rules and ontologies – MKNF Well-Founded Semantics [34] – that possesses some desirable decidability and complexity prop- erties. To finish, we conclude with two prototype implementations for such approach – a first bottom-up implementation based on the definition of the semantics; and a goal-driven imple- mentation that, by being query-oriented, reduces the computational process to the set of atoms of which the given query depend on. For these implementations we use XSB along with CDF, an ontology management system complete for ALCQ extended with relational hierarchies and product classes. This represents an important contribution as it may be the first implementa- tions for hybrid knowledge bases that combine nonmonotonic rules and DL ontologies, where the combination is such that both the rules can refer to predicates in the ontology, and the on- tology can refer to predicates defined in the rules.
Role of Midkine in Cancer Drug Resistance: Regulators of Its Expression and Its Molecular Targeting

Molecules involved in drug resistance can be targeted for better therapeutic efficacies. Research on midkine (MDK) has escalated in the last few decades, which affirms a positive correlation between disease progression and MDK expression in most cancers and indicates its association with multi-drug resistance in cancer. MDK, a secretory cytokine found in blood, can be exploited as a potent biomarker for the non-invasive detection of drug resistance expressed in various cancers and, thereby, can be targeted. We summarize the current information on the involvement of MDK in drug resistance, and transcriptional regulators of its expression and highlight its potential as a cancer therapeutic target.
The ELIXIR Biodiversity Community: Understanding short- and long-term changes in biodiversity

Biodiversity loss is now recognised as one of the major challenges for humankind to address over the next few decades. Unless major actions are taken, the sixth mass extinction will lead to catastrophic effects on the Earth’s biosphere and human health and well-being. ELIXIR can help address the technical challenges of biodiversity science, through leveraging its suite of services and expertise to enable data management and analysis activities that enhance our understanding of life on Earth and facilitate biodiversity preservation and restoration. This white paper, prepared by the ELIXIR Biodiversity Community, summarises the current status and responses, and presents a set of plans, both technical and community-oriented, that should both enhance how ELIXIR Services are applied in the biodiversity field and how ELIXIR builds connections across the many other infrastructures active in this area. We discuss the areas of highest priority, how they can be implemented in cooperation with the ELIXIR Platforms, and their connections to existing ELIXIR Communities and international consortia. The article provides a preliminary blueprint for a Biodiversity Community in ELIXIR and is an appeal to identify and involve new stakeholders.
Evaluation of Parameter Sweeps for Computationally Efficient Infection Risk Analysis Using Pedestrian Dynamics

Pedestrian dynamics is an approach for modeling the fine-scaled movement of people. It is finding increasing application in the analysis of infection risk for directly transmitted diseases during air travel. A parameter sweep is often needed to evaluate infection risk for a variety of possible scenarios to account for inherent variability in human behavior. A low discrepancy parameter sweep was recently introduced to reduce the computational effort by one to three orders of magnitude. However, it has the following limitations: (i) a low-overhead parallelization leads to significant load imbalance, and (ii) the convergence rate worsens with dimension. This paper examines whether pseudorandom and hybrid sequences can overcome these defects and whether the convergence criteria can be changed to yield accurate solutions faster. We simulate the deplaning process of an airplane using different parameter sweep strategies and evaluate their relative computational efficiencies. Our results show that hybrid and pseudorandom parameter sweeps are advantageous for moderate accuracy, while a low discrepancy sweep is preferable for high accuracy. Our results also show that the convergence criteria could be relaxed substantially to yield accurate solutions around a factor of 20 faster. They promise to help a variety of applications that employ large parameter sweeps for modeling infection risk.
Tracking: Prediction of Future Values from Serial Measurements

Repeated measurements of the same characteristic, obtained over time from each of a cohort of individuals, often show systematic change that facilitates prediction of future values. Several investigators have called this regular behavior 'tracking'. Most definitions of the term are related to the idea that a single individual's repeated measurements have expectations equal to a constant percentile of the population distribution asit changes over time. This paper investigates the relationship between this concept and the pattern of change implicit in growth-curve models. Growth-curve models are shown to be more general in some ways, more restrictive in others. We illustrate prediction of future values by growth techniques with an analysis of serial blood pressure measurements from the Framingham Heart Study. We also compare growth-curve analysis with recent work by McMahan (1981, Biometrics 37, 447-455) and suggest a more general class of random-effects models that may be useful in the study of tracking.
The Discrete Poisson-Sujatha Distribution

In this paper, Poisson-Sujatha distribution has been obtained by compounding Poisson distribution with Sujatha distribution introduced by Shanker (2015 a). The first four moments about origin and the moments about mean has been obtained. The expression for coefficient of variation, skewness and kurtosis has been obtained. Important mathematical and statistical properties of the distribution have been derived and discussed. The estimation of its parameter has been discussed using both maximum likelihood estimation and the method of moments. The proposed distribution has been fitted using maximum likelihood estimate to some count data - sets to test its goodness of fit and the fit is compared with that obtained using other one parameter discrete distributions.
Clonal Hematopoiesis of Indeterminate Potential is Associated with Acute Kidney Injury

Age is a predominant risk factor for acute kidney injury (AKI), yet the biological mechanisms underlying this risk are largely unknown and to date no genetic mechanisms for AKI have been established. Clonal hematopoiesis of indeterminate potential (CHIP) is a recently recognized biological mechanism conferring risk of several chronic aging diseases including cardiovascular disease, pulmonary disease and liver disease. In CHIP, blood stem cells acquire mutations in myeloid cancer driver genes such as DNMT3A, TET2, ASXL1 and JAK2 and the myeloid progeny of these mutated cells contribute to end-organ damage through inflammatory dysregulation. We sought to establish whether CHIP causes acute kidney injury (AKI). To address this question, we first evaluated associations with incident AKI events in three population-based epidemiology cohorts (N = 442,153). We found that CHIP was associated with a greater risk of AKI (adjusted HR 1.26, 95% CI: 1.19-1.34, p<0.0001), which was more pronounced in patients with AKI requiring dialysis (adjusted HR 1.65, 95% CI: 1.24-2.20, p=0.001). The risk was particularly high in the subset of individuals where CHIP was driven by mutations in genes other than DNMT3A (HR: 1.49, 95% CI: 1.37-1.61, p<0.0001). We then examined the association between CHIP and recovery from AKI in the ASSESS-AKI cohort and identified that non-DNMT3A CHIP was more common among those with a non-resolving pattern of injury (HR 2.3, 95% CI: 1.14-4.64, p = 0.03). To gain mechanistic insight, we evaluated the role of Tet2-CHIP to AKI in ischemia-reperfusion injury (IRI) and unilateral ureteral obstruction (UUO) mouse models. In both models, we observed more severe AKI and greater post-AKI kidney fibrosis in Tet2-CHIP mice. Kidney macrophage infiltration was markedly increased in Tet2-CHIP mice and Tet2-CHIP mutant renal macrophages displayed greater pro-inflammatory responses. In summary, this work establishes CHIP as a genetic mechanism conferring risk of AKI and impaired kidney function recovery following AKI via an aberrant inflammatory response in CHIP-derived renal macrophages.
The Effect of Interstock on The Development of Huanglongbing Disease and Vegetative Growth of Three Commercial Citrus Varieties in Indonesia

Huanglongbing (HLB) disease, causes a 60-95% decline in Indonesia citrus production in almost all production centers. The use of suitable citrus interstocks can increase fruit production and quality, improve compatibility, and can also increase tolerance to HLB with tolerant interstocks. Potential interstock, providing an alternative for citrus farmers to be able to save their plants from the destruction of HLB. The purpose of this study was to determine the effect of several interstocks in inhibiting the development of HLB disease and the growth of citrus plants. Eight interstock combination treatments (JC, JC+Rough lemon, JC+Carrizo citrange, JC+citrumelo, JC+Poncirus trifoliata, JC+Volkamer lemon, JC+Troyer citrange and JC+ Kanci kumquat) on 3 commercial citrus varieties (C. reticulata var. Batu 55, C. nobilis var. Pontianak and C. aurantifolia var. Nimas Agrihorti). The combination of interstock affects plant tolerance to HLB. In the three upper limit varieties tested, the mildest symptoms of HLB and the healthiest was found in the combination of interstock JC+P.trifoliata, and Candidatus Liberibacter asiaticus were detected after inoculation using conventional PCR amplification in the second test. Based on the PCR index and the severity of the symptoms, the combination of interstock JC+P. trifoliata was classified as mild severity and the healthiest against CLas infection in the 3 scion varieties tested (C. reticulata var. Batu 55, C. nobilis var. Pontianak and C. aurantifolia var. Nimas Agrihorti). Vegetative growth was found to be varied, and almost all of them could grow well except shoot growth which was stagnant at 5-6 months after inoculation.
A Non-Intrusive Load Decomposition Method Based on Generative Adversarial Networks and Asynchronous Federated Learning

The non-intrusive load decomposition method is an essential approach of appreciating the water requirements of consumers. Traditional techniques of load breakdown have a number of drawbacks, including a poor level of water identification precision. In order to do this, a model for load decomposition based on generative adversarial networks has been developed. By applying a convolutional autoencoder to the total power noise, the generative network constructs the power breakdown sequence of the given home appliance. The discriminant network is then utilized to determine whether or not the created sequence is legitimate. In order to achieve a more natural and authentic sequence, the participants compete against one another. In light of the shortcomings of the centralized model training method, the network model implementation scheme based on asynchronous federated learning is utilized to replace the traditional convolution in order to realize the model’s lightweight nature. This allows the model to be applied to terminal devices such as smart water meters, and it is carried out in a cloud-edge collaborative manner. In addition, the model is carried out in a manner that is collaborative between the cloud and the edge. Training not only eases the strain of communication transmission but also protects the privacy of users and the integrity of their data.
Robot positioning and navigation technology is based on Integration of the Global Navigation Satellite System and real-time kinematics

This paper provides a new robot positioning and navigation technique that combines GNSS-RTK and visual SLAM to address the shortcomings of existing single robot positioning and navigation techniques, such as limited adaptation to scenarios and susceptibility to environmental influences. Real-time kinematics (RTK) is a real-time dynamic observation technique based on carrier phase observations, which can provide real-time 3D positioning results of the measurement site in a specified coordinate system, and can be positioned with centimetre-level accuracy, which is suitable for It is suitable for outdoor positioning of mobile robots. In this paper, a dataset is studied to compare and analyse the advantages and disadvantages of different SLAMs in order to determine the most suitable robot positioning and navigation technique for autonomous indoor and outdoor mobility. These sensors were well calibrated and the composition was applied to the RK3588. The results show good performance in indoor-outdoor switching scenarios, obtaining a mapping technique that cannot be achieved with a single visual SLAM, effectively improving the ability to adapt to multiple scenarios and enhancing the operational range of the transportable robot.
One-photo excitation pathway in 2D in-plane heterostructures for effective visible-light-driven photocatalytic degradation

Broad-spectrum absorption and highly effective charge-carrier separation are two essential requirements to improve the photocatalytic performance of semiconductor-based photocatalysts. In this work, a fascinating one-photon system is reported by rationally fabricating 2D in-plane Bi2O3/BiOCl (i-Cl) heterostructures for efficient photocatalytic degradation of RhB and TC. Systematic investigations revealed that the matched band structure generated an internal electric field and a chemical bond connection between the Bi2O3 and BiOCl in the Bi2O3/BiOCl composite that could effectively improve the utilization ratio of visible light and the separation effectivity of photo-generated carriers in space. The formed interactions at the 2D in-plane heterojunction interface induced the one-photon excitation pathway which has been confirmed by the experiment and DFT calculations. As a result, the i-Cl samples showed significantly enhanced photocatalytic efficiency towards the degradation of RhB and TC (RhB: 0.106 min−1; TC: 0.048 min−1) under visible light. The degradation activities of RhB and TC for i-Cl were 265.08 and 4.08 times that of pure BiOCl, as well as 9.27 and 2.14 times that of mechanistically mixed Bi2O3/BiOCl samples, respectively. This work provides a logical strategy to construct other 2D in-plane heterojunctions with a one-photon excitation pathway with enhanced performance.
Ultrasensitive plasmonic sensors mold the flow of light and fluidics

Detection of large numbers of proteins and determination of their functions may enable early diagnosis of complex diseases such as cancer and Alzheimer’s, as well their treatment, by discovering effective drugs. Although large-scale studies of proteins are significant because of such potentially far-reaching implications, their realization is challenging owing to the limitations of current detection techniques. In particular, label-free, ultrasensitive, real-time, and high-throughput on-chip biosensing systems are urgently needed. Over the last decade, electrical, mechanical, and optical surface biosensors used to explore novel science at nanoscale dimensions have attracted significant attention.1 Several highly multiplexed, ultrasensitive nanobiosensors offering detection with single-particle sensitivity have been proposed.2 Concurrently, researchers are integrating sensitive and compact biosensors with microfluidics for automation and improved throughput. However, the performance of surface nanosensors operating in fluidic environments is strongly limited by the efficiency of analyte delivery to the sensing surface rather than by the sensors’ intrinsic detection capabilities. Relying only on passive diffusion for mass transport can severely limit sensor performance. At low concentrations, this ‘mass-transport problem’ causes impractically long detection times for nanosensors, from days to weeks.3 To overcome this fundamental limitation, we recently demonstrated a biosensing system that merges photonics and fluidics.4, 5 By efficiently delivering analytes to the device surface, our system dramatically increases the response time of ultrasensitive sensors. Thus far, one of the main conceptual constraints was that biosensors and microfluidics are always considered as different parts of the sensor platform, completing Figure 1. (a) Suspended plasmonic nanohole arrays. (b) Multilayered nanoplasmonic-nanofluidic sensor scheme allows 3D control of flow for efficient analyte delivery. Au: Gold. PDMS: Polydimethylsiloxane. EOT: Extraordinary optical transmission.
Comparison of the Effect of Preoperative Oral Tizanidine and Pregabalin on Shoulder Pain in Laparoscopic Cholecystectomy Under General Anesthesia

Background: Shoulder pain is considered as the most important and relatively common postoperative cholecystectomy complications that often controls in recovery room by systemic narcotics that may have some side effects. The aim of this study was to evaluate the effect of premedication with oral tizanidine on shoulder pain relief after elective laparoscopic cholecystectomy. Materials and Methods: In this double-blinded clinical trial, 75 adults of American Society of Anesthesiologist physiologic state 1 and 2 scheduled for elective laparoscopic cholecystectomy under general anesthesia were selected and randomly divided in three groups of T, P, and control groups. Ninety minutes before the induction of anesthesia, patients received either 4 mg tizanidine (T group), 100 mg pregabalin (P group), or orally in 50cc or the same volume of plain water as a placebo (control group). Then, the vital signs, pain intensity, and the need for analgesic were measured during 24 hours and then compared in the groups. Results: There was no significant difference in patient characteristics, with respect to age, weight, gender, and duration of anesthesia and surgery between the groups (P > 0.05). The pain intensity and need for analgesic were significantly lower in tizanidine and pregabalin groups than the control group (P < 0.003) vs (P < 0.001). There was no significant difference in vital signs characteristics between the groups. Conclusion: Oral administration of 4 mg tizanidine and 100 mg pregabalin 90 minutes before laparoscopic cholecystectomy significantly relive postoperative shoulder pain and analgesic consumption without any complication.
Inflammatory mechanisms and intervention strategies for sepsis‐induced myocardial dysfunction

Sepsis‐induced myocardial dysfunction (SIMD) is the leading cause of death in patients with sepsis in the intensive care units. The main manifestations of SIMD are systolic and diastolic dysfunctions of the myocardium. Despite our initial understanding of the SIMD over the past three decades, the incidence and mortality of SIMD remain high. This may be attributed to the large degree of heterogeneity among the initiating factors, disease processes, and host states involved in SIMD. Previously, organ dysfunction caused by sepsis was thought to be an impairment brought about by an excessive inflammatory response. However, many recent studies have shown that SIMD is a consequence of a combination of factors shaped by the inflammatory responses between the pathogen and the host. In this article, we review the mechanisms of the inflammatory responses and potential novel therapeutic strategies in SIMD.