Miracle-dz/UniFAR-FTUs huggingface dataset:
House price prediction using hedonic pricing model and machine learning techniques

The problem with property valuation is that it is extremely complex. It is difficult to objectively model the pricing process or fairly estimate a property value. Many factors can contribute to this complexity such as spatial and time factors. Evaluators and researchers have been trying to model the process for centuries. Up until recently, when computer‐aided valuation systems provided better solutions in the data evaluation and real estate valuation. Nevertheless, they may suffer from low transparency, inaccuracy, and inefficiency. This work explores the ability of machine learning techniques (MLTs) in enhancing economic activities by increasing the accuracy of house price prediction. In this article, XGBoost algorithm has been integrated with outlier sum‐statistic (OS) approach. In the real estate industry, the price of property plays a crucial role in economic growth. The research attempts to predict the price of a house using MLTs. Here, the price of the property is predicted using Extreme Gradient (XG) boosting algorithm and hedonic regression pricing. Both XGBoost and hedonic pricing models use 13 variables as inputs to predict house prices. The contribution of this research lies in the practicality of using XGboost technique to predict house prices. Finally, the accuracy of the prediction algorithms is reported with XGBoosting showing the highest accuracy of 84.1% while the accuracy of the hedonic regression algorithm is 42%.
Population Structure of Finless Porpoise (Neophocaena phocaenoides) Discovered off Coastal Waters, Republic of Korea

The finless porpoise (Neophocaena phocaenoides Cuvier, 1829) is distributed in the coastal waters of Asia, throughout Indonesia to the east, and as far north as the Taiwan Strait. The finless porpoise has been declared critically endangered by the WWF (World Wide Fund for Nature), and in 2017 was rated vulnerable on the IUCN Red Threatened Species List. Since this species is distributed near the coast and has many interactions with humans, effective conservation of the species requires further studies into their genetic diversity and population. In this study, 45 samples were obtained from bycatch or stranded individuals in the East, South, and West Seas, where Korean porpoises were mainly distributed from 2017–2021. We compared 473 bp mtDNA sequences from the control region. Pairwise fixation indices (FST) revealed that the two populations differed significantly (FST = 0.4557, p = 0.000). In contrast to high levels of genetic differentiation, gene flow was identified as medium levels (Nm of 0.04–0.71). Our data suggest that finless porpoises may have undergone a historic differentiation event, and that finless porpoises in the three regions could be divided into two populations: West and East/South.
The Effect of Fu's Subcutaneous Needling Combined with Reperfusion Approach on Surface Electromyography Signals in Patients with Cervical Spondylosis and Neck Pain: A Clinical Trial Protocol

Background Neck pain is one of the most common musculoskeletal diseases. Fu's subcutaneous needling therapy is a special acupuncture method that targets muscle trigger points. It has been proven to have a positive effect on the treatment of neck pain. The access to its curative effect may be related to the improvement of muscle and soft tissue condition. The purpose of this study is to evaluate the outcome of Fu's subcutaneous needling therapy for patients with neck pain by collecting changes in the sEMG of the patient's neck muscles and related data from evaluation scales and explore the feasibility and safety of Fu's subcutaneous needling therapy for neck pain. Methods 72 patients meeting the inclusion criteria were randomly divided into FSN group and acupuncture group for corresponding treatment. FSN group was treated once every other day for 5 consecutive treatments; the acupuncture group was treated once a day for 10 consecutive treatments. Result Outcome indicators were measured at baseline, after the first treatment and the end of the treatment. Primary outcome indicators: average EMG (AEMG) and (mean power frequency) MPF of sternocleidomastoid muscle and superior trapezius muscle. Secondary outcome indicators: Mc Gill pain questionnaire (MPQ), neck disability index (NDI), and adverse reactions. Conclusions This study will explore the efficacy, safety, and possible mechanism of Fu's subcutaneous needling therapy for patients with neck pain, thus to provide more evidence support for clinical decision-making. This trial is registered with Chinese Clinical Trial Register Center (registration number ChiCTR2100043529).
Deep Learning Techniques for the Effective Prediction of Alzheimer’s Disease: A Comprehensive Review

“Alzheimer’s disease” (AD) is a neurodegenerative disorder in which the memory shrinks and neurons die. “Dementia” is described as a gradual decline in mental, psychological, and interpersonal qualities that hinders a person’s ability to function autonomously. AD is the most common degenerative brain disease. Among the first signs of AD are missing recent incidents or conversations. “Deep learning” (DL) is a type of “machine learning” (ML) that allows computers to learn by doing, much like people do. DL techniques can attain cutting-edge precision, beating individuals in certain cases. A large quantity of tagged information with multi-layered “neural network” architectures is used to perform analysis. Because significant advancements in computed tomography have resulted in sizable heterogeneous brain signals, the use of DL for the timely identification as well as automatic classification of AD has piqued attention lately. With these considerations in mind, this paper provides an in-depth examination of the various DL approaches and their implementations for the identification and diagnosis of AD. Diverse research challenges are also explored, as well as current methods in the field.
Managing, multiple feature models : foundations, languages and applications

L’ingenierie des lignes de produits logiciels (LdPs) est un paradigme pour la modelisation et le developpement de familles de systemes logiciels plutot que de systemes logiciels individuels. Son objectif porte sur les moyens de produire et maintenir efficacement des produits logiciels similaires en exploitant ce qu’ils ont en commun et en gerant ce qui varie entre eux. Par analogie, la pratique dans l’industrie automobile est de construire une ligne de production dans laquelle des variations personnalisees mais tout de meme similaires de modeles de voitures sont produits. Les feature models (FMs) sont une representation fondamentale pour specifier et raisonner sur la commonalite et la variabilite des LdPs en termes de features (caracteristiques). Les FMs deviennent de plus en plus complexes, manipules par plusieurs developpeurs ou organisations, utilises pour decrire des features a divers niveaux d’abstraction et qui sont mises en relation de differentes facons. Maintenir un seul gros FM n’est ni realiste ni souhaitable. Au contraire une tendance forte est de considerer de multiples FMs. Dans cette these, nous developpons les fondations theoriques et un support pratique pour gerer de multiples FMs. Nous concevons et developpons un ensemble d’operateurs de composition et de decomposition (aggregate, merge, slice) pour supporter la separation des preoccupations. Les operateurs sont formellement definis et implementes avec un algorithme qui garantit des proprietes semantiques. Nous montrons comment les operateurs de composition et de decomposition peuvent etre combines ensemble ou avec d’autres operateurs d’edition ou de raisonnement pour realiser des taches complexes. Nous proposons un langage textuel, FAMILIAR (pour FeAture Model scrIpt Language for manIpulation and Automatic Reasoning), qui fournit une solution operationnelle a la gestion de multiples FMs a large echelle. Un utilisateur des FMs peut combiner les differents operateurs et manipuler un ensemble restreint de concepts (FMs, features, configurations, etc. ) en utilisant une notation concise et des facilites linguistiques. FAMILIAR cache les details d’implementations (e. G. , solveurs) et est supporte par un environnement de developpement complet. Nous decrivons plusieurs applications de ces operateurs et utilisations de FAMILIAR dans differents domaines (messagerie medicale, video protection) et pour differents objectifs (conception de workflows scientifiques, modelisation de la variabilite des exigences a l’execution, retro ingenierie), demontrant l’applicabilite a la fois des operateurs et du langage de support. Sans les nouvelles capacites fournies par les operateurs et FAMILIAR, certaines operations d’analyse et de raisonnement n’auraient pas ete possibles dans les differents cas d’etudes. Pour conclure, nous discutons les differentes perspectives de recherche a moyen terme (operateurs, langage, elements de validation) et a long terme (e. G. Relations entre les FMs et les autres modeles).
Gamification Intensity in Web-Based Virtual Training Environments and Its Effect on Learning

Gamification approaches to learning use game-inspired design elements to improve learning. Given manifold design options to implement gamification in virtual environments, an important but underexplored research area is how the composition of gamification elements affects learning. To advance research in this area, we systematically identified key design elements that have shown promise in leading to positive learning results. We then conducted an experiment in which we varied gamification intensity in web-based virtual training environments for a procedural industrial task. In total, 355 participants were divided into a baseline group without gamification, a basic, and an advanced gamification group. Analysis of participants’ learning included learning outcomes (time-to-completion and number of mistakes), affective learning factors (motivation, self-efficacy, and satisfaction), learning system usability, and perceived cognitive load throughout the learning process. The results did not show any statistically significant differences between the lower and higher levels of gamification intensity. Conversely, we found that participants’ computer gaming habits and technical equipment (display size and computer pointing device) significantly influenced learning.
Performance of Remsima® Monitor Drug Level versus RIDASCREEN IFX Monitoring in therapeutic drug monitoring of infliximab in patients with inflammatory bowel disease: A study of diagnostic accuracy

Therapeutic drug monitoring (TDM) is effective in optimizing the efficacy of infliximab in patients with inflammatory bowel disease (IBD). An affordable way of monitoring is in high demand. This study evaluated the analytical and clinical performances of the newly available Remsima monitor kits and compared them with the established enzyme-linked immunosorbent assay kits. The trough level of infliximab in patients with IBD treated with an infliximab originator (Remicade) or biosimilar compounds (Remsima and Remaloce) was measured using a Remsima® Monitor Drug Level (Remsima) kit at the Samsung Medical Center, Seoul, Korea. Twenty-six plasma samples were collected immediately before the infusion of infliximab from 18 patients with IBD (Remicade, n = 8; Remsima, n = 6; and Remaloce, n = 4). The intra-assay intraclass correlation coefficient (ICC) of the RIDA and Remsima kits was 0.951 (95% CI = 0.908–0.976) and 0.990 (95% CI = 0.981–0.995). The inter-assay ICC of infliximab trough level between the RIDA and Remsima kits was very high (R = 0.971; 95% CI = 0.935–0.987), and the mean difference between the kits was 1.458 (95% limits of agreement = −3.302 to 6.219). The intra- and inter-assay reliabilities of all types of infliximab did not show significant differences. Qualitative stratification revealed substantial similarities between the kits (weighted kappa = 0.798). This study indicated that the Remsima kit was reproducible and highly correlated with the RIDA kit.
The REFLEX galaxy cluster survey - VII. Omega(m) and sigma(8) from cluster abundance and large-scale clustering

For the first time the large-scale clustering and the mean abundance of galaxy clusters are analysed simultaneously to get precise constraints on the normalized cosmic matter density $\Omega_{\rm m}$ and the linear theory RMS fluctuations in mass $\sigma_8$. A self-consistent likelihood analysis is described which combines, in a natural and optimal manner, a battery of sensitive cosmological tests where observational data are represented by the (Karhunen-Loeve) eigenvectors of the sample correlation matrix. This method breaks the degeneracy between $\Omega_{\rm m}$ and $\sigma_8$. The cosmological tests are performed with the ROSAT ESO Flux-Limited X-ray (REFLEX) cluster sample. The computations assume cosmologically flat geometries and a non-evolving cluster population mainly over the redshift range $0<z<0.3$. The REFLEX sample gives the cosmological constraints and their $1\sigma$ random errors of $\Omega_{\rm m}\,=\,0.341\,^{+0.031}_{-0.029}$ and $\sigma_8\,=\,0.711\,^{+0.039}_{-0.031}$. Possible systematic errors are evaluated by estimating the effects of uncertainties in the value of the Hubble constant, the baryon density, the spectral slope of the initial scalar fluctuations, the mass/X-ray luminosity relation and its intrinsic scatter, the biasing scheme, and the cluster mass density profile. All these contributions sum up to total systematic errors of $\sigma_{\Omega_{\rm m}}=^{+0.087}_{-0.071}$ and $\sigma_{\sigma_8}=^{+0.120}_{-0.162}$.
Objective Supervised Machine Learning-Based Classification and Inference of Biological Neuronal Networks

The classification of biological neuron types and networks poses challenges to the full understanding of the human brain’s organisation and functioning. In this paper, we develop a novel objective classification model of biological neuronal morphology and electrical types and their networks, based on the attributes of neuronal communication using supervised machine learning solutions. This presents advantages compared to the existing approaches in neuroinformatics since the data related to mutual information or delay between neurons obtained from spike trains are more abundant than conventional morphological data. We constructed two open-access computational platforms of various neuronal circuits from the Blue Brain Project realistic models, named Neurpy and Neurgen. Then, we investigated how we could perform network tomography with cortical neuronal circuits for the morphological, topological and electrical classification of neurons. We extracted the simulated data of 10,000 network topology combinations with five layers, 25 morphological type (m-type) cells, and 14 electrical type (e-type) cells. We applied the data to several different classifiers (including Support Vector Machine (SVM), Decision Trees, Random Forest, and Artificial Neural Networks). We achieved accuracies of up to 70%, and the inference of biological network structures using network tomography reached up to 65% of accuracy. Objective classification of biological networks can be achieved with cascaded machine learning methods using neuron communication data. SVM methods seem to perform better amongst used techniques. Our research not only contributes to existing classification efforts but sets the road-map for future usage of brain–machine interfaces towards an in vivo objective classification of neurons as a sensing mechanism of the brain’s structure.
Diabatic potential energy surfaces and semiclassical multi-state dynamics for fourteen coupled 3 A′ states of O3

Dissociation and energy transfer in high-energy collisions of O2 play important roles in simulating thermal energy content and heat flux in flows around hypersonic vehicles. Furthermore, atomic oxygen reactions on the vehicle surface are an important contributor to heat shield erosion. Molecular dynamics modeling is needed to better understand the relevant rate processes. Because it is necessary to model the gas flows in high-temperature shock waves, electronically excited states of O2 and O can be populated, and molecular dynamics simulations should include collisions of electronically excited species and electronically nonadiabatic collisions. This requires potential energy surfaces and state couplings for many energetically accessible electronic states. Here we report a systematic strategy to calculate such surfaces and couplings. We have applied this method to the fourteen lowest-energy potential energy surfaces in the 3 A′ manifold of O3, and we report a neural-network fit to diabatic potential energy matrix (DPEM). We illustrate the use of the resulting DPEM by carrying out semiclassical dynamics calculations of cross sections for excitation of O2 in 3 A′ collisions with O at two collision energies; these dynamics calculations are carried out by the curvature-driven coherent switching with decay of mixing method.
Groundwater Resources in a Complex Karst Environment Involved by Wind Power Farm Construction

The need to produce energy from clean energy sources has caused public administrations and private companies to look for suitable places. The windiness detected in the eastern area of the Matese karst massif (southern Italy) has favored the construction of wind farms to produce electricity from clean energy sources. During the installation of the first wind turbines, some alterations in the supply of drinking water, fed by the springs of this area, were attributed by the population to this installation. Therefore, in order to assess whether there has been an impact produced by the wind farms on the quality of groundwater, a detailed hydrogeological study was developed. Karst hydrogeological features of the area were mapped, focusing on endorheic areas, sinkholes and karst springs. Artificial tracer tests were then carried out to investigate groundwater flow circulation and connection between surface karst landforms and springs. Chemical and physical characteristics of the groundwater were monitored during the construction of the wind farms and, for the following months, by infield measurements and laboratory analysis of spring water samples. This study highlights that wind farms mainly develop along the boundary of endorheic areas, which are important recharge zones for groundwater resources, and are directly connected to the major karst springs through sinkholes and a dense network of karst conduits. The results of the monitoring did not reveal any anomalies in the quality of the water and, therefore, any alterations cannot be attributed to the wind farms. Our investigation appears useful for a better understanding of the possible actual and future effects of the wind farms on both groundwater circulation and spring water quality in this karst area.
Toward a contextualized understanding of inside sales: the role of sales development in effective lead funnel management

Purpose The purpose of this study is to provide a contextualized understanding of how business-to-business (B2B) firms use the sales development function for efficient and effective lead funnel management. Design/methodology/approach The authors adopted a qualitative field-study approach and interviewed 13 people from eight firms. While software as a service (SaaS) firms are the most prevalent application context for the sales development function, the authors also included respondents from non-SaaS firms to develop an in-depth understanding of the contextualized nature of the sales development process. Findings Sales development processes can be applied in outbound prospect-focused, outbound account-based, inbound prospect-focused and inbound account-based lead management contexts. The sales development processes of lead research, engagement and handover vary depending on the nature of the lead management context. These processes are supported by the appropriate design of organizational, technological and people platforms. Practical implications The authors explain how sales development as a form of inside sales can support effective lead funnel management in B2B firms through technology-enabled lead research and nurture processes designed to prepare customers for meaningful conversations with field sales. Originality/value To the best of the authors’ knowledge, this study is the first to focus purely on the sales development function as a form of inside sales. They explain how the sales development processes relating to lead research, engagement and handover are conducted in four distinct application contexts to qualify leads for the outside salesforce.
Alterations of Microorganisms in Tongue Coating of Gastric Precancerous Lesion Patients with a Damp Phlegm Pattern

Objective. In the research, the microbial changes in the tongue coating of patients with a damp phlegm pattern of gastric precancerous lesion (GPL) were investigated. Methods. This was a case-control study, in which 80 tongue coating samples were collected including 40 patients with a damp phlegm pattern of GPL, 20 patients with a nondamp phlegm pattern of GPL, and 20 healthy control people. The 16S rRNA microbiome technology was used to analyze the alterations of microorganisms in tongue coating of GPL patients with a damp phlegm pattern. Results. Microorganisms in the genus level were analyzed. Compared with the healthy control group, the relative abundance of 4 microorganisms (Solobacterium, Rothia, Oribacterium, and Alloprevotella) in the GPL group was significantly higher ( P < 0.05 ). The relative abundance of 10 microorganisms (Terrisporobacter, Solobacterium, Porphyromonas, Parvimonas, Lactobacillus, Johnsonella, Gemella, Fusibacter, Azoarcus, and Acidothermus) in the GPL damp phlegm pattern group was significantly lower than that in the GPL nondamp phlegm pattern group ( P < 0.05 ). In the comparison of phenotype “forms biofilms,” the relative abundance of microorganisms in the GPL group was significantly higher than that in the healthy control group ( P < 0.05 ). In the comparison of phenotype “contains mobile elements,” the relative abundance of microorganisms in the GPL damp phlegm pattern group was significantly lower than that in the GPL nondamp phlegm pattern group ( P < 0.05 ). In the comparison of microbial metabolic functions, the abundance ratio of “infectious diseases: bacterial” in the GPL group was significantly lower than that in the healthy control group ( P < 0.05 ). The abundance ratio of the “excretory system” and “folding, sorting, and degradation” in the GPL group was significantly higher than that in the healthy control group ( P < 0.05 ). Conclusions. Solobacterium may be a marker microorganism of the GPL damp phlegm pattern. The differential phenotype of microorganisms in tongue coating of the GPL damp tongue pattern is mainly reflected in “forms biofilms” and “contains mobile elements. ”
Nonlinear Fractional Backward Doubly Stochastic Differential Equations with Hurst Parameter in (1/2,1)

We first state a special type of It\^o formula involving stochastic integrals of both standard and fractional Brownian motions. Then we use Doss-Sussman transformation to establish the link between backward doubly stochastic differential equations, driven by both standard and fractional Brownian motions, and backward stochastic differential equations, driven only by standard Brownian motions. Following the same technique, we further study associated nonlinear stochastic partial differential equations driven by fractional Brownian motions and partial differential equations with stochastic coefficients.