Miracle-dz/UniFAR-FTUs huggingface dataset:
Spectroscopic and chemical analysis of burnt earth under Amazonian homegarden systems and anthropic Amazonian dark soils

The practice of burning organic residues is locally known, in Amazon basin, as burnt earth (Terra Queimada). Among burnt earth soils, there are differences on their origins, as earth soils from Oxisol and Ultisol soil profiles. To understand the main physical and chemical attributes responsible for the improvement of the fertility of these soils, spectroscopic and elemental analyses were performed at four different sites: three in an Oxisol profile and one in an Ultisol profile, both collected in Amazonas State, Brazil. The burnt earth soil exhibited improved fertility indices compared with a sample from Yellow Oxisol within a secondary forest profile. The major differences were a higher pH value and increased C, N, available P, exchangeable Ca, Mg and K contents. An anthropic Amazonian Dark soil was investigated for comparison with burnt earth soils and showed intermediate values in relation to the other two studied samples for the most of the chemical attributes as pH, total base cations and effective cation exchange capacity. The lower fertility of the Yellow Oxisol under secondary forest sample can be explained by the high content of Al 3+ and H + cations that directly impact its potential cation exchange capacity. Fourier-transform infrared spectroscopy and X-ray photoelectron spectroscopy revealed the presence of calcium in different forms, being these important in the correction of the soil acidity. In addition, Raman spectroscopy indicated that burnt earth contains carbon with crystallite sizes that are typical of structures within the limit region between nanographite and amorphous carbons.
O Déficit da previdência social no Brasil: simulações de reforma com um modelo EGC dinâmico

Este artigo examina o impacto da transição demográfica no equilíbrio financeiro do Regime Geral de Previdência Social (RGPS) nas próximas décadas e quantifica os efeitos de três cenários de reforma paramétrica, entre elas, a introdução de uma idade mínima obrigatória semelhante àquela introduzida pela reforma da previdência, Emenda Constitucional (EC) no 103, aprovada em 2019. A análise baseia-se em um modelo de equilíbrio geral computável dinâmico integrado com um módulo previdenciário. Os resultados mostram que a introdução de uma idade mínima, por si só, é insuficiente para estabilizar o deficit do RGPS, o que reforça a necessidade de combinar essa medida com outros dispositivos que reduzam os valores médios dos benefícios. Entre os cenários de reforma avaliados, aquele que produz o maior custo para a sociedade é o aumento de alíquotas de contribuição previdenciária.
Post-combustion CO2capture with chemical absorption and hybrid system: current status and challenges

Post‐combustion CO2 capture from the power and industrial sectors has gained widespread attention due to its ability to retrofit easily with newly installed and existing plants. This state‐of‐the‐art, updated review provides an overview of technical issues in post‐combustion CO2 capture related to process performance, energy requirements for CO2 capture, and inconsistencies in the prediction of CO2 capture cost. Recent updates are presented regarding chemical absorption‐desorption systems, and particularly CO2 absorption and solubility enhancement studies, absorber operation at elevated pressure, recent developments in absorber configuration, and methods for reducing the heat duty of strippers. Furthermore, recently developed post‐combustion hybrid techniques such as the membrane‐absorbent hybrid process, the membrane‐cryogenic hybrid process, the membrane‐adsorption hybrid process, the absorption‐electrolysis hybrid process, and the solar thermal electrochemical process (STEP) are discussed. Finally, we suggest areas for improvement and indicate that there are significant future prospects for post‐combustion CO2 capture. © 2018 Society of Chemical Industry and John Wiley & Sons, Ltd.
Exploration of Vehicle Target Detection and Classification Method Based on Sea Lion Optimization with Deep Convolutional Neural Network

Presently, urban environments over the globe are highly employed to obtain solutions for enhancing the quality of the livers and enhance the usage of city infrastructure and resources with minimal operational cost. Urban remote sensing acts as a significant part in the ability of mapping, monitoring, and controlling infrastructure. High-resolution remote sensing data renders worldwide images faster than traditional data collection strategies. Hence, small objects such as cars are easily detected. Vehicle recognition on aerial remote sensing images (RSIs) in the complicated background of urban zones has always gained a lot of interest in the remote sensing field. The automatic vehicles enumeration research domain had a significant contribution in several applications, including traffic management and monitoring. Target detection technology will be a crucial part of computer vision (CV) technology, and target detection techniques were enforced in several domains. Therefore, this study develops a new Vehicle Recognition and Classification using Sea Lion Optimization with Deep Learning (VRC-SLODL) model on RSI. In the presented VRC-SLODL technique, the major intention lies in recognising and classifying vehicles present in the images. The bilateral filtering (BF) technique can initially improve the RSI quality. The VRC-SLODL technique employs a modified residual network (ResNet) model to produce a collection of feature vectors. Eventually, the SLO approach with long short-term memory (LSTM) technique was exploited for vehicle classification, where the SLO algorithm acts as a hyperparameter optimizer. The experiments were performed on a benchmark dataset to examine the better performance of the VRC-SLODL method. The obtained values reported the improved classification performance of the VRC-SLODL technique over other models.
Modelling foraging strategies of honey bees as agents in a dynamic landscape representation

Introduction: Both intrinsic colony mechanisms and external environmental variables affect the honey bee colony development rates and response and a key aspect of this is the use of resources within the landscape by honey bees. Although several models have been developed to explore the foraging behaviour of bees, none fully considered the spatial and temporal dynamics of landscape resources and the role of the colony in the process. Methodology: Here, we introduce a new honey bee foraging model being developed as a part of the ApisRAM honey bee colony model. Based on agent-based modelling, we used a dynamic ALMaSS landscape model enhanced with floral resource modelling to assess the impacts of weather conditions and resource availability on the possible foraging behaviour of honey bees. Several possible mechanisms (defined, based on honey bee traits) for scouting and foraging were investigated, separately for nectar and pollen collection, including prioritising foraging polygons for nectar foraging according to their distance to the colony, the quality or the energetic efficiency and, for pollen foraging, according to their distance to the colony and pollen quantity. Results: If model foraging bees prioritised the polygons, based on their distance from the colony, the number of unsuccessful flights increased compared to other tested strategies and the total amount of sugar collected showed a high variability. Contrary to expectations, the energetic efficiency strategy did not provide the colony with the highest amount of sugar. Overall, the tested strategies provide different outcomes on the collection of resources, the number of performed flights and their success rate, evidencing that the model's outcome at the colony level arises from the individual types of behaviour. Conclusions and Relevance: Variability in the mass of collected nectar and pollen was found mostly when scout bees applied the distance strategy. This higher variability in sugar collection means that model bees were not able to find the most profitable foraging sites at the landscape level, but only at the local level. Other strategies showed less dependence on the surrounding landscape (i.e. quality or random), but it comes at a cost (i.e. lower production for both nectar and pollen collection). These outputs help us evaluate which strategies could be used for future model development and confirm the models' ability to create dynamic responses. These responses at the colony level were only possible thanks to the implementation of a dynamic landscape model and dynamic spatiotemporal resource model, as well as implementing a social communication mechanism for bees to share information about the resources. Plant nectar production and quality information is essential to predict honey bee foraging distribution. In future model development, the implementation of pollen quality should also be explored to evaluate if it influences the overall pollen collection.
COLLABORATIVE GOVERNANCE PENANGANAN BAGI KORBAN KEKERASAN PEREMPUAN DAN ANAK MELALUI RUMAH AMAN KEMENTERIAN PEMBERDAYAAN PEREMPUAN DAN PERLINDUNGAN ANAK

This study aims to identify and analyze the criteria and prerequisites for safe houses for women and children victims of violence with a victim's perspective and responsive to gender and to obtain a model of collaboration between government agencies (collaborative governance) in handling women and children victims of violence in safe homes. This study uses a qualitative method with a grounded theory approach. Data collection techniques were carried out by interviews and documentation. The data analysis technique uses qualitative analysis with 4 stages, namely data reduction, data collection, data presentation, and drawing conclusions. The results showed that the criteria and conditions for safe houses for women and children who were victims of violence had been regulated in the Pergub Prov. DKI Jakarta No. 44 of 2019 concerning Amendments to the Provincial Governor Regulation. DKI Jakarta No. 48 of 2018 concerning Safe Homes for Women and Children Victims of Violence, and can be divided into 3 (three) types of requirements, namely location and security requirements, infrastructure and physical facilities requirements, and service requirements. Referring to the perspective of victims and gender, the requirements for a safe house are safe and comfortable and able to meet all the needs of victims, both physical and non-physical. Collaborative models in safe houses require initial conditions accompanied by good institutional dimensions. In the facilitative leadership dimension, it is necessary to establish or appoint a leader and other administrators in a management structure who are responsible for coordinating and managing the collaboration process and in the collaboration process dimension, strengthening is carried out in the formation of inter-agency trust and commitment from each stakeholder.
International Journal of Management Sciences and Business Research, April-2023 ISSN (2226-8235) Vol-12, Issue 4

The primary goal of this research is to explore the impact of corporate governance on the company performance of Sri Lanka's banking, finance, and insurance sectors. The study specifically looked at CEO duality, the number of directors, the adoption of three key committees, the proportion of independent directors on the board, the proportion of independent directors on the audit committee, the proportion of independent directors on the remuneration committee, the proportion of shares held by the independent directors on the audit committee, the proportion of shares held by the CEO, and the proportion of shares held by the directors excluding the CEO, and how the proportion of shares held by the directors excluding the CEO. Return on Assets was used to assess corporate performance. (ROA). The population comprised 25 companies registered on the Colombo Stock Exchange in the banking, finance, and insurance sectors. (CSE). The study relied on secondary data, including documentary information from Company annual reports from 2016 to 2020. A multiple linear regression model was used to evaluate the data. The study discovered a weak association between the Corporate Governance procedures under consideration and the financial performance of the firms. The formation of three Board sub-committees, the number of independent directors, and the proportion of shares held by independent directors on the audit committee and CEO were found to positively improve insurance firms' financial performance. However, the CEO duality, Board size, and proportion of independent directors on the audit committee, proportion of independent directors on the pay committee, and proportion of shares held by the directors excluding the CEO all had a negative impact on financial performance. In order to improve financial performance, the banking, finance, and insurance industries should establish suitable board subcommittees and increase the number of independent directors on the board. Board size should also be lowered as much as feasible, as larger board sizes significantly reduce financial performance.
The Sunet System for Monitoring Noise Pollution in Cluj-Napoca

Abstract In Europe the need for local authorities to provide a high standard of living for their communities led to the establishment of the European noise mapping directive 2002/49/EC. Whilst Noise mapping itself doesn’t need continuous noise monitoring, states must produce a noise action plan which highlights actions they intend to take to reduce the number of people affected. SUNET platform (System for Urban Noise and Eco-Traffic) was designed for improved management of the noise pollution in Cluj-Napoca and to provide up-to-date public data on a user-friendly interface. The implementation of the application over the entire municipality is highly advantageous for it creates a link between the town’s administration (the local authorities, the City Hall of Cluj-Napoca) and the user (the citizens) through this system which is dynamic, modern and compliant with the European regulations.
SISTEM REKOMENDASI MAKANAN TIO CIU MENGGUNAKAN COLLABORATIVE FILTERING

With the advancement of technological innovation, people can order any kind of food easily and this impacts the culinary industries. As people became aware of any kind of foods available to be ordered, people began to eat a different kinds of food varieties. One kind of food variety available to be ordered is the tio ciu food. Therefore, an information system program was designed that can be used by people who like to eat tio ciu foods to be able to get another tio ciu food recommendation so that they can find out more about tio ciu foods that they haven’t eaten yet. The design of this program applies the Collaborative Filtering method. Based on the test, Collaborative Filtering can give user a food recommendation based on their rating beforehand.
Physiological adaptability of livestock to heat stress: an updated review.

Climate change associated heat stress was established to be the crucial factor, which negatively influences animal production. The animals possess several adaptive mechanisms which are helpful for their survival in harsh environmental conditions, but while doing so their productive performances are compromised. Among the various mechanisms, which helps to maintain homeostasis in animals, Physiological adaptability was considered one of the primary response mechanisms by which the heat stressed animals survive the heat stress. This review is therefore an attempt to provide the audience an updated review on the subject to signify the importance of physiological responses in animals and their role in surviving the stressful environment. Respiration rate (RR), pulse rate (PR), rectal temperature (RT), sweating rate (SR) and skin temperature (ST) are the cardinal physiological variables which helps to maintain the heat balance and homeostasis in the stressed animals. Among the various physiological variables, RR, RT and ST were established to be ideal indicators for quantifying heat stress in several farm animals. Therefore, these variables can be effectively used to assess the severity of heat stress in livestock. Although these variables are considered significant in establishing the heat stress impact in animals, a wide genetic variation was also established in these response mechanisms. Further, variations in physiological adaptability were also established between indigenous, cross bred and pure bred animals. The indigenous livestock breeds were tipped to be exhibiting less physiological variability as compared to their counterparts. These discussions vividly indicate the importance of studying in detail the physiological adaptive mechanisms in different farm animals and these efforts can help the farming community to identify the suitable agroclimatic zone specific livestock breeds which can go in long way to help to ensure climate resilient livestock production.
The Role and Therapeutic Potential of α-secretase in Alzheimer’s Disease

Currently, more than six million Americans suffer from Alzheimer's Disease (AD), a number that is set to become 12.7 million by 2050. But despite killing more people than breast cancer and prostate cancer combined, there are no available pharmaceuticals that can cure AD, halt its pathogenesis, or reverse its effects. Therefore, the need for a thorough understanding of the physiological mechanisms behind AD progression has captured the attention of countless researchers and clinicians worldwide. In the last 30 years alone, the scientific community has made unprecedented progress towards an effective therapeutic. Notably, the amyloid cascade hypothesis has been one of the most dominating theories in recent AD research and drug discovery. The hypothesis suggests that AD’s main trigger lies in the accumulation of amyloid-beta protein fragments within brain tissue. Here, I present a review of some of the latest efforts to inhibit the production of these neurotoxic amyloid-beta peptides through the modulation of α-secretase enzyme activity in APP proteolysis.
Temporal variability in composition and fluxes of Yellow River particulate organic matter

This study examines temporal variations of the abundance and carbon isotopic characteristics of particulate organic carbon (POC) and specific‐source compounds in the context of hydrological variability in the Yellow River. The content and bulk carbon isotopic characteristics (13C and 14C) of POC were relatively uniform over the hydrologic (seasonal) cycle. We attribute these temporally invariant geochemical characteristics to the dominant contribution of loess material to the suspended particulate matter (SPM). In contrast, molecular‐level signals revealed that hydrologic conditions exert a significant influence on the proportional contributions of petrogenic and especially fresh plant‐derived OC, while pre‐aged soil OC is mobilized via deeper erosion processes (e.g., gully erosion, mudslides) and is independent of hydrodynamics and surface runoff. A coupled biomarker‐isotope mixing model was applied to estimate the time‐varying supply of contemporary/modern biomass, pre‐aged soil, and fossil OC components to Chinese marginal seas from the Yellow River. We found that natural (e.g., precipitation) and human‐induced (e.g., water and sediment regulation) variations in hydrological regime strongly influence the flux with the magnitude of the corresponding annual fluxes of POC ranging between 0.343 ± 0.122 Mt yr−1 and 0.581 ± 0.213 Mt yr−1, but less strongly infleunce proportions of the different OC constituents. Inter‐annual differences in pre‐aged soil and fossil OC fluxes imply that extreme climate events (e.g., floods) modulate the exhumation and export of old carbon to the ocean, but the OC homogeneity in the pre‐aged mineral soil‐dominated watersheds facilitates robust predictions in terms of OC transport dynamics in the past (sediment cores) and in the future.
Arborescences and Shortest Path Trees when Colors Matter

Color-constrained subgraph problems are those where we are given an edge-colored (directed or undirected) graph and the task is to find a specific type of subgraph, like a spanning tree, an arborescence, a single-source shortest path tree, a perfect matching etc., with constraints on the number of edges of each color. Some of these problems, like color-constrained spanning tree, have elegant solutions and some of them, like color-constrained perfect matching, are longstanding open questions. In this work, we study color-constrained arborescences and shortest path trees. Computing a color-constrained shortest path tree on weighted digraphs turns out to be NP-hard in general but polynomial-time solvable when all cycles have positive weight. This polynomial-time solvability is due to the fact that the solution space is essentially the set of all color-constrained arborescences of a directed acyclic subgraph of the original graph. While finding color-constrained arborescence of digraphs is NP-hard in general, we give efficient algorithms when the input graph is acyclic. Consequently, a color-constrained shortest path tree on weighted digraphs having only positive weight cycles can be efficiently computed. Our algorithms also generalize to the problem of finding a color-constrained shortest path tree with minimum total weight. En route, we sight nice connections to colored matroids and color-constrained bases.
Lightweight Deep Neural Network Model With Padding-Free Downsampling

Deep neural networks have achieved impressive performance in image classification tasks. However, due to limitations in hardware resources, including computing units and storage capacity, deploying these networks directly on resource-constrained devices such as mobile and edge devices is challenging. While lightweight network models have made significant advancements, the downsampling stage has received little attention. As the feature map is reused multiple times, the reduction of its size during the downsampling stage not only reduces the computational cost of the downsampling module itself but also lowers the computational burden of subsequent stages. This letter addresses this gap by proposing a padding-free downsampling module that effectively reduces computational costs and can seamlessly integrates into various deep learning models. Furthermore, we introduce a hybrid stem layer to obtain competitive accuracy. Extensive experiments were conducted on CIFAR-100, Stanford Dogs, and ImageNet datasets. On the CIFAR-100 dataset, the results show that the proposed module reduces computational costs by approximately 20% and improves inference speed on resource-constrained devices by around 10%.