Miracle-dz/UniFAR-FTUs huggingface dataset:
Mitigating Soil Erosion through Biomass-Derived Biochar: Exploring the Influence of Feedstock Types and Pyrolysis Temperature

Soil erosion is one of the major emerging threats to the Himalayan ecosystem. There is a dearth of diverse, cost-effective erosion control measures in the region. In the Himalayan region, where agriculture plays a pivotal role in local livelihoods and environmental stability, the management of soil erosion is of paramount importance. Hence, this study investigates the impact of biochar application on soil erosion and its related indices in the temperate Himalayas of India. This study employs a combination of physicochemical analysis and field experiments to assess the influence of biochar on soil erodibility. The research objectives include an examination of the influence of different temperature pyrolyzed biomasses and varying application rates on soil erodibility indices, viz., dispersion ratio (DR), percolation ratio (PR), clay ratio (CR), erosion ratio (ER), and mean weight diameter (MWD), considering two distinct fertilizer regimes. This study yielded quantitative results that shed light on the impact of various soil amendments and application rates on soil erodibility in the temperate Himalayas. Results showed that the mean values of the DR exhibited by amendment levels NB, AB400, AB600, RAC, DW400, DW600, and RDW were 0.37, 0.35, 0.51, 0.44, 0.51, 0.47, and 0.91, respectively. The mean values of DR for different amendment levels varied, with RDW exhibiting the highest erodibility at 0.91, while DW400 and DW600 demonstrated less soil disturbance, making them promising choices for soil erosion mitigation. Notably, the application of pyrolyzed weed residue improved soil erodibility, whereas AB600 resulted in increased soil erosion due to aggregate disintegration, as indicated by the MWD. Aquatic weed residues and apple wood chips applied without pyrolysis increased the soil erodibility, while pyrolyzed residues improved soil erodibility. The DR was 0.41 at the high application rate, 0.48 at the medium rate, and 0.61 at the low application rate. Among application rates, low rates (1 t ha−1) had the highest DR, followed by medium rates (2 t ha−1) and high rates (3 t ha−1). The no-fertilizer level exhibited higher DR (0.49) compared to the fertilized level (0.38). Results inferred that the application of AB400 °C at 3 t ha−1 can be adopted to minimize soil erosion and maintain ecological security in the temperate Himalayas.
Trajectory Tracking of Wheeled Mobile Robot Based on Model Predictive Control without Terminal Constraints

Model Predictive Control(MPC) without terminal constraints can make the controller have higher dynamic performance and less computation. However, how to design a MPC algorithm without terminal constraints for a specific system while ensuring the stability of the system has become a challenging research. In this paper, a feasible form of MPC without terminal constraints is given for nonlinear systems by constructing cost functions and Lyapunov functions. We show that for MPC algorithms without terminal constraints, there is control input to stabilize the system. Then we propose a new trajectory tracking error model based on non-linear the wheeled mobile robot(WMR) system and design a MPC algorithm without terminal constraints under the framework of the model. Finally, the simulation results show that the algorithm can also guarantee the gradual convergence of WMR error tracking system to the origin without terminal constraints.
Using Deep Learning for Classification of Lung Cancer on CT Images in Ardabil Province : Classification of Lung Cancer using Xception

In the past, doctors had to physically locate the region they thought could have lung cancer. Several studies have shown that manual segmentation is time-consuming and dependent on the operator and instrument. To aid healthcare providers in making an early diagnosis of lung cancer, an algorithm known as the XCEPTION Based Classification for Lung Cancer (XCEPTION) has been created. XCEPTION enhances the categorization of lung cancer lesions by using a neural network to assist doctors in diagnosing lung cancer. Medical pictures of lung cancer in malignant, benign, and healthy people may all be classified by XCEPTION. Each and every ethical guideline has been followed while doing research for this study. This approach uses a convolutional neural network to categorize lung cancer in order to help medical practitioners identify lung cancer lesions. With precision and sensitivity, XCEPTION can identify incoming medical photos as belonging to cancer patients or healthy people.
Global travel time tomography with 3-D reference models

In this study, a global high-resolution P-wave velocity model is obtained for the Earth's crust and mantle using travel time tomography. Improvements to previous models are achieved by incorporating additional data and advancing the method to use 3-D reference models. The newly compiled data set consists of a selection of travel times from the updated EHB catalog (Engdahl et al., 1998), which forms a subset of 445,000 events from the ISC bulletins and NEIC data up to September 2004. The data selection contains 17.7 million first and later-arriving P phases and additionally 1.5 million core phases. The data set is extended by 14,000 events from the EMSC bulletins for the period 1998-2003. Furthermore, over 200,000 newly picked, high-accuracy arrival times are incorporated originating from recordings of seismic networks and temporary experiments in Europe and North America which did not report arrival times to the ISC. Methodologically, instead of a standard 1-D Earth reference model, 3-D reference models are used for the tomographic inversion based on a combination of tomography models that use travel time residuals and models that use independent observations from surface waves, normal modes and long period body waves. In addition, the selected events are relocated in the respective 3-D reference model prior to tomography using a directed grid-search technique to establish consistency between travel times and earthquake parameters. The impact of the new regional data contained in the EHB catalog is investigated in a regional Pn tomography study of the crust and uppermost mantle beneath Europe. This analysis shows that for P velocities in the crust and uppermost mantle anomalies of a lateral extent of 0.4 degrees can be resolved and for S velocities anomalies of 0.8 degrees providing detailed images of the crust and uppermost mantle structure. Due to the newly picked data, more detail is seen in the upper 400 km, particularly in regions with few seismic stations and/or earthquakes. For instance, the mantle beneath western North America is imaged in high resolution comparable to regional tomographic studies and due to the new data set the remnants of the Farallon plate, an ancient oceanic plate, can be located in the upper mantle transition zone. Furthermore, features that were already observed earlier are enhanced. Besides that, the core phases help to constrain anomalies in the lowermost mantle and greatly improve, for example, imaging of the hot upwelling beneath Central Europe. Due to the use of 3-D reference models, the resulting model combines the long wavelength structure as seen by long period seismic information with the detailed mantle structure obtained from short period data. As an application of the new high-resolution tomography model, tests with well-located events demonstrate that the new model predicts, particularly at regional distances, arrival times better than a 1-D velocity model, and that epicenter mislocations and origin time errors are reduced using the new model.
Application of seismic refraction methods in groundwater studies in Calabar Southeastern Nigeria.

Calabar, the Cross River State capital, is underlain by Benin Formation. The formation is partly marine, partly deltaic and partly fluviolacustrine in origin. Seismic refraction surveys in the area show that the aquiferous zone has seismic wave velocity of 700-800ms while the non aquiferous zone has velocity ranging from 400-600ms. The velocity is larger in the aquiferous zone because the density in the water bearing sand is higher. Vertical electric sounding confirms the hydrogeologic boundaries between these zones established by the seismic refraction survey. Key words: Seismic Refraction, Groundwater, Velocity, Vertical Electrical Sounding, Aquifer
Enhancing Government Communication Strategies for Effective Health In-formation and Public Health Education

Effective government communication is pivotal in disseminating health information and education to the public, shaping public perceptions, and influencing behavior change. Government agencies play a central role in disseminating critical health information, shaping public perceptions, and fostering behavioral change. This research investigates the multifaceted landscape of government communication strategies within the context of public health. Through an analysis of trust and credibility, messaging effectiveness, challenges related to misinformation, community and stakeholder engagement, and lessons learned, this study provides valuable insights into the strengths and challenges of government health communication. The research reveals that trust in government health sources remains a cornerstone of effective communication, necessitating transparency, responsiveness, and credibility preservation. Clear and accessible messaging emerges as a linchpin for motivating positive health behaviors, with implications for public compliance with guidelines and recommendations. The prevalence of misinformation on digital platforms underscores the urgency of addressing this issue collaboratively, demanding proactive strategies and media literacy promotion. Collaboration with community organizations and stakeholders, coupled with public participation, contributes to the tailoring of health messages and the cultivation of trust among diverse populations. Lessons learned underscore the need for real-time adaptation, proactive strategies to combat misinformation, empowerment of the public through health literacy, and a commitment to transparency. In conclusion, this research offers a roadmap for government agencies seeking to enhance their health communication strategies. The policy implications highlight the importance of prioritizing clarity and accessibility in messaging, fostering partnerships, combating misinformation, building trust, empowering the public, and engaging in real-time monitoring and adaptation. As we navigate the dynamic landscape of health communication, this study reaffirms the pivotal role of government agencies in shaping healthier, more informed, and resilient communities.
Soft Sensor of Effluent Ammonia Nitrogen Concentration Based on Modular Stochastic Configuration Networks

This paper proposes a soft sensing method based on a brain-inspired modular stochastic configuration network for the real-time and accurate measurement of effluent ammonia nitrogen concentration in wastewater treatment processes. This method first uses grey relational analysis to extract the main variables affecting the effluent ammonia nitrogen concentration. Then, based on the fuzzy clustering method, the task is decomposed to alleviate the complexity of each modeling task. After that, following the principle of divide and conquer, a corresponding stochastic configuration network sub-model is constructed for each sub-task, and the results of each sub-model are integrated to improve the measurement performance of effluent ammonia nitrogen concentration. Finally, the effectiveness and accuracy of the proposed method are verified by simulation based on actual water quality data.
Lipid Profile Paradox: Investigating Improved Lipid Levels in Diabetic Mellitus Patients with Foot Ulcer Infections—A Prospective Descriptive Study

Type 2 diabetes mellitus (DM) is a chronic metabolic disorder posing multifaceted challenges to global public health. Among its numerous complications, infected diabetic foot ulcers (IDFUs) represent a particularly debilitating consequence. Beyond cardiovascular implications, there is an emerging understanding of the interconnectedness among IDFUs, neuropathy, atherosclerosis, and dyslipidemia. IDFUs, peripheral neuropathy, and atherosclerosis share common risk factors and mechanistic pathways. The primary objective of this study was to characterize the lipid profiles in DM patients with IDFUs, comparing them with DM patients without foot ulcers, and with a control group of healthy subjects. The secondary objectives included evaluating apolipoprotein E (ApoE) levels across these study groups and comparing lipid profiles within IDFU subgroups. A total of 160 patients were assessed for this study. After applying exclusion criteria, 140 participants were included, divided into three groups: Group IDFU (80 patients with IDFUs), Group DM (32 patients with DM but no foot ulcers), and Group Controls (28 healthy controls). Compared to Group DM, Group IDFU demonstrated lower levels of high-density lipoprotein cholesterol (HDL-C) (30.9 ± 12.6 mg/dL vs. 40.8 ± 16.6 mg/dL, p = 0.002), but improved levels of ApoE (160.9 ± 68.4 mg/dL vs. 197.2 ± 69.6 mg/dL, p = 0.01), triglycerides (TG) (126.9 ± 56.2 mg/dL vs. 165.8 ± 79.0 mg/dL, p = 0.004), low-density lipoprotein cholesterol (LDL-C) (84.2 ± 32.3 mg/dL vs. 92.3 ± 39.3 mg/dL, p = 0.1), and total cholesterol (133.6 ± 43 mg/dL vs. 164.6 ± 44.4 mg/dL, p = 0.002). The IDFU patients exhibit improved lipid profiles, excepting HDL-C, which is unusual because IDFU follows complications related to dyslipidemia for DM patients. Anemia, impaired renal function, and elevated TG levels were identified as biomarkers for mortality among patients with IDFUs. The data suggest that a lower level of HDL-C and an improved lipid profile may indicate a systemic end-stage disease manifestation in DM patients with IDFUs.
Personalization-based deep hybrid E-learning model for online course recommendation system

Deep learning, a subset of artificial intelligence, gives easy way for the analytical and physical tasks to be done automatically. There is a less necessity for human intervention while performing these tasks. Deep hybrid learning is a blended approach to combine machine learning with deep learning. A hybrid deep learning (HDL) model using convolutional neural network (CNN), residual network (ResNet) and long short term memory (LSTM) is proposed for better course selection of the enrolled candidates in an online learning platform. In this work, a hybrid framework that facilitates the analysis and design of a recommendation system for course selection is developed. A student’s schedule for the next course should consist of classes in which the student has shown interest. For universities to schedule classes optimally, they need to know what courses each student wants to take before each course begins. The proposed recommendation system selects the most appropriate course that can encourage students to base their selection on informed decision making. This system will enable learners to obtain the correct choices of courses to be studied.
Thermal diffusivity of thermokarst lake ice in the Beiluhe basin of the Qinghai–Tibetan Plateau

Abstract The ice cover on the Qinghai–Tibetan Plateau plays an important role in the environmental and ecological systems. We analyze the in situ measurements of ice growth and examine the thermal diffusivity of thermokarst lake ice in the Beiluhe basin. We evaluate numerically the change of thermal diffusivity of thermokarst lake ice with changing ice temperature using an optimal control model. In a higher ice temperature regime (–3 to 0°C), the thermal diffusivity of thermokarst lake ice decreases exponentially with increasing ice temperature, and approaches the thermal diffusivity value of fresh water near the freezing-point temperature. In a lower ice temperature regime (–15 to –3°C), the thermal diffusivity increases slowly with decreasing ice temperature.
Validity and Reliability of POM-Checker for Measuring Shoulder Range of Motion in Healthy Participants: A Pilot Single-Center Comparative Study

Background. The aim of this study was to compare shoulder movement measurements between a Kinect-based markerless ROM assessment device (POM-Checker) and a 3D motion capture analysis system (BTS SMART DX-400). Methods. This was a single-visit clinical trial designed to evaluate the validity and reliability of the POM-Checker. The primary outcome was to assess the equivalence between two measurement devices within the same set of participants, aiming to evaluate the validity of the POM-Checker compared to the gold standard device (3D Motion Analysis System). As this was a pilot study, six participants were included. Results. The intraclass correlation coefficient (ICC) and the corresponding 95% confidence intervals (CIs) were used to assess the reproducibility of the measurements. Among the 18 movements analyzed, 16 exhibited ICC values of >0.75, indicating excellent reproducibility. Conclusion. The results showed that the POM-checker is reliable and validated to measure the range of motion of the shoulder joint.
A Framework for Automated Cardiovascular Magnetic Resonance Image Quality Scoring based on EuroCMR Registry Criteria

Cardiovascular magnetic resonance (CMR) imaging is a radiation-free modality widely used for functional and structural evaluation of the cardiovascular system. Achieving an accurate diagnosis requires having good-quality images. Subjective CMR image quality assessment is a tedious, time-consuming and costly process. This paper presents an automated scoring framework for CMR image quality assessment that uses deep learning models to evaluate left ventricular coverage and CMR imaging artefacts. The quality scoring in the proposed framework is an attempt to automate some of the subjective quality control criteria of the EuroCMR registry for the short-axis cine steady-state free precession (SSFP) CMR images. The scores given by a radiologist and a cardiologist with experience in CMR imaging for the images of 50 subjects from the UK Biobank were used to validate the proposed framework. The Pearson correlation coefficient (PCC) and the Spearman rank-order correlation coefficient (SRCC) calculated for the experts’ quality scores versus ones obtained from the proposed framework are 0.908 and 0.806 on average. The results show that the quality scoring by the proposed framework is highly correlated with the experts’ opinions. The proposed framework can be used for post-imaging quality assessment of short-axis cine SSFP CMR images and quality control of large population studies such as the UK Biobank.
COMPARISON OF INERTIAL NAVIGATION SYSTEM ERROR MODELS IN APPLICATION TO IMU TRANSFER ALIGNMENT

,This paper compares IMU transfer alignment results from using different INS system error model forms for a local level wander azimuth system. Actual recorded test data is processed by Kalman filter implementations of three INS error dynamic models. These three INS error model forms are obtained by using velocity error representations referred to as either computer frame or perturbation forms. For the computer frame form, two variations of that form are obtained from either of the assumptions - the local vertical axis tilt error, $z, is zero, or the navigation frame vertical axis misalignment, 66z, is zero. The perturbation form assumes the local vertical axis tilt error, 2 = 0 , and perturbation model results are similar for estimating the body referenced misalignment between the IMU and the aircraft's reference navigation system; and 3) the computer frame model, with ddt = 0 , produces results that more accurately reflect navigation system errors during aircraft maneuvers.
NTT Technical Review, Vol. 17, No. 11, Nov. 2019

Noninvasive glucose measurement without needle pricking is anticipated as a novel medical and healthcare application. We introduce here our research on the use of near-infrared photoacoustic spectroscopy and microwave dielectric spectroscopy for noninvasive glucose measurement using electromagnetic waves. We also present our recent work involving in vivo measurement. Both measurement techniques are based on optical and wireless components and system integration, which have been investigated in telecommunication system development at NTT.
The Cultural Production of the Educated Person: A Case Study the New Student Admision in Pangkalpinang Bangka

The purpose of this article is to produce new findings or the development of existing theories stating that “the more mature the child the easier it will be for the teacher to teach”. The findings from this research can be used to provide criticism and information for policy, education, and teachers and parents regarding whether children should enter school at the age of 6 and why parents must comply with state regulations on new student admission process (PPDB). The research was conducted in Pangkalpinang, Bangka Belitung Island Province. The researcher observes 4 schools, 7 homeroom teachers of first grade, 4 principles and 235 students of first grade. The technique in collecting data in this article are structured interview and participant observation. This study uses qualitative research method through anthropological approach using ethnography. Finally, the data analysis technique in this article uses reduction, display and verification of data. The article asks whether the culture of school in Bangka Island promote or hinders the principle of the proper age of School. We found that Jean Piaget’s stages of cognitive development have influenced the proper age for school in Bangka Island. The authors believe that Bangka follows the cultural global construction in determining the proper age of a child to enter the elementary school. Keywords: Cultural Production, Student Admission Process, School Age