Miracle-dz/UniFAR-FTUs huggingface dataset:
Ensemble equivalence and asymptotic equipartition property in information theory

There is a consensus in science that information theory and statistical physics have a close relationship but the literary proofs of the equivalence between most of the conceptions in the two disciplines are still missing. In this work, according to the statistical ensembles' description of the information sequences that are generated by the i.i. d. single variable and multivariate information source, the relationship between the ensemble equivalence and asymptotic equipartition property is established. We find that the description of information sequences in classical information theory is a special case of the canonical ensemble description of information sequences. They are both the maximum entropy approximation of the real signal generation. Vice versa, the conjugate microcanonical ensemble description of the information sequences under hard constraints satisfies the condition in real signal generation exactly. Thus, the microcanoincal ensemble description is closer to the real signal generation than the conjugate canonical ensemble, but the ensemble equivalence between the microcanonical and canonical ensemble in the thermodynamic limit guarantees the effectiveness of classical information theory, i.e., the asymptotic equipartition property in information theory is an isotope of ensemble equivalence from statistical physics.
TaskFlex Solver for Multi-Agent Pursuit via Automatic Curriculum Learning

This paper addresses the problem of multi-agent pursuit, where slow pursuers cooperate to capture fast evaders in a confined environment with obstacles. Existing heuristic algorithms often lack expressive coordination strategies and are highly sensitive to task conditions, requiring extensive hyperparameter tuning. In contrast, reinforcement learning (RL) has been applied to this problem and is capable of obtaining cooperative pursuit strategies. However, RL-based methods face challenges in training for complex scenarios due to the vast amount of training data and limited adaptability to varying task conditions, such as different scene sizes, varying numbers and speeds of obstacles, and flexible speed ratios of the evader to the pursuer. In this work, we combine RL and curriculum learning to introduce a flexible solver for multiagent pursuit problems, named TaskFlex Solver (TFS), which is capable of solving multi-agent pursuit problems with diverse and dynamically changing task conditions in both 2-dimensional and 3-dimensional scenarios. TFS utilizes a curriculum learning method that constructs task distributions based on training progress, enhancing training efficiency and final performance. Our algorithm consists of two main components: the Task Evaluator, which evaluates task success rates and selects tasks of moderate difficulty to maintain a curriculum archive, and the Task Sampler, which constructs training distributions by sampling tasks from the curriculum archive to maximize policy improvement. Experiments show that TFS produces much stronger performance than baselines and achieves close to 100% capture rates in both 2-dimensional and 3-dimensional multi-agent pursuit problems with diverse and dynamically changing scenes. The project website is at https://sites.google. com/view/tfs-2023.
The 6-minute Run Test: Validation and Reference Equations for Adults

Abstract Endurance performance tests directly measuring cardiorespiratory fitness are complex, but field tests indirectly assessing maximum oxygen uptake (VO 2max ) are an alternative. This study aimed to validate the 6-minute run test in adults, comparing it to the established shuttle run test, and to create reference equations. The cross-over design involved healthy adults aged 18–65 undertaking both tests, separated by a two-hour interval. The 6-minute run test required participants to run around a volleyball court for six minutes, aiming to maximize distance covered. The shuttle run involved participants covering 20 meters in defined time intervals at increasing speeds. Parameters measured included 6-minute run test distance, heart rates, calculated maximum oxygen uptake during the shuttle run, and total shuttle count. The study enrolled 250 participants (134 men and 116 women). Men averaged 1195.7 m (SD=161.4), while women averaged 1051.2 m (SD=148.0) in six minutes. The strongest correlation was found between the distance covered in the 6-minute run test and the total shuttle count (r=0.91, p<0.001). Two predictive models for 6-minute run test distance were developed and normative values for different sex-specific age clusters were established. The study showed that the 6-minute run test is valid as a practical endurance test for adults aged 18–65.
Transcriptomic responses to location learning by honeybee dancers are partly mirrored in the brains of dance-followers

The waggle dances of honeybees are a strikingly complex form of animal communication that underlie the collective foraging behaviour of colonies. The mechanisms by which bees assess the locations of forage sites that they have visited for representation on the dancefloor are now well-understood, but few studies have considered the remarkable backward translation of such information into flight vectors by dance-followers. Here, we explore whether the gene expression patterns that are induced through individual learning about foraging locations are mirrored when bees learn about those same locations from their nest-mates. We first confirmed that the mushroom bodies of honeybee dancers show a specific transcriptomic response to learning about distance, and then showed that approximately 5% of those genes were also differentially expressed by bees that follow dances for the same foraging sites, but had never visited them. A subset of these genes were also differentially expressed when we manipulated distance perception through an optic flow paradigm, and responses to learning about target direction were also in part mirrored in the brains of dance followers. Our findings show a molecular footprint of the transfer of learnt information from one animal to another through this extraordinary communication system, highlighting the dynamic role of the genome in mediating even very short-term behavioural changes.
Giant baleen whales emerged from a cold southern cradle

Baleen whales (mysticetes) include the largest animals on the Earth. How they achieved such gigantic sizes remains debated, with previous research focusing primarily on when mysticetes became large, rather than where. Here, we describe an edentulous baleen whale fossil (21.12–16.39 mega annum (Ma)) from South Australia. With an estimated body length of 9 m, it is the largest mysticete from the Early Miocene. Analysing body size through time shows that ancient baleen whales from the Southern Hemisphere were larger than their northern counterparts. This pattern seemingly persists for much of the Cenozoic, even though southern specimens contribute only 19% to the global mysticete fossil record. Our findings contrast with previous ideas of a single abrupt shift towards larger size during the Plio-Pleistocene, which we here interpret as a glacially driven Northern Hemisphere phenomenon. Our results highlight the importance of incorporating Southern Hemisphere fossils into macroevolutionary patterns, especially in light of the high productivity of Southern Ocean environments.
Deep Neural Networks Based Modeling to Optimize Water Productivity of a Passive Solar Still

Solar stills (SSs) have emerged as highly efficient solutions for converting saline or contaminated water into potable water, addressing a critical need for water purification. This study aims to predict and optimize SS performance, emphasizing the importance of enhancing productivity in various applications, including domestic, agricultural, and industrial settings. Several influencing factors, such as sunlight intensity, ambient temperature, wind speed, and structural design, are crucial in determining SS performance. By harnessing the power of contemporary machine learning techniques, this study adopts Deep Neural Networks, with a special emphasis on the Multilayer Perceptron (MLP) model, aiming to more accurately predict SS output. The research presents a head-to-head comparison of diverse hyperparameter optimization techniques, with Particle Swarm Optimization (PSO) notably outpacing the rest when combined with MLP. This optimized PSO-MLP model was particularly proficient when paired with a specific type of solar collector, registering impressive metrics like a COD of 0.98167 and an MSE of 0.00006. To summarize, this research emphasizes the transformative potential of integrating sophisticated computational models in predicting and augmenting SS performance, laying the groundwork for future innovations in this essential domain of water purification.
Vaşaklarda (Lynx lynx) Arka Bacak Kemiklerinin Makro-Anatomik Olarak İncelenmesi

Bu calismanin amaci, vasakta arka bacak kemiklerini makro-anatomik olarak incelemekti. Bu amacla, 2 vasak ( Lynx lynx ) kullanildi. Her iki os coxae birbirine paraleldi. Fossa acetabuli derin, incisura acetabuli genisti. Foramen obturatum oldukca genis ve ovaldi. Crista iliaca disbukeydi. Ramus cranialis ossis pubis genisti ve bu genislik acetabulum’a dogru artmisti. Spina ischiadica alcak ve kuttu. Incisura ischiadica major ve incisura ischiadica minor fazla belirgin degildi. Femur’da trochanter tertius bulunmamaktaydi. Condylus lateralis’in uzerinde kucuk bir kabarti seklinde tuberositas supracondylaris mevcuttu. Condylus medialis ve condylus lateralis iyi gelismisti. Bu yuzden fossa intercondylaris derin ve genisti. Apex patellae dar, basis patellae ise genis olup ucgen seklindeydi. Eminentia intercondylaris alcakti. Buyuk bir tuberositas tibia vardi. Bu olusum uzerinde sulcus tuberositatis tibia bulunmamaktaydi. Tibia’nin distal ucunun tarsal kemiklerle eklem yapan yuzu oblik bir kabartiyla iki eklem cukuruna ayrilmisti. Fibula, tibia’nin lateralinde bulunan, proximal ve distalde onunla eklem yapan ince bir kemikti. Vasaklarin arka bacaklarinda birbirinden bagimsiz 5 adet metatarsus bulunmaktaydi. Birinci metatarsus rudimenterdi. Sonuc olarak; vasakta arka bacak kemiklerinin makro-anatomik ozellikleri incelenerek diger carnivor turleriyle olan benzerlik ve farkliliklari tespit edildi.
Effect of Mechanically Created Pits Pattern for Direct Diamond Deposition on Stainless-Steel Surface

Direct diamond deposition on a steel surface has been considered very difficult. Recently, we found that high-quality diamond films can be deposited on the surface of stainless-steel X5CrNi 18-10 by drilling multiple regularly arranged pits without interlayers or seeding. The following two hypotheses (A) and (B) can be considered as the reason why a high-quality diamond film can be deposited: (A) unoxidized Cr and Ni exposed to the stainless-steel X5CrNi 18-10 surface prevent carbon diffusion into interior of the stainless-steel, resulting diamond core generation, (B) Surface geometry with regular roughness contributes to stress relaxation and delamination prevention. In the present study, those hypotheses have been examined by quantum chemistry calculation and experimental. For the quantum chemistry calculation, energy barrier and kinetic energy for a carbon atom intrudes into a model cluster has been calculated with an ab-initio computational chemistry software package, Gaussian. The calculation result has supported hypothesis (A). For the experiment, X5CrNi 18-10 stainless-steel substrates with different surface characteristics are prepared by using various mechanical machining methods and used in the direct deposition process for diamond with in-liquid plasma CVD. The experimental result has supported both hypothesis (A) and (B).
Study the End Milling Machinability Properties of Al 6061/WC Aluminium Metal Matrix Composites

The end milling process plays a crucial role in shaping the material into surface structures that meet the stringent requirements of these demanding industries. The present paper aims to optimize the end milling process of Al6061 with nano tungsten carbide reinforcement at 6% by studying metal removal rate (MRR), and surface finish based on the parameters of depth of cut, feed, and cutting speed. The aluminium metal matrix composite is prepared with 6 weight percentage of nano tungsten carbide is reinforced with Al6061 alloy is casted by using stir casting method. RSM analysis is used to find the best operating condition by using L9 orthogonal array. The ANOVA result shows that cutting speed is the most domineering factor which influences the MRR and feed rate is the most influencing factor on surface roughness.
Model-Based Approach for Leak Detection and Localization in Water Distribution Networks: A Literature Survey

Water loss poses a significant problem for water utilities and has received a lot of attention. To fulfill the increasing global demand for water, water supply system operations must be streamlined, making leak detection and location crucial. Water utilities have developed a number of techniques over time for finding leaks in water distribution networks (WDNs). These methodologies range from simple visual inspection to the use of hardware systems and now software using models and algorithms. Data from flow or pressure measurements, which are required for the analysis of water networks, is becoming more readily available with the introduction of intelligent sensor devices. Along with the introduction of geographic information systems (GIS) and supervisory control and data acquisition (SCADA) in the water sector, the deployment of model-driven methodologies for leak detection and localization has found extensive use. This paper aims to provide a concise introductory reference for early researchers in the development of a model-based approach for leak detection in WDNs. Thus, a survey of model-based approaches is presented, along with current research trends and applications of model-driven methodologies for leak detection in water supply networks. Several model-driven approaches and research studies for each case are discussed. Some challenges and research gaps are also discussed.
Cross-Chain Smart Contract Invocations: A Systematic Multi-Vocal Literature Review

The introduction of smart contracts has expanded the applicability of blockchains to many domains beyond finance and cryptocurrencies. Moreover, different blockchain technologies have evolved that target special requirements. As a result, in practice, often a combination of different blockchain systems is required to achieve an overall goal. However, due to the heterogeneity of blockchain protocols, the execution of distributed business transactions that span several blockchains leads to multiple interoperability and integration challenges. Therefore, in this article, we examine the domain of Cross-Chain Smart Contract Invocations (CCSCIs), which are distributed transactions that involve the invocation of smart contracts hosted on two or more blockchain systems. We conduct a systematic multi-vocal literature review to get an overview of the available CCSCI approaches. We select 20 formal literature studies and 13 high-quality gray literature studies, extract data from them, and analyze it to derive the CCSCI Classification Framework. With the help of the framework, we group the approaches into two categories and eight subcategories. The approaches differ in multiple characteristics, e.g., the mechanisms they follow, and the capabilities and transaction processing semantics they offer. Our analysis indicates that all approaches suffer from obstacles that complicate real-world adoption, such as the low support for handling heterogeneity and the need for trusted third parties.
Applying Immune Feature Weighted Support Vector Machine in Multi-Finger Feature Extraction in the Control of the Right-Hand Prosthetics

Brain Computer Interface (BCI) is a new technology that helps device to connect to the brain without undergoing an invasive operation. The major role of BCI is to help disabled people or amputees to rehabilitate. The goal of this paper is to create a device that will help people who had a right hand but lost it due to accident. Our group is able to create a prosthetic hand using a combined Support Vector Machine and Immune algorithm called Immune feature weighted Support Vector Machine. This combined algorithm proved to work giving it an accuracy of 81.5%.
Incident congestion propagation prediction using incident reports

Accurate predictions of how congestion propagates are essential for mitigating its effects on traffic and the urban environment. However, the vast majority of state-of-the-art traffic prediction models focus on regular traffic scenarios and struggle to adapt to the conditions following incidents. This is particularly problematic since the irregular periods after incidents are arguably when traffic predictions are most critical. Current traffic models struggle with non-recurring congestion for two reasons: they lack inputs alerting them an incident has happened, and traffic data containing incident information is scarce. We create two new such datasets: one by simulating incidents and their congestion in an open-source microscopic simulator and another by fusing real-world traffic flow data with incident reports. We then propose a framework that integrates incident reports into deep learning models for congestion propagation prediction. Our framework utilizes the recent traffic flow data and fuses it with information from incident reports. We perform a detailed empirical comparison between recurrent and graph-based models utilizing incident reports against baselines. Our study demonstrates that our framework significantly outperforms state-of-the-art graph-based models that do not account for incident reports.
A City-level High-performance Spatio-temporal Mobility Simulation System

Urban mobility simulation refers to simulating human fine-grained spatio-temporal mobility and activity behaviors in cities, which facilitates the measurement of traffic operations, assesses the impacts of transportation on other areas of the city like environment, and supports the designation of simulation-driven mobility-related sustainable policies. It is becoming one of the important tools in the development of sustainable cities. However, the main challenge currently restricting the mobility simulation and its applications is poor performance when dealing with city-scale million or even ten million people simulation. In mobility simulation, agents like people and vehicles need to take actions based on the previous step's states of other agents nearby, which reflects the spatio-temporal dependencies among agents. Facing city-scale scenarios, the spatio-temporal dependencies become the main barrier to achieving efficient parallel computation acceleration. To alleviate the impact of spatio-temporal dependencies on parallel acceleration, we propose a city-level high-performance spatio-temporal mobility simulation system designed with a two-stage parallel process based on read/write separation and a parallel-friendly indexing subsystem. The two-stage parallel process optimizes cross-step state read/write processes among agents by reorganizing all states into three categories (public read-only, public write-only, and private) and introducing a two-stage control flow design to divide the entire data flow into two easily parallelized groups. The indexing subsystem optimizes spatial belonging relationship maintenance and relative location queries through parallel-friendly data structure selection with addition, deletion, and query process design. We implement the whole system on both CPU and GPU to adapt to different hardware environments. We also conduct extensive experiments and build use cases to demonstrate that the system achieves the expected results in terms of performance and can support innovative applications about sustainable mobility research. The experiments show that our proposed system achieves a computational speedup of 278.77 times the wall clock time, i.e. 3.59 milliseconds per step, in a city-level simulation with nearly 1 million people simultaneously.
Assisting Stroke Gait with Cable Driven Lower Limb Rehabilitation Exoskeleton (C-LREX): Simulation Study

Cable driven exoskeletons have demonstrated promising results in the rehabilitation of stroke survivors. Usually, the performance of an exoskeleton is tested via multiple experimental trials to allow iterative adjustment and optimization, which can be costly and time-consuming. Preliminary numerical model studies which quantify the effects of various design parameters on the exoskeleton's behavior and compare the responses of the impaired limb with and without the exoskeleton are valuable as they provide time and cost-effective means to fine-tune key design parameters while minimizing unnecessary testing. This study employed a generalized framework of cable driven exoskeleton analysis to investigate the ability of a cable driven lower limb rehabilitation exoskeleton (C-LREX) to correct stroke impaired gait and track reference healthy trajectories. Five different impaired gait patterns were studied. The simulation results demonstrated that the C-LREX model was able to assist the impaired limb to track the reference trajectory successfully in all impaired gait patterns except in the case of high deviation in the range of motion, which required a cable tension that exceed the allowable limit implemented in the model. This study confirmed that our developed simulation framework was able to test the C-LREX model ability to assist various impaired lower limb patterns in the sagittal plane during gait and determine the cable tension requirements. Future work includes expanding the framework to incorporate the frontal plane motion toward testing the performance of C-LREX in assisting three-dimensional impaired motion. CCS CONCEPTS •Mathematics of computing → Mathematical analysis → Mathematical optimization •Human-centered computing → Human computer interaction (HCI) •General and reference → Cross-computing tools and techniques → Design •Human-centered computing → Interaction design → Systems and tools for interaction design
ROS reducing and skin-whitening potentials of corn silk peptide FMFFVYK

Human skin is constantly exposed to oxidative stress due to the ubiquitous presence of free radicals which may be destructive to skin cells. Discovery of antioxidative peptides from corn silk could encourage the channelling of corn silk waste into useful materials with cosmeceutical values. Hence, this study aimed to evaluate the antioxidative and skin-whitening properties of FMFFVYK (Phe-Met-Phe-Phe-Val-Tyr-Lys), a corn silk-derived peptide. MTT assay was first performed to evaluate the cytotoxicity of FMFFVYK on HaCaT skin cells, while DCFH-DA assay was used to determine the cellular antioxidative activity of FMFFVYK. In order to evaluate the skin-whitening ability, the inhibitory potential of FMFFVYK towards tyrosinase, a key enzyme in melanogenesis, was investigated. Results obtained showed that the peptide did not reduce cellular viability of skin cells at tested concentrations up to 150 μg/mL. Furthermore, reduction of endogenous ROS after treatment with FMFFVYK was observed in the HaCat cell line as compared to untreated control. FMFFVYK also demonstrated dose-dependent inhibition of the activity of tyrosinase enzyme in vitro, indicating possible skin whitening effect. As such, FMFFVYK is an effective and novel antioxidative and skin-whitening peptide that could be integrated into cosmeceutical products, thus, warrants further investigation.