Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of Marketing Strategies on Tourists’ Choice of Destination Area in Cross River State, Nigeria

Development of tourism destination areas all over the world has increased the international arrivals. The study evaluated tourism destination attractions and the influence of marketing strategies and communication tools on tourists’ choice of destination area in Cross River State, Nigeria. The study relied on secondary data from government tourism organizations (GTOs) and private tourism service providers (PTSPs) who are involved in marketing and promotion of tourism products. Also, data was collected through questionnaire survey, interviews, observation and content analysis of tourism brochures. A total of 215 respondents consisting of tourists and staff of GOTs and PTSPs were used for the study. Data generated was analyzed using descriptive and quantitative statistics. The findings revealed that, from the six natures of activities participated by tourists in the destination area, 64 percent of them were involved in leisure/recreational park and historical site/museum activities. The one-way analysis of variance (ANOVA) of the level of attractiveness of tourism attractions and events produced F-ratio of 14.245 which was significant at 0.05 level, this established statistical significant difference in the level of attractiveness of tourism products in the destination area. The findings showed that the higher the responses on the level of attractiveness of a product, the higher the population of tourists on that attraction site. It was further discovered that Obudu Ranch Resort was the most attractive site accounting for 91 percent of the responses, while Obubra Crocodile Lake and Akatcha festival were considered not attractive by the tourists based on the nature of marketing strategies and communication tools. Furthermore, the Kruskal-Wallis test (H) for several independent samples was significant at x2 (df=5) = 111.76, with p<0.05 establishing significant difference in the level of influence of tourism marketing strategies on tourists’ choice of destination area. The study observed that all the marketing strategies were influential on aggregate representing 39 percent, however festivals/events and security/environmental beautification were rated very high as the most influential marketing strategies on tourists’ choice of destination attraction. They accounted for 66 percent and 63 percent respectively. The high rating was dependent on the quality of packaging of the programmes in marketing communication methods such as radio and television, internet, magazines/newspapers etc about the destination attractions. Based on the above findings, the study recommended more persuasive and appealing marketing strategies that will have great influence on tourists’ choice of destination in Cross River State, Nigeria.
Complete uncertainty analysis of a simple undergraduate experiment for the measurement of Planck’s constant using python

The detailed analysis of propagation of errors in an undergraduate experiment is not dealt with utmost carefulness in general. Here an experiment is chosen from the proposed B.Sc. (Hons.) Physics syllabus submitted to the University Grant Commission, India under choice based credit system. The step by step determination of Planck’s constant and uncertainties introduced in each step are described. The least square fittings between various data sets and the evaluation of the associated uncertainties are done using python codes. Although similar experiments were studied previously, simple modification is made to improve the accuracy of the result. The major portion of the uncertainty in determining Planck’s constant arises from the least squares fitting of a particular data set containing the logarithms of the photodetector currents and the reciprocal of bulb temperatures. Another significant contribution in this regard comes from the measurement of the bulb resistance at room temperature. So a relatively high-resolution measuring instrument is used.
Urban Noise Monitoring using Edge Computing with CNN-LSTM on Jetson Nano

With the negative impact of noise pollution on human health and well-being, distributed wireless sensor network with edge devices like Jetson Nano as the centre of the network for noise data classification is an effective approach to urban noise monitoring. However, most classification systems were not developed for edge computing or did not include timing performance as a metric during development. The weaknesses can limit edge computing devices' real-time ability, stability, etc. This study addresses the weaknesses by proposing a classification system using MFCC and CNN-LSTM for Jetson Nano's low-latency computation. We consider both accuracy and timing performance as critical metrics in this study. The proposed approach is based on MFCC with a window length of 0.128 seconds and overlap of 50%, classified by a CNN-LSTM trained with 30% dropout probabilities, which has obtained an accuracy of 94.94% and an execution time of 35 ms to classify 6.144 seconds audio at an audio segment length of 1.024 seconds on a Jetson Nano. The experiment demonstrates that the proposed system has outstanding accuracy and timing performance compared to the existing urban sound classification systems.
The Impact of Artificial Intelligence in Human Resource Management Systems on the Performance of Workers in the Sultanate of Oman

This study aimed to know the impact of artificial intelligence in human resource management systems on the performance of workers in the Sultanate of Oman. The study population included workers in the upper and middle management in educational institutions in Muscat, Sultanate of Oman, who numbered (860) individuals. The questionnaire was used as a main tool for collecting data and information, and then it was analyzed by a set of statistical methods, namely. In data path analysis . The study reached a set of results, the most important of which are: the high interest of workers in educational institutions with the requirements of commitment to work, keenness to apply its correct values and avoiding negative phenomena resulting from delay in work and evasion of its performance . In light of these results, the study recommended several recommendations, including: Attracting specialized labor in the field of the necessity of adopting an integrated and equal incentive system that includes material and moral incentives, taking into account the modern technological developments and techniques that the world is witnessing in the field of education
A new Mars Climate Database v5.1

What is the Mars Climate Database? The Mars Climate Database (MCD) is a database of meteorological fields derived from General Circulation Model (GCM) numerical simulations of the Martian atmosphere and validated using available observational data. The MCD includes complementary post-processing schemes such as high spatial resolution interpolation of environmental data and means of reconstructing the variability thereof. The GCM is developed at Laboratoire de Meteorologie Dynamique du CNRS (Paris, France) [1-3] in collaboration with the Open University (UK), the Oxford University (UK) and the Instituto de Astrofisica de Andalucia (Spain) with support from the European Space Agency (ESA) and the Centre National d'Etudes Spatiales (CNES). The MCD is freely distributed and intended to be useful and used in the framework of engineering applications as well as in the context of scientific studies which require accurate knowledge of the state of the Martian atmosphere. The MCD may be accessed either online (in a somewhat simplified form) via an interactive server available at http://www-mars.lmd.jussieu.fr (useful for moderate needs), or from the complete version which includes advanced access and post-processing software (contact millour@lmd.jussieu.fr and/or forget@lmd.jussieu.fr to obtain a free copy). Overview of MCDv5 contents: The MCD provides mean values and statistics of the main meteorological variables (atmospheric temperature, density, pressure and winds) as well as atmospheric composition (including dust and water vapor and ice content), as the GCM from which the datasets are obtained includes water cycle [4-6], chemistry [7,8], and ionosphere [9,10] models. The database extends up to and including the thermosphere[11-13] (~350km). Since the influence of Extreme Ultra Violet (EUV) input from the sun is significant in the latter, 3 EUV scenarios (solar minimum, average and maximum inputs) account for the impact of the various states of the solar cycle .
Association of medication adherence quality measures for diabetes, hypertension, and hyperlipidemia with cognitive decline

Background: While diabetes, hypertension, and hyperlipidemia each are associated with increased risk of cognitive decline, little is known regarding how nonadherence to medications for these conditions is associated with cognitive decline risk. Methods: We identified patients enrolled in a Medicare Advantage Prescription Drug plan who were eligible for inclusion in the CMS Star Medication Adherence quality measures for diabetes, hypertension, and hyperlipidemia in 2018, 2019, and 2020. To achieve an adherence quality measure, patients had to meet 80% of the proportion of days for the medication. We used propensity score with inverse probability of treatment weighting to balance outcomes for baseline characteristics and logistic regression models to compare odds of cognitive decline outcomes across patient groups. Results: The study population of 99,774 individuals had a mean age of 71.0 years and was 49.1% female, 73.9% White, and 17.8% Black, with 62.0% living in an urban setting. Compared with patients who missed zero adherence measures, those who missed one measure had 23%–33% increased odds of cognitive decline (any decline OR = 1.23; dementia OR = 1.33; Alzheimer's disease OR = 1.27; all P values <0.01). Patients who missed 2–3 measures had 37%–96% increased odds of cognitive decline (any decline OR = 1.37; dementia OR = 1.58; Alzheimer's disease OR = 1.96; all P values <0.01). Patients who missed ≥4 adherence measures had the greatest odds of cognitive decline (any decline OR = 1.64; dementia OR = 2.05; Alzheimer's disease OR = 2.48; all P values <0.01). Conclusion: Not achieving CMS Star Medication Adherence quality measures for diabetes, hypertension, and hyperlipidemia therapies was associated with increased risk of cognitive decline outcomes.
Robotic Stirring Mechanism with Novel Actuator for an Automated Drug Discovery Workcell

The pharmaceutical market has been growing rapidly, but concerns about energy and resource sustainability have made it important to consider the economical and sustainable aspects of discovering functional molecules in synthetic chemistry. One of the main challenges of traditional chemical synthesis is that it is labor-intensive and generates a lot of waste due to repetitive reaction manipulation. To address this issue, this paper presents a robotic end effector system with three degrees of freedom (DOF) to facilitate the automation of tasks in a drug discovery workcell. This robotics system features a unique remote center of motion (RCM) spherical-linear mechanism and a novel hollow double spring vacuum actuator (HDSVA) that uses soft elastic material and springs for actuation and structural integrity. This paper covers the design, kinematics, and actuation of the system. The HDSVA is modeled analytically and the interaction between the spring and soft membrane is examined. Through kinematic analysis, simulation results, and experimental evaluations, we examine the motion capabilities and validate the feasibility of this robotic end effector system in automated stirring tasks.
The Influence of Positive and Negative Aspects of Perfectionism on Psychological Distress in Emerging Adulthood: Exploring the Mediating Role of Self-Compassion

Perfectionism constitutes a multidimensional personality trait. According to the diathesis–stress model, perfectionism may predispose individuals to experience increased psychological distress during stressful periods. Since self-compassion is considered as a protective factor within the context of mental health difficulties, the primary objective of this study was to explore the mediating function of self-compassion in the relationship between positive (i.e., high standards and order) and negative (i.e., discrepancy) aspects of perfectionism and psychological distress among Greek emerging adults. The sample consisted of 410 university students (47.6% males and 52.4% females), with a mean age of 20.61 ± 1.88 years. The Almost Perfect Scale-Revised (APS-R), the Self-Compassion Scale (SCS), and the General Health Questionnaire-28 (GHQ-28) were used to assess perfectionism, self-compassion, and psychological distress, respectively. The results indicated that self-compassion mediated the association between positive and negative aspects of perfectionism and different dimensions of psychological distress (i.e., somatic symptoms, anxiety/insomnia, social dysfunction, and severe depression). Specifically, discrepancy and increased levels of high standards were related to lower levels of self-compassion, thus leading to greater endorsement of psychological distress. In contrast, order was associated with higher levels of self-compassion, thus leading to lower levels of psychological distress. The findings of the present study highlight that self-compassion is an overall important area to examine in nonclinical populations. These findings have implications for the effectiveness of cognitive behavioral therapy (CBT) in enhancing individuals’ self-acceptance and awareness of their perfectionistic tendencies, as well as compassion-focused therapy (CFT) in elevating self-compassion, mindfulness, and overall psychological well-being while reducing psychological distress.
MODELLING OF FIRE RESISTANCE AND MECHANICAL PERFORMANCE OF GLASS FAÇADES

Glass façades are widely used in modern high-rise buildings because of their aesthetic merits and environmentally friendly characteristics. However, collapsing glass in an accidental fire can lead to huge casualties and economic losses. Here, we develop a meshfree computational framework based on peridynamics to investigate the fire resistance and mechanical performance of glass façades. The proposed model is well verified and applied to explore the damage and failure mechanisms of glass façades. The effects of the temperature distribution on the thermomechanical fracture behaviour of glazing are also explored. Results show that cracks tend to initiate at fixed supporting points such as bolts and their propagation paths are greatly influenced by the temperature distributions. Both temperature gradient and local boundary conditions can play a significant role in the thermomechanical cracking of glass façades. This work provides important insights on cracking mechanisms of glass façades during a fire and provide the building and construction companies with new recommendations and design guidelines for improved fire safety.
Development of an Advanced Navigation System for Autonomous Mobile Robots for Logistics Environments

In this paper, we proposes the development of a novel navigation system for Autonomous Mobile Robots (AMR) in a logistics environment. AMR aim to provide a more flexible and innovative solution compare with traditional Automated Guided Vehicle (AGV) systems, in order to alleviate the inconvenience of line reconfiguration due to environmental changes. AMR equipped with various functionalities and sensors play a significant role in automating logistics and factory operations.The proposed navigation system includes methods for optimal path planning for single or multiple robots in a warehouse environment, improved localization techniques, and obstacle detection methods. By utilizing three maps, the system plans specialized paths for logistics warehouses, and addresses the limitations of conventional probability-based localization methods by utilizing semantic features extracted from the radar sensor for position recognition.Furthermore, this system incorporates LIDAR sensors for obstacle detection and ensures that the robot stops when obstacles are detected within the predicted area based on size and speed. The implementation of this navigation system enhances the autonomy and efficiency of AMR in logistics environments, improving position measurement, path planning, and obstacle detection capabilities. It offers the potential for increased productivity and cost savings in automated logistics system operations
Biological and physical processes influencing sea ice, under‐ice algae, and dimethylsulfoniopropionate during spring in the Canadian Arctic Archipelago

This study presents temporal variations in concentrations of chlorophyll a (Chl a), particulate and dissolved dimethylsulfoniopropionate (DMSPp and DMSPd) in the sea ice and underlying water column in the Canadian Arctic Archipelago during the spring of 2010 and 2011. During both years, bottom ice Chl a, DMSPp and DMSPd concentrations were high (up to 1328 µg L−1, 15,082 nmol L−1, and 6110 nmol L−1, respectively) in May and decreased thereafter. The release of bottom ice algae and DMSPp in the water column was gradual in 2010 and rapid (8 days) in 2011. Bottom brine drainage during the presnowmelt period in 2010 and a rapid loss of the snow cover in 2011 coinciding with rain events explain most of the difference between the 2 years. During both years, less than 13% of the DMSPd lost from the ice was detected in the water column, suggesting a rapid microbial consumption. An under-ice diatom bloom developed in both years. In 2010, the bloom was dominated by centric diatoms while in 2011 pennates dominated, likely reflecting seeding by ice algae following the faster snowmelt progression induced by rainfall events in 2011. Both under-ice blooms were associated with high DMSPp concentrations (up to 185 nmol L−1), but pennate diatoms showed DMSPp/Chl a ratios twice higher than centrics. These results highlight the key role of snowmelt and precipitation on the temporal pattern of ice-DMSP release to the water column and on the timing, taxonomic composition, and DMSP content of phytoplankton under-ice blooms in the Arctic.
Implicit Time-Integration Simulation of Robots With Rigid Bodies and Cosserat Rods Based on a Newton–Euler Recursive Algorithm

In this article, we propose a new algorithm for solving the forward dynamics of multibody systems consisting of rigid bodies connected in arbitrary topologies by localized joints and/or soft links, possibly actuated or not. The simulation is based on the implicit time integration of the Lagrangian model of these systems, where the soft links are modeled by Cosserat rods parameterized by assumed strain modes. This choice imposes a predictor–corrector structure on the approach, and requires computing both the residual vector and the Jacobian of the residual vector of the dynamics constrained by the time integrator. These additional calculations are handled here with a new Newton–Euler recursive inverse dynamics algorithm and its linearized tangent version. The approach is illustrated with numerical examples from the Cosserat rod literature and from recent robotic applications.
A COMPREHENSIVE STUDY OF THE RECOMMENDATION SYSTEMS IN LOCATION BASED SOCIAL NETWORKS

With the astonishing growth of the social networks, especially Location-Based Social Networks (LBSNs), and the increase in the number of users, there is a vigorous need for services based on location-based social networks. These LBSNs allow users to check-in at various locations and to share their current location among their friends. They provide a large amount of data for analysis. This can be used for location recommendation, activity recommendation, generic and personalized travel recommendations, itinerary planning etc. In this paper we are going to have an extensive study on the current trends in LBSN. Then we identify the demerits of the existing works and propose new methodologies that would essentially overcome the demerits of the existing methodologies.
Numerical Performance Evaluation of ℓ1 - ℓ2 Sparse Reconstruction Using Optical Analog Circuit

Iterative shrinkage thresholding algorithm (ISTA), fast ISTA (FISTA) and alternating direction method of multipliers (ADMM) based algorithm are well known convex optimization algorithms based on proximal splitting for ℓ1 - ℓ2 optimization problem in compressed sensing. In our previous study, aiming at the implementation with optical analog devices, we have proposed constant inertial FISTA (CIFISTA) and an approximated ADMM based algorithm, which do not require the operation of division. In this paper, we consider to realize those algorithms with optical analog devices and show concrete configurations of analog circuits using optical devices. We have conducted computer experiments to evaluate the performance of each algorithm, taking into account the effects of the additive noise introduced by optical amplifiers assuming the implementation using optical devices for common optical fiber communications systems. From the numerical results, it has been confirmed that both the proposed CIFISTA and the approximated ADMM based algorithm can achieve faster convergence than ISTA, while the degradation of the steady state mean squared error (MSE) due to the circuit noise is much smaller than that of the conventional ADMM algorithm.
Private hospital model in Rayong Province that satisfied consumer needs

This research objectives are to examine the private hospital model in Rayong Province which relations among hospital policy, hospital performance, overall total quality, and hospital personnel’s public mind directly and indirectly influence customer requirements. This study used a hybrid methodology that combined quantitative and qualitative research. A questionnaire was used as the research instrument for the quantitative research investigation. A simple random selection method was used to select four hundred participants from inhabitants in Rayong, Thailand. The model confirmation was examined by the Structural Equation Model (SEM). The results illustrated those exogenous latent variables of hospital policy, hospital performance, and total quality positively have a direct effect on hospital personnel’s public mind and are additionally positive towards customer requirements. The prediction for customer requirements can be made from hospital policy, hospital performance, total quality and hospital personnel’s public mind with a variance of ninety-seven percent. Throughout the qualitative research of in-depth interviews with tri-angular analysis, the conclusion can be made with the fact that hospitals satisfy consumer needs and increase customers with firstly public relations communication presenting the outstanding features in medical treatment of particular hospital, secondly proactive operations with well-defined customer segmentation such as factories and business companies within the area of Rayong Province, thirdly good services that leads to word of mouth, fourthly reasonable pricing, fifthly maintaining medical standards, sixthly providing services conforming to reasonable prices, and lastly emphasizing the care and be active to the satisfaction of consumer needs.