Miracle-dz/UniFAR-FTUs huggingface dataset:
A Pathway to a Better Future: Community-Based Tourism for Local Economic Growth in Sayan Village,

Community Based Tourism (CBT) has become increasingly popular in recent years. This is due to the increasing interest of tourists in traveling more authentically. The CBT concept seeks to create sustainable tourism that positively impacts the local economy. Sayan Tourism Village is one of the Tourism Villages that has implemented the concept. The purpose of this study is to analyze the contribution of CBT in supporting local economic growth in Sayan Tourism Village. The research method used is qualitative research with an exploratory study approach. Data were collected through literature review, observation, and interviews with the Village Government and tourism industry players in Sayan Tourism Village. The results showed that the implementation of CBT in Sayan Tourism Village has had a positive impact on the local economy.
Development of IoT-Based Sensor Tag for Smart Factory

To reduce the rate of defective products and ensure the health of employees, it has become increasingly necessary to improve air quality in factories. To do so, it is important to accurately determine the air quality in the factory first. However, currently available industry-grade sensors require complex and often separate construction processes, making them difficult to use in complicated industrial sites. In order to solve such an issue, this study developed an IoT-based sensor that can collect data such as factory temperature, humidity, CO2 levels, dust, and stench. This study has also developed software to manage the IoT-based sensors, which can transmit the data via RF communication. Sensor tags were tested for basic environmental durability, including waterproof, dustproof, and vibration standards. The sensors can be connected via USB to a computer to configure its settings and access the stored data within the sensors. Unlike other existing environmental sensors, these can be easily installed anywhere as they transmit data via wireless communication. These sensors can also be installed in the factory to check the factory’s internal pollution level in real time. The application of this sensor will enable the real-time monitoring of air pollution in factories. By locating the polluted areas accurately, it will be possible to exercise distributed control over ventilation devices to prevent further spread of pollutants, while also pushing out the polluted air to maintain an optimal working environment. Future studies should develop a simulation model for determining the ideal installation location for the sensors. There also needs to be further studies in developing a distributed-controlled air-conditioning system to run empirical tests.
Pragmatic critical realism and Mixed methods in Inter-disciplinary Research—Management and Information systems

Mixed methods research traditionally follows one paradigm based on established typologies. However, this study deviates from conventional research paradigms by combining two paradigms in one study. Specifically, it employs a critical realist method to analyze data, complemented by a pragmatist approach. This combination allows for the exploration of various phenomena in multidisciplinary research, such as cyber risk management, which intersects with management and information systems. By delving into the causal mechanisms underlying observed social and organizational phenomena, the pragmatic critical realism (PCR) approach, in conjunction with mixed methods research (MMR), offers a valuable framework for understanding the study's findings. Importantly, PCR has not been widely discussed or adopted in a multidisciplinary context for examining mixed data and advancing theory using MMR. This approach can be applied to multidisciplinary research in different contexts, enabling a deeper understanding of research participants' perspectives, the observable events resulting from those perspectives, and their causal relationships. The study systematically reviews the literature on pragmatism and critical realism in the field of management and information system; compares and combines these two research paradigms to identify common ground and explore their potential for developing a new framework to address interdisciplinary research questions. As a result, a new multiparadigm conceptual model has been established that integrates pragmatism and critical realism, making an original contribution to the field of mixed methods research. The study suggests that academics and researchers in management and information systems should broaden their use of methodologies and embrace interdisciplinary thinking in order to advance theoretical debates within their discipline.
Monitoring of biophysical parameters in vineyards through hyperspectral reflectance techniques

The Mediterranean climate, characterized by warm and drought summers, gathers ideal conditions for the practice of viticulture. Such conditions are found in Douro region, in the Northeast of Portugal, where viticulture represents the most important agricultural and economic activity. The application of remote sensing (RS) tools in precision agriculture (PA) is revolutionizing the input management in agriculture, including in viticulture. This dissertation presents three case studies of RS techniques application in Portuguese vineyards, based on physiological assumptions and supported by modelling approaches. The first case study is related to the use of hyperspectral and thermal data to assess the application of kaolin in grapevines. According to the bibliography, kaolin is reported as a potential tool to minimize the effect of high temperatures over the vegetation during the Mediterranean summer. The main findings presented in this case study report the wavelength 535 nm, related to foliar pigments responsible for the heat dissipation (i.e. xanthophyll), and the wavelength 733 nm as possible indicators of thermal stress promoted by the solar radiation. Moreover, a principal component analysis (PCA) showed that the thermal data also highly contribute for explaining the vines response to kaolin application throughout the grapevine cycle, while considering the time-losing effect after kaolin application. The other two case studies present different modelling approaches for the estimation of predawn leaf water potential (ѱpd) in vineyards of two sub-regions in Douro wine region using hyperspectral data: (i) the second case study presents a generalized predictive model of (ѱpd) based on machine learning techniques applied in regression mode – Random Forest (RF), Bagging Trees (BT), Gaussian Process Regression (GPR), and Variational Heteroscedastic Gaussian Process Regression (VH-GPR); (ii) in the third case study a multisite predictive model of (ѱpd) were developed based on a machine learning technique applied in the classification mode – ordinal logistic regression (OLR) using the data-set from 2017. A large set of spectral vegetation indices (VIs) were tested, optimized, and validated as models’ predictors. In both case studies, the VIs selected encompassed the visible and near-infrared (NIR) zones of the electromagnetic spectrum. Analyzing model’s performance, the second case study provided overall accuracy between 82-83% and the third case study an overall accuracy of 73.2% in the estimation of classes of ѱpd values. The results obtained from both case studies showed potential for the models’ operational use to estimate the ѱpd in support to irrigation management.
Evaluation of High-Resolution Gridded Precipitation Data in Arid and Semiarid Regions: Heihe River Basin, Northwest China

AbstractThe reliability of three satellite-derived precipitation products, Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 and the Climate Prediction Center morphing technique (CMORPH) satellite-only (CMORPH-RAW) and gauge-corrected versions (CMORPH-CRT), and three gauge-based precipitation datasets, Asian Precipitation–Highly Resolved Observational Data Integration Toward Evaluation of Water Resources (APHRODITE), National Climate Center of China Meteorological Administration (CN05.1), and Institute of Tibetan Plateau Research, Chinese Academy of Sciences (ITPCAS), is evaluated via comparisons with rain gauge observations from stations over the Heihe River basin (HRB) for the period from 1998 to 2012. The results show that the observed climatology, interannual variability, the detection of precipitation events, and probability density functions (PDFs) are reasonably well represented by the high-resolution precipitation products (HRPPs), with APHRODITE presenting the best performance, CN05.1 and ITPCAS.
Letters in Animal Biology

Mastitis is among the most popular ailments in dairy cow that results in the greatest financial losses for dairy farms. Different strains of bacteria, fungus, and algae are responsible for causing mastitis. Mostly this disease is typically caused by bacteria that grow in the udder and lymph nodes, causing damage to the udder parenchyma. Antibiotics are the primary method of treatment for mastitis. However, long-term use of antibiotics has caused pathogens that cause mastitis to become more resistant to antibiotics. As a result, various strategies for eliminating pathogenic bacteria that cause mastitis are being researched. Researchers have looked for alternative therapeutic modalities to antibiotics for the treatment of mastitis. These efforts are helped by the innovative invention of nanotechnology for mastitis therapy and management. As a result, nanotechnology may become the primary type of mastitis treatment in the near future. In vitro-In vivo research of alternative remedy for mastitis have indicated that many microorganisms responsible for this disease can be successfully inhibited and destroyed. The current review article discusses different nanoparticles for mastitis treatment in dairy cow herds.
RESEARCH OF LSTM MODEL FOR VEHICLE CONTROL SYSTEM OF AUTOMATED DRIVING SYSTEMS (ADS)

Research Question/Objective: This study aims to construct a long short-term memory (LSTM) model of the vehicle control system for automated driving systems (ADSs) that does not cause annoyance or distrust. Furthermore, this study investigates the effect of LSTM hyperparameters on model accuracy. A survey showed that certain drivers did not use levels 1 and 2 of the ADS function because they were annoyed with the driving behavior of the ADS-controlled vehicle. Although the driving behavior of the ADS-controlled vehicle causes distrust in passengers, it cannot effectively enable safe driving. This study focuses on a novel vehicle control method that reduces annoyance and distrust in passengers and contributes to the safe operation of ADSs. These control methods involve the application of a long short-term memory (LSTM) model that learns long-term time-series data. This system enables the construction of ADS control algorithms from LSTM models based on personalized driver operations during ordinary driving. Methods and Data Sources: LSTM models were constructed for highway driving in the following three driving scenarios. Scenario-1: following a preceding vehicle, Scenario-2: passing a preceding vehicle at low speed with lane change, Scenario-3: a sudden lane change by a vehicle in the passing lane in front of the vehicle. The effect of LSTM hyperparameters on the accuracy of the LSTM model was investigated for each driving scene. The data of these models were sourced from an experiment using a driving simulator conducted to determine driver behavior. Results: The results verified the accuracy of the model that simulated the driving operation of the driver. The model accuracy was improved by setting LSTM hyperparameters. In Scenario-1, the number of units, learning rate, and the number of epochs affected the coefficient of determination. The coefficient of determination tends to be particularly high for a large number of units. In Scenario-2, unlike Scenario-1, a large number of units was not required to obtain a
Assessing the Efficacy of Marijuana Prevention Initiatives for Promoting Students' Well-Being at Lagos State University

This study assessed the efficacy of marijuana prevention initiatives for a drug-free university campus with a focus on Lagos State University. The study used the descriptive survey research design. With the aid of the convenience sampling technique, out of a population of 500 sampled students, responses were obtained from 432 respondents and were used for final analysis. Using the Health Belief Model (HBM) as a theoretical framework, descriptive statistics and content analysis were adopted to analyse the collected and collated data. Results showed that there exists a notable lack of awareness among the majority of the respondents (62.3%) regarding marijuana prevention programs or initiatives at Lagos State University. Also, the study highlights the potential health and social risks associated with marijuana use, emphasizing the importance of proactive prevention efforts. These risks include impaired cognitive function, mental health issues, strained interpersonal relationships, and legal consequences. As a result of these findings, the study concluded by noting the pressing need for enhanced and consistent educational efforts, particularly through awareness campaigns during student orientation programs and the utilization of diverse communication channels. It is recommended amongst others, that Lagos State University implement regular awareness campaigns using diverse communication channels such as social media, workshops, seminars, and posters as well as enhance and promote the services of the university’s counselling unit to ensure that students are aware of the available support for substance use issues.
INVESTIGATION ON THE CONDITIONS FOR DISTURBANCES IN THE SAFETY PERFORMANCE ASSESSMENT ON THE PERCEPTION FUNCTION OF AUTOMATED DRIVING VEHICLES

This study focuses on perception as a fundamental part of the function chain of automated driving systems. The dynamic control of automated driving vehicles will be operated based on the perception function resulting from processing information gathered by sensors. Factors influencing perception should be identified and determined for the safety performance assessment because such factors consequence the behavior of automated driving vehicle. Especially, the characteristics of radar on perception function in ADS was investigated, and conditions for disturbances to developing safety performance assessments was discussed.
COLLISION RISK PREDICTION UTILIZING ROAD SAFETY MIRRORS AT BLIND INTERSECTIONS

Traffic accident number in Japan has been reduced year by year by growing ADAS technologies, revising the traffic rules, improving traffic environment. However, to realize the Vision Zero world which is zero traffic accidents, zero fatal accidents and zero injured seems far away currently. According to the traffic accident statistics data in Japan, more than half of accidents are occurring both in and around intersection areas [1]. The accident number at the intersections without traffic light is bigger than that with traffic light and has been seen at residential areas. To reduce the accident number at the intersection without traffic light, road safety mirrors have been installed in the intersection frequently [2]. In our study, using the front camera, which is one of ADAS sensors, even if it is a scene where the front camera cannot detect the object directly, our purpose is to reduce the collision risk by detecting the approaching vehicle using its image in road safety mirrors. In this paper, our collision avoidance method which consists of the 3 steps "Road safety mirror detection", "Object detection in the road safety mirror" and "Risk prediction" has been proposed. Especially, in road safety mirror detection, one countermeasure for false positives (FP) has been introduced. Our proposed method has
International Journal of

Much of the theory that embodies the framework for community risk assessments has been drawn from varying peripheral disciplines. Many potentially important factors such as the suitability of existing disaster management philosophies in differing contexts, the need for methodologies accessible to those with limited training and the development of appropriate indicators for monitoring the success of participation have been largely neglected. Further to these issues that relate directly to the risk assessment, issues surrounding the role of governments in risk reduction and the recognition of the importance of risk perception amongst communities were also encountered during the research process discussed below and have been examined in the context of developing appropriate methodologies. The importance of small-scale threats to the surveyed community was identified during the field study as well as the need for the de-professionalization of risk assessment procedures, particularly for areas that would not ordinarily receive attention from disaster management practitioners but that would benefit from the principles used in the discipline. The study concluded that the development of a more flexible methodology that had the ability to adapt to the multifarious contexts that the process is employed in is a key factor in ensuring the positive future development of the process
WCBAODV: An Efficacious approach to detect Wormhole attack in VANET using CBAODV Algorithm

Vehicular nodes in VANET are high energy nodes that can travel randomly to form an Ad-hoc network. These high energy nodes are dynamic in nature and moves more quickly within the network, leading to drastic changes in network topology. Additionally, VANET faces more obstacles and new challenges compared to mobile nodes in MANET. Due to open access atmosphere, vehicular nodes share information with other nearby nodes that are exposed to several security issues. Security attacks in VANET arise because of absence nodes, driver confidentiality and legitimacy. Wormhole attack is one of the major security issues faced by vehicular nodes in VANET. This attack helps the attacker to receive a data packet from one end of the network and redirects that data packet to another. Then, it makes attacker to replay them into the participating network from that end point itself. To solve this issue, the CBAODV algorithm is chosen to detect Wormhole attacks based on the early detection of the characteristics of each vehicular node. Wormhole attack is detected by Route Analysis model and by calculating the Round Trip Time (RTT) in VANET. Compared to other traditional detection techniques in VANET, the proposed WCBAODV algorithm performs better for secure VANET communication. In order to simulate and analyze the results, the NS2 simulator is used to design and examine the VANET environment.
International Journal of Advance Research in Computer Science and Management Studies

In modern security systems, to recognize a person earlier recognition techniques such as ATM cards, pin code have been replaced with person’s biological characteristics as fingerprint, eye, hand, etc. This technology is referred to as biometrics. In this paper, a biometric technique based on human hand’s shape and features that are hand geometry will be discussed. It has gained popularity in low to medium security system because of its ease of collectability, public acceptance and low template size. This paper present survey of the hand geometry biometric technology and basic steps of hand geometry recognition. To determine performance of hand geometry system various parameters like FAR, EER, FFR, ROC curve will be discussed This paper will compare the work done by various researchers on hand biometrics based on image acquisition technique, population size, features extracted, performance, etc. At last, few limitations of the system will be provided and future work will be discussed that may further improve biometrics.
Stacked Auto-Encoder-Based Transients Recognition in VSC-HVDC

For overhead long-distance high voltage direct current (HVDC) transmission lines, transients are produced due to complicated field conditions and lightning activities. To ensure reliable operation of protection devices, accurate recognition of faults and disturbances is quite critical. The most popular recognition methods include threshold-based ones which require the proper setting of the threshold value, and classifier-based ones that need suitable feature extractions. These methods depend heavily on the experience of engineers or experts and are ineffective in dealing with the variation of system parameters. In this paper, a transient recognition method based on stack auto-encoder (SAE) is proposed to characterize different transients and to avoid human interferences. A symmetrical ±500kv HVDC system is modeled to illustrate the performance of the proposed method. The effect of some factors, such as noises and conductors, are discussed and compared. The simulation results demonstrate that the proposed SAE-based recognition has excellent potential in transient recognition of practical HVDC systems.
“THIS TWEET IS UNAVAILABLE”: #BLACKLIVESMATTER TWEETS DECAY

Previous studies show that random tweet collections that include more than one hashtag had relatively low rate of unavailable tweets while political related datasets have a higher rate of tweet decay (Almuhimedi et al., 2013; Bastos, 2021; Bhattacharya & Ganguly, 2016; McCammon, 2022; Zubiaga, 2018). For example, Zubiaga’s (2018) study indicates that 81.4% of their 30 randomly selected real-world events datasets remained available after 4 years, while Bastos’ (2021) study shows that only 67% of Brexit debate related tweets were available after the same amount of time. Our preliminary study looks at the #BlackLivesMatter discussion on Twitter and we find that only 63% tweets remain available after 2 years, which is significantly higher compared to the previous studies. This preliminary work adds to the existing literature in two keyways. First, we broaden the kinds of discussion spaces where information decay has been studied by focusing on #BlackLivesMatter (hereafter, BLM), which in the U.S. has become a highly politicized movement (Stewart et al., 2018). Second, to our best knowledge, this is the first study looking at social movement tweets since Musk took over and it may highlight how the landscape on Twitter has shifted.
Biodistribution and human absorbed dose evaluation of an organic-inorganic nanocomposite containing 198Au (198Au/PG4D)

An organic-inorganic nanocomposite 198 Au/PG4D was synthesized using polyamidoamine G4 dendrimer and applied as an anticancer agent against 4T1 carcinoma tumor as well as for biodistribution and human absorbed dose investigation. Radionuclide 198 Au was produced by irradiation of natural gold ( 197 Au) in a medium flux reactor with 3 × 10 11 n/cm 2 . s flux of thermal neutron. Gamma spectroscopy exhibited only one characteristic peak of 198 Au at 411 KeV as well as a radiochemical purity of more than 82% (using ITLC) was obtained for final formulation of 198 Au/PG4D (37MBq). A single intratumor injection of 3 . 26MBq (88 μ Ci) of 198 Au/PG4D resulted in statistically significant 65% growth inhibition in 4T1 tumor volume after 20 days. Biodistribution investigations showed that the tumor had a maximum accommodated activity owing to 198 Au/PG4D of 81.27% and 79.37% at 4 and 24 h post injection. The human’s absorbed dose, furthermore, was extrapolated via the biokinetics data of mice so that the doses absorbed in the critical organs such as the bone, lung, spleen, kidney, and liver are 0.0669, 1.1, 0.221, 0.0983 and 0.282 mGy/MBq, respectively.