Miracle-dz/UniFAR-FTUs huggingface dataset:
Sampling Clear Sky Models using Truncated Gaussian Mixtures

Parametric clear sky models are often represented by simple analytic expressions that can efﬁciently generate plausible, natural radiance maps of the sky, taking into account expensive and hard to simulate atmospheric phenomena. In this work, we show how such models can be complemented by an equally simple, elegant and generic analytic continuous probability density function (PDF) that provides a very good approximation to the radiance-based distribution of the sky. We describe a ﬁtting process that is used to properly parameterise a truncated Gaussian mixture model, which allows for exact, constant-time and minimal-memory sampling and evaluation of this PDF, without rejection sampling, an important property for practical applications in ofﬂine and real-time rendering. We present experiments in a standard importance sampling framework that showcase variance reduction approaching that of a more expensive inversion sampling method using Summed Area Tables.
Improved Aerodynamics of a Hollow-Blade Axial Flow Fan by Controlling the Leakage Flow Rate by Air Injection at the Rotating Shroud

Axial flow fans are used in many fields in order to ensure the mass and heat transfer from air, chiefly in the heating, ventilation and air conditioning industry (HVAC). A more proper understanding of the airflow behavior through the systems is necessary to manage and optimize the fan operation. Computational fluid dynamics (CFD) represents a real tool providing the ability to access flow structures in areas that measuring equipment cannot reach. Reducing the leakage flow rate, inherent in operation, by synthetic-jet techniques improves performance. This paper presents the CFD results performed on a hollow blade fan developed by our team. The leakage flow is controlled by blowing air from 16 designated circular holes and arranged on the fan shroud. We discuss the results for two rotational speeds (1000 and 2000 rpm) and two injection rates (400 and 800 L/min). The numerical results consistent with the experimental show, for the low rotation speed and high injection ratio, significant gains in power (53%), torque (80%) and leakage flow rate (80%).
Channel Quality-Based Optimal Status Update for Information Freshness in Internet of Things

This paper investigates the status updating policy for information freshness in Internet of things (IoT) systems, where the channel quality is fed back to the sensor at the beginning of each time slot. Based on the channel quality, we aim to strike a balance between the information freshness and the update cost by minimizing the weighted sum of the age of information (AoI) and the energy consumption. The optimal status updating problem is formulated as a Markov decision process (MDP), and the structure of the optimal updating policy is investigated. We prove that, given the channel quality, the optimal policy is of a threshold type with respect to the AoI. In particular, the sensor remains idle when the AoI is smaller than the threshold, while the sensor transmits the update packet when the AoI is greater than the threshold. Moreover, the threshold is proven to be a non-increasing function of channel state. A numerical-based algorithm for efficiently computing the optimal thresholds is proposed for a special case where the channel is quantized into two states. Simulation results show that our proposed policy performs better than two baseline policies.
NeRF‐Tex: Neural Reflectance Field Textures

We investigate the use of neural fields for modelling diverse mesoscale structures, such as fur, fabric and grass. Instead of using classical graphics primitives to model the structure, we propose to employ a versatile volumetric primitive represented by a neural reflectance field (NeRF‐Tex), which jointly models the geometry of the material and its response to lighting. The NeRF‐Tex primitive can be instantiated over a base mesh to ‘texture’ it with the desired meso and microscale appearance. We condition the reflectance field on user‐defined parameters that control the appearance. A single NeRF texture thus captures an entire space of reflectance fields rather than one specific structure. This increases the gamut of appearances that can be modelled and provides a solution for combating repetitive texturing artifacts. We also demonstrate that NeRF textures naturally facilitate continuous level‐of‐detail rendering. Our approach unites the versatility and modelling power of neural networks with the artistic control needed for precise modelling of virtual scenes. While all our training data are currently synthetic, our work provides a recipe that can be further extended to extract complex, hard‐to‐model appearances from real images.
Commitment Leading Towards Success: A Study of Bank Employees of Pakistan

Talent competition is so high in today’s work environment. Employees are also more concerned regarding those factors, that can bring success in their life. Hence, this article aims to create a linkage of commitment and career success amongst banking sector employees residing in Punjab, Pakistan. Employees (Managers and officers) of public sector banks were taken as a population from which the sample was selected using two-stage cluster sampling. Data was collected using a questionnaire, which was distributed among 240 bankers. The usable response received is from 130 respondents, making the response rate, 54 percent. Data was analyzed by using SPSS and STATISTICA 10.0v software. Through analysis, it is established that organizational commitment (OC) has a direct influence on career commitment (CC). Moreover, two indicators of career success (CS) i.e. a) objective CS and; b) subjective CS appeared as important consequences of career commitment. Therefore, this study generates an insight that commitment to the organization and with career, leads employees towards career success. This is a prerequisite in strengthening the belief of today’s dynamic environment that both the employees and organizations are mutually responsible for achieving career success.
A Nomogram-based Model to Predict Neoplastic Risk for Patients with Gallbladder Polyps

Background and Aims Gallbladder polyp (GBP) assessment aims to identify the early stages of gallbladder carcinoma. Many studies have analyzed the risk factors for malignant GBPs. In this retrospective study, we aimed to establish a more accurate predictive model for potential neoplastic polyps in patients with GBPs. Methods We developed a nomogram-based model in a training cohort of 233 GBP patients. Clinical information, ultrasonographic findings, and blood test findings were analyzed. Mann-Whitney U test and multivariate logistic regression analyses were used to identify independent predictors and establish the nomogram model. An internal validation was conducted in 225 consecutive patients. Performance and clinical benefit of the model were evaluated using receiver operating characteristic curves and decision curve analysis (DCA), respectively. Results Age, cholelithiasis, carcinoembryonic antigen, polyp size, and sessile shape were confirmed as independent predictors of GBP neoplastic potential in the training group. Compared with five other proposed prediction methods, the established nomogram model presented better discrimination of neoplastic GBPs in the training cohort (area under the curve [AUC]: 0.846) and the validation cohort (AUC: 0.835). DCA demonstrated that the greatest clinical benefit was provided by the nomogram compared with the other five methods. Conclusions Our developed preoperative nomogram model can successfully be used to evaluate the neoplastic potential of GBPs based on simple clinical variables that maybe useful for clinical decision-making.
A Qualitative Study of the Process of Knowledge Utilization in the Social Sciences

This research aimed to propose a model of knowledge utilization in social sciences based on the analysis of the views of Iranian academics. This research was conducted based on a qualitative approach using grounded theory. The structured interview method was used to obtain the opinions of the experts. The research population consisted of academic members of Social Sciences in Iranian universities. Due to the extensive statistical population, sequential theoretical sampling was employed. We selected 25 academics with the highest number of both publications and citations (hindex) as the interviewees to determine the reliability and validity of this research. The evaluative criteria proposed by Lincoln and Guba were adopted. Moreover, to analyze the collected data and reach a theory, Theoretical Coding was applied. After coding, classifying, and analyzing data, the research findings led to a model derived from the ideas of Social Sciences experts in four areas as Knowledge Production, Knowledge Transfer, Knowledge Sharing, and Knowledge Utilization. According to the results achieved from analyzing the ideas of Social Sciences experts, knowledge utilization was determined as the central core of the model.
Musical Features Modification for Less Intrusive Delivery of Popular Notification Sounds

Less intrusive information delivery has been a popular research topic for auditory displays. While most research has addressed this issue by creating new notification cues such as rendering ambient soundscapes or modifying background music, we present a novel method to gently deliver artificial notification sounds that have been commonly used in digital devices and for popular applications. We propose to play a notification sound by embedding it into the music that a user is listening to, after changing the musical timbre, amplitude, tempo, and octave of the notification to match these features of the music. To implement this concept, we extend a melody extraction algorithm for notification timbre transfer, and we present a pipeline that algorithmically selects a proper time spot and harmoniously embeds the notification into music. To validate our design concept, we present a user study comparing our method with the standard method of playing notification sounds on digital devices. Through an extensive analysis of 96 tasks performed by 32 participants, we demonstrate that our method can deliver notification sounds in a less intrusive but adequately noticeable manner and is preferred by most participants.
Influencing Behavioral Attributions to Robot Motion During Task Execution

—Recent literature has proposed algorithms for autonomous generation of robot motion that communicates functional attributes of a robot’s state such as intent or incapability. However, less is known about how to automate the generation of motion for communicating higher-level behavioral attributes such as curiosity or competence. In this work, we consider a coverage task resembling robot vacuum cleaning in a household. Through a virtual interface, we collect a dataset of human attributions to robot trajectories during task execution and extract a probabilistic model that maps robot trajectories to human attributions. We then incorporate this model into an trajectory generation mechanism that balances between task completion and communication of a desired behavioral attribution. Through an online user study on a different household layout, we ﬁnd that our prediction model accurately captures human attribution for coverage tasks. Further, our generation mechanism produces trajectories that are thematically consistent, but more research is required to understand how to balance attribution and task performance.
Facile patterning of amorphous indium oxide thin films based on a gel-like aqueous precursor for low-temperature, high performance thin-film transistors

A “green precursor” and a “green patterning technique” were used to fabricate low temperature processed indium oxide (InOx) semiconductors. For the InOx precursor, chloride ligand-based indium(III) was dissolved in deionized (DI) water without any additives to form a gel-like precursor. The as-spin-coated precursor films could be facilely patterned using the “green patterning technique”, which requires only ultraviolet (UV) irradiation and DI water. A systematic study was carried out to investigate the chemical reaction of the chloride-based precursor films as well as the semiconductor properties. It was found that UV irradiation and water treatment not only helped to transform In–Cl into In–OH, but also helped to remove the Cl-related impurities. It led to the activation of InOx films at temperatures as low as 180 °C. The mobility of InOx TFTs based on an anodized aluminium oxide (AlOx:Nd) insulator with patterning was improved by more than 1 order compared to that without patterning at an annealing temperature of 280 °C. In addition, flexible InOx TFTs on polyimide (PI) substrates were demonstrated. They showed only a little degradation in the subthreshold region of the transfer curve even at a bending curvature (R) of 5 mm.
The first report of antimicrobial residues in cultured tilapia fillets in three provinces of Lao PDR

The objective of this study was to investigate antimicrobial residues in the fillets of Nile tilapia fish (n=180) from 3 provinces (Vientiane, Savannakhet Province and Champasak Province) in Lao PDR in the summer, rainy and winter seasons. Clean meat test (CMT) was used for screening the antimicrobial residue samples. Then, the antimicrobial residue groups were examined and identified by the european six-plate test (ESPT), and the concentration of each positive antimicrobial residue from the ESPT was further assessed by high-performance liquid chromatography (HPLC). The evaluation results of the three antimicrobial detection methods were 19.44% (35/180) for the CMT, 22.86% (8/35) for the ESPT and 75% (6/8) for the HPLC. The levels of enrofloxacin and ciprofloxacin residues detected by HPLC did not exceed the maximum residue limits according to the European Union standard. The highest occurrence of positive CMT results among three provinces was in Champasak Province at 26.67%. The occurrence of antimicrobial residues in the summer season was significantly higher than the rainy season (P<0.05), implying that the seasonal climate is an important factor influencing the use of antimicrobials as well as the occurrence of antimicrobial residues in the cultured tilapia fillets in Lao PDR.
Dynamics and evolution of genotype 1 porcine reproductive and respiratory syndrome virus following its introduction into a herd concurrently infected with genotypes 1 and 2

This study was conducted to investigate the dynamics and evolution of porcine reproductive and respiratory syndrome virus (PRRSV) genotype 1 following its introduction into a herd concurrently infected with both PRRSV genotypes. The results demonstrated that 2 novel genotype 1 groups emerged, termed clusters II and III. The emergence of cluster II suggested that this cluster of genotype 1 virus potentially correlated with the outbreak. Then, cluster III was detected with the disappearance of cluster II. The evolution of the genotype 1 virus from cluster II to cluster III is likely to be associated with immune evasion induced by amino acid substitutions at N-linked glycosylation sites. In contrast, evolution of the genotype 2 isolates was not observed. This result suggests that genotype 2 viruses were not affected by the emergence of the genotype 1 virus and each genotype developed independently.
Antioxidant activity of solvent extracts of Inula viscosa from Morocco

The antioxidative activity of Inula viscosa was evaluated by using a DPPH test system to suggest it as a new potential source of natural antioxidants. The amounts of total phenolics and flavonoids in the solvent extracts (diethyl ether and ethyl acetate) were determined spectrometrically. Furthermore, The DPPH scavenging activity of extracts increased in the order ascorbic acid < diethyl ether extract < ethyl acetate extract. Finally, a relationship was observed between the antioxidant activity potential and total phenolic and flavonoid levels of the extract.
Impact of Jeva Evaporation on Storage Stability and Physiochemical Characteristics of Vietnam Red Dragon Fruit (hylocereus Polyrhizus)

Red dragon fruit (Hylocereus polyrhizus) is a tropical fruit rich in nutrients vitamin C, vitamin A, protein, polyphenols, and flavonoids. The fruit can be, therefore, processed into different high valued products. Concentrated fruit juice had a long storage life with lesser transportation cost than that of fresh juice. Juice of red dragon fruit from Binh Thuan Province in Vietnam was concentrated using novel evaporation launched under ambient pressure and moderate temperature named JEVA. The obtained concentrates were from 13.8°Brix to above 60°Brix, while at the same time, it allowed retaining more than 90% polyphenol content (92.89%) and flavonoid content (98.58%); the retention of vitamin C and betacyanin content was 70.17% and 74%, respectively. Meanwhile, the evaporation under vacuum technology at 40°C allows retaining approx. 57% vitamin C and 40% betacyanin in red dragon fruit juice concentrate. With betacyanin retention of 85.76% and vitamin C retention of 81.55% after 8-weeks-storage at 4°C, dragon fruit concentrate obtained by JEVA evaporation at 35°C showed much higher stability than that of fresh juices. These obtained results suggested that JEVA evaporation can offer a better product than vacuum evaporation. JEVA evaporation is highly potential for launching in a large scale to process red dragon fruit juice.
Computational modeling of behavioral tasks: An illustration on a classic reinforcement learning paradigm

There has been a growing interest among psychologists, psychiatrists and neuroscien-tists in applying computational modeling to behavioral data to understand animal and human behavior. Such approaches can be daunting for those without experience. This paper presents a step-by-step tutorial to conduct parameter estimation in R via three techniques: Maximum Likelihood Estimation (MLE), Maximum A Posteriori (MAP) and Expectation-Maximization with Laplace approximation (EML). We ﬁrst demonstrate how to simulate a classic reinforcement learning paradigm – the two-armed bandit task – for N = 100 subjects; and then explain how to develop the computational model and implement the MLE, MAP and EML methods to recover the parameters. By presenting a suﬃciently detailed walkthrough on a familiar behavioral task, we hope this tutorial could beneﬁt readers interested in applying parameter estimation methods in their own research.
Auto tuning SIR model parameters using genetic algorithm

Earlier studies comparing Covid-19 simulations using extended SIR model with observed new cases in New Jersey and United States showed good agreement between simulated results and observational data. The parameters of the SIR model controlling the behavior of the model have to be manually adjusted until the modeled results and observations reach good agreement. The parameter tuning process is tedious and time consuming. In this work, we have developed an approach using genetic algorithm to automatically select the most optimal set of parameters to minimize the residual between simulated result and observational data. The parameter tuning process applying SIR model can now be automated without tedious and time consuming manual intervention.
Analysis on the Organic Bromine in the Atmosphere in Beijing

Gaseous and particulate phase samples are simultaneously collected at a downtown site in Beijing from April 2004 to December 2004 using a modified high-volume air sampler and determined by in strumental neutron activation analysis(INAA) for the concentrations of extractable organic-bromine(EOBr) and extractable persistent organic-bromine(EPOBr). Results show that the average concentrations of EOBr are 6.2 ng/m~3(1.0~18.5 ng/m~3) and 18.3 ng/m~3(2.3~73.4 ng/m~3) in gaseous and particulate phase,respectively;the average concentrations of EPOBr are 0.95 ng/m~3(0.02~2.2 ng/m~3) and 1.0 ng/m~3(0.09~3.0 ng/m~3) in gaseous and particulate phase;EPOBr accounts for(15.3%)(1.9%~62.8%)in gaseous phase and 5.5%(0.8%~55.6%) in TSP of total EOBr.
Kernel-based Attribute-aware Self adaptation and Multi thresholding for Rating Prediction

In recommender systems, our main task is to predict the rating of a new product from the authorized user and then return the best rating for the particular item and this technique reduced the existing prediction error rate. Our proposed system is user-item rating matrix prediction based on Synergetic filtering techniques and this technique is more efficiency compared to other technique. Proposed System are providing personalized recommendation for help the users accord with information overburden problem. However, the techniques are the data insufficiency of the user-item rating matrix underlying for brand-new items and users are severely affected by Synergetic filtering technique. Since the character of common links and items between more accessible by the users in the Internet and this paper exploits the common links of users and the character of items to overcome the existing problems and to ease the rating insufficient effect. However they may need excessive computational moment, and they often accost the insufficient problem which negatively modify the ability of the system. Specifically, we initially propose a Kernel-based Attribute-aware Self adaptation and multi thresholding model to blend the character information of items into matrix factorization and then introduce self-adaptation and multi thresholding. KASM can find the indefinite interactions among characters, users, and items, which reduce the rating insufficient effect for brand-new items by nature. In this paper we suggest a quick recommendation algorithm based on self-adaptation and multi thresholding. Self-adaptation in its genuine meaning is a state-of-the-art method to alter the setting of control specification. It is called self-adaptive because the algorithm controls the setting of these specification itself sink them into a distinctive genome and emerging them. It is construct to deal with the specified drawbacks and enhance the prediction quality. Extended analysis on two real world data sets establish that our proposed method can attain necessarily improve performance than other state-of the-art-methods. In this method we get the accuracy rate of predicting user rating will be 95%
A Review on Unmanned Crop Detection Techniques

The programmed water system frameworks are received as of late to provide support to the agriculturists in turn reducing the manpower and improving cost effective techniques. In this paper, a detailed survey is done analysing the crop detection techniques which are in existence. This paper provides an insight study on pH level monitoring in the soil, water and soil nutrients management techniques, temperature and humidity measurement, surveillance of crop field and other smart irrigation techniques utilized for monitoring the farmland. This paper discusses about the effective methodology with enhanced technique that could be implemented in future to attain better performance and user-friendly for the farmers.
Smart Autonomous Car Parking For the Modern Vehicles

The objective of intelligent car park management is to use advanced sensing & monitoring technology to maximize parking areas within large car parks and make car parking easy for passengers by transferring these to accessible parking spaces. An effective and insightful way to simplify parking infrastructure operations by utilizing the Internet for parking technologies. The IoT gives wireless connectivity to the device, which helps the customer control the parking area’s capacity. With the rising in automobile numbers in metropolitan areas, the main issue confronting road congestion is. This paper aims to solve the question. Typically the customer expends his time and energy to pursue the limited room accessible in a particular car park. The user is told of the parking detail. Therefore, it is reduced the waiting period of the customer searching for a car park. RFID technology is used to prevent car fraud.