Miracle-dz/UniFAR-FTUs huggingface dataset:
The Income Distribution and Household Food Security of Beef Cattle Farmers in The Special Region of Yogyakarta, Indonesia

This research aimed to calculate income and its distribution as well as the food security rate and to analyze factors affecting food security rate of independent and profit-sharing farmers of beef cattle fattening in Special Region of Yogyakarta. An explanatory was used as the method for the research in Gunungkidul, Sleman, Bantul, and Kulonprogo Regencies by quota sampling on 120 independent and 120 profit-sharing farmers. Data were analyzed through income formulation, R/C Ratio, Gini Ratio, cross-classification of food expenditure share and energy consumption (Table of Jonsson and Toole), and ordered logistic model. The research results indicate that the incomes of independent and profit-sharing farmers were IDR1,079,033.00 and IDR1,023,324.00, lower than the Minimum Wage of Special Region of Yogyakarta which amounted to IDR1,337,645.25. The business was still accountable because the R/C ratio had a value larger than one. The values of the Gini Ratio of independent and sharing farmers were 0.310 and 0.219 which categorized as the low-scale unequal income distribution. Food security rates of independent and profit-sharing farmers were 22.5% and 13.33% of food secure, 15.83% and 18.33% of food vulnerable, 40.0% and 38.34% of less-secure food, and 21.67% and 30.0% of food insecure. The determining variables were the price of rice (p<0.01), price of vegetable (p<0.05), price of beef (p<0.01), price of cooking oil (p<0.05), and the price of instant noodle (p<0.01), household income (p>0.05), education dummy (p<0.05), cattle group dummy (p>0.05), the number of family member (p<0.01), and the housewife education (p>0.05).
A Deep Learning Framework for End-to-End Control of Powered Prostheses

Deep learning offers a potentially powerful alternative to hand-tuned control of active lower-limb prostheses, being capable of generating continuous joint-level assistance end-to-end. This eliminates the need for conventional task classification, state machines and mid-level control equations by collapsing the entire control problem into a deep neural network. In this letter, sensor data and conventional commanded torque from an open-source powered knee-ankle prosthesis (OSL) were collected across five locomotion modes: level ground, ramp incline/decline and stair ascent/descent. Reference commanded torques were generated using an expert-tuned finite state machine-based impedance controller for each mode and transfemoral amputee participant (N = 12). Stance phases of the output were then estimated using a temporal convolutional network (TCN), which produced mode- and user-independent knee and ankle torques with RMSE of 0.154 ± 0.06 and 0.106 ± 0.06 Nm/kg, respectively. Training the model on mode-specific data only produced significant reductions in stair descent, lowering knee and ankle RMSE by 0.06 ± 0.028 and 0.033 ± 0.008 Nm/kg respectively (p < 0.05). In addition, the TCN adapted to walking speed and slope shifts in reference commanded torque. These results demonstrate that this deep learning model not only removes the need for heuristic state machines and mode classification but can also reduce or remove the need for prosthesis assistance tuning entirely.
Genome-Wide Identification and Drought Stress Response Pattern of the NF-Y Gene Family in Cymbidium sinense

Cymbidium sinense, a type of orchid plant, is more drought-resistant and ornamental than other terrestrial orchids. Research has shown that many members of the NUCLEAR FACTOR Y (NF-Y) transcription factor family are responsive to plant growth, development, and abiotic stress. However, the mechanism of the NF-Y gene family’s response to abiotic stress in orchids has not yet been reported. In this study, phylogenetic analysis allowed for 27 CsNF-Y genes to be identified (5 CsNF-YAs, 9 CsNF-YBs, and 13 CsNF-YC subunits), and the CsNF-Ys were homologous to those in Arabidopsis and Oryza. Protein structure analysis revealed that different subfamilies contained different motifs, but all of them contained Motif 2. Secondary and tertiary protein structure analysis indicated that the CsNF-YB and CsNF-YC subfamilies had a high content of alpha helix structures. Cis-element analysis showed that elements related to drought stress were mainly concentrated in the CsNF-YB and CsNF-YC subfamilies, with CsNF-YB3 and CsNF-YC12 having the highest content. The results of a transcriptome analysis showed that there was a trend of downregulation of almost all CsNF-Ys in leaves under drought stress, while in roots, most members of the CsNF-YB subfamily showed a trend of upregulation. Additionally, seven genes were selected for real-time reverse transcription quantitative PCR (qRT-PCR) experiments. The results were generally consistent with those of the transcriptome analysis. The regulatory roles of CsNF-YB 1, 2, and 4 were particularly evident in the roots. The findings of our study may make a great contribution to the understanding of the role of CsNF-Ys in stress-related metabolic processes.
Modelling mosquito population suppression based on competition system with strong and weak Allee effect

Mosquito-borne diseases are threatening half of the world's population. To prevent the spread of malaria, dengue fever, or other mosquito-borne diseases, a new disease control strategy is to reduce or eradicate the wild mosquito population by releasing sterile mosquitoes. To study the effects of sterile insect technique on mosquito populations, we developed a mathematical model of constant release of sterile Aedes aegypti mosquitoes with strong and weak Allee effect and considered interspecific competition with Anopheles mosquitoes. We calculated multiple release thresholds and investigated the dynamical behavior of this model. In order to get closer to reality, an impulsive differential equation model was also introduced to study mosquito suppression dynamics under the strategy of releasing $ c $ sterile male mosquitoes at each interval time $ T $. Finally, the relationship between the releasing amount or the waiting period and the number of days required to suppress mosquitoes was illustrated by numerical simulations.
Measures to Improve Nurses' Pain Management

Pain is the most common medical issue that older people face in a long-term care facility. Registered nurses have a critical role in helping residents manage their pain. This research looked at measures to improve pain management practices in long-term care facilities in Ontario. The site for this research was a chosen long-term care facility in Ontario, Canada, a 160-bed nursing home for the elderly that provides various nursing and medical care services. Semi-structured focus group interviews lasting about an hour were done. This study's population consisted of 45 nurses. The researcher chose a sample of 25 registered nurses using a purposive sampling strategy. The data was reviewed using qualitative data analysis to detect recurring concerns. This research revealed the necessity of identifying measures to improve pain management and reinforcing good practices in long-term care homes; better pain management practices are necessary to manage pain in a long-term care home. This study demonstrated the importance of recognizing and overcoming measures to improve pain management and reinforce good practices in long-term care homes. Therefore, improved measures to improve pain management practices are required to manage pain in a long-term care home effectively. Education about safe pain management will help to prevent the undertreatment of pain and its negative consequences. The overall benefits of measures to improve pain management practices in long-term care homes expand nurses' clinical knowledge in the care of residents living in nursing homes.
Geochemistry of diverse lava types from the Lau Basin (South West Pacific): Implications for complex back‐arc mantle dynamics

Lavas from back‐arc basins are generally diverse, and their geochemical compositions indicate that they received variable contributions from subduction components during petrogenesis. Obtaining comprehensive geochemistry characteristics for the entire back‐arc basin are helpful in gaining a better understanding of the complexities of back‐arc evolution. In this study, we systematically reviewed the major and trace element compositions of Lau Basin lavas. This data set covered all of the major tectonic units of the basin, for example, the Eastern Lau spreading center (ELSC), the Valu Fa ridge (VFR), the Central Lau spreading center (CLSC), the Northwestern Lau spreading center (NWLSC), the Northeastern Lau spreading center (NELSC), the Mangatolu Triple Junction (MTJ), and the Fonualei Spreading Center (FSC). The lava types from these locations are diverse, including tholeiitic basalts, alkali basalts, basaltic andesites, andesites, dacites, and rhyolites. In addition, the NELSC and FSC contain boninites. In this study, the geochemical compositions of these diverse rock types are used to investigate the spreading dynamics of the Lau Basin back‐arc. We conclude that (a) the subduction components mainly include hydrous fluids released from the subducted slab, and hydrous melt directly from the melting subducted sediment, and the influence of the subducted components on the mantle wedge and the petrogenesis of the central and southern Lau Basin lavas decreases with increasing distance from the Tonga Trench; (b) during the propagation and spreading of the Samoan mantle into the Lau Basin, lavas from the NELSC, NWLSC, MTJ, and even from the northern part of the FSC may receive variable degrees of influence from the Samoan plume; and (c) the migration of Indian MORB‐like mantle southwards into the Pacific mantle is consistent with the opening of the Lau Basin. With the gradual migration of Indian mantle into the Lau Basin, a series of spreading centers were formed, for example, in order, the NWLSC, CLSC, ELSC, and the propagating VFR.
Frequency Domain Auto-tuning of Structured LPV Controllers for High-Precision Motion Control

Motion systems are a vital part of many industrial processes. However, meeting the increasingly stringent demands of these systems, especially concerning precision and throughput, requires novel control design methods that can go beyond the capabilities of traditional solutions. Traditional control methods often struggle with the complexity and position-dependent effects inherent in modern motion systems, leading to compromises in performance and a laborious task of controller design. This paper addresses these challenges by introducing a novel structured feedback control auto-tuning approach for multiple-input multiple-output (MIMO) motion systems. By leveraging frequency response function (FRF) estimates and the linear-parameter-varying (LPV) control framework, the proposed approach automates the controller design, while providing local stability and performance guarantees. Key innovations include norm-based magnitude optimization of the sensitivity functions, an automated stability check through a novel extended factorized Nyquist criterion, a modular structured MIMO LPV controller parameterization, and a controller discretization approach which preserves the continuous-time (CT) controller parameterization. The proposed approach is validated through experiments using a state-of-the-art moving-magnet planar actuator prototype.
Beneficiamento de sementes de hortaliças.

INTRODUÇÃO A obtenção de sementes de alta qualidade representa a meta prioritária do processo de produção. Neste contexto, o beneficiamento constitui-se numa etapa essencial dentro do programa de produção de sementes, visto que o lote de sementes necessita ser beneficiado e manipulado de forma adequada, caso contrário, os esforços anteriores com a fase de produção das sementes podem ser anulados. Após a colheita de um lote de sementes, junto com as sementes de qualidade superior encontram-se algumas impurezas (palhas, torrões, pedras, restos de frutos, sementes de plantas daninhas e de outras espécies cultivadas, estruturas propagativas de patógenos, etc) e também sementes com qualidade inferior (sementes imaturas, mal formadas, chochas, deformadas, atacadas por patógenos e insetos, etc). O beneficiamento de sementes constitui-se num conjunto de operações visando melhorar, ou aprimorar, as características de um lote de sementes, uma vez que esse processo promove a eliminação das impurezas, das sementes de outras espécies ou cultivares, das sementes da espécie ou da cultivar, que por ventura apresentem características indesejáveis e por fim permite a separação em frações mais uniformes (Carvalho & Nakagawa, 2000). Além disso, várias espécies de hortaliças apresentam crescimento indeterminado, de forma que nessas espécies, os lotes de sementes contêm além das demais impurezas, sementes imaturas, que precisam ser removidas durante o processo de beneficiamento. Assim, a operação de beneficiamento é fundamental para que a qualidade do lote de sementes seja satisfatória para comercialização, pois, de maneira geral, tem a finalidade de eliminar esses materiais indesejáveis nas diferentes etapas e, quando conveniente, classificar as sementes principalmente quanto ao tamanho. O beneficiamento realça as boas características do lote de sementes e também concorre para o seu aprimoramento. Conseqüentemente, as etapas seguintes da produção de sementes (tratamento, revestimento, embalagem, armazenamento, etc) serão favorecidas, sendo que a principal preocupação durante o beneficiamento é a preservação da qualidade das sementes. A qualidade final de um lote de sementes depende do cuidado em manter, durante o beneficiamento,
Asymptotics of the finite-temperature sine kernel determinant

In the present paper, we study the asymptotics of the Fredholm determinant $D(x,s)$ of the finite-temperature deformation of the sine kernel, which represents the probability that there is no particles on the interval $(-x/\pi,x/\pi)$ in the bulk scaling limit of the finite-temperature fermion system. The variable $s$ in $D(x,s)$ is related to the temperature. The determinant also corresponds to the finite-temperature correlation function of one dimensional Bose gas. We derive the asymptotics of $D(x,s)$ in several different regimes in the $(x,s)$-plane. A third-order phase transition is observed in the asymptotic expansions as both $x$ and $s$ tend to positive infinity at certain related speed. The phase transition is then shown to be described by an integral involving the Hastings-McLeod solution of the second Painlev\'e equation.
From Instructions to Constraints: Language Model Alignment with Automatic Constraint Verification

User alignment is crucial for adapting general-purpose language models (LMs) to downstream tasks, but human annotations are often not available for all types of instructions, especially those with customized constraints. We observe that user instructions typically contain constraints. While assessing response quality in terms of the whole instruction is often costly, efficiently evaluating the satisfaction rate of constraints is feasible. We investigate common constraints in NLP tasks, categorize them into three classes based on the types of their arguments, and propose a unified framework, ACT (Aligning to ConsTraints), to automatically produce supervision signals for user alignment with constraints. Specifically, ACT uses constraint verifiers, which are typically easy to implement in practice, to compute constraint satisfaction rate (CSR) of each response. It samples multiple responses for each prompt and collect preference labels based on their CSR automatically. Subsequently, ACT adapts the LM to the target task through a ranking-based learning process. Experiments on fine-grained entity typing, abstractive summarization, and temporal question answering show that ACT is able to enhance LMs' capability to adhere to different classes of constraints, thereby improving task performance. Further experiments show that the constraint-following capabilities are transferable.
Practically adaptable CPABE based Health-Records sharing framework

With recent elevated adaptation of cloud services in almost every major public sector, the health sector emerges as a vulnerable segment, particularly in data exchange of sensitive Health records, as determining the retention, exchange, and efficient use of patient records without jeopardizing patient privacy, particularly on mobile-applications remains an area to expand. In the existing scenarios of cloud-mobile services, several vulnerabilities can be found including trapping of data within a single cloud-service-provider and loss of resource control being the significant ones. In this study, we have suggested a CPABE and OAuth2.0 based framework for efficient access-control and authorization respectively to improve the practicality of EHR sharing across a single client-application. In addition to solving issues like practicality, data entrapment, and resource control loss, the suggested framework also aims to provide two significant functionalities simultaneously, the specific operation of client application itself, and straightforward access of data to institutions, governments, and organizations seeking delicate EHRs. Our implementation of the suggested framework along with its analytical comparison signifies its potential in terms of efficient performance and minimal latency as this study would have a considerable impact on the recent literature as it intends to bridge the pragmatic deficit in CPABE-based EHR services.
Style Blind Domain Generalized Semantic Segmentation via Covariance Alignment and Semantic Consistence Contrastive Learning

Deep learning models for semantic segmentation often experience performance degradation when deployed to unseen target domains unidentified during the training phase. This is mainly due to variations in image texture (\ie style) from different data sources. To tackle this challenge, existing domain generalized semantic segmentation (DGSS) methods attempt to remove style variations from the feature. However, these approaches struggle with the entanglement of style and content, which may lead to the unintentional removal of crucial content information, causing performance degradation. This study addresses this limitation by proposing BlindNet, a novel DGSS approach that blinds the style without external modules or datasets. The main idea behind our proposed approach is to alleviate the effect of style in the encoder whilst facilitating robust segmentation in the decoder. To achieve this, BlindNet comprises two key components: covariance alignment and semantic consistency contrastive learning. Specifically, the covariance alignment trains the encoder to uniformly recognize various styles and preserve the content information of the feature, rather than removing the style-sensitive factor. Meanwhile, semantic consistency contrastive learning enables the decoder to construct discriminative class embedding space and disentangles features that are vulnerable to misclassification. Through extensive experiments, our approach outperforms existing DGSS methods, exhibiting robustness and superior performance for semantic segmentation on unseen target domains.
BRDF-Based Photometric Modeling of LEO Constellation Satellite from Massive Observations

Modeling the brightness of satellites in large Low-Earth Orbit (LEO) constellations can not only assist the astronomical community in assessing the impact of reflected light from satellites, optimizing observing schedules and guiding data processing, but also motivate satellite operators to improve their satellite designs, thus facilitating cooperation and consensus among different stakeholders. This work presents a photometric model of the Starlink satellites based on the Bidirectional Reflectance Distribution Function (BRDF) using millions of photometric observations. To enhance model accuracy and computational efficiency, data filtering and reduction are employed, and chassis blocking on the solar array and the earthshine effect are taken into account. The assumptions of the model are also validated by showing that the satellite attitude is as expected, the solar array is nearly perpendicular to the chassis, and both the solar array pseudo-specular reflection and the chassis earthshine should be included in the model. Reflectance characteristics of the satellites and the apparent magnitude distributions over station are finally discussed based on the photometric predictions from the model. In addition to assessing the light pollution and guiding the development of response measures, accurate photometric models of satellites can also play an important role in areas such as space situational awareness.
Assessing User Apprehensions About Mixed Reality Artifacts and Applications: The Mixed Reality Concerns (MRC) Questionnaire

Current research in Mixed Reality (MR) presents a wide range of novel use cases for blending virtual elements with the real world. This yet-to-be-ubiquitous technology challenges how users currently work and interact with digital content. While offering many potential advantages, MR technologies introduce new security, safety, and privacy challenges. Thus, it is relevant to understand users' apprehensions towards MR technologies, ranging from security concerns to social acceptance. To address this challenge, we present the Mixed Reality Concerns (MRC) Questionnaire, designed to assess users' concerns towards MR artifacts and applications systematically. The development followed a structured process considering previous work, expert interviews, iterative refinements, and confirmatory tests to analytically validate the questionnaire. The MRC Questionnaire offers a new method of assessing users' critical opinions to compare and assess novel MR artifacts and applications regarding security, privacy, social implications, and trust.
LLMs Still Can't Avoid Instanceof: An Investigation Into GPT-3.5, GPT-4 and Bard's Capacity to Handle Object-Oriented Programming Assignments

Large Language Models (LLMs) have emerged as promising tools to assist students while solving programming assignments. However, object-oriented programming (OOP), with its inherent complexity involving the identification of entities, relationships, and responsibilities, is not yet mastered by these tools. Contrary to introductory programming exercises, there exists a research gap with regard to the behavior of LLMs in OOP contexts. In this study, we experimented with three prominent LLMs - GPT-3.5, GPT-4, and Bard - to solve real-world OOP exercises used in educational settings, subsequently validating their solutions using an Automatic Assessment Tool (AAT). The findings revealed that while the models frequently achieved mostly working solutions to the exercises, they often overlooked the best practices of OOP. GPT-4 stood out as the most proficient, followed by GPT-3.5, with Bard trailing last. We advocate for a renewed emphasis on code quality when employing these models and explore the potential of pairing LLMs with AATs in pedagogical settings. In conclusion, while GPT-4 showcases promise, the deployment of these models in OOP education still mandates supervision.