Miracle-dz/UniFAR-FTUs huggingface dataset:
Lower Salaries and No Options: The Optimal Structure of Executive Pay

We estimate a standard principal agent model with constant relative risk aversion and lognormal stock prices for a sample of 598 US CEOs. The model is widely used in the compensation literature, but it predicts that almost all of the CEOs in our sample should hold no stock options. Instead, CEOs should have lower base salaries and receive additional shares in their companies. For a typical value of relative risk aversion, almost half of the CEOs in our sample would be required to purchase additional stock in their companies from their private savings. The model predicts contracts that would reduce average compensation costs by 20% while providing the same incentives and the same utility to CEOs. We investigate a number of extensions and modi.cations of the standard model, but . nd none of them to be satisfactory. We conclude that the standard principal agent model typically used in the literature cannot rationalize observed contracts. One reason may be that executive pay contracts are suboptimal.
The Impact of Permanent Magnet Geometry on the Reading of Soft Magnetic Barcodes Using Magnetoresistive Sensors

Encoding information in patterns printed with magnetic ink can extend the usability of barcode and QR code technologies to applications that do not require a line of sight between the sensor and the code. Such an implementation garners an interest in security, track-and-trace, and recyclable packaging applications. Previous work has demonstrated a device that can read a barcode obscured by a layer of opaque and 1 mm thick material. However, given that the most widespread magnetic toners are soft magnetic, a permanent magnet (PM) is needed in the system alongside the sensor to magnetically bias the barcode, which affects the sensor sensitivity. Here, we describe a protocol to evaluate the impact of different PM geometries on the performance of the sensor and validate it with experimental results, enabling active feedback for integration in the scanner system. As a result, we have reached an improved geometry that increases the reading distance of the device to at least 2 mm. This study is also useful for other applications that require optimization of the interaction between a PM and a magnetic sensor (e.g., nanoparticle tracking in magnetoresistive biosensors).
Characterisation of New Foxunavirus Phage Murka with the Potential of Xanthomonas campestris pv. campestris Control

Phages of phytopathogenic bacteria are considered to be promising agents for the biological control of bacterial diseases in plants. This paper reports on the isolation and characterisation of a new Xanthomonas campestris pv. campestris phage, Murka. Phage morphology and basic kinetic characteristics of the infection were determined, and a phylogenomic analysis was performed. The phage was able to lyse a reasonably broad range (64%, 9 of the 14 of the Xanthomonas campestris pv. campestris strains used in the study) of circulating strains of the cabbage black rot pathogen. This lytic myovirus has a DNA genome of 44,044 bp and contains 83 predicted genes. Taxonomically, it belongs to the genus Foxunavirus. This bacteriophage is promising for use as a possible means of biological control of cabbage black rot.
Importance of Data Preprocessing and Parameters Tuning for Supervised Machine Learning Models on Tweets Sentiment Analysis

This paper shows the comparison of five different supervised machine learning models by showing the accuracy and classification report of these models when used for tweets sentiments analysis while showing the improvement in accuracy when data was preprocessed and parameters were tuned. The five different models that were used are: NaiveBayes, Support Vector Machine, Random Forest, Long Short-Term Memory (LSTM) and XG Boost. Total of 25000 tweets were processed, analyzed and predicted the output as positive, negative, or neutral using those models. This research would help to understand which models should be used and followed and which model would yield higher accuracy while using various approaches of data preprocessing and parameters tuning. The paper also tries to show that the standard models can still perform better and are still viable for sentiment analysis while SVM and Random Forest classifiers maybe viewed as standard learning strategies.
Sectoral Utilization of Foreign Exchange and the Policy Implications for Economic Growth in Nigeria (1997 – 2022)

Purpose: The purpose of this study is to investigate the effect of sectoral utilization of foreign exchange and the policy implications for economic growth in Nigeria. Methodology: The methodology involved the use of annual time series data covering 1997 to 2022 for pre-diagnostic tests for unit roots which showed that the variables used in the study: economic growth (proxied by real gross domestic product), visible transactions, invisible transactions, treasury bill rate, and exchange rate, were stationary in the first difference. Hence, after first differencing the data, the Ordinary Least Squares (OLS) estimation method was used to estimate the model. Findings: The findings of the study showed that visible and invisible foreign exchange utilization valid for foreign exchange transactions, in addition to exchange rate and Treasury bill rate impacted positively and significantly on economic growth in Nigeria during the study period. The results of the study strengthened the theoretical basis for the use of relevant policies to grow economies generally, and that of Nigeria in particular. Unique contributor to theory, policy and practice: By implication, the governments at all tiers should endeavor to provide the enabling environment for greater availability and utilization of foreign exchange to further improve economic growth and sustainable development in Nigeria.
Research on Improving Amplifier Used in Brain-Computer Interface Technology

With the rapid development of science and technology, the demand for human-computer interaction in many fields is increasing, and the requirements are getting higher and higher. As the most important part in human-computer interaction, the demand for Brain-Computer Interface (BCI) technology in medicine, games, military fields, etc. is increasing. Therefore, more and more scholars and institutions have begun to study BCI technology. In this paper, an experiment is designed to improve the performance of two-stage CMOS amplifiers, an important component of BCI technology. The experiment uses CoolSpice to build a basic model of the two-stage CMOS amplifier for simulation, replacing brain waves with a small signal, and adjusting the performance of the two-stage CMOS amplifier by adding a compensated capacitor and resistor. Through experimentation, it can be concluded that selecting an appropriate compensated capacitor can significantly improve the performance of the amplifier. Improving the performance of amplifiers can help improve the efficiency of BCI technology and help people collected the information emitted by brain waves more accurately in the future.
Cognitive Job Demands and Sports Participation among Young Workers: What Moderates the Relationship?

Cognitive job demands are theoretically and empirically associated with sports participation in various ways. Workers may be overwhelmed by stress and fatigue from their workload and therefore refrain from sports activities, but they can also feel the need to use sports as a way to recover and detach from work. The strategy to which workers adhere can depend on workers’ resources that moderate the cognitive job demands and sports participation relationship, such as educational attainment, being a parent, or having worktime and work location control. To test our expectations, we used recent information on sports participation by young working adults from the Netherlands (N = 2032). Using multinomial logistic regression modelling, we found that workers in mentally demanding jobs were more likely to participate in sports more than three times a week. In particular, workers without children reported a higher likelihood of participating in sports more than three times a week when they experienced high cognitive job demands. Among the higher-educated, workers with high cognitive job demands were less likely to participate in sports one to three times a week. We reflect on the academic and policy-related implications of our findings.
A Bidirectional DC–DC Converter With Direct Power Transfer

This paper presents a novel bidirectional DC–DC converter for several applications such as energy storage systems. The proposed power circuit topology not only has inherent soft switching but also offers reduced conduction losses. The reduction in conduction losses is achieved through a direct power transfer (DPT) path, which can effectively bypass the transformer as well as power semiconductors at the primary side, and transfer power directly to the secondary side. In addition, The power circuit offers flexibility to shape the input current waveform to have lower peak values, resulting in further reduction in conduction losses. Thus, the proposed circuit can potentially provide high efficiency by reducing both the conduction losses and switching losses simultaneously. Theoretical analysis, simulation results, and experimental results demonstrate the feasibility of the proposed circuit and its superior performance.
K-MEANS CLUSTERING CALCULATION TO DETERMINE MAINSTREAM DOMINATION OF COURSES

Abstract : In Semester 6 students are required to choose mainstream elective courses at the STMIK Royal campus. In this study, researchers chose 2 mainstraim elective courses, namely Logic and Programming, and Database and Information Management. The purpose of choosing this elective course is for students to focus more on their majors. The process of selecting the data, researchers carry out an analysis process of student learning outcomes. This research data is semester 1 to semester 5 grades. All data on the value of student learning outcomes are divided into 2 parts, namely theory and practice and divided into the number of courses. For many uses, a data mining method using K-means cluster is needed. K-Means Clustering is a data analysis method or Data Mining method that performs an unsupervised learning modeling process and uses methods that group data from various partitions. The results of the k means clutering grouping with 2 clusters, namely cluster 0 of the mainstream group of logic and programming courses with a total of 28 students, and cluster 1 of the mainstream group of informatics database and management courses with a total of 72 students with a total of 100 students. Keywords: data; K-means; student Abstrak : Mahasiswa semester 6 diwajibkan memilih mainstream matakuliah pilihan yang ada di kampus STMIK Royal. Pada penelitian ini, peneliti memilih 2 mainstraim matakuliah pilihan yaitu Logika dan Pemprograman, serta Basis Data dan Manajemen Informatika. Tujuan pemilihan matakuliah pilihan ini adalah agar mahasiswa lebih terfokus pada jurusannya. Proses pemilihan datanya, peneliti melakukan proses analisa dari hasil belajar mahasiswa. Data penelitian ini nilai semester 1 sampai semester 5. Seluruh data nilai hasil belajar mahasiswa dibagi menjadi 2 bagian yaitu teori dan praktek serta dibagi jumlah banyaknya matakuliah. untuk penggunaan yang banyak, maka dperlukan metode data mining dengan menggunakan k-means cluster. K-Means Clustering adalah metode analisis data atau metode Data Mining yang melakukan proses pemodelan pembelajaran tanpa pengawasan dan menggunakan metode yang mengelompokkan data dari berbagai partisi. Hasil pengelompokan k means clutering dengan 2 cluster yaitu cluster 0 kelompok mainstream matakuliah logika dan pemprograman dengan jumlah mahasiswa 28 orang, dan cluster 1 kelompok mainstream matakuliah basis data dan manajemen informatika dengan jumlah mahasiswa 72 orang dengan total keseluruhan 100 mahasiswa. Keyword : data; K-means; mahasiswa;
A DNA-barcoding-based approach to quantitatively investigate larval food resources of cavity-nesting wasps from trap nests

Artificial nesting resources, also known as trap nests, have proven to be an ideal method for monitoring cavity-nesting bees and wasps, their collected food resources, and natural enemies. Nowadays, trap nests are frequently used to assess responses to environmental and biodiversity changes based on multi-trophic interaction networks. Here, we reconstructed quantitative trophic interaction networks of five apoid wasps (Trypoxylon clavicerum, Passaloecus corniger, Passaloecus gracilis, Psenulus fuscipennis, Isodontia mexicana and two vespid wasp species (Ancistrocerus nigricornis, Microdynerus parvulus) using DNA barcoding. Sampling the nests during their construction period allowed us to give an accurate count and identification of the provided food items. We recovered highly resolved bi- and tripartite networks including wasp-beetle larva, wasp-cricket, natural enemy-wasp-moth larva, natural enemy-wasp-spider, and natural enemy-wasp-aphid associations. The latter include aphid species that are known as agricultural and forest pests. Although the quantitative sampling of nests entails increased time costs, it enables not only high-quality DNA barcoding but also to reconstruct quantitative interaction networks. Thus, our approach is a highly promising monitoring tool for gaining deeper knowledge on the ecology, habitat requirements and the impact of environmental and biodiversity change on cavity-nesting bees and wasps.
The Impact of Loneliness and Resilience on Quality of Life among Elderly Living in Geriatric Homes

Background: Quality of life is a vital element in the care of old people and is an indicator of overall health. Loneliness and resilience are among the factors that affect elderly quality of life. This study aimed to evaluate the impact of loneliness and resilience on quality of Life among elderly living in geriatric homes. Methods: a descriptive correlational research design was utilized in the current study, a convenience sample of (30) elderly was selected. The research was implemented at three geriatric homes; Islamic Acquaintance Association for Elderly Care (Dar El Hana), Hedya Barakat Geriatric Home, and Maana Geriatric Home which all located at Giza Governorate, data were collected using four tools; personal data questionnaire, loneliness scale, resilience scale, and World Health organization quality of life scale. Results: results revealed low levels of loneliness among residents, moderate to high level of resilience and quality of life among majority of residents, significant negative statistical correlation between loneliness and both resilience and quality of life and positive statistical correlation between resilience and quality of life. Conclusion: both loneliness and resilience has impact on quality of life among elderly. Recommendations: programs to enhance social functioning and resilience may be effective in improving quality of life among elderly living at geriatric homes.
Fusarium and fumonisin in GM maize grown by small-scale farmers in KwaZulu-Natal, South Africa

The genetic modification (GM) of maize to contain proteins that act to control insects has become a widespread agricultural practice. Although the reduction of insect damage to maize ears could potentially increase crop yield, rural small-scale farmers might be reluctant to buy expensive GM seed every season even when the lower fungal infection of the GM maize might also result in health benefits. This study was conducted over 5 years in three districts of northern KwaZulu-Natal, South Africa, to study the Fusarium verticillioides and F. proliferatum infection and fumonisin contamination levels in Bt maize, Roundup- Ready® maize, conventional commercial maize and traditional landrace maize planted by rural farmers following their traditional agricultural practices. Mean Fusarium infection rates varied between 3.0% and 38.3% with large standard deviations. Fusarium infection was not significantly different (p>0.05) between the various genotypes, possibly due to the wide variation in results and low sample numbers. Although the fumonisin results also showed wide variation, the trend of contamination was lower in Bt maize compared to conventional commercial genotypes. The mean fumonisin levels in Bt hybrids were mostly <300 μg/kg, ensuring a safe maize supply in populations consuming maize as a dietary staple. The wide variations in Fusarium and fumonisin levels within each district point to the influence of local agricultural practices, local environmental conditions, and seasonal variations. Reducing exposure to fumonisins in these communities requires both further attention to the possible influence of these factors, as well as the use of appropriate post-harvest strategies.
Single‐Probe Heat Pulse Method for Soil Water Content Determination: Comparison of Methods

Six methods to obtain soil water content by single probe are presented and compared. Errors of estimated soil water content by six methods increase with water content. Heating duration affects soil water content estimation errors. Four of the six methods are probe dependent but can be easily calibrated. Each method works for some soils, and combining different methods is a solution.
Deep Learning-Based Lane Detection: An In-depth Analysis

Lane detection technology is mainly studied in the traditional method at first, but this method is not suitable for practice. Because in some unfavorable conditions usually do not have the required robustness of autonomous driving systems. Later, Deep learning-based techniques that could increase recognition speed and accuracy were put forth. However, in the complex and changing environment, the recognition accuracy would be reduced, so the current methods need to be strengthened. This paper compares the classical methods based on image segmentation and object detection. The classical object detection algorithms such as VPGNet, Spatial CNN and spatial-temporal deep learning are introduced. In addition, the classical image segmentation methods such as Lanenet, UNet-ConvLSTM and MMA-Net are also introduced. By comparing the performance of these methods on different data sets, the advantages and problems of these methods are analyzed, and the development trend of these methods in this field is predicted. Overall, at the end of this review, the full text is summarized and prospected.
Time-varying generalized linear models: characterizing and decoding neuronal dynamics in higher visual areas

To create a behaviorally relevant representation of the visual world, neurons in higher visual areas exhibit dynamic response changes to account for the time-varying interactions between external (e.g., visual input) and internal (e.g., reward value) factors. The resulting high-dimensional representational space poses challenges for precisely quantifying individual factors’ contributions to the representation and readout of sensory information during a behavior. The widely used point process generalized linear model (GLM) approach provides a powerful framework for a quantitative description of neuronal processing as a function of various sensory and non-sensory inputs (encoding) as well as linking particular response components to particular behaviors (decoding), at the level of single trials and individual neurons. However, most existing variations of GLMs assume the neural systems to be time-invariant, making them inadequate for modeling nonstationary characteristics of neuronal sensitivity in higher visual areas. In this review, we summarize some of the existing GLM variations, with a focus on time-varying extensions. We highlight their applications to understanding neural representations in higher visual areas and decoding transient neuronal sensitivity as well as linking physiology to behavior through manipulation of model components. This time-varying class of statistical models provide valuable insights into the neural basis of various visual behaviors in higher visual areas and hold significant potential for uncovering the fundamental computational principles that govern neuronal processing underlying various behaviors in different regions of the brain.
TeamBridge 2.0: an extensible and non-invasive middleware for control and adapting games for motor rehabilitation

This work present the TeamBridge 2.0, a middleware able to perform the communication between digital games and hardware devices, like joysticks, mice and cameras (both rgbd and monocular). That communication does not require any modification to the game source code, allowing an old game to be adapted to work with a new hardware device. To prove this, tests were carried out with several games, including one of them being a commercial game. This middleware also allows the use of more than one device at the same time, so we can obtain more accurate information, one device can supply the deficiencies of the other. Finally, we performed tests to make sure that the middleware would not interfere with the user experience. Tests have shown that TeamBridge 2.0 can receive, interpret and send information quickly, the time varies according to the device used, getting 33ms when used with Kinect, 40ms with Leap Motion and 255ms with a DIY Data-Glove.