Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanism of One-Step Hydrothermally Synthesized Titanate Catalysts for Ozonation

A titanate nanotube catalyst for ozonation was synthesized with a simple one-step NaOH hydrothermal treatment without energy-consuming calcination. The synthesized titania catalysts were characterized by X-ray diffraction (XRD), Raman, porosimetry analysis, high-resolution transmission electron microscopy (HR-TEM), Fourier transformed infrared (FTIR), and electron paramagnetic resonance (EPR) analysis. The catalyst treated with a higher concentration of NaOH was found to be more catalytically active for phenol removal due to its higher titanate content that would facilitate more OH groups on its surface. Furthermore, the main active oxidizing species of the catalytic ozonation process were recognized as singlet oxygen and superoxide radical, while the hydroxyl radical may only play a minor role. This work provides further support for the correlation between the properties of titania and catalytic performance, which is significant for understanding the mechanism of catalytic ozonation with titania-based materials.
Successful Congenital Kyphoscoliosis (with Severe Truncal Shift) Correction Associated with Syrinx and Diastematomylia without Prior Neurosurgical Intervention: A Case Report and Brief Review of Literature

Introduction and Importance: The use of halo-femoral traction to ease correction in patients with scoliosis with multiple intraspinal pathologies is an evolving technique that is not yet fully understood. This report aimed to dem-onstrate the efficiency of using this technique to achieve maximum deformity correction along with decreasing complication rates. Case Presentation: Congenital scoliosis with two intraspinal pathologies was corrected in a 17-year-old male with severe truncal shift, who was easily fatigued due to respiratory symptoms, utilizing the traction method with no neurosurgical intervention. The patient had consulted many hospitals locally and internationally, and was told that surgery poses a great risk of neurological deficit. He needed neurosurgical release of the cord prior to any deformity correction. Management and Outcome: The patient did not undergo any neurosurgical intervention, but rather posterior spinal correction and instrumented fusion preceded by ha-lo-femoral traction. He was followed up for 5 years and showed complete recovery without any short-term or long-term complications. The patient was able to return to full activity and resolution of respiratory symptoms, and a good alignment on follow-up radiography was observed. Discussion: Not all patients with scoliosis associated with intraspinal pathology need treatment before spinal correction. Perioperative halo-femoral traction seems to
Evaluation of Low-cost Particulate Matter (PM) Sensors: A Preliminary Investigation

The new generation of low cost and highly portable Particulate Matter (Williams et al.) sensors, which offer significant advantages in terms of compact size and low cost, opens an exciting opportunity for people to use such sensors for a wide range of applications. There have been an increased number of studies focusing on the evaluation and calibration of various PM sensor systems, but how these sensors actually perform in the real situation still remains unknown to the society. In this study, three low-cost PM sensors based on light scattering and two commercial high-cost PM monitoring products were tested to preliminarily investigate their performances in three realistic indoor environmental settings (incense burning, spraying air-freshener, and spraying whiteboard cleaner) to help us understand their availability, usability, and overall feasibility. Two metrics, i.e., response time and the linearity of response are selected as the evaluation indexes. A preliminary investigation of low- cost PM sensors’ potential applications with building automation system in smart buildings is presented. The conclusion suggests that low-cost sensors perform reasonably well in quick resphighonse and yield good linearities. Besides, low-cost sensors have a potential for smart building applications including building filtration control.
How to Sustain Quality Education in a Fully Online Environment: A Qualitative Study of Students’ Perceptions and Suggestions

This study explored the learning experience of university students in Hong Kong, where remote online instruction was adopted to sustain student learning during the first 2 years of the COVID-19 pandemic. Based on student voices, this study aimed to improve online instruction. A qualitative case study approach was adopted, involving 12 university students from different subject disciplines and universities. Individual semi-structured interviews were conducted, followed by a series of qualitative data analysis procedures. The findings of this study suggested that our participants were generally satisfied with the way synchronous online lessons progressed, but that instructors’ technical problems were common. Furthermore, the students’ self-reported level of engagement tended to be negative due to, for example, lengthy lessons and poor group dynamics in virtual rooms. We also revealed the need for professional development opportunities for instructors to enhance their online teaching skills. Recommendations for improving online instruction are discussed, such as limiting online lessons to between 90 and 120 min, and allowing students to form their own groups, while helping shy students to join a group. This study thus has important implications for sustaining quality education in a fully online environment.
CHANNEL ESTIMATION USING GAUSSIAN PROCESS REGRESSION

Gaussian process (GP) regression can be used in the interpolation of observed periodic channel estimates in OFDM transmission systems over both time and frequency in small-scale fading environments. Previous GP regression studies used the popular radial basis function as the GP kernel. In this study, we examine the performance of GP regression using a Bessel kernel with a semi-static hyperparameter vector. Results show that GP regression using the Bessel kernel out-performs the radial basis kernel, as well as traditional interpolation methods such as cubic spline and FIR interpolation, especially when training symbols are spaced far apart in time with respect to the channel coherence time.
Multiperiod planning and uncertainty issues in cellular manufacturing: a review and future directions

In this paper we review research that has been done to address cellular manufacturing under conditions of multi-period planning horizons, with demand and resource uncertainties. Most traditional cell formation procedures ignore any changes in demand over time caused by product redesign and uncertainties due to volume variation, part mix variation, and resource unreliability. However in today‟s business environment, product life cycles are short, and demand volumes and product mix can vary frequently. Thus cell design needs to address these issues. It is only recently that researchers have been modelling uncertainty and multi-period issues. In this paper we conduct a comprehensive review of the work that addresses these issues. We present mathematical programming formulations as well as a taxonomy of existing models. Finally we suggest some directions for future research.
Bubbly Collateral and Economic Activity PRELIMINARY AND INCOMPLETE: PLEASE DO NOT CIRCULATE

This paper develops a model of the bubbly economy and uses it to study the eﬀects of bailout policies. In the bubbly economy, weak enforcement institutions do not allow ﬁrms to pledge future revenues to their creditors. As a result, ‘fundamental’ collateral is scarce and this impairs the intermediation process that transforms savings into capital. To overcome this shortage of ‘fundamental’ collateral, the bubbly economy creates ‘bubbly’ collateral. This additional collateral supports an intricate array of intra- and inter-generational transfers that allow savings to be transformed into capital and bubbles. Swings in investor sentiment lead to ﬂuctuations in the amount of bubbly collateral, giving rise to bubbly business cycles with very rich and complex dynamics. Bailout policies can aﬀect these dynamics in a variety of ways. Expected bailouts provide additional collateral and expand investment and the capital stock. Realized bailouts reduce the supply of funds and contract investment and the capital stock. Thus, bailout policies tend to foster investment and growth in normal times, but to depress investment and growth during crisis periods. We show how to design bailout policies that maximize various policy objectives.
A Review of Path Loss Models for UHFRADIOWaves Propagation: Trends andAssessment .

: Wave propagation models are essential and very important tools for determining the wave propagation characteristics for a particular environment. Path loss predictions are therefore required for the coverage planning, determination of multipath effects as well as interference and cell calculations. These calculations lead to high level network planning. The planning process includes the prediction of the received power which determines the parameter of the base station transceiver. In wireless broad band services, delay spread, angular speed and impulse response of the mobile channel have become more and more important for the planning process. This paper outlines the trends in the development of various path loss models and assessing these models.
Blended Learning and employability- A case study

- In today’s technological world there is a lot of transformation in the field of education. The traditional teaching approach has been changed to modern techno oriented approach by incorporating use of Information and Communication Technology (ICT) tools such as use of internet, mobile computing, learning management system etc. This also is helping in student engagement and better employment. In this paper the authors tracks the placement information, for the last few years for a post graduate programme. It was observed that use of active & collaborative learning techniques at the institute has helped in preparing the students for the industry. Typical placement data shows an improvement in the placement records.
Intelligent dashboard to support for decision making in business courier company

The aim of this paper was to research, evaluate and present a study on an intelligent dashboard to support decision making in Courier Company based on Artificial Intelligence techniques. Brazil has gone through several transformations of general services have been adapting to the new demands of customers and market. As a result, the courier service has become highly complex and competitive. The transport, treatment and distribution remained follow these trends. In this context, the application of intelligent techniques to support decision-making is an alternative, seeking productivity and high level of service. The methodological synthesis of the article is to develop a dashboard supported by Artificial Intelligence techniques. An Artificial Neural Network (ANN) Type Multilayer Perceptron (MLP), trained by error back-propagation algorithm was developed and applied to make demand forecast and forecast absenteeism, these forecasts were presented in intelligent dashboard to support decision. In addition we applied the Self-Organizing Map of Kohonen to generate clusters seeking better visualization to be used on the dashboard . The data for the experiments were collected in a courier company. It was concluded that the application of techniques helped in the creation of an intelligent dashboard to support decision making.
PATIENT SATISFACTION IN A TERTIARY CARE GOVERNMENT HOSPITAL

: OBJECTIVES: To determine patient satisfaction in terms of various services such as ease of getting care, in-patient admission, waiting for check-up, staﬀ interaction, medical care, cost of care, and cleanliness in a Tertiary Care Government Hospital. METHODOLOGY: The study design was cross sectional observational. A total of 200 patients meeting our inclusion criteria were enrolled through convenient sampling techniques in this research study. A well-designed questionnaire was used for data collection of admitted patients. RESULTS: Out of 200 patients, 96 (48%) male and 104 (47%) female patients were interviewed. 72% were satisﬁed with clinical care services, 14% had mixed views while 14% patients were not satisﬁed at all. CONCLUSION: Patients in general showed their satisfaction in some of the aspects, however, keeping in view the burden a public sector hospital absorbs on a daily basis, and it was imperative to receive unsatisfactory feedback from patients in most of the aspects. Thus, the results of my study recommend to the health care leaders to take practical steps to bring further improvement by developing a proper mechanism for the uplift of public sector hospitals.
Case Report Psychosis-Induced Takotsubo Syndrome

Background: Takotsubo Syndrome (TS) is an acute and reversible wall motion abnormality of the left or right ventricular myocardium, most commonly induced by emotional or physical stress. Mood and anxiety disorders are the most common mental triggers to TS. Objective: The relationship between psychotic affective disorders and Takotsubo syndrome has been described very rarely. Methods: The authors describe the case of a 55-year old woman with a previous history of depression and alcohol abuse, who presented to the emergency department with an acute psychotic episode that triggers an asymptomatic Takotsubo Syndrome. Results/Conclusions: The authors wants to alert to the atypical presentation of TS and the multidisciplinary management crucial to favorable outcome.
Recalibrating probabilistic forecasts to improve their accuracy

The accuracy of human forecasters is often reduced because of incomplete information and cognitive biases that affect the judges. One approach to improve the accuracy of the forecasts is to recalibrate them by means of non-linear transformations that are sensitive to the direction and the magnitude of the biases. Previous work on recalibration has focused on binary forecasts. We propose an extension of this approach by developing an algorithm that uses a single free parameter to recalibrate complete subjective probability distributions. We illustrate the approach with data from the quarterly Survey of Professional Forecasters (SPF) conducted by the European Central Bank (ECB), document the potential benefits of this approach, and show how it can be used in practical applications.
e-Publications@Marquette e-Publications@Marquette

: Microwave measurements and machine learning algorithms are presented to estimate metal ion concentrations in drinking water. A novel block loop gap resonator (BLGR) as a microwave probe is designed and fabricated to estimate Pb ion concentrations in city water as low as 1 ppb with an rms error of 0.18 ppb. No physical contact between the BLGR probe and the water sample allows on-site/in situ continuous detection of ppb-level metal ion concentrations. The S 11 raw data (amplitude and phase) from the BLGR are used to classify and estimate metal ion concentrations using a support vector regression algorithm. The performance of the proposed method to estimate Pb concentrations in the presence of interfering metal ions (Cu 2+ , Fe 3+ , and Zn 2+ ) is also evaluated, and it is found that the average measurement error remains less than 13%.
Transformation in der chemischen Industrie - Das Beispiel der Ticona GmbH

Zusammenfassung: Transformationsprozesse innerhalb der chemischen Industrie im Rahmen von Mergerund Demerger-Aktivitäten sowie überbetrieblichen Integrationsanstrengungen zählen zu den Herausforderungen globaler Unternehmen. Der vorliegende Artikel beschreibt diese Transformation am Beispiel der Ticona GmbH, einem Unternehmen der ehemaligen Hoechst Gruppe. Gezeigt wird der Beitrag konzeptioneller Architekturmodelle als Instrumente zur Planung, Dokumentation und Standardisierung. Im Mittelpunkt steht eine Gesamtsicht des vernetzten Unternehmens, die kundenund lieferantenseitige Transformationsmaßnahmen umfasst.
UK/306/2021. Stop Consonants in Chinese and Slovak: Contrastive Analysis by Using Istvanova

 Abstract — The acquisition of the correct pronunciation in Chinese is closely linked to the initial phase of the study. Based on the contrastive analysis, we determine the differences in the pronunciation of stop consonants in Chinese and Slovak taking into consideration the place and manner of articulation to gain a better understanding of the students' main difficulties in the process of acquiring correct pronunciation of Chinese stop consonants. We employ the software Praat for the analysis of the recorded samples with an emphasis on the pronunciation of the students with a varying command of Chinese. The comparison of the voice onset time (VOT) length for the individual consonants in the students' pronunciation and the pronunciation of the native speaker exposes the differences between the correct pronunciation and the deviant pronunciation of the
A safety navigation method for integrating global path planning and local obstacle avoidance for self-driving cars in a dynamic environment

This paper proposes a novel method to obtain high-quality paths for self-driving cars in underground parking lots. Self-driving cars require fast and accurate planning of collision less paths. When the self-driving car arrives at a parking lot, it downloads the layout from the corresponding intelligent system and is assigned a parking space; then, the locations of the designated parking space and the car are provided and pinpointed by the intelligent system. A global path is planned by the global algorithm according to the loca-tion of both parking space and car. In case of detecting dynamic or unknown obstacles in the process of moving along the global path, the parameters of obstacles can be estimated using the obstacle-detection algorithm. According to these obtained parameters, the local obstacle avoidance path can be planned through the behavior dynamics method. After complet-ing the obstacle avoidance, the car returns to the global path and continues to move toward the target parking space. Finally, the proposed method was simulated by MATLAB, and the results showed that the car could safely park in the target parking space. This method could simultaneously satisfy the smooth and real-time requirements of path planning.
Face Detection Using Haar Cascade Classifiers

: The main building block on which all automated systems concerned with human faces are designed is face detection. In many person-system user interfaces, precise face identification is important; the real implication is the precise approach to face identification. This paper proposes, a 3-stage face detection system architecture that is based on the Haar Cascade Classifiers. Pictures of classical Indian kathak and kuchipudi dances are included. In the proposed method, 94 percent of the faces were correctly identified using 1000 images for research.
Effects of Aegle marmelos (L.) Leaf Extract and Green Nanoparticles on Lipid Profile

Green silver nanoparticles (GNPs) synthesized mediated by A. marmelos leaf extract has been characterized and A. marmelos leaf extract (LE) and GNPs were evaluated for the regulation of hyperlipidaemia in rat model. Colour change of solution (1mg/mL LE, 1mMol AgNO 3 and 99mL distilled water) from pale yellow to dark brown, highest absorption of spectrum at 200nm and a broad spectrum at 474nm of UV-visible spectroscopy and Scanning electron microscopy (SEM) provides confirmation of synthesis of GNPs with diameter of 60nm to 120nm in size and spherical in shape. Fourier transform infrared spectroscopy (FTIR) analysis shows transmission peak at 3275 cm -1 corresponding to O-H and H- stretch, 1604 cm -1 corresponding to C=C stretch, 1384 cm -1 corresponding to N=O bend, 1072 cm -1 corresponding to C=N stretch which provides the confirmation about the presence of alcohols and phenols, represents alkenes, aliphatic nitro compound, aliphatic amines in the GNPs. The results show that both LE and GNPs significantly (p<0.001) reduced the total cholesterol, low density lipoprotein (LDL) and triglycerides concentration in the serum while LE at LD and HD and GNPs at LD had no significant effect on serum high density lipoprotein (HDL) but HDL concentration significantly increases at HD of LE when compared with controls. However, the GNPs shows significantly (p<0.001) higher efficacy in the reduction of total cholesterol, LDL and triglyceride concentration of serum when compare to LE. The study suggests that, both LE and GNPs may have beneficial effect on hyperlipidemia and associated diseases.
Automatic Painting with Economized Strokes

We present a method that takes a raster image as input and produces a painting-like image composed of strokes rather than pixels. Unlike previous automatic painting methods, we attempt to use very few brush-strokes. This is accomplished by first segmenting the image into features, finding the medial axes points of these features, converting the medial axes points into ordered lists of image tokens, and finally rendering these lists as brush strokes. Our process creates images reminiscent of modem realist painters who often want an abstract or sketchy quality in their work.
Precision Probiotics in Agroecosystems: Multiple Strategies of Native Soil Microbiotas for Conquering the Competitor Ralstonia solanacearum

In the current work, the relationship between the soil microbiota and tomato bacterial wilt on a large scale offered us a comprehensive understanding of the disease. The delicate strategy of the microbiota in soil used for growing tomatoes to conquer the strong competitor, Rs, was revealed by microbiome research. ABSTRACT Ralstonia solanacearum (Rs), a soilborne phytopathogen, causes bacterial wilt disease in a broad range of hosts. Common approaches, for example, the direct reduction of the pathogen using classic single broad-spectrum probiotics, suffer from poor colonization efficiency, interference by resident microbiota, and nonnative-microorganism invasion. The soil microbiota plays an important role in plant health. Revealing the intrinsic linkage between the microbiome and the occurrence of disease and then applying it to agroecosystems for the precise control of soilborne diseases should be an effective strategy. Here, we surveyed the differences in the microbiome between healthy and diseased soils used for tomato planting across six climatic regions in China by using 16S rRNA amplicon and metagenomic sequencing. The roles of species associated with disease symptoms were further validated. Healthy soil possessed more diverse bacterial communities and more potential plant probiotics than diseased soil. Healthy soil simultaneously presented multiple strategies, including specifically antagonizing Rs, decreasing the gene expression of the type III secretion system of Rs, and competing for nutrition with Rs. Bacteria enriched in diseased samples promoted the progression of tomato bacterial wilt by strengthening the chemotaxis of pathogens. Therefore, Rs and its collaborators should be jointly combatted for disease suppression. Our research provides integrated insights into a multifaceted strategy for the biocontrol of tomato bacterial wilt based on the individual network of local microbiota. IMPORTANCE In the current work, the relationship between the soil microbiota and tomato bacterial wilt on a large scale offered us a comprehensive understanding of the disease. The delicate strategy of the microbiota in soil used for growing tomatoes to conquer the strong competitor, Rs, was revealed by microbiome research. The collaborators of Rs that coexist in a common niche with Rs strengthened our understanding of the pathogenesis of bacterial wilt. Bacteria enriched in healthy soil that antagonized pathogens with high specificity provide a novel view for ecofriendly probiotics mining. Our study offers new perspectives on soilborne-pathogen biocontrol in agroecosystems by decoding the rule of the natural ecosystem.