Miracle-dz/UniFAR-FTUs huggingface dataset:
The role of job satisfaction in mediating the effect of transactional leadership and organizational commitment on intention to stay

This study aims to investigate the effect of transactional leadership and organizational commitment on intention to stay mediated by job satisfaction at PT X and PT Y. This is an explanatory study using a quantitative approach with saturated sampling, where all members of the population are used as research samples. The questionnaire was distributed to employees of PT X and PT Y with a total of 122 respondents. Data collection in this study was collected using a questionnaire distributed in person and using Likert scales to test the instrument. The data obtained were analyzed using Partial Least Square (PLS). The results revealed that transactional leadership and job satisfaction had a positive and significant effect on the intention to stay, while organizational commitment had a positive and insignificant effect on the intention to stay. The results also show that job satisfaction mediates the effect of transactional leadership and organizational commitment to intention to stay. The findings of this study suggest that leaders at PT X and PT Y need to increase the role of transactional leadership and the company needs to pay more attention to the factors that affect employee job satisfaction.
Callus formation of Coffea canephora induced with 2,4 dichlorophenoxyacetic acid and tomato extract supplements

Robusta coffee (Coffea canephora) is Indonesia's essential leading trade commodity. Robusta coffee is preferred for its bitter taste. However, robusta coffee productivity decreased due to bad agriculture practice. Tissue culture is an alternative to cultivation with various advantages. The plant growth regulators (PGRs) influence tissue culture’s success. Due to its stability, 2,4 dichlorophenoxyacetic acid has become a common synthetic PGRs. PGRs can be obtained from natural ingredients, such as tomatoes. This study aimed to analyze the effect of 2,4-dichlorophenoxyacetic acid and tomato extract addition and its appropriate concentration for callus induction of robusta coffee in vitro. This research was conducted at the Tissue Culture Laboratory, Department of Biology, Faculty of Mathematics and Natural Sciences, Hasanuddin University. This study used a Complete Randomized Design with two factors. The first factor was the concentration of tomato extract (0%; 7.5%; 10%; and 12.5%). Meanwhile, the second factor was the concentration of 2,4 dichlorophenoxyacetic acid (0 ppm; 1 ppm; 2 ppm; and 3 ppm). Observation parameters include the percentage of callus formation, callus growing time, callus fresh weight, callus color, and callus texture. The quantitative data were analyzed by the Kruskal-Wallis test followed by the Mann-Whitney Test to compare the effect of each treatment. The results showed that among other treatment combinations, treatment with the addition of 2 ppm 2,4-D and 10% tomato extract (T2D2) and treatment with the addition of 3 ppm 2,4-D and 10% tomato extract (T2D3) had the best effect in inducing the callus of robusta coffee.
Modernizing Lung Cancer Detection: The Federated Learning CNN Approach

Deep learning paradigms, particularly Convolutional Neural Networks (CNNs), have carved out a place in imaging-based illness identification in the modern age of medical diagnostics. Through a decentralised approach of federated learning among six different clients, the study penetrates the field of lung cancer diagnosis, dissecting its severity over four gradient levels. With its capacity for dispersed data processing, federated learning enables on-site data training without transferring any data, protecting data privacy and improving scalability. The first Result Analysis examined how the federated CNN model performed for specific customers. In contrast to Client 6, who fluctuated between 89.50% and 93.26%, Client 1 had diagnostic accuracy across severity levels ranging from 85.80% to 95.76%. These variations highlight the inherent variations in local data and the flexibility of the approach after leaving the microcosm of local data to start exploring how local data transforms into a more comprehensive Global Data Model. When results were averaged, they varied from Client 1’s 90.80% to Client 3’s 88.36%. The model’s capacity to combine a wide range of disparate information into a single, unified diagnostic framework was highlighted by this consolidation. Finally, the study used three averaging metrics to evaluate the model’s effectiveness: macro, weighted, and micro. The macro average ranged from 88.40% (Client 3) to 92.67% (Client 2). Client 3 had a weighted average factoring class distribution of 90.25%, while Client 2 peaked at 92.91%. Client 3 scored 90.27% on the micro average based on aggregate classifications, whereas Client 2 continuously dominated at 92.91%. In conclusion, this study demonstrates the revolutionary potential of CNNs and federated learning for determining the severity of lung cancer.
Food Demand in Slovenia

The objective of this research is to analyse food consumption patterns in Slovenia. Crosssectional household data from Household budget survey in year 2001 were used. We estimate expenditure and price elasticities for food demand for households segmented by quartile income levels and for Slovenia total. Food items are divided into the following commodity groups: bread and cereals, meat and fish, dairy products, oils and other fats, fruit, vegetables, confectionary. For a complete demand system analysis, we apply linearly approximated Almost Ideal Demand System (LA/AIDS). Empirical results show positive expenditure elasticities being close to one for all food groups. In general, demands for dairy products and vegetables could be regarded as the most sensitive to food expenditures. Further on, all Marshallian (uncompensated) and Hicksian (compensated) own price elasticities are negative and less than one. Own price elasticities for meat and fish are estimated as the lowest and for vegetables as the highest. With cross price elasticities close to zero the studied commodity groups seem to be unrelated. The negative sign of uncompensated cross price elasticities indicates complementary type of food groups, while substitution relationship of aggregate foods is indicated by mostly positive Hicksian cross price elasticities. According to these expenditure and price elasticities inhabitants of Slovenia seem to be losing consumption characteristics typical for countries in transition. However some unique food habits persist.
Belonging and Alienation in the new Northern Ireland

As the repercussions of the Christmas Flag Protests continued into 2013, the Office of the First and Deputy First Minister produced a new version of a good relations strategy for Northern Ireland: Together: Building a United Community. At the heart of this document is the idea that building a ‘united community’ for Northern Ireland must begin at local level (e.g. by removing interface barriers) and with the younger generation (e.g. through shared school campuses).
A Novel Spinel Ferrite-Hexagonal Ferrite Composite for Enhanced Magneto-Electric Coupling in a Bilayer with PZT

The magnetoelectric effect (ME) is an important strain mediated-phenomenon in a ferromagnetic-piezoelectric composite for a variety of sensors and signal processing devices. A bias magnetic field, in general, is essential to realize a strong ME coupling in most composites. Magnetic phases with (i) high magnetostriction for strong piezomagnetic coupling and (ii) large anisotropy field that acts as a built-in bias field are preferred so that miniature, ME composite-based devices can operate without the need for an external magnetic field. We are able to realize such a magnetic phase with a composite of (i) barium hexaferrite (BaM) with high magnetocrystalline anisotropy field and (ii) nickel ferrite (NFO) with high magnetostriction. The BNx composites, with (100 − x) wt. % of BaM and x wt. % NFO, for x = 0–100, were prepared. X-ray diffraction analysis shows that the composites did not contain any impurity phases. Scanning electron microscopy images revealed that, with an increase in NFO content, hexagonal BaM grains become prominent, leading to a large anisotropy field. The room temperature saturation magnetization showed a general increase with increasing BaM content in the composites. NFO rich composites with x ≥ 60 were found to have a large magnetostriction value of around −23 ppm, comparable to pure NFO. The anisotropy field HA of the composites, determined from magnetization and ferromagnetic resonance (FMR) measurements, increased with increasing NFO content and reached a maximum of 7.77 kOe for x = 75. The BNx composite was cut into rectangular platelets and bonded with PZT to form the bilayers. ME voltage coefficient (MEVC) measurements at low frequencies and at mechanical resonance showed strong coupling at zero bias for samples with x ≥ 33. This large in-plane HA acted as a built-in field for strong ME effects under zero external bias in the bilayers. The highest zero-bias MEVC of ~22 mV/cm Oe was obtained for BN75-PZT bilayers wherein BN75 also has the highest HA. The Bilayer of BN95-PZT showed a maximum MEVC ~992 mV/cm Oe at electromechanical resonance at 59 kHz. The use of hexaferrite–spinel ferrite composite to achieve strong zero-bias ME coupling in bilayers with PZT is significant for applications related to energy harvesting, sensors, and high frequency devices.
Changes in semi-natural grassland distribution in relation to common agricultural policy 2014–2020 area-based payments in Latvia

Semi-natural grasslands (SNG) are essential in the context of farmland biodiversity. The Common Agricultural Policy (CAP), on the other hand, is an important tool for the continuation of the management of semi-natural grasslands. Explicit knowledge on the pattern of grassland area changes is important to foster a more effective use of CAP for grassland biodiversity conservation. Our aim was to determine the habitat-specific changes in the distribution of Latvian SNG across different management regimes. Based on state-owned geospatial data of SNG distribution in 2014 and 2022, we assessed changes in semi-natural grassland areas over nearly ten years by analyzing grassland management and spatial distribution including areas inside and outside the Natura 2000 network. The spatial distribution of SNG remained similar in both periods. Eastern Latvia retained the highest share of SNG while the central part showed the most pronounced increase in new localities of SNG due to extensive habitat mapping carried out in recent years. Abandonment appeared as a more important threat to SNG in the Natura 2000 network and more profoundly to unproductive habitat types while transformation into arable land and grassland intensification was a considerable threat to SNG outside the network and to more productive habitat types. We suggest that agri-environment schemes should address the different needs of SNG in the Natura 2000 network and outside it.
An Electromagnetic-Thermal Modeling Method for the Joint Resistance of the CORC Cable

The Conductor on Round Core (CORC) cable has attracted wide attention with its strong current carrying capacity and high flexibility. Due to the limitation of soldering technology, joint resistances of a CORC cable are evitable, which can impact the performance of CORC cable significantly. In this work, a 3D model of a typical CORC cable with joint resistance is proposed based on H-formulation and heat transfer theory. Then, investigations are carried out on impacts of the joint resistance on the current distribution of CORC cable with DC transport current and AC transport current. Besides, the influence of Joule heat generated by the joint resistance on the temperature of the CORC cable is explored. The proposed model can serve as a powerful tool to analyze the electromagnetic-thermal behaviors of CORC cable in real applications.
Activation of sperm Toll-like receptor 2 induces hyperactivation to enhance the penetration to mucus and uterine glands: a trigger for the uterine inflammatory cascade in cattle

It is known that sperm and seminal plasma (SP) affect uterine immunity. In cattle, artificial insemination enables breeding by depositing frozen and largely diluted sperm with a negligible amount of SP into the uterus. Thus, the present study focused on the impact of frozen-thawed sperm on bovine uterine immunity. We have previously shown that in the bovine uterus, sperm swim smoothly over the luminal epithelium and some sperm interact with uterine glands to induce a weak inflammatory response mainly via the endometrial Toll-like receptor 2 (TLR2) signaling. However, the process by which sperm is encountered in the uterine glands is not completely clear. The present study intended to evaluate the role of sperm-TLR2 in sperm-uterine mucus penetration for reaching the glandular epithelium to induce the uterine immune response. To activate and block sperm-TLR2, they were treated with TLR2 agonist and antagonist, respectively. TLR2 activation enhanced sperm hyperactivation and improved its capacity to penetrate the artificial viscoelastic fluid and estrous-uterine-mucus. In contrast, TLR2-blocked sperm showed completely opposite effects. It is noteworthy, that the TLR2-activated sperm that penetrated the uterine mucus exhibited increased motile activity with hyperactivation. In the sperm-endometrial ex-vivo model, a greater amount of TLR2-activated sperm entered the uterine glands with an immune response, which was seen as the upregulation of mRNA expression for TNFA, IL1B, IL8, PGES, and TLR2 similar to those in control sperm. On the other hand, a lesser amount of TLR2-blocked sperm entered the uterine glands and weakened the sperm-induced increase only in PGES, suggesting that penetration of a certain number of sperm in the uterine gland is necessary enough to trigger the inflammatory response. Altogether, the present findings indicate that activation of sperm-TLR2 promotes their hyperactivation and mucus penetration with greater motility, allowing them to enter into the uterine glands more. This further suggests that the hyperactivated sperm contributes to triggering the pro-inflammatory cascade partly via TLR2 in the uterus.
Amenity in Sustainable Stormwater: A Preliminary Assessment of the Toronto Green Standard

AMENITY IN SUSTAINABLE STORMWATER: A PRELIMINARY ASSESSMENT OF THE TORONTO GREEN STANDARD Eriks Kalvins! Advisor: University of Guelph, 2012! Sean Kelly ! Stormwater management systems reduce the impact of surface runoff in urban environments. The Toronto Green Standard (TGS) has been developed to further mitigate effects of urban runoff by mandating runoff control implementation in new developments at the site level. The intended effect of these measures is to reduce the quantity and improve the quality of water. Recent concepts of sustainable stormwater suggest that amenity is an equally important aspect of such a system. This study evaluated Best Management Practices suggested by the TGS for their performance and amenity characteristics. Design professionals selected for prior experience with the TGS were consulted to determine industry perceptions of the requirements, and how the standards could be altered to improve amenity value. Several concerns were identified which appear to impede the successful implementation of sustainable stormwater management solutions, to which recommendations are proposed which may help balance performance and amenity requirements in the TGS.
A Detection of Vehicle Velocity Using Deep Learning

In order to keep track of traffic flow, recognize unlawful vehicle classes, flag accidents, and calculate vehicle velocities, Intelligent Transportation Systems (ITS) need to integrate vehicle detection. Even if there is a growing interest in study, this perplexing challenge has not yet been answered and must be addressed. Although hardware options like radars and LIDAR have been suggested, they are excessively costly to run and give operators of traffic monitoring systems with very little data that can be used to take action. The Histogram of Gradients (HOG) and the Gaussian Mixed Model (GMM) are two instances of classical approaches that are employed in software-based solutions. Despite the fact that these methods are employed, they are operationally sluggish and are not suited for real-time traffic detection. As a result, this project will explore and assess a wide variety of approaches to the detection of motor vehicles. In addition, the Spiking Neural Network (SpikeNet) is used to recognize autos from the outputs of highway camera systems in order to carry out video processing procedures and get the required data. In this paper, the anchor frame optimization was done using the YOLOv5s design and the k-means method. This was done under a variety of lighting circumstances. The suggested model was able to attain a mean accuracy performance (mAP) of 97.8 for the daylight dataset as well as 95.1 for the nighttime dataset, in accordance with the results of the simulations and evaluations of the algorithm.
Retrieving Forest Characteristics from High-Resolution Airborne S-Band Radar Data

Synthetic Aperture Radar (SAR) data are utilized for improved mapping of forest cover and biophysical retrieval due to its sensitivity to forest canopy and structure. It is important to study the forest structure and biophysical parameters because it constitutes the major forest aboveground biomass (AGB). The S-band SAR frequency has not been consistently investigated for forest monitoring due to the lack of long-term data. Using the recent AirSAR campaign (2010-2014) over Savernake Forest and Wytham Woods in southern England, this thesis presents methods for analysing S-band SAR data for soil and forest canopies using the radiative transfer Michigan Microwave Canopy Scattering (MIMICS-I) model. The first result chapter shows that dominant scattering behaviour of S-band frequency arises from ground/trunk interactions with little direct crown scattering across all polarisations and incidence angles. The S-band backscatter shows significant sensitivity to both soil moisture content and surface roughness. Simulation experiments related to forest degradation show low co-polarisation backscatter due to reduced canopy component and tree density at S-band. Using the above information, the second result chapter shows that S-band HHand VVbackscatter and Radar Forest Degradation Index (RFDI) data produces forest/non-forest classification map at 6 m resolution with 70% overall accuracy (kappa coefficient, κ = 0.41) while 63% overall accuracy (kappa coefficient, κ = 0.27) for the 20 m resolution map in a Maximum Likelihood algorithm. S-band data is also useful for mapping various non-forest cover types and monitoring forest cover changes over time due to the loss of volume scattering when forest canopies are removed. Using the field measured forest biomass, the third result chapter reveals that S-band radar backscatter correlates well with forest AGB. A consistent S-band backscatter/ biomass relationship is found, suggesting increasing backscatter sensitivity to forest AGB up to 100 t/ha with least error varying 90.46 98.65 t/ha at 25 m resolution (stand level) in low to medium incidence angles. The implications of these results are that S-band SAR data like the longer L-band SAR is highly suitable for mapping forest cover and monitoring cover changes and be able to retrieve low biomass stands below 100 t/ha.
Remote Symptom Monitoring to Enhance the Delivery of Palliative Cancer Care in Low-Resource Settings: Emerging Approaches from Africa

This paper brings together researchers, clinicians, technology developers and digital innovators to outline current applications of remote symptom monitoring being developed for palliative cancer care delivery in Africa. We outline three remote symptom monitoring approaches from three countries, highlighting their models of delivery and intended outcomes, and draw on their experiences of implementation to guide further developments and evaluations of this approach for palliative cancer care in the region. Through highlighting these experiences and priority areas for future research, we hope to steer efforts to develop and optimise remote symptom monitoring for palliative cancer care in Africa.
Oruta: Privacy- Preserving Public Auditing for Shared Data in the Cloud

We believe that sharing data among multiple users is perhaps one of the most engaging features that motivates cloud storage. A unique problem introduced during the process of public auditing for shared data in the cloud is how to preserve identity privacy from the TPA, because the identities of signers on shared data may indicate that a particular user in the group or a special block in shared data is a higher valuable target than others. Abstract—With cloud storage services, it is common place for data to be not only stored in the cloud, but also shared across multiple users. However, public auditing for such shared data — while preserving identity privacy— remains to be an open challenge. In this paper, we propose the first privacy-preserving mechanism that allows public auditing on shared data stored in the cloud. In particular, we exploit ring signatures to compute the verification information needed to audit the integrity of shared data. With our mechanism, the identity of the signer on each block in shared data is kept private from a third party auditor (TPA), who is still able to publicly verify the integrity of shared data without retrieving the entire file. Our experimental results demonstrate the effectiveness and efficiency of our proposed mechanism when auditing shared data. mechanism for cloud data, so that during public auditing, the content of private data belonging to a personal user is not disclosed to the third party auditor.
Validation of an HPLC-DAD Method for Quercetin Quantification in Nanoparticles

The evaluation of the efficacy of incorporation of quercetin in nanoparticles is crucial, both for the development and quality control of pharmaceutical formulations. The validation of analytical methods for the precise quantification of quercetin is useful for the evaluation of various potential quercetin delivery systems and quercetin pharmacokinetics. This work aimed to validate a high-performance liquid chromatography with diode array detection (HPLC-DAD) method for quercetin detection and quantification in nanoparticles. Different mobile phase conditions and detection wavelengths (254 and 368 nm) were tested, and the major validation parameters were assessed (precision, accuracy, linearity, sensitivity, stability, and selectivity). The best peak resolution was obtained when quercetin was analyzed at 368 nm with a mobile phase of 1.5% acetic acid and a water/acetonitrile/methanol ratio of 55:40:5. Under these conditions, quercetin also eluted rapidly (retention time of 3.6 min). The method proved to be linear (R2 > 0.995), specific, and repeatable (variation coefficient between 2.4% and 6.7%) and presented intermediate precision (variation coefficient between 7.2% and 9.4%). The accuracy of the analysis ranged between 88.6% and 110.7%, and detection and quantification limits were 0.046 and 0.14 µg/mL, respectively. Quercetin solutions were more stable when stored at 4 °C than at room temperature or −20 °C. This validated method satisfied more parameters of bias assessment than most recent methods for quercetin determination and presented itself as more sensitive and efficient than general spectrophotometric methods. The method was successfully used for the analysis of quercetin incorporation in nanoparticles and will be evaluated in the future for its adequacy for the determination of quercetin in more complex matrices.