Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-level framework for optimal operation of gasifier system utilising crop residues from small and medium-scale farms

Lack of techno-economic framework for optimal gasification and the identification of critical parameters for optimal operations is one of the major challenges restricting the gasification of crop residues. This study aims to develop an optimal techno-economic framework for the gasification of crop residues from clustered small/medium-scale farms. The developed model was applied to a case study in Adiembra, a farming community for a 10-kW gasifier engine system. Eight scenarios of individual feedstock and their blends were considered. The results revealed specific fuel consumption ranging from 1.79 – 3.53 kg/kWh. The economic analysis showed marginal profitability except for rice husk and straw which are not profitable. At the current grid electricity price, the minimum level of subsidies required to ensure the financial viability of the feedstocks is within the range of 30 – 70 % of the investment cost based on the various feedstock scenarios considered. The study revealed individual feedstocks with the best technical and economic prospects for optimal gasification to be cocoa pod husk, maize stalk and husk, maize cobs, rice straw and rice husk in the order of best to worst. The use of feedstock blends generally improved the overall syngas characteristics and financial viability. A total number of farms ranging between 107 – 532 are required to be clustered within a radius of 0.74 – 2.12 km with a cluster radius greater than 3.91 km not being financially viable. The fraction of each feedstock type in the blends were optimised with corresponding increase in syngas generation within the range of 9 – 35 % and decrease in the required number of farms within the range of 30 – 57 %. The outcome of the study demonstrates that sustainable gasification of crop residues for minigrid electricity generation requires co-gasification of various residue types, valorisation of by-products and increase in the current feed-in-tariff rate in Ghana.
Clustering based Intrusion Detection System for effective Detection of known and Zero-day Attacks

Developing effective security measures is the most challenging task now a days and hence calls for the development of intelligent intrusion detection systems. Most of the existing intrusion detection systems perform best at detecting known attacks but fail to detect zero-day attacks due to the lack of labeled examples. Authors in this paper, comes with a clustering-based IDS framework that can effectively detect both known and zero-day attacks by following unsupervised machine learning techniques. This research uses NSL-KDD dataset for the motive of experimentation and the experimental results exhibit best performance with an accuracy of 78%.
Evaluation and spatial analysis of the equalization of basic public service in urban and rural areas in China

With the rapid socio-economic development, and the progresses of urbanization, the demand for basic public service is increasing both in rural and urban areas. Thus, regional and urban-rural non-equalization are increasingly prominent, which are critical to the development of the harmonious society. Against this background, the paper established an index system for basic public service level evaluation from six aspects, including education services, cultural services, health services, social security services, infrastructure services and information services, in order to assess the basic public service level in both urban and rural areas of China's 31 provincial level administrative units. Taking the assessment outcomes as variables,we evaluated equalizations of basic public service in urban and rural areas of China's 31 provincial level administrative units by the method of information entropy. Finally, we explored the spatial pattern of the equalization of basic public service in urban and rural areas with the method of "Exploratory Spatial Data Analysis". As a result, firstly, there are significant inter-provincial differences in the public service level between urban and rural areas. The spatial distribution of basic public service level in urban areas present the letter "T" shape, and the basic public service level decreases from east to central part then to west, while the spatial distribution of basic public service level in rural areas, in contrast, decreases from east to west then central region. These results suggested that to speed up the economic development of relatively backward areas is an effective way to improve the level of basic public service in urban areas. Secondly, equalizations of basic public service in urban and rural areas are generally low, with a significant global autocorrelation in the spatial distribution. Thirdly, the relationship between the equalization of basic public service in urban and rural areas and the basic public service level of rural areas is similar to the Matthew Effect. The influence of basic public service level of rural areas on the index of the equalization of basic public service is greater than that of urban areas. So, it is important for the improvement of the equalization of basic public service to enhance the level of basic public service of rural areas.
Heat-Bath and Metropolis Dynamics in Ising-like Models on Directed Regular Random Graphs

Using a single-site mean-field approximation (MFA) and Monte Carlo simulations, we examine Ising-like models on directed regular random graphs. The models are directed-network implementations of the Ising model, Ising model with absorbing states, and majority voter models. When these nonequilibrium models are driven by the heat-bath dynamics, their stationary characteristics, such as magnetization, are correctly reproduced by MFA as confirmed by Monte Carlo simulations. It turns out that MFA reproduces the same result as the generating functional analysis that is expected to provide the exact description of such models. We argue that on directed regular random graphs, the neighbors of a given vertex are typically uncorrelated, and that is why MFA for models with heat-bath dynamics provides their exact description. For models with Metropolis dynamics, certain additional correlations become relevant, and MFA, which neglects these correlations, is less accurate. Models with heat-bath dynamics undergo continuous phase transition, and at the critical point, the power-law time decay of the order parameter exhibits the behavior of the Ising mean-field universality class. Analogous phase transitions for models with Metropolis dynamics are discontinuous.
Urban growth pattern and sustainable development: a comparative study of municipalities in the Seoul Metropolitan Region

Urban Growth Pattern and Sustainable Development: A Comparative Study of Municipalities in the Seoul Metropolitan Region. (August 2006) Seunggeun Paek, B.A., Seoul National University, Korea; M.A., Seoul National University, Korea Chair of Advisory Committee: Dr. Arthur L. Sullivan The main purpose of this study was to obtain a better understanding of the impact of urban growth and change on sustainability based on a comparative study of municipalities comprising Gyeonggi Province within the Seoul Metropolitan Region, Korea over the 1990-2000 period. To examine the impact of urban growth and change on sustainability, this study selected 38 sustainability indicators (population density, waste recycling rate, time spent commuting, etc.) and then measured progress towards sustainability in 31 study areas for the years 1990, 1995 and 2000. Data for this study were drawn from the 1990-2000 censuses and local government publications. Statistical methods such as t-test, analysis of variance and factor analysis were used to answer the research questions. This study led to five major findings. First, the study areas with higher densities showed the lower mean values of sustainability. This result implies that increased density does not necessarily result in improved sustainability. Second, the level of sustainability has increased over time in urban areas with green belt, but the mean differences were not
Formulation, Characterization, and Sunscreen Potential Evaluation of Nutmeg Leaf Essential Oil Nanoemulsions (Myristica fragrans Houtt.)

Nutmeg leaf essential oil (Myristica Fragrans Houtt.) is one of the natural ingredients which have antioxidant activity and potential as a sunscreen. The research aims to formulate nutmeg leaf essential oil nanoemulsion, characterize, and determine its potency as a sunscreen. Nutmeg leaf essential oil nanoemulsion was formulated with 1; 3; and 5 mL of oil content with two variations of surfactant. The characterization included organoleptic, pH, viscosity, %T, nanoemulsion type, droplet size, thermodynamic and centrifugation stability tests. Evaluation was conducted using UV-Vis spectrophotometer at a wavelength range of 290-400 nm with ethanol as a blank and non-nano-emulsified as a comparison. The result showed that the nutmeg leaf essential oil nanoemulsion had clear and stable appearance, safe for skin, viscosity values of <200 mPas, %T values of near 100%, nanoemulsions type of o/w, and particles size of <200 nm. The evaluation showed that the sunscreen activities of nutmeg leaf oil nanoemulsion was higher than non-nano-emulsified. The highest sunscreen activity was the formula A nanoemulsion which an oil content of 5 mL and had an SPF value of 1.475; %Te value of 50.571%; and %Tp value of 77.218%. The nanoemulsion sunscreen activity of formula A was categorized as a regular suntan
AoI-Energy Tradeoff for Data Collection in UAV-Assisted Wireless Networks

Unmanned aerial vehicle (UAV)-assisted wireless communication systems are able to provide high-quality services and ubiquitous connectivity for massive Internet of Things (IoT) devices. In this paper, we study the Age of Information (AoI) and energy tradeoff in a system where an employed UAV performs data collection for multiple IoT nodes (INs). Bearing in mind the importance of AoI and energy consumption during the data collection process, we present a multi-objective optimization problem to minimize the AoI and UAV energy consumption. To explore the tradeoff between AoI and energy consumption, we jointly optimize the collection time, the UAV trajectory, and the duration of time slots. Due to the non-convexity of the formulated problem, we divide the main problem into three sub-problems and address them by leveraging successive convex approximation (SCA) and Lagrangian dual methods. Finally, we design a multi-variable fixed algorithm to iteratively solve the three sub-problems. Simulations are carried out to investigate the tradeoff between AoI and UAV energy consumption, revealing that reducing AoI and energy consumption simultaneously is unattainable. Furthermore, the convergence and validity of the proposed algorithm are presented and analyzed.
A Novel CNN-based Model for Medical Image Registration

—The registration of the deformable image is applied widely to image diagnosis, the monitoring of the disease, and the navigation of the surgery with the aim of learning the correspondence of the anatomist among an image of motion and an image of static. The procedure of the registration of an image mainly includes three steps: the creation of a model of the deformation, a function design for the mensuration of the similarity, and the step of learning for the optimization of the parameter. In the current article, 2-stream architecture is designed, which has the ability to sequentially estimate the fields of the registration of the multi-level by a couple of the pyramids of the feature. In this paper, a 3D network of the encoder-decoder with the 2-stream is designed, which calculates 2 pyramids of the feature of the convolutional as separately by 2 volumes of the input. Also, the registration of the pyramid of the sequential is proposed, which in it, a trail of the modules of the pyramid registration (PR) for the prediction of the fields of the registration of the multi-level is designed as straight by the pyramids of the feature of the decoding. In addition, the modules of PR can be augmented with the computation of the 3D correlations of the local among the pyramids of the feature, which this work leads to the further improvement of the presented approach. Thus, it is capable of collecting the detailed anatomical structure of the brain. The proposed method is tested in three criterion datasets about the registration of MRI of the brain. The evaluation outcomes display that the presented approach outperforms the advanced approaches with a big value.
Simultaneous Interpreting with Accented Mandarin

This study investigates the perception of simultaneous interpreting (SI) quality among accented Mandarin interpreters in Taiwan. Four hundred Taiwanese individuals were recruited online for a controlled experiment, where participants were randomly assigned, in equal numbers, to listen to one of four SI versions. Each version was distinguished solely by accent: Taiwan Mandarin, Chinese Standard Mandarin, Malaysian Mandarin, and US-accented Mandarin. Subsequently, participants were asked to evaluate the SI quality using a nine-item questionnaire, and optional comment sections were available for them to provide written feedback. Ratings were analyzed quantitatively, while comments were subjected to qualitative analysis. Results indicate that SI quality was evaluated less favorably in terms of comprehensibility and performance satisfaction with increasing markedness of accent. The Chinese Standard Mandarin accent was regarded as the suitable stylistic choice because of its “standard” prestige. However, it also faced some criticism, particularly for what certain participants perceived as mispronunciations. Among the non-Taiwanese Mandarin accents, Malaysian Mandarin was perceived as the most favorable for comprehensibility and satisfaction, likely due to its perceived similarities with Taiwan Mandarin. In contrast, American-accented Mandarin received the lowest ratings for comprehensibility. Nevertheless, there was a general sense of support for the US interpreter, possibly suggesting that perceived deficiencies in SI were attributed to factors beyond the interpreter’s control, such as their non-native background, as inferred from their accent. This study reveals that accent significantly influences interpreting quality perceptions, challenging prior beliefs of its minimal impact and underscoring its importance in interpreter training and development. The interpreting market is affected by complex dynamics involving diverse stakeholder biases towards accents, so interpreters face the dual challenge of adapting to a neutral accent to satisfy client expectations while preserving their own accent identity. However, increasing global and local focus on linguistic diversity may enhance accent tolerance and reduce accent-based judgments.
HDAC1: An Essential and Conserved Member of the Diverse Zn2+-Dependent HDAC Family Driven by Divergent Selection Pressure

Zn2+-dependent histone deacetylases (HDACs) are enzymes that regulate gene expression by removing acetyl groups from histone proteins. These enzymes are essential in all living systems, playing key roles in cancer treatment and as potential pesticide targets. Previous phylogenetic analyses of HDAC in certain species have been published. However, their classification and evolutionary origins across biological kingdoms remain unclear, which limits our understanding of them. In this study, we collected the HDAC sequences from 1451 organisms and performed analyses. The HDACs are found to diverge into three classes and seven subclasses under divergent selection pressure. Most subclasses show species specificity, indicating that HDACs have evolved with high plasticity and diversification to adapt to different environmental conditions in different species. In contrast, HDAC1 and HDAC3, belonging to the oldest class, are conserved and crucial in major kingdoms of life, especially HDAC1. These findings lay the groundwork for the future application of HDACs.
The study on the self-cleaning property of hydrophobic polydimethylsiloxane/nano-CaCO3 coating

Dust depositions always degrade the building’s value due to the growth of mold and fungi. Manual cleaning is required to clean the building glass frequently which is time-consuming and costly. Self-cleaning coating is an effective and practical way to reduce dust accumulation. Using the sol-gel method, this study synthesized the self-cleaning polydimethylsiloxane (PDMS)/nano-calcium carbonate (CaCO3) coating. The coating recorded the water contact angle (WCA) of 99.5° ± 0.5°, indicating the hydrophobic property. The embedded nanoparticles create air pockets that reduce the adhesion between water droplets and glass surface. The hydrophobic coated glass showed the self-cleaning effect where the coating achieves a low dust haze of about 32% at the sliding angle 25°. The water contact angle (WCA) of the coating system degrades to 98.2° ± 1.2°, indicating the coated glass can maintain its hydrophobic property in the real environment after 1 month of outdoor exposure. In comparison, the bare glass showed large dust haze value above 50% due to the dust accumulation impact.
Associations between subjective well-being, number of teeth, and self-rated mastication in Japanese adults: A cross-sectional study

This cross-sectional study aimed to investigate the risk factors for low levels of subjective well-being (SWB) in the general population of Japan, specifically, the impact of the number of teeth and self-rated mastication. The surveyed population consisted of individuals aged between 40 and 79 years from Yamagata prefecture, Japan. A postal self-administered questionnaire survey of respondents lifestyles, medical history, oral health, and dietary intake, was conducted from 2017 to 2021. We included 6846 participants to confirm the independent associations between SWB and several parameters using multivariate ordinal logistic regression analysis to estimate adjusted odds ratios and 95% confidence intervals. Individuals with < 20 teeth and poor mastication ability had a 1.3-fold risk for low levels of SWB compared with individuals with ≥ 20 teeth with good mastication ability (adjusted odds ratios = 1.300, 95% confidence intervals = 1.043–1.621, P = .020). There were no differences between individuals with good mastication ability, regardless of the number of remaining teeth. Our study emphasizes not only the importance of having ≥ 20 teeth and good mastication ability for high levels of SWB but also the importance of restoring mastication ability using some form of prosthesis, to facilitate a high level of SWB when the number of remaining teeth is < 20.
Monitoraggio via Satellite dei Flussi Migratori nell'Area del Mediterraneo (Remote Monitoring of Migrants Vessels in the Mediterranean Sea)

2 Executive Summary . 4 Part 1: Generalities . 12 1 Introduction and Statistical Data. 12 1.1 Demographics of Immigration in Italy . 12 1.2 Illegal Immigration to Italy. 13 1.3 Illegal Immigration to Italy through the Mediterranean Sea . 15 1.4 Illegal Immigration in Europe through the Mediterranean Sea . 20 2 Problem Analysis . 21 3 Proposed Solution . 26 Part 2: Technical Report . 30 4 Introduction to Ship Detection with SAR. 30 5 System Architecture. 37 5.1 The Vessel Monitoring System. 39 5.2 The Vessel Detection System . 43 5.3 VMS and VDS Data Fusion. 49 5.4 Guidelines for Migrant Vessels Identification. 55 6 Overview on Orbit Selection . 58 7 Performance Assessment of Existing Satellites. 64 7.1 Overview of COSMO-SkyMed . 64 7.1.1 Payload Instrument . 67 Remote Monitoring of Migrants Vessels in the Mediterranean Sea 11 7.1.2 Operative Orbit . 72 7.1.3 Constellation Deployment . 74 7.2 Performances assessment. 74 7.2. 1 Problem modeling and proposed analyses. 74 7.2. 2 Coverage Time. 78 7.2. 3 Revisit Time. 82 7.2. 4 Ground Station Access. 88 7.2. 5 Modified COSMO-SkyMed and Augmented Constellation . 90 8 Feasibility of ad-hoc Systems . 92 8.1 Performance Assessment . 93 8.1.1 Coverage Time. 94 8.1.2 Revisit Time. 97 8.1.3 Ground Station access. 100 8.2 Space segment feasibility. 100 8.2. 1 Satellite Bus . 100 9 Conclusions and Recommendations . 103 9.1 Conclusions. 103 9.2 Recommendations. 108 References . 110 Remote Monitoring of Migrants Vessels in the Mediterranean Sea 12 Part 1: Generalities
ACLC: Automatic Calibration for Nonrepetitive Scanning LiDAR-Camera System Based on Point Cloud Noise Optimization

The recent nonrepetitive scanning (NRS)-light detection and ranging (LiDAR), using rosette or Lissajous curves, has higher spatial scanning efficiency and lower price than traditional mechanical LiDAR with fixed scanning angle. NRS-LiDAR has garnered significant attention in the field, particularly in perception research areas such as detection, tracking, and simultaneously localization and mapping (SLAM). However, the NRS mode introduces new challenges, including high-intensity reflections in overlapping areas, nonuniform distribution of point clouds, and scattering at target edges. This article proposes a target-based automatic calibration for NRS-LiDAR and camera (ACLC) system to address the challenges mentioned above and achieve precise calibration of the external parameters for LiDAR-camera fusion. Initially, we model the noise distribution of the point cloud generated by NRS-LiDAR and mitigate systematic measurement errors by suppressing the noise in the point cloud. Next, we employ automatic detection techniques to obtain a stable and accurate point cloud of a checkerboard pattern. Subsequently, we extract the 3-D corner coordinates of the checkerboard by optimizing the point cloud reflectivity intensity using a nonlinear approach. Finally, by utilizing the perspective-n-point (PnP) algorithm with the optimized 3-D corners from the point cloud and the 2-D corners from the camera image, we can determine the external parameters of the LiDAR-camera system. The proposed algorithm’s effectiveness and accuracy are validated through qualitative and quantitative analyses of various LiDAR and camera combinations in real-world environments. Additionally, its open-source implementation on GitHub has gained significant recognition within the industry and academia: https://github.com/HViktorTsoi/ACSC.git.
Smart Cities, Smarter Roads: A Review of Leveraging Cutting-Edge Technologies for Intelligent Event Detection from Social Media

—The rapidly evolving landscape of smart cities and intelligent transportation systems makes the timely detection of traffic events a critical element for optimizing urban mobility. Furthermore, social media emerges as a valuable source of real-time information, with users acting as active sensors who spontaneously share observations and experiences related to traffic incidents. This review paper offers a comprehensive understanding of the state-of-the-art in traffic event detection from social media. The paper explores leveraging cutting-edge technologies including machine learning, and deep learning with big data technologies and high-performance computing. The discussion unfolds with an in-depth examination of the recent approaches for event detection followed by an exploration of the techniques of spatio-temporal information extraction and sentiment analysis, which are both considered fundamental aspects in enhancing the contextual understanding of traffic events. Further, the review explores the pivotal role of big data technologies in addressing scalability challenges inherent in the vast expanse of social data. The examination encompasses how big data frameworks facilitate efficient storage, processing, and analysis of large-scale social media datasets, thereby empowering machine learning and deep learning models for robust and real-time traffic event detection. Subsequently, the challenges and future directions have been highlighted. Addressing these challenges and leveraging advanced technologies, facilitates the proactive detection and management of these events, paving the way for smart mobility systems.