Miracle-dz/UniFAR-FTUs huggingface dataset:
Longitudinal glued joints of timber beams and the influence of quality manufacturing onto their carrying capacity

The content of this article is to analyse destructive testing results of longitudinal solid wood joints of structural size beams with internal and external glued wood-based panels (plywood) stressed in bending, which was mostly focused on simulating the effect of the glued line thickness (1 and 3 mm) and the influence of contact surfaces of longitudinally connected elements when bending loads. The aim of this article is to compare the carrying capacity and the joint real behaviour under load with values obtained using numerical modelling and calculation according to valid standards.
MICRO-PERFORATED PANELS AND SOUND ABSORPTION

Micro-perforated panels (MPP) are used more and more for sound absorption. The characteristics of micro-perforated panels have been studied widely by a lot of researchers and it is recognized that this kind of absorbers is the next generation acoustical materials due to fiber-free nature and attractive design. Helmholtz’s principle is underlying MPP structures that present o series of advantages. In this study two kinds of wood MPP placed at different distances from the rigid back-plate are presented. Their sound absorption coefficient has been measured in the impedance tube and compared with the one that resulted from a mathematical model. Key words: impedance, micro-perforated panels, sound absorption, wood
Estimating Biomass and Carbon Sequestration Capacity of Phragmites australis Using Remote Sensing and Growth Dynamics Modeling: A Case Study in Beijing Hanshiqiao Wetland Nature Reserve, China

Estimating the biomass of Phragmites australis (Cav.) Trin. ex Steud. , i.e., a common wetland macrophyte, and the associated carbon sequestration capacity has attracted increasing attention. Hanshiqiao Wetland Nature Reserve (HWNR) is a large P. australis wetland in Beijing, China, and provides an ideal case study site for such purpose in an urban setting. In this study, an existing P. australis growth dynamics model was adapted to estimate the plant biomass, which was in turn converted to the associated carbon sequestration capacity in the HWNR throughout a typical year. To account for local differences, the modeling parameters were calibrated against the above-ground biomass (AGB) of P. australis retrieved from hyperspectral images of the study site. We also analyzed the sensitivity of the modeling parameters and the influence of environmental factors, particularly the nutrient availability, on the growth dynamics and carbon sequestration capacity of P. australis. Our results show that the maximum AGB and below-ground biomass (BGB) of P. australis in the HWNR are 2.93 × 103 and 2.49 × 103 g m−2, respectively, which are higher than the reported level from nearby sites with similar latitudes, presumably due to the relatively high nutrient availability and more suitable inundation conditions in the HWNR. The annual carbon sequestration capacity of P. australis in the HWNR was estimated to be 2040.73 gC m−2 yr−1, which was also found to be highly dependent on nutrient availability, with a 50% increase (decrease) in the constant of the nutrient availability KNP, resulting in a 12% increase (23% decrease) in the annual carbon sequestration capacity. This implies that a comprehensive management of urban wetlands that often encounter eutrophication problems to synergize the effects of nutrient control and carbon sequestration is worth considering in future practices.
Review of a Specialty Fiber for Distributed Acoustic Sensing Technology

Specialty fibers have introduced new levels of flexibility and variability in distributed fiber sensing applications. In particular, distributed acoustic sensing (DAS) systems utilized] the unique functions of specialty fibers to achieve performance enhancements in various distributed sensing applications. This paper provides an overview of recent preparations and developments of specialty-fiber-based DAS systems and their sensing applications. The specialty-fiber-based DAS systems are categorized and reviewed based on the differences in scattering enhancement and methods of preparation. The prospects of using specialty fibers for DAS systems are also discussed.
Extending MQTT with Real-Time Communication Services Based on SDN

MQTT is one of the most popular application-layer protocols used in the scope of the Internet-of-Things (IoT) and Industrial-Internet-of-Things (IIoT), given its suitability for resource-constrained embedded systems. However, MQTT Quality-of-Service policies do not support timeliness requirements, which is common in IIoT. The literature reports several research works that address this limitation, but they are limited in scope (e.g., improvements in the broker’s internal operation, control of the publisher’s data rate, and path optimizations). Conversely, this paper presents a comprehensive architectural approach, proposing a set of extensions to the MQTT protocol that allow applications to explicitly specify real-time requirements and instantiate corresponding network reservations to enforce the desired temporal behavior. Such reservations are enforced via Software Defined Networking, specifically the OpenFlow protocol, but other protocols that allow bandwidth reservations, e.g., TSN, can also be used. This paper presents the proposed system architecture together with extensive emulation and implementation results that validate the feasibility of the approach, showing that time-sensitive MQTT traffic can be effectively segregated and prioritized to meet application-defined real-time requirements. Using several combinations of network topologies and load levels and comparing to the absence of the proposed real-time mechanisms, both average and worst-case latencies of the time-sensitive traffic decreased to approximately half, while for the normal traffic, they increased by approximately 10%.
A Bahá’í concept of peace as a resource for peace education: Case study of ‘The Problem of Prejudice’

ABSTRACT This article describes a Bahá’í concept of peace in the context of discussions about the nature and focus of peace education, in particular the role of moral education as an element of peace education. It introduces the notions of human nobility and the oneness of humanity as the moral basis for holistic peace within a framework of the collective social evolution of humanity, and explores the idea of identifying, understanding, and removing barriers to unity, specifically in the form of inequalities and prejudices, as the foundation of an approach to peace education. The application of such an approach to a university level course is shared through a case study of ‘The Problem of Prejudice’, offered by the Bahá’í Chair for World Peace at the University of Maryland, College Park. Key strands of content and pedagogy are described, and qualitative data from students participating in the course in 2021 (n = 20), collected in the form of self-perceptions of changes in knowledge, skills, attitudes and commitment, are presented. The article concludes with a discussion of the learning gained from this study and how a Bahá’í concept of peace may serve as a resource for university peace educators and students.
Metagenomic Analyses of the Soybean Root Mycobiome and Microbiome Reveal Signatures of the Healthy and Diseased Plants Affected by Taproot Decline

Invading pathogens interact with plant-associated microbial communities, which can be altered under the pressure of pathogen infection. Limited information exists on plant–microbe interactions occurring during natural outbreaks in agricultural fields. Taproot decline (TRD) of soybean is an emerging disease caused by Xylaria necrophora. TRD disease occurrence and yield loss associated with TRD are outstanding issues in soybean production. We applied nuclear ribosomal DNA Internal Transcribed Spacers and 16S rRNA gene taxonomic marker sequencing to define the composition of the fungal and bacterial communities associated with healthy and diseased soybean roots collected from the Mississippi Delta. The plant compartment was a significant factor regulating taxonomic diversity, followed by the disease status of the plant. TRD impacted the root endophytes, causing imbalances; at the intermediate and advanced stages of TRD, X. necrophora decreased mycobiome diversity, whereas it increased microbiome richness. Networks of significant co-occurrence and co-exclusion relationships revealed direct and indirect associations among taxa and identified hubs with potential roles in assembling healthy and TRD-affected soybean biomes. These studies advance the understanding of host–microbe interactions in TRD and the part of biomes in plant health and disease.
Dynamic threshold strategy optimization for security protection in Internet of Things: An adversarial deep learning‐based game‐theoretical approach

As mobile communications, the Internet, databases, distributed computing, and other technologies continue to develop, the Internet of Things (IoT) has emerged as prevalent technique. However, attacks on security and sensitive data in IoT occur frequently, and these attacks often evade intrusion detection systems strategically by mutating their traffic. To prevent security threats and sensitive data leakage, we propose a game approach based on adversarial deep learning to optimize a dynamic security threshold strategy. We introduce a mobile edge computing framework and utilize a game model to describe the adversarial interaction between the two participants. To solve the complexity of the game problem to gain dynamically randomized adversarial attacks, we present a column generation (CG) framework, which uses a feedforward neural network to quantify data flowing through IoT devices. Considering the limited resources of IoT devices, we calculate an optimal response to cyberattacks via a particle swarm optimization algorithm, aiming to reduce the false alarm rate. The adversarial dynamic threshold (ADT)‐based column generation (CG‐ADT) algorithm generates the set of detection threshold and the probability. Finally, we present the results of experiments conducted to demonstrate the effectiveness and robustness of the proposed dynamic threshold scheme for sensitive data security protection in IoT and its suitability for implementation in production systems.
Comparison of solar radiation data sources for design and performance appraisal of CSP systems in India

ABSTRACT The choice of a solar radiation data source is expected to have a significant impact on the predicted performance of a concentrating solar power (CSP) system and consequently on its technical and financial feasibility. In the present study, an attempt has been made to analyse the effect of choice of various solar radiation data sources on the predicted performance of a CSP system at 13 different locations in 5 different climatic zones of India. It was observed that there is significant variation in the amount of annual electricity output obtained for various locations using different solar radiation data sources resulting in variations in levelised cost of electricity. For a 50-MW parabolic trough-based CSP plant located in Jaisalmer, the estimated value of annual electricity output varies from 63 to 124 GWh. For a CSP plant based on the central tower receiver technology, the corresponding range is from 106 to 145 GWh.
Shaping families’ local biodiversity awareness while using a place-based mobile device during a nature walk

Abstract This research study focuses on 31 families (n = 105 individuals) and their experience using a place-based e-Trailguide to observe and learn about the local biodiversity during a hike. Open-ended interviews were conducted with each family after their hike with the e-Trailguide to assess shifts in biodiversity awareness. 190 interview responses were thematically coded into 17 different categories. Results showed that new awareness of tree characteristics, plant identification, and insect species were reported most often. Overall, families’ new awareness of plants was reported more than animals. Implications highlight the value of place-based and temporal-specific content when designing outdoor-based mobile programs for families.
Intelligent Deep Transfer Learning Based Malaria Parasite Detection and Classification Model Using Biomedical Image

: Malaria is a severe disease caused by Plasmodium parasites, which can be detected through blood smear images. The early identification of the disease can effectively reduce the severity rate. Deep learning (DL) models can be widely employed to analyze biomedical images, thereby min-imizing the misclassification rate. With this objective, this study developed an intelligent deep-transfer-learning-based malaria parasite detection and classification (IDTL-MPDC) model on blood smear images. The proposed IDTL-MPDC technique aims to effectively determine the presence of malarial parasites in blood smear images. In addition, the IDTL-MPDC technique derives median filtering (MF) as a pre-processing step. In addition, a residual neural network (Res2Net) model was employed for the extraction of feature vectors, and its hyperparameters were optimally adjusted using the differential evolution (DE) algorithm. The k -nearest neighbor (KNN) classifier was used to assign appropriate classes to the blood smear images. The optimal selection of Res2Net hyperparameters by the DE model helps achieve enhanced classification outcomes. A wide range of simulation analyses of the IDTL-MPDC technique are carried out using a benchmark dataset, and its performance seems to be highly accurate (95.86%), highly sensitive (95.82%), highly specific (95.98%), with a high F1 score (95.69%), and high precision (95.86%), and it has been proven to be better than the other existing methods.
Magnetization Roasting of Specularite Ore: Phase Transformation, Magnetism and Kinetics

ABSTRACT Specularite (α-Fe2O3) is a hematite aggregate with a metallic luster, presenting the shape of sheet stacking. Specularite ore resource is abundant globally, such as in Brazil and China. However, unlike hematite ore, specularite ore has not been efficiently utilized in the iron-making industry. Magnetization roasting technology is an effective method for processing specularite ore. In this study, the phase transformation and magnetism variation of specularite ore in magnetization roasting was characterized by X-ray diffraction, vibrating sample magnetometry, and Mössbauer spectroscopy. The reduction kinetics of specularite ore followed the nucleation model A2, with the pre-exponential factor of 20.43 s−1 and activation energy of 56.61 kJ/mol. It has implications for the utilization of specularite ore resources using magnetization roasting technology.
Resource Allocation With Workload-Time Windows for Cloud-Based Software Services: A Deep Reinforcement Learning Approach

As the workloads and service requests in cloud computing environments change constantly, cloud-based software services need to adaptively allocate resources for ensuring the Quality-of-Service (QoS) while reducing resource costs. However, it is very challenging to achieve adaptive resource allocation for cloud-based software services with complex and variable system states. Most of the existing methods only consider the current condition of workloads, and thus cannot well adapt to real-world cloud environments subject to fluctuating workloads. To address this challenge, we propose a novel Deep Reinforcement learning based resource Allocation method with workload-time Windows (DRAW) for cloud-based software services that considers both the current and future workloads in the resource allocation process. Specifically, an original Deep Q-Network (DQN) based prediction model of management operations is trained based on workload-time windows, which can be used to predict appropriate management operations under different system states. Next, a new feedback-control mechanism is designed to construct the objective resource allocation plan under the current system state through iterative execution of management operations. Extensive simulation results demonstrate that the prediction accuracy of management operations generated by the proposed DRAW method can reach 90.69%. Moreover, the DRAW can achieve the optimal/near-optimal performance and outperform other classic methods by 3<inline-formula><tex-math notation="LaTeX">$\sim$</tex-math><alternatives><mml:math><mml:mo>∼</mml:mo></mml:math><inline-graphic xlink:href="chen-ieq1-3169157.gif"/></alternatives></inline-formula>13% under different scenarios.
Development of 3D standard-part library of stamping die CAD in I-DEAS

It is an important way to enhance efficiency of products design to establish standard parts library in CAD system. There is no standard parts library in current CAD system. This paper introduces the techniques of the development on I-DEAS focused on the parameterized feature modeling technology of I-DEAS, and describes the key techniques on the development of 3D standard-part library of stamping die CAD in detail.
A Comprehensive Identification and Function Analysis of Serine/Arginine-Rich (SR) Proteins in Cotton (Gossypium spp.)

As one of the most important factors in alternative splicing (AS) events, serine/arginine-rich (SR) proteins not only participate in the growth and development of plants but also play pivotal roles in abiotic stresses. However, the research about SR proteins in cotton is still lacking. In this study, we performed an extensive comparative analysis of SR proteins and determined their phylogeny in the plant lineage. A total of 169 SR family members were identified from four Gossypium species, and these genes could be divided into eight distinct subfamilies. The domain, motif distribution and gene structure of cotton SR proteins are conserved within each subfamily. The expansion of SR genes is mainly contributed by WGD and allopolyploidization events in cotton. The selection pressure analysis showed that all the paralogous gene pairs were under purifying selection pressure. Many cis-elements responding to abiotic stress and phytohormones were identified in the upstream sequences of the GhSR genes. Expression profiling suggested that some GhSR genes may involve in the pathways of plant resistance to abiotic stresses. The WGCNA analysis showed that GhSCL-8 co-expressed with many abiotic responding related genes in a salt-responding network. The Y2H assays showed that GhSCL-8 could interact with GhSRs in other subfamilies. The subcellular location analysis showed that GhSCL-8 is expressed in the nucleus. The further VIGS assays showed that the silencing of GhSCL-8 could decrease salt tolerance in cotton. These results expand our knowledge of the evolution of the SR gene family in plants, and they will also contribute to the elucidation of the biological functions of SR genes in the future.
PENGEMBANGAN INVESTASI WILAYAH PERBATASAN, INDUSTRI MARITIM DAN KAWASAN PERDAGANGAN BEBAS DALAM MENINGKATKAN KESEJAHTERAAN MASYARAKAT PESISIR DI PROVINSI KEPULAUAN RIAU

The purpose of this research to create the boder region of investment development model, the maritime industry investment development model and Free Trade Zone development model effect on increase the welfare of coastal communities.The research location is in the of Riau Islands Province at Batam, Bintan and Karimun in 2016 with 200 research data. Analysis of the research through modeling path diagram, goodness of fit test (GOF) and the test results prove that the model evaluation through feasibility test models of assessment criteria Goodness of Fit (GOF) proved to be a model drawn up by researchers is the model has fit so that the model is said to fit the observed data development investment has met the criteria GOF through the test. Analysis of the research through modeling path diagram, goodness of fit test (GOF) and the test results prove that the model evaluation through feasibility studies Goodness of Fit (GOF) proven models arranged by researchers is the model has fit so that the model is said to fit the observed data to develop investment meets the criteria GOF through Chi-square test, probability of> 0.05, CMIN / DF≤2,00, RMSEA≤0,08, GFI≥0,90, AGFI≥0,90, TLI≥0,90, NFI≥0,90, CFI≥ 0.95. The results of the research hypotheses prove that the investment development of boder region, maritime industry and Free Trade Zone significant effect on increase the welfare of coastal communities in Riau Islands Province at Batam, Bintan and Karimun.
Airmass Analysis of Size-Resolved Black Carbon Particles Observed in the Arctic Based on Cluster Analysis

Here we apply new analysis methods and approaches to existing long-term measurement series that provide additional insights into the atmospheric processes that control black carbon (BC) in the Arctic. Based on clustering size distribution data from Zeppelin Observatory for the years 2002–2010, observations classified as ‘Polluted’ were further investigated based on BC properties. The data were split into two subgroups, and while the microphysical and chemical fingerprints of the two subgroups are very similar, they show larger differences in BC concentration and correlation with the particle size distribution. Therefore, a source–receptor analysis was performed with HYSPLIT 10-days backward trajectories for both subsets. We demonstrate that within this ‘Polluted’ category, the airmasses that contributed to the largest BC signal at the Zeppelin station are not necessarily associated with traditional transport pathways from Eurasia. Instead, the strongest signal is from a region east of the Ural Mountains across the continent to the Kamchatka Peninsula.
Optimal Terminal Missile Guidance Law Based on Fuzzy Neural Networks

Because fuzzy neural networks technique has the advantages not only strong adaptability to nonlinear systems but also quick learning ability, it has been applied to nonlinear systems successfully. an optimal terminal missile guidance law based on fuzzy neural networks for the advanced missiles with thruster vector control is presented. In the design of optimal guidance law, the minimum of missile energy loss and miss distance is required while the nonlinear character of the thruster vector control is considered. In addition, fuzzy learning rules are presented to improve the learning ability of the neural networks system. Numerical simulation results are given to illustrate that the presented fuzzy neural networks guidance law has good performance to hit maneuvering targets.
Cooperative Navigation for Low-Cost UAV Swarm Based on Sigma Point Belief Propagation

As navigation is a key to task execution of micro unmanned aerial vehicle (UAV) swarm, the cooperative navigation (CN) method that integrates relative measurements between UAVs has attracted widespread attention due to its performance advantages. In view of the precision and efficiency of cooperative navigation for low-cost micro UAV swarm, this paper proposes a sigma point belief propagation-based (SPBP) CN method that can integrate self-measurement data and inter-UAV ranging in a distributed manner so as to improve the absolute positioning performance of UAV swarm. The method divides the sigma point filter into two steps: the first is to integrate local measurement data; the second is to approximate the belief of position based on the mean and covariance of the state, and pass message by augmentation, resampling and cooperative measurement update of the state to realize a low-complexity approximation to traditional message passing method. The simulation results and outdoor flight test results show that with similar performance, the proposed CN method has a calculation load more than 20 times less than traditional BP algorithms.