Important Data Research Articles

Assessment of the stand structure of protective forest monitoring based on statistical models in Irano-Turanian phytogeographical regions of Iran.

One of the most important data that forest management planners need for effective decisions is the data related to the forest structure. The aim of the study is to investigate and analyse the structure of protective forests in Irano-Turanian phytogeographical regions, Iran. Since there are many shrubs in this phytogeographical region, it is very difficult to measure the stand diameter at any height (breast or root height). For this reason, it is necessary to analyse the parameters of height and crown cover to investigate and analyse forest structure. For that purpose, two study plots were selected, and basic data were analysed by using statistical distributions, scatter plots and R2 coefficients. With EasyFit software and Anderson‒Darling test, it was found that the Weibull (3P) and Pearson 6 (4P) distributions for the crown cover factor and the Gen-Pareto and Pert distributions for the height factor have the best goodness-of-fit for the distribution of the different crown cover classes and heights in the studied forest. Moreover, the results confirm that there is a very weak R2 coefficient between crown cover and root collar diameter, with R2 = 0.513 and 0.369 in plots 1 and 2, respectively. Therefore, the combination of crown cover and height parameters is more suitable for use when analysing stand structure in such forests, although the values of R2 are still low (0.673 and 0.524 in plots 1 and 2, respectively). The results of this study show that in protective forests with many shrubs, it is better to focus on the height and crown cover of ​trees and of shrubs rather than on parameters related to stand/tree diameter when analysing stand structure.

Phenotypic Environmental Sensitivity and Mental Health During Pregnancy and Post Partum: Protocol for the Experiences of Pregnancy Longitudinal Cohort Study.

Mental health problems during pregnancy and post partum are common and associated with negative short- and long-term impacts on pregnant individuals, obstetric outcomes, and child socioemotional development. Socio-environmental factors are important predictors of perinatal mental health, but the effects of the environment on mental health are heterogeneous. The differential susceptibility theory and the environmental sensitivity framework suggest that individuals differ in their degree of sensitivity to positive and negative environments, which can be captured by individual phenotypes such as temperament and personality. While there is strong evidence for these models in childhood, few studies examined them in adults, and they were not examined in pregnancy. The primary objective of the Experiences of Pregnancy study is to explore whether childhood and current environments are associated with mental health and well-being in pregnancy and whether these effects depend on individual sensitivity phenotypes (personality). This study also aims to gather important psychosocial and health data for potential secondary data analyses and integrative data analyses. We will conduct a longitudinal cohort study. The study was not registered elsewhere, other than this protocol. Participants will be recruited through social media advertisements linking to the study website, followed by an eligibility call on Zoom (Zoom Video Communications). Participants must be aged 18 years or older, currently residing in the United States as citizens or permanent residents, and currently planning to continue the pregnancy. A minimum of 512 participants will be recruited based on power analyses for the main objectives. Since the data will also be a resource for secondary analyses, up to 1000 participants will be recruited based on the available budget. Participants will be in their first trimester of pregnancy, and they will be followed at each trimester and once post partum. Data will be obtained through self-reported questionnaires assessing demographic factors; pregnancy-related factors; delivery, labor, and birth outcomes; early infant feeding; individual personality factors; childhood and current environments; mental health and well-being; attachment; and infant temperament. A series of measures were taken to safeguard the study from web robots and fraudulent participants, as well as to reduce legal and social risks for participants following Dobbs v. Jackson. The study received ethics approval in April 2023 from the University of Oklahoma-Norman Campus Institutional Review Board. Recruitment occurred from May to August 2023, with 3 follow-ups occurring over 10 months. The Experiences of Pregnancy study will extend theories of environmental sensitivity, mainly applied in children to the perinatal period. This will help better understand individual sensitivity factors associated with risk, resilience, plasticity, and receptivity to negative and positive environmental influences during pregnancy for pregnant individuals. PRR1-10.2196/49243.

Linezolid does not improve bactericidal activity of rifampin-containing first-line regimens in animal models of TB meningitis

Tuberculous meningitis (TB meningitis) is the most devastating form of tuberculosis (TB) and there is a critical need to optimize treatment. Linezolid is approved for multidrug resistant TB and has shown encouraging results in retrospective TB meningitis studies, with several clinical trials underway assessing its additive effects on high-dose (35 mg/kg/day) or standard-dose (10 mg/kg/day) rifampin-containing regimens. However, the efficacy of adjunctive linezolid to rifampin-containing first-line TB meningitis regimens and the tissue pharmacokinetics (PK) in the central nervous system (CNS) are not known. We therefore conducted cross-species studies in two mammalian (rabbits and mice) models of TB meningitis to test the efficacy of linezolid when added to the first-line TB regimen and measure detailed tissue PK (multicompartmental positron emission tomography [PET] imaging and mass spectrometry). Addition of linezolid did not improve the bactericidal activity of the high-dose rifampin-containing regimen in either animal model. Moreover, the addition of linezolid to standard-dose rifampin in mice also did not improve its efficacy. Linezolid penetration (tissue/plasma) into the CNS was compartmentalized with lower than previously reported brain and cerebrospinal fluid (CSF) penetration, which decreased further two weeks after initiation of treatment. These results provide important data regarding the addition of linezolid for the treatment of TB meningitis.

Biomimetic Wearable Sensors: Emerging Combination of Intelligence and Electronics.

Owing to the advancement of interdisciplinary concepts, for example, wearable electronics, bioelectronics, and intelligent sensing, during the microelectronics industrial revolution, nowadays, extensively mature wearable sensing devices have become new favorites in the noninvasive human healthcare industry. The combination of wearable sensing devices with bionics is driving frontier developments in various fields, such as personalized medical monitoring and flexible electronics, due to the superior biocompatibilities and diverse sensing mechanisms. It is noticed that the integration of desired functions into wearable device materials can be realized by grafting biomimetic intelligence. Therefore, herein, the mechanism by which biomimetic materials satisfy and further enhance system functionality is reviewed. Next, wearable artificial sensory systems that integrate biomimetic sensing into portable sensing devices are introduced, which have received significant attention from the industry owing to their novel sensing approaches and portabilities. To address the limitations encountered by important signal and data units in biomimetic wearable sensing systems, two paths forward are identified and current challenges and opportunities are presented in this field. In summary, this review provides a further comprehensive understanding of the development of biomimetic wearable sensing devices from both breadth and depth perspectives, offering valuable guidance for future research and application expansion of these devices.

Structure-function correlation of retinal photoreceptors in PRPH2-associated central areolar choroidal dystrophy patients assessed by high-resolution scanning laser imaging and microperimetry.

High Magnification Module (HMM™, Heidelberg Engineering, Heidelberg, Germany) imaging is a novel technique, designed to visualize the retina at a cellular level. To assess the potential of HMM™-based metrics as endpoints for future trials, we evaluated correlations between structural HMM™ cone metrics, spectral-domain OCT (SD-OCT, Heidelberg Engineering, Heidelberg, Germany) and retinal sensitivity on microperimetry (MP, MAIA, CenterVue, Padova, Italy) in healthy subjects and p.(Arg142Trp) PRPH2-associated Central Areolar Choroidal Dystrophy (CACD) patients. We projected a default 10° MP grid on composite HMM™ images and performed automated cone density (CD), intercell distance (ICD) and nearest neighbour distance (NND) analysis at stimuli located at 3° and 5° retinal eccentricity. We manually measured intrasubject outer retinal thickness on SD-OCT in absolute and relative scotomas, located outside of focal atrophy. We included 15 CACD patients and five healthy subjects. We found moderate-to-strong correlations of HMM™ metrics and MP sensitivity at 3° eccentricity from the fovea. We found the outer retina at the locations of absolute scotomas to be statistically significant thinner (p = 0.000003, one-sample t-test), as the outer retinal thickness at locations of relative scotomas. Interestingly, HMM™ metrics of these areas did not differ significantly. We found significant correlations between structural photoreceptors metrics on HMM™ imaging and retinal sensitivity on MP in healthy subjects and CACD patients. A multimodal approach, combining SD-OCT, MP and HMM™ imaging, allows for detailed mapping of retinal photoreceptor integrity and restitution potential, important data that could serve as biomarkers in future clinical trials.

Peran Kepala Madrasah serta Guru dalam Pengembangan Kurikulum Pendidikan Agama Islam

Science and innovation are continuously created to keep up with the times and the progress of human thought. Increased knowledge and innovation moreover encouraged for the advancement of human life. Developing the curriculum is carried out as a proactive step to provide answers to challenges that have grown from the progress of the times. This study uses a literature search method on the use of subjective strategies and socio-historical approaches that focus on important data about curriculum development. A teacher plays an irreplaceable role in curriculum development. The teacher carries out two main tasks, namely the task of delivering subjects and tasks related to the administration of lessons. In addition to teachers who have a strategic role in improving PAI education programs, the head of the madrasa as the head of the institution also has a very important contribution. Madrasah heads have a strategic role and high obligations, including in terms of curriculum development.

Conducting a Rasch Analysis in jMetrik

This step-by-step guide to conducting a Rasch analysis using the jMetrik software package describes how to format and import data, conduct a simple classical test theory item analysis, and then conduct a simple Rasch analysis. Some basic graphical outputs are also described. After completing the steps described in this guide, novice users should be able to conduct a basic analysis unaided and be able to explore the more advanced features of jMetrik by referring to the manual. Keywords: jMetrik, Rasch analysis, item analysis

CASBA neurological assessment: Noninvasive EEG biomarkers classify amyloid‐PET, clinical diagnosis, cognitive dementia rating, and FCSRT free recall scores in patients with cognitive decline

AbstractBackgroundAlzheimer’s Disease (AD) is difficult to diagnose and stage, often relying on burdensome clinical tests for neural protein aggregation and cognitive performance. Delays and errors in the accurate diagnosis of AD reduce patient outcomes and prevent early therapeutic intervention. The CASBA electroencephalography (EEG) system leverages a custom sequence of dynamic visual stimuli to elicit and translate novel and noninvasive neural signals biomarkers into accurate estimates of amyloid burden, cognitive performance, and clinical diagnosis, to facilitate clinical decision‐making in patients with cognitive decline.MethodIn a board‐approved study, human patients with mild cognitive impairment (MCI; 71.2 ± 8.2 years old), subjective cognitive decline (SCD; 70.7 ± 6.3 years old), and AD diagnosis (71.2 ± 6.6 years old), alongside amyloid PET (Anti‐Serum Amyloid P Antibody), cognitive testing (i.e. FCSRT), and clinical dementia rating (CDR). Neural signals were collected on a 64‐channel equidistant EEG system (2048Hz, Ag/AgCl; ANT Neuro, Enschede)]. Automated preprocessing and machine learning classification pipelines were developed to extract EEG features and calculate estimates of physiological and cognitive AD metrics from these EEG biomarkers.ResultCASBA accurately classified normal vs. subnormal FCSRT free recall score (threshold of 18, 77.3% balanced accuracy, 84.0% specificity, 70.6% sensitivity), SCD v. MCI/AD clinical diagnosis (79.4% balanced accuracy, 77.3% specificity, 81.5% sensitivity), CDR (threshold of 1, 79.2% balanced accuracy, 76.9% specificity, 81.5% sensitivity), and amyloid‐PET (composite threshold of 1.25, 83.0% balanced accuracy, 75.0% specificity, 91.9% sensitivity).ConclusionThe CASBA neurological assessment accurately predicts important clinical data including patient diagnosis and staging, amyloid burden, and performance on key evaluations of cognition in AD. CASBA supports physician decision making and patient stratification and suggests a new method for noninvasive tracking and repeat assessment during clinical trials. As such, the CASBA neurological assessment offers a promising new tool to advance clinical care and innovation for patients with neurodegenerative disease.

A Catholic correction of Max Weber’s thesis on Protestant ethic in the view of Michael Novak

Abstract Max Weber’s thesis on the decisive influence of Protestant ethic on the formation and development of modern capitalism has become one of the best-known and widely shared canonical claims in social sciences. Since its publication at the beginning of the 20th century, this thesis, supported by subsequent great works by the German classic, has rarely been the subject of major controversy. The work of correcting Weber’s thesis was undertaken in the late 20th century by Michael Novak. Novak’s correction is not confrontational, but complementary. The American thinker abandons the research area of minority Protestant communities, which caught Weber’s attention, and undertakes an analysis of the broader religious tradition, developing both before and after the Reformation. In particular, the achievements of Pope John Paul II, interpreted by Novak in terms of the renewal of a liberty current of Catholic social thought, provide important corrective data.

Changing the Game: Well Integrity Measurements Acquired on Drillpipe

The industry is continuously challenged to improve the efficiency and safety of operations. This is evident over the last 30 years in the development and improvement of measurements acquired while drilling. However, this has, in general, until now not been applied to well integrity measurements such as casing integrity and cement evaluation, which have traditionally been acquired utilizing wireline deployment. This paper will show the results of a new drillpipe-deployed tool that can be run in parallel with existing well operations. The results from two differing North Sea wells will be compared to traditionally acquired wireline-deployed tools and will demonstrate that these measurements and the resultant interpretation can successfully be acquired on drillpipe. This allows for much improved efficiency of operations and, in fact, the ability to acquire this important data in well conditions and environments where it is difficult or, in some cases, impossible to log with conventional wireline techniques. Two wells were selected with different degrees of difficulty in terms of measurement acquisition and showing different well trajectories and mud types. Both wells were logged with both the new drillpipe-deployed technology and traditional wireline technology, allowing a direct comparison of the techniques and tools and paving the way for acceptance of the new drillpipe-conveyed technology. The new drillpipe-conveyed tool can be run anytime drillpipe is utilized in the well. A radial distribution of ultrasonic transducers arranged on the circumference of a drill collar allows for full azimuthal interpretation of the casing and cement while rotating the drillpipe. Analysis of the acquired data allows for the interpretation of caliper thickness and an evaluation of the material in the annular space behind the casing. In addition, the tool can provide casing collar location in real time and has the ability to orient downhole devices such as whipstocks, perforating guns, and oriented cutters. The two well examples conclusively demonstrate that the tool can be run in parallel with existing operations to minimize rig time and eliminate the need for a dedicated, standalone wireline operation. Also, the cement evaluation interpretation was comparable to the equivalent wireline technology. We will investigate which measurements and applications the new tool can be used for and where there may be further room for improvement.

Privacy-Preserving Range Query Quantum Scheme With Single Photons in Edge-Based Internet of Things

Range query in cloud-based outsourcing applications is an important data search service, but it can suffer from privacy disclosure. In this paper, we generalize and redefine privacy-preserving range query in edge-based Internet of Things (IoT). Furthermore, to enhance security and privacy of sensitive data, we introduce quantum cryptographic technologies and present a novel quantum approach to solve the problem. First, we present a feasible protocol for Quantum Secret Permutating (QSP) with single photons, which will be later utilized in the final scheme as a basic block. Second, we generalize traditional privacy-preserving range query to a secure one-sided two-party computation, i.e., specific dot product. Third, we present a synchronously grouping strategy based on Quantum Key Distribution (QKD) to reduce the complexity and improve the efficiency of solving this two-party computation, so that it can easily get the desired result by repeatedly using feasible quantum privacy query group by group. Finally, adopting our previously proposed quantum protocols, we design privacy-preserving range query quantum scheme in edge-based IoT. Compared with the classical related schemes, our proposed quantum scheme has higher security, because the security of our proposed protocols is based on the basic physical principles of quantum mechanics, instead of unproven computational difficulty assumptions.

A Conceptual Framework of Data Readiness for the Health and Aging Brain Study‐Health Disparities

AbstractBackgroundThe Health and Aging Brain Study: Health Disparities (HABS‐HD) seeks to understand the biological, social and environmental factors that impact brain aging among diverse communities. HABS‐HD, like many other NIH funded data‐sharing projects, has important data assets for various uses, including social, environmental and behavioral data, and multiple data flow pathways. Machine learning (ML) develops algorithms and models to continuously improve itself over time, but the determination of data quality and its readiness are needed for these models to operate efficiently. Therefore, developing a data readiness reporting methodology has become a very urgent task for HABS‐HD.MethodIn this study, we developed a conceptual framework of data readiness. First, we analyzed the missing data percentage and used ML‐Based Multiple Imputation (MLMI) for missing data imputation. Then, we performed SVM based on Recursive Feature Elimination and Cross Validation (SVM‐RFE‐CV) for feature elimination and outlier removal. Lastly, we rated the data readiness based on the three metrics: missing data percentage, performance before feature engineering, and performance after feature engineering to rate data readiness. All the three scores were averaged to rate the overall readiness of data.ResultA framework for calculating overall average score for readiness of data was presented (1 stands for completely accessible, 0 for not accessible at all, and 0.5 for neutral). Our results show that the framework of data readiness was straightforward and useful in assessing how ready the HABS‐HD data is for ML.ConclusionThe systematic analysis of readiness of data before building ML models is of utmost importance. And it has a significant impact on biomarker discovery and disease prediction application for Alzheimer’s disease. The conceptual framework of data readiness works well for our Alzheimer’s disease models in HABS‐HD. It can also be applied to other disease data readiness reporting.

Large-Scale Meta-Heuristic Feature Selection Based on BPSO Assisted Rough Hypercuboid Approach.

The selection of prominent features for building more compact and efficient models is an important data preprocessing task in the field of data mining. The rough hypercuboid approach is an emerging technique that can be applied to eliminate irrelevant and redundant features, especially for the inexactness problem in approximate numerical classification. By integrating the meta-heuristic-based evolutionary search technique, a novel global search method for numerical feature selection is proposed in this article based on the hybridization of the rough hypercuboid approach and binary particle swarm optimization (BPSO) algorithm, namely RH-BPSO. To further alleviate the issue of high computational cost when processing large-scale datasets, parallelization approaches for calculating the hybrid feature evaluation criteria are presented by decomposing and recombining hypercuboid equivalence partition matrix via horizontal data partitioning. A distributed meta-heuristic optimized rough hypercuboid feature selection (DiRH-BPSO) algorithm is thus developed and embedded in the Apache Spark cloud computing model. Extensive experimental results indicate that RH-BPSO is promising and can significantly outperform the other representative feature selection algorithms in terms of classification accuracy, the cardinality of the selected feature subset, and execution efficiency. Moreover, experiments on distributed-memory multicore clusters show that DiRH-BPSO is significantly faster than its sequential counterpart and is perfectly capable of completing large-scale feature selection tasks that fail on a single node due to memory constraints. Parallel scalability and extensibility analysis also demonstrate that DiRH-BPSO could scale out and extend well with the growth of computational nodes and the volume of data.

Antimicrobial Protein LECT2-b Helps Maintain Gut Microbiota Homeostasis via Selectively Targeting Certain Pathogenic Bacteria.

Antimicrobial peptides/proteins (AMPs) constitute a critical component of gut immunity in animals, protecting the gut from pathogenic bacteria. However, the interactions between AMPs and gut microbiota remain elusive. In this study, we show that leukocyte-derived chemotaxin-2 (LECT2)-b, a recently discovered AMP, helps maintain gut homeostasis in grass carp (Ctenopharyngodon idella), one of the major farmed fish species globally, by directly regulating the gut microbiota. Knockdown of LECT2-b resulted in dysregulation of the gut microbiota. Specifically, LECT2-b deficiency led to the dominance of Proteobacteria, consisting of proinflammatory bacterial species, over Firmicutes, which includes anti-inflammatory bacteria. In addition, the opportunistic pathogenic bacteria genus Aeromonas became the dominant genus replacing the probiotic bacteria Lactobacillus and Bacillus. Further analysis revealed that this effect was due to the direct and selective inhibition of certain pathogenic bacterial species by LECT2-b. Moreover, LECT2-b knockdown promoted biofilm formation by gut microbiota, resulting in tissue damage and inflammation. Importantly, LECT2-b treatment alleviated the negative effects induced by LECT2-b knockdown. These findings highlight the crucial role of LECT2-b in maintaining the gut microbiota homeostasis and mucosal health. Overall, our study provides important data for understanding the roles of AMPs in the regulation of gut homeostasis in animals.

Southern province of the first African state: discovery of the Kerman settlement in Letti, Sudan

Discoveries at Letti provide important data on the functioning and reach of one of the oldest African civilisations: the kingdom of Kerma (2500–1500 BC). Extensive surveys and preliminary excavations have recorded numerous settlement and funerary sites in the region. Our results help to expand the economic data and chronology.

A Comparative Study of Minimum Data Set of Speech Therapy: A Systematic Literature Review

Introduction: A minimum data set improves the potential of data standardization and overcoming the problem of low-quality speech therapy data by providing coherent, complete, and uniform data elements. Therefore, this study was conducted to compare speech therapy minimum data set among different countries.Material and Methods: A systematic review was conducted without time limits in PubMed, Scopus, Web of Science, Embase, SID, Magiran, Elmnet databases, and in the Google search engine to retrieve articles, speech therapy forms, and speech therapy registry sites. Keywords related to speech therapy minimum data set including minimum data set, registry, and speech therapy, were used. First, studies were reviewed based on titles and abstracts. Then, the selected studies from the previous stage were examined independently by two researchers. A similar standard checklist was used to extract and compare the findings.Results: A total of 1710 related records were extracted for review, and finally, six main articles and 11 forms were included in this review. The six original articles included two related to speech therapy minimum data set in the United States, two related to Iran, and one related to Australia and Germany. A comparative review of the most important data elements obtained from the articles and input forms in this review, including identity and admission information, referral information, history, assessment of verbal skills, assessment of non-verbal skills, assessment of organs of production, assessment of cognitive skills, assessment of other aspects of speech, and linguistic and cultural considerations, were information elements related to diagnoses, recommendations, and treatment plans.Conclusion: It could be concluded that an agreed classification system is needed to facilitate communication between speech therapists. This potentially enables further testing of diagnostic and therapeutic hypotheses with more coherent and simultaneous data collection. The challenge ahead is to create a comprehensive and universally agreed-upon classification system that meets the needs of professionals and researchers.

Coronary artery disease in patients with rheumatic valvular heart disease- An analysis from eastern India

IntroductionThere is insufficient information on the angiographic characteristics of individuals with rheumatic valvular heart disease (VHD) from eastern India. The objective of this research is to gather important data in this area to aid the best surgical outcomes for patients with rheumatic VHD. Materials and methods978 consecutive patients with rheumatic VHD, scheduled for surgical intervention, were recruited. Result and conclusionMitral valve involvement was observed in 66.1 %, aortic valve in 7.3 % and both valves in 26.6 %. Patients with CAD had significantly higher proportions of severe aortic stenosis (AS). Therefore, addressing the risk factors for CAD is crucial in patients with rheumatic VHD.

Prominent expression method for line features in multiview reconstruction

In multi-view reconstruction, mesh models are one of the most important data carriers. However, existing reconstruction methods have not adequately considered the existence of real-world edge features, resulting in insufficient utilization of geometric feature information from images. This leads to issues such as surface distortion and a lack of prominent edge features in the reconstructed mesh models. To address these challenges, this paper proposes a method of line features prominent expression during the multi-view reconstruction process. This algorithm integrates image line features and the mesh model to enhance geometric features. Specifically, in this paper, the line features on the image are first extracted, and the 2D line features are sampled and fitted to synthesize 3D line features by depth map. Then, it clusters the 3D line features and establishes minimum bounding boxes. Finally, the mesh line features are determined based on graph cut methods and are prominently expressed in the mesh model. Experimental validation demonstrates that in terms of the accuracy of line feature expression, the average reprojection pass rate for line features in five datasets reaches 93.7 %. This proves that this method significantly mitigates the issue of unclear edge features on the model surface, greatly improving the visual quality of mesh models while maintaining a high accuracy.

Sentiment analysis of reviews on cappadocia: The land of beautiful horses in the eyes of tourists

Abstract The Cappadocia region is one of the most popular tourist destinations in Turkey, and its tourism sector has a significant share in the Turkish economy. In this study, we scraped TripAdvisor reviews of visitors of the Cappadocia region with the Python programming language and used them to analyse public sentiment using various supervised machine learning algorithms. The main purpose of the study is to help create competitive intelligence on both regional and global scales using social media data. For this, we applied Random Forest, Naïve Bayes, and Support Vector Machine methods to classify 4,770 reviews and get insights about the visitors’ perspectives. Results show that the majority of the tourists (90%) had a positive experience during their visit. Most of the complaints focused on the attitudes of staff members. In addition, all three supervised machine learning methods achieved high accuracy in their classification of the reviews. This study is significant in terms of providing a meaningful database for understanding visitor comments, the most important data for the development of tourism in the region, through state-of-the-art machine learning methods, and to direct improvements accordingly.

Energy-Efficient and QoS-Aware Data Routing in Node Fault Prediction Based IoT Networks

Internet of Things (IoT) enables important data collection of the sensor nodes at desired location through device-to-device communication. It helps us in various applications including tracking of physical objects, human health monitoring, climate smart agriculture, smart manufacturing, field surveillance, and intelligent transportation. Since, these applications utilize multi-hop data transmission framework, Quality-of-Service (QoS) such as data transmission delay, data throughput, and data gathering reliability degrades at the destination nodes. In addition, multi-hop data routing causes more data interference and reduced network lifetime due to non-uniform energy consumption across the network. Moreover, in an IoT network, faulty nodes further reduce the performance of data collection and network lifetime. In order to address the aforementioned challenges, various fault detection and fault tolerant-data routing methods have been proposed in the literature. However, predicting the faulty nodes a prior can result in improved network lifetime and QoS. Hence, in this work, a novel joint node fault prediction based optimal data routing method is proposed in an IoT network. The method utilizes a novel unsupervised learning based Local Outlier Factor (LOF) method for predicting forthcoming faults. The method classifies the faulty state of a sensor node as an outlier when plotted with normal state of the sensor node. Subsequently, a novel data routing method is proposed which utilizes Q-learning framework towards multi-hop data routing. Here, the Q-values decide the optimal routing path, where, data transmission path is altered based on predictions made on the faulty nodes. The performance of the proposed methods is evaluated over both, simulated IoT testbed and real-field dataset. The obtained results demonstrate that the proposed methods are able to successfully predict the faulty nodes and avoid data transmission through them, resulting in improved energy-efficiency and QoS over the network.